def test_tla_per_region():
sp_land_dist_all = [
331.702828, 181.9634517, 88.98630743, 130.15193962, 201.18287123,
933.98739798, 37.60589239, 7.02032552, 43.64118779, 88.61837725
]
index = pd.Index([
'OECD90', 'Eastern Europe', 'Asia (Sans Japan)',
'Middle East and Africa', 'Latin America', 'Global', 'China', 'India',
'EU', 'USA'
])
land_dist = pd.DataFrame(sp_land_dist_all, columns=['All'], index=index)
expected = pd.read_csv(datadir.joinpath('sp_tla.csv'), index_col=0)
result = tla.tla_per_region(land_dist=land_dist)
pd.testing.assert_frame_equal(expected, result)
def test_tla_per_region_sub_world_vals():
sp_land_dist_all = [
331.702828, 181.9634517, 88.98630743, 130.15193962, 201.18287123,
933.98739798, 37.60589239, 7.02032552, 43.64118779, 88.61837725
]
index = pd.Index([
'OECD90', 'Eastern Europe', 'Asia (Sans Japan)',
'Middle East and Africa', 'Latin America', 'Global', 'China', 'India',
'EU', 'USA'
])
land_dist = pd.DataFrame(sp_land_dist_all, columns=['All'], index=index)
custom_world_vals = list(range(100, 149))
index = pd.Index(data=list(range(2012, 2061)), name='Year')
custom_world_vals_df = pd.DataFrame(custom_world_vals,
columns=['Source 1'],
index=index)
tla_per_region = tla.tla_per_region(
land_dist=land_dist, custom_world_values=custom_world_vals_df)
pd.testing.assert_series_equal(tla_per_region.loc[:, 'World'],
custom_world_vals_df.loc[2014:, 'Source 1'],
check_names=False)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name, cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
if self.ac.use_custom_tla and self.ac.custom_tla_fixed_value is not None:
self.c_tla = tla.CustomTLA(fixed_value=self.ac.custom_tla_fixed_value)
custom_world_vals = self.c_tla.get_world_values()
elif self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
adconfig_list = [
['param', 'World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)',
'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA'],
['trend', self.ac.soln_pds_adoption_prognostication_trend, 'Medium',
'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'],
['growth', self.ac.soln_pds_adoption_prognostication_growth, 'NOTE',
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE', 'NOTE', 'NOTE'],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]]
adconfig = pd.DataFrame(adconfig_list[1:], columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {
}
self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
tla_world_2050 = self.tla_per_region.loc[2050, 'World']
ad_2018 = self.ac.ref_base_adoption['World']
growth_initial = pd.DataFrame([[2018] + list(self.ac.ref_base_adoption.values())],
columns=ca_pds_columns).set_index('Year')
degrade_rate_high = pd.Series(0.00506640879193976, index=range(2014, 2061))
degrade_rate_low = pd.Series(0, index=range(2014, 2061))
degrade_rate_low.loc[2014:2019] = 0.00506640879193976
degrade_rate_low.loc[2020:2029] = 0.00253320439596988
degrade_rate_low.loc[2030:] = 0.0
degrade_rate_alt = pd.Series(0, index=range(2014, 2061))
degrade_rate_alt.loc[2014] = 0.00506640879193976
degrade_rate_alt.loc[2015] = 0.00464420805927811
degrade_rate_alt.loc[2016] = 0.00422200732661647
degrade_rate_alt.loc[2017] = 0.00379980659395482
degrade_rate_alt.loc[2018] = 0.00337760586129317
degrade_rate_alt.loc[2019] = 0.00295540512863153
degrade_rate_alt.loc[2020] = 0.00253320439596988
degrade_rate_alt.loc[2021] = 0.00227988395637289
degrade_rate_alt.loc[2022] = 0.00202656351677590
degrade_rate_alt.loc[2023] = 0.00177324307717892
degrade_rate_alt.loc[2024] = 0.00151992263758193
degrade_rate_alt.loc[2025] = 0.00126660219798494
degrade_rate_alt.loc[2026] = 0.00101328175838795
degrade_rate_alt.loc[2027] = 0.00075996131879096
degrade_rate_alt.loc[2028] = 0.00050664087919398
degrade_rate_alt.loc[2029] = 0.00025332043959699
degrade_rate_alt.loc[2030:] = 0.0
# In Excel, the Custom PDS Scenarios tab columns AC:AF compute a limit on adoption
# based on the amount of degraded land remaining. We compute the same limit here.
# This version takes the adoption into account.
def constrained_tla_adoption(rate, datapoints):
degrade_df = pd.DataFrame(0, index=range(2014, 2061), columns=[
'Total undegraded land at the start of the year',
'Degraded land at the end of the year',
'Total undegraded land at the end of the year', 'Constrained TLA'])
for year in range(2014, 2061):
if year == 2014:
# Not a typo, Excel uses the 2018 base adoption number starting in 2014.
# We are bug-for-bug compatible here.
undeg_start = tla_world_2050 - ad_2018
else:
last = degrade_df.loc[year-1, 'Total undegraded land at the end of the year']
new = datapoints.loc[year, 'World'] - datapoints.loc[year-1, 'World']
undeg_start = last - new
degrade_df.loc[year, 'Total undegraded land at the start of the year'] = undeg_start
degraded_end = max(0, undeg_start * rate[year])
degrade_df.loc[year, 'Degraded land at the end of the year'] = degraded_end
degrade_df.loc[year, 'Total undegraded land at the end of the year'] = (
undeg_start - degraded_end)
degrade_df.loc[year, 'Constrained TLA'] = (tla_world_2050 -
degrade_df['Degraded land at the end of the year'].sum())
return degrade_df
def constrained_tla(rate):
degrade_df = pd.DataFrame(0, index=range(2014, 2061), columns=[
'Total undegraded land at the start of the year',
'Degraded land at the end of the year',
'Total undegraded land at the end of the year', 'Constrained TLA'])
for year in range(2014, 2061):
if year == 2014:
# Not a typo, Excel uses the 2018 base adoption number starting in 2014.
# We are bug-for-bug compatible here.
undeg_start = tla_world_2050 - ad_2018
else:
last = degrade_df.loc[year-1, 'Constrained TLA']
undeg_start = last - ad_2018
degrade_df.loc[year, 'Total undegraded land at the start of the year'] = undeg_start
degraded_end = max(0, undeg_start * rate[year])
degrade_df.loc[year, 'Degraded land at the end of the year'] = degraded_end
degrade_df.loc[year, 'Total undegraded land at the end of the year'] = (
undeg_start - degraded_end)
degrade_df.loc[year, 'Constrained TLA'] = (tla_world_2050 -
degrade_df['Degraded land at the end of the year'].sum())
return degrade_df
# DATA SOURCE 1
# https://rsis.ramsar.org/ris-search/peatland?pagetab=2&f%5B0%5D=wetlandTypes_en_ss%3AInland%20wetlands&f%5B1%5D=wetlandTypes_en_ss%3AU%3A%20Permanent%20Non-forested%20peatlands&f%5B2%5D=wetlandTypes_en_ss%3AXp%3A%20Permanent%20Forested%20peatlands&f%5B3%5D=managementPlanAvailable_i%3A-1
ds1_growth_rate = 0.0622897686767343
# silvopasture and farmlandrestoration use a growth_rate feature of customadoption
# to do something like this. However those two solutions calculate a linear growth
# and then use it to call TREND() to fit a line through it. peatlands uses the raw
# linear growth data as its Custom Adoption. not quite compatible with what
# silvopasture and farmlandrestoration do, so we calculate it here.
ds1_datapoints = pd.DataFrame(0.0, index=range(2012, 2061), columns=dd.REGIONS)
ds1_datapoints.loc[2012:2018, 'World'] = ad_2018
for year in range(2019, 2061):
rate = 1 + ds1_growth_rate
ds1_datapoints.loc[year, 'World'] = ds1_datapoints.loc[year-1, 'World'] * rate
# DATA SOURCE 2
df = constrained_tla_adoption(rate=degrade_rate_high, datapoints=ds1_datapoints)
ds2_adoption_2050 = df.loc[2050, 'Constrained TLA']
ds2_adoption_2030 = 0.7 * ds2_adoption_2050
# DATA SOURCE 4
df = constrained_tla_adoption(rate=degrade_rate_alt, datapoints=ds1_datapoints)
ds4_adoption_2050 = df.loc[2050, 'Constrained TLA']
ds4_adoption_2030 = 0.7 * ds4_adoption_2050
# DATA SOURCE 5
df = constrained_tla(rate=degrade_rate_high)
ds5_constrained_tla = df.loc[2050, 'Constrained TLA']
# DATA SOURCE 7
ds7_constrained_tla = constrained_tla(degrade_rate_low).loc[2050, 'Constrained TLA']
# DATA SOURCE 9
ds9_growth_rate = 0.0394112383173051
# silvopasture and farmlandrestoration use a growth_rate feature of customadoption
# to do something like this. However those two solutions calculate a linear growth
# and then use it to call TREND() to fit a line through it. peatlands uses the raw
# linear growth data as its Custom Adoption. So calculate it here.
ds9_datapoints = pd.DataFrame(0.0, index=range(2012, 2061), columns=dd.REGIONS)
ds9_datapoints.loc[2012:2018, 'World'] = ad_2018
for year in range(2019, 2061):
rate = 1 + ds9_growth_rate
ds9_datapoints.loc[year, 'World'] = ds9_datapoints.loc[year-1, 'World'] * rate
df = constrained_tla_adoption(degrade_rate_high, ds9_datapoints)
ds9_constrained_tla = df.loc[:, 'Constrained TLA'].min()
# DATA SOURCE 10
df = constrained_tla_adoption(degrade_rate_low, ds9_datapoints)
ds10_constrained_tla = df.loc[:, 'Constrained TLA'].min()
ca_pds_data_sources = [
{'name': 'High adoption and conservative degradation rate', 'include': True,
'description': (
'The Peatlands Ramsar sites with management plans evolution (1985-2015) was '
'used to estimate the yearly increase rate of 6,23% corresponding to 1985- '
"2015 coupled with the TLA's current degradation rate of 0.51%. "
),
'datapoints': ds1_datapoints, 'maximum': 331},
{'name': 'Scenario 1 + 70% conservative degradation TLA in 2030', 'include': True,
'description': (
'Scenario 1 with linear increase up to 70% of TLA 2050 value in 2030 and '
'then linear increase to 100% TLA value in 2050 from then onwards '
),
'maximum': ds2_adoption_2050,
'datapoints': pd.DataFrame([
[2014, ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2030, ds2_adoption_2030, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2050, ds2_adoption_2050, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
], columns=ca_pds_columns).set_index('Year')},
{'name': 'High adoption and low degradation rate', 'include': True,
'description': (
'The Peatlands Ramsar sites with management plans evolution (1985-2015) was '
'used to estimate the yearly increase rate of 6,23% corresponding to 1985- '
'2015 coupled with the he New York declaration on Forests is used to '
'estimate an alternative degradation rate which starts from 0.51% and '
'linearly decreases to half its value in 2020 and then to zero in 2030. '
),
'datapoints': ds1_datapoints, 'maximum': 396},
{'name': 'Scenario 3 + 70% low degradation TLA in 2030', 'include': True,
'description': (
'Scenario 3 with linear increase up to 70% of TLA 2050 value in 2030 and '
'then linear increase to 100% TLA value in 2050 from then onwards '
),
'maximum': ds4_adoption_2050,
'datapoints': pd.DataFrame([
[2014, ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2030, ds4_adoption_2030, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2050, ds4_adoption_2050, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
], columns=ca_pds_columns).set_index('Year')},
{'name': '100% conservative degradation TLA in 2050', 'include': True,
'description': (
'100% adoption ofconservative degradation rate TLA by 2050 '
),
'maximum': ds5_constrained_tla,
'datapoints': pd.DataFrame([
[2014, ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2050, ds5_constrained_tla, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
], columns=ca_pds_columns).set_index('Year')},
{'name': '80% conservative degradation TLA in 2030', 'include': True,
'description': (
'Linear increase up to 80% of TLA 2050 value in 2030 (calculated with the '
'conservative degradation rate) and then linear increase to 100% TLA value '
'in 2050 from then onwards '
),
'maximum': ds5_constrained_tla,
'datapoints': pd.DataFrame([
[2014, ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2030, 0.8 * ds5_constrained_tla, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2050, ds5_constrained_tla, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
], columns=ca_pds_columns).set_index('Year')},
{'name': '100% low degradation TLA in 2050', 'include': True,
'description': (
'100% adoption of low degradation rate TLA by 2050 '
),
'maximum': ds7_constrained_tla,
'datapoints': pd.DataFrame([
[2014, ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2050, ds7_constrained_tla, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
], columns=ca_pds_columns).set_index('Year')},
{'name': '80% low degradation TLA in 2030', 'include': True,
'description': (
'80% low degradation TLA in 2030 '
),
'maximum': ds7_constrained_tla,
'datapoints': pd.DataFrame([
[2014, ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2030, 0.8 * ds7_constrained_tla, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2050, ds7_constrained_tla, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
], columns=ca_pds_columns).set_index('Year')},
{'name': 'Medium adoption and conservative degradation rate', 'include': True,
'description': (
'The Peatlands Ramsar sites with management plans evolution (1985-2015) was '
'used to estimate the yearly increase rate of 3,94% corresponding to 1990- '
"2015 coupled with the TLA's current degradation rate of 0.51%. "
),
'datapoints': ds9_datapoints, 'maximum': ds9_constrained_tla},
{'name': 'Medium adoption and low degradation rate', 'include': True,
'description': (
'The Peatlands Ramsar sites with management plans evolution (1985-2015) was '
'used to estimate the yearly increase rate of 3,94% corresponding to 1990- '
"2015 coupled with the TLA's low degradation rate of 0.51%. "
),
'datapoints': ds9_datapoints, 'maximum': ds10_constrained_tla},
]
self.pds_ca = customadoption.CustomAdoption(data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0, low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
for s in self.pds_ca.scenarios.values():
df = s['df']
df.loc[2014:2018, 'World'] = ad_2018
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region()
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region()
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(
list(self.ac.ref_base_adoption.values()), index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (ht_ref_adoption_initial /
self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2018] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(ac=self.ac,
ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region, pds_adoption_limits=self.tla_per_region,
use_first_pds_datapoint_main=True,
adoption_base_year=2018,
copy_pds_to_ref=True,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted=self.ua.soln_net_annual_funits_adopted()
self.fc = firstcost.FirstCost(ac=self.ac, pds_learning_increase_mult=2,
ref_learning_increase_mult=2, conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
tot_red_in_deg_land=self.ua.cumulative_reduction_in_total_degraded_land(),
pds_protected_deg_land=self.ua.pds_cumulative_degraded_land_protected(),
ref_protected_deg_land=self.ua.ref_cumulative_degraded_land_protected(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name,
cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
if self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(
filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
adconfig_list = [
[
'param', 'World', 'OECD90', 'Eastern Europe',
'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America',
'China', 'India', 'EU', 'USA'
],
[
'trend', self.ac.soln_pds_adoption_prognostication_trend,
'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'
],
[
'growth', self.ac.soln_pds_adoption_prognostication_growth,
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE'
],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
]
adconfig = pd.DataFrame(adconfig_list[1:],
columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {
'Raw Data for ALL LAND TYPES': {
'FAO 2010': THISDIR.joinpath('ad', 'ad_FAO_2010.csv'),
},
}
self.ad = adoptiondata.AdoptionData(ac=self.ac,
data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
adoption_2014 = self.ac.ref_base_adoption['World']
tla_2050 = self.tla_per_region.loc[2050, 'World']
ds4_percent_adoption_2050 = 0.85
ds4_adoption_2050 = ds4_percent_adoption_2050 * tla_2050
ca_pds_data_sources = [
{
'name':
'Low growth, linear trend',
'include':
True,
'datapoints_degree':
1,
# This scenario projects the future adoption of bamboo based on historical regional
# growth reported for the 1990-2010 period in the Global Forest Resource Assessment
# 2010 report, published by the FAO.
'datapoints':
pd.DataFrame([
[
1990, np.nan, 0.0, 0.0, 15.412, 3.688, 10.399, 0.0,
0.0, 0.0, 0.0
],
[
2000, np.nan, 0.0, 0.0, 16.311, 3.656, 10.399, 0.0,
0.0, 0.0, 0.0
],
[
2005, np.nan, 0.0, 0.0, 16.943, 3.640, 10.399, 0.0,
0.0, 0.0, 0.0
],
[
2010, np.nan, 0.0, 0.0, 17.360, 3.627, 10.399, 0.0,
0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Medium growth, linear trend',
'include':
True,
# This scenario projects the future adoption of bamboo based on the highest
# historical regional annual growth rate, based on 1990-2010 FAO data. The highest
# annual growth rate was reported in the Asia region (0.0974 Mha/year). Thus, it
# was assumed that bamboo plantation in other regions will grow by half of the
# growth rate calculated in Asia (0.05 Mha/year), while bamboo plantation in Asia
# continues to grow with the same rate.
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Medium_growth_linear_trend.csv')
},
{
'name':
'High growth, linear trend',
'include':
True,
# This scenario projects the future adoption of bamboo based on the highest
# historical regional annual growth rate, based on 1990-2010 FAO data. The highest
# annual growth rate was reported in the Asia region. Thus, it was assumed that
# bamboo plantation in other regions will grow at the same growth rate calculated
# in Asia (0.0974 Mha/year), while bamboo plantation in Asia continues to grow at
# double this rate (0.19 Mha/year).
'filename':
THISDIR.joinpath('ca_pds_data',
'custom_pds_ad_High_growth_linear_trend.csv')
},
{
'name':
'Max growth, linear trend',
'include':
True,
# Considering the limited total land available for bamboo, this scenario
# projects an aggressive adoption of bamboo plantation and projects a worldwide
# 85% adoption of bamboo plantation by 2050.
'datapoints':
pd.DataFrame([
[
2014, adoption_2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
[
2050, ds4_adoption_2050, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Song et al. 2013',
'include':
True,
# "Annual increase in global bamboo forests based on a global historical annual
# expansion of bamboo forests of 3%, as reported in Song et al. 2013 (see p.7
# of publication).
'filename':
THISDIR.joinpath('ca_pds_data',
'custom_pds_ad_Song_et_al__2013.csv')
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
# Manual adjustment made in spreadsheet for Drawdown 2020.
for s in self.pds_ca.scenarios.values():
df = s['df']
df.loc[2014] = [
32.8913636108367000, 0.0, 0.0, 18.0440250214314000,
3.9611495064729000, 10.8861890829324000, 0.0, 0.0, 0.0, 0.0
]
df.loc[2015] = [
33.0453659423780000, 0.0, 0.0, 18.1703910504150000,
3.9772571687142000, 10.8977177232488000, 0.0, 0.0, 0.0, 0.0
]
df.loc[2016] = [
33.2014226143695000, 0.0, 0.0, 18.2979284861039000,
3.9938159101118100, 10.9096782181539000, 0.0, 0.0, 0.0, 0.0
]
df.loc[2017] = [
33.3595689913150000, 0.0, 0.0, 18.4266654288099000,
4.0108468526825300, 10.9220567098226000, 0.0, 0.0, 0.0, 0.0
]
df.loc[2018] = [
33.5198404377181000, 0.0, 0.0, 18.5566299788448000,
4.0283711184431500, 10.9348393404302000, 0.0, 0.0, 0.0, 0.0
]
# Custom REF Data
ca_ref_data_sources = [
{
'name':
'[Type Scenario 1 Name Here (REF CASE)...]',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_ref_data',
'custom_ref_ad_Type_Scenario_1_Name_Here_REF_CASE_.csv')
},
]
self.ref_ca = customadoption.CustomAdoption(
data_sources=ca_ref_data_sources,
soln_adoption_custom_name=self.ac.soln_ref_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
if self.ac.soln_ref_adoption_basis == 'Custom':
ref_adoption_data_per_region = self.ref_ca.adoption_data_per_region(
)
else:
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(list(
self.ac.ref_base_adoption.values()),
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2018] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
ref_adoption_data_per_region=ref_adoption_data_per_region,
use_first_pds_datapoint_main=False,
adoption_base_year=2018,
copy_pds_to_ref=False,
copy_ref_datapoint=False,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name,
cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
if self.ac.use_custom_tla and self.ac.custom_tla_fixed_value is not None:
self.c_tla = tla.CustomTLA(
fixed_value=self.ac.custom_tla_fixed_value)
custom_world_vals = self.c_tla.get_world_values()
elif self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(
filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
adconfig_list = [
[
'param', 'World', 'OECD90', 'Eastern Europe',
'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America',
'China', 'India', 'EU', 'USA'
],
[
'trend', self.ac.soln_pds_adoption_prognostication_trend,
'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'
],
[
'growth', self.ac.soln_pds_adoption_prognostication_growth,
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE'
],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
]
adconfig = pd.DataFrame(adconfig_list[1:],
columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {}
self.ad = adoptiondata.AdoptionData(ac=self.ac,
data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
tla_2050 = self.tla_per_region.loc[2050, 'World']
# Page 9, Green India Mission- Mission document
# http://moef.gov.in/wp-content/uploads/2017/08/GIM_Mission-Document-1.pdf
i_percent = 300000 / 346713
# Guatemala
# Table on page 35 of "Mesa de Restauración del Paisaje Forestal de Guatemala 2015.
# Estrategia de Restauración del Paisaje Forestal: Mecanismo para el Desarrollo Rural
# Sostenible de Guatemala, 58 pp."
g_percent = 10132 / 26464
avg_percent = (i_percent + g_percent) / 2.0
ca_pds_data_sources = [
{
'name':
'India restoration commitment applied to TLA',
'include':
True,
'description':
('The National Mission for a Green India included a commitment to restore 0.1 '
'Mha of mangroves in 10 years (up to 2020). We assume that this commitment '
'will be replicated in the next three decades resulting in 300,000 hectares '
'to be restored by 2050. This would represent a restoration of 89% of '
'mangrove restorable area in India. We apply this % to the global mangrove '
'area. '),
'datapoints':
pd.DataFrame([
[2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[
2050, i_percent * tla_2050, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
],
[
2051, i_percent * tla_2050, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'India + Guatemala restoration commitment applied to TLA',
'include':
True,
'description':
("Guatemala's NDC includes the restoration of 10,000 ha of mangroves by 2045. "
'This would represent 38% of total mangrove restorable area in the country. '
'We use the average of both India (87%) and Guatemala % and apply them to '
'the TLA '),
'datapoints':
pd.DataFrame([
[2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[
2050, avg_percent * tla_2050, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
],
[
2051, avg_percent * tla_2050, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'India restoration commitment applied to TLA with 100% adoption by 2030',
'include':
True,
'description': ('Scenario 1 + 100% of adoption by 2030 '),
'datapoints':
pd.DataFrame([
[2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[
2030, i_percent * tla_2050, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
],
[
2031, i_percent * tla_2050, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'India + Guatemala restoration commitment applied to TLA with 100% adoption in 2030',
'include':
True,
'description': ('Scenario 2 + 100% adoption by 2030 '),
'datapoints':
pd.DataFrame([
[2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[
2030, avg_percent * tla_2050, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
],
[
2031, avg_percent * tla_2050, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'100% TLA by 2050',
'include':
True,
'description': ('Linear increase to 100% TLA by 2050 '),
'datapoints':
pd.DataFrame([
[2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[
2050, 1.0 * tla_2050, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0
],
[
2051, 1.0 * tla_2050, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'100% TLA by 2030',
'include':
True,
'description': ('Linear increase to 100% TLA by 2030 '),
'datapoints':
pd.DataFrame([
[2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[
2030, 1.0 * tla_2050, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0
],
[
2031, 1.0 * tla_2050, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
for s in self.pds_ca.scenarios.values():
df = s['df']
for y in range(2014, 2019):
df.loc[y, 'World'] = 0.0
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(list(
self.ac.ref_base_adoption.values()),
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2018] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
use_first_pds_datapoint_main=True,
adoption_base_year=2018,
copy_pds_to_ref=True,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name,
cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
if self.ac.use_custom_tla and self.ac.custom_tla_fixed_value is not None:
self.c_tla = tla.CustomTLA(
fixed_value=self.ac.custom_tla_fixed_value)
custom_world_vals = self.c_tla.get_world_values()
elif self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(
filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
adconfig_list = [
[
'param', 'World', 'OECD90', 'Eastern Europe',
'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America',
'China', 'India', 'EU', 'USA'
],
[
'trend', self.ac.soln_pds_adoption_prognostication_trend,
'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'
],
[
'growth', self.ac.soln_pds_adoption_prognostication_growth,
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE'
],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
]
adconfig = pd.DataFrame(adconfig_list[1:],
columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {}
self.ad = adoptiondata.AdoptionData(ac=self.ac,
data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
w_2018 = self.ac.ref_base_adoption['World']
w_2050 = self.tla_per_region.loc[2050, 'World']
ca_pds_data_sources = [
{
'name':
'International Pledges, Low Growth (Linear trend)',
'include':
True,
'description':
('In the absence of accurate studies predicting future adoption of '
'multistrata agroforests, Drawdown evaluated current pledges for the '
'conversion of degraded land to protect land or buffer as part of the Bonn '
'Challenge and NY deceleration. (See hidden "Adoption - Multistrata" data- '
'sheet for a breakdown of calculation). Based on these pledges, projected '
'low-growth adoption was set at 10%. '),
'datapoints':
pd.DataFrame([
[
2018, w_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0
],
[
2050, w_2018 + (0.1 * w_2050), 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Medium, linear trend',
'include':
True,
'description':
('In the absence of accurate studies predicting future adoption of '
'multistrata agroforests, Drawdown evaluated current pledges for the '
'conversion of degraded land to protect land or buffer as part of the Bonn '
'Challenge and NY deceleration. (See hidden "Adoption - Multistrata" data- '
'sheet for a breakdown of calculation). Based on these pledges, projected '
'low-growth adoption was set at 20%. '),
'datapoints':
pd.DataFrame([
[
2018, w_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0
],
[
2050, w_2018 + (0.2 * w_2050), 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'High, linear trend',
'include':
True,
'description':
('In the absence of accurate studies predicting future adoption of '
'multistrata agroforests, Drawdown evaluated current pledges for the '
'conversion of degraded land to protect land or buffer as part of the Bonn '
'Challenge and NY deceleration. (See hidden "Adoption - Multistrata" data- '
'sheet for a breakdown of calculation). Based on these pledges, projected '
'low-growth adoption was set at 30%. '),
'datapoints':
pd.DataFrame([
[
2018, w_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0
],
[
2050, w_2018 + (0.3 * w_2050), 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Low, early adoption, linear trend',
'include':
True,
'description':
('This is Scenario 1 with 70% adoption by 2030 '),
'datapoints':
pd.DataFrame([
[
2018, w_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0
],
[
2030, w_2018 + (0.07 * w_2050), 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0
],
[
2050, w_2018 + (0.1 * w_2050), 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Medium, early adoption, linear trend',
'include':
True,
'description':
('This is Scenario 2 with 70% adoption by 2030 '),
'datapoints':
pd.DataFrame([
[
2018, w_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0
],
[
2030, w_2018 + (0.14 * w_2050), 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0
],
[
2050, w_2018 + (0.2 * w_2050), 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'High, early adoption, linear trend',
'include':
True,
'description':
('This is Scenario 3 with 70% adoption by 2030 '),
'datapoints':
pd.DataFrame([
[
2018, w_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0
],
[
2030, w_2018 + (0.21 * w_2050), 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0
],
[
2050, w_2018 + (0.3 * w_2050), 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Max growth, linear trend',
'include':
False,
'description':
('This is the Drawdown Optimum scenario, assuming 100% adoption of '
'Multistrata Agroforestry in the allocated TLA. '),
'datapoints':
pd.DataFrame([
[
2018, w_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0
],
[
2050, w_2018 + (1.0 * w_2050), 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
for s in self.pds_ca.scenarios.values():
df = s['df']
for y in range(2012, 2019):
df.loc[y, 'World'] = 0.0001
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(list(
self.ac.ref_base_adoption.values()),
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
use_first_pds_datapoint_main=False,
adoption_base_year=2018,
copy_pds_to_ref=False,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name,
cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
if self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(
filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
adconfig_list = [
[
'param', 'World', 'OECD90', 'Eastern Europe',
'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America',
'China', 'India', 'EU', 'USA'
],
[
'trend', self.ac.soln_pds_adoption_prognostication_trend,
'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'
],
[
'growth', self.ac.soln_pds_adoption_prognostication_growth,
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE'
],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
]
adconfig = pd.DataFrame(adconfig_list[1:],
columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {
'Raw Data for ALL LAND TYPES': {
'Sum of regional prognostications below':
THISDIR.joinpath(
'ad', 'ad_Sum_of_regional_prognostications_below.csv'),
},
'Region: Asia (Sans Japan)': {
'Raw Data for ALL LAND TYPES': {
'Dara et al. 2018; Kazakshstan recultivation':
THISDIR.joinpath(
'ad',
'ad_Dara_et_al__2018_Kazakshstan_recultivation.csv'),
},
},
'Region: China': {
'Tropical-Humid Land': {
'Lin, L, et al 2018, Wenzhou province only':
THISDIR.joinpath(
'ad', 'ad_Lin_L_et_al_2018_Wenzhou_province_only.csv'),
},
},
'Region: EU': {
'Tropical-Humid Land': {
'Estel et al. 2015, 2nd Poly, capped at 94.7mha':
THISDIR.joinpath(
'ad',
'ad_Estel_et_al__2015_2nd_Poly_capped_at_94_7mha.csv'),
},
},
}
self.ad = adoptiondata.AdoptionData(ac=self.ac,
data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
growth_initial = pd.DataFrame(
[[2018] + list(self.ac.ref_base_adoption.values())],
columns=ca_pds_columns).set_index('Year')
tla_init = self.ac.ref_base_adoption['World']
tla_grow = self.tla_per_region.loc[2050, 'World'] - tla_init
ca_pds_data_sources = [
{
'name': '1.32% ann, Linear Trend',
'include': True,
'growth_rate': 0.013219212962963,
# Limited information on restoration of abandoned farmland is available (see
# data interpolation sheet). As abandoned farmlands area a subset of degraded
# land, it is asumed that the restoration of abandoned farmland will also follow
# similar trends. Refer sheet, "Adoption rates-Deg Area".
'growth_initial': growth_initial
},
{
'name': '2.64% annual rate, Linear Trend',
'include': False,
# This scenario projects future growth by doubling of historical annual
# rate in India
'growth_rate': (2 * 0.013219212962963),
'growth_initial': growth_initial
},
{
'name':
'63% of TLA, Linear Trend',
'include':
True,
# It is assumed that by 2050, 63% of the total degraded farmlands will be restored
# for cropping. This is the highest reported adoption - from rainfed cultivated
# areas in India over several decades: see Adoption trends-Deg Area worksheet.
'datapoints':
pd.DataFrame([
[2012] + list(self.ac.ref_base_adoption.values()),
[2018] + list(self.ac.ref_base_adoption.values()),
[
2050, tla_init + (tla_grow * 0.63), 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Very high: 85% of TLA, Linear Trend',
'include':
False,
# It is assumed that by 2050, 85% of the total abandoned farmlands will be
# restored for cropping.
'datapoints':
pd.DataFrame([
[2012] + list(self.ac.ref_base_adoption.values()),
[2018] + list(self.ac.ref_base_adoption.values()),
[
2050, tla_init + (tla_grow * 0.85), 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Maximum, Linear Trend',
'include':
True,
# It is assumed that by 2050, 100% of the total abandoned farmlands will be
# restored for cropping.
'datapoints':
pd.DataFrame([
[2012] + list(self.ac.ref_base_adoption.values()),
[2018] + list(self.ac.ref_base_adoption.values()),
[
2050, tla_init + (tla_grow * 1.00), 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Historical linear based on Whenzhou province China reclaimed lands',
'include': True,
# Based on low interpolated linear trend from historical data on annual adoption
# rate for Whenzhoue province China; Lin, Jia et al 2017
'include': True,
'growth_rate': 0.0696,
'growth_initial': growth_initial
},
{
'name': 'Historical linear based on EU -Estel et al 2015',
'include': True,
# Based on low interpolated linear trend from historical data on annual adoption
# rate for EU, based on Estel et al 2015
'growth_rate': 0.0568,
'growth_initial': growth_initial
},
{
'name':
'Historical linear trend based on Kazakstan, Dara et al 2017',
'include': True,
# Based on low interpolated linear trend from historical data on annual adoption
# rate for Kazahkstan, based on Dara et al 2017
'growth_rate': 0.0512,
'growth_initial': growth_initial
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
for s in self.pds_ca.scenarios.values():
df = s['df']
for year in range(2012, 2019):
df.loc[year] = [
20.029602999999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0
]
df.sort_index(inplace=True)
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(list(
self.ac.ref_base_adoption.values()),
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
use_first_pds_datapoint_main=True,
adoption_base_year=2018,
copy_pds_to_ref=False,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name,
cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
if self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(
filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
adconfig_list = [
[
'param', 'World', 'OECD90', 'Eastern Europe',
'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America',
'China', 'India', 'EU', 'USA'
],
[
'trend', self.ac.soln_pds_adoption_prognostication_trend,
'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'
],
[
'growth', self.ac.soln_pds_adoption_prognostication_growth,
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE'
],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
]
adconfig = pd.DataFrame(adconfig_list[1:],
columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {
'Raw Data for ALL LAND TYPES': {
'Lassaletta et al 2014':
THISDIR.joinpath('ad', 'ad_Lassaletta_et_al_2014.csv'),
},
}
self.ad = adoptiondata.AdoptionData(ac=self.ac,
data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
adoption_2014 = self.ac.ref_base_adoption['World']
tla_2050 = self.tla_per_region.loc[2050, 'World']
area_N_eff_1990_1995 = 225.049083333333
area_N_eff_1996_2000 = 89.7290333333333
area_N_eff_2001_2005 = 42.7382
area_N_eff_2006_2010 = 98.7301833333333
ds1_o90_2050 = 0.46568375652467 * self.tla_per_region.loc[2050,
'OECD90']
ds1_ee_2050 = 0.169344179047277 * self.tla_per_region.loc[
2050, 'Eastern Europe']
ds1_asj_2050 = 0.194261406995049 * self.tla_per_region.loc[
2050, 'Asia (Sans Japan)']
ds1_mea_2050 = 0.141859451675443 * self.tla_per_region.loc[
2050, 'Middle East and Africa']
ds1_la_2050 = 0.712235299292505 * self.tla_per_region.loc[
2050, 'Latin America']
ds1_world_2050 = ds1_o90_2050 + ds1_ee_2050 + ds1_asj_2050 + ds1_mea_2050 + ds1_la_2050
ds2_o90_2050 = 0.791283756524669 * self.tla_per_region.loc[2050,
'OECD90']
ds2_ee_2050 = 0.494944179047277 * self.tla_per_region.loc[
2050, 'Eastern Europe']
ds2_asj_2050 = 0.51986140699505 * self.tla_per_region.loc[
2050, 'Asia (Sans Japan)']
ds2_mea_2050 = 0.467459451675442 * self.tla_per_region.loc[
2050, 'Middle East and Africa']
ds2_la_2050 = 1.03783529929251 * self.tla_per_region.loc[
2050, 'Latin America']
ds2_world_2050 = ds2_o90_2050 + ds2_ee_2050 + ds2_asj_2050 + ds2_mea_2050 + ds2_la_2050
ds3_o90_2050 = 1.0 * self.tla_per_region.loc[2050, 'OECD90']
ds3_ee_2050 = 1.0 * self.tla_per_region.loc[2050, 'Eastern Europe']
ds3_asj_2050 = 1.0 * self.tla_per_region.loc[2050, 'Asia (Sans Japan)']
ds3_mea_2050 = 1.0 * self.tla_per_region.loc[2050,
'Middle East and Africa']
ds3_la_2050 = 1.0 * self.tla_per_region.loc[2050, 'Latin America']
ds3_world_2050 = ds3_o90_2050 + ds3_ee_2050 + ds3_asj_2050 + ds3_mea_2050 + ds3_la_2050
ds_2018 = [
self.ac.ref_base_adoption['World'],
self.ac.ref_base_adoption['OECD90'],
self.ac.ref_base_adoption['Eastern Europe'],
self.ac.ref_base_adoption['Asia (Sans Japan)'],
self.ac.ref_base_adoption['Middle East and Africa'],
self.ac.ref_base_adoption['Latin America']
]
ca_pds_data_sources = [
{
'name':
'Aggressive-Low, Linear Trend',
'include':
True,
# The future adoption of nutrient management is build on the extent of the future
# adoption of Conservation Agrculture (CA). Nutrient management is one of the
# integral component of CA, thus, it is assumed that the adoption of nutrient
# management will follow the similar trend of CA. This scenario projects the
# future adoption of the solution based on the average annual future growth rate
# of conservation agriculture.
'datapoints':
pd.DataFrame([
[2018] + ds_2018 + [0.0, 0.0, 0.0, 0.0],
[
2050, ds1_world_2050, ds1_o90_2050, ds1_ee_2050,
ds1_asj_2050, ds1_mea_2050, ds1_la_2050, 0.0, 0.0, 0.0,
0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Aggressive-High, Linear Trend',
'include':
True,
# The future adoption of nutrient management is build on the extent of the future
# adoption of Conservation Agrculture (CA). Nutrient management is one of the integral
# component of CA, thus, it is assumed that the adoption of nutrient management will
# follow the similar trend of CA. This scenario projects the future adoption of the
# solution based on the high annual future growth rate of conservation agriculture.
'datapoints':
pd.DataFrame([
[2018] + ds_2018 + [0.0, 0.0, 0.0, 0.0],
[
2050, ds2_world_2050, ds2_o90_2050, ds2_ee_2050,
ds2_asj_2050, ds2_mea_2050, ds2_la_2050, 0.0, 0.0, 0.0,
0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Aggressive-Max, Linear Trend',
'include':
True,
# This is an aggressive scenario assumes adoption of the solution in 100% of the TLA
# by 2050 in all regions.
'datapoints':
pd.DataFrame([
[2018] + ds_2018 + [0.0, 0.0, 0.0, 0.0],
[
2050, ds3_world_2050, ds3_o90_2050, ds3_ee_2050,
ds3_asj_2050, ds3_mea_2050, ds3_la_2050, 0.0, 0.0, 0.0,
0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Aggressive-High, high early growth',
'include':
False,
# This scenario is assuming 60% of the total adoption will be achieved by 2030.
'datapoints':
pd.DataFrame([
[2018] + ds_2018 + [0.0, 0.0, 0.0, 0.0],
[
2030, 0.6 * ds2_world_2050, 0.6 * ds2_o90_2050, 0.6 *
ds2_ee_2050, 0.6 * ds2_asj_2050, 0.6 * ds2_mea_2050,
0.6 * ds2_la_2050, 0.0, 0.0, 0.0, 0.0
],
[
2050, ds2_world_2050, ds2_o90_2050, ds2_ee_2050,
ds2_asj_2050, ds2_mea_2050, ds2_la_2050, 0.0, 0.0, 0.0,
0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Aggressive-Low, high early growth',
'include':
False,
# "This is scenario 1, assuming 60% of the total adoption will be achieved by 2030.
'datapoints':
pd.DataFrame([
[2018] + ds_2018 + [0.0, 0.0, 0.0, 0.0],
[
2030, 0.6 * ds1_world_2050, 0.6 * ds1_o90_2050, 0.6 *
ds1_ee_2050, 0.6 * ds1_asj_2050, 0.6 * ds1_mea_2050,
0.6 * ds1_la_2050, 0.0, 0.0, 0.0, 0.0
],
[
2050, ds1_world_2050, ds1_o90_2050, ds1_ee_2050,
ds1_asj_2050, ds1_mea_2050, ds1_la_2050, 0.0, 0.0, 0.0,
0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Lassaletta et al 2014, conservative growth',
'include':
True,
# Future adoption is projected based on the maximum global area reported under
# Nitrogen Use Efficiency in the past by Lassaletta et al 2014.
'datapoints':
pd.DataFrame([
[2018] + ds_2018 + [0.0, 0.0, 0.0, 0.0],
[
2050, area_N_eff_1990_1995, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Lassaletta et al 2014, moderate growth',
'include':
True,
# Future adoption is projected based on the maximum global area reported under
# Nitrogen Use Efficiency in the past by Lassaletta et al 2014.
'datapoints':
pd.DataFrame([
[2018] + ds_2018 + [0.0, 0.0, 0.0, 0.0],
[
2050, 2 * area_N_eff_1990_1995, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Lassaletta et al 2014, high growth',
'include':
True,
# Future adoption is projected based on the maximum global area reported under
# Nitrogen Use Efficiency in the past by Lassaletta et al 2014.
'datapoints':
pd.DataFrame([
[2018] + ds_2018 + [0.0, 0.0, 0.0, 0.0],
[
2050, 4 * area_N_eff_1990_1995, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Sutton et al 2013, linear trend',
'include':
True,
# Future adoption is projected based on the projected global area under
# Nitrogen Use Efficiency by 2020 by Sutton et al 2013.
'datapoints':
pd.DataFrame([
[2018] + ds_2018 + [0.0, 0.0, 0.0, 0.0],
[
2020, 335.92674, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
# Manual adjustment made in spreadsheet for Drawdown 2020.
for s in self.pds_ca.scenarios.values():
df = s['df']
df.loc[2012:2018, 'World'] = 139.129613374975
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(list(
self.ac.ref_base_adoption.values()),
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2018] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
use_first_pds_datapoint_main=False,
adoption_base_year=2018,
copy_pds_to_ref=False,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name,
cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution())
adconfig_list = [
[
'param', 'World', 'OECD90', 'Eastern Europe',
'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America',
'China', 'India', 'EU', 'USA'
],
[
'trend', self.ac.soln_pds_adoption_prognostication_trend,
'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'
],
[
'growth', self.ac.soln_pds_adoption_prognostication_growth,
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE'
],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
]
adconfig = pd.DataFrame(adconfig_list[1:],
columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {
'Raw Data for ALL LAND TYPES': {
'FAOSTAT (Sum of all Regions)':
THISDIR.joinpath('ad', 'ad_FAOSTAT_Sum_of_all_Regions.csv'),
'Prestele baseline and Topdown (moderate growth) projection':
THISDIR.joinpath(
'ad',
'ad_Prestele_baseline_and_Topdown_moderate_growth_projection.csv'
),
'Prestele baseline and BottoUp: (maximum)':
THISDIR.joinpath(
'ad', 'ad_Prestele_baseline_and_BottoUp_maximum.csv'),
},
'Region: OECD90': {
'Raw Data for ALL LAND TYPES': {
'FAOSTAT':
THISDIR.joinpath('ad', 'ad_FAOSTAT.csv'),
'Prestele baseline and Topdown (moderate growth) projection':
THISDIR.joinpath(
'ad',
'ad_Prestele_baseline_and_Topdown_moderate_growth_projection.csv'
),
'Prestele baseline and BottoUp: (maximum)':
THISDIR.joinpath(
'ad', 'ad_Prestele_baseline_and_BottoUp_maximum.csv'),
},
},
'Region: Eastern Europe': {
'Raw Data for ALL LAND TYPES': {
'FAOSTAT':
THISDIR.joinpath('ad', 'ad_FAOSTAT.csv'),
'Prestele baseline and Topdown (moderate growth) projection':
THISDIR.joinpath(
'ad',
'ad_Prestele_baseline_and_Topdown_moderate_growth_projection.csv'
),
'Prestele baseline and BottoUp: (maximum)':
THISDIR.joinpath(
'ad', 'ad_Prestele_baseline_and_BottoUp_maximum.csv'),
},
},
'Region: Asia (Sans Japan)': {
'Raw Data for ALL LAND TYPES': {
'FAOSTAT':
THISDIR.joinpath('ad', 'ad_FAOSTAT.csv'),
'Prestele baseline and Topdown (moderate growth) projection':
THISDIR.joinpath(
'ad',
'ad_Prestele_baseline_and_Topdown_moderate_growth_projection.csv'
),
'Prestele baseline and BottoUp: (maximum)':
THISDIR.joinpath(
'ad', 'ad_Prestele_baseline_and_BottoUp_maximum.csv'),
},
},
'Region: Middle East and Africa': {
'Raw Data for ALL LAND TYPES': {
'FAOSTAT':
THISDIR.joinpath('ad', 'ad_FAOSTAT.csv'),
'Prestele baseline and Topdown (moderate growth) projection':
THISDIR.joinpath(
'ad',
'ad_Prestele_baseline_and_Topdown_moderate_growth_projection.csv'
),
'Prestele baseline and BottoUp: (maximum)':
THISDIR.joinpath(
'ad', 'ad_Prestele_baseline_and_BottoUp_maximum.csv'),
},
},
'Region: Latin America': {
'Raw Data for ALL LAND TYPES': {
'FAOSTAT':
THISDIR.joinpath('ad', 'ad_FAOSTAT.csv'),
'Prestele baseline and Topdown (moderate growth) projection':
THISDIR.joinpath(
'ad',
'ad_Prestele_baseline_and_Topdown_moderate_growth_projection.csv'
),
'Prestele baseline and BottoUp: (maximum)':
THISDIR.joinpath(
'ad', 'ad_Prestele_baseline_and_BottoUp_maximum.csv'),
},
},
}
self.ad = adoptiondata.AdoptionData(ac=self.ac,
data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
tla_2050 = self.tla_per_region.loc[2050]
ad_2018 = pd.Series(self.ac.ref_base_adoption)
# % smallholder area, based on updates to smallholder area VMA sheet
# (34.1% was estimated in Book Ed1)
smallholder_land = 0.315909724971454
large_farm_land = 1.0 - smallholder_land
max_cons_ag_large_farm_tla = self.tla_per_region.loc[
2050] * large_farm_land
max_cons_ag_large_farm_tla[dd.SPECIAL_COUNTRIES] = 0.0
ad_2080 = 1.5 * pd.Series(self.ac.ref_base_adoption)
# Maximum technical potential adoption area available for CA out of the 685 Mha based
# on percentage in Prestele et al 2018 (81.4%, bottom up, likely maximum future area)
# Values used in 2017 Project Drawdown book publication.
prestele_pct_book = pd.Series(
[0., 0.3952, 0.1662, 0.1632, 0.1422, 0.7332, 0., 0., 0., 0.],
index=dd.REGIONS)
prestele_mult = 0.814
prestele_ad_book = self.tla_per_region.loc[
2050] * prestele_mult * prestele_pct_book
# Data Source 1
# Source: Refer sheet "PD regional CA annual adoption"
if 'BookVersion' in self.ac.name:
pct = pd.Series(
[0., 0.3952, 0.1662, 0.1632, 0.1422, 0.7332, 0., 0., 0., 0.],
index=dd.REGIONS)
else:
pct = pd.Series([
0., 0.465683756524670, 0.169344179047277, 0.194261406995049,
0.141859451675443, 0.712235299292505, 0., 0., 0., 0.
],
index=dd.REGIONS)
ds1_poss_adopt_2050 = max_cons_ag_large_farm_tla * pct
ds1_datapoints = pd.DataFrame(columns=ca_pds_columns).set_index('Year')
ds1_datapoints.loc[2018] = ad_2018
ds1_datapoints.loc[2030] = (1.0 * ds1_poss_adopt_2050)
ds1_datapoints.loc[2050] = (1.0 * ds1_poss_adopt_2050)
ds1_datapoints.loc[2080] = ad_2080
ds1_datapoints['World'] = 0.0
ds1_datapoints['World'] = ds1_datapoints.sum(axis=1)
# Data Source 2
# Source: Refer sheet "PD regional CA annual adoption"
if 'BookVersion' in self.ac.name:
pct = pd.Series(
[0., 0.7208, 0.4918, 0.4888, 0.4678, 1.0, 0., 0., 0., 0.],
index=dd.REGIONS)
else:
pct = pd.Series([
0., 0.791283756524669, 0.494944179047277, 0.519861406995050,
0.467459451675442, 1.037835299292510, 0., 0., 0., 0.
],
index=dd.REGIONS)
ds2_poss_adopt_2050 = max_cons_ag_large_farm_tla * pct
ds2_datapoints = pd.DataFrame(columns=ca_pds_columns).set_index('Year')
ds2_datapoints.loc[2018] = ad_2018
ds2_datapoints.loc[2030] = (1.0 * ds2_poss_adopt_2050)
ds2_datapoints.loc[2050] = (1.0 * ds2_poss_adopt_2050)
ds2_datapoints.loc[2080] = ad_2080
ds2_datapoints['World'] = 0.0
ds2_datapoints['World'] = ds2_datapoints.sum(axis=1)
# Data Source 3
# Source: Refer sheet "PD regional CA annual adoption"
ds3_datapoints = pd.DataFrame(columns=ca_pds_columns).set_index('Year')
ds3_datapoints.loc[2018] = ad_2018
ds3_datapoints.loc[2030] = (0.8 * max_cons_ag_large_farm_tla)
ds3_datapoints.loc[2050] = (1.0 * max_cons_ag_large_farm_tla)
ds3_datapoints.loc[2080] = ad_2080
ds3_datapoints['World'] = 0.0
ds3_datapoints['World'] = ds3_datapoints.sum(axis=1)
# Data Source 4
# Source: Refer sheet "PD regional CA annual adoption"
ds4_datapoints = pd.DataFrame(columns=ca_pds_columns).set_index('Year')
ds4_datapoints.loc[2018] = ad_2018
ds4_datapoints.loc[2030] = (0.8 * ds2_poss_adopt_2050)
ds4_datapoints.loc[2050] = (0.6 * ds2_poss_adopt_2050)
ds4_datapoints.loc[2080] = ad_2080
ds4_datapoints['World'] = 0.0
ds4_datapoints['World'] = ds4_datapoints.sum(axis=1)
# Data Source 5
# Source: Refer sheet "PD regional CA annual adoption"
ds5_datapoints = pd.DataFrame(columns=ca_pds_columns).set_index('Year')
ds5_datapoints.loc[2018] = ad_2018
ds5_datapoints.loc[2030] = (0.8 * ds1_poss_adopt_2050)
ds5_datapoints.loc[2050] = (0.6 * ds1_poss_adopt_2050)
ds5_datapoints.loc[2080] = ad_2080
ds5_datapoints['World'] = 0.0
ds5_datapoints['World'] = ds5_datapoints.sum(axis=1)
# Data Source 6
# Source: see PresteleAdoptionCalc sheet
if 'BookVersion' in self.ac.name:
ds6_prestele_poss_adopt_2050 = prestele_ad_book
else:
pct = pd.Series([
0., 0.740285597497468, 0.61878404520945, 0.581170964964222,
0.0900760503108076, 0.803736844958406, 0., 0., 0., 0.
],
index=dd.REGIONS)
ds6_prestele_poss_adopt_2050 = self.tla_per_region.loc[
2050] * prestele_mult * pct
ds6_datapoints = pd.DataFrame(columns=ca_pds_columns).set_index('Year')
ds6_datapoints.loc[2018] = ad_2018
ds6_datapoints.loc[2030] = (0.8 * ds6_prestele_poss_adopt_2050)
ds6_datapoints.loc[2050] = (0.6 * ds6_prestele_poss_adopt_2050)
ds6_datapoints.loc[2080] = ad_2080
ds6_datapoints['World'] = 0.0
ds6_datapoints['World'] = ds6_datapoints.sum(axis=1)
# Data Source 7
# Source: see PresteleAdoptionCalc sheet
if 'BookVersion' in self.ac.name:
ds7_prestele_poss_adopt_2050 = prestele_ad_book
else:
pct = pd.Series(
[0., 1.0, 1.0, 1.0, 0.320741884451365, 1.0, 0., 0., 0., 0.],
index=dd.REGIONS)
ds7_prestele_poss_adopt_2050 = self.tla_per_region.loc[
2050] * prestele_mult * pct
ds7_datapoints = pd.DataFrame(columns=ca_pds_columns).set_index('Year')
ds7_datapoints.loc[2018] = ad_2018
ds7_datapoints.loc[2030] = (0.8 * ds7_prestele_poss_adopt_2050)
ds7_datapoints.loc[2050] = (0.6 * ds7_prestele_poss_adopt_2050)
ds7_datapoints.loc[2080] = ad_2080
ds7_datapoints['World'] = 0.0
ds7_datapoints['World'] = ds7_datapoints.sum(axis=1)
# Data Source 8
# Source: see PresteleAdoptionCalc sheet
if 'BookVersion' in self.ac.name:
ds8_prestele_poss_adopt_2050 = prestele_ad_book
else:
pct = pd.Series([
0., 0.46568375652467, 0.169344179047277, 0.194261406995049,
0.141859451675443, 0.712235299292505, 0., 0., 0., 0.
],
index=dd.REGIONS)
ds8_prestele_poss_adopt_2050 = self.tla_per_region.loc[
2050] * prestele_mult * pct
ds8_datapoints = pd.DataFrame(columns=ca_pds_columns).set_index('Year')
ds8_datapoints.loc[2018] = ad_2018
ds8_datapoints.loc[2050] = (1.0 * ds8_prestele_poss_adopt_2050)
ds8_datapoints['World'] = 0.0
ds8_datapoints['World'] = ds8_datapoints.sum(axis=1)
ca_pds_data_sources = [
{
'name':
'Low,High Early Adoption, Polynomial Trend',
'include':
False,
'description':
('An annual growth rate of 0.36% was used to forecast regional adoption. '
'Regional adoption of CA in the year 2050 was calculated and their '
'percentage to the total arable land area was estimated, and are used as '
'assumptions 1-5, below. It is also assumed that 100% of the projected '
'adoption will be achieved by 2030. As, it is a bridge solution to RA, the '
'area under CA will decline post 2030 and will remains to only 80% of the '
'estimated adoption by 2050. '),
'datapoints':
ds1_datapoints,
'datapoints_degree':
3
},
{
'name':
'High,High Early Adoption, Polynomial Trend',
'include':
False,
'description':
('Adoption of CA was reported maximum in Brazil, across the globe. Thus, its '
'annual growth rate of 1.24% was used to forecast regional adoption. '
'Regional adoption of CA in the year 2050 was calculated and their '
'percentage to the total arable land area was estimated, and are used as '
'assumptions 1-5, below. It is also assumed that 100% of the projected '
'adoption will be achieved by 2030. As, it is a bridge solution to RA, the '
'area under CA will decline post 2030 and will remains to only 80% of the '
'estimated adoption by 2050. '),
'datapoints':
ds2_datapoints,
'datapoints_degree':
3
},
{
'name':
'Max, Polynomial Trend',
'include':
False,
'description':
('A 100% regional adoption was assumed in this scenario. '),
'datapoints':
ds3_datapoints,
'datapoints_degree':
3
},
{
'name':
'High, Early Adoption, Polynomial Trend',
'include':
False,
'description':
('Adoption of CA was reported maximum in Brazil, across the globe. Thus, its '
'annual growth rate of 1.24% was used to forecast regional adoption. '
'Regional adoption of CA in the year 2050 was calculated and their '
'percentage to the total arable land area was estimated, and are used as '
'assumptions 1-5, below. It is also assumed that 80% of the projected '
'adoption will be achieved by 2030. As, it is a bridge solution to RA, the '
'area under CA will decline post 2030 and will remains to only 60% of the '
'estimated adoption by 2050. '),
'datapoints':
ds4_datapoints,
'datapoints_degree':
3
},
{
'name':
'Low, Early Adoption, Polynomial Trend',
'include':
False,
'description':
('An annual growth rate of 0.36% was used to forecast regional adoption. '
'Regional adoption of CA in the year 2050 was calculated and their '
'percentage to the total arable land area was estimated, and are used as '
'assumptions 1-5, below. It is also assumed that 80% of the projected '
'adoption will be achieved by 2030. As, it is a bridge solution to RA, the '
'area under CA will decline post 2030 and will remains to only 60% of the '
'estimated adoption by 2050. '),
'datapoints':
ds5_datapoints,
'datapoints_degree':
3
},
{
'name':
'Moderate CA Adoption rates based on Prestele Topdown map, polynomial',
'include':
False,
'description':
('Regional adoption of CA in the year 2050 was calculated and their '
'percentage to the total arable land area was estimated, and are used as '
'assumptions 1-5, below. It is also assumed that 80% of the projected '
'adoption will be achieved by 2030. It is a bridge solution to RA so the '
'area under CA will decline post 2030 and will remain to only 60% of the '
'estimated adoption by 2050. SEI added current and projected adoption and '
'max potential adoption (assumption 10) based on Prestele et al 2018. after '
'applying Prestele regional percent adoption by TopDown projection '
'(intermediate adoption rate to 2050), to country -level data available in '
'their supplementary materials, then summing by PDrawdown regions (which '
'differ from Prestele regions). The % of regional Drawdown TLA projected by '
'2050 using Prestele projections (not mean of data) was calculated and used '
'in Revised growth assumptions 1-5. '),
'datapoints':
ds6_datapoints,
'datapoints_degree':
3
},
{
'name':
'Likely max CA based on Prestele BottomUp Map, polynominal',
'include':
False,
'description':
('Regional adoption of CA in the year 2050 was calculated and their '
'percentage to the total arable land area was estimated, and are used as '
'assumptions 1-5, below. It is also assumed that 80% of the projected '
'adoption will be achieved by 2030. It is a bridge solution to RA so the '
'area under CA will decline post 2030 and will remain to only 60% of the '
'estimated adoption by 2050. SEI added current and projected adoption and '
'max potential adoption (assumption 10) based on Prestele et al 2018. after '
'applying Prestele regional percent adoption by BOTTOM UP projection (forced '
'to 2050), to country -level data available in their supplementary '
'materials, then summing by PDrawdown regions (which differ from Prestele '
'regions). The % of regional Drawdown TLA projected by 2050 using Prestele '
'projections (not mean of data) was calculated OR if the bottom up '
'projection result in extent larger than Drawdown regional TLA, then 100% '
'was used in Revised growth assumptions 1-5. '),
'datapoints':
ds7_datapoints,
'datapoints_degree':
3
},
{
'name': 'Baseline scenario',
'include': True,
'description': ('tis is the average of scenarios 1-7 '),
'datapoints': ds8_datapoints,
'maximum': max_cons_ag_large_farm_tla['World']
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
for s in self.pds_ca.scenarios.values():
df = s['df']
df.loc[2014] = [
108.926170040128000, 43.700575542980000, 9.040960261572450,
8.934730992728910, 1.113417893008100, 46.136485349838400, 0.0,
0.0, 0.0, 0.0
]
df.loc[2015] = [
118.522634649202000, 47.190416175021700, 10.277201842542000,
10.390765035686000, 1.355692262948170, 49.308559333003700, 0.0,
0.0, 0.0, 0.0
]
df.loc[2016] = [
128.231557482326000, 50.685767480789800, 11.580017143547000,
11.936077959964700, 1.613327416836600, 52.416367481188000, 0.0,
0.0, 0.0, 0.0
]
df.loc[2017] = [
138.054065103220000, 54.187020002847500, 12.949707603363100,
13.570892429945200, 1.886419963055470, 55.460025104009000, 0.0,
0.0, 0.0, 0.0
]
df.loc[2018] = [
147.991284075603000, 57.694564283758300, 14.386574660765900,
15.295431110007600, 2.175066509986860, 58.439647511084400, 0.0,
0.0, 0.0, 0.0
]
# Custom REF Data
ca_ref_data_sources = [
{
'name':
'[Type Scenario 1 Name Here (REF CASE)...]',
'include':
True,
'description':
('[PLEASE DESCRIBE IN DETAIL THE METHODOLOGY YOU USED IN THIS ANALYSIS. BE '
'SURE TO INCLUDE ANY ADDITIONAL EQUATIONS YOU UTILIZED] '),
'filename':
THISDIR.joinpath(
'ca_ref_data',
'custom_ref_ad_Type_Scenario_1_Name_Here_REF_CASE_.csv')
},
]
self.ref_ca = customadoption.CustomAdoption(
data_sources=ca_ref_data_sources,
soln_adoption_custom_name=self.ac.soln_ref_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
if self.ac.soln_ref_adoption_basis == 'Custom':
ref_adoption_data_per_region = self.ref_ca.adoption_data_per_region(
)
else:
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(list(
self.ac.ref_base_adoption.values()),
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2014] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2018] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
ref_adoption_data_per_region=ref_adoption_data_per_region,
use_first_pds_datapoint_main=False,
adoption_base_year=2018,
copy_pds_to_ref=False,
copy_ref_datapoint=False,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name)
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution())
# Custom PDS Data
ca_pds_data_sources = [
{
'name':
'Linear trend based on Zomers >30% tree cover percent area applied in grassland area',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Linear_trend_based_on_Zomers_30_tree_cover_percent_area_applied_in_grassland_area.csv'
)
},
{
'name':
'Linear trend based on Zomers >30% tree cover percent area and conversion of >10% are to 30% tree cover area applied in grassland area',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Linear_trend_based_on_Zomers_30_tree_cover_percent_area_and_conversion_of_10_are_to_30_t_d419700f.csv'
)
},
{
'name':
'Medium growth, linear trend',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Medium_growth_linear_trend.csv')
},
{
'name':
'High growth, linear trend',
'include':
False,
'filename':
THISDIR.joinpath('ca_pds_data',
'custom_pds_ad_High_growth_linear_trend.csv')
},
{
'name':
'Low growth, linear trend (based on improved pasture area)',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Low_growth_linear_trend_based_on_improved_pasture_area.csv'
)
},
{
'name':
'High growth, linear trend (based on improved pasture area)',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_High_growth_linear_trend_based_on_improved_pasture_area.csv'
)
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=0.5,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
ht_ref_adoption_initial = pd.Series(
[314.15, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2014] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_regions, ht_percentages = zip(
*self.ac.pds_adoption_final_percentage)
ht_pds_adoption_final_percentage = pd.Series(list(ht_percentages),
index=list(ht_regions))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution())
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name)
if self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(self.ae.get_land_distribution(), custom_world_values=custom_world_vals)
# Custom PDS Data
ca_pds_columns = ['Year', 'World'] + dd.MAIN_REGIONS
ca_pds_data_sources = [
{'name': 'Average growth, linear trend', 'include': True,
# This scenario is built on the historical (1962-2012) average global growth rate of
# tropical staple crops. The historical data available for each decade was interpolated
# based on the best curve fit and the interpolated data were then used in the
# AdoptionData. The calculation uses the 2050 adopted value and calculates the
# percentage with reference to the TLA, which is used to build this adoption scenario.
'datapoints': pd.DataFrame([
[2014, 25.42446198181150, 0.0, 0.0, 0.0, 0.0, 0.0],
[2050, self.tla_per_region.loc[2050, 'World'] * 0.6179095040166380,
0.0, 0.0, 0.0, 0.0, 0.0],
], columns=ca_pds_columns).set_index('Year')
},
{'name': 'Medium growth, linear trend', 'include': True,
# This scenario is built on the historical (1962-2012) average global growth rate of
# tropical staple crops. The historical data available for each decade was interpolated
# based on the best curve fit and the interpolated data were then used in the
# AdoptionData. This scenario presents the result assuming a 5% increase on the
# historical growth rate. The calculation uses the 2050 adopted value and calculates
# the percentage with reference to the TLA, which is used to build this adoption scenario.
'datapoints': pd.DataFrame([
[2014, 25.42446198181150, 0.0, 0.0, 0.0, 0.0, 0.0],
[2050, self.tla_per_region.loc[2050, 'World'] * 0.648804979217470,
0.0, 0.0, 0.0, 0.0, 0.0],
], columns=ca_pds_columns).set_index('Year')
},
{'name': 'Low growth linear trend', 'include': True,
# It is assumed that the adoption of tropical tree staples will reach 55% of its
# TLA by 2050.
'datapoints': pd.DataFrame([
[2014, 25.42446198181150, 0.0, 0.0, 0.0, 0.0, 0.0],
[2050, self.tla_per_region.loc[2050, 'World'] * 0.55, 0.0, 0.0, 0.0, 0.0, 0.0],
], columns=ca_pds_columns).set_index('Year')
},
{'name': 'Low early growth, linear trend', 'include': True,
# This scenario assumes 60% adoption of the solution by 2030 and remains same until 2050.
# The early adoption of the solution was considered because of the higher carbon and
# financial impact of the solution and lower land availability.
'datapoints': pd.DataFrame([
[2014, 25.42446198181150, 0.0, 0.0, 0.0, 0.0, 0.0],
[2030, self.tla_per_region.loc[2030, 'World'] * 0.6, 0.0, 0.0, 0.0, 0.0, 0.0],
[2050, self.tla_per_region.loc[2050, 'World'] * 0.6, 0.0, 0.0, 0.0, 0.0, 0.0],
], columns=ca_pds_columns).set_index('Year')
},
{'name': 'Max early growth, linear trend', 'include': True,
# This scenario assumes 100% adoption of the solution by 2050 with the assumption that
# 70% of that adoption will be acheived by 2030. The early adoption of the solution was
# considered because of the higher carbon and financial impact of the solution and lower
# land availability.
'datapoints': pd.DataFrame([
[2014, 25.42446198181150, 0.0, 0.0, 0.0, 0.0, 0.0],
[2030, self.tla_per_region.loc[2030, 'World'] * 0.7, 0.0, 0.0, 0.0, 0.0, 0.0],
[2050, self.tla_per_region.loc[2050, 'World'] * 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
], columns=ca_pds_columns).set_index('Year')
},
]
self.pds_ca = customadoption.CustomAdoption(data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0, low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region()
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region()
pds_adoption_is_single_source = None
ht_ref_adoption_initial = pd.Series(
[25.424461981811515, 0.03142549034684199, 0.0, 28.59165470128803, 17.01389600193888,
5.211947770049295, 0.0, 0.0, 0.0, 0.0],
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2014] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_regions, ht_percentages = zip(*self.ac.pds_adoption_final_percentage)
ht_pds_adoption_final_percentage = pd.Series(list(ht_percentages), index=list(ht_regions))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(ac=self.ac,
ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region, pds_adoption_limits=self.tla_per_region,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(ac=self.ac,
ref_total_adoption_units=self.tla_per_region, pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted=self.ua.soln_net_annual_funits_adopted()
self.fc = firstcost.FirstCost(ac=self.ac, pds_learning_increase_mult=2,
ref_learning_increase_mult=2, conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution())
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name)
if self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(
filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
commit_13_dec_2016 = 136.32 # commitment to reforestation made Dec 13, 2016.
commit_13_dec_2016_tmr = 0.93 # % commitment that are projects in tropical TMR (based on country level commitments)
commit_13_dec_2016_mha = commit_13_dec_2016 * commit_13_dec_2016_tmr # Mha of current commitments in tropical TMR
intact_13_dec_2016 = 0.328 # % commitment in tropical TMR available are for intact forest
# restoration (based on country level commitments)
max_land_bonn = 350.0 # Max land Expected total land commited under Bonn Challenge and NY Declaration
max_land_wri = self.tla_per_region.loc[
2050, 'World'] # Total degraded land suitable for
# restoration, considered to be in tropical TMRs by WRI
commit_1_mha_new = max_land_bonn - commit_13_dec_2016 # Mha of total degraded land available for
# new commitments in tropical TMR + additional degraded land available from the Forest
# Protection solution, for which exact area has to be estimated.
final_adoption_1 = (commit_13_dec_2016_mha *
intact_13_dec_2016) + (commit_1_mha_new * 1.0)
data_source_1 = {
'name':
'Optimistic-Achieve Commitment in 15 years w/ 100% intact, NYDF/2030 (Charlotte Wheeler, 2016)',
'include':
True,
# The adoption scenarios are calculated using the linear trendline based on
# (i) current restoration commitments to date (as taken on 13/12/2016 from Bonn
# Challenge website);
# (ii) potential future commitments for intact forest restoration in tropics {32.80%
# (based on current commitments) or 100% (projected)};
# (iii) the proportion of committed land restored to intact forest {304 million
# hectares (based on WRI calculation) or 350 million hectares (based on New York
# Declaration)}; and
# (iv) the year commitments are realized (2030, 2045 or 2060).
# The current restoration commitments is fixed in all custom scenarios, while the rest
# variables changes from scenario to scenario.This scenario projects the future adoption
# of the solution considering, (1) 100% intact forest restoration, (2) 350 million
# hectare land availability for future restoration of tropical forests, and
# (3) the commitments will be realized by the year 2030.
'datapoints':
pd.DataFrame([
[2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[
2030, final_adoption_1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
[
2031, final_adoption_1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
}
commit_2_mha_new = max_land_wri - (commit_13_dec_2016 *
commit_13_dec_2016_tmr)
final_adoption_2 = (commit_13_dec_2016_mha *
intact_13_dec_2016) + (commit_2_mha_new * 1.0)
data_source_2 = {
'name':
'Optimistic-Achieve Commitment in 15 years w/ 100% intact, WRI/2030 (Charlotte Wheeler, 2016)',
'include':
True,
# The adoption scenarios are calculated using the linear trendline based on
# (i) current restoration commitments to date (as taken on 13/12/2016 from Bonn
# Challenge website);
# (ii) potential future commitments for intact forest restoration in tropics {32.80%
# (based on current commitments) or 100% (projected)};
# (iii) the proportion of committed land restored to intact forest {304 million hectares
# (based on WRI calculation) or 350 million hectares (based on New York
# Declaration)}; and
# (iv) the year commitments are realized (2030, 2045 or 2060).
# The current restoration commitments is fixed in all custom scenarios, while the rest
# variables changes from scenario to scenario.This scenario projects the future adoption
# of the solution considering, (1) 100% intact forest restoration, (2) 304 million
# hectare land availability for future restoration of tropical forests, and (3) the
# commitments will be realized by the year 2030.
'datapoints':
pd.DataFrame([
[2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[
2030, final_adoption_2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
[
2031, final_adoption_2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
}
future_3_tmr = 0.328 # % of future commitments in tropical thermal moisture regime available are for intact forest restoration
final_adoption_3 = ((commit_13_dec_2016_mha * intact_13_dec_2016) + (
(max_land_wri - commit_13_dec_2016_mha) * future_3_tmr))
data_source_3 = {
'name':
'Conservative-Achieve Commitment in 15 years w/ 32.8% intact, WRI/2030 (Charlotte Wheeler,2016)',
'include':
True,
# The adoption scenarios are calculated using the linear trendline based on
# (i) current restoration commitments to date (as taken on 13/12/2016 from Bonn
# Challenge website);
# (ii) potential future commitments for intact forest restoration in tropics {32.80%
# (based on current commitments) or 100% (projected)};
# (iii) the proportion of committed land restored to intact forest {304 million hectares
# (based on WRI calculation) or 350 million hectares (based on New York
# Declaration)}; and
# (iv) the year commitments are realized (2030, 2045 or 2060).
# The current restoration commitments is fixed in all custom scenarios, while the rest
# variables changes from scenario to scenario.This scenario projects the future
# adoption of the solution considering, (1) 32.8% intact forest restoration,
# (2) 304 million hectare land availability for future restoration of tropical forests,
# and (3) the commitments will be realized by the year 2030.
'datapoints':
pd.DataFrame([
[2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[
2030, final_adoption_3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
[
2031, final_adoption_3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
}
future_4_tmr = 0.328 # % of future commitments in tropical thermal moisture regime available are for intact forest restoration
final_adoption_4 = ((commit_13_dec_2016_mha * intact_13_dec_2016) + (
(max_land_wri - commit_13_dec_2016_mha) * future_4_tmr))
data_source_4 = {
'name':
'Conservative-Achieve Commitment in 15 years w/ 32.8% intact with continued growth post-2030, WRI/2030 and beyond (Charlotte Wheeler,2016)',
'include':
True,
# The adoption scenarios are calculated using the linear trendline based on
# (i) current restoration commitments to date (as taken on 13/12/2016 from Bonn
# Challenge website);
# (ii) potential future commitments for intact forest restoration in tropics {32.80%
# (based on current commitments) or 100% (projected)};
# (iii) the proportion of committed land restored to intact forest {304 million hectares
# (based on WRI calculation) or 350 million hectares (based on New York
# Declaration)}; and
# (iv) the year commitments are realized (2030, 2045 or 2060).
# The current restoration commitments is fixed in all custom scenarios, while the rest
# variables changes from scenario to scenario.This scenario projects the future
# adoption of the solution considering, (1) 32.8% intact forest restoration,
# (2) 304 million hectare land availability for future restoration of tropical forests,
# and (3) the commitments will be realized by the year 2030.
#
# This is scenario 3, except, it is also assumed that the adoption of the solution
# will continue post 2030 as well.
'datapoints':
pd.DataFrame([
[2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[
2030, final_adoption_4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
}
future_5_tmr = 1.0 # % of future commitments in tropical thermal moisture regime available are for intact forest restoration
final_adoption_5 = ((commit_13_dec_2016_mha * intact_13_dec_2016) + (
(max_land_wri - commit_13_dec_2016_mha) * future_5_tmr))
data_source_5 = {
'name':
'Conservative-Achieve Commitment in 30 years w/ 100% intact, WRI/2045 (Charlotte Wheeler,2016)',
'include':
True,
# The adoption scenarios are calculated using the linear trendline based on
# (i) current restoration commitments to date (as taken on 13/12/2016 from Bonn
# Challenge website);
# (ii) potential future commitments for intact forest restoration in tropics {32.80%
# (based on current commitments) or 100% (projected)};
# (iii) the proportion of committed land restored to intact forest {304 million hectares
# (based on WRI calculation) or 350 million hectares (based on New York
# Declaration)}; and
# (iv) the year commitments are realized (2030, 2045 or 2060).
# The current restoration commitments is fixed in all custom scenarios, while the rest
# variables changes from scenario to scenario.This scenario projects the future
# adoption of the solution considering, (1) 100% intact forest restoration,
# (2) 304 million hectare land availability for future restoration of tropical forests,
# and (3) the commitments will be realized by the year 2045.
'datapoints':
pd.DataFrame([
[2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[
2045, final_adoption_5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
[
2046, final_adoption_5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
}
future_6_tmr = 0.328 # % of future commitments in tropical thermal moisture regime available are for intact forest restoration
final_adoption_6 = ((commit_13_dec_2016_mha * intact_13_dec_2016) + (
(max_land_wri - commit_13_dec_2016_mha) * future_6_tmr))
data_source_6 = {
'name':
'Conservative-Achieve Commitment in 30 years w/ 32.8% intact, WRI/2045 (Charlotte Wheeler,2016)',
'include':
True,
# The adoption scenarios are calculated using the linear trendline based on
# (i) current restoration commitments to date (as taken on 13/12/2016 from Bonn
# Challenge website);
# (ii) potential future commitments for intact forest restoration in tropics {32.80%
# (based on current commitments) or 100% (projected)};
# (iii) the proportion of committed land restored to intact forest {304 million hectares
# (based on WRI calculation) or 350 million hectares (based on New York
# Declaration)}; and
# (iv) the year commitments are realized (2030, 2045 or 2060).
# The current restoration commitments is fixed in all custom scenarios, while the rest
# variables changes from scenario to scenario.This scenario projects the future
# adoption of the solution considering, (1) 32.8% intact forest restoration,
# (2) 304 million hectare land availability for future restoration of tropical forests,
# and (3) the commitments will be realized by the year 2045.
'datapoints':
pd.DataFrame([
[2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[
2045, final_adoption_6, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
[
2046, final_adoption_6, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
}
future_7_tmr = 0.328 # % of future commitments in tropical thermal moisture regime available are for intact forest restoration
final_adoption_7 = ((commit_13_dec_2016_mha * intact_13_dec_2016) + (
(max_land_wri - commit_13_dec_2016_mha) * future_7_tmr))
data_source_7 = {
'name':
'Conservative-Achieve Commitment in 30 years w/ 32.8% intact with continued growth, WRI/2045 (Charlotte Wheeler,2016)',
'include':
True,
# The adoption scenarios are calculated using the linear trendline based on
# (i) current restoration commitments to date (as taken on 13/12/2016 from Bonn
# Challenge website);
# (ii) potential future commitments for intact forest restoration in tropics {32.80%
# (based on current commitments) or 100% (projected)};
# (iii) the proportion of committed land restored to intact forest {304 million hectares
# (based on WRI calculation) or 350 million hectares (based on New York
# Declaration)}; and
# (iv) the year commitments are realized (2030, 2045 or 2060).
# The current restoration commitments is fixed in all custom scenarios, while the rest
# variables changes from scenario to scenario. This scenario projects the future
# adoption of the solution considering, (1) 32.8% intact forest restoration,
# (2) 304 million hectare land availability for future restoration of tropical forests,
# and (3) the commitments will be realized by the year 2045.
#
# This is scenario 6, except, it is also assumed that the adoption of the solution
# will continue post 2045 as well.
'datapoints':
pd.DataFrame([
[2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[
2045, final_adoption_7, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
}
future_8_tmr = 1.0 # % of future commitments in tropical thermal moisture regime available are for intact forest restoration
final_adoption_8 = ((commit_13_dec_2016_mha * intact_13_dec_2016) + (
(max_land_wri - commit_13_dec_2016_mha) * future_8_tmr))
data_source_8 = {
'name':
'Conservative-Achieve Commitment in 45 years w/ 100% intact, WRI/2060 (Charlotte Wheeler,2016)',
'include':
False,
# The adoption scenarios are calculated using the linear trendline based on
# (i) current restoration commitments to date (as taken on 13/12/2016 from Bonn
# Challenge website);
# (ii) potential future commitments for intact forest restoration in tropics {32.80%
# (based on current commitments) or 100% (projected)};
# (iii) the proportion of committed land restored to intact forest {304 million hectares
# (based on WRI calculation) or 350 million hectares (based on New York
# Declaration)}; and
# (iv) the year commitments are realized (2030, 2045 or 2060).
# The current restoration commitments is fixed in all custom scenarios, while the rest
# variables changes from scenario to scenario.
#
# This scenario projects the future adoption of the solution considering,
# (1) 100% intact forest restoration, (2) 304 million hectare land availability
# for future restoration of tropical forests, and (3) the commitments will be
# realized by the year 2060.
'datapoints':
pd.DataFrame([
[2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[
2060, final_adoption_8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
}
future_9_tmr = 0.328 # % of future commitments in tropical thermal moisture regime available are for intact forest restoration
final_adoption_9 = ((commit_13_dec_2016_mha * intact_13_dec_2016) + (
(max_land_wri - commit_13_dec_2016_mha) * future_9_tmr))
data_source_9 = {
'name':
'Conservative-Achieve Commitment in 45 years w/ 32.8% intact, WRI/2060 (Charlotte Wheeler,2016)',
'include':
False,
# The adoption scenarios are calculated using the linear trendline based on
# (i) current restoration commitments to date (as taken on 13/12/2016 from Bonn
# Challenge website);
# (ii) potential future commitments for intact forest restoration in tropics {32.80%
# (based on current commitments) or 100% (projected)};
# (iii) the proportion of committed land restored to intact forest {304 million hectares
# (based on WRI calculation) or 350 million hectares (based on New York
# Declaration)}; and
# (iv) the year commitments are realized (2030, 2045 or 2060).
# The current restoration commitments is fixed in all custom scenarios, while the rest
# variables changes from scenario to scenario.
#
# This scenario projects the future adoption of the solution considering,
# (1) 32.8% intact forest restoration, (2) 304 million hectare land availability
# for future restoration of tropical forests, and (3) the commitments will be realized
# by the year 2060.
'datapoints':
pd.DataFrame([
[2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[
2060, final_adoption_9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
}
ca_pds_data_sources = [
data_source_1, data_source_2, data_source_3, data_source_4,
data_source_5, data_source_6, data_source_7, data_source_8,
data_source_9
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
ht_ref_adoption_initial = pd.Series(
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2014] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_regions, ht_percentages = zip(
*self.ac.pds_adoption_final_percentage)
ht_pds_adoption_final_percentage = pd.Series(list(ht_percentages),
index=list(ht_regions))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution())
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name,
cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
if self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(
filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
adconfig_list = [
[
'param', 'World', 'OECD90', 'Eastern Europe',
'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America',
'China', 'India', 'EU', 'USA'
],
[
'trend', self.ac.soln_pds_adoption_prognostication_trend,
'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'
],
[
'growth', self.ac.soln_pds_adoption_prognostication_growth,
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE'
],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
]
adconfig = pd.DataFrame(adconfig_list[1:],
columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {
'Raw Data for ALL LAND TYPES': {
'FAOSTAT 2016 + Literature (Exponential)':
THISDIR.joinpath('ad',
'ad_FAOSTAT_2016_Literature_Exponential.csv'),
'FAOSTAT 2016 + Literature (2nd order)':
THISDIR.joinpath('ad',
'ad_FAOSTAT_2016_Literature_2nd_order.csv'),
'FAOSTAT 2016 + Literature (linear)':
THISDIR.joinpath('ad',
'ad_FAOSTAT_2016_Literature_linear.csv'),
},
'Region: OECD90': {
'Raw Data for ALL LAND TYPES': {
'FAOSTAT 2016 + Literature (Exponential)':
THISDIR.joinpath(
'ad', 'ad_FAOSTAT_2016_Literature_Exponential.csv'),
'FAOSTAT 2016 + Literature (2nd order)':
THISDIR.joinpath(
'ad', 'ad_FAOSTAT_2016_Literature_2nd_order.csv'),
'FAOSTAT 2016 + Literature (3rd order)':
THISDIR.joinpath(
'ad', 'ad_FAOSTAT_2016_Literature_3rd_order.csv'),
},
},
'Region: Asia (Sans Japan)': {
'Raw Data for ALL LAND TYPES': {
'FAOSTAT 2016 + Literature (Exponential)':
THISDIR.joinpath(
'ad', 'ad_FAOSTAT_2016_Literature_Exponential.csv'),
'FAOSTAT 2016 + Literature (2nd order)':
THISDIR.joinpath(
'ad', 'ad_FAOSTAT_2016_Literature_2nd_order.csv'),
'FAOSTAT 2016 + Literature (3rd order)':
THISDIR.joinpath(
'ad', 'ad_FAOSTAT_2016_Literature_3rd_order.csv'),
},
},
'Region: Middle East and Africa': {
'Raw Data for ALL LAND TYPES': {
'FAOSTAT 2016 + Literature (Exponential)':
THISDIR.joinpath(
'ad', 'ad_FAOSTAT_2016_Literature_Exponential.csv'),
'FAOSTAT 2016 + Literature (2nd order)':
THISDIR.joinpath(
'ad', 'ad_FAOSTAT_2016_Literature_2nd_order.csv'),
'FAOSTAT 2016 + Literature (3rd order)':
THISDIR.joinpath(
'ad', 'ad_FAOSTAT_2016_Literature_3rd_order.csv'),
},
},
'Region: Latin America': {
'Raw Data for ALL LAND TYPES': {
'FAOSTAT 2016 + Literature (Exponential)':
THISDIR.joinpath(
'ad', 'ad_FAOSTAT_2016_Literature_Exponential.csv'),
'FAOSTAT 2016 + Literature (2nd order)':
THISDIR.joinpath(
'ad', 'ad_FAOSTAT_2016_Literature_2nd_order.csv'),
'FAOSTAT 2016 + Literature (3rd order)':
THISDIR.joinpath(
'ad', 'ad_FAOSTAT_2016_Literature_3rd_order.csv'),
},
},
}
self.ad = adoptiondata.AdoptionData(ac=self.ac,
data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
tla_2050 = self.tla_per_region.loc[2050]
ref_base_2018 = pd.Series(self.ac.ref_base_adoption)
# Data Source 1: Jong 2018
# (https://link.springer.com/content/pdf/10.1007%2F978-981-10-5269-9.pdf);
# FAOSTAT, accessed in 2018
# 2050 medium adoption value based on historical data interpolation, based on FAOSTAT data
ds1_adoption_data = self.ad.adoption_data(region='World')
ds1_ad_2050 = ds1_adoption_data.loc[
2050, 'FAOSTAT 2016 + Literature (Exponential)']
ds1_percent = ds1_ad_2050 / tla_2050['World']
ds1_percentages = pd.Series([
0.0, ds1_percent, ds1_percent, ds1_percent, ds1_percent,
ds1_percent, 0.0, 0.0, 0.0, 0.0
],
index=dd.REGIONS)
ds1_regional = tla_2050 * ds1_percentages
ds1_regional.clip(lower=ref_base_2018, inplace=True)
ds1_regional['World'] = 0.0
ds1_world_2050 = ds1_regional.sum(axis=0)
# Excel uses regional TLA to compute World, but doesn't populate regional adoption
ds1_2050 = [
ds1_world_2050, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
]
# Data Source 2: Jong 2018
# (https://link.springer.com/content/pdf/10.1007%2F978-981-10-5269-9.pdf);
# FAOSTAT, accessed in 2018
# 2050 average adoption value based on historical data interpolation,
# based on FAOSTAT data (without TLA limitation)
ds2_adoption_data = self.ad.adoption_data(region='World')
ds2_ad_2050 = ds2_adoption_data.loc[
2050, 'FAOSTAT 2016 + Literature (2nd order)']
ds2_percent = ds2_ad_2050 / tla_2050['World']
ds2_percentages = pd.Series([
0.0, ds2_percent, ds2_percent, ds2_percent, ds2_percent,
ds2_percent, 0.0, 0.0, 0.0, 0.0
],
index=dd.REGIONS)
ds2_regional = tla_2050 * ds2_percentages
ds2_regional.clip(lower=ref_base_2018, inplace=True)
ds2_regional['World'] = 0.0
ds2_world_2050 = ds2_regional.sum(axis=0)
# Excel uses regional TLA to compute World, but doesn't populate regional adoption
ds2_2050 = [
ds2_world_2050, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
]
# Data Source 3: Jong 2018
# (https://link.springer.com/content/pdf/10.1007%2F978-981-10-5269-9.pdf);
# FAOSTAT, accessed in 2018
# 2050 minimum adoption value based on historical data interpolation,
# based on FAOSTAT data (without TLA limitation)
ds3_adoption_data = self.ad.adoption_data(region='World')
ds3_ad_2050 = ds3_adoption_data.loc[
2050, 'FAOSTAT 2016 + Literature (linear)']
ds3_percent = ds3_ad_2050 / tla_2050['World']
ds3_percentages = pd.Series([
0.0, ds3_percent, ds3_percent, ds3_percent, ds3_percent,
ds3_percent, 0.0, 0.0, 0.0, 0.0
],
index=dd.REGIONS)
ds3_regional = tla_2050 * ds3_percentages
ds3_regional.clip(lower=ref_base_2018, inplace=True)
ds3_regional['World'] = 0.0
ds3_world_2050 = ds3_regional.sum(axis=0)
# Excel uses regional TLA to compute World, but doesn't populate regional adoption
ds3_2050 = [
ds3_world_2050, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
]
ca_pds_data_sources = [
{
'name':
'Medium growth based on exponential interpolation of historical data, linear trend',
'include':
True,
# This scenario is built on the historical (1962-2016) average global
# growth rate of tropical staple crops based on FAOSTAT 2018 and Jong
# et al. 2018 data (see "Master - Adoption Data" sheet for details).
# Future projections up to 2060 were interpolated based on an exponential
# best-curve fit applied to historical data available at decadal intervals
# for the given time-period.
'datapoints':
pd.DataFrame([
[2018] + list(self.ac.ref_base_adoption.values()),
[2050] + ds1_2050,
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'High growth based on 2nd order polynomial interpolation of historical data, linear trend',
'include':
True,
# This scenario is built on the historical (1962-2016) average global
# growth rate of tropical staple crops based on FAOSTAT 2018 and Jong
# et al. 2018 data (see "Master - Adoption Data" sheet for details).
# Future projections up to 2060 were interpolated based on a 2nd order
# polynomial best-curve fit applied to historical data available at
# decadal intervals for the given time-period.
'datapoints':
pd.DataFrame([
[2018] + list(self.ac.ref_base_adoption.values()),
[2050] + ds2_2050,
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Low growth based on linear interpolation of historical data, linear trend',
'include':
True,
# This scenario is built on the historical (1962-2016) average global
# growth rate of tropical staple crops based on FAOSTAT 2018 and Jong
# et al. 2018 data (see "Master - Adoption Data" sheet for details).
# Future projections up to 2060 were interpolated based on a linear
# best-curve fit applied to historical data available at decadal
# intervals for the given time-period.
'datapoints':
pd.DataFrame([
[2018] + list(self.ac.ref_base_adoption.values()),
[2050] + ds3_2050,
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Medium growth with 70% adoption 2030 (based on exponential interpolations of historical data), linear trend',
'include':
True,
# Scenario 1, with 70% adoption by 2030
'datapoints':
pd.DataFrame([
[2018] + list(self.ac.ref_base_adoption.values()),
[2030] + [0.7 * x for x in ds1_2050],
[2050] + ds1_2050,
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'High growth with 70% adoption 2030 (based on 2nd order polynomial interpolations of historical data), linear trend',
'include':
True,
# Scenario 2, with 70% adoption by 2030
'datapoints':
pd.DataFrame([
[2018] + list(self.ac.ref_base_adoption.values()),
[2030] + [0.7 * x for x in ds2_2050],
[2050] + ds2_2050,
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Low growth with 70% adoption 2030 (based on linear interpolations of historical data), linear trend',
'include':
True,
# Scenario 3, with 70% adoption by 2030
'datapoints':
pd.DataFrame([
[2018] + list(self.ac.ref_base_adoption.values()),
[2030] + [0.7 * x for x in ds3_2050],
[2050] + ds3_2050,
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Max adoption, linear growth',
'include':
True,
# "100% adoption by 2050
'datapoints':
pd.DataFrame([
[2018] + list(self.ac.ref_base_adoption.values()),
[2050] + [
tla_2050['World'], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
for s in self.pds_ca.scenarios.values():
df = s['df']
for y in range(2012, 2019):
df.loc[y] = [
0.0001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
]
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(list(
self.ac.ref_base_adoption.values()),
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
use_first_pds_datapoint_main=True,
adoption_base_year=2018,
copy_pds_to_ref=False,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name,
cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
if self.ac.use_custom_tla and self.ac.custom_tla_fixed_value is not None:
self.c_tla = tla.CustomTLA(
fixed_value=self.ac.custom_tla_fixed_value)
custom_world_vals = self.c_tla.get_world_values()
elif self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(
filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
adconfig_list = [
[
'param', 'World', 'OECD90', 'Eastern Europe',
'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America',
'China', 'India', 'EU', 'USA'
],
[
'trend', self.ac.soln_pds_adoption_prognostication_trend,
'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'
],
[
'growth', self.ac.soln_pds_adoption_prognostication_growth,
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE'
],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
]
adconfig = pd.DataFrame(adconfig_list[1:],
columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {
'Raw Data for ALL LAND TYPES': {
'Henwood 1994 and Henwood 2010':
THISDIR.joinpath('ad', 'ad_Henwood_1994_and_Henwood_2010.csv'),
'Henwood 2010':
THISDIR.joinpath('ad', 'ad_Henwood_2010.csv'),
},
}
self.ad = adoptiondata.AdoptionData(ac=self.ac,
data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
tla_world_2050 = self.tla_per_region.loc[2050, 'World']
ad_2014 = 132.860079407797 # adoption in year 2014 (refer B647, adoption data sheet)
ad_2018 = self.ac.ref_base_adoption['World']
total_temperate_grassland = 1209.098
# In Excel, the Custom PDS Scenarios tab columns AC:AF compute a limit on adoption
# based on the amount of degraded land remaining. We compute the same limit here.
def constrained_tla(ad_2050):
(slope, intercept) = np.polyfit(x=[2018, 2050],
y=[ad_2018, ad_2050],
deg=1)
degrade_df = pd.DataFrame(
0,
index=range(2014, 2061),
columns=[
'Total undegraded land at the start of the year',
'Degraded land at the end of the year',
'Total undegraded land at the end of the year',
'Constrained TLA'
])
for year in range(2014, 2061):
if year == 2014:
undeg_start = tla_world_2050 - ad_2014
else:
last = degrade_df.loc[
year - 1,
'Total undegraded land at the end of the year']
undeg_start = last - slope
degrade_df.loc[
year,
'Total undegraded land at the start of the year'] = undeg_start
degraded_end = max(0, undeg_start * self.ac.degradation_rate)
degrade_df.loc[
year,
'Degraded land at the end of the year'] = degraded_end
degrade_df.loc[
year, 'Total undegraded land at the end of the year'] = (
undeg_start - degraded_end)
degrade_df.loc[year, 'Constrained TLA'] = (
tla_world_2050 -
degrade_df['Degraded land at the end of the year'].sum())
return degrade_df['Constrained TLA'].min()
# Data Source 1: Linear growth, conservative
# SOURCE: Henwood, W. D. (2010). Toward a strategy for the conservation and
# protection of the world's temperate grasslands. Great Plains Research, 121-134.
ds1_commit_2014 = 0.25 # % commitment for protection of temperate grassland by 2014
ds1_adoption_2050 = total_temperate_grassland * ds1_commit_2014
ds1_constrained_tla = constrained_tla(ad_2050=ds1_adoption_2050)
# Data Source 3: Linear growth, High
# SOURCE: Henwood, W. D. (2010). Toward a strategy for the conservation and
# protection of the world's temperate grasslands. Great Plains Research, 121-134.
ds3_commit_2014 = 0.5 # % commitment for protection of temperate grassland by 2014
ds3_adoption_2018 = self.ac.ref_base_adoption['World']
ds3_adoption_2050 = total_temperate_grassland * ds3_commit_2014
ds3_constrained_tla = constrained_tla(ad_2050=ds3_adoption_2050)
# Data Source 4: Linear growth, High
# SOURCE: Henwood, W. D. (2010). Toward a strategy for the conservation and
# protection of the world's temperate grasslands. Great Plains Research, 121-134.
ds4_commit_2014 = 1.0 # % commitment for protection of temperate grassland by 2014
ds4_adoption_2018 = self.ac.ref_base_adoption['World']
ds4_adoption_2050 = total_temperate_grassland * ds4_commit_2014
ds4_constrained_tla = constrained_tla(ad_2050=ds4_adoption_2050)
ca_pds_data_sources = [
{
'name':
'Linear growth, conservative',
'include':
True,
'maximum':
ds1_constrained_tla,
'description':
('The future adoption is projected based on the protection commitment made '
'for the temperate grassland, which is 10% of the total temperate grassland '
'area. Since, the current protection is much higher than the 10% commitment, '
'so 25% commitment target is considered for this projection. '
),
'datapoints':
pd.DataFrame([
[2018, ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[
2050, ds1_adoption_2050, 0., 0., 0., 0., 0., 0., 0.,
0., 0.
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Exsisting progonostication',
'include':
True,
'maximum':
ds1_constrained_tla,
'description':
('The future adoption value was projected using the data interpolator and '
'adoption data sheet based on the data available on protected grassland area '
'globally in the temperate in the year 1994/2010 and 1996/2009. '
'NOTE: in actual implementation, this scenario is set equal to the "Linear '
'Growth, conservative" scenario not from adoption data.'),
'datapoints':
pd.DataFrame([
[2018, ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[
2050, ds1_adoption_2050, 0., 0., 0., 0., 0., 0., 0.,
0., 0.
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Linear growth, High',
'include':
True,
'maximum':
ds3_constrained_tla,
'description':
('The future adoption is projected based on the protection commitment made '
'for the temperate grassland, which is 10% of the total temperate grassland '
'area. Since, the current protection is much higher than the 10% commitment, '
'so 50% commitment target is considered for this projection. '
),
'datapoints':
pd.DataFrame([
[2018, ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[
2050, ds3_adoption_2050, 0., 0., 0., 0., 0., 0., 0.,
0., 0.
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Max adoption, linear trend',
'include':
True,
'maximum':
ds4_constrained_tla,
'description':
('In this scenario, it is assumed that 100% of the grassland as per the TLA '
'will be protected by 2050 '),
'datapoints':
pd.DataFrame([
[2018, ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[
2050, ds4_adoption_2050, 0., 0., 0., 0., 0., 0., 0.,
0., 0.
],
],
columns=ca_pds_columns).set_index('Year')
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
for s in self.pds_ca.scenarios.values():
df = s['df']
df.loc[2014, 'World'] = 132.860079407797
df.loc[2015, 'World'] = 139.475887288368
df.loc[2016, 'World'] = 146.114200458296
df.loc[2017, 'World'] = 152.77501982823
df.loc[2018, 'World'] = 159.457782791276
# Custom REF Data
ca_ref_data_sources = [
{
'name':
'[Type Scenario 1 Name Here (REF CASE)...]',
'include':
True,
'description':
('[PLEASE DESCRIBE IN DETAIL THE METHODOLOGY YOU USED IN THIS ANALYSIS. BE '
'SURE TO INCLUDE ANY ADDITIONAL EQUATIONS YOU UTILIZED] '),
'filename':
THISDIR.joinpath(
'ca_ref_data',
'custom_ref_ad_Type_Scenario_1_Name_Here_REF_CASE_.csv')
},
]
self.ref_ca = customadoption.CustomAdoption(
data_sources=ca_ref_data_sources,
soln_adoption_custom_name=self.ac.soln_ref_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
if self.ac.soln_ref_adoption_basis == 'Custom':
ref_adoption_data_per_region = self.ref_ca.adoption_data_per_region(
)
else:
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(list(
self.ac.ref_base_adoption.values()),
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
ref_adoption_data_per_region=ref_adoption_data_per_region,
use_first_pds_datapoint_main=False,
adoption_base_year=2018,
copy_pds_to_ref=False,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=False)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
tot_red_in_deg_land=self.ua.
cumulative_reduction_in_total_degraded_land(),
pds_protected_deg_land=self.ua.
pds_cumulative_degraded_land_protected(),
ref_protected_deg_land=self.ua.
ref_cumulative_degraded_land_protected(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name, cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
if self.ac.use_custom_tla and self.ac.custom_tla_fixed_value is not None:
self.c_tla = tla.CustomTLA(fixed_value=self.ac.custom_tla_fixed_value)
custom_world_vals = self.c_tla.get_world_values()
elif self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
adconfig_list = [
['param', 'World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)',
'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA'],
['trend', self.ac.soln_pds_adoption_prognostication_trend, 'Medium',
'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'],
['growth', self.ac.soln_pds_adoption_prognostication_growth, 'NOTE',
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE', 'NOTE', 'NOTE'],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]]
adconfig = pd.DataFrame(adconfig_list[1:], columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {
'Raw Data for ALL LAND TYPES': {
'RR 2018, https://rightsandresources.org/wp-content/uploads/2018/09/At-A-Crossroads_RRI_Sept-2018.pdf': THISDIR.joinpath('ad', 'ad_RR_2018_httpsrightsandresources_orgwpcontentuploads201809AtACrossroads_RRI_Sept2018_pdf.csv'),
},
}
self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
tla_world_2050 = self.tla_per_region.loc[2050, 'World']
ad_2018 = self.ac.ref_base_adoption['World']
ad_2012 = 431.635465343388 # current adoption in year 2012 (refer U46, adoption data sheet)
# https://rightsandresources.org/wp-content/uploads/2018/09/At-A-Crossroads_RRI_Sept-2018.pdf
lmic_18_3 = 337.0 # 18.3% adoption in 33 Low and Middle Income Countries
lmic_22_1 = 425.0 # 22.1% adoption in 33 Low and Middle Income Countries
lmic_23_0 = 458.0 # 23% adoption in 33 Low and Middle Income Countries
lmic_24_1 = 484.0 # 24.1% adoption in 33 Low and Middle Income Countries
tla_lmic = (lmic_18_3 / 18.3) * 100
lmic_50_0 = tla_lmic * 0.5 # 50% adoption in 33 Low and Middle Income Countries
lmic_75_0 = tla_lmic * 0.75 # 75% adoption in 33 Low and Middle Income Countries
wtla_50 = tla_world_2050 * 0.5
wtla_75 = tla_world_2050 * 0.75
def constrained_tla(datapoints):
(slope, intercept) = np.polyfit(x=datapoints.index, y=datapoints['World'], deg=1)
degrade_df = pd.DataFrame(0, index=range(2014, 2061), columns=[
'Total undegraded land at the start of the year',
'Degraded land at the end of the year',
'Total undegraded land at the end of the year', 'Constrained TLA'])
for year in range(2014, 2061):
if year == 2014:
undeg_start = tla_world_2050 - ad_2012
else:
last = degrade_df.loc[year-1, 'Total undegraded land at the end of the year']
undeg_start = last - slope
degrade_df.loc[year, 'Total undegraded land at the start of the year'] = undeg_start
degraded_end = max(0, undeg_start * self.ac.degradation_rate)
degrade_df.loc[year, 'Degraded land at the end of the year'] = degraded_end
degrade_df.loc[year, 'Total undegraded land at the end of the year'] = (
undeg_start - degraded_end)
degrade_df.loc[year, 'Constrained TLA'] = (tla_world_2050 -
degrade_df['Degraded land at the end of the year'].sum())
return degrade_df
# Data Source 1
ds1_datapoints = pd.DataFrame([
[2002, lmic_18_3, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2008, lmic_22_1, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2013, lmic_23_0, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2017, lmic_24_1, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2030, lmic_50_0, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
], columns=ca_pds_columns).set_index('Year')
ds1_maximum = constrained_tla(datapoints=ds1_datapoints)['Constrained TLA'].min()
ds2_datapoints = pd.DataFrame([
[2002, lmic_18_3, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2008, lmic_22_1, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2013, lmic_23_0, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2017, lmic_24_1, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2030, lmic_50_0, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2050, lmic_75_0, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
], columns=ca_pds_columns).set_index('Year')
ds2_maximum = constrained_tla(datapoints=ds2_datapoints)['Constrained TLA'].min()
ds3_datapoints = pd.DataFrame([
[2002, lmic_18_3, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2008, lmic_22_1, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2013, lmic_23_0, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2017, lmic_24_1, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2030, tla_lmic, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2050, tla_lmic, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
], columns=ca_pds_columns).set_index('Year')
ds3_maximum = constrained_tla(datapoints=ds3_datapoints)['Constrained TLA'].min()
ds4_datapoints = pd.DataFrame([
[2002, lmic_18_3, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2008, lmic_22_1, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2013, lmic_23_0, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2017, lmic_24_1, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2030, wtla_50, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
], columns=ca_pds_columns).set_index('Year')
ds4_maximum = constrained_tla(datapoints=ds4_datapoints)['Constrained TLA'].min()
ds5_datapoints = pd.DataFrame([
[2002, lmic_18_3, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2008, lmic_22_1, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2013, lmic_23_0, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2017, lmic_24_1, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2030, wtla_50, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2050, wtla_75, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
], columns=ca_pds_columns).set_index('Year')
ds5_maximum = constrained_tla(datapoints=ds5_datapoints)['Constrained TLA'].min()
ds6_datapoints = pd.DataFrame([
[2002, lmic_18_3, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2008, lmic_22_1, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2013, lmic_23_0, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2017, lmic_24_1, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2030, wtla_50, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2050, tla_world_2050, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
], columns=ca_pds_columns).set_index('Year')
ds6_maximum = constrained_tla(datapoints=ds6_datapoints)['Constrained TLA'].min()
ca_pds_data_sources = [
{'name': 'Moderate growth, Linear Trend', 'include': True,
'description': (
'Future adoption of forest area under Indigenous People (IP) management was '
'built based on the adoption in Low and Middle Income Countries given for '
'the year 2002, 2008, 2013, and targeted percentage for the year 2030 by '
'Rights and Resources 2018 publication. '
),
'datapoints_degree': 1, 'datapoints': ds1_datapoints, 'maximum': ds1_maximum},
{'name': 'High growth, Linear Trend', 'include': True,
'description': (
'Future adoption of forest area under Indigenous People (IP) management was '
'built based on the adoption in Low and Middle Income Countries given for '
'the year 2002, 2008, 2013, and targeted percentage for the year 2030 by '
'Rights and Resources 2018 publication. Further assumption was made that '
'the forest area under indigenous people management will increase to 75% by '
'2050 in Low and Middle Income Countries. '
),
'datapoints_degree': 1, 'datapoints': ds2_datapoints, 'maximum': ds2_maximum},
{'name': 'Max growth, Linear Trend', 'include': True,
'description': (
'Future adoption of forest area under Indigenous People (IP) management was '
'built based on the adoption in Low and Middle Income Countries given for '
'the year 2002, 2008, 2013, and targeted percentage for the year 2030 by '
'Rights and Resources 2018 publication. Further assumption was made that '
'the forest area under indigenous people management will increase to 100% by '
'2050 in Low and Middle Income Countries. '
),
'datapoints_degree': 1, 'datapoints': ds3_datapoints, 'maximum': ds3_maximum},
{'name': 'Moderate growth, AEZ TLA, Linear Trend', 'include': True,
'description': (
'Future adoption of forest area under Indigenous People (IP) management was '
'built based on the adoption in Low and Middle Income Countries given for '
'the year 2002, 2008, 2013, and targeted percentage for the year 2030 by '
'Rights and Resources 2018 publication. However, the 2030 projected area is '
'calculated with reference to the assigned TLA of the solution and not by '
'the total area as calculated for theLow and Middle Income Countries. '
),
'datapoints_degree': 1, 'datapoints': ds4_datapoints, 'maximum': ds4_maximum},
{'name': 'High growth, AEZ TLA, Linear Trend', 'include': True,
'description': (
'Future adoption of forest area under Indigenous People (IP) management was '
'built based on the adoption in Low and Middle Income Countries given for '
'the year 2002, 2008, 2013, and targeted percentage for the year 2030 by '
'Rights and Resources 2018 publication. Further assumption was made that '
'the forest area under indigenous people management will increase to 75% by '
'2050 in Low and Middle Income Countries. However, the 2030 and 2050 '
'projected area is calculated with reference to the assigned TLA of the '
'solution and not by the total area as calculated for the Low and Middle '
'Income Countries. '
),
'datapoints_degree': 1, 'datapoints': ds5_datapoints, 'maximum': ds5_maximum},
{'name': 'Max growth, AEZ TLA, Linear Trend', 'include': True,
'description': (
'Future adoption of forest area under Indigenous People (IP) management was '
'built based on the adoption in Low and Middle Income Countries given for '
'the year 2002, 2008, 2013, and targeted percentage for the year 2030 by '
'Rights and Resources 2018 publication. Further assumption was made that '
'the forest area under indigenous people management will increase to 100% by '
'2050 in Low and Middle Income Countries. However, the 2030 and 2050 '
'projected area is calculated with reference to the assigned TLA of the '
'solution and not by the total area as calculated for the Low and Middle '
'Income Countries. '
),
'datapoints_degree': 1, 'datapoints': ds6_datapoints, 'maximum': ds6_maximum},
]
self.pds_ca = customadoption.CustomAdoption(data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0, low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
for s in self.pds_ca.scenarios.values():
df = s['df']
df.loc[2012, 'World'] = 431.635465343388
df.loc[2013, 'World'] = 442.123227592586
df.loc[2014, 'World'] = 453.987693921633
df.loc[2015, 'World'] = 464.65888472248
df.loc[2016, 'World'] = df.loc[2015, 'World']
df.loc[2017, 'World'] = 486.074210468988
df.loc[2018, 'World'] = 496.809418409265
# Custom REF Data
ca_ref_data_sources = [
{'name': '[Type Scenario 1 Name Here (REF CASE)...]', 'include': True,
'description': (
'Reference adoption wrt change in current adoption from 2014 to 2018 '
),
'filename': THISDIR.joinpath('ca_ref_data', 'custom_ref_ad_Type_Scenario_1_Name_Here_REF_CASE_.csv')},
]
self.ref_ca = customadoption.CustomAdoption(data_sources=ca_ref_data_sources,
soln_adoption_custom_name=self.ac.soln_ref_adoption_custom_name,
high_sd_mult=1.0, low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
if self.ac.soln_ref_adoption_basis == 'Custom':
ref_adoption_data_per_region = self.ref_ca.adoption_data_per_region()
else:
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region()
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region()
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(
list(self.ac.ref_base_adoption.values()), index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (ht_ref_adoption_initial /
self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2018] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(ac=self.ac,
ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region, pds_adoption_limits=self.tla_per_region,
ref_adoption_data_per_region=ref_adoption_data_per_region,
use_first_pds_datapoint_main=False,
adoption_base_year=2018,
copy_pds_to_ref=False,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=False)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted=self.ua.soln_net_annual_funits_adopted()
self.fc = firstcost.FirstCost(ac=self.ac, pds_learning_increase_mult=2,
ref_learning_increase_mult=2, conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
tot_red_in_deg_land=self.ua.cumulative_reduction_in_total_degraded_land(),
pds_protected_deg_land=self.ua.pds_cumulative_degraded_land_protected(),
ref_protected_deg_land=self.ua.ref_cumulative_degraded_land_protected(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name)
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution())
# Custom PDS Data
ca_pds_data_sources = [
{
'name':
'Low,High Early Adoption, Polynomial Trend',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_LowHigh_Early_Adoption_Polynomial_Trend.csv'
)
},
{
'name':
'High,High Early Adoption, Polynomial Trend',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_HighHigh_Early_Adoption_Polynomial_Trend.csv'
)
},
{
'name':
'Max, Polynomial Trend',
'include':
True,
'filename':
THISDIR.joinpath('ca_pds_data',
'custom_pds_ad_Max_Polynomial_Trend.csv')
},
{
'name':
'High, Early Adoption, Polynomial Trend',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_High_Early_Adoption_Polynomial_Trend.csv')
},
{
'name':
'Low, Early Adoption, Polynomial Trend',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Low_Early_Adoption_Polynomial_Trend.csv')
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
ht_ref_adoption_initial = pd.Series([
71.6843318543261, 21.7864515209236, 5.79724982776745,
7.68605153649748, 0.244451347436256, 36.1701276217012, 0.0, 0.0,
0.0, 0.0
],
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2014] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_regions, ht_percentages = zip(
*self.ac.pds_adoption_final_percentage)
ht_pds_adoption_final_percentage = pd.Series(list(ht_percentages),
index=list(ht_regions))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution())
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name,
cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
if self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(
filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
adconfig_list = [
[
'param', 'World', 'OECD90', 'Eastern Europe',
'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America',
'China', 'India', 'EU', 'USA'
],
[
'trend', self.ac.soln_pds_adoption_prognostication_trend,
'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'
],
[
'growth', self.ac.soln_pds_adoption_prognostication_growth,
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE'
],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
]
adconfig = pd.DataFrame(adconfig_list[1:],
columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {
'Raw Data for ALL LAND TYPES': {
'FAO 2015': THISDIR.joinpath('ad', 'ad_FAO_2015.csv'),
},
}
self.ad = adoptiondata.AdoptionData(ac=self.ac,
data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
adoption_2014 = self.ac.ref_base_adoption['World']
tla_2050 = self.tla_per_region.loc[2050, 'World']
tla_afforestation = 717.972721643776
tla_bamboo = 363.757256176669
tla_perennial_bioenergy = 264.89533875
tla_all_biomass = tla_afforestation + tla_bamboo + tla_perennial_bioenergy
ds6_percent_afforestation = tla_afforestation / tla_all_biomass
ds6_percent_adoption_2050 = (
1614 * ds6_percent_afforestation) / tla_all_biomass
ds6_adoption_2050 = ds6_percent_adoption_2050 * tla_2050
ds7_percent_adoption_2050 = 1614 / tla_all_biomass
ds7_adoption_2050 = ds7_percent_adoption_2050 * tla_2050
ca_pds_data_sources = [
{
'name':
'Regional linear trend',
'include':
True,
'datapoints_degree':
1,
# Future forest plantation area is projected based on country level data available
# for the year 1990, 2000, 2005, and 2010 from the FAO's Forest Resource Assessment
# report from 2015.
#
# Country level data about planted forest area from 1990 - 2014 was aggregated at
# the regional level (for details see hidden ""HistoricAndProjectedData"" sheet).
# This data was then used to project future adoption using a linear trend.
'datapoints':
pd.DataFrame([
[
1990, np.nan, 96.8914, 44.1631, 106.1562, 16.5338,
14.9281, 0.0, 0.0, 0.0, 0.0
],
[
2000, np.nan, 97.2555, 44.2605, 107.5489, 16.7251,
15.2771, 0.0, 0.0, 0.0, 0.0
],
[
2005, np.nan, 97.6355, 44.3794, 109.8028, 16.9607,
15.6649, 0.0, 0.0, 0.0, 0.0
],
[
2010, np.nan, 97.8700, 44.4540, 111.5210, 17.1760,
16.0730, 0.0, 0.0, 0.0, 0.0
],
[
2014, np.nan, 98.1783330003811, 44.5558196042818,
113.7890763825080, 17.4259450169749, 16.5131623025461,
0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Regional max linear trend',
'include':
True,
# "Future forest plantation area is projected based on country level data available
# for the year 1990, 2000, 2005, and 2010 from the FAO's Forest Resource Assessment
# report from 2015. Country level data about planted forest area from 1990 - 2014
# was aggregated at the regional level, and used to determine regional annual growth
# rates. For details about the calculations of regional rates see hidden
# ""HistoricAndProjectedData"" sheet).
#
# In this scenario, regional future adoption of forest plantations is projected
# based on the maximum historical regional growth rate reported in the respective
# region (between 1990 - 2014). For each region, future adoption projected based
# on the maximum linear regional trend between any two given time periods.
'datapoints':
pd.DataFrame([
[
2010, np.nan, 97.8700, 44.4540, 111.5210, 17.1760,
16.0730, 0.0, 0.0, 0.0, 0.0
],
[
2014, np.nan, 98.1783330003811, 44.5558196042818,
113.7890763825080, 17.4259450169749, 16.5131623025461,
0.0, 0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'50% of the max historical annual afforestation rate across the regions',
'include':
True,
# Future forest plantation area is projected based on country level data available
# for the year 1990, 2000, 2005, and 2010 from the FAO's Forest Resource Assessment
# report from 2015. Country level data about planted forest area from 1990 - 2014
# was aggregated at the regional level, and used to determine regional annual growth
# rates. For details about the calculations of regional rates see hidden
# "HistoricAndProjectedData" sheet).
#
# In this scenario, regional future adoption of forest plantations is projected
# based on the maximum historical regional growth rate across all regions (between
# 1990 - 2014), which occurred in Asia (0.57 Mha annual increase between 2010 -2014).
# For the ASIA region this same rate (0.57 Mha annual increase) is used for
# projecting the future adoption of forest plantation. For all other regions half
# of this growth rate is applied (0.29 Mha annual increase).
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_50_of_the_max_historical_annual_afforestation_rate_across_the_regions.csv'
)
},
{
'name':
'100% of the max historical annual afforestation rate across the regions',
'include':
True,
# Future forest plantation area is projected based on country level data available
# for the year 1990, 2000, 2005, and 2010 from the FAO's Forest Resource Assessment
# report from 2015. Country level data about planted forest area from 1990 - 2014
# was aggregated at the regional level, and used to determine regional annual growth
# rates. For details about the calculations of regional rates see hidden
# "HistoricAndProjectedData" sheet).
#
# In this scenario, regional future adoption of forest plantations is projected
# based on the maximum historical regional growth rate across all regions (between
# 1990 - 2014), which occurred in Asia (0.57 Mha increase between 2010 -2014).
# To project future adoption of forest plantations, this rate was applied to all
# regions, including Asia (0.57 Mha annual increase).
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_100_of_the_max_historical_annual_afforestation_rate_across_the_regions.csv'
)
},
{
'name':
'200% increase for Asia and 100% for the other regions',
'include':
True,
# Future forest plantation area is projected based on country level data available
# for the year 1990, 2000, 2005, and 2010 from the FAO's Forest Resource Assessment
# report from 2015. Country level data about planted forest area from 1990 - 2014
# was aggregated at the regional level, and used to determine regional annual growth
# rates. For details about the calculations of regional rates see hidden
# "HistoricAndProjectedData" sheet).
#
# In this scenario, regional future adoption of forest plantations is projected
# based on the maximum historical regional growth rate across all regions (between
# 1990 - 2014), which occurred in Asia (0.57 Mha increase between 2010 -2014).
# To project future adoption of forest plantations, this rate was applied to all
# regions (0.57 Mha annual increase) except for Asia region where the rate was
# doubled (1.14 Mha annual increase).
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_200_increase_for_Asia_and_100_for_the_other_regions.csv'
)
},
{
'name':
'Conservative growth projection based on Kreidenweis et al. (2016)',
'include':
True,
# This projection of future adoption of afforestation is based on Kreidenweis
# et al. (2016)'s ""global afforestation"" scenario, which assumes 1614 Mha total
# afforested area by 2050.
# We calculated the proportion of this projection based on the percent of
# afforestation to the total TLA set for the biomass crops as per 2019 land
# allocation model. This proportion was applied to TLA set for this solution
# allocated on forest AEZs.
'datapoints':
pd.DataFrame([
[
2014, adoption_2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
[
2050, ds6_adoption_2050, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year'),
'maximum':
tla_bamboo
},
{
'name':
'High growth projection based on Kreidenweis et al. (2016)',
'include':
True,
# "This projection of future adoption of afforestation is based on Kreidenweis
# et al. (2016)'s ""global afforestation"" scenario, which assumes 1614 Mha total
# afforested area by 2050.
# We calculated the proportion of this projection based on the percent of
# afforestation to the total TLA set for the biomass crops as per 2019 land
# allocation model. This proportion was applied to TLA set for this solution
# allocated on forest AEZs.
'datapoints':
pd.DataFrame([
[
2014, adoption_2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
[
2050, ds7_adoption_2050, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Linear projections based on Evans (2009) publication',
'include':
True,
# Projections of future adoption of forest plantations are based on the total
# predicted area of planted forest in 2030 (344.5 Mha), as reported by Evans (2009),
# see Table 5.5. The predictions are made according to two scenarios (Scenario 2:
# "Business as Usual" and Scenario 3: "Higher Productivity") described in
# Table 5.2 of the publication.
'datapoints':
pd.DataFrame([
[
2014, 294.140179643776, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0
],
[
2030, 344.500000000000, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=0.5,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
# Manual adjustment made in spreadsheet for Drawdown 2020.
for s in self.pds_ca.scenarios.values():
df = s['df']
df.loc[2014] = [
290.462336306692, 98.1783330003811, 44.5558196042818,
113.789076382508, 17.4259450169749, 16.5131623025461, 0.0, 0.0,
0.0, 0.0
]
df.loc[2015] = [
291.336550416405, 98.2542011640878, 44.5812227272287,
114.378174359749, 17.4918608321974, 16.6310913331414, 0.0, 0.0,
0.0, 0.0
]
df.loc[2016] = [
292.240912741787, 98.3317866108997, 44.6073285492058,
114.988603296961, 17.5600505509306, 16.7531437337891, 0.0, 0.0,
0.0, 0.0
]
df.loc[2017] = [
293.175447683892, 98.4110924492030, 44.6341380907823,
115.620380827999, 17.6305154458053, 16.8793208701027, 0.0, 0.0,
0.0, 0.0
]
df.loc[2018] = [
294.140179643776, 98.4921217873836, 44.6616523725276,
116.273524586716, 17.7032567894526, 17.0096241076960, 0.0, 0.0,
0.0, 0.0
]
# Custom REF Data
ca_ref_data_sources = [
{
'name':
'[Type Scenario 1 Name Here (REF CASE)...]',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_ref_data',
'custom_ref_ad_Type_Scenario_1_Name_Here_REF_CASE_.csv')
},
]
self.ref_ca = customadoption.CustomAdoption(
data_sources=ca_ref_data_sources,
soln_adoption_custom_name=self.ac.soln_ref_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
if self.ac.soln_ref_adoption_basis == 'Custom':
ref_adoption_data_per_region = self.ref_ca.adoption_data_per_region(
)
else:
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(list(
self.ac.ref_base_adoption.values()),
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2018] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
ref_adoption_data_per_region=ref_adoption_data_per_region,
use_first_pds_datapoint_main=False,
adoption_base_year=2018,
copy_pds_to_ref=False,
copy_ref_datapoint=False,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name,
cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
if self.ac.use_custom_tla and self.ac.custom_tla_fixed_value is not None:
self.c_tla = tla.CustomTLA(
fixed_value=self.ac.custom_tla_fixed_value)
custom_world_vals = self.c_tla.get_world_values()
elif self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(
filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
adconfig_list = [
[
'param', 'World', 'OECD90', 'Eastern Europe',
'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America',
'China', 'India', 'EU', 'USA'
],
[
'trend', self.ac.soln_pds_adoption_prognostication_trend,
'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'
],
[
'growth', self.ac.soln_pds_adoption_prognostication_growth,
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE'
],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
]
adconfig = pd.DataFrame(adconfig_list[1:],
columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {}
self.ad = adoptiondata.AdoptionData(ac=self.ac,
data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
tla_world_2050 = self.tla_per_region.loc[2050, 'World']
ad_2018 = self.ac.ref_base_adoption['World']
# Data Source 4-5
# Zomer et al. 2014; Zomer, R. J., Trabucco, A., Coe, R., Place, F.,
# Van Noordwijk, M., & Xu, J. C. (2014). Trees on farms: an update and
# reanalysis of agroforestry’s global extent and socio-ecological characteristics.
# World Agroforestry Center Working Paper, 179.
ds5_adoption = ad_2018
for y in range(2015, 2049):
ds5_adoption = ds5_adoption * (1 + 0.0128)
# In Excel, the 2018 adoption is used as 2014 and the 2048 adoption is used as 2050.
ds5_ad_2050 = ds5_adoption
# Data Source 6-9
# Thomson et al. 2018; Thomson, A., Misselbrook, T., Moxley, J., Buys, G.,
# Evans, C., Malcolm, H., ... & Reinsch, S. (2018). Quantifying the impact
# of future land use scenarios to 2050 and beyond. Final report.
ds6_cropland_percent = 0.05
global_cropland = 1411.0
remaining_cropland = global_cropland - ad_2018
ds6_ad_2018 = 248.0 # No idea why ds6 uses a different adoption in 2018
ds6_ad_2050 = (remaining_cropland * ds6_cropland_percent) + ds6_ad_2018
ds7_cropland_percent = 0.1
ds7_ad_2050 = (remaining_cropland * ds7_cropland_percent) + ad_2018
ds8_cropland_percent = 0.2
ds8_ad_2050 = (remaining_cropland * ds8_cropland_percent) + ad_2018
ds9_cropland_percent = 0.39
ds9_ad_2050 = (remaining_cropland * ds9_cropland_percent) + ad_2018
ca_pds_data_sources = [
{
'name':
'Medium, linear growth',
'include':
False,
'description':
('Due to the unavailability of any historical data or future prognostication, '
'we have assumed a 75% adoption of the solution under this scenario. '
),
'maximum':
tla_world_2050,
'datapoints':
pd.DataFrame([
[2018, ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[
2050, 0.75 * tla_world_2050, 0., 0., 0., 0., 0., 0.,
0., 0., 0.
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'High, linear growth',
'include':
False,
'description':
('Due to the unavailability of any historical data or future prognostication, '
'we have assumed a 90% adoption of the solution under this scenario. '
),
'maximum':
tla_world_2050,
'datapoints':
pd.DataFrame([
[2018, ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[
2050, 0.9 * tla_world_2050, 0., 0., 0., 0., 0., 0., 0.,
0., 0.
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Max, linear growth',
'include':
True,
'description':
('Due to the unavailability of any historical data or future prognostication, '
'we have assumed a 100% adoption of the solution under this scenario. '
),
'maximum':
tla_world_2050,
'datapoints':
pd.DataFrame([
[2018, ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[
2050, 1.0 * tla_world_2050, 0., 0., 0., 0., 0., 0., 0.,
0., 0.
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'0.64% annual adoption increase based on Zomer 2014 global increase rates of <20% tree cover on cropland',
'include':
True,
'description':
('Linear projection based on current global increase rates of tree cover '
'greater than 10% on cropland, as calculated based on Zomer et al. (2014) '
'data. Percent of agricultural land under >10% and >20% tree cover in 2000 '
"and 2010 was calculated (see 'Zomer2014-CurrentAdoption' sheet). We "
'compared this to total global cropland (1473 Mha according to GAEZ zones) '
'and calculated a global increase in 10-20% tree cover on cropland of 6.4% '
'from 2000 - 2010, which was converted to an annual increase rate of 0.64 % '
'for this scenario. '),
'maximum':
tla_world_2050,
'growth_initial':
pd.DataFrame(
[[2018, ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
columns=ca_pds_columns).set_index('Year'),
'growth_rate':
0.0064
},
{
'name':
'1.28% annual adoption increase based on Zomer 2014 global increase rates of <10% tree cover on cropland',
'include':
True,
'description':
('Linear projection based on current global increase rates of tree cover '
'greater than 10% on cropland, as calculated based on Zomer et al. (2014) '
'data. Percent of agricultural land under >10% and >20% tree cover in 2000 '
"and 2010 was calculated (see 'Zomer2014-CurrentAdoption' sheet). We "
'compared this to total global cropland (1473 Mha according to GAEZ zones) '
'and calculated a global increase in 10-20% tree cover on cropland of 6.4% '
'from 2000 - 2010, which was doubled and converted to an annual increase '
'rate of 1.28 % for this scenario. '),
'maximum':
tla_world_2050,
'datapoints':
pd.DataFrame([
[2018, ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2050, ds5_ad_2050, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Low conversion of remaining cropland by 2050 (5% - UK medium ambition scenario)',
'include':
True,
'description':
('Linear projection based on the projected rate of 5% conversion of remaining '
'cropland by 2050 as reported by Thomson et al. (2018) for UK projections of '
'future land-use change. Total remaining global cropland in 2014 is '
'estimated at 1225 Mha, which was calculated based on the difference between '
'total current cropland of 1473 Mha and the total global area already under '
'tree intercropping. '),
'maximum':
tla_world_2050,
'datapoints':
pd.DataFrame([
[2018, ds6_ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2050, ds6_ad_2050, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Medium conversion of remaining cropland by 2050 (10% - UK medium ambition scenario)',
'include':
True,
'description':
('Linear projection based on the projected rate of 10% conversion of '
'remaining cropland by 2050 as reported by Thomson et al. (2018) for UK '
'projections of future land-use change. Total remaining global cropland in '
'2014 is estimated at 1225 Mha, which was calculated based on the difference '
'between total current cropland of 1473 Mha and the total global area '
'already under tree intercropping. '),
'maximum':
tla_world_2050,
'datapoints':
pd.DataFrame([
[2018, ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2050, ds7_ad_2050, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'High conversion of remaining cropland by 2050 (20%)',
'include':
True,
'description':
('Linear projection estimating a medium current adoption rate of 20% of tree '
'intercropping on remaining global cropland. Total remaining global cropland '
'in 2014 is estimated at 1225 Mha, which was calculated based on the '
'difference between total current cropland of 1473 Mha and the total global '
'area already under tree intercropping. '),
'maximum':
tla_world_2050,
'datapoints':
pd.DataFrame([
[2018, ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2050, ds8_ad_2050, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Very high conversion of remaining cropland by 2050 (39% - current adoption in EU)',
'include':
True,
'description':
('Linear projection based on current adoption rate of tree intercropping on '
'cropland in the EU, which is 39% as reported in den Herder et al. (2017). '
'This rate was applied to all current remaining global cropland by 2050. '
'Total remaining global cropland in 2014 is estimated at 1225 Mha, which was '
'calculated based on the difference between total current cropland of 1473 '
'Mha and the total global area already under tree intercropping. '
),
'maximum':
tla_world_2050,
'datapoints':
pd.DataFrame([
[2018, ad_2018, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2050, ds9_ad_2050, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
],
columns=ca_pds_columns).set_index('Year')
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
for s in self.pds_ca.scenarios.values():
df = s['df']
df.loc[2012, 'World'] = 254.91717271225
df.loc[2013, 'World'] = 256.900453733641
df.loc[2014, 'World'] = 259.672330723017
df.loc[2015, 'World'] = 261.743911973822
df.loc[2016, 'World'] = 263.820565911005
df.loc[2017, 'World'] = 265.901052727049
df.loc[2018, 'World'] = 267.984718062521
# Custom REF Data
ca_ref_data_sources = [
{
'name':
'[Type Scenario 1 Name Here (REF CASE)...]',
'include':
True,
'description':
('[PLEASE DESCRIBE IN DETAIL THE METHODOLOGY YOU USED IN THIS ANALYSIS. BE '
'SURE TO INCLUDE ANY ADDITIONAL EQUATIONS YOU UTILIZED] '),
'filename':
THISDIR.joinpath(
'ca_ref_data',
'custom_ref_ad_Type_Scenario_1_Name_Here_REF_CASE_.csv')
},
]
self.ref_ca = customadoption.CustomAdoption(
data_sources=ca_ref_data_sources,
soln_adoption_custom_name=self.ac.soln_ref_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
if self.ac.soln_ref_adoption_basis == 'Custom':
ref_adoption_data_per_region = self.ref_ca.adoption_data_per_region(
)
else:
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(list(
self.ac.ref_base_adoption.values()),
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
use_first_pds_datapoint_main = False
if (self.ac.name == 'PDS-100p2050-Optimum'
or self.ac.name == 'PDS-100p2050-Optimum-Jan2019'
or self.ac.name == 'PDS-97p2050-Drawdown-Jan2019'):
use_first_pds_datapoint_main = True
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
ref_adoption_data_per_region=ref_adoption_data_per_region,
use_first_pds_datapoint_main=use_first_pds_datapoint_main,
adoption_base_year=2018,
copy_pds_to_ref=False,
copy_ref_datapoint=False,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name)
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution())
# Custom PDS Data
ca_pds_data_sources = [
{
'name':
'Regional linear trend',
'include':
True,
'filename':
THISDIR.joinpath('ca_pds_data',
'custom_pds_ad_Regional_linear_trend.csv')
},
{
'name':
'Regional max linear trend',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Regional_max_linear_trend.csv')
},
{
'name':
'50% of the max annual afforestation rate across the regions',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_50_of_the_max_annual_afforestation_rate_across_the_regions.csv'
)
},
{
'name':
'100% of the max annual afforestation rate across the regions',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_100_of_the_max_annual_afforestation_rate_across_the_regions.csv'
)
},
{
'name':
'200% increase for Asia and 100% for the other regions',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_200_increase_for_Asia_and_100_for_the_other_regions.csv'
)
},
{
'name':
'Global high - medium early adoption',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Global_high_medium_early_adoption.csv')
},
{
'name':
'Global high - high early adoption',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Global_high_high_early_adoption.csv')
},
{
'name':
'Global max - max early adoption',
'include':
False,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Global_max_max_early_adoption.csv')
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
ht_ref_adoption_initial = pd.Series([
297.78662238675923, 98.41788764519316, 44.68660278943361,
119.60170249905194, 17.61544828747855, 17.464981165602055, 0.0,
0.0, 0.0, 0.0
],
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2014] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_regions, ht_percentages = zip(
*self.ac.pds_adoption_final_percentage)
ht_pds_adoption_final_percentage = pd.Series(list(ht_percentages),
index=list(ht_regions))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution())
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name,
cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
if self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(
filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
adconfig_list = [
[
'param', 'World', 'OECD90', 'Eastern Europe',
'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America',
'China', 'India', 'EU', 'USA'
],
[
'trend', self.ac.soln_pds_adoption_prognostication_trend,
'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'
],
[
'growth', self.ac.soln_pds_adoption_prognostication_growth,
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE'
],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
]
adconfig = pd.DataFrame(adconfig_list[1:],
columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {}
self.ad = adoptiondata.AdoptionData(ac=self.ac,
data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
tla_2050 = self.tla_per_region.loc[2050]
total_grazing_area = 2700.0
ds1_percentages = pd.Series([
0.0, 0.413624302911244, 0.0, 0.070550343761321, 0.138183856774756,
0.845901090636972, 0.0, 0.0, 0.0, 0.0
],
index=dd.REGIONS)
ds1_2050 = tla_2050 * ds1_percentages
ds1_2050['World'] = ds1_2050.sum()
ds2_percentages = pd.Series([
0.0, 0.596920577658838, 0.0, 0.0836983623713848, 0.178254518319696,
0.933216308519849, 0.0, 0.0, 0.0, 0.0
],
index=dd.REGIONS)
ds2_2050 = tla_2050 * ds2_percentages
ds2_2050['World'] = ds2_2050.sum()
ds3_percentages = pd.Series([
0.494785212286615, 0.494785212286615, 0.0, 0.0743985443301198,
0.153620863162148, 0.853238399576085, 0.0, 0.0, 0.0, 0.0
],
index=dd.REGIONS)
ds3_2050 = tla_2050 * ds3_percentages
# Data source 4 is described as scenario 1 with 70% adoption by 2030, but in actual
# implementation it does not make the World be a sum of the regions. Instead, it
# makes the World use the percentage of the OECD90 region.
ds4_percentages = pd.Series([
0.413624302911244, 0.413624302911244, 0.0, 0.070550343761321,
0.138183856774756, 0.845901090636972, 0.0, 0.0, 0.0, 0.0
],
index=dd.REGIONS)
ds4_2050 = tla_2050 * ds4_percentages
# Data source 5 is described as scenario 2 with 70% adoption by 2030, but in actual
# implementation it does not make the World be a sum of the regions. Instead, it
# makes the World use the percentage of the OECD90 region.
ds5_percentages = pd.Series([
0.596920577658838, 0.596920577658838, 0.0, 0.0836983623713848,
0.178254518319696, 0.933216308519849, 0.0, 0.0, 0.0, 0.0
],
index=dd.REGIONS)
ds5_2050 = tla_2050 * ds5_percentages
# SOURCE: Organic World 2009 Table 47, Organic World 2019 and FAOStat.
# Region, Organic grazing area 2009 Mha, Organic grazing area 2009 %,
# Organic grazing area 2019 Mha, Organic grazing area 2019 %
# Africa, 0.03, 0%, 0.08, 0%
# Asia, 0.6, 0%, 1, 0%
# EU, 2.1, 3%, 5.7, 8%
# Latin America, 0.006, 0%, 4.9, 3%
# N. America, 1.1, 0%, 1.4, 0%
# Oceania, 11.7, 8%, 34.9, 24%
# Global Total, 15.5, 0.50%, 48, 2%
ds7_mha_2009 = 15.5
ds7_mha_2019 = 48
ds7_growth_rate = (ds7_mha_2019 - ds7_mha_2009) / (2019 - 2009)
# Note that Excel says the ref_base_adoption is for 2018, when computing a growth
# projection it uses it as 2014. We do the same here, bug-for-bug compatibility.
ds7_2050 = self.ac.ref_base_adoption['World'] + (ds7_growth_rate *
(2050 - 2014))
# Costa Rica proposes a 20-40% increase in 20 years; increase managed grazing
# area by 1-2% per year (Navarro 2015).
ds8_2050 = 0.4 * total_grazing_area
# Brazil INDC proposes to restore 15 Mha of degraded grassland, (20% of national
# grazing area), (Federative Republic of Brazil 2015)
ds9_2050 = 0.2 * total_grazing_area
# Sá (2016) projects an increase in restoration of degraded pasture in Latin America
# from 10Mha in 2015 to 20Mha in 2020. That’s 11% of 177.4 Mha grazing land for
# South America (FAOstat 2019)
ds10_2050 = 0.11 * total_grazing_area
ca_pds_data_sources = [
{
'name':
'Low Growth, Linear Trend',
'include':
True,
# The adoption of managed grazing is projected based on weighted "average, medium,
# and high" growth rates, calculated based on the available country specifc area
# under managed grazing and the region specific total grazing area (Using Henderson
# et al 2015 estimates). This scenario builds the future projection based on the
# low growth rate.
'datapoints':
pd.DataFrame([
[2018] + list(self.ac.ref_base_adoption.values()),
[2050] + ds1_2050.tolist(),
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'High Growth, Linear Trend',
'include':
True,
# The adoption of managed grazing is projected based on weighted "average, medium,
# and high" growth rates, calculated based on the available country specifc area
# under managed grazing and the region specific total grazing area (Using Henderson
# et al 2015 estimates). This scenario builds the future projection based on the
# high growth rate.
'datapoints':
pd.DataFrame([
[2018] + list(self.ac.ref_base_adoption.values()),
[2050] + ds2_2050.tolist(),
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Medium Growth, Linear Trend',
'include':
True,
# The adoption of managed grazing is projected based on weighted "average, medium,
# and high" growth rates, calculated based on the available country specifc area
# under managed grazing and the region specific total grazing area (Using Henderson
# et al 2015 estimates). This scenario builds the future projection based on the
# medium growth rate.
'datapoints':
pd.DataFrame([
[2018] + list(self.ac.ref_base_adoption.values()),
[2050] + ds3_2050.tolist(),
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Low High Early Growth, Linear Trend',
'include':
True,
# This is scenario 1 with 70% adoption by 2030.
'datapoints':
pd.DataFrame([
[2018] + list(self.ac.ref_base_adoption.values()),
[2030] + (0.7 * ds4_2050).tolist(),
[2050] + ds4_2050.tolist(),
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'High High Early Growth, Linear Trend',
'include':
True,
# This is scenario 2 with 70% adoption by 2030.
'datapoints':
pd.DataFrame([
[2018] + list(self.ac.ref_base_adoption.values()),
[2030] + (0.7 * ds5_2050).tolist(),
[2050] + ds5_2050.tolist(),
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Medium High Early Growth, Linear Trend',
'include':
True,
# This is scenario 3 with 70% adoption by 2030.
'datapoints':
pd.DataFrame([
[2018] + list(self.ac.ref_base_adoption.values()),
[2030] + (0.7 * ds3_2050).tolist(),
[2050] + ds3_2050.tolist(),
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Linear growth, based on Organic Grazing Historic Growth',
'include':
True,
# The future adoption of managed grazing is projected based on the organic
# grazing historic growth rate using the linear trend.
'datapoints':
pd.DataFrame([
[2018] + list(self.ac.ref_base_adoption.values()),
[2050] +
[ds7_2050, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Future commitments, max growth',
'include':
True,
# Future adoption of managed grazing is projected based on the country/regional
# commitments as given in the table below. It is assumed that the country/regional
# level trends will be adopted at the global level. This scenario is based on the
# high end commitments given for the Costa Rica.
'datapoints':
pd.DataFrame([
[2018] + list(self.ac.ref_base_adoption.values()),
[
2050, ds8_2050, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Future Commitments, moderate growth',
'include':
True,
# Future adoption of managed grazing is projected based on the country/regional
# commitments as given in the table below. It is assumed that the country/regional
# level trends will be adopted at the global level. This scenario is based on the
# high end commitments given for the Brazil.
'datapoints':
pd.DataFrame([
[2018] + list(self.ac.ref_base_adoption.values()),
[
2050, ds9_2050, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Future Commitments, low growth',
'include':
True,
# Future adoption of managed grazing is projected based on the country/regional
# commitments as given in the table below. It is assumed that the country/regional
# level trends will be adopted at the global level. This scenario is based on the
# high end commitments given for the Latin America.
'datapoints':
pd.DataFrame([
[2018] + list(self.ac.ref_base_adoption.values()),
[
2050, ds10_2050, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
for s in self.pds_ca.scenarios.values():
df = s['df']
for y in range(2012, 2019):
df.loc[y] = [
71.6320447618946, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0
]
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(list(
self.ac.ref_base_adoption.values()),
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
use_first_pds_datapoint_main=True,
adoption_base_year=2018,
copy_pds_to_ref=False,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name,
cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
if self.ac.use_custom_tla and self.ac.custom_tla_fixed_value is not None:
self.c_tla = tla.CustomTLA(
fixed_value=self.ac.custom_tla_fixed_value)
custom_world_vals = self.c_tla.get_world_values()
elif self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(
filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
adconfig_list = [
[
'param', 'World', 'OECD90', 'Eastern Europe',
'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America',
'China', 'India', 'EU', 'USA'
],
[
'trend', self.ac.soln_pds_adoption_prognostication_trend,
'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'
],
[
'growth', self.ac.soln_pds_adoption_prognostication_growth,
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE'
],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
]
adconfig = pd.DataFrame(adconfig_list[1:],
columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {}
self.ad = adoptiondata.AdoptionData(ac=self.ac,
data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_data_sources = [
{
'name':
'Constant degradation rate, 100% adoption by 2050, linear',
'include':
True,
'description':
('The TLA_Envelope sheet was used to compute the annual degradation of the '
'wetland. The annual protection rate was adjusted so that 100% of the '
'remaining wetlands in 2050 were protected. This adoption is less than 100% '
'of the wetlands in 2014. '),
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Constant_degradation_rate_100_adoption_by_2050_linear.csv'
)
},
{
'name':
'Constant degradation rate, 80% adoption by 2050, linear',
'include':
True,
'description':
('The TLA_Envelope sheet was used to compute the annual degradation of the '
'wetland. The annual protection rate was adjusted so that 80% of the '
'remaining wetlands in 2050 were protected. This is the same as Scenario 1 '
'except for a smaller percentage protected by 2050. '),
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Constant_degradation_rate_80_adoption_by_2050_linear.csv'
)
},
{
'name':
'Constant degradation rate, 100% adoption by 2030, linear',
'include':
True,
'description':
('The TLA_Envelope sheet was used to compute the annual degradation of the '
'wetland. The annual protection rate was adjusted so that 100% of the '
'remaining wetlands in 2030 were protected. Because there is 100% '
'protection in 2030, all values after that are constant. '),
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Constant_degradation_rate_100_adoption_by_2030_linear.csv'
)
},
{
'name':
'Constant degradation rate, 80% adoption by 2030, linear',
'include':
True,
'description':
('The TLA_Envelope sheet was used to compute the annual degradation of the '
'wetland. The annual protection rate was adjusted so that 80% of the '
'remaining wetlands in 2030 were protected. Because there is only 80% '
'protection in 2030 linear interpolation was used to bridge the 2014, 2030, '
'and 2050 values. '),
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Constant_degradation_rate_80_adoption_by_2030_linear.csv'
)
},
{
'name':
'Variable degradation rate, 100% adoption by 2050, linear',
'include':
True,
'description':
('The TLA_Envelope sheet was used to compute the annual degradation of the '
'wetland. The annual protection rate was adjusted so that 100% of the '
'remaining wetlands in 2050 were protected. This adoption is less than 100% '
'of the wetlands in 2014. This is the same as Scenario 1 except an annual '
'reduction in degradation rate is applied. '),
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Variable_degradation_rate_100_adoption_by_2050_linear.csv'
)
},
{
'name':
'Variable degradation rate, 80% adoption by 2050, linear',
'include':
True,
'description':
('The TLA_Envelope sheet was used to compute the annual degradation of the '
'wetland. The annual protection rate was adjusted so that 80% of the '
'remaining wetlands in 2050 were protected. This is the same as Scenario 5 '
'except for a smaller percentage protected by 2050. '),
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Variable_degradation_rate_80_adoption_by_2050_linear.csv'
)
},
{
'name':
'Variable degradation rate, 100% adoption by 2030, linear',
'include':
True,
'description':
('The TLA_Envelope sheet was used to compute the annual degradation of the '
'wetland. The annual protection rate was adjusted so that 100% of the '
'remaining wetlands in 2030 were protected. Because there is 100% '
'protection in 2030, all values after that are constant. This is the same as '
'Scenario 3 except a variable degradation rate is used. '),
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Variable_degradation_rate_100_adoption_by_2030_linear.csv'
)
},
{
'name':
'Variable degradation rate, 80% adoption by 2030, linear',
'include':
True,
'description':
('The TLA_Envelope sheet was used to compute the annual degradation of the '
'wetland. The annual protection rate was adjusted so that 80% of the '
'remaining wetlands in 2030 were protected. Because there is only 80% '
'protection in 2030 linear interpolation was used to bridge the 2014, 2030, '
'and 2050 values. This scenario is the same as Scenario 4 except a variable '
'degradation rate is used. '),
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Variable_degradation_rate_80_adoption_by_2030_linear.csv'
)
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(list(
self.ac.ref_base_adoption.values()),
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
use_first_pds_datapoint_main=False,
adoption_base_year=2018,
copy_pds_to_ref=False,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
tot_red_in_deg_land=self.ua.
cumulative_reduction_in_total_degraded_land(),
pds_protected_deg_land=self.ua.
pds_cumulative_degraded_land_protected(),
ref_protected_deg_land=self.ua.
ref_cumulative_degraded_land_protected(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name, cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
if self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
adconfig_list = [
['param', 'World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)',
'Middle East and Africa', 'Latin America', 'China', 'India', 'EU', 'USA'],
['trend', self.ac.soln_pds_adoption_prognostication_trend, 'Medium',
'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'],
['growth', self.ac.soln_pds_adoption_prognostication_growth, 'NOTE',
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE', 'NOTE', 'NOTE'],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]]
adconfig = pd.DataFrame(adconfig_list[1:], columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {
}
self.ad = adoptiondata.AdoptionData(ac=self.ac, data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
# Current commitments made under Bonn Challenge (taken on 16/12/2019)
bonn_mha = 149.012768
# % current commitments are projects in temperate thermal moisture regime
temperate_percent = 0.198187597540394
# % current commitments in temperate thermal moisture regime available are
# for intact forest restoration
intact_percent = 0.4423
# Calculated by subtracting the total degraded land suitable for tropical
# forest restoration (304 Mha) and boreal (46 Mha) from the WRI estimates
# of 500 Mha as the potential global area for forest restoration
# TODO: the spatial-aez work in late 2019 provided direct data input of temperate
# forest area, distinct from boreal and tropical, which could be used here.
title = 'Percent of Degraded Land Suitable for Intact Temperate Forest Restoration'
intact_mha = self.ac.lookup_vma(vma_title=title)
if intact_mha is None:
intact_mha = VMAs[title].avg_high_low(key='mean')
# Max land Expected total land commited under Bonn Challenge and NY Declaration
max_land = 350.0
# Mha of current commitments in temperate thermal moisture regime
committed_mha = bonn_mha * temperate_percent
# Mha of total degraded land available for new commitments in
# temperate thermal moisture regime, from New York Declaration
nydf_new_mha = (max_land - bonn_mha) * temperate_percent
# WRI prediction for max available land area
wri_new_mha = intact_mha - committed_mha
# DATA SOURCE 1, using NYDF land area
ds1_future_commit = 1.0
ds1_2030 = (committed_mha * intact_percent) + (nydf_new_mha * ds1_future_commit)
# future land area with WRI prediction plus 44.23% and 100% commitment
future_mha_44p = (committed_mha * intact_percent) + (wri_new_mha * 0.4423)
future_mha_100p = (committed_mha * intact_percent) + (wri_new_mha * 1.0)
ca_pds_data_sources = [
{'name': 'Optimistic-Achieve Commitment in 15 years w/ 100% intact, (Charlotte Wheeler, 2016)', 'include': True,
'description': (
'The adoption scenarios are calculated using the linear trendline based '
'on: (1) Current commitments to date, (2) Potential future commitments, (3) '
'The proportion of committee land restored to intact forest (100% or 44.23%), '
'and (4) The year commitments are realised (2030, 2045 or 2060). In this '
'scenario, NYDF prediction for max area available for temperate forest '
'restoration, 100% new commitment for intact forest and year 2030 was '
'considered when the commitments will be realized.'),
'maximum': intact_mha,
'datapoints': pd.DataFrame([
[2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[2030, ds1_2030, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[2031, ds1_2030, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
], columns=ca_pds_columns).set_index('Year')},
{'name': 'Optimistic-Achieve Commitment in 15 years w/ 100% intact, WRI estimates (Charlotte Wheeler, 2016)', 'include': True,
'description': (
'The adoption scenarios are calculated using the linear trendline based '
'on: (1) Current commitments to date, (2) Potential future commitments, (3) '
'The proportion of committee land restored to intact forest (100% or 44.23%), '
'and (4) The year commitments are realised (2030, 2045 or 2060). In this '
'scenario, WRI prediction for max area available for temperate forest '
'restoration, 100% new commitment for intact forest and year 2030 was '
'considered when the commitments will be realized. '),
'maximum': intact_mha,
'datapoints': pd.DataFrame([
[2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[2030, future_mha_100p, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[2031, future_mha_100p, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
], columns=ca_pds_columns).set_index('Year')},
{'name': 'Conservative-Achieve Commitment in 15 years w/ 44.2% intact, (Charlotte Wheeler,2016)', 'include': True,
'description': (
'The adoption scenarios are calculated using the linear trendline based '
'on: (1) Current commitments to date, (2) Potential future commitments, (3) '
'The proportion of committee land restored to intact forest (100% or 44.23%), '
'and (4) The year commitments are realised (2030, 2045 or 2060). In this '
'scenario, WRI prediction for max area available for temperate forest '
'restoration, 44.23% new commitment for intact forest and year 2030 was '
'considered when the commitments will be realized. '),
'maximum': intact_mha,
'datapoints': pd.DataFrame([
[2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[2030, future_mha_44p, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[2031, future_mha_44p, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
], columns=ca_pds_columns).set_index('Year')},
{'name': 'Conservative-Achieve Commitment in 15 years w/ 44.2% intact with continued growth post-2030, (Charlotte Wheeler,2016)', 'include': True,
'description': (
'The adoption scenarios are calculated using the linear trendline based '
'on: (1) Current commitments to date, (2) Potential future commitments, (3) '
'The proportion of committee land restored to intact forest (100% or 44.23%), '
'and (4) The year commitments are realised (2030, 2045 or 2060). In this '
'scenario, WRI prediction for max area available for temperate forest '
'restoration, 44.23% new commitment for intact forest and year 2030 was '
'considered when the commitments will be realized, with continued growth '
'in post 2030.'),
'maximum': intact_mha,
'datapoints': pd.DataFrame([
[2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[2030, future_mha_44p, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
], columns=ca_pds_columns).set_index('Year')},
{'name': 'Conservative-Achieve Commitment in 30 years w/ 100% intact, (Charlotte Wheeler,2016)', 'include': True,
'description': (
'The adoption scenarios are calculated using the linear trendline based '
'on: (1) Current commitments to date, (2) Potential future commitments, (3) '
'The proportion of committee land restored to intact forest (100% or 44.23%), '
'and (4) The year commitments are realised (2030, 2045 or 2060). In this '
'scenario, WRI prediction for max area available for temperate forest '
'restoration, 100% new commitment for intact forest and year 2045 was '
'considered when the commitments will be realized. '),
'maximum': intact_mha,
'datapoints': pd.DataFrame([
[2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[2045, future_mha_100p, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[2046, future_mha_100p, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
], columns=ca_pds_columns).set_index('Year')},
{'name': 'Conservative-Achieve Commitment in 30 years w/ 44.2% intact, (Charlotte Wheeler,2016)', 'include': True,
'description': (
'The adoption scenarios are calculated using the linear trendline based '
'on: (1) Current commitments to date, (2) Potential future commitments, (3) '
'The proportion of committee land restored to intact forest (100% or 44.23%), '
'and (4) The year commitments are realised (2030, 2045 or 2060). In this '
'scenario, WRI prediction for max area available for temperate forest '
'restoration, 44.23% new commitment for intact forest and year 2045 was '
'considered when the commitments will be realized. '),
'maximum': intact_mha,
'datapoints': pd.DataFrame([
[2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[2045, future_mha_44p, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[2046, future_mha_44p, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
], columns=ca_pds_columns).set_index('Year')},
{'name': 'Conservative-Achieve Commitment in 30 years w/ 44.2% intact with continued growth, (Charlotte Wheeler,2016)', 'include': True,
'description': (
'The adoption scenarios are calculated using the linear trendline based '
'on: (1) Current commitments to date, (2) Potential future commitments, (3) '
'The proportion of committee land restored to intact forest (100% or 44.23%), '
'and (4) The year commitments are realised (2030, 2045 or 2060). In this '
'scenario, WRI prediction for max area available for temperate forest '
'restoration, 44.23% new commitment for intact forest and year 2045 was '
'considered when the commitments will be realized, with continued growth '
'in post 2045. '),
'maximum': intact_mha,
'datapoints': pd.DataFrame([
[2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[2045, future_mha_44p, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
], columns=ca_pds_columns).set_index('Year')},
{'name': 'Conservative-Achieve Commitment in 45 years w/ 100% intact (Charlotte Wheeler,2016)', 'include': True,
'description': (
'The adoption scenarios are calculated using the linear trendline based '
'on: (1) Current commitments to date, (2) Potential future commitments, (3) '
'The proportion of committee land restored to intact forest (100% or 44.23%), '
'and (4) The year commitments are realised (2030, 2045 or 2060). In this '
'scenario, WRI prediction for max area available for temperate forest '
'restoration, 100% new commitment for intact forest and year 2060 was '
'considered when the commitments will be realized. '),
'maximum': intact_mha,
'datapoints': pd.DataFrame([
[2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[2060, future_mha_100p, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
], columns=ca_pds_columns).set_index('Year')},
{'name': 'Conservative-Achieve Commitment in 45 years w/ 44.2% intact (Charlotte Wheeler,2016)', 'include': True,
'description': (
'The adoption scenarios are calculated using the linear trendline based '
'on: (1) Current commitments to date, (2) Potential future commitments, (3) '
'The proportion of committee land restored to intact forest (100% or 44.23%), '
'and (4) The year commitments are realised (2030, 2045 or 2060). In this '
'scenario, WRI prediction for max area available for temperate forest '
'restoration, 44.23% new commitment for intact forest and year 2060 was '
'considered when the commitments will be realized. '),
'maximum': intact_mha,
'datapoints': pd.DataFrame([
[2014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[2060, future_mha_44p, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
], columns=ca_pds_columns).set_index('Year')},
]
self.pds_ca = customadoption.CustomAdoption(data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0, low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region()
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region()
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(
list(self.ac.ref_base_adoption.values()), index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (ht_ref_adoption_initial /
self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(ac=self.ac,
ref_datapoints=ht_ref_datapoints, pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region, pds_adoption_limits=self.tla_per_region,
use_first_pds_datapoint_main=True,
adoption_base_year=2018,
copy_pds_to_ref=False,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted=self.ua.soln_net_annual_funits_adopted()
self.fc = firstcost.FirstCost(ac=self.ac, pds_learning_increase_mult=2,
ref_learning_increase_mult=2, conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name,
cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
if self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(
filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
adconfig_list = [
[
'param', 'World', 'OECD90', 'Eastern Europe',
'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America',
'China', 'India', 'EU', 'USA'
],
[
'trend', self.ac.soln_pds_adoption_prognostication_trend,
'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'
],
[
'growth', self.ac.soln_pds_adoption_prognostication_growth,
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE'
],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
]
adconfig = pd.DataFrame(adconfig_list[1:],
columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {}
self.ad = adoptiondata.AdoptionData(ac=self.ac,
data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
ad_vma = VMAs['Future adoption (million hectares)']
ad_cached = self.ac.lookup_vma(
vma_title='Future adoption (million hectares)')
adoption_2050_mean = ad_cached if ad_cached else ad_vma.avg_high_low(
key='mean')
adoption_2050_high = ad_vma.avg_high_low(key='high')
ca_pds_data_sources = [
{
'name':
'Average growth, linear trend',
'include':
True,
# In this scenario, the future adoption of the solution was based on the average
# percent future adoption of perennial bioenergy crops as derived from the VMA sheet.
'datapoints':
pd.DataFrame([
[
2014, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[
2018, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[
2050, adoption_2050_mean, 0., 0., 0., 0., 0., 0., 0.,
0., 0.
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Medium growth, linear trend',
'include':
True,
# In this scenario, the future adoption of the solution was based on the high
# percent future adoption of perennial bioenergy crops as derived from the VMA sheet.
'datapoints':
pd.DataFrame([
[
2014, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[
2018, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[
2050, adoption_2050_high, 0., 0., 0., 0., 0., 0., 0.,
0., 0.
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Max growth, linear trend',
'include':
True,
# This scenario assumes that perennial bioenergy crops will be cultivated on
# 100% of the total land allocated to this solution by 2050.
'datapoints':
pd.DataFrame([
[
2014, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[
2018, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[
2050, self.tla_per_region.loc[2050, 'World'], 0., 0.,
0., 0., 0., 0., 0., 0., 0.
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Average early growth, linear trend',
'include':
True,
# This is scenario 1, with the assumption that 70% of the total adoption will
# be achieved by 2030.
'datapoints':
pd.DataFrame([
[
2014, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[
2018, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[
2030, 0.7 * adoption_2050_mean, 0., 0., 0., 0., 0., 0.,
0., 0., 0.
],
[
2050, adoption_2050_mean, 0., 0., 0., 0., 0., 0., 0.,
0., 0.
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Medium early growth, linear trend',
'include':
True,
# This is scenario 2, with the assumption that 70% of the total adoption will
# be achieved by 2030.
'datapoints':
pd.DataFrame([
[
2014, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[
2018, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[
2030, 0.7 * adoption_2050_high, 0., 0., 0., 0., 0., 0.,
0., 0., 0.
],
[
2050, adoption_2050_high, 0., 0., 0., 0., 0., 0., 0.,
0., 0.
],
],
columns=ca_pds_columns).set_index('Year')
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=None)
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(list(
self.ac.ref_base_adoption.values()),
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2018] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
use_first_pds_datapoint_main=True,
adoption_base_year=2018,
copy_pds_to_ref=True,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=False)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name,
cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution())
adconfig_list = [
[
'param', 'World', 'OECD90', 'Eastern Europe',
'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America',
'China', 'India', 'EU', 'USA'
],
[
'trend', self.ac.soln_pds_adoption_prognostication_trend,
'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'
],
[
'growth', self.ac.soln_pds_adoption_prognostication_growth,
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE'
],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
]
adconfig = pd.DataFrame(adconfig_list[1:],
columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {
'Raw Data for ALL LAND TYPES': {
'Project Drawdown 2018_Direct Seeded Rice Area (2nd Polynomial Growth)':
THISDIR.joinpath(
'ad',
'ad_Project_Drawdown_2018_Direct_Seeded_Rice_Area_2nd_Polynomial_Growth.csv'
),
'Project Drawdown 2018_Rice Area under water management (Linear Growth)':
THISDIR.joinpath(
'ad',
'ad_Project_Drawdown_2018_Rice_Area_under_water_management_Linear_Growth.csv'
),
},
}
self.ad = adoptiondata.AdoptionData(ac=self.ac,
data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
tla_2050 = self.tla_per_region.loc[2050, 'World']
ad_2018 = self.ac.ref_base_adoption['World']
# 2050 adoption based on Project Drawdown 2018 Direct Seeded
# Rice Area (2nd order Polynomial Growth); Refer "Adoption Data" sheet
ad_2050_2ndpoly = 69.9380489958066000
# 2050 adoption based on Project Drawdown 2018_Rice Area under water
# management (Linear Growth); Refer "Adoption Data" sheet
ad_2050_linear = 170.3736841446970000
# 2050 adoption based on Project Drawdown 2018_Rice Area under water
# management (Linear Growth, divided by 2); Refer "Adoption Data" sheet
ad_2050_linear_half = 85.1868420723484000
ad_2050_avg = (ad_2050_2ndpoly + ad_2050_linear +
ad_2050_linear_half) / 3.0
ad_2050_min = min(
[ad_2050_2ndpoly, ad_2050_linear, ad_2050_linear_half])
ad_2050_max = max(
[ad_2050_2ndpoly, ad_2050_linear, ad_2050_linear_half])
ca_pds_data_sources = [
{
'name':
'Medium Growth',
'include':
True,
'description':
('Future adoption of improved rice cultivation is projected based on the '
'historical growth rate of direct seeded rice and water management available '
'in the literature (refer "Current Adoption_LR" sheet). The historical data '
'was interpolated, a 2nd order polynomial growth was considered for direct '
'seeded rice data and linear growth for the water management, The '
'interpolated data is then placed in the "adoption data" sheet. The future '
'adoption in the present scenario is based on the "average adoption value '
'projected for the year 2050" based on the given data set. '),
'datapoints':
pd.DataFrame([
[
2018, ad_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0
],
[
2050, ad_2050_avg, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Low Growth',
'include':
True,
'description':
('Future adoption of improved rice cultivation is projected based on the '
'historical growth rate of direct seeded rice and water management available '
'in the literature (refer "Current Adoption_LR" sheet). The historical data '
'was interpolated, a 2nd order polynomial growth was considered for direct '
'seeded rice data and linear growth for the water management, The '
'interpolated data is then placed in the "adoption data" sheet. The future '
'adoption in the present scenario is based on the "minimum adoption value '
'projected for the year 2050" based on the given data set. '),
'datapoints':
pd.DataFrame([
[
2018, ad_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0
],
[
2050, ad_2050_min, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'High Growth',
'include':
True,
'description':
('Future adoption of improved rice cultivation is projected based on the '
'historical growth rate of direct seeded rice and water management available '
'in the literature (refer "Current Adoption_LR" sheet). The historical data '
'was interpolated, a 2nd order polynomial growth was considered for direct '
'seeded rice data and linear growth for the water management, The '
'interpolated data is then placed in the "adoption data" sheet. The future '
'adoption in the present scenario is based on the "maximum adoption value '
'projected for the year 2050" based on the given data set. '),
'datapoints':
pd.DataFrame([
[
2018, ad_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0
],
[
2050, ad_2050_max, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Medium Growth, Early Adoption',
'include':
True,
'description':
('Future adoption of improved rice cultivation is projected based on the '
'historical growth rate of direct seeded rice and water management available '
'in the literature (refer "Current Adoption_LR" sheet). The historical data '
'was interpolated, a 2nd order polynomial growth was considered for direct '
'seeded rice data and linear growth for the water management, The '
'interpolated data is then placed in the "adoption data" sheet. The future '
'adoption in the present scenario is based on the "average adoption value '
'projected for the year 2050" based on the given data set. In addition, it '
'is also assumed that 75% of the projected adoption for the year 2050 will '
'be achieved by 2030. '),
'datapoints':
pd.DataFrame([
[
2018, ad_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0
],
[
2030, 0.75 * ad_2050_avg, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0
],
[
2050, ad_2050_avg, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Low Growth, Early Adoption',
'include':
True,
'description':
('Future adoption of improved rice cultivation is projected based on the '
'historical growth rate of direct seeded rice and water management available '
'in the literature (refer "Current Adoption_LR" sheet). The historical data '
'was interpolated, a 2nd order polynomial growth was considered for direct '
'seeded rice data and linear growth for the water management, The '
'interpolated data is then placed in the "adoption data" sheet. The future '
'adoption in the present scenario is based on the "minimum adoption value '
'projected for the year 2050" based on the given data set. In addition, it '
'is also assumed that 75% of the projected adoption for the year 2050 will '
'be achieved by 2030. '),
'datapoints':
pd.DataFrame([
[
2018, ad_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0
],
[
2030, 0.75 * ad_2050_min, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0
],
[
2050, ad_2050_min, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'High Growth, Early Adoption',
'include':
False,
'description':
('Future adoption of improved rice cultivation is projected based on the '
'historical growth rate of direct seeded rice and water management available '
'in the literature (refer "Current Adoption_LR" sheet). The historical data '
'was interpolated, a 2nd order polynomial growth was considered for direct '
'seeded rice data and linear growth for the water management, The '
'interpolated data is then placed in the "adoption data" sheet. The future '
'adoption in the present scenario is based on the "maximum adoption value '
'projected for the year 2050" based on the given data set. In addition, it '
'is also assumed that 75% of the projected adoption for the year 2050 will '
'be achieved by 2030. '),
'datapoints':
pd.DataFrame([
[
2018, ad_2018, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0
],
[
2030, 0.75 * ad_2050_max, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0
],
[
2050, ad_2050_max, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
],
],
columns=ca_pds_columns).set_index('Year')
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
for s in self.pds_ca.scenarios.values():
df = s['df']
df.loc[2014, 'World'] = 31.2753335238409000
df.loc[2015, 'World'] = 33.6257358946259000
df.loc[2016, 'World'] = 35.9761382654110000
df.loc[2017, 'World'] = 38.3265406361960000
df.loc[2018, 'World'] = 40.6769430069810000
# Custom REF Data
ca_ref_data_sources = [
{
'name':
'[Type Scenario 1 Name Here (REF CASE)...]',
'include':
True,
'description':
'No description entered.',
'filename':
THISDIR.joinpath(
'ca_ref_data',
'custom_ref_ad_Type_Scenario_1_Name_Here_REF_CASE_.csv')
},
]
self.ref_ca = customadoption.CustomAdoption(
data_sources=ca_ref_data_sources,
soln_adoption_custom_name=self.ac.soln_ref_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
if self.ac.soln_ref_adoption_basis == 'Custom':
ref_adoption_data_per_region = self.ref_ca.adoption_data_per_region(
)
else:
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(list(
self.ac.ref_base_adoption.values()),
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2018] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
ref_adoption_data_per_region=ref_adoption_data_per_region,
use_first_pds_datapoint_main=False,
adoption_base_year=2018,
copy_pds_to_ref=False,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=False)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name,
cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
if self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(
filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
adconfig_list = [
[
'param', 'World', 'OECD90', 'Eastern Europe',
'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America',
'China', 'India', 'EU', 'USA'
],
[
'trend', self.ac.soln_pds_adoption_prognostication_trend,
'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'
],
[
'growth', self.ac.soln_pds_adoption_prognostication_growth,
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE'
],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
]
adconfig = pd.DataFrame(adconfig_list[1:],
columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {
'Raw Data for ALL LAND TYPES': {
'Nair 2012 & Lal et al. 2018':
THISDIR.joinpath('ad', 'ad_Nair_2012_Lal_et_al__2018.csv'),
},
}
self.ad = adoptiondata.AdoptionData(ac=self.ac,
data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
tla_2050 = self.tla_per_region.loc[2050, 'World']
adoption_vma = VMAs['Current Adoption']
adoption_2018 = adoption_vma.avg_high_low(key='mean')
# SOURCE: den Herder, M., Moreno, G., Mosquera-Losada, R. M., Palma, J. H., Sidiropoulou,
# A., Freijanes, J. J. S., ... & Papanastasis, V. P. (2017). Current extent and
# stratification of agroforestry in the European Union. Agriculture, Ecosystems &
# Environment, 241, 121-132.
ds4_silvo_of_grassland = 0.35
ds4_total_grassland = 3621.237045
ds4_potential_adoption = ds4_silvo_of_grassland * ds4_total_grassland
ds4_adopt_2050 = 0.6 * ds4_potential_adoption
# SOURCE: Somarriba, E., Beer, J., Alegre-Orihuela, J., Andrade, H. J., Cerda, R., DeClerck,
# F., ... & Krishnamurthy, L. (2012). Mainstreaming agroforestry in Latin America. In
# Agroforestry-The Future of Global Land Use (pp. 429-453). Springer, Dordrecht.
ds5_adoption_rate = 0.45
pg_vma = VMAs['Total Pasture/Grazing Area']
pasture_grassland_area = pg_vma.avg_high_low(key='mean')
ds5_potential_adoption = ds5_adoption_rate * pasture_grassland_area
ds5_adopt_2050 = 0.6 * ds5_potential_adoption
# SOURCE: Holman et al., 2004, http://www.lrrd.org/lrrd16/12/holm16098.htm
growth_initial = pd.DataFrame(
[[2018] + list(self.ac.ref_base_adoption.values())],
columns=ca_pds_columns).set_index('Year')
ds6_rate = 0.006
# SOURCE: Holman et al., 2004, http://www.lrrd.org/lrrd16/12/holm16098.htm
ds7_rate = 0.013
ca_pds_data_sources = [
{
'name':
'Linear trend based on Zomers >30% tree cover percent area applied in grassland area',
'include':
False,
# This is a proxy adoption scenario which is created in the absence of any data
# available either on historical growth rate of silvopasture or any future
# projections. Thus, the present scenario builds the future adoption using the
# Zoomer 2014 information available on tree coverage in the agricultural area.
# Country level data on agricultural area with > 30 percent tree cover was available
# at Zomer 2014. This data was compiled at the Project Drawdown regions, which is
# then used to get their percent with respect to the total agricultural area.
# Those percentages were then applied on the grassland area to get the regional
# grassland area under >30 percent tree cover. The future adoption of the silvopasture
# area under was thus projected based on the regional linear trend applied to the
# grassland area with >30 percent tree coverage. The projections were based on the
# regional linear trend. (Refer PD region wise - silvopasture sheet)
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Linear_trend_based_on_Zomers_30_tree_cover_percent_area_applied_in_grassland_area.csv'
)
},
{
'name':
'Linear trend based on Zomers >30% tree cover percent area and conversion of >10% are to 30% tree cover area applied in grassland area',
'include':
False,
# In this scenario, the future area in silvopasture is projected based on scenario
# one, in addition it was assumed that there will be some extra area available for
# silvopasture by the conversion of 0-10 percent/11-20 percent tree coverage
# grassland area to >30 percent tree coverage areas as required for a silvopasture
# system. The projections are based on regional linear trends.
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Linear_trend_based_on_Zomers_30_tree_cover_percent_area_and_conversion_of_10_are_to_30_t_d419700f.csv'
)
},
{
'name':
'Linear Interpolation for Adoption Data based on Nair 2012 & Lal et al. 2018',
'include':
True,
# In the absence of comprehensive historical data for silvopasture adoption, this
# scenario uses available global adoption estimates reported in peer-reviewed
# publications. Data points ffrom 2012 (Nair 2012) and 2018 (Lal et al. 2018) were
# used for a linear interpolation of future adoption based on historic expansion of
# silvopasture adoption.
'datapoints':
pd.DataFrame([
[
2018, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[
2050, 1083.33333333333, 0., 0., 0., 0., 0., 0., 0., 0.,
0.
],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Medium interpolation based on current adoption, linear trend (high regional proportion of grazing land under silvopasture)',
'include':
True,
# Future area in silvopasture is projected based on the proportion of current area
# of grazing or pasture land under silvopasture practice in the EU, which currently
# has the highest regional proportion of grazing land under silvopasture worldwide
# (35%), as reported by den Herder et al 2017 (see VMA, Variable 31). This percentage
# was applied to the total global grazing area to obtain a medium estimate of
# potential projected area for future silvopasture adoption. This scenario assumes
# 60 percent of future silvopasture adoption by 2050.
'datapoints':
pd.DataFrame([
[
2018, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[2050, ds4_adopt_2050, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'High interpolation based on current adoption, linear trend (high national proportion of grazing land under silvopasture)',
'include':
True,
# Future area in silvopasture is projected based on the proportion of current area
# of grazing or pasture land under silvopasture practice in Nicaragua, which currently
# has the highest national proportion of grazing land under silvopasture worldwide
# (45%), as reported by Somarriba et al 2012 (see VMA, Variable 31). This percentage
# was applied to the total global grazing area to obtain a high estimate of potential
# projected area for future silvopasture adoption. This scenario assumes 60 percent
# of future silvopasture adoption by 2050.
'datapoints':
pd.DataFrame([
[
2018, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[2050, ds5_adopt_2050, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Low growth, linear trend (based on improved pasture area)',
'include': True,
# This is a proxy adoption scenario which is created in the absence of any data
# available either on historical growth rate of silvopasture or any future projections
# on silvopasture. In this scenario future adoption of silvopasture area was projected
# using the Thorton 2010 future adoption rates given for improved pasture. The
# silvopasture adoption is projected based on the average annual adoption percent
# (0.60%) increase in the improved pasture area given for the five countries (Mexico,
# Honduras, Nicaragua, Costa Rica, and Panama) by Holman et al 2004 and reported by
# Thorton et al 2010. With the limitation of data at the regional level, the
# projections are made only at the global scale.
'growth_rate': ds6_rate,
'growth_initial': growth_initial
},
{
'name':
'High growth, linear trend (based on improved pasture area)',
'include': True,
# This is a proxy adoption scenario which is created in the absence of any data
# available either on historical growth rate of silvopasture or any future projections
# on silvopasture. In this scenario future adoption of silvopasture area was projected
# using the Thorton 2010 future adoption rates given for improved pasture. The
# silvopasture adoption is projected based on the maximum annual adoption percent
# (1.30%) increase in the improved pasture area given for the five countries (Mexico,
# Honduras, Nicaragua, Costa Rica, and Panama) by Holman et al 2004 and reported by
# Thorton et al 2010. With the limitation of data at the regional level, the
# projections are made only at the global scale.
'growth_rate': ds7_rate,
'growth_initial': growth_initial
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
# Manual adjustment made in spreadsheet for Drawdown 2020.
for source in ca_pds_data_sources:
if 'filename' in source:
# only the interpolated sources are adjusted
continue
name = source['name']
s = self.pds_ca.scenarios[name]
df = s['df']
df.loc[2012, 'World'] = 450.0
df.loc[2013, 'World'] = 466.666666666667
df.loc[2014, 'World'] = 483.333333333333
df.loc[2015, 'World'] = 500.0
df.loc[2016, 'World'] = 516.666666666667
df.loc[2017, 'World'] = 533.333333333333
df.loc[2018, 'World'] = 550.0
# Custom REF Data
ca_ref_data_sources = [
{
'name':
'[Type Scenario 1 Name Here (REF CASE)...]',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_ref_data',
'custom_ref_ad_Type_Scenario_1_Name_Here_REF_CASE_.csv')
},
]
self.ref_ca = customadoption.CustomAdoption(
data_sources=ca_ref_data_sources,
soln_adoption_custom_name=self.ac.soln_ref_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
if self.ac.soln_ref_adoption_basis == 'Custom':
ref_adoption_data_per_region = self.ref_ca.adoption_data_per_region(
)
else:
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(list(
self.ac.ref_base_adoption.values()),
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2018] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
ref_adoption_data_per_region=ref_adoption_data_per_region,
use_first_pds_datapoint_main=False,
adoption_base_year=2018,
copy_pds_to_ref=False,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name,
cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
if self.ac.use_custom_tla and self.ac.custom_tla_fixed_value is not None:
self.c_tla = tla.CustomTLA(
fixed_value=self.ac.custom_tla_fixed_value)
custom_world_vals = self.c_tla.get_world_values()
elif self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(
filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
adconfig_list = [
[
'param', 'World', 'OECD90', 'Eastern Europe',
'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America',
'China', 'India', 'EU', 'USA'
],
[
'trend', self.ac.soln_pds_adoption_prognostication_trend,
'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'
],
[
'growth', self.ac.soln_pds_adoption_prognostication_growth,
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE'
],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
]
adconfig = pd.DataFrame(adconfig_list[1:],
columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {}
self.ad = adoptiondata.AdoptionData(ac=self.ac,
data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
ad_world_2018 = self.ac.ref_base_adoption['World']
ad_2018 = pd.Series(self.ac.ref_base_adoption)
tla_world_2050 = self.tla_per_region.loc[2050, 'World']
degrade_rate_conservative = pd.Series(self.ac.degradation_rate,
index=range(2014, 2061))
degrade_rate_low = pd.Series(0, index=range(2014, 2061))
degrade_rate_low.loc[2014] = self.ac.degradation_rate
degrade_rate_low.loc[2020] = degrade_rate_low.loc[2014] / 2.0
step = (degrade_rate_low.loc[2014] - degrade_rate_low.loc[2020]) / 6.0
for year in range(2015, 2020):
degrade_rate_low.loc[year] = degrade_rate_low.loc[2014] - (
(year - 2014) * step)
step = (degrade_rate_low.loc[2020] - degrade_rate_low.loc[2030]) / 10.0
for year in range(2021, 2030):
degrade_rate_low.loc[year] = degrade_rate_low.loc[2020] - (
(year - 2020) * step)
degrade_rate_low.loc[2030:] = 0.0
# In Excel, the Custom PDS Scenarios tab columns AC:AF compute a limit on adoption
# based on the amount of degraded land remaining. We compute the same limit here.
def constrained_tla(rate):
degrade_df = pd.DataFrame(
0,
index=range(2014, 2061),
columns=[
'Total undegraded land at the start of the year',
'Degraded land at the end of the year',
'Total undegraded land at the end of the year',
'Constrained TLA'
])
for year in range(2014, 2061):
if year == 2014:
# Not a typo, Excel uses the 2018 base adoption number starting in 2014.
# We are bug-for-bug compatible here.
undeg_start = tla_world_2050 - ad_world_2018
else:
last = degrade_df.loc[year - 1, 'Constrained TLA']
undeg_start = last - ad_world_2018
degrade_df.loc[
year,
'Total undegraded land at the start of the year'] = undeg_start
degraded_end = max(0, undeg_start * rate[year])
degrade_df.loc[
year,
'Degraded land at the end of the year'] = degraded_end
degrade_df.loc[
year, 'Total undegraded land at the end of the year'] = (
undeg_start - degraded_end)
degrade_df.loc[year, 'Constrained TLA'] = (
tla_world_2050 -
degrade_df['Degraded land at the end of the year'].sum())
return degrade_df
# Data Source 1
ds1_growth_rate = 0.0197583057035311
ds1_datapoints = pd.DataFrame(0.0,
index=range(2012, 2061),
columns=dd.REGIONS)
ds1_datapoints.loc[2012:2018, 'World'] = ad_world_2018
ds1_maximum = 1035.0
for year in range(2019, 2061):
rate = 1 + ds1_growth_rate
newval = ds1_datapoints.loc[year - 1, 'World'] * rate
ds1_datapoints.loc[year, 'World'] = min(ds1_maximum, newval)
# Data Source 2
ds2_growth_rate = 0.0102232658124979
ds2_datapoints = pd.DataFrame(0.0,
index=range(2012, 2061),
columns=dd.REGIONS)
ds2_datapoints.loc[2012:2018, 'World'] = ad_world_2018
ds2_maximum = 1016.0
for year in range(2019, 2061):
rate = 1 + ds2_growth_rate
newval = ds2_datapoints.loc[year - 1, 'World'] * rate
ds2_datapoints.loc[year, 'World'] = min(ds2_maximum, newval)
# Data Source 3
ds3_growth_rate = 0.00550564152557609
ds3_datapoints = pd.DataFrame(0.0,
index=range(2012, 2061),
columns=dd.REGIONS)
ds3_datapoints.loc[2012:2018, 'World'] = ad_world_2018
ds3_maximum = 998.0
for year in range(2019, 2061):
rate = 1 + ds3_growth_rate
newval = ds3_datapoints.loc[year - 1, 'World'] * rate
ds3_datapoints.loc[year, 'World'] = min(ds3_maximum, newval)
# Data Source 4
ds4_growth_rate = 0.0197583057035311
ds4_datapoints = pd.DataFrame(0.0,
index=range(2012, 2061),
columns=dd.REGIONS)
ds4_datapoints.loc[2012:2018, 'World'] = ad_world_2018
ds4_maximum = 1047.9
for year in range(2019, 2061):
rate = 1 + ds4_growth_rate
newval = ds4_datapoints.loc[year - 1, 'World'] * rate
ds4_datapoints.loc[year, 'World'] = min(ds4_maximum, newval)
# Data Source 5
ds5_growth_rate = 0.0102232658124979
ds5_datapoints = pd.DataFrame(0.0,
index=range(2012, 2061),
columns=dd.REGIONS)
ds5_datapoints.loc[2012:2018, 'World'] = ad_world_2018
ds5_maximum = 1046.8
for year in range(2019, 2061):
rate = 1 + ds5_growth_rate
newval = ds5_datapoints.loc[year - 1, 'World'] * rate
ds5_datapoints.loc[year, 'World'] = min(ds5_maximum, newval)
# Data Source 6
ds6_growth_rate = 0.00550564152557609
ds6_datapoints = pd.DataFrame(0.0,
index=range(2012, 2061),
columns=dd.REGIONS)
ds6_datapoints.loc[2012:2018, 'World'] = ad_world_2018
ds6_minimum = 1046.0
for year in range(2019, 2061):
rate = 1 + ds6_growth_rate
newval = ds6_datapoints.loc[year - 1, 'World'] * rate
ds6_datapoints.loc[year, 'World'] = min(ds6_minimum, newval)
# Data Source 7
ds7_constrained = constrained_tla(rate=degrade_rate_conservative)
ds7_constrained_2050 = ds7_constrained.loc[2050, 'Constrained TLA']
ds7_datapoints = pd.DataFrame(columns=ca_pds_columns).set_index('Year')
ds7_datapoints.loc[2014] = ad_2018
ds7_datapoints.loc[2030] = (1.0 * ds7_constrained_2050)
ds7_datapoints.loc[2050] = ds7_constrained_2050
# Data Source 8
ds8_datapoints = pd.DataFrame(columns=ca_pds_columns).set_index('Year')
ds8_datapoints.loc[2014] = ad_2018
ds8_datapoints.loc[2030] = (0.9 * ds7_constrained_2050)
ds8_datapoints.loc[2050] = ds7_constrained_2050
# Data Source 9
ds9_constrained = constrained_tla(rate=degrade_rate_low)
ds9_constrained_2050 = ds9_constrained.loc[2050, 'Constrained TLA']
ds9_datapoints = pd.DataFrame(columns=ca_pds_columns).set_index('Year')
ds9_datapoints.loc[2014] = ad_2018
ds9_datapoints.loc[2030] = (1.0 * ds9_constrained_2050)
ds9_datapoints.loc[2050] = ds9_constrained_2050
# Data Source 10
ds10_datapoints = pd.DataFrame(
columns=ca_pds_columns).set_index('Year')
ds10_datapoints.loc[2014] = ad_2018
ds10_datapoints.loc[2030] = (0.9 * ds9_constrained_2050)
ds10_datapoints.loc[2050] = ds9_constrained_2050
ca_pds_data_sources = [
{
'name':
'High adoption and conservative degradation rate',
'include':
True,
'description':
('The historical protected area evolution (1990-2015) in Morales-Hidalgo was '
'used to estimate three protected areas\' yearly increase rates: 1,98%; 1,02% '
'and 0,55% for the forest PA corresponding to 1990-2015, 2005- 2015 and '
'2010- 2015 respectively. This, coupled with the TLA\'s current degradation '
'rate of 0.31% result in scenarios 1, 2 and 3. The New York declaration on '
'Forests is used to estimate an alternative degradation rate which starts '
'from 0.31% and linearly decreases to half its value in 2020 and then to '
'zero in 2030. The yearly increase rates from Morales- Hidalgo plus this '
'degradation rate result in scenarios 4, 5 and 6. '),
'maximum':
1035.0,
'datapoints':
ds1_datapoints
},
{
'name':
'Medium adoption and conservative degradation rate',
'include':
True,
'description':
('The historical protected area evolution (1990-2015) in Morales-Hidalgo was '
'used to estimate three protected areas yearly increase rates: 1,98%; 1,02% '
'and 0,55% for the forest PA corresponding to 1990-2015, 2005- 2015 and '
'2010- 2015 respectively. This, coupled with the TLA\'s current degradation '
'rate of 0.31% result in scenarios 1, 2 and 3. The New York declaration on '
'Forests is used to estimate an alternative degradation rate which starts '
'from 0.31% and linearly decreases to half its value in 2020 and then to '
'zero in 2030. The yearly increase rates from Morales- Hidalgo plus this '
'degradation rate result in scenarios 4, 5 and 6. '),
'maximum':
1016.0,
'datapoints':
ds2_datapoints
},
{
'name':
'Low adoption and conservative degradation rate',
'include':
True,
'description':
('The historical protected area evolution (1990-2015) in Morales-Hidalgo was '
'used to estimate three protected areas yearly increase rates: 1,98%; 1,02% '
'and 0,55% for the forest PA corresponding to 1990-2015, 2005- 2015 and '
'2010- 2015 respectively. This, coupled with the TLA\'s current degradation '
'rate of 0.31% result in scenarios 1, 2 and 3. The New York declaration on '
'Forests is used to estimate an alternative degradation rate which starts '
'from 0.31% and linearly decreases to half its value in 2020 and then to '
'zero in 2030. The yearly increase rates from Morales- Hidalgo plus this '
'degradation rate result in scenarios 4, 5 and 6. '),
'maximum':
998.0,
'datapoints':
ds3_datapoints
},
{
'name':
'High adoption and low degradation rate',
'include':
True,
'description':
('The historical protected area evolution (1990-2015) in Morales-Hidalgo was '
'used to estimate three protected areas yearly increase rates: 1,98%; 1,02% '
'and 0,55% for the forest PA corresponding to 1990-2015, 2005- 2015 and '
'2010- 2015 respectively. This, coupled with the TLA\'s current degradation '
'rate of 0.31% result in scenarios 1, 2 and 3. The New York declaration on '
'Forests is used to estimate an alternative degradation rate which starts '
'from 0.31% and linearly decreases to half its value in 2020 and then to '
'zero in 2030. The yearly increase rates from Morales- Hidalgo plus this '
'degradation rate result in scenarios 4, 5 and 6. '),
'maximum':
1047.9,
'datapoints':
ds4_datapoints
},
{
'name':
'Medium adoption and low degradation rate',
'include':
True,
'description':
('The historical protected area evolution (1990-2015) in Morales-Hidalgo was '
'used to estimate three protected areas\' yearly increase rates: 1,98%; 1,02% '
'and 0,55% for the forest PA corresponding to 1990-2015, 2005- 2015 and '
'2010- 2015 respectively. This, coupled with the TLA\'s current degradation '
'rate of 0.31% result in scenarios 1, 2 and 3. The New York declaration on '
'Forests is used to estimate an alternative degradation rate which starts '
'from 0.31% and linearly decreases to half its value in 2020 and then to '
'zero in 2030. The yearly increase rates from Morales- Hidalgo plus this '
'degradation rate result in scenarios 4, 5 and 6. '),
'maximum':
1046.8,
'datapoints':
ds5_datapoints
},
{
'name':
'Low adoption and low degradation rate',
'include':
True,
'description':
('The historical protected area evolution (1990-2015) in Morales-Hidalgo was '
'used to estimate three protected areas\' yearly increase rates: 1,98%; 1,02% '
'and 0,55% for the forest PA corresponding to 1990-2015, 2005- 2015 and '
'2010- 2015 respectively. This, coupled with the TLA\'s current degradation '
'rate of 0.31% result in scenarios 1, 2 and 3. The New York declaration on '
'Forests is used to estimate an alternative degradation rate which starts '
'from 0.31% and linearly decreases to half its value in 2020 and then to '
'zero in 2030. The yearly increase rates from Morales- Hidalgo plus this '
'degradation rate result in scenarios 4, 5 and 6. '),
'maximum':
1046,
'datapoints':
ds6_datapoints
},
{
'name':
'100% conservative degradation TLA in 2030',
'include':
True,
'description':
('Linear increase up to 100% of TLA value in 2030 (calculated with the '
'conservative degradation rate) and TLA value from then onwards '
),
'maximum':
ds7_constrained_2050,
'datapoints':
ds7_datapoints
},
{
'name':
'90% conservative degradation TLA in 2030',
'include':
True,
'description':
('Linear increase up to 90% of TLA value in 2030 (calculated with the '
'conservative degradation rate) and then linear increase to 100% TLA value '
'in 2050 from then onwards '),
'maximum':
ds7_constrained_2050,
'datapoints':
ds8_datapoints
},
{
'name':
'100% low degradation TLA by 2030',
'include':
True,
'description':
('Linear increase up to 100% of TLA value in 2030 (calculated with the low '
'degradation rate) and TLA value from then onwards '),
'maximum':
ds9_constrained_2050,
'datapoints':
ds9_datapoints
},
{
'name':
'90% low degradation TLA by 2030',
'include':
True,
'description':
('Linear increase up to 90% of TLA value in 2030 (calculated with the low '
'degradation rate) and TLA value from then onwards '),
'maximum':
ds9_constrained_2050,
'datapoints':
ds10_datapoints
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=self.ac.soln_pds_adoption_custom_high_sd_mult,
low_sd_mult=self.ac.soln_pds_adoption_custom_low_sd_mult,
total_adoption_limit=self.tla_per_region)
for s in self.pds_ca.scenarios.values():
df = s['df']
df.loc[2014:2018, 'World'] = 651.0
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(list(
self.ac.ref_base_adoption.values()),
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2018] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
use_first_pds_datapoint_main=True,
adoption_base_year=2018,
copy_pds_to_ref=True,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=False)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
tot_red_in_deg_land=self.ua.
cumulative_reduction_in_total_degraded_land(),
pds_protected_deg_land=self.ua.
pds_cumulative_degraded_land_protected(),
ref_protected_deg_land=self.ua.
ref_cumulative_degraded_land_protected(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name)
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution())
# Custom PDS Data
ca_pds_data_sources = [
{
'name':
'Conservative-Low, Linear Trend',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_ConservativeLow_Linear_Trend.csv')
},
{
'name':
'Conservative-Medium, Linear Trend',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_ConservativeMedium_Linear_Trend.csv')
},
{
'name':
'High-linear trend',
'include':
True,
'filename':
THISDIR.joinpath('ca_pds_data',
'custom_pds_ad_Highlinear_trend.csv')
},
{
'name':
'High-high early growth, linear trend',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Highhigh_early_growth_linear_trend.csv')
},
{
'name':
'High, very high early growth, linear trend',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_High_very_high_early_growth_linear_trend.csv'
)
},
{
'name':
'Max, high early growth, linear trend',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Max_high_early_growth_linear_trend.csv')
},
{
'name':
'Aggressive Max, urgent adoption',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_Aggressive_Max_urgent_adoption.csv')
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
ht_ref_adoption_initial = pd.Series(
[3.27713, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2014] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_regions, ht_percentages = zip(
*self.ac.pds_adoption_final_percentage)
ht_pds_adoption_final_percentage = pd.Series(list(ht_percentages),
index=list(ht_regions))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
tot_red_in_deg_land=self.ua.
cumulative_reduction_in_total_degraded_land(),
pds_protected_deg_land=self.ua.
pds_cumulative_degraded_land_protected(),
ref_protected_deg_land=self.ua.
ref_cumulative_degraded_land_protected(),
regime_distribution=self.ae.get_land_distribution())
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name)
if self.ac.use_custom_tla:
self.c_tla = tla.CustomTLA(
filename=THISDIR.joinpath('custom_tla_data.csv'))
custom_world_vals = self.c_tla.get_world_values()
else:
custom_world_vals = None
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution(),
custom_world_values=custom_world_vals)
# Custom PDS Data
ca_pds_data_sources = [
{
'name':
'Optimistic-Achieve Commitment in 15 years w/ 100% intact, (Charlotte Wheeler, 2016)',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_OptimisticAchieve_Commitment_in_15_years_w_100_intact_Charlotte_Wheeler_2016.csv'
)
},
{
'name':
'Optimistic-Achieve Commitment in 15 years w/ 100% intact, WRI estimates (Charlotte Wheeler, 2016)',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_OptimisticAchieve_Commitment_in_15_years_w_100_intact_WRI_estimates_Charlotte_Wheeler_2016.csv'
)
},
{
'name':
'Conservative-Achieve Commitment in 15 years w/ 44.2% intact, (Charlotte Wheeler,2016)',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_ConservativeAchieve_Commitment_in_15_years_w_44_2_intact_Charlotte_Wheeler2016.csv'
)
},
{
'name':
'Conservative-Achieve Commitment in 15 years w/ 44.2% intact with continued growth post-2030, (Charlotte Wheeler,2016)',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_ConservativeAchieve_Commitment_in_15_years_w_44_2_intact_with_continued_growth_post2030__967aa8cc.csv'
)
},
{
'name':
'Conservative-Achieve Commitment in 30 years w/ 100% intact, (Charlotte Wheeler,2016)',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_ConservativeAchieve_Commitment_in_30_years_w_100_intact_Charlotte_Wheeler2016.csv'
)
},
{
'name':
'Conservative-Achieve Commitment in 30 years w/ 44.2% intact, (Charlotte Wheeler,2016)',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_ConservativeAchieve_Commitment_in_30_years_w_44_2_intact_Charlotte_Wheeler2016.csv'
)
},
{
'name':
'Conservative-Achieve Commitment in 30 years w/ 44.2% intact with continued growth, (Charlotte Wheeler,2016)',
'include':
True,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_ConservativeAchieve_Commitment_in_30_years_w_44_2_intact_with_continued_growth_Charlotte_4167ecee.csv'
)
},
{
'name':
'Conservative-Achieve Commitment in 45 years w/ 100% intact (Charlotte Wheeler,2016)',
'include':
False,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_ConservativeAchieve_Commitment_in_45_years_w_100_intact_Charlotte_Wheeler2016.csv'
)
},
{
'name':
'Conservative-Achieve Commitment in 45 years w/ 44.2% intact (Charlotte Wheeler,2016)',
'include':
False,
'filename':
THISDIR.joinpath(
'ca_pds_data',
'custom_pds_ad_ConservativeAchieve_Commitment_in_45_years_w_44_2_intact_Charlotte_Wheeler2016.csv'
)
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
ht_ref_adoption_initial = pd.Series(
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2014] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_regions, ht_percentages = zip(
*self.ac.pds_adoption_final_percentage)
ht_pds_adoption_final_percentage = pd.Series(list(ht_percentages),
index=list(ht_regions))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2014] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution())
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name,
cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution())
adconfig_list = [
[
'param', 'World', 'OECD90', 'Eastern Europe',
'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America',
'China', 'India', 'EU', 'USA'
],
[
'trend', self.ac.soln_pds_adoption_prognostication_trend,
'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'
],
[
'growth', self.ac.soln_pds_adoption_prognostication_growth,
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE'
],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
]
adconfig = pd.DataFrame(adconfig_list[1:],
columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {
'Raw Data for ALL LAND TYPES': {
'Organic annual cropland estimate (sum of low growth by regions)':
THISDIR.joinpath(
'ad',
'ad_Organic_annual_cropland_estimate_sum_of_low_growth_by_regions.csv'
),
'Organic annual cropland estimate (sum of medium growth by region)':
THISDIR.joinpath(
'ad',
'ad_Organic_annual_cropland_estimate_sum_of_medium_growth_by_region.csv'
),
'Organic annual cropland estimate (sum of high growth by region)':
THISDIR.joinpath(
'ad',
'ad_Organic_annual_cropland_estimate_sum_of_high_growth_by_region.csv'
),
},
'Region: OECD90': {
'Raw Data for ALL LAND TYPES': {
'Willer 2018 SEI calc RA lin':
THISDIR.joinpath('ad',
'ad_Willer_2018_SEI_calc_RA_lin.csv'),
'Willer 2018 SEI calc RA exp':
THISDIR.joinpath('ad',
'ad_Willer_2018_SEI_calc_RA_exp.csv'),
'Willer 2018 SEI calc RA 3rd poly':
THISDIR.joinpath(
'ad', 'ad_Willer_2018_SEI_calc_RA_3rd_poly.csv'),
},
},
'Region: Eastern Europe': {
'Raw Data for ALL LAND TYPES': {
'Willer 2018 SEI calc RA lin':
THISDIR.joinpath('ad',
'ad_Willer_2018_SEI_calc_RA_lin.csv'),
'Willer 2018 SEI calc RA 3rd poly':
THISDIR.joinpath(
'ad', 'ad_Willer_2018_SEI_calc_RA_3rd_poly.csv'),
'Willer 2018 SEI calc RA exp':
THISDIR.joinpath('ad',
'ad_Willer_2018_SEI_calc_RA_exp.csv'),
},
},
'Region: Asia (Sans Japan)': {
'Raw Data for ALL LAND TYPES': {
'Willer 2018 SEI calc RA lin':
THISDIR.joinpath('ad',
'ad_Willer_2018_SEI_calc_RA_lin.csv'),
'Willer 2018 SEI calc RA exp':
THISDIR.joinpath('ad',
'ad_Willer_2018_SEI_calc_RA_exp.csv'),
'Willer 2018 SEI calc RA 3rd poly':
THISDIR.joinpath(
'ad', 'ad_Willer_2018_SEI_calc_RA_3rd_poly.csv'),
},
},
'Region: Middle East and Africa': {
'Raw Data for ALL LAND TYPES': {
'Willer 2018 SEI calc RA lin':
THISDIR.joinpath('ad',
'ad_Willer_2018_SEI_calc_RA_lin.csv'),
'Willer 2018 SEI calc RA 3rd poly':
THISDIR.joinpath(
'ad', 'ad_Willer_2018_SEI_calc_RA_3rd_poly.csv'),
'Willer 2018 SEI calc RA exp':
THISDIR.joinpath('ad',
'ad_Willer_2018_SEI_calc_RA_exp.csv'),
},
},
'Region: Latin America': {
'Raw Data for ALL LAND TYPES': {
'Willer 2018 SEI calc RA lin':
THISDIR.joinpath('ad',
'ad_Willer_2018_SEI_calc_RA_lin.csv'),
'Willer 2018 SEI calc RA exp':
THISDIR.joinpath('ad',
'ad_Willer_2018_SEI_calc_RA_exp.csv'),
'Willer 2018 SEI calc RA 3rd poly':
THISDIR.joinpath(
'ad', 'ad_Willer_2018_SEI_calc_RA_3rd_poly.csv'),
},
},
'Region: USA': {
'Tropical-Humid Land': {
'USDA NASS Organic Surv; Cropland area 2016, 2008 summaries':
THISDIR.joinpath(
'ad',
'ad_USDA_NASS_Organic_Surv_Cropland_area_2016_2008_summaries.csv'
),
},
},
}
self.ad = adoptiondata.AdoptionData(ac=self.ac,
data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
tla_2050 = self.tla_per_region.loc[2050]
world_2014 = self.tla_per_region.loc[2014, 'World']
ad_2018 = list(self.ac.ref_base_adoption.values())
# % smallholder area, based on updates to smallholder area VMA sheet
# (34.1% was estimated in Book Ed1)
smallholder_pct = 0.315909724971454
# Maximum technical potential adoption area available for conservation agriculture,
# based on % large farmers (Note: not a typo, we're accounting for leftover
# Conservation Agriculture land here)
max_adoption = smallholder_pct * world_2014
def _get_datapoints(percent, extra=0.0):
w = self.tla_per_region.loc[2014, 'World']
dp = [
0.0, # World is the sum of the regions.
((percent['OECD90'] + extra) *
self.tla_per_region.loc[2050, 'OECD90']),
((percent['Eastern Europe'] + extra) *
self.tla_per_region.loc[2050, 'Eastern Europe']),
((percent['Asia (Sans Japan)'] + extra) *
self.tla_per_region.loc[2050, 'Asia (Sans Japan)']),
((percent['Middle East and Africa'] + extra) *
self.tla_per_region.loc[2050, 'Middle East and Africa']),
((percent['Latin America'] + extra) *
self.tla_per_region.loc[2050, 'Latin America']),
0.0,
0.0,
0.0,
0.0
] # China, India, USA, EU
dp[0] = sum(dp)
return dp
# Data Source 1
lin = 'Willer 2018 SEI calc RA lin'
ds1_percent = {
'OECD90':
self.ad.adoption_data(region='OECD90').loc[2050, lin] / world_2014,
'Eastern Europe':
self.ad.adoption_data(region='Eastern Europe').loc[2050, lin] /
world_2014,
'Asia (Sans Japan)':
self.ad.adoption_data(region='Asia (Sans Japan)').loc[2050, lin] /
world_2014,
'Middle East and Africa':
self.ad.adoption_data(region='Middle East and Africa').loc[2050,
lin] /
world_2014,
'Latin America':
self.ad.adoption_data(region='Latin America').loc[2050, lin] /
world_2014,
'China':
0.0,
'India':
0.0,
'EU':
0.0,
'USA':
0.0
}
ds1_regen = 0.2 # RA adoption in addition to organic agriculture adoption
ds1_2050 = _get_datapoints(percent=ds1_percent, extra=ds1_regen)
ds1_2030 = [x * 0.6 for x in ds1_2050]
# Data Source 2
ply = 'Willer 2018 SEI calc RA 3rd poly'
exp = 'Willer 2018 SEI calc RA exp'
ds2_percent = {
'OECD90':
self.ad.adoption_data(region='OECD90').loc[2050, ply] / world_2014,
'Eastern Europe':
self.ad.adoption_data(region='Eastern Europe').loc[2050, exp] /
world_2014,
'Asia (Sans Japan)':
self.ad.adoption_data(region='Asia (Sans Japan)').loc[2050, ply] /
world_2014,
'Middle East and Africa':
self.ad.adoption_data(region='Middle East and Africa').loc[2050,
exp] /
world_2014,
'Latin America':
self.ad.adoption_data(region='Latin America').loc[2050, ply] /
world_2014,
'China':
0.0,
'India':
0.0,
'EU':
0.0,
'USA':
0.0
}
ds2_regen = 0.2 # RA adoption in addition to organic agriculture adoption
ds2_2050 = _get_datapoints(percent=ds2_percent, extra=ds2_regen)
ds2_2030 = [x * 0.6 for x in ds2_2050]
# Data Source 3
percent = {
'OECD90': 0.6,
'Eastern Europe': 0.6,
'Asia (Sans Japan)': 0.6,
'Middle East and Africa': 0.6,
'Latin America': 0.6,
'China': 0.0,
'India': 0.0,
'EU': 0.0,
'USA': 0.0
}
ds3_2050 = _get_datapoints(percent=percent)
ds3_2030 = [x * 0.8 for x in ds3_2050]
# Data Source 4
ds4_regen = 0.3 # RA adoption in addition to organic agriculture adoption
ds4_2050 = _get_datapoints(percent=ds2_percent, extra=ds4_regen)
ds4_2030 = [x * 0.8 for x in ds4_2050]
# Data Source 5, SOURCE: Project Drawdown 2016, constrained TLA for RA
ds5_regen = 0.3 # RA adoption in addition to organic agriculture adoption
ds5_2050 = _get_datapoints(percent=ds1_percent, extra=ds5_regen)
ds5_2030 = [x * 0.8 for x in ds5_2050]
# Data Source 6
ds6_2050 = ds3_2050
ds6_2040 = [x * 0.75 for x in ds6_2050]
# Data Source 7
ds7_2050 = ds3_2050
ds7_2040 = [x * 0.8 for x in ds7_2050]
# Data Source 8
ds8_percent = {}
for region in ds1_percent.keys():
ds8_percent[region] = (ds1_percent[region] +
ds2_percent[region]) / 2.0
ds8_2050 = _get_datapoints(percent=ds8_percent)
ca_pds_data_sources = [
{
'name':
'Low, Linear Trend',
'include':
False,
'description':
('This scenario projected the future growth of regenerative agriculture based '
'on the low historical growth rate of the organic agriculture in different '
'regions (Willer et al 2016). In addition, it was assumed that the adoption '
'of RA will be higher (20%) than that of the organic agriculture and 60% of '
'the estimated adoption by 2050 will be achieved by 2030. Along with this, '
'this scenario also includes the leftover area from conservation '
'agriculture, as discussed above. Current adoption values for the year '
'2012-2017 are taken from the sheet "org adoption by country". This change '
'was made in response to the new year set for the currrent adoption, i.e., '
'2018. '),
'datapoints':
pd.DataFrame([
[2018] + ad_2018,
[2030] + ds1_2030,
[2050] + ds1_2050,
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'High, Linear Trend',
'include':
False,
'description':
('This scenario projected the future growth of regenerative agriculture based '
'on the high historical growth rate of the organic agriculture in different '
'regions (Willer et al 2018, after calculation for arable land). In '
'addition, it was assumed that the adoption of RA will be higher (20%) than '
'that of the organic agriculture and 60% of the estimated adoption by 2050 '
'will be achieved by 2030. Along with this, this scenario also includes the '
'leftover area from conservation agriculture, as discussed above. Current '
'adoption values for the year 2012-2017 are taken from the sheet "org '
'adoption by country". This change was made in response to the new year set '
'for the currrent adoption, i.e., 2018. '),
'datapoints':
pd.DataFrame([
[2018] + ad_2018,
[2030] + ds2_2030,
[2050] + ds2_2050,
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Max, Linear Trend',
'include':
False,
'description':
('This scenario projected a 60% adoption of the solution by 2050 and assumed '
'80% of the that will be achieved by 2030. Along with this, this scenario '
'also includes the leftover area from conservation agriculture, as discussed '
'above. Current adoption values for the year 2012-2017 are taken from the '
'sheet "org adoption by country". This change was made in response to the '
'new year set for the currrent adoption, i.e., 2018. '),
'datapoints':
pd.DataFrame([
[2018] + ad_2018,
[2030] + ds3_2030,
[2050] + ds3_2050,
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Aggressive-high, early growth, linear trend',
'include':
False,
'description':
('This scenario projected the future growth of regenerative agriculture based '
'on the high historical growth rate of the organic agriculture in different '
'regions (Willer et al 2018, after calculaion for arable land). In addition, '
'it was assumed that the adoption of RA will be higher (30%) than that of '
'the organic agriculture and 80% of the estimated adoption by 2050 will be '
'achieved by 2030. Along with this, this scenario also includes the leftover '
'area from conservation agriculture, as discussed above. Current adoption '
'values for the year 2012-2017 are taken from the sheet "org adoption by '
'country". This change was made in response to the new year set for the '
'currrent adoption, i.e., 2018. '),
'datapoints':
pd.DataFrame([
[2018] + ad_2018,
[2030] + ds4_2030,
[2050] + ds4_2050,
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Aggressive-low, early growth, linear trend',
'include':
False,
'description':
('This scenario projected the future growth of regenerative agriculture based '
'on the low historical growth rate of the organic agriculture in different '
'regions (Willer et al 2016). In addition, it was assumed that the adoption '
'of RA will be higher (30%) than that of the organic agriculture and 80% of '
'the estimated adoption by 2050 will be achieved by 2030. Along with this, '
'this scenario also includes the leftover area from conservation '
'agriculture, as discussed above. Current adoption values for the year '
'2012-2017 are taken from the sheet "org adoption by country". This change '
'was made in response to the new year set for the currrent adoption, i.e., '
'2018. '),
'datapoints':
pd.DataFrame([
[2018] + ad_2018,
[2030] + ds5_2030,
[2050] + ds5_2050,
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Conservative, low growth 1, only RA',
'include':
False,
'description':
('Assuming a lower current adoption of the solution, this scenario has taken '
'a conservative approach and projected a 60% adoption of the solution by '
'2050 and assumed 75% of the that will be achieved by 2040. This scenario is '
'built independent of conservation agriculture, so no leftover area of '
'conservation agriculture was added to this solution. Current adoption '
'values for the year 2012-2017 are taken from the sheet "org adoption by '
'country". This change was made in response to the new year set for the '
'currrent adoption, i.e., 2018. '),
'datapoints':
pd.DataFrame([
[2018] + ad_2018,
[2040] + ds6_2040,
[2050] + ds6_2050,
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Conservative, low growth 2, only RA',
'include':
False,
'description':
('Assuming a lower current adoption of the solution, this scenario has taken '
'a conservative approach and projected a 60% adoption of the solution by '
'2050 and assumed 80% of the that will be achieved by 2040. This scenario is '
'built independent of conservation agriculture, so no leftover area of '
'conservation agriculture was added to this solution. Current adoption '
'values for the year 2012-2017 are taken from the sheet "org adoption by '
'country". This change was made in response to the new year set for the '
'currrent adoption, i.e., 2018. '),
'datapoints':
pd.DataFrame([
[2018] + ad_2018,
[2040] + ds7_2040,
[2050] + ds7_2050,
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Baseline scenario',
'include':
True,
'description': ('Baseline adoption '),
'datapoints':
pd.DataFrame([
[2018] + ad_2018,
[2050] + ds8_2050,
],
columns=ca_pds_columns).set_index('Year')
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
# Shrinkage in land area used for the Conservation Agriculture solution are added to
# the land area available for Regenerative Agriculture.
for idx, s in enumerate(self.pds_ca.scenarios.values(), start=1):
if 'BookVersion1' in self.ac.name:
fname = f'ds{idx}_incr_change_in_cons_ag_book.csv'
else:
fname = f'ds{idx}_incr_change_in_cons_ag_current.csv'
cons_ag_file = THISDIR.joinpath('ca_pds_data', fname)
if cons_ag_file.is_file():
cons_ag_adopt = pd.read_csv(str(cons_ag_file),
header=0,
index_col=0,
comment='#',
skipinitialspace=True,
skip_blank_lines=True,
dtype=np.float64)
# growth in Conservation Agriculture is not a factor, only reduction in area
cons_ag_adopt[cons_ag_adopt >= 0.0] = 0.0
cons_ag_adopt['China', 'India', 'EU', 'USA'] = 0.0
s['df'] += cons_ag_adopt.abs()
# Current adoption values for the year 2012-2017 are taken from the sheet
# "org adoption by country" in the Excel file for this solution.
for s in self.pds_ca.scenarios.values():
df = s['df']
df.loc[2014] = [
10.0425, 6.230983199564, 1.024820758485, 1.495740017536,
0.280348210165, 0.342272547086, 0.0, 0.0, 0.0, 0.0
]
df.loc[2015] = [
10.4821, 6.392187207238, 1.186149025025, 1.609802907785,
0.313522708425, 0.349200927281, 0.0, 0.0, 0.0, 0.0
]
df.loc[2016] = [
10.9217, 6.560831715351, 1.372873741988, 1.732564062959,
0.350622850918, 0.356484379405, 0.0, 0.0, 0.0, 0.0
]
df.loc[2017] = [
11.3613, 6.743428946088, 1.588992842952, 1.864686799696,
0.392113171655, 0.364411206077, 0.0, 0.0, 0.0, 0.0
]
df.loc[2018] = [
11.8009, 6.946491121632, 1.839133620034, 2.006885018162,
0.438513174434, 0.373269709916, 0.0, 0.0, 0.0, 0.0
]
# Custom REF Data
ca_ref_data_sources = [
{
'name':
'[Type Scenario 1 Name Here (REF CASE)...]',
'include':
True,
'description':
('[PLEASE DESCRIBE IN DETAIL THE METHODOLOGY YOU USED IN THIS ANALYSIS. BE '
'SURE TO INCLUDE ANY ADDITIONAL EQUATIONS YOU UTILIZED] '),
'filename':
THISDIR.joinpath(
'ca_ref_data',
'custom_ref_ad_Type_Scenario_1_Name_Here_REF_CASE_.csv')
},
]
self.ref_ca = customadoption.CustomAdoption(
data_sources=ca_ref_data_sources,
soln_adoption_custom_name=self.ac.soln_ref_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
if self.ac.soln_ref_adoption_basis == 'Custom':
ref_adoption_data_per_region = self.ref_ca.adoption_data_per_region(
)
else:
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
ht_ref_adoption_initial = pd.Series(list(
self.ac.ref_base_adoption.values()),
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2018] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
ref_adoption_data_per_region=ref_adoption_data_per_region,
use_first_pds_datapoint_main=False,
adoption_base_year=2018,
copy_pds_to_ref=False,
copy_ref_datapoint=False,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=True)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)
def __init__(self, scenario=None):
if scenario is None:
scenario = list(scenarios.keys())[0]
self.scenario = scenario
self.ac = scenarios[scenario]
# TLA
self.ae = aez.AEZ(solution_name=self.name,
cohort=2020,
regimes=dd.THERMAL_MOISTURE_REGIMES8)
self.tla_per_region = tla.tla_per_region(
self.ae.get_land_distribution())
adconfig_list = [
[
'param', 'World', 'OECD90', 'Eastern Europe',
'Asia (Sans Japan)', 'Middle East and Africa', 'Latin America',
'China', 'India', 'EU', 'USA'
],
[
'trend', self.ac.soln_pds_adoption_prognostication_trend,
'Medium', 'Medium', 'Medium', 'Medium', 'Medium', 'Medium',
'Medium', 'Medium', 'Medium'
],
[
'growth', self.ac.soln_pds_adoption_prognostication_growth,
'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE', 'NOTE',
'NOTE'
],
['low_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
['high_sd_mult', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
]
adconfig = pd.DataFrame(adconfig_list[1:],
columns=adconfig_list[0],
dtype=np.object).set_index('param')
ad_data_sources = {
'Raw Data for ALL LAND TYPES': {
'Norman Uphoff (Personal Comunication)':
THISDIR.joinpath('ad',
'ad_Norman_Uphoff_Personal_Comunication.csv'),
},
'Region: Asia (Sans Japan)': {
'Raw Data for ALL LAND TYPES': {
'IRIN (Adoption of SRI in China)':
THISDIR.joinpath('ad',
'ad_IRIN_Adoption_of_SRI_in_China.csv'),
'IRIN (Adoption of SRI in India)':
THISDIR.joinpath('ad',
'ad_IRIN_Adoption_of_SRI_in_India.csv'),
'IRIN (Adoption of SRI in Indonesia)':
THISDIR.joinpath(
'ad', 'ad_IRIN_Adoption_of_SRI_in_Indonesia.csv'),
},
},
}
self.ad = adoptiondata.AdoptionData(ac=self.ac,
data_sources=ad_data_sources,
main_includes_regional=True,
adconfig=adconfig)
# Custom PDS Data
ca_pds_columns = ['Year'] + dd.REGIONS
tla_world_2050 = self.tla_per_region.loc[2050, 'World']
irin_sri_adopt_fname = THISDIR.joinpath('ca_pds_data',
'IRIN_SRI_Adoption.csv')
irin_sri_adopt = pd.read_csv(str(irin_sri_adopt_fname),
header=0,
comment='#',
skipinitialspace=True,
skip_blank_lines=True,
dtype=np.float64)
irin_avg = irin_sri_adopt.mean().mean()
irin_high = irin_sri_adopt.max().max()
irin_mid = (irin_avg + irin_high) / 2.0
ca_pds_data_sources = [
{
'name':
'Average, Linear Trend',
'include':
True,
'maximum':
tla_world_2050,
'description':
('The future area under SRI is projected based on the adoption projections '
'given for China, India, and Indonesia by IRIN. The data interpolator was '
'used to extrapolate the data and the average and high adoption value for '
'the year 2050 is summarized in the given assumptions. This scenario assumes '
'that the area will increase by average growth. '),
'datapoints':
pd.DataFrame([
[
2018, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[2050, irin_avg, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'High, Linear Trend',
'include':
False,
'maximum':
tla_world_2050,
'description':
('The future area under SRI is projected based on the adoption projections '
'given for China, India, and Indonesia by IRIN. The data interpolator was '
'used to extrapolate the data and the average and high adoption value for '
'the year 2050 is summarized in the given assumptions. This scenario assumes '
'that the area will increase by maximum growth. '),
'datapoints':
pd.DataFrame([
[
2018, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[2050, irin_high, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Average, Linear Trend, Early Growth',
'include':
False,
'maximum':
tla_world_2050,
'description':
('This is scenario 1 with 100% adoption by 2030 '),
'datapoints':
pd.DataFrame([
[
2018, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[2030, irin_avg, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2050, irin_avg, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'High, Linear Trend, Early Growth',
'include':
False,
'description':
('This is scenario 2 with 100% adoption by 2030 '),
'maximum':
tla_world_2050,
'datapoints':
pd.DataFrame([
[
2018, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[2030, irin_high, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2050, irin_high, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Medium, Linear Trend',
'include':
False,
'description':
(' The future area under SRI is projected based on the adoption projections '
'given for China, India, and Indonesia by IRIN. The data interpolator was '
'used to extrapolate the data and the average and high adoption value for '
'the year 2050 is summarized in the given assumptions. This scenario assumes '
'that the area will increase by average growth based on assumption 1 and 2. '
),
'maximum':
tla_world_2050,
'datapoints':
pd.DataFrame([
[
2018, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[2050, irin_mid, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'Medium, Linear Trend, Early Growth',
'include':
False,
'description':
('This is scenario 5 with 100% adoption by 2030 '),
'maximum':
tla_world_2050,
'datapoints':
pd.DataFrame([
[
2018, self.ac.ref_base_adoption['World'], 0., 0., 0.,
0., 0., 0., 0., 0., 0.
],
[2030, irin_mid, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2050, irin_mid, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
],
columns=ca_pds_columns).set_index('Year')
},
{
'name':
'World Adoption, linear trend',
'include':
False,
'description':
('The future growth is projected based on the world adoption of SRI in 2013 '
'and 2018, based on the information received from personal communication '
'through Prof. Norman Uphoff, Cornell University '),
'maximum':
tla_world_2050,
'datapoints':
pd.DataFrame([
[2013, 3.5, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[2018, 6.7, 0., 0., 0., 0., 0., 0., 0., 0., 0.],
],
columns=ca_pds_columns).set_index('Year')
},
]
self.pds_ca = customadoption.CustomAdoption(
data_sources=ca_pds_data_sources,
soln_adoption_custom_name=self.ac.soln_pds_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
for s in self.pds_ca.scenarios.values():
df = s['df']
df.loc[2012, 'World'] = 2.86
df.loc[2013, 'World'] = 3.5
df.loc[2014, 'World'] = 4.15259261268174
df.loc[2015, 'World'] = 4.79610333421305
df.loc[2016, 'World'] = 5.44024605689438
df.loc[2017, 'World'] = 6.08503665221758
df.loc[2018, 'World'] = 6.73044601473859
# Custom REF Data
ca_ref_data_sources = [
{
'name':
'[Type Scenario 1 Name Here (REF CASE)...]',
'include':
True,
'description':
('[PLEASE DESCRIBE IN DETAIL THE METHODOLOGY YOU USED IN THIS ANALYSIS. BE '
'SURE TO INCLUDE ANY ADDITIONAL EQUATIONS YOU UTILIZED] '),
'filename':
THISDIR.joinpath(
'ca_ref_data',
'custom_ref_ad_Type_Scenario_1_Name_Here_REF_CASE_.csv')
},
]
self.ref_ca = customadoption.CustomAdoption(
data_sources=ca_ref_data_sources,
soln_adoption_custom_name=self.ac.soln_ref_adoption_custom_name,
high_sd_mult=1.0,
low_sd_mult=1.0,
total_adoption_limit=self.tla_per_region)
if self.ac.soln_ref_adoption_basis == 'Custom':
ref_adoption_data_per_region = self.ref_ca.adoption_data_per_region(
)
else:
ref_adoption_data_per_region = None
if False:
# One may wonder why this is here. This file was code generated.
# This 'if False' allows subsequent conditions to all be elif.
pass
elif self.ac.soln_pds_adoption_basis == 'Fully Customized PDS':
pds_adoption_data_per_region = self.pds_ca.adoption_data_per_region(
)
pds_adoption_trend_per_region = self.pds_ca.adoption_trend_per_region(
)
pds_adoption_is_single_source = None
elif self.ac.soln_pds_adoption_basis == 'Existing Adoption Prognostications':
pds_adoption_data_per_region = self.ad.adoption_data_per_region()
pds_adoption_trend_per_region = self.ad.adoption_trend_per_region()
pds_adoption_is_single_source = self.ad.adoption_is_single_source()
copy_pds_to_ref = (len(self.ac.pds_adoption_use_ref_years) == 0)
ht_ref_adoption_initial = pd.Series(list(
self.ac.ref_base_adoption.values()),
index=dd.REGIONS)
ht_ref_adoption_final = self.tla_per_region.loc[2050] * (
ht_ref_adoption_initial / self.tla_per_region.loc[2014])
ht_ref_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_ref_datapoints.loc[2018] = ht_ref_adoption_initial
ht_ref_datapoints.loc[2050] = ht_ref_adoption_final.fillna(0.0)
ht_pds_adoption_initial = ht_ref_adoption_initial
ht_pds_adoption_final_percentage = pd.Series(
list(self.ac.pds_adoption_final_percentage.values()),
index=list(self.ac.pds_adoption_final_percentage.keys()))
ht_pds_adoption_final = ht_pds_adoption_final_percentage * self.tla_per_region.loc[
2050]
ht_pds_datapoints = pd.DataFrame(columns=dd.REGIONS)
ht_pds_datapoints.loc[2018] = ht_pds_adoption_initial
ht_pds_datapoints.loc[2050] = ht_pds_adoption_final.fillna(0.0)
self.ht = helpertables.HelperTables(
ac=self.ac,
ref_datapoints=ht_ref_datapoints,
pds_datapoints=ht_pds_datapoints,
pds_adoption_data_per_region=pds_adoption_data_per_region,
ref_adoption_limits=self.tla_per_region,
pds_adoption_limits=self.tla_per_region,
ref_adoption_data_per_region=ref_adoption_data_per_region,
use_first_pds_datapoint_main=False,
adoption_base_year=2018,
copy_pds_to_ref=copy_pds_to_ref,
pds_adoption_trend_per_region=pds_adoption_trend_per_region,
pds_adoption_is_single_source=pds_adoption_is_single_source)
self.ef = emissionsfactors.ElectricityGenOnGrid(ac=self.ac)
self.ua = unitadoption.UnitAdoption(
ac=self.ac,
ref_total_adoption_units=self.tla_per_region,
pds_total_adoption_units=self.tla_per_region,
electricity_unit_factor=1000000.0,
soln_ref_funits_adopted=self.ht.soln_ref_funits_adopted(),
soln_pds_funits_adopted=self.ht.soln_pds_funits_adopted(),
bug_cfunits_double_count=False)
soln_pds_tot_iunits_reqd = self.ua.soln_pds_tot_iunits_reqd()
soln_ref_tot_iunits_reqd = self.ua.soln_ref_tot_iunits_reqd()
conv_ref_tot_iunits = self.ua.conv_ref_tot_iunits()
soln_net_annual_funits_adopted = self.ua.soln_net_annual_funits_adopted(
)
self.fc = firstcost.FirstCost(
ac=self.ac,
pds_learning_increase_mult=2,
ref_learning_increase_mult=2,
conv_learning_increase_mult=2,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_tot_iunits=conv_ref_tot_iunits,
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_first_cost_uses_tot_units=True,
fc_convert_iunit_factor=land.MHA_TO_HA)
self.oc = operatingcost.OperatingCost(
ac=self.ac,
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
conv_ref_annual_tot_iunits=self.ua.conv_ref_annual_tot_iunits(),
soln_pds_annual_world_first_cost=self.fc.
soln_pds_annual_world_first_cost(),
soln_ref_annual_world_first_cost=self.fc.
soln_ref_annual_world_first_cost(),
conv_ref_annual_world_first_cost=self.fc.
conv_ref_annual_world_first_cost(),
single_iunit_purchase_year=2017,
soln_pds_install_cost_per_iunit=self.fc.
soln_pds_install_cost_per_iunit(),
conv_ref_install_cost_per_iunit=self.fc.
conv_ref_install_cost_per_iunit(),
conversion_factor=land.MHA_TO_HA)
self.c4 = ch4calcs.CH4Calcs(
ac=self.ac,
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
self.c2 = co2calcs.CO2Calcs(
ac=self.ac,
ch4_ppb_calculator=self.c4.ch4_ppb_calculator(),
soln_pds_net_grid_electricity_units_saved=self.ua.
soln_pds_net_grid_electricity_units_saved(),
soln_pds_net_grid_electricity_units_used=self.ua.
soln_pds_net_grid_electricity_units_used(),
soln_pds_direct_co2eq_emissions_saved=self.ua.
direct_co2eq_emissions_saved_land(),
soln_pds_direct_co2_emissions_saved=self.ua.
direct_co2_emissions_saved_land(),
soln_pds_direct_n2o_co2_emissions_saved=self.ua.
direct_n2o_co2_emissions_saved_land(),
soln_pds_direct_ch4_co2_emissions_saved=self.ua.
direct_ch4_co2_emissions_saved_land(),
soln_pds_new_iunits_reqd=self.ua.soln_pds_new_iunits_reqd(),
soln_ref_new_iunits_reqd=self.ua.soln_ref_new_iunits_reqd(),
conv_ref_new_iunits=self.ua.conv_ref_new_iunits(),
conv_ref_grid_CO2_per_KWh=self.ef.conv_ref_grid_CO2_per_KWh(),
conv_ref_grid_CO2eq_per_KWh=self.ef.conv_ref_grid_CO2eq_per_KWh(),
soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
annual_land_area_harvested=self.ua.
soln_pds_annual_land_area_harvested(),
regime_distribution=self.ae.get_land_distribution(),
regimes=dd.THERMAL_MOISTURE_REGIMES8)