prob_scale=1.0)
bau_default_model = DLWBusinessAsUsual()
bau_default_model.bau_emissions_setup(t)
c = DLWCost(t,
bau_default_model.emit_level[0],
g=92.08,
a=3.413,
join_price=joinp,
max_price=maxp,
tech_const=tech_chg,
tech_scale=tech_scale,
cons_at_0=30460.0)
df = DLWDamage(tree=t,
bau=bau_default_model,
cons_growth=growth,
ghg_levels=[450, 650, 1000],
subinterval_len=5)
#df.damage_simulation(draws=4000000, peak_temp=temp, disaster_tail=tail, tip_on=on,
# temp_map=maps, temp_dist_params=None, maxh=100.0, cons_growth=growth)
df.import_damages()
u = EZUtility(tree=t,
damage=df,
cost=c,
period_len=5.0,
eis=eis,
ra=ra,
time_pref=pref)
prob_scale=1.0)
bau_default_model = DLWBusinessAsUsual()
bau_default_model.bau_emissions_setup(t)
c = DLWCost(t,
bau_default_model.emit_level[0],
g=92.08,
a=3.413,
join_price=joinp,
max_price=maxp,
tech_const=tech_chg,
tech_scale=tech_scale,
cons_at_0=30460.0)
df = DLWDamage(tree=t,
bau=bau_default_model,
cons_growth=growth,
ghg_levels=[450, 650, 1000],
subinterval_len=5)
df.damage_simulation(draws=4000000,
peak_temp=temp,
disaster_tail=tail,
tip_on=on,
temp_map=maps,
temp_dist_params=None,
maxh=100.0,
cons_growth=growth)
u = EZUtility(tree=t,
damage=df,
cost=c,
if __name__ == "__main__": from tree import TreeModel from bau import DLWBusinessAsUsual from cost import DLWCost from damage import DLWDamage from utility import EZUtility from output_functions import * t = TreeModel(decision_times=[0, 15, 45, 85, 185, 285, 385], prob_scale=1.0) bau_default_model = DLWBusinessAsUsual() bau_default_model.bau_emissions_setup(t) c = DLWCost(t, bau_default_model.emit_level[0], g=92.08, a=3.413, join_price=2000.0, max_price=2500.0, tech_const=1.5, tech_scale=0.0, cons_at_0=30460.0) df = DLWDamage(tree=t, bau=bau_default_model, cons_growth=0.015, ghg_levels=[450, 650, 1000]) #df.damage_simulation(draws=4000000, peak_temp=6.0, disaster_tail=18.0, tip_on=True, # temp_map=1, temp_dist_params=None, maxh=100.0, cons_growth=0.015) df.import_damages() df.forcing_init(sink_start=35.596, forcing_start=4.926, ghg_start=400, partition_interval=5, forcing_p1=0.13173, forcing_p2=0.607773, forcing_p3=315.3785, absorbtion_p1=0.94835, absorbtion_p2=0.741547, lsc_p1=285.6268, lsc_p2=0.88414) m = np.array([0.61053004,0.79246812,0.58157155,1.01485702,0.88055513,0.78830025,0.63919678, 1.27605078,1.23010852,1.27284628,0.93278258,1.02821083,0.94334363,0.94319353, 0.57852567,1.30397034,1.15580694,1.22157915,1.13071263,1.25878603,1.1162571, 1.57046613,1.4283019, 1.70395503,1.59262015,1.68384978,1.56395966,1.74293419, 1.63780257,1.38892971,0.4412861, 2.03792726,1.58020076,1.7380882, 1.50968748, 1.75140343,1.62919952,1.32564331,1.25431191,1.75616436,1.5240563, 1.65222977, 1.3323968, 1.8620841, 1.32573398,1.94555827,1.54948187,2.04135856,1.72350611,