world = world,
has_renewable_subsidy = False,
has_emissions_tax = False,
has_fossil_ban = False,
emissions_tax_intro_threshold = 1, # disabled
renewable_subsidy_intro_threshold = 0.5, # disabled
fossil_ban_intro_threshold = 0.5, # disabled
emissions_tax_level = 20 * 200e9, # see Wikipedia social cost of carbon. 100e9*3.5,
time_between_votes = 4 if with_voting else 1e100,
) for s in range(nsocs)]
(boreal, temperate, subtropical, tropical) = cells = [M.Cell(
social_system = social_systems[c//2],
renewable_sector_productivity = [.7, .9, 1.1, 1.3][c]
* 100000 * M.Cell.renewable_sector_productivity.default,
# represents dependency of solar energy on solar insolation angle
fossil_sector_productivity = M.Cell.fossil_sector_productivity.default *280,
#biomass_sector_productivity = M.Cell.biomass_sector_productivity.default * 5
biomass_sector_productivity=3e5*10**(0.4)*900,
# these values result in realistic total energy production for the year 2000, see below
) for c in range(ncells)]
individuals = [M.Individual(
cell = cells[i%4],
is_environmentally_friendly =
random.choice([False, True], p=[1-p_env_friendly,
p_env_friendly]), # represents the "20% suffice" assumption
)
for i in range(ninds)]
# initialize block model acquaintance network:
target_degree = 10 # = 2.5% of all agents. Dunbar's number would be too large
emissions_tax_intro_threshold=0.5, # disabled
renewable_subsidy_intro_threshold=1, # disabled
fossil_ban_intro_threshold=1, # disabled
emissions_tax_level=30 *
200e9, # see Wikipedia social cost of carbon. 100e9*3.5,
time_between_votes=4 if with_voting else 1e100,
) for s in range(nsocs)
]
(boreal, temperate, subtropical, tropical) = cells = [
M.Cell(
social_system=social_systems[c // 2],
renewable_sector_productivity=[.7, .9, 1.1, 1.3][c] * 2000 *
M.Cell.renewable_sector_productivity.default,
# represents dependency of solar energy on solar insolation angle
fossil_sector_productivity=M.Cell.fossil_sector_productivity.default *
7,
biomass_sector_productivity=M.Cell.biomass_sector_productivity.default
* 5
# these values result in realistic total energy production for the year 2000, see below
) for c in range(ncells)
]
individuals = [
M.Individual(
cell=cells[i % 4],
is_environmentally_friendly=random.choice(
[False, True], p=[.8,
.2]), # represents the "20% suffice" assumption
) for i in range(ninds)
]
def run(seed, updates, with_social, p_env_friendly):
"""Run the model."""
np.random.seed(seed)
if with_social:
filename = "esd_example_with_social_update_rate_{0}_seed_{1}.p".format(
updates, seed)
else:
filename = "esd_example_without_social_seed_{0}.p".format(seed)
model = M.Model()
# instantiate process taxa:
environment = M.Environment()
metabolism = M.Metabolism(renewable_energy_knowledge_spillover_fraction=0)
culture = M.Culture(awareness_lower_carbon_density=1e-5,
awareness_upper_carbon_density=4e-5,
awareness_update_rate=updates * with_social,
environmental_friendliness_learning_rate=updates *
with_social)
# generate entities and plug them together:
world = M.World(environment=environment,
metabolism=metabolism,
culture=culture,
atmospheric_carbon=830 * D.gigatonnes_carbon,
upper_ocean_carbon=(5500 - 830 - 2480 - 1125) * D.GtC)
social_systems = []
for _ in range(NUMBER_SOCIAL_SYSTEMS):
_soc = M.SocialSystem(world=world,
has_renewable_subsidy=False,
has_emissions_tax=False,
has_fossil_ban=False,
emissions_tax_intro_threshold=1,
renewable_subsidy_intro_threshold=0.5,
fossil_ban_intro_threshold=0.5,
renewable_subsidy_keeping_threshold=0.5,
fossil_ban_keeping_threshold=0.5,
emissions_tax_level=20 * 200e9,
time_between_votes=4 if with_social else 1e100)
social_systems.append(_soc)
cells = []
for cell_id in range(NUMBER_CELLS):
_cell = M.Cell(
social_system=social_systems[cell_id // 2],
renewable_sector_productivity=[.7, .9, 1.1, 1.3][cell_id] *
RENEWABLE_SCALING * M.Cell.renewable_sector_productivity.default,
fossil_sector_productivity=M.Cell.fossil_sector_productivity.
default * 280,
biomass_sector_productivity=3e5 * 10**(0.4) * 900)
cells.append(_cell)
individuals = []
for individual_id in range(NUMBER_INDIVIDUALS):
_individual = M.Individual(
cell=cells[individual_id % 4],
is_environmentally_friendly=np.random.choice(
[False, True], p=[1 - p_env_friendly, p_env_friendly]))
individuals.append(_individual)
# initialize block model acquaintance network:
target_degree = 10 # = 2.5% of all agents. Dunbar's number would be too large
target_degree_samecell = 0.5 * target_degree
target_degree_samesoc = 0.35 * target_degree
target_degree_other = 0.15 * target_degree
p_samecell = target_degree_samecell / (NUMBER_INDIVIDUALS / NUMBER_CELLS -
1)
p_samesoc = target_degree_samesoc / (
NUMBER_INDIVIDUALS / NUMBER_SOCIAL_SYSTEMS -
NUMBER_INDIVIDUALS / NUMBER_CELLS - 1)
p_other = target_degree_other / (
NUMBER_INDIVIDUALS - NUMBER_INDIVIDUALS / NUMBER_SOCIAL_SYSTEMS - 1)
for index, i in enumerate(individuals):
for j in individuals[:index]:
if i.cell == j.cell:
prop = p_samecell
elif i.social_system == j.social_system:
prop = p_samesoc
else:
prop = p_other
if np.random.uniform() < prop:
culture.acquaintance_network.add_edge(i, j)
# distribute area and vegetation uniformly since it seems there are no real
# differences between the actual zones:
sigma_0 = 1.5e8 * D.square_kilometers * np.array([0.25, 0.25, 0.25, 0.25])
M.Cell.land_area.set_values(cells, sigma_0)
# 2480 is yr 2000
l_0 = 2480 * D.gigatonnes_carbon * np.array([0.25, 0.25, 0.25, 0.25])
M.Cell.terrestrial_carbon.set_values(cells, l_0)
# distribute fossils linearly from north to south, 1125 is yr 2000:
g_0 = 1125 * D.gigatonnes_carbon * np.array([.4, .3, .2, .1])
M.Cell.fossil_carbon.set_values(cells, g_0)
# distribute population 1:3 between north and south, 6e9 is yr 2000:
#r = np.random.uniform(size=nsocs)
p_0 = 6e9 * D.people * np.array([0.25, 0.75])
M.SocialSystem.population.set_values(social_systems, p_0)
# distribute capital 2:1:
k_0 = sum(p_0) * 1e4 * D.dollars / D.people * np.array([2 / 3, 1 / 3])
M.SocialSystem.physical_capital.set_values(social_systems, k_0)
s_0 = 2e11 * D.gigajoules * np.array([1, 1])
M.SocialSystem.renewable_energy_knowledge.set_values(social_systems, s_0)
# do simulation:
runner = Runner(model=model)
starttime = time()
traj = runner.run(t_0=2000,
t_1=FINAL_TIME,
dt=1,
add_to_output=[M.Individual.represented_population])
save(traj=traj, filename=filename)
print("Runtime:", time() - starttime, " seconds")