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Parameter reference

time

start

Year in which the model starts

  • Type: int

  • Default: 2020

  • Can be false: False

  • Min: 1900

  • Max: 2100

Example usage: 

params = load_params()
params["time"]["start"] = 2020
model = MIMOSA(params)
end

Last year of the model run

  • Type: int

  • Default: 2150

  • Can be false: False

  • Min: 1901

  • Max: 2300

Example usage: 

params = load_params()
params["time"]["end"] = 2150
model = MIMOSA(params)
dt

Timestep in years

  • Type: float

  • Default: 5

  • Can be false: False

  • Min: 0.5

  • Max: 20

Example usage: 

params = load_params()
params["time"]["dt"] = 5
model = MIMOSA(params)
SSP

SSP, used for population, baseline GDP and baseline emissions

  • Type: enum

  • Default: SSP2

  • Can be false: False

  • Allowed values:

    • SSP1
    • SSP2
    • SSP3
    • SSP4
    • SSP5

Example usage: 

params = load_params()
params["SSP"] = "SSP2"
model = MIMOSA(params)

economics

PRTP

Pure rate of time preference

  • Type: float

  • Default: 0.015

  • Can be false: False

  • Min: 0

  • Max: 0.2

Example usage: 

params = load_params()
params["economics"]["PRTP"] = 0.015
model = MIMOSA(params)
elasmu

Elasticity of marginal utility

  • Type: float

  • Default: 1.001

  • Can be false: False

  • Min: 0.1

  • Max: 10

Example usage: 

params = load_params()
params["economics"]["elasmu"] = 1.001
model = MIMOSA(params)
inequal_aversion

Parameter of inequality aversion. Should be between 0 and elasmu. Only used when welfare_module='inequal_aversion_general'

  • Type: float

  • Default: 0.5

  • Can be false: False

  • Min: 0.0

  • Max: 3

Example usage: 

params = load_params()
params["economics"]["inequal_aversion"] = 0.5
model = MIMOSA(params)

economics > GDP

alpha

Output elasticity of capital

  • Type: float

  • Default: 0.3

  • Can be false: False

  • Min: 0

  • Max: 1

Example usage: 

params = load_params()
params["economics"]["GDP"]["alpha"] = 0.3
model = MIMOSA(params)
depreciation of capital

Yearly depreciation rate of capital stock

  • Type: float

  • Default: 0.05

  • Can be false: False

  • Min: 0

  • Max: inf

Example usage: 

params = load_params()
params["economics"]["GDP"]["depreciation of capital"] = 0.05
model = MIMOSA(params)
savings rate

Fraction of GDP used for investments

  • Type: float

  • Default: 0.21

  • Can be false: False

  • Min: 0

  • Max: 1

Example usage: 

params = load_params()
params["economics"]["GDP"]["savings rate"] = 0.21
model = MIMOSA(params)

economics > MAC

beta

Power factor of the MAC curve

  • Type: float

  • Default: 3

  • Can be false: False

  • Min: 0.01

  • Max: 10

Example usage: 

params = load_params()
params["economics"]["MAC"]["beta"] = 3
model = MIMOSA(params)
gamma

Calibration level of the MAC (carbon price for 100% reduction)

  • Type: quantity

  • Default: 2601 USD2005/tCO2

  • Can be false: False

  • Unit: currency_unit/emissionsrate_unit

Example usage: 

params = load_params()
params["economics"]["MAC"]["gamma"] = "2601 USD2005/tCO2"
model = MIMOSA(params)
LBD_rate

Learning by doing rate: reduction in marginal mitigation costs for doubling cumulative mitigation capacity. Empirical studies show values between 0.65 (high learning) and 0.95 (low learning)

  • Type: float

  • Default: 0.82

  • Can be false: False

  • Min: 0.1

  • Max: 1

Example usage: 

params = load_params()
params["economics"]["MAC"]["LBD_rate"] = 0.82
model = MIMOSA(params)
LBD_scaling

Scaling factor for learning by doing to transform the units of cumulative mitigation in relative terms (compared to baseline emissions in t=0). Only used for calibration, and should not be used to change the amount of LBD (for this, use the economics > MAC > rho parameter)

  • Type: quantity

  • Default: 40 GtCO2

  • Can be false: False

  • Unit: emissions_unit

Example usage: 

params = load_params()
params["economics"]["MAC"]["LBD_scaling"] = "40 GtCO2"
model = MIMOSA(params)
LOT_rate

Learning rate of exogenous learning (learning over time). By default, there is no exogenous learning assumed, as all the technological learning happens endogenously (learning by doing).

  • Type: float

  • Default: 0

  • Can be false: False

  • Min: 0

  • Max: inf

Example usage: 

params = load_params()
params["economics"]["MAC"]["LOT_rate"] = 0
model = MIMOSA(params)
regional calibration factor

Column from mac.csv to be used for the regional MACs. The MACs are calibrated from TIMER SSP2-RCP2.6 MACs at a given year and a given carbon price / abatement level.

  • Type: enum

  • Default: kappa_rel_abatement_0.75_2050

  • Can be false: False

  • Allowed values:

    • kappa_carbonprice_200_2030
    • kappa_carbonprice_200_2050
    • kappa_carbonprice_200_2070
    • kappa_carbonprice_200_2100
    • kappa_carbonprice_300_2030
    • kappa_carbonprice_300_2050
    • kappa_carbonprice_300_2070
    • kappa_carbonprice_300_2100
    • kappa_carbonprice_500_2030
    • kappa_carbonprice_500_2050
    • kappa_carbonprice_500_2070
    • kappa_carbonprice_500_2100
    • kappa_carbonprice_1000_2030
    • kappa_carbonprice_1000_2050
    • kappa_carbonprice_1000_2070
    • kappa_carbonprice_1000_2100
    • kappa_rel_abatement_0.25_2030
    • kappa_rel_abatement_0.25_2050
    • kappa_rel_abatement_0.25_2070
    • kappa_rel_abatement_0.25_2100
    • kappa_rel_abatement_0.4_2030
    • kappa_rel_abatement_0.4_2050
    • kappa_rel_abatement_0.4_2070
    • kappa_rel_abatement_0.4_2100
    • kappa_rel_abatement_0.5_2050
    • kappa_rel_abatement_0.5_2070
    • kappa_rel_abatement_0.5_2100
    • kappa_rel_abatement_0.75_2050
    • kappa_rel_abatement_0.75_2070
    • kappa_rel_abatement_0.75_2100

Example usage: 

params = load_params()
params["economics"]["MAC"]["regional calibration factor"] = "kappa_rel_abatement_0.75_2050"
model = MIMOSA(params)
rel_mitigation_costs_min_level

Minimum level of mitigation costs (rel to GDP). By default, this is 0: no negative abatement costs are allowed. For certain burden sharing regimes, this value can become negative to allow certain (small) financial transfers.

  • Type: float

  • Default: 0

  • Can be false: False

  • Min: -2

  • Max: 0

Example usage: 

params = load_params()
params["economics"]["MAC"]["rel_mitigation_costs_min_level"] = 0
model = MIMOSA(params)

economics > emission trade

min rel payment level

Which percentage of the area under the MAC of a region should it pay itself (minimum)? If false: no limt

  • Type: float

  • Default: False

  • Can be false: True

  • Min: 0

  • Max: 1

Example usage: 

params = load_params()
params["economics"]["emission trade"]["min rel payment level"] = False
model = MIMOSA(params)
max rel payment level

Which percentage of the area under the MAC of a region should it pay itself (maximum)? If false: no limit

  • Type: float

  • Default: False

  • Can be false: True

  • Min: 1

  • Max: inf

Example usage: 

params = load_params()
params["economics"]["emission trade"]["max rel payment level"] = False
model = MIMOSA(params)

economics > damages

percentage reversible

Factor of damages that are reversible

  • Type: float

  • Default: 1

  • Can be false: False

  • Min: 0

  • Max: 1

Example usage: 

params = load_params()
params["economics"]["damages"]["percentage reversible"] = 1
model = MIMOSA(params)
scale factor

Manual scaling factor to increase or decrease damages

  • Type: float

  • Default: 1

  • Can be false: False

  • Min: -inf

  • Max: inf

Example usage: 

params = load_params()
params["economics"]["damages"]["scale factor"] = 1
model = MIMOSA(params)
ignore damages

Flag to not take into account the damages in the GDP (but damages are calculated)

  • Type: bool

  • Default: False

  • Can be false: False

Example usage: 

params = load_params()
params["economics"]["damages"]["ignore damages"] = False
model = MIMOSA(params)
quantile

Damage quantile (Only used for COACCH)

  • Type: enum

  • Default: 0.5

  • Can be false: False

  • Allowed values:

    • 0.025
    • 0.05
    • 0.16
    • 0.25
    • 0.33
    • 0.5
    • 0.67
    • 0.75
    • 0.84
    • 0.95
    • 0.975

Example usage: 

params = load_params()
params["economics"]["damages"]["quantile"] = 0.5
model = MIMOSA(params)
coacch_slr_withadapt

Flag to use the SLR-with-Adapation damage functions (Only used for COACCH)

  • Type: bool

  • Default: True

  • Can be false: False

Example usage: 

params = load_params()
params["economics"]["damages"]["coacch_slr_withadapt"] = True
model = MIMOSA(params)
coacch_combined_slr_nonslr_damages

If true, do not model SLR damages separately from non-SLR, but use the combined damage functions (Only used for COACCH)

  • Type: bool

  • Default: False

  • Can be false: False

Example usage: 

params = load_params()
params["economics"]["damages"]["coacch_combined_slr_nonslr_damages"] = False
model = MIMOSA(params)

emissions

carbonbudget

Value of the carbon budget. Example: "800 GtCO2" (the unit is important). If set to False, no carbon budget is imposed: this is cost-benefit mode. Default: False.

  • Type: quantity

  • Default: False

  • Can be false: True

  • Unit: emissions_unit

Example usage: 

params = load_params()
params["emissions"]["carbonbudget"] = False
model = MIMOSA(params)
global min level

Limit on the emission level (globally), mostly used for negative emissions. Can also be false, then no limit is imposed

  • Type: quantity

  • Default: -20 GtCO2/yr

  • Can be false: True

  • Unit: emissionsrate_unit

Example usage: 

params = load_params()
params["emissions"]["global min level"] = "-20 GtCO2/yr"
model = MIMOSA(params)
regional min level

Limit on the emission level (per region), mostly used for negative emissions. Can also be false, then no limit is imposed

  • Type: quantity

  • Default: -10 GtCO2/yr

  • Can be false: True

  • Unit: emissionsrate_unit

Example usage: 

params = load_params()
params["emissions"]["regional min level"] = "-10 GtCO2/yr"
model = MIMOSA(params)
not positive after budget year

If true, impose net-zero emissions after budget year (2100)

  • Type: bool

  • Default: True

  • Can be false: False

Example usage: 

params = load_params()
params["emissions"]["not positive after budget year"] = True
model = MIMOSA(params)
non increasing emissions after 2100

If true, the regional emissions after 2100 are not allowed to climb.

  • Type: bool

  • Default: True

  • Can be false: False

Example usage: 

params = load_params()
params["emissions"]["non increasing emissions after 2100"] = True
model = MIMOSA(params)
baseline carbon intensity

If true, use baseline carbon intensity to calculate baseline emissions. If false, the SSP baseline emissions are used, regardless of lower GDP.

  • Type: bool

  • Default: True

  • Can be false: False

Example usage: 

params = load_params()
params["emissions"]["baseline carbon intensity"] = True
model = MIMOSA(params)

emissions > inertia

global

Maximum reduction speed, in % of initial emissions (should be negative) Can also be false, then no inertia limit is imposed

  • Type: float

  • Default: False

  • Can be false: True

  • Min: -inf

  • Max: 0

Example usage: 

params = load_params()
params["emissions"]["inertia"]["global"] = False
model = MIMOSA(params)
regional

Maximum reduction speed, in % of initial emissions (should be negative) Can also be false, then no inertia limit is imposed

  • Type: float

  • Default: -0.05

  • Can be false: True

  • Min: -inf

  • Max: 0

Example usage: 

params = load_params()
params["emissions"]["inertia"]["regional"] = -0.05
model = MIMOSA(params)
cumulative_emissions_trapz

If true, calculate cumulative emissions using trapezoidal interpolation. If false, cum. emissions are simply cum_emissions[t-1] + dt * cum_emissions[t]. This is less accurate, but better for numerical stability. For small dt the approximation is usually still acceptable.

  • Type: bool

  • Default: True

  • Can be false: False

Example usage: 

params = load_params()
params["emissions"]["cumulative_emissions_trapz"] = True
model = MIMOSA(params)

effort sharing

regime

Type of effort sharing to be used

  • Type: enum

  • Default: noregime

  • Can be false: False

  • Allowed values:

    • noregime
    • equal_mitigation_costs
    • equal_total_costs
    • per_cap_convergence

Example usage: 

params = load_params()
params["effort sharing"]["regime"] = "noregime"
model = MIMOSA(params)
percapconv_year

Year of convergence to per capita emission allowance (only used when effort sharing - regime is per_cap_convergence) Can also be false, then always use grandfathering

  • Type: float

  • Default: 2050

  • Can be false: True

  • Min: 2020

  • Max: 2200

Example usage: 

params = load_params()
params["effort sharing"]["percapconv_year"] = 2050
model = MIMOSA(params)

temperature

initial

Temperature in initial year of model run (2020 by default).

  • Type: quantity

  • Default: 1.16 delta_degC

  • Can be false: False

  • Unit: temperature_unit

Example usage: 

params = load_params()
params["temperature"]["initial"] = "1.16 delta_degC"
model = MIMOSA(params)
TCRE

Transient Climate Response to CO2 Emissions

  • Type: quantity

  • Default: 0.62 delta_degC/(TtCO2)

  • Can be false: False

  • Unit: (temperature_unit)/(emissions_unit)

Example usage: 

params = load_params()
params["temperature"]["TCRE"] = "0.62 delta_degC/(TtCO2)"
model = MIMOSA(params)
target

Temperature target in 2100 (and beyond). Can also be false, then no temperature target is imposed

  • Type: quantity

  • Default: False

  • Can be false: True

  • Unit: temperature_unit

Example usage: 

params = load_params()
params["temperature"]["target"] = False
model = MIMOSA(params)

model

damage module

Damage module to be used

  • Type: enum

  • Default: COACCH

  • Can be false: False

  • Allowed values:

    • COACCH
    • nodamage

Example usage: 

params = load_params()
params["model"]["damage module"] = "COACCH"
model = MIMOSA(params)
emissiontrade module

Emission trade module to be used

  • Type: enum

  • Default: notrade

  • Can be false: False

  • Allowed values:

    • notrade
    • emissiontrade
    • globalcostpool

Example usage: 

params = load_params()
params["model"]["emissiontrade module"] = "notrade"
model = MIMOSA(params)
financialtransfer module

Financial transfer module to be used

  • Type: enum

  • Default: notransfer

  • Can be false: False

  • Allowed values:

    • notransfer
    • globaldamagepool

Example usage: 

params = load_params()
params["model"]["financialtransfer module"] = "notransfer"
model = MIMOSA(params)
welfare module

Welfare and utility module to be used

  • Type: enum

  • Default: welfare_loss_minimising

  • Can be false: False

  • Allowed values:

    • welfare_loss_minimising
    • cost_minimising
    • inequal_aversion_general

Example usage: 

params = load_params()
params["model"]["welfare module"] = "welfare_loss_minimising"
model = MIMOSA(params)
objective module

Objective module to be used

  • Type: enum

  • Default: utility

  • Can be false: False

  • Allowed values:

    • utility
    • globalcosts

Example usage: 

params = load_params()
params["model"]["objective module"] = "utility"
model = MIMOSA(params)
regionstype

Name of the region definition. Used in the mapping of the regional parameters.

  • Type: enum

  • Default: IMAGE26

  • Can be false: False

  • Allowed values:

    • IMAGE26
    • SSP5
    • World

Example usage: 

params = load_params()
params["regionstype"] = "IMAGE26"
model = MIMOSA(params)
regionsmappings

List of region types and their conversion tables. Only used for regional parameters, not for aggregating or disaggregating variables or other output.

  • Type: list

  • Default: [{'regionstype1': 'IMAGE26', 'regionstype2': 'COACCH', 'conversiontable': 'inputdata/regions/IMAGE26_COACCH.csv'}, {'regionstype1': 'IMAGE26', 'regionstype2': 'ADRICE2010', 'conversiontable': 'inputdata/regions/IMAGE26_ADRICE2010.csv'}, {'regionstype1': 'IMAGE26', 'regionstype2': 'ADRICE2012', 'conversiontable': 'inputdata/regions/IMAGE26_ADRICE2012.csv'}]

  • Can be false: False

Example usage: 

params = load_params()
params["regionsmappings"] = [{'regionstype1': 'IMAGE26', 'regionstype2': 'COACCH', 'conversiontable': 'inputdata/regions/IMAGE26_COACCH.csv'}, {'regionstype1': 'IMAGE26', 'regionstype2': 'ADRICE2010', 'conversiontable': 'inputdata/regions/IMAGE26_ADRICE2010.csv'}, {'regionstype1': 'IMAGE26', 'regionstype2': 'ADRICE2012', 'conversiontable': 'inputdata/regions/IMAGE26_ADRICE2012.csv'}]
model = MIMOSA(params)
regional_parameter_files

Dictionary of regional parameter files. If the regionstype of the file is different from the regionstype of the model, the file is converted using the regionsmappings parameter.

  • Type: dict

  • Default: {'economics': {'filename': 'inputdata/regionalparams/economics.csv', 'regionstype': 'IMAGE26'}, 'MAC': {'filename': 'inputdata/regionalparams/mac.csv', 'regionstype': 'IMAGE26'}, 'COACCH': {'filename': 'inputdata/regionalparams/COACCH.csv', 'regionstype': 'COACCH'}}

  • Can be false: False

Example usage: 

params = load_params()
params["regional_parameter_files"] = {'economics': {'filename': 'inputdata/regionalparams/economics.csv', 'regionstype': 'IMAGE26'}, 'MAC': {'filename': 'inputdata/regionalparams/mac.csv', 'regionstype': 'IMAGE26'}, 'COACCH': {'filename': 'inputdata/regionalparams/COACCH.csv', 'regionstype': 'COACCH'}}
model = MIMOSA(params)
regions

Dictionary of all regions with optional dictionaries defining, optionally, manual values for certain parameters for that specific region.

  • Type: dict

  • Default: {'CAN': None, 'USA': None, 'MEX': None, 'RCAM': None, 'BRA': None, 'RSAM': None, 'NAF': None, 'WAF': None, 'EAF': None, 'SAF': None, 'WEU': None, 'CEU': None, 'TUR': None, 'UKR': None, 'STAN': None, 'RUS': None, 'ME': None, 'INDIA': None, 'KOR': None, 'CHN': None, 'SEAS': None, 'INDO': None, 'JAP': None, 'OCE': None, 'RSAS': None, 'RSAF': None}

  • Can be false: False

Example usage: 

params = load_params()
params["regions"] = {'CAN': None, 'USA': None, 'MEX': None, 'RCAM': None, 'BRA': None, 'RSAM': None, 'NAF': None, 'WAF': None, 'EAF': None, 'SAF': None, 'WEU': None, 'CEU': None, 'TUR': None, 'UKR': None, 'STAN': None, 'RUS': None, 'ME': None, 'INDIA': None, 'KOR': None, 'CHN': None, 'SEAS': None, 'INDO': None, 'JAP': None, 'OCE': None, 'RSAS': None, 'RSAF': None}
model = MIMOSA(params)

input

input > variables

GDP

Data source of GDP

  • Type: datasource

  • Default: {'variable': 'GDP|PPP', 'unit': 'currency_unit', 'scenario': '{SSP}-Ref-SPA0-V17', 'model': 'IMAGE', 'file': 'inputdata/data/data_IMAGE_SSP.csv'}

  • Can be false: False

Example usage: 

params = load_params()
params["input"]["variables"]["GDP"] = {'variable': 'GDP|PPP', 'unit': 'currency_unit', 'scenario': '{SSP}-Ref-SPA0-V17', 'model': 'IMAGE', 'file': 'inputdata/data/data_IMAGE_SSP.csv'}
model = MIMOSA(params)
emissions

Data source of baseline emissions

  • Type: datasource

  • Default: {'variable': 'Emissions|CO2', 'unit': 'emissionsrate_unit', 'scenario': '{SSP}-Ref-SPA0-V17', 'model': 'IMAGE', 'file': 'inputdata/data/data_IMAGE_SSP.csv'}

  • Can be false: False

Example usage: 

params = load_params()
params["input"]["variables"]["emissions"] = {'variable': 'Emissions|CO2', 'unit': 'emissionsrate_unit', 'scenario': '{SSP}-Ref-SPA0-V17', 'model': 'IMAGE', 'file': 'inputdata/data/data_IMAGE_SSP.csv'}
model = MIMOSA(params)
population

Data source of population

  • Type: datasource

  • Default: {'variable': 'Population', 'unit': 'population_unit', 'scenario': '{SSP}-Ref-SPA0-V17', 'model': 'IMAGE', 'file': 'inputdata/data/data_IMAGE_SSP.csv'}

  • Can be false: False

Example usage: 

params = load_params()
params["input"]["variables"]["population"] = {'variable': 'Population', 'unit': 'population_unit', 'scenario': '{SSP}-Ref-SPA0-V17', 'model': 'IMAGE', 'file': 'inputdata/data/data_IMAGE_SSP.csv'}
model = MIMOSA(params)

simulation

simulationmode

If true, the model is run in simulation mode: then some variables will be imposed exogenously and fixed. If false, constraint_variables and deactivated_constraints are ignored.

  • Type: bool

  • Default: False

  • Can be false: False

Example usage: 

params = load_params()
params["simulation"]["simulationmode"] = False
model = MIMOSA(params)
constraint_variables

Dictionary of variable names with associated path to file containing values for that variable

  • Type: dict

  • Default: None

  • Can be false: False

Example usage: 

params = load_params()
params["simulation"]["constraint_variables"] = None
model = MIMOSA(params)
deactivated_constraints

List of constraint names to be disabled

  • Type: list

  • Default: None

  • Can be false: False

Example usage: 

params = load_params()
params["simulation"]["deactivated_constraints"] = None
model = MIMOSA(params)
custom_mapping

Custom mapping of parameter values or variables

  • Type: dict

  • Default: None

  • Can be false: False

Example usage: 

params = load_params()
params["simulation"]["custom_mapping"] = None
model = MIMOSA(params)