IAMC-Documentation - User contributions [en]

GCAM

2023-10-11T15:22:07Z

Matthew Binsted:

{{ModelTemplate}}
{{ModelInfoTemplate
|Name=GCAM
|Version=7.0
|ModelLink=https://github.com/JGCRI/gcam-core; http://jgcri.github.io/gcam-doc/toc.html
|participation=full
|processState=under review
}}
{{ScopeMethodTemplate
|ModelTypeOption=Integrated assessment model
|GeographicalScopeOption=Global
|Objective=GCAM is an integrated, multi-sector model that explores both human and Earth system dynamics. The role of models like GCAM is to bring multiple human and physical Earth systems together in one place to shed light on system interactions and provide scientific insights that would not otherwise be available from the pursuit of traditional disciplinary scientific research alone. GCAM is constructed to explore these interactions in a single computational platform with a sufficiently low computational requirement to allow for broad explorations of scenarios and uncertainties. Components of GCAM are designed to capture the behavior of human and physical systems, but they do not necessarily include the most detailed process-scale representations of its constituent components. On the other hand, model components in principle provide a faithful representation of the best current scientific understanding of underlying behavior.
|SolutionConceptOption=General equilibrium (closed economy)
|SolutionConcept=GCAM solves all energy, water, and land markets simultaneously
|SolutionHorizonOption=Recursive dynamic (myopic)
|SolutionMethod=Recursive dynamic solution method
|Anticipation=GCAM is a dynamic recursive model, meaning that decision-makers do not know the future when making a decision today. After it solves each period, the model then uses the resulting state of the world, including the consequences of decisions made in that period - such as resource depletion, capital stock retirements and installations, and changes to the landscape - and then moves to the next time step and performs the same exercise. For long-lived investments, decision-makers may account for future profit streams, but those estimates would be based on current prices. For some parts of the model, economic agents use prior experience to form expectations based on multi-period experiences.
|BaseYear=2015
|TimeSteps=5-year (default), minimum time step is 1-year
|Horizon=2100
|Nr=32 (default)
|Region=USA; Canada; Mexico; Australia_NZ; Japan; South Korea; EU-12; EU-15; European Free Trade Association; Europe_Non_EU; Europe_Eastern; Russia; China; Taiwan; Central Asia; South Asia; Southeast Asia; Indonesia; India; Pakistan; Middle East; Africa_Eastern; Africa_Northern; Africa_Southern; Africa_Western; South Africa; Argentina; Brazil; Central America and Caribbean; Colombia; South America_Northern; South America_Southern;
|SpatialText=Dimensionality is flexible and can be expanded by adding additional information about regions. For example, a version of GCAM (GCAM-USA) exists with 82 regions that includes the 50 U.S. states, the District of Columbia and the remaining 31 non-US regions.
|PoliciesOption=Emission tax; Emission pricing; Cap and trade; Fuel taxes; Fuel subsidies; Feed-in-tariff; Portfolio standard; Capacity targets; Emission standards; Energy efficiency standards; Agricultural producer subsidies; Agricultural consumer subsidies; Land protection; Pricing carbon stocks
}}
{{Socio-economicTemplate
|PopulationOption=Yes (exogenous)
|PopulationAgeStructureOption=Yes (exogenous)
|UrbanizationRateOption=Yes (exogenous)
|GDPOption=Yes (exogenous)
|IncomeDistributionOption=Yes (exogenous)
|LaborProductivityOption=Yes (exogenous)
|TotalFactorProductivityOption=Yes (exogenous)
|AutonomousEnergyEfficiencyImprovementsOption=Yes (exogenous)
}}
{{Macro-economyTemplate
|TradeOption=Coal; Oil; Gas; Uranium; Electricity; Bioenergy crops; Food crops; Emissions permits
|CostMeasureOption=Area under MAC
|CategorizationByGroupOption=Income; Urban - rural
|InstitutionalAndPoliticalFactorsOption=Early retirement of capital allowed; Interest rates differentiated by country/region; Regional risk factors included; Technology costs differentiated by country/region; Technological change differentiated by country/region; Behavioural change differentiated by country/region
|CoalRUOption=Yes (process model)
|ConventionalOilRUOption=Yes (process model)
|UnconventionalOilRUOption=Yes (process model)
|ConventionalGasRUOption=Yes (process model)
|UnconventionalGasRUOption=Yes (process model)
|UraniumRUOption=Yes (process model)
|BioenergyRUOption=Yes (process model)
|WaterRUOption=Yes (process model)
|RawMaterialsRUOption=Yes (process model)
|LandRUOption=Yes (process model)
|IndustryESOption=Yes (physical & economic)
|EnergyESOption=Yes (physical & economic)
|TransportationESOption=Yes (physical & economic)
|ResidentialAndCommercialESOption=Yes (physical & economic)
|AgricultureESOption=Yes (physical & economic)
|ForestryESOption=Yes (physical & economic)
|EnergyConversionTechnologyTCOption=Exogenous technological change
|EnergyEnd-useTCOption=Exogenous technological change
|MaterialUseTCOption=Exogenous technological change
|AgricultureTCOption=Exogenous technological change
}}
{{EnergyTemplate
|EnergyTechnologyChoiceOption=Logit choice model
|EnergyTechnologySubstitutabilityOption=Mixed high and low substitutability
|EnergyTechnologyDeploymentOption=System integration constraints
|ElectricityTechnologyOption=Coal w/o CCS; Coal w/ CCS; Gas w/o CCS; Gas w/ CCS; Oil w/o CCS; Oil w/ CCS; Bioenergy w/o CCS; Bioenergy w/ CCS; Geothermal power; Nuclear power; Solar power; Solar power-central PV; Solar power-distributed PV; Solar power-CSP; Wind power; Wind power-onshore; Wind power-offshore; Hydroelectric power
|HydrogenProductionOption=Coal to hydrogen w/o CCS; Coal to hydrogen w/ CCS; Natural gas to hydrogen w/o CCS; Natural gas to hydrogen w/ CCS; Oil to hydrogen w/o CCS; Oil to hydrogen w/ CCS; Biomass to hydrogen w/o CCS; Biomass to hydrogen w/ CCS; Nuclear thermochemical hydrogen; Electrolysis
|RefinedLiquidsOption=Coal to liquids w/o CCS; Coal to liquids w/ CCS; Gas to liquids w/o CCS; Gas to liquids w/ CCS; Bioliquids w/o CCS; Bioliquids w/ CCS; Oil refining
|RefinedGasesOption=Coal to gas w/o CCS; Coal to gas w/ CCS; Oil to gas w/o CCS; Oil to gas w/ CCS; Biomass to gas w/o CCS; Biomass to gas w/ CCS
|HeatGenerationOption=Coal heat; Natural gas heat; Oil heat; Biomass heat; CHP (coupled heat and power)
|ElectricityGIOption=Yes (aggregate)
|GasGIOption=Yes (aggregate)
|HeatGIOption=Yes (aggregate)
|CO2GIOption=Yes (aggregate)
|HydrogenGIOption=Yes (aggregate)
|PassengerTransportationOption=Passenger trains; Buses; Light Duty Vehicles (LDVs); Electric LDVs; Hydrogen LDVs; Hybrid LDVs; Gasoline LDVs; Diesel LDVs; Passenger aircrafts
|PassengerTransportation=CNG Buses; CNG Three-wheelers; Diesel Three-wheelers; Electric Buses; Electric Three-wheelers; LPG/CNG LDVs
|FreightTransportationOption=Freight trains; Heavy duty vehicles; Freight aircrafts; Freight ships
|IndustryOption=Steel production; Aluminium production; Cement production; Petrochemical production
|ResidentialAndCommercialOption=Space heating; Space cooling
}}
{{Land-useTemplate
|LandCoverOption=Cropland; Cropland irrigated; Cropland food crops; Cropland feed crops; Cropland energy crops; Forest; Managed forest; Natural forest; Pasture; Shrubland; Built-up area
|AgricultureAndForestryDemandsOption=Agriculture food; Agriculture food crops; Agriculture food livestock; Agriculture feed; Agriculture feed crops; Agriculture feed livestock; Agriculture non-food; Agriculture non-food crops; Agriculture non-food livestock; Agriculture bioenergy; Agriculture residues; Forest industrial roundwood; Forest fuelwood; Forest residues
|AgriculturalCommoditiesOption=Wheat; Rice; Other coarse grains; Oilseeds; Sugar crops; Ruminant meat; Non-ruminant meat and eggs; Dairy products
}}
{{EmissionClimateTemplate
|GHGOption=CO2 fossil fuels; CO2 cement; CO2 land use; CH4 energy; CH4 land use; CH4 other; N2O energy; N2O land use; N2O other; CFCs; HFCs; SF6; PFCs
|PollutantOption=CO energy; CO land use; CO other; NOx energy; NOx land use; NOx other; VOC energy; VOC land use; VOC other; SO2 energy; SO2 land use; SO2 other; BC energy; BC land use; BC other; OC energy; OC land use; OC other; NH3 energy; NH3 land use; NH3 other
|ClimateIndicatorOption=Concentration: CO2; Concentration: CH4; Concentration: N2O; Concentration: Kyoto gases; Radiative forcing: CO2; Radiative forcing: CH4; Radiative forcing: N2O; Radiative forcing: F-gases; Radiative forcing: Kyoto gases; Radiative forcing: aerosols; Radiative forcing: land albedo; Radiative forcing: AN3A; Radiative forcing: total; Temperature change; Sea level rise; Ocean acidification
|ClimateIndicator=Radiative Forcing (Land Albedo) - Yes (exogenous)
|CarbonDioxideRemovalOption=Bioenergy with CCS; Reforestation; Afforestation
|ClimateChangeImpactsOption=Agriculture; Energy demand
|Co-LinkagesOption=Energy security: Fossil fuel imports & exports (region); Energy access: Household energy consumption; Air pollution & health: Source-based aerosol emissions; Food access; Water availability
}}
{{InstitutionTemplate
|abbr=PNNL, JGCRI
|institution=Pacific Northwest National Laboratory, Joint Global Change Research Institute
|link=https://www.pnnl.gov/projects/jgcri
|country=USA
}}
The Global Change Assessment Model (GCAM) is a global model that represents the behavior of, and interactions between five systems: the energy system, water, agriculture and land use, the economy, and the climate. It is used in a wide range of different applications from the exploration of fundamental questions about the complex dynamics between human and Earth systems to the those associated with response strategies to address important environmental questions. GCAM is a community model stewarded by The Joint Global Change Research Institute (JGCRI). This wiki page provides the documentation for GCAM.

An overview of GCAM is available at GCAM Model Overview (https://jgcri.github.io/gcam-doc/overview.html). The five main systems in GCAM (energy, water, land, economics, and the climate), along with other key considerations in the construction annd use of the model are covered in the following pages.

GCAM

2023-10-11T15:16:46Z

Matthew Binsted:

{{ModelTemplate}}
{{ModelInfoTemplate
|Name=GCAM
|Version=7.0
|ModelLink=https://github.com/JGCRI/gcam-core; http://jgcri.github.io/gcam-doc/toc.html
|participation=full
|processState=under review
}}
{{ScopeMethodTemplate
|ModelTypeOption=Integrated assessment model
|GeographicalScopeOption=Global
|Objective=GCAM is an integrated, multi-sector model that explores both human and Earth system dynamics. The role of models like GCAM is to bring multiple human and physical Earth systems together in one place to shed light on system interactions and provide scientific insights that would not otherwise be available from the pursuit of traditional disciplinary scientific research alone. GCAM is constructed to explore these interactions in a single computational platform with a sufficiently low computational requirement to allow for broad explorations of scenarios and uncertainties. Components of GCAM are designed to capture the behavior of human and physical systems, but they do not necessarily include the most detailed process-scale representations of its constituent components. On the other hand, model components in principle provide a faithful representation of the best current scientific understanding of underlying behavior.
|SolutionConceptOption=General equilibrium (closed economy)
|SolutionConcept=GCAM solves all energy, water, and land markets simultaneously
|SolutionHorizonOption=Recursive dynamic (myopic)
|SolutionMethod=Recursive dynamic solution method
|Anticipation=GCAM is a dynamic recursive model, meaning that decision-makers do not know the future when making a decision today. After it solves each period, the model then uses the resulting state of the world, including the consequences of decisions made in that period - such as resource depletion, capital stock retirements and installations, and changes to the landscape - and then moves to the next time step and performs the same exercise. For long-lived investments, decision-makers may account for future profit streams, but those estimates would be based on current prices. For some parts of the model, economic agents use prior experience to form expectations based on multi-period experiences.
|BaseYear=2015
|TimeSteps=5-year (default), minimum time step is 1-year
|Horizon=2100
|Nr=32 (default)
|Region=USA; Canada; Mexico; Australia_NZ; Japan; South Korea; EU-12; EU-15; European Free Trade Association; Europe_Non_EU; Europe_Eastern; Russia; China; Taiwan; Central Asia; South Asia; Southeast Asia; Indonesia; India; Pakistan; Middle East; Africa_Eastern; Africa_Northern; Africa_Southern; Africa_Western; South Africa; Argentina; Brazil; Central America and Caribbean; Colombia; South America_Northern; South America_Southern;
|SpatialText=Dimensionality is flexible and can be expanded by adding additional information about regions. For example, a version of GCAM (GCAM-USA) exists with 82 regions that includes the 50 U.S. states, the District of Columbia and the remaining 31 non-US regions.
|PoliciesOption=Emission tax; Emission pricing; Cap and trade; Fuel taxes; Fuel subsidies; Feed-in-tariff; Portfolio standard; Capacity targets; Emission standards; Energy efficiency standards; Agricultural producer subsidies; Agricultural consumer subsidies; Land protection; Pricing carbon stocks
}}
{{Socio-economicTemplate
|PopulationOption=Yes (exogenous)
|PopulationAgeStructureOption=Yes (exogenous)
|UrbanizationRateOption=Yes (exogenous)
|GDPOption=Yes (exogenous)
|IncomeDistributionOption=Yes (exogenous)
|LaborProductivityOption=Yes (exogenous)
|TotalFactorProductivityOption=Yes (exogenous)
|AutonomousEnergyEfficiencyImprovementsOption=Yes (exogenous)
}}
{{Macro-economyTemplate
|TradeOption=Coal; Oil; Gas; Uranium; Electricity; Bioenergy crops; Food crops; Emissions permits
|CostMeasureOption=Area under MAC
|CategorizationByGroupOption=Income; Urban - rural
|InstitutionalAndPoliticalFactorsOption=Early retirement of capital allowed; Interest rates differentiated by country/region; Regional risk factors included; Technology costs differentiated by country/region; Technological change differentiated by country/region; Behavioural change differentiated by country/region
|CoalRUOption=Yes (process model)
|ConventionalOilRUOption=Yes (process model)
|UnconventionalOilRUOption=Yes (process model)
|ConventionalGasRUOption=Yes (process model)
|UnconventionalGasRUOption=Yes (process model)
|UraniumRUOption=Yes (process model)
|BioenergyRUOption=Yes (process model)
|WaterRUOption=Yes (process model)
|RawMaterialsRUOption=Yes (process model)
|LandRUOption=Yes (process model)
|IndustryESOption=Yes (physical & economic)
|EnergyESOption=Yes (physical & economic)
|TransportationESOption=Yes (physical & economic)
|ResidentialAndCommercialESOption=Yes (physical & economic)
|AgricultureESOption=Yes (physical & economic)
|ForestryESOption=Yes (physical & economic)
|EnergyConversionTechnologyTCOption=Exogenous technological change
|EnergyEnd-useTCOption=Exogenous technological change
|MaterialUseTCOption=Exogenous technological change
|AgricultureTCOption=Exogenous technological change
}}
{{EnergyTemplate
|EnergyTechnologyChoiceOption=Logit choice model
|EnergyTechnologySubstitutabilityOption=Mixed high and low substitutability
|EnergyTechnologyDeploymentOption=System integration constraints
|ElectricityTechnologyOption=Coal w/o CCS; Coal w/ CCS; Gas w/o CCS; Gas w/ CCS; Oil w/o CCS; Oil w/ CCS; Bioenergy w/o CCS; Bioenergy w/ CCS; Geothermal power; Nuclear power; Solar power; Solar power-central PV; Solar power-distributed PV; Solar power-CSP; Wind power; Wind power-onshore; Wind power-offshore; Hydroelectric power
|HydrogenProductionOption=Coal to hydrogen w/o CCS; Coal to hydrogen w/ CCS; Natural gas to hydrogen w/o CCS; Natural gas to hydrogen w/ CCS; Oil to hydrogen w/o CCS; Oil to hydrogen w/ CCS; Biomass to hydrogen w/o CCS; Biomass to hydrogen w/ CCS; Nuclear thermochemical hydrogen; Solar thermochemical hydrogen; Electrolysis
|RefinedLiquidsOption=Coal to liquids w/o CCS; Coal to liquids w/ CCS; Gas to liquids w/o CCS; Gas to liquids w/ CCS; Bioliquids w/o CCS; Bioliquids w/ CCS; Oil refining
|RefinedGasesOption=Coal to gas w/o CCS; Coal to gas w/ CCS; Oil to gas w/o CCS; Oil to gas w/ CCS; Biomass to gas w/o CCS; Biomass to gas w/ CCS
|HeatGenerationOption=Coal heat; Natural gas heat; Oil heat; Biomass heat; Geothermal heat; Solarthermal heat; CHP (coupled heat and power)
|ElectricityGIOption=Yes (aggregate)
|GasGIOption=Yes (aggregate)
|HeatGIOption=Yes (aggregate)
|CO2GIOption=Yes (aggregate)
|HydrogenGIOption=Yes (aggregate)
|PassengerTransportationOption=Passenger trains; Buses; Light Duty Vehicles (LDVs); Electric LDVs; Hydrogen LDVs; Hybrid LDVs; Gasoline LDVs; Diesel LDVs; Passenger aircrafts
|PassengerTransportation=CNG Buses; CNG Three-wheelers; Diesel Three-wheelers; Electric Buses; Electric Three-wheelers; LPG/CNG LDVs
|FreightTransportationOption=Freight trains; Heavy duty vehicles; Freight aircrafts; Freight ships
|IndustryOption=Steel production; Aluminium production; Cement production; Petrochemical production
|ResidentialAndCommercialOption=Space heating; Space cooling
}}
{{Land-useTemplate
|LandCoverOption=Cropland; Cropland irrigated; Cropland food crops; Cropland feed crops; Cropland energy crops; Forest; Managed forest; Natural forest; Pasture; Shrubland; Built-up area
|AgricultureAndForestryDemandsOption=Agriculture food; Agriculture food crops; Agriculture food livestock; Agriculture feed; Agriculture feed crops; Agriculture feed livestock; Agriculture non-food; Agriculture non-food crops; Agriculture non-food livestock; Agriculture bioenergy; Agriculture residues; Forest industrial roundwood; Forest fuelwood; Forest residues
|AgriculturalCommoditiesOption=Wheat; Rice; Other coarse grains; Oilseeds; Sugar crops; Ruminant meat; Non-ruminant meat and eggs; Dairy products
}}
{{EmissionClimateTemplate
|GHGOption=CO2 fossil fuels; CO2 cement; CO2 land use; CH4 energy; CH4 land use; CH4 other; N2O energy; N2O land use; N2O other; CFCs; HFCs; SF6; PFCs
|PollutantOption=CO energy; CO land use; CO other; NOx energy; NOx land use; NOx other; VOC energy; VOC land use; VOC other; SO2 energy; SO2 land use; SO2 other; BC energy; BC land use; BC other; OC energy; OC land use; OC other; NH3 energy; NH3 land use; NH3 other
|ClimateIndicatorOption=Concentration: CO2; Concentration: CH4; Concentration: N2O; Concentration: Kyoto gases; Radiative forcing: CO2; Radiative forcing: CH4; Radiative forcing: N2O; Radiative forcing: F-gases; Radiative forcing: Kyoto gases; Radiative forcing: aerosols; Radiative forcing: land albedo; Radiative forcing: AN3A; Radiative forcing: total; Temperature change; Sea level rise; Ocean acidification
|ClimateIndicator=Radiative Forcing (Land Albedo) - Yes (exogenous)
|CarbonDioxideRemovalOption=Bioenergy with CCS; Reforestation; Afforestation
|ClimateChangeImpactsOption=Agriculture; Energy demand
|Co-LinkagesOption=Energy security: Fossil fuel imports & exports (region); Energy access: Household energy consumption; Air pollution & health: Source-based aerosol emissions; Food access; Water availability
}}
{{InstitutionTemplate
|abbr=PNNL, JGCRI
|institution=Pacific Northwest National Laboratory, Joint Global Change Research Institute
|link=https://www.pnnl.gov/projects/jgcri
|country=USA
}}
The Global Change Assessment Model (GCAM) is a global model that represents the behavior of, and interactions between five systems: the energy system, water, agriculture and land use, the economy, and the climate. It is used in a wide range of different applications from the exploration of fundamental questions about the complex dynamics between human and Earth systems to the those associated with response strategies to address important environmental questions. GCAM is a community model stewarded by The Joint Global Change Research Institute (JGCRI). This wiki page provides the documentation for GCAM.

An overview of GCAM is available at GCAM Model Overview (https://jgcri.github.io/gcam-doc/overview.html). The five main systems in GCAM (energy, water, land, economics, and the climate), along with other key considerations in the construction annd use of the model are covered in the following pages.

GCAM

2023-10-11T14:54:58Z

Matthew Binsted:

{{ModelTemplate}}
{{ModelInfoTemplate
|Name=GCAM
|Version=7.0
|ModelLink=https://github.com/JGCRI/gcam-core; http://jgcri.github.io/gcam-doc/toc.html
|participation=full
|processState=under review
}}
{{ScopeMethodTemplate
|ModelTypeOption=Integrated assessment model
|GeographicalScopeOption=Global
|Objective=GCAM is an integrated, multi-sector model that explores both human and Earth system dynamics. The role of models like GCAM is to bring multiple human and physical Earth systems together in one place to shed light on system interactions and provide scientific insights that would not otherwise be available from the pursuit of traditional disciplinary scientific research alone. GCAM is constructed to explore these interactions in a single computational platform with a sufficiently low computational requirement to allow for broad explorations of scenarios and uncertainties. Components of GCAM are designed to capture the behavior of human and physical systems, but they do not necessarily include the most detailed process-scale representations of its constituent components. On the other hand, model components in principle provide a faithful representation of the best current scientific understanding of underlying behavior.
|SolutionConceptOption=General equilibrium (closed economy)
|SolutionConcept=GCAM solves all energy, water, and land markets simultaneously
|SolutionHorizonOption=Recursive dynamic (myopic)
|SolutionMethod=Recursive dynamic solution method
|Anticipation=GCAM is a dynamic recursive model, meaning that decision-makers do not know the future when making a decision today. After it solves each period, the model then uses the resulting state of the world, including the consequences of decisions made in that period - such as resource depletion, capital stock retirements and installations, and changes to the landscape - and then moves to the next time step and performs the same exercise. For long-lived investments, decision-makers may account for future profit streams, but those estimates would be based on current prices. For some parts of the model, economic agents use prior experience to form expectations based on multi-period experiences.
|BaseYear=2015
|TimeSteps=5-year (default), minimum time step is 1-year
|Horizon=2100
|Nr=32 (default)
|Region=USA; Canada; Mexico; Australia_NZ; Japan; South Korea; EU-12; EU-15; European Free Trade Association; Europe_Non_EU; Europe_Eastern; Russia; China; Taiwan; Central Asia; South Asia; Southeast Asia; Indonesia; India; Pakistan; Middle East; Africa_Eastern; Africa_Northern; Africa_Southern; Africa_Western; South Africa; Argentina; Brazil; Central America and Caribbean; Colombia; South America_Northern; South America_Southern;
|SpatialText=Dimensionality is flexible and can be expanded by adding additional information about regions. For example, a version of GCAM (GCAM-USA) exists with 82 regions that includes the 50 U.S. states, the District of Columbia and the remaining 31 non-US regions.
|PoliciesOption=Emission tax; Emission pricing; Cap and trade; Fuel taxes; Fuel subsidies; Feed-in-tariff; Portfolio standard; Capacity targets; Emission standards; Energy efficiency standards; Agricultural producer subsidies; Agricultural consumer subsidies; Land protection; Pricing carbon stocks
}}
{{Socio-economicTemplate
|PopulationOption=Yes (exogenous)
|PopulationAgeStructureOption=Yes (exogenous)
|UrbanizationRateOption=Yes (exogenous)
|GDPOption=Yes (exogenous)
|IncomeDistributionOption=Yes (exogenous)
|LaborProductivityOption=Yes (exogenous)
|TotalFactorProductivityOption=Yes (exogenous)
|AutonomousEnergyEfficiencyImprovementsOption=Yes (exogenous)
}}
{{Macro-economyTemplate
|TradeOption=Coal; Oil; Gas; Uranium; Electricity; Bioenergy crops; Food crops; Emissions permits
|CostMeasureOption=Area under MAC
|CategorizationByGroupOption=Income; Urban - rural
|InstitutionalAndPoliticalFactorsOption=Early retirement of capital allowed; Interest rates differentiated by country/region; Regional risk factors included; Technology costs differentiated by country/region; Technological change differentiated by country/region; Behavioural change differentiated by country/region
|CoalRUOption=Yes (process model)
|ConventionalOilRUOption=Yes (process model)
|UnconventionalOilRUOption=Yes (process model)
|ConventionalGasRUOption=Yes (process model)
|UnconventionalGasRUOption=Yes (process model)
|UraniumRUOption=Yes (process model)
|BioenergyRUOption=Yes (process model)
|WaterRUOption=Yes (process model)
|RawMaterialsRUOption=Yes (process model)
|LandRUOption=Yes (process model)
|IndustryESOption=Yes (physical & economic)
|EnergyESOption=Yes (physical & economic)
|TransportationESOption=Yes (physical & economic)
|ResidentialAndCommercialESOption=Yes (physical & economic)
|AgricultureESOption=Yes (physical & economic)
|ForestryESOption=Yes (physical & economic)
|EnergyConversionTechnologyTCOption=Exogenous technological change
|EnergyEnd-useTCOption=Exogenous technological change
|MaterialUseTCOption=Exogenous technological change
|AgricultureTCOption=Exogenous technological change
}}
{{EnergyTemplate
|EnergyTechnologyChoiceOption=Logit choice model
|EnergyTechnologySubstitutabilityOption=Mixed high and low substitutability
|EnergyTechnologyDeploymentOption=System integration constraints
|ElectricityTechnologyOption=Coal w/o CCS; Coal w/ CCS; Gas w/o CCS; Gas w/ CCS; Oil w/o CCS; Oil w/ CCS; Bioenergy w/o CCS; Bioenergy w/ CCS; Geothermal power; Nuclear power; Solar power; Solar power-central PV; Solar power-distributed PV; Solar power-CSP; Wind power; Wind power-onshore; Wind power-offshore; Hydroelectric power
|HydrogenProductionOption=Coal to hydrogen w/o CCS; Coal to hydrogen w/ CCS; Natural gas to hydrogen w/o CCS; Natural gas to hydrogen w/ CCS; Oil to hydrogen w/o CCS; Oil to hydrogen w/ CCS; Biomass to hydrogen w/o CCS; Biomass to hydrogen w/ CCS; Nuclear thermochemical hydrogen; Solar thermochemical hydrogen; Electrolysis
|RefinedLiquidsOption=Coal to liquids w/o CCS; Coal to liquids w/ CCS; Gas to liquids w/o CCS; Gas to liquids w/ CCS; Bioliquids w/o CCS; Bioliquids w/ CCS; Oil refining
|RefinedGasesOption=Coal to gas w/o CCS; Coal to gas w/ CCS; Oil to gas w/o CCS; Oil to gas w/ CCS; Biomass to gas w/o CCS; Biomass to gas w/ CCS
|HeatGenerationOption=Coal heat; Natural gas heat; Oil heat; Biomass heat; Geothermal heat; Solarthermal heat; CHP (coupled heat and power)
|ElectricityGIOption=Yes (aggregate)
|GasGIOption=Yes (aggregate)
|HeatGIOption=Yes (aggregate)
|CO2GIOption=Yes (aggregate)
|HydrogenGIOption=Yes (aggregate)
|PassengerTransportationOption=Passenger trains; Buses; Light Duty Vehicles (LDVs); Electric LDVs; Hydrogen LDVs; Hybrid LDVs; Gasoline LDVs; Diesel LDVs; Passenger aircrafts
|PassengerTransportation=CNG Buses; CNG Three-wheelers; Diesel Three-wheelers; Electric Buses; Electric Three-wheelers; LPG/CNG LDVs
|FreightTransportationOption=Freight trains; Heavy duty vehicles; Freight aircrafts; Freight ships
|IndustryOption=Cement production; Petrochemical production; Plastics production
|ResidentialAndCommercialOption=Space heating; Space cooling; Cooking; Refrigeration; Washing; Lighting
|ResidentialAndCommercial=Other electrical uses; Water heating
}}
{{Land-useTemplate
|LandCoverOption=Cropland; Cropland irrigated; Cropland food crops; Cropland feed crops; Cropland energy crops; Forest; Managed forest; Natural forest; Pasture; Shrubland; Built-up area
|AgricultureAndForestryDemandsOption=Agriculture food; Agriculture food crops; Agriculture food livestock; Agriculture feed; Agriculture feed crops; Agriculture feed livestock; Agriculture non-food; Agriculture non-food crops; Agriculture non-food livestock; Agriculture bioenergy; Agriculture residues; Forest industrial roundwood; Forest fuelwood; Forest residues
|AgriculturalCommoditiesOption=Wheat; Rice; Other coarse grains; Oilseeds; Sugar crops; Ruminant meat; Non-ruminant meat and eggs; Dairy products
}}
{{EmissionClimateTemplate
|GHGOption=CO2 fossil fuels; CO2 cement; CO2 land use; CH4 energy; CH4 land use; CH4 other; N2O energy; N2O land use; N2O other; CFCs; HFCs; SF6; PFCs
|PollutantOption=CO energy; CO land use; CO other; NOx energy; NOx land use; NOx other; VOC energy; VOC land use; VOC other; SO2 energy; SO2 land use; SO2 other; BC energy; BC land use; BC other; OC energy; OC land use; OC other; NH3 energy; NH3 land use; NH3 other
|ClimateIndicatorOption=Concentration: CO2; Concentration: CH4; Concentration: N2O; Concentration: Kyoto gases; Radiative forcing: CO2; Radiative forcing: CH4; Radiative forcing: N2O; Radiative forcing: F-gases; Radiative forcing: Kyoto gases; Radiative forcing: aerosols; Radiative forcing: land albedo; Radiative forcing: AN3A; Radiative forcing: total; Temperature change; Sea level rise; Ocean acidification
|ClimateIndicator=Radiative Forcing (Land Albedo) - Yes (exogenous)
|CarbonDioxideRemovalOption=Bioenergy with CCS; Reforestation; Afforestation
|ClimateChangeImpactsOption=Agriculture; Energy demand
|Co-LinkagesOption=Energy security: Fossil fuel imports & exports (region); Energy access: Household energy consumption; Air pollution & health: Source-based aerosol emissions; Food access; Water availability
}}
{{InstitutionTemplate
|abbr=PNNL, JGCRI
|institution=Pacific Northwest National Laboratory, Joint Global Change Research Institute
|link=https://www.pnnl.gov/projects/jgcri
|country=USA
}}
The Global Change Assessment Model (GCAM) is a global model that represents the behavior of, and interactions between five systems: the energy system, water, agriculture and land use, the economy, and the climate. It is used in a wide range of different applications from the exploration of fundamental questions about the complex dynamics between human and Earth systems to the those associated with response strategies to address important environmental questions. GCAM is a community model stewarded by The Joint Global Change Research Institute (JGCRI). This wiki page provides the documentation for GCAM.

An overview of GCAM is available at GCAM Model Overview (https://jgcri.github.io/gcam-doc/overview.html). The five main systems in GCAM (energy, water, land, economics, and the climate), along with other key considerations in the construction annd use of the model are covered in the following pages.

GCAM

2023-10-11T14:37:46Z

Matthew Binsted:

{{ModelTemplate}}
{{ModelInfoTemplate
|Name=GCAM
|Version=7.0
|ModelLink=https://github.com/JGCRI/gcam-core; http://jgcri.github.io/gcam-doc/toc.html
|participation=full
|processState=under review
}}
{{InstitutionTemplate
|abbr=PNNL, JGCRI
|institution=Pacific Northwest National Laboratory, Joint Global Change Research Institute
|link=https://www.pnnl.gov/projects/jgcri
|country=USA
}}
{{ScopeMethodTemplate
|ModelTypeOption=Integrated assessment model
|GeographicalScopeOption=Global
|Objective=GCAM is an integrated, multi-sector model that explores both human and Earth system dynamics. The role of models like GCAM is to bring multiple human and physical Earth systems together in one place to shed light on system interactions and provide scientific insights that would not otherwise be available from the pursuit of traditional disciplinary scientific research alone. GCAM is constructed to explore these interactions in a single computational platform with a sufficiently low computational requirement to allow for broad explorations of scenarios and uncertainties. Components of GCAM are designed to capture the behavior of human and physical systems, but they do not necessarily include the most detailed process-scale representations of its constituent components. On the other hand, model components in principle provide a faithful representation of the best current scientific understanding of underlying behavior.
|SolutionConceptOption=General equilibrium (closed economy)
|SolutionConcept=GCAM solves all energy, water, and land markets simultaneously
|SolutionHorizonOption=Recursive dynamic (myopic)
|SolutionMethod=Recursive dynamic solution method
|Anticipation=GCAM is a dynamic recursive model, meaning that decision-makers do not know the future when making a decision today. After it solves each period, the model then uses the resulting state of the world, including the consequences of decisions made in that period - such as resource depletion, capital stock retirements and installations, and changes to the landscape - and then moves to the next time step and performs the same exercise. For long-lived investments, decision-makers may account for future profit streams, but those estimates would be based on current prices. For some parts of the model, economic agents use prior experience to form expectations based on multi-period experiences.
|BaseYear=2015
|TimeSteps=5-year (default), minimum time step is 1-year
|Horizon=2100
|Nr=32 (default)
|Region=USA; Canada; Mexico; Australia_NZ; Japan; South Korea; EU-12; EU-15; European Free Trade Association; Europe_Non_EU; Europe_Eastern; Russia; China; Taiwan; Central Asia; South Asia; Southeast Asia; Indonesia; India; Pakistan; Middle East; Africa_Eastern; Africa_Northern; Africa_Southern; Africa_Western; South Africa; Argentina; Brazil; Central America and Caribbean; Colombia; South America_Northern; South America_Southern;
|SpatialText=Dimensionality is flexible and can be expanded by adding additional information about regions. For example, a version of GCAM exists with 82 regions that include 50 states, the District of Columbia and the remaining 31 non-US regions.
|PoliciesOption=Emission tax; Emission pricing; Cap and trade; Fuel taxes; Fuel subsidies; Feed-in-tariff; Portfolio standard; Capacity targets; Emission standards; Energy efficiency standards; Agricultural producer subsidies; Agricultural consumer subsidies; Land protection; Pricing carbon stocks
}}
{{Socio-economicTemplate
|PopulationOption=Yes (exogenous)
|PopulationAgeStructureOption=Yes (exogenous)
|UrbanizationRateOption=Yes (exogenous)
|GDPOption=Yes (exogenous)
|IncomeDistributionOption=Yes (exogenous)
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{{Macro-economyTemplate
|TradeOption=Coal; Oil; Gas; Uranium; Electricity; Bioenergy crops; Food crops; Emissions permits
|CostMeasureOption=Area under MAC
|CategorizationByGroupOption=Income; Urban - rural
|InstitutionalAndPoliticalFactorsOption=Early retirement of capital allowed; Interest rates differentiated by country/region; Regional risk factors included; Technology costs differentiated by country/region; Technological change differentiated by country/region; Behavioural change differentiated by country/region
|CoalRUOption=Yes (process model)
|ConventionalOilRUOption=Yes (process model)
|UnconventionalOilRUOption=Yes (process model)
|ConventionalGasRUOption=Yes (process model)
|UnconventionalGasRUOption=Yes (process model)
|UraniumRUOption=Yes (process model)
|BioenergyRUOption=Yes (process model)
|WaterRUOption=Yes (process model)
|RawMaterialsRUOption=Yes (process model)
|LandRUOption=Yes (process model)
|IndustryESOption=Yes (physical & economic)
|EnergyESOption=Yes (physical & economic)
|TransportationESOption=Yes (physical & economic)
|ResidentialAndCommercialESOption=Yes (physical & economic)
|AgricultureESOption=Yes (physical & economic)
|ForestryESOption=Yes (physical & economic)
|EnergyConversionTechnologyTCOption=Exogenous technological change
|EnergyEnd-useTCOption=Exogenous technological change
|MaterialUseTCOption=Exogenous technological change
|AgricultureTCOption=Exogenous technological change
}}
{{EnergyTemplate
|EnergyTechnologyChoiceOption=Logit choice model
|EnergyTechnologySubstitutabilityOption=Mixed high and low substitutability
|EnergyTechnologyDeploymentOption=System integration constraints
|ElectricityTechnologyOption=Coal w/o CCS; Coal w/ CCS; Gas w/o CCS; Gas w/ CCS; Oil w/o CCS; Oil w/ CCS; Bioenergy w/o CCS; Bioenergy w/ CCS; Geothermal power; Nuclear power; Solar power; Solar power-central PV; Solar power-distributed PV; Solar power-CSP; Wind power; Wind power-onshore; Wind power-offshore; Hydroelectric power
|HydrogenProductionOption=Coal to hydrogen w/o CCS; Coal to hydrogen w/ CCS; Natural gas to hydrogen w/o CCS; Natural gas to hydrogen w/ CCS; Oil to hydrogen w/o CCS; Oil to hydrogen w/ CCS; Biomass to hydrogen w/o CCS; Biomass to hydrogen w/ CCS; Nuclear thermochemical hydrogen; Solar thermochemical hydrogen; Electrolysis
|RefinedLiquidsOption=Coal to liquids w/o CCS; Coal to liquids w/ CCS; Gas to liquids w/o CCS; Gas to liquids w/ CCS; Bioliquids w/o CCS; Bioliquids w/ CCS; Oil refining
|RefinedGasesOption=Coal to gas w/o CCS; Coal to gas w/ CCS; Oil to gas w/o CCS; Oil to gas w/ CCS; Biomass to gas w/o CCS; Biomass to gas w/ CCS
|HeatGenerationOption=Coal heat; Natural gas heat; Oil heat; Biomass heat; Geothermal heat; Solarthermal heat; CHP (coupled heat and power)
|ElectricityGIOption=Yes (aggregate)
|GasGIOption=Yes (aggregate)
|HeatGIOption=Yes (aggregate)
|CO2GIOption=Yes (aggregate)
|HydrogenGIOption=Yes (aggregate)
|PassengerTransportationOption=Passenger trains; Buses; Light Duty Vehicles (LDVs); Electric LDVs; Hydrogen LDVs; Hybrid LDVs; Gasoline LDVs; Diesel LDVs; Passenger aircrafts
|PassengerTransportation=CNG Buses; CNG Three-wheelers; Diesel Three-wheelers; Electric Buses; Electric Three-wheelers; LPG/CNG LDVs
|FreightTransportationOption=Freight trains; Heavy duty vehicles; Freight aircrafts; Freight ships
|IndustryOption=Cement production; Petrochemical production; Plastics production
|ResidentialAndCommercialOption=Space heating; Space cooling; Cooking; Refrigeration; Washing; Lighting
|ResidentialAndCommercial=Other electrical uses; Water heating
}}
{{Land-useTemplate
|LandCoverOption=Cropland; Cropland irrigated; Cropland food crops; Cropland feed crops; Cropland energy crops; Forest; Managed forest; Natural forest; Pasture; Shrubland; Built-up area
|AgricultureAndForestryDemandsOption=Agriculture food; Agriculture food crops; Agriculture food livestock; Agriculture feed; Agriculture feed crops; Agriculture feed livestock; Agriculture non-food; Agriculture non-food crops; Agriculture non-food livestock; Agriculture bioenergy; Agriculture residues; Forest industrial roundwood; Forest fuelwood; Forest residues
|AgriculturalCommoditiesOption=Wheat; Rice; Other coarse grains; Oilseeds; Sugar crops; Ruminant meat; Non-ruminant meat and eggs; Dairy products
}}
{{EmissionClimateTemplate
|GHGOption=CO2 fossil fuels; CO2 cement; CO2 land use; CH4 energy; CH4 land use; CH4 other; N2O energy; N2O land use; N2O other; CFCs; HFCs; SF6; PFCs
|PollutantOption=CO energy; CO land use; CO other; NOx energy; NOx land use; NOx other; VOC energy; VOC land use; VOC other; SO2 energy; SO2 land use; SO2 other; BC energy; BC land use; BC other; OC energy; OC land use; OC other; NH3 energy; NH3 land use; NH3 other
|ClimateIndicatorOption=Concentration: CO2; Concentration: CH4; Concentration: N2O; Concentration: Kyoto gases; Radiative forcing: CO2; Radiative forcing: CH4; Radiative forcing: N2O; Radiative forcing: F-gases; Radiative forcing: Kyoto gases; Radiative forcing: aerosols; Radiative forcing: land albedo; Radiative forcing: AN3A; Radiative forcing: total; Temperature change; Sea level rise; Ocean acidification
|ClimateIndicator=Radiative Forcing (Land Albedo) - Yes (exogenous)
|CarbonDioxideRemovalOption=Bioenergy with CCS; Reforestation; Afforestation
|ClimateChangeImpactsOption=Agriculture; Energy demand
|Co-LinkagesOption=Energy security: Fossil fuel imports & exports (region); Energy access: Household energy consumption; Air pollution & health: Source-based aerosol emissions; Food access; Water availability
}}
The Global Change Assessment Model (GCAM) is a global model that represents the behavior of, and interactions between five systems: the energy system, water, agriculture and land use, the economy, and the climate. It is used in a wide range of different applications from the exploration of fundamental questions about the complex dynamics between human and Earth systems to the those associated with response strategies to address important environmental questions. GCAM is a community model stewarded by The Joint Global Change Research Institute (JGCRI). This wiki page provides the documentation for GCAM.

An overview of GCAM is available at GCAM Model Overview (https://jgcri.github.io/gcam-doc/overview.html). The five main systems in GCAM (energy, water, land, economics, and the climate), along with other key considerations in the construction annd use of the model are covered in the following pages.

Reference card - GCAM

2023-10-11T14:31:37Z

Matthew Binsted:

Trade - GCAM

2023-10-10T19:50:03Z

Matthew Binsted: Add trade details

{{ModelDocumentationTemplate
|IsDocumentationOf=GCAM
|DocumentationCategory=Trade
}}
International trade in most commodities in GCAM is done by one of two methods: (1) [https://jgcri.github.io/gcam-doc/details_trade.html#heckscher-ohlin Heckscher-Ohlin] (single global markets), or (2) [https://jgcri.github.io/gcam-doc/details_trade.html#armington-style-trade Armington Style Trade] (global trade with regionally-differentiated markets with Armington-like preferences between domestic and imported commodities). Other approaches for trade can also be implemented in the GCAM framework (such as GCAM USA where logit based decisions are made to facilitate trade between the 50-states) See [http://jgcri.github.io/gcam-doc/details_trade.html trade details] and [https://jgcri.github.io/gcam-doc/outputs_trade.html trade outputs].

== [https://jgcri.github.io/gcam-doc/details_trade.html#heckscher-ohlin Heckscher-Ohlin] ==
The Heckscher-Ohlin theorem explains trade using factor endowments and predicts that each country produces goods with more intensive use of its abundant factor of production (Peter Debaere, 2003<ref>Peter Debaere (2003) Relative factor abundance and trade. Journal of Political Economy 111, 589-610. 10.1086/374179</ref>; Vanek, 1968<ref>Vanek, J. (1968) The factor proportions theory: The n—factor case. Kyklos 21, 749-756. 10.1111/j.1467-6435.1968.tb00141.x</ref>). The empirical use of the Heckscher-Ohlin approach assumes products are homogeneous across sources and traded in a single global market (i.e., fully integrated world market). Markets clear at the world level and each region will see the same global price and independently decide how much each will supply and demand of each commodity given that price. A region’s net trade position is dynamic depending on economics, technical change, demand, growth, resources, etc. Under this method for trading goods there is no modeled preference for a given region to demand a commodity from any other specific region.

The trade of agricultural products were mostly modeled using the Heckscher-Ohlin approach in early versions of GCAM (e.g., GCAM v4), and trade of livestock products was fixed in these versions. But GCAM has been updated to the Armington style trade modeling approach for most of the agricultural and livestock products. However, FodderHerb is still modeled using the Heckscher-Ohlin approach and FodderGrass is not traded. Also, major energy commodities such as coal, gas, oil, bio-energy, etc. are also traded in a single world market with the Heckscher-Ohlin approach.

== [https://jgcri.github.io/gcam-doc/details_trade.html#armington-style-trade Armington Style Trade] ==
For the agricultural, livestock, and forestry commodities in GCAM (except fodder crops and fish & other meats), we use an Armington style distinction between domestic and imported goods. The Armington approach assumes products are differentiated by source and consumers view goods produced in different countries as imperfect substitutes (Armington, 1969)<ref>Armington, P.S. (1969) A theory of demand for products distinguished by place of production. Staff Papers 16, 159-178.</ref>. The theoretical background and the derivation of the logit-based Armington approach are documented in Zhao et al. (2020)<ref>Zhao, Xin, Marshall A. Wise, Stephanie T. Waldhoff, G. Page Kyle, Jonathan E. Huster, Christopher W. Ramig, Lauren E. Rafelski, Pralit L. Patel, and Katherine V. Calvin. “The impact of agricultural trade approaches on global economic modeling.” ''Global Environmental Change'' 73 (2022): 102413. <nowiki>https://doi.org/10.1016/j.gloenvcha.2021.102413</nowiki></ref>. In this approach, the competition between imports and domestic is governed by a logit sharing function. Imports are from a single global pool that draws from all regions and is also governed by a logit. The logit-based Armington approach requires a segmented regional markets, as opposed to the integrated world market in the Heckscher-Ohlin approach. Thus, it allows differentiating regional prices and tracing gross trade flows.

Spatial dimension - GCAM

2023-10-10T19:27:04Z

Matthew Binsted:

{{ModelDocumentationTemplate
|IsDocumentationOf=GCAM
|DocumentationCategory=Spatial dimension
}}
The GCAM Core represents the entire world, but it is constructed with different levels of resolution for each different system. In the current release version of GCAM, the energy-economy system operates at 32 regions globally, land is divided into 384 subregions, and water is tracked for 235 basins worldwide. The Earth system module operates at a global scale. See [https://jgcri.github.io/gcam-doc/overview.html#overview-of-integrated-dynamics-in-the-gcam-core Overview of Integrated Dynamics in the GCAM Core] and [https://jgcri.github.io/gcam-doc/common_assumptions.html Common Assumptions].
{| class="wikitable"
|+Spatial scale of systems in release version of GCAM
!GCAM Component
!Geospatial Resolution
|-
|Macro-Economy
|32 Geopolitical Regions
|-
|Energy System
|32 Geopolitical Regions
|-
|Land System
|384 subregions
|-
|Water Supplies
|235 Hydrologic Basins
|-
|Physical Earth System
|Global
|}

Appendices - GCAM

2023-10-10T18:09:43Z

Matthew Binsted: Remove broken link

{{ModelDocumentationTemplate
|IsDocumentationOf=GCAM
|DocumentationCategory=Appendices
}}
Some useful GCAM guides include:

* [http://jgcri.github.io/gcam-doc/index.html GCAM Documentation] (main documentation page)
* [https://jgcri.github.io/gcam-doc/user-guide.html How to Get Started Running GCAM] (user guide)
* [http://jgcri.github.io/gcam-doc/gcam-build.html How to Set Up and Build] (build instructions)
* [https://gcims.pnnl.gov/community GCAM Video Tutorials]
* [https://github.com/JGCRI/gcam-core/releases GCAM Releases]

To download GCAM, and for questions, issues, and discussions, please visit the [https://github.com/jgcri/gcam-core gcam-core GitHub Repository].

Water - GCAM

2023-10-10T18:07:33Z

Matthew Binsted: Small changes to water section

{{ModelDocumentationTemplate
|IsDocumentationOf=GCAM
|DocumentationCategory=Water
}}
== Supply of Water ==
Three distinct sources of fresh water are modeled, renewable water, non-renewable groundwater, and desalinated water. Renewable water is water that is replenished naturally by surface runoff and subsurface infiltration and release (groundwater recharge). Non-renewable groundwater is water from aquifers whose recharge is sufficiently low as to be depletable on a human time scale and which have replenishment timescales greater than 100 years. Renewable water and non-renewable groundwater are separately modeled for each basin. Desalinated water of brackish groundwater and seawater is available as an additional source of freshwater within each basin and for municipal and industrial end-use demands for water. See [http://jgcri.github.io/gcam-doc/supply_water.html Supply of Water] for a full description.

== Demand for Water ==
Water demand is calculated for six major sectors: agriculture, electricity generation, industrial manufacturing, primary energy production, livestock, and municipal uses. For each sector, up to four types of water demand are represented. Types of water include
* water withdrawals: water diverted or withdrawn from a surface water or groundwater source (Vickers 2001).<ref name="vickers2001">Vickers, A. 2001. Handbook of Water Use and Conservation. WaterPlow Press, Amherst, MA, USA.</ref>
* water consumption: water use that permanently withdraws water from its source; water that is no longer available because it has evaporated, been transpired by plants, incorporated into products or crops, consumed by people or livestock, or otherwise removed from the immediate water environment (Vickers 2001).<ref name="vickers2001" />
* biophysical water consumption: total water required for crop evapo-transpiration; the sum of “blue” and “green” water in Mekonnen and Hoekstra 2011.<ref>Mekonnen, M.M., and Hoekstra, A.Y. 2011. The green, blue and grey water footprint of crops and derived crop products. Hydrology and Earth System Sciences 15, pp 1577–1600.</ref>
* seawater: water from the oceans, including brackish estuaries, that is withdrawn for cooling thermo-electric power plants, or used in primary energy production.

For more information, see [http://jgcri.github.io/gcam-doc/demand_water.html Demand for Water].

== Water Module Details ==
For more information on water in GCAM including on basin-to-region and basin-to-sector mappings and water markets, visit the [https://jgcri.github.io/gcam-doc/details_water.html detailed water] page.
<references />

Climate - GCAM

2023-10-10T17:44:30Z

Matthew Binsted: Update Hector info to 7.0

{{ModelDocumentationTemplate
|IsDocumentationOf=GCAM
|DocumentationCategory=Climate
}}
Hector v3.1.1 is the default climate model (Hartin et al., 2015)<ref name="hartin2015">Hartin, C. A., Patel, P., Schwarber, A., Link, R. P., and Bond-Lamberty, B. P.: A simple object-oriented and open-source model for scientific and policy analyses of the global climate system – Hector v1.0, Geosci. Model Dev., 8, 939-955, doi:10.5194/gmd-8-939-2015, 2015.</ref> within GCAM.

Hector, an open-source, object-oriented, reduced-form global climate carbon-cycle model, is written in C++. This model runs essentially instantaneously while still representing the most critical global-scale earth system processes. Hector has a three-part main carbon cycle: a one-pool atmosphere, three-pool land, and 4-pool ocean. The model’s terrestrial carbon cycle includes primary production and respiration fluxes, accommodating arbitrary geographic divisions into, e.g., ecological biomes or political units. Hector actively solves the inorganic carbon system in the surface ocean, directly calculating air– sea fluxes of carbon and ocean pH. Hector reproduces the global historical trends of atmospheric [CO<nowiki>2</nowiki>], radiative forcing, and surface temperatures. Hector’s flexibility, open-source nature, and modular design facilitate a broad range of research.

Currently the GCAM sectors interact with Hector via emissions. At every time step, emissions from GCAM are passed to Hector. Hector converts these emissions to concentrations when necessary, and calculates the associated radiative forcing, as well as the response of the climate system and earth system (e.g., temperature, carbon-fluxes, etc.). Hector's climate information can be used as a climate constraint for in a [https://jgcri.github.io/gcam-doc/policies.html GCAM policy run]. See [http://jgcri.github.io/gcam-doc/hector.html Hector] for more details. <references />

Socio-economic drivers - GCAM

2023-10-10T17:35:48Z

Matthew Binsted:

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=GCAM
|DocumentationCategory=Socio-economic drivers
}}The socioeconomic component of GCAM sets the scale of economic activity and associated demands for model simulations. Assumptions about population and per capita GDP growth for each of the 32 geo-political regions together determine the Gross Domestic Product (GDP). GDP and population both can drive the demands for a range of different demands within GCAM.

One of the most important determinants of energy, agriculture, and land-use is the scale of economic activity, which we assume is proportional to GDP. In previous versions of GCAM, dating back to the model’s earliest formulations, the level of GDP was prescribed exogenously. There has been an option to endogenously modify the initial GDP assumption to reflect changes in the cost of delivering energy services within a scenario (Edmonds and Reilly, 1983; Edmonds and Reilly, 1985). However, that feedback elasticity was not determined structurally and was a simple scalar parameter. In other words, population and economic activity are used in GCAM through a one-way transfer of information to other GCAM components. For example, neither the price nor quantity of energy nor the quantity of energy services provided to the economy affect the calculation of the principle model output of the GCAM macro-economic system, GDP.

Since GCAM v7, GCAM incorporates a macroeconomic module that allows for fully endogenizing GDP responses. This model creates a two-way coupling between the scale of economic activity, measured as GDP, and the existing energy sector module. In the simple macro-economic model that we employ here, the two-way interaction is developed for each geo-political region in GCAM. The system is assumed to be open, with each of the regions interacting with others in the global economy via trade. See the [http://jgcri.github.io/gcam-doc/economy.html economy] and [https://jgcri.github.io/gcam-doc/inputs_economy.html economic inputs] sections for details, including a detailed description of the GCAM-Macro model.

Macro-economy - GCAM

2023-10-10T15:27:36Z

Matthew Binsted: Added GCAM-Macro description

{{ModelDocumentationTemplate
|IsDocumentationOf=GCAM
|DocumentationCategory=Macro-economy
}}
The socioeconomic component of GCAM sets the scale of economic activity and associated demands for model simulations. Assumptions about population and per capita GDP growth for each of the 32 geo-political regions together determine the Gross Domestic Product (GDP). GDP and population both can drive the demands for a range of different demands within GCAM.

One of the most important determinants of energy, agriculture, and land-use is the scale of economic activity, which we assume is proportional to GDP. In previous versions of GCAM, dating back to the model’s earliest formulations, the level of GDP was prescribed exogenously. There has been an option to endogenously modify the initial GDP assumption to reflect changes in the cost of delivering energy services within a scenario (Edmonds and Reilly, 1983; Edmonds and Reilly, 1985). However, that feedback elasticity was not determined structurally and was a simple scalar parameter. In other words, population and economic activity are used in GCAM through a one-way transfer of information to other GCAM components. For example, neither the price nor quantity of energy nor the quantity of energy services provided to the economy affect the calculation of the principle model output of the GCAM macro-economic system, GDP.

Since GCAM v7, GCAM incorporates a macroeconomic module that allows for fully endogenizing GDP responses. This model creates a two-way coupling between the scale of economic activity, measured as GDP, and the existing energy sector module. In the simple macro-economic model that we employ here, the two-way interaction is developed for each geo-political region in GCAM. The system is assumed to be open, with each of the regions interacting with others in the global economy via trade.

See the [https://jgcri.github.io/gcam-doc/economy.html economy] and [https://jgcri.github.io/gcam-doc/inputs_economy.html economic inputs] sections for details, including a detailed description of the GCAM-Macro model.

Socio-economic drivers - GCAM

2023-10-06T16:59:02Z

Matthew Binsted: Added info about GCAM-Macro to socioeconomics section

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=GCAM
|DocumentationCategory=Socio-economic drivers
}}The socioeconomic component of GCAM sets the scale of economic activity and associated demands for model simulations. Assumptions about population and per capita GDP growth for each of the 32 geo-political regions together determine the Gross Domestic Product (GDP). GDP and population both can drive the demands for a range of different demands within GCAM.

One of the most important determinants of energy, agriculture, and land-use is the scale of economic activity, which we assume is proportional to GDP. In previous versions of GCAM, dating back to the model’s earliest formulations, the level of GDP was prescribed exogenously. There has been an option to endogenously modify the initial GDP assumption to reflect changes in the cost of delivering energy services within a scenario (Edmonds and Reilly, 1983; Edmonds and Reilly, 1985). However, that feedback elasticity was not determined structurally and was a simple scalar parameter. In other words, population and economic activity are used in GCAM through a one-way transfer of information to other GCAM components. For example, neither the price nor quantity of energy nor the quantity of energy services provided to the economy affect the calculation of the principle model output of the GCAM macro-economic system, GDP.

Since GCAM v7, GCAM incorporates a macroeconomic module that allows for fully endogenizing GDP responses. This model creates a two-way coupling between the scale of economic activity, measured as GDP, and the existing energy sector module. In the simple macro-economic model that we employ here, the two-way interaction is developed for each geo-political region in GCAM. The system is assumed to be open, with each of the regions interacting with others in the global economy via trade. See the [http://jgcri.github.io/gcam-doc/economy.html economy] section for details, including a detailed description of the GCAM-Macro model.

Policy - GCAM

2023-10-06T16:44:31Z

Matthew Binsted: Added detail to GCAM policy section

{{ModelDocumentationTemplate
|IsDocumentationOf=GCAM
|DocumentationCategory=Policy
}}
One of GCAM’s uses is to explore the implications of different future policies. There are a number of types of policies that can be easily modeled in GCAM, including policies related to emissions, land-use, and energy production. The most common of these are discussed in the documentation's [http://jgcri.github.io/gcam-doc/policies.html policy] section.

== [https://jgcri.github.io/gcam-doc/policies.html#emissions-policies Emissions-Related Policies] ==
There are three main policy approaches that can be applied in GCAM to reduce emissions of CO2 or other greenhouse gases: carbon or GHG prices, emissions constraints, or climate constraints. In all cases, GCAM implements the policy approach by placing a price on emissions. This price then filters down through all the systems in GCAM and alters production and demand. For example, a price on carbon would put a cost on emitting fossil fuels. This cost would then influence the cost of producing electricity from fossil-fired power plants that emit CO2, which would then influence their relative cost compared to other electricity generating technologies and increase the price of electricity. The increased price of electricity would then make its way to consumers that use electricity, decreasing its competitiveness relative to other fuels and leading to a decrease in electricity demand. The three policy approaches, carbon or GHG prices, emissions constraints, and climate constraints, are discussed in detail [https://jgcri.github.io/gcam-doc/policies.html#emissions-policies here].

== [https://jgcri.github.io/gcam-doc/policies.html#energy-production-policies Energy Production Policies] ==
There are times in which users would like to explore the implications of a constraint on production or a minimum production requirement. This capability allows GCAM users to model policies such as renewable portfolio standards and biofuels standards. Across sectors, these constraints must be applied as quantity constraints, but they can be applied as share constraints within individual sectors (e.g., fraction of electricity that comes from solar power). In implementing these policies, this can either be a lower bound or upper bound. The model will solve for the tax (upper bound) or subsidy (lower bound) required to reach the given constraint.

== [https://jgcri.github.io/gcam-doc/policies.html#land-use-policies Land-Use Policies] ==
There are a number of ways that policies can be applied directly to influence the land sector in GCAM. These include the following.
* Protected lands
* Valuing carbon in land
* Bioenergy constraints
* Land constraints
See the [https://jgcri.github.io/gcam-doc/policies.html#land-use-policies Land-Use Policies] section in the documentation for more details.

== [https://jgcri.github.io/gcam-doc/policies.html#policy-costs Calculating Emissions Policy Costs] ==
The cost of GHG emissions mitigation is a concept that is not uniquely defined. A wide range of measures are used in the literature. These include, the price of carbon (or as appropriate given the policy) needed to achieve a desired emission mitigation goal, reduction in Gross Domestic Product (GDP), consumption loss, deadweight loss, and equivalent variation. Beyond that the concept of net cost, which includes the benefits of emissions mitigation as well as the resource cost of emissions reduction and the social cost of carbon are also encountered. GCAM makes no attempt to calculate the benefits. See [https://jgcri.github.io/gcam-doc/policies.html#policy-costs Calculating Emissions Policy Costs] for details.

Water - GCAM

2022-06-24T14:15:06Z

Matthew Binsted:

{{ModelDocumentationTemplate
|IsDocumentationOf=GCAM
|DocumentationCategory=Water
}}
== Supply of Water ==
Three distinct sources of fresh water are modeled, renewable water (surface and ground), non-renewable groundwater, and desalinated water. Renewable water is water that is replenished naturally by surface runoff and subsurface infiltration and release. Non-renewable groundwater is fossil groundwater resources or groundwater where abstraction exceeds recharge. Renewable water and non-renewable groundwater are separately modeled for each basin. Desalinated water is available as an additional source of freshwater within each basin and for alternative end-use demands for water. See [http://jgcri.github.io/gcam-doc/supply_water.html Supply of Water] for a full description.

== Demand for Water ==
Water demand is calculated for six major sectors: agriculture, electricity generation, industrial manufacturing, primary energy production, livestock, and municipal uses. For each sector, up to four types of water demand are represented. Types of water include
* water withdrawals: water diverted or withdrawn from a surface water or groundwater source (Vickers 2001).<ref name = "vickers2001">Vickers, A. 2001. Handbook of Water Use and Conservation. WaterPlow Press, Amherst, MA, USA.</ref>
* water consumption: water use that permanently withdraws water from its source; water that is no longer available because it has evaporated, been transpired by plants, incorporated into products or crops, consumed by people or livestock, or otherwise removed from the immediate water environment (Vickers 2001).<ref name = "vickers2001"/>
* biophysical water consumption: total water required for crop evapo-transpiration; the sum of “blue” and “green” water in Mekonnen and Hoekstra 2011.<ref>Mekonnen, M.M., and Hoekstra, A.Y. 2011. The green, blue and grey water footprint of crops and derived crop products. Hydrology and Earth System Sciences 15, pp 1577–1600.</ref>
* seawater: water from the oceans, including brackish estuaries, that is withdrawn for cooling thermo-electric power plants, or used in primary energy production.

For more information, see [http://jgcri.github.io/gcam-doc/demand_water.html Demand for Water].

== Water Module Details ==
For more information on water in GCAM including on basin-to-region and basin-to-sector mappings and water markets, visit the [https://jgcri.github.io/gcam-doc/details_water.html detailed water] page.

Climate - GCAM

2022-06-24T14:13:16Z

Matthew Binsted:

{{ModelDocumentationTemplate
|IsDocumentationOf=GCAM
|DocumentationCategory=Climate
}}
Hector v2.0 is the default climate model (Hartin et al., 2015)<ref name = "hartin2015">Hartin, C. A., Patel, P., Schwarber, A., Link, R. P., and Bond-Lamberty, B. P.: A simple object-oriented and open-source model for scientific and policy analyses of the global climate system – Hector v1.0, Geosci. Model Dev., 8, 939-955, doi:10.5194/gmd-8-939-2015, 2015.</ref> within GCAM.

Hector, an open-source, object-oriented, reduced-form global climate carbon-cycle model, is written in C++. This model runs essentially instantaneously while still representing the most critical global-scale earth system processes. Hector has a three-part main carbon cycle: a one-pool atmosphere, three-pool land, and 4-pool ocean. The model’s terrestrial carbon cycle includes primary production and respiration fluxes, accommodating arbitrary geographic divisions into, e.g., ecological biomes or political units. Hector actively solves the inorganic carbon system in the surface ocean, directly calculating air– sea fluxes of carbon and ocean pH. Hector reproduces the global historical trends of atmospheric [CO2], radiative forcing, and surface temperatures. The model simulates all four Representative Concentration Pathways (RCPs) with equivalent rates of change of key variables over time compared to current observations, MAGICC (Wigley, 2008),<ref>Wigley, T. M. (2008), MAGICC/SENGEN 5.3: User manual (version 2), edited, p. 80, NCAR, Boulder CO.</ref> and models from CMIP5.<ref name = "hartin2015"/> Hector’s flexibility, open-source nature, and modular design facilitates a broad range of research in various areas.

Currently the GCAM sectors interact with Hector via their emissions. At every time step, emissions from GCAM are passed to Hector. Hector converts these emissions to concentrations when necessary, and calculates the associated radiative forcing, as well as the response of the climate system (e.g., temperature, carbon-fluxes, etc.). See [http://jgcri.github.io/gcam-doc/hector.html Hector] for more details.

Liquid fuels - GCAM

2022-06-24T14:08:12Z

Matthew Binsted:

{{ModelDocumentationTemplate
|IsDocumentationOf=GCAM
|DocumentationCategory=Liquid fuels
}}

== Refining ==
The refining sector, or liquid fuels production sector, explicitly tracks all energy inputs, emissions, and costs involved with converting primary energy forms into liquid fuels. Liquid fuels include gasoline, diesel, kerosene, ethanol and many other liquid hydrocarbon fuels; for the full mapping see [http://jgcri.github.io/gcam-doc/details_inputs.html#mapping-the-iea-energy-balances Mapping the IEA Energy Balances]. The refining sector includes subsectors of oil refining, biomass liquids, gas to liquids, and coal to liquids, each of which are described below. Each of these four subsectors is available starting in the first future time period, and the capital stocks of refineries are explicitly tracked. Click on the headings for links to the corresponding section in the documentation, and see the documentation sections on [https://jgcri.github.io/gcam-doc/supply_energy.html#refining refining] and [https://jgcri.github.io/gcam-doc/details_energy.html#refining refining details].

=== [https://jgcri.github.io/gcam-doc/supply_energy.html#oil-refining Oil Refining] ===
The oil refining subsector accounts for the vast majority of the historical output of the refining sector, globally and in all regions. Each region is assigned a single production technology for oil refining; this technology does not differentiate between conventional and unconventional oil, whose competition is explicitly modeled upstream of the refining sector. In a typical region, the oil refining technology consumes three energy inputs: crude oil, natural gas, and electricity. The coefficients of the oil refining production technology reflect whole-process inputs and liquid fuel outputs; there is no explicit tracking of the production and on-site use of intermediate products such as refinery gas (still gas). Electricity produced at refineries (both the fuel inputs and electricity outputs) is modeled in the electricity and/or industrial energy use sectors, as the IEA Energy Balances (IEA 2019)<ref>International Energy Agency, 2019, Energy Balances of OECD Countries 1960-2017 and Energy Balances of Non-OECD Countries 1971-2017, International Energy Agency, Paris, France.</ref> do not disaggregate autoproducer electric power plants at refineries from elsewhere. There is no oil refining technology option with CO2 capture and storage (CCS) considered.

=== [https://jgcri.github.io/gcam-doc/supply_energy.html#biomass-liquids Biomass Liquids] ===
The biomass liquids subsector includes up to eight technologies in each region, with a global total of 11 production technologies. The biomass liquids technologies include up to four “first-generation” biofuels in each region, defined as biofuels produced from agricultural crops that are also used as food, animal feed, or other modeled uses (described in the [https://jgcri.github.io/gcam-doc/land.html land module]). The model tracks secondary feed outputs of first generation biofuel production, as DDGS (dried distillers grains and solubles) from ethanol production, and as feedcakes from biodiesel production. Second-generation technologies consume the “biomass” or “biomassOil” commodities, which include purpose-grown bioenergy crops, as well as residues from forestry and agriculture, and municipal and industrial wastes. Starting in 2020, second-generation biofuels (cellulosic ethanol and Fischer-Tropsch syn-fuels) are introduced, each with three levels of CCS: none, level 1, and level 2. The first CCS level generally consists of relatively pure and high-concentration CO2 sources (e.g., from gasifiers or fermenters), which have relatively low capture and compression costs. The second CCS level includes a broader set of sources (e.g., post-combustion emissions), and incurs higher costs but has a higher CO2 removal fraction.

=== [https://jgcri.github.io/gcam-doc/supply_energy.html#coal-to-liquids Coal to Liquids] ===
The majority of the world’s coal to liquids production is in South Africa (IEA 2012),<ref name = "iea2012">International Energy Agency, 2011, Energy Balances of OECD Countries 1960-2010 and Energy Balances of Non-OECD Countries 1971-2010, International Energy Agency, Paris, France.</ref> but the technology is available to all regions in GCAM starting in the first future time period. Note that the CO2 emissions intensity is substantially higher than all other liquid fuel production technologies, due to high process energy intensities, and high primary fuel carbon contents. Where crude oil refining emits about 5.5 kg of CO2 per GJ of fuels produced, coal to liquids emits over 130 kg of CO2 per GJ of fuel produced. The upstream emissions from fuel production by this pathway are substantially higher than the “tailpipe” emissions from combustion of the fuels produced (about 70 kg CO2 per GJ). As with biomass liquids, two different production technologies with CCS are represented, with costs and CO2 removal fractions based on Dooley and Dahowski (2009).<ref>Dooley, J.J., and Dahowski, R.T. 2009. Large-scale U.S. unconventional fuels production and the role of carbon dioxide capture and storage technologies in reducing their greenhouse gas emissions. Energy Procedia 1(1), pp. 4225-4232.</ref>

=== [https://jgcri.github.io/gcam-doc/supply_energy.html#gas-to-liquids Gas to Liquids] ===
While a minor contributor to liquid fuels production globally (about 0.1%; (IEA 2012),<ref name = "iea2012"/>) gas to liquids has received increased attention in recent years, with several large-scale plants completed in the last decade (Glebova 2013),<ref>Glebova, O. 2013. Gas to Liquids: Historical Development and Future Prospects, Report NG 80, Oxford Institute for Energy Studies.</ref> and others in various stages of planning and construction (Enerdata 2014).<ref>Enerdata, 2016. The Future of Gas-to-Liquid (GTL) Industry.</ref> Because of the relatively low carbon content of natural gas, and whole-process energy efficiency ratings typically about 60%, the net CO2 emissions from the process are about 20 kg CO2 per GJ of fuel, significantly lower than coal to liquids. There is only one production technology represented in GCAM, with no CCS option available.

Liquid fuels - GCAM

2022-06-24T14:07:12Z

Matthew Binsted:

{{ModelDocumentationTemplate
|IsDocumentationOf=GCAM
|DocumentationCategory=Liquid fuels
}}

== Refining ==
The refining sector, or liquid fuels production sector, explicitly tracks all energy inputs, emissions, and costs involved with converting primary energy forms into liquid fuels. Liquid fuels include gasoline, diesel, kerosene, ethanol and many other liquid hydrocarbon fuels; for the full mapping see [http://jgcri.github.io/gcam-doc/details_inputs.html#mapping-the-iea-energy-balances Mapping the IEA Energy Balances]. The refining sector includes subsectors of oil refining, biomass liquids, gas to liquids, and coal to liquids, each of which are described below. Each of these four subsectors is available starting in the first future time period, and the capital stocks of refineries are explicitly tracked. Click on the headings for links to the corresponding section in the documentation, and see the documentation sections on [https://jgcri.github.io/gcam-doc/supply_energy.html#refining refining] and [https://jgcri.github.io/gcam-doc/details_energy.html#refining refining details].

=== [https://jgcri.github.io/gcam-doc/supply_energy.html#oil-refining Oil Refining] ===
The oil refining subsector accounts for the vast majority of the historical output of the refining sector, globally and in all regions. Each region is assigned a single production technology for oil refining; this technology does not differentiate between conventional and unconventional oil, whose competition is explicitly modeled upstream of the refining sector. In a typical region, the oil refining technology consumes three energy inputs: crude oil, natural gas, and electricity. The coefficients of the oil refining production technology reflect whole-process inputs and liquid fuel outputs; there is no explicit tracking of the production and on-site use of intermediate products such as refinery gas (still gas). Electricity produced at refineries (both the fuel inputs and electricity outputs) is modeled in the electricity and/or industrial energy use sectors, as the IEA Energy Balances (IEA 2019)<ref>International Energy Agency, 2019, Energy Balances of OECD Countries 1960-2017 and Energy Balances of Non-OECD Countries 1971-2017, International Energy Agency, Paris, France.</ref> do not disaggregate autoproducer electric power plants at refineries from elsewhere. There is no oil refining technology option with CO2 capture and storage (CCS) considered.

=== [https://jgcri.github.io/gcam-doc/supply_energy.html#biomass-liquids Biomass Liquids] ===
The biomass liquids subsector includes up to eight technologies in each region, with a global total of 11 production technologies. The biomass liquids technologies include up to four “first-generation” biofuels in each region, defined as biofuels produced from agricultural crops that are also used as food, animal feed, or other modeled uses (described in the [https://jgcri.github.io/gcam-doc/land.html land module]). The model tracks secondary feed outputs of first generation biofuel production, as DDGS (dried distillers grains and solubles) from ethanol production, and as feedcakes from biodiesel production. Second-generation technologies consume the “biomass” or “biomassOil” commodities, which include purpose-grown bioenergy crops, as well as residues from forestry and agriculture, and municipal and industrial wastes. Starting in 2020, second-generation biofuels (cellulosic ethanol and Fischer-Tropsch syn-fuels) are introduced, each with three levels of CCS: none, level 1, and level 2. The first CCS level generally consists of relatively pure and high-concentration CO2 sources (e.g., from gasifiers or fermenters), which have relatively low capture and compression costs. The second CCS level includes a broader set of sources (e.g., post-combustion emissions), and incurs higher costs but has a higher CO2 removal fraction.

=== [https://jgcri.github.io/gcam-doc/supply_energy.html#coal-to-liquids Coal to Liquids] ===
The majority of the world’s coal to liquids production is in South Africa (IEA 2012),<ref name = "iea2012">International Energy Agency, 2011, Energy Balances of OECD Countries 1960-2010 and Energy Balances of Non-OECD Countries 1971-2010, International Energy Agency, Paris, France.</ref> but the technology is available to all regions in GCAM starting in the first future time period. Note that the CO2 emissions intensity is substantially higher than all other liquid fuel production technologies, due to high process energy intensities, and high primary fuel carbon contents. Where crude oil refining emits about 5.5 kg of CO2 per GJ of fuels produced, coal to liquids emits over 130 kg of CO2 per GJ of fuel produced. The upstream emissions from fuel production by this pathway are substantially higher than the “tailpipe” emissions from combustion of the fuels produced (about 70 kg CO2 per GJ). As with biomass liquids, two different production technologies with CCS are represented, with costs and CO2 removal fractions based on Dooley and Dahowski (2009).<ref>Dooley, J.J., and Dahowski, R.T. 2009. Large-scale U.S. unconventional fuels production and the role of carbon dioxide capture and storage technologies in reducing their greenhouse gas emissions. Energy Procedia 1(1), pp. 4225-4232.</ref>

=== [https://jgcri.github.io/gcam-doc/supply_energy.html#gas-to-liquids Gas to Liquids] ===
While a minor contributor to liquid fuels production globally (about 0.1%; (IEA 2012),<ref name = "iea2012"/> gas to liquids has received increased attention in recent years, with several large-scale plants completed in the last decade (Glebova 2013),<ref>Glebova, O. 2013. Gas to Liquids: Historical Development and Future Prospects, Report NG 80, Oxford Institute for Energy Studies.</ref> and others in various stages of planning and construction (Enerdata 2014).<ref>Enerdata, 2016. The Future of Gas-to-Liquid (GTL) Industry.</ref> Because of the relatively low carbon content of natural gas, and whole-process energy efficiency ratings typically about 60%, the net CO2 emissions from the process are about 20 kg CO2 per GJ of fuel, significantly lower than coal to liquids. There is only one production technology represented in GCAM, with no CCS option available.

Model Documentation - GCAM

2022-06-24T14:02:39Z

Matthew Binsted:

{{ModelDocumentationTemplate
|IsDocumentationOf=GCAM
|DocumentationCategory=Model Documentation
}}

GCAM is a global model that represents the behavior of, and interactions between five systems: the energy system, water, agriculture and land use, the economy, and the climate. GCAM has been under development for over 30 years. Work began in 1980 with the work first documented in 1982 in working papers (Edmonds and Reilly, 1982a,b,c)<ref>Edmonds, J. and J. Reilly. 1982a. “Global energy and CO2 to the year 2050,” IEA/ORAU Working Paper Contribution No. 82-6.</ref> <ref>Edmonds, J. and J. Reilly. 1982b. “Global energy production and use to the year 2050,” IEA/ORAU Working Paper Contribution No. 82-7.</ref>
<ref>Edmonds, J. and J. Reilly. 1982c. An introduction to the use of the IEA/ORAU, Long-term, global energy model,” IEA/ORAU Working Paper Contribution No. 82-9.</ref> and the first peer-reviewed publications in 1983 (Edmonds and Reilly, 1983a,b,c)<ref>Edmonds, J. and J. Reilly. 1983a. “Global Energy and CO2 to the Year 2050,” The Energy Journal, 4(3):21-47.</ref><ref>Edmonds, J. and J. Reilly. 1983b. “A Long-Term, Global, Energy-Economic Model of Carbon Dioxide Release From Fossil Fuel Use,” Energy Economics, 5(2):74-88.</ref><ref>Edmonds, J. and J. Reilly. 1983c. “Global Energy Production and Use to the Year 2050,” Energy, 8(6):419-32.</ref>. At this point, the model was known as the Edmonds-Reilly (and subsequently the Edmonds-Reilly-Barnes) model. The model was renamed MiniCAM in the mid-1990s, the model code was re-written in object-oriented C++ (Kim et al. 2006)<ref>Kim, S.H., J. Edmonds, J. Lurz, S. J. Smith, and M. Wise (2006) The ObjECTS Framework for Integrated Assessment: Hybrid Modeling of Transportation. The Energy Journal 27(Special Issue 2): pp 63-91.</ref> and renamed to GCAM in the mid-2000s. The first coupling to a carbon cycle model was published in Edmonds et al. (1984)<ref>Edmonds, J., J. Reilly, J.R. Trabalka and D.E. Reichle. 1984. An Analysis of Possible Future Atmospheric Retention of Fossil Fuel CO2. TR013, DOE/OR/21400-1. National Technical Information Service, U.S. Department of Commerce, Springfield Virginia 22161.</ref>. The first use of GCAM (MiniCAM at the time) in conjunction with a Monte Carlo uncertainty analysis was published in Reilly et al. (1987)<ref name = "reilly1987">Reilly, J.M., Edmonds, J.A., Gardner, R.H., and Brenkert, A.L. 1987. “Uncertainty Analysis of the IEA/ORAU CO2 Emissions Model,” The Energy Journal, 8(3):1-29. Response Strategies Working Group, Intergovernmental Panel on Climate Change. 1990. Emissions Scenarios.</ref>.

Throughout its lifetime, GCAM has evolved in response to the need to address an expanding set of science and assessment questions. The original question that the model was developed to address was the magnitude of mid-21st-century global emissions of fossil fuel CO2. Over time GCAM has expanded its scope to include a wider set of energy producing, transforming, and using technologies, emissions of non-CO2 greenhouse gases, agriculture and land use, water supplies and demands, and physical Earth systems. GCAM has been used to produce scenarios for national and international assessments ranging from the very first IPCC scenarios (Response Strategies Working Group, 1990)<ref name = "reilly1987"/> through the present Shared Socioeconomic Pathways (Calvin et al., 2017)<ref>Calvin, K., B. Bond-Lamberty, L. Clarke, J. Edmonds, J. Eom, C. Hartin, S. Kim, P. Kyle, R. Link, R. Moss, H. McJeon, P. Patel, S. Smith, S. Waldhoff and M. Wise (2017). “The SSP4: A world of deepening inequality.” Global Environmental Change 42: 284-296.</ref>. GCAM is increasingly being used in multi-model, multi-scale analysis, in which it is either soft- or hard-coupled to other models with different focuses and often greater resolution in key sectors. For example, a range of downscaling tools have been developed for use with GCAM to be able to land and water outputs at a grid resolution. Similarly, it has been coupled to a state of the art Earth system model (Collins, et al., 2015)<ref>Collins, William D., Anthony P. Craig, John E. Truesdale, A. V. Di Vittorio, Andrew D. Jones, Benjamin Bond-Lamberty, Katherine V. Calvin, James A. Edmonds, Allison M. Thomson, Benjamine Bond-Lamberty, Pralit Patel, Sonny H. Kim, Peter E. Thornton, Jiafu Mao, Xiaoying Shi, Louise P. Chini, and George C. Hurtt. “The integrated Earth system model version 1: formulation and functionality.” Geoscientific Model Development 8, no. 7 (2015): 2203-2219.</ref>. Hundreds of papers have been published in peer-reviewed journals using GCAM over its lifetime and the GCAM system continues to be an important international tool for scientific inquiry. GCAM is also a community model being used by researchers across the globe, creating a shared global research enterprise. GCAM can be run on Windows, Linux, Mac, and high-performance computing systems.

The official documentation for GCAM can be found [http://jgcri.github.io/gcam-doc/index.html here].

Model Documentation - GCAM

2022-06-21T22:29:28Z

Matthew Binsted:

{{ModelDocumentationTemplate
|IsDocumentationOf=GCAM
|DocumentationCategory=Model Documentation
}}

GCAM is a global model that represents the behavior of, and interactions between five systems: the energy system, water, agriculture and land use, the economy, and the climate. GCAM has been under development for over 30 years. Work began in 1980 with the work first documented in 1982 in working papers (Edmonds and Reilly, 1982a,b,c)<ref>Edmonds, J. and J. Reilly. 1982a. “Global energy and CO2 to the year 2050,” IEA/ORAU Working Paper Contribution No. 82-6.</ref> <ref>Edmonds, J. and J. Reilly. 1982b. “Global energy production and use to the year 2050,” IEA/ORAU Working Paper Contribution No. 82-7.</ref>
<ref>Edmonds, J. and J. Reilly. 1982c. An introduction to the use of the IEA/ORAU, Long-term, global energy model,” IEA/ORAU Working Paper Contribution No. 82-9.</ref> and the first peer-reviewed publications in 1983 (Edmonds and Reilly, 1983a,b,c)<ref>Edmonds, J. and J. Reilly. 1983a. “Global Energy and CO2 to the Year 2050,” The Energy Journal, 4(3):21-47.</ref><ref>Edmonds, J. and J. Reilly. 1983b. “A Long-Term, Global, Energy-Economic Model of Carbon Dioxide Release From Fossil Fuel Use,” Energy Economics, 5(2):74-88.</ref><ref>Edmonds, J. and J. Reilly. 1983c. “Global Energy Production and Use to the Year 2050,” Energy, 8(6):419-32.</ref>. At this point, the model was known as the Edmonds-Reilly (and subsequently the Edmonds-Reilly-Barnes) model. The model was renamed MiniCAM in the mid-1990s, the model code was re-written in object-oriented C++ (Kim et al. 2006)<ref>Kim, S.H., J. Edmonds, J. Lurz, S. J. Smith, and M. Wise (2006) The ObjECTS Framework for Integrated Assessment: Hybrid Modeling of Transportation. The Energy Journal 27(Special Issue 2): pp 63-91.</ref> and renamed to GCAM in the mid-2000s. The first coupling to a carbon cycle model was published in Edmonds et al. (1984)<ref>Edmonds, J., J. Reilly, J.R. Trabalka and D.E. Reichle. 1984. An Analysis of Possible Future Atmospheric Retention of Fossil Fuel CO2. TR013, DOE/OR/21400-1. National Technical Information Service, U.S. Department of Commerce, Springfield Virginia 22161.</ref>. The first use of GCAM (MiniCAM at the time) in conjunction with a Monte Carlo uncertainty analysis was published in Reilly et al. (1987)<ref>Reilly, J.M., Edmonds, J.A., Gardner, R.H., and Brenkert, A.L. 1987. “Uncertainty Analysis of the IEA/ORAU CO2 Emissions Model,” The Energy Journal, 8(3):1-29. Response Strategies Working Group, Intergovernmental Panel on Climate Change. 1990. Emissions Scenarios.</ref>.

Throughout its lifetime, GCAM has evolved in response to the need to address an expanding set of science and assessment questions. The original question that the model was developed to address was the magnitude of mid-21st-century global emissions of fossil fuel CO2. Over time GCAM has expanded its scope to include a wider set of energy producing, transforming, and using technologies, emissions of non-CO2 greenhouse gases, agriculture and land use, water supplies and demands, and physical Earth systems. GCAM has been used to produce scenarios for national and international assessments ranging from the very first IPCC scenarios (Response Strategies Working Group, 1990)<ref>Reilly, J.M., Edmonds, J.A., Gardner, R.H., and Brenkert, A.L. 1987. “Uncertainty Analysis of the IEA/ORAU CO2 Emissions Model,” The Energy Journal, 8(3):1-29. Response Strategies Working Group, Intergovernmental Panel on Climate Change. 1990. Emissions Scenarios.</ref> through the present Shared Socioeconomic Pathways (Calvin et al., 2017)<ref>Calvin, K., B. Bond-Lamberty, L. Clarke, J. Edmonds, J. Eom, C. Hartin, S. Kim, P. Kyle, R. Link, R. Moss, H. McJeon, P. Patel, S. Smith, S. Waldhoff and M. Wise (2017). “The SSP4: A world of deepening inequality.” Global Environmental Change 42: 284-296.</ref>. GCAM is increasingly being used in multi-model, multi-scale analysis, in which it is either soft- or hard-coupled to other models with different focuses and often greater resolution in key sectors. For example, a range of downscaling tools have been developed for use with GCAM to be able to land and water outputs at a grid resolution. Similarly, it has been coupled to a state of the art Earth system model (Collins, et al., 2015)<ref>Collins, William D., Anthony P. Craig, John E. Truesdale, A. V. Di Vittorio, Andrew D. Jones, Benjamin Bond-Lamberty, Katherine V. Calvin, James A. Edmonds, Allison M. Thomson, Benjamine Bond-Lamberty, Pralit Patel, Sonny H. Kim, Peter E. Thornton, Jiafu Mao, Xiaoying Shi, Louise P. Chini, and George C. Hurtt. “The integrated Earth system model version 1: formulation and functionality.” Geoscientific Model Development 8, no. 7 (2015): 2203-2219.</ref>. Hundreds of papers have been published in peer-reviewed journals using GCAM over its lifetime and the GCAM system continues to be an important international tool for scientific inquiry. GCAM is also a community model being used by researchers across the globe, creating a shared global research enterprise. GCAM can be run on Windows, Linux, Mac, and high-performance computing systems.

The official documentation for GCAM can be found [http://jgcri.github.io/gcam-doc/index.html here].

Water - GCAM

2022-06-21T22:25:13Z

Matthew Binsted:

{{ModelDocumentationTemplate
|IsDocumentationOf=GCAM
|DocumentationCategory=Water
}}
== Supply of Water ==
Three distinct sources of fresh water are modeled, renewable water (surface and ground), non-renewable groundwater, and desalinated water. Renewable water is water that is replenished naturally by surface runoff and subsurface infiltration and release. Non-renewable groundwater is fossil groundwater resources or groundwater where abstraction exceeds recharge. Renewable water and non-renewable groundwater are separately modeled for each basin. Desalinated water is available as an additional source of freshwater within each basin and for alternative end-use demands for water. See [http://jgcri.github.io/gcam-doc/supply_water.html Supply of Water] for a full description.

== Demand for Water ==
Water demand is calculated for six major sectors: agriculture, electricity generation, industrial manufacturing, primary energy production, livestock, and municipal uses. For each sector, up to four types of water demand are represented. Types of water include
* water withdrawals: water diverted or withdrawn from a surface water or groundwater source (Vickers 2001).<ref>Vickers, A. 2001. Handbook of Water Use and Conservation. WaterPlow Press, Amherst, MA, USA.</ref>
* water consumption: water use that permanently withdraws water from its source; water that is no longer available because it has evaporated, been transpired by plants, incorporated into products or crops, consumed by people or livestock, or otherwise removed from the immediate water environment (Vickers 2001).<ref>Vickers, A. 2001. Handbook of Water Use and Conservation. WaterPlow Press, Amherst, MA, USA.</ref>
* biophysical water consumption: total water required for crop evapo-transpiration; the sum of “blue” and “green” water in Mekonnen and Hoekstra 2011.<ref>Mekonnen, M.M., and Hoekstra, A.Y. 2011. The green, blue and grey water footprint of crops and derived crop products. Hydrology and Earth System Sciences 15, pp 1577–1600.</ref>
* seawater: water from the oceans, including brackish estuaries, that is withdrawn for cooling thermo-electric power plants, or used in primary energy production.

For more information, see [http://jgcri.github.io/gcam-doc/demand_water.html Demand for Water].

== Water Module Details ==
For more information on water in GCAM including on basin-to-region and basin-to-sector mappings and water markets, visit the [https://jgcri.github.io/gcam-doc/details_water.html detailed water] page.

Climate - GCAM

2022-06-21T22:19:58Z

Matthew Binsted:

{{ModelDocumentationTemplate
|IsDocumentationOf=GCAM
|DocumentationCategory=Climate
}}
Hector v2.0 is the default climate model (Hartin et al., 2015)<ref>Hartin, C. A., Patel, P., Schwarber, A., Link, R. P., and Bond-Lamberty, B. P.: A simple object-oriented and open-source model for scientific and policy analyses of the global climate system – Hector v1.0, Geosci. Model Dev., 8, 939-955, doi:10.5194/gmd-8-939-2015, 2015.</ref> within GCAM.

Hector, an open-source, object-oriented, reduced-form global climate carbon-cycle model, is written in C++. This model runs essentially instantaneously while still representing the most critical global-scale earth system processes. Hector has a three-part main carbon cycle: a one-pool atmosphere, three-pool land, and 4-pool ocean. The model’s terrestrial carbon cycle includes primary production and respiration fluxes, accommodating arbitrary geographic divisions into, e.g., ecological biomes or political units. Hector actively solves the inorganic carbon system in the surface ocean, directly calculating air– sea fluxes of carbon and ocean pH. Hector reproduces the global historical trends of atmospheric [CO2], radiative forcing, and surface temperatures. The model simulates all four Representative Concentration Pathways (RCPs) with equivalent rates of change of key variables over time compared to current observations, MAGICC (Wigley, 2008),<ref>Wigley, T. M. (2008), MAGICC/SENGEN 5.3: User manual (version 2), edited, p. 80, NCAR, Boulder CO.</ref> and models from CMIP5.<ref>Hartin, C. A., Patel, P., Schwarber, A., Link, R. P., and Bond-Lamberty, B. P.: A simple object-oriented and open-source model for scientific and policy analyses of the global climate system – Hector v1.0, Geosci. Model Dev., 8, 939-955, doi:10.5194/gmd-8-939-2015, 2015.</ref> Hector’s flexibility, open-source nature, and modular design facilitates a broad range of research in various areas.

Currently the GCAM sectors interact with Hector via their emissions. At every time step, emissions from GCAM are passed to Hector. Hector converts these emissions to concentrations when necessary, and calculates the associated radiative forcing, as well as the response of the climate system (e.g., temperature, carbon-fluxes, etc.). See [http://jgcri.github.io/gcam-doc/hector.html Hector] for more details.

GHGs - GCAM

2022-06-21T22:12:28Z

Matthew Binsted:

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== CO2 Emissions ==
GCAM endogenously estimates CO2 fossil-fuel related emissions based on fossil fuel consumption and global emission factors by fuel (oil, unconventional oil, natural gas, and coal). These emission factors are consistent with global emissions by fuel from the CDIAC global inventory (CDIAC 2017).<ref>Boden, T., and Andres, B. 2017, National CO2 Emissions from Fossil-Fuel Burning, Cement Manufacture, and Gas Flaring: 1751-2014, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory.</ref>

GCAM can be considered as a process model for CO2 emissions and reductions. CO2 emissions change over time as fuel consumption in GCAM endogenously changes. Application of Carbon Capture and Storage (CCS) is explicitly considered as separate technological options for a number of processes, such as electricity generation and fertilizer manufacturing. GCAM, in effect, produces a Marginal Abatement Curve for CO2 as a carbon-price is applied within the model. Documentation for CO2 emissions can be found [http://jgcri.github.io/gcam-doc/emissions.html#co2-emissions here].

== Non-CO2 GHG Emissions ==
The non-CO2 greenhouse gases include methane (CH4), nitrous oxide (N2O) and fluorinated gases. These emissions, ''E'', are modeled for any given technology in time period ''t'' as:<math display="block">E_{t}=A_{t}*F_{t0}*(1-MAC(Cprice_{t})) </math>where:
{| class="wikitable"
|-
|F
|Emissions factor: base-year emissions per unit activity
|-
|A
|Activity level (e.g., output of a technology)
|-
|MAC
|Marginal Abatement Cost Curve
|-
|Cprice
|Carbon Price
|}
Non-CO2 GHG emissions are proportional to the activity except for any reductions in emission intensity due to the MAC curve. As noted above, the MAC curves are assigned to a wide variety of technologies, mapped directly from EPA 2019<ref>US EPA, 2019, Global Non-CO2 Greenhouse Gas Emission Projection & Mitigation Potential Report. United States Environmental Protection Agency, Office of Atmospheric Programs.</ref> (Ou et al. 2021).<ref>Ou, Y., Roney, C., Alsalam, J., et al. 2021. Deep mitigation of CO2 and non-CO2 greenhouse gases toward 1.5 °C and 2 °C futures. Nature Communications 12. doi:10.1038/s41467-021-26509-z</ref> Under a carbon policy, emissions are reduced by an amount determined by the MAC curve. Documentation for non CO2 emissions can be found [http://jgcri.github.io/gcam-doc/emissions.html#non-co2-ghg-emissions here].

=== Fluorinated Gases ===
Most fluorinated gas emissions are linked either to the industrial sector as a whole (e.g., semiconductor-related F-gas emissions are driven by growth in the “industry” sector), or population and GDP (e.g., fire extinguishers). As those drivers change, emissions will change. Additionally, we include abatement options based on EPA MAC curves. Documentation for fluorinated gas emissions can be found [http://jgcri.github.io/gcam-doc/emissions.html#fluorinated-gases here].

GHGs - GCAM

2022-06-21T22:11:39Z

Matthew Binsted:

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== CO2 Emissions ==
GCAM endogenously estimates CO2 fossil-fuel related emissions based on fossil fuel consumption and global emission factors by fuel (oil, unconventional oil, natural gas, and coal). These emission factors are consistent with global emissions by fuel from the CDIAC global inventory (CDIAC 2017).<ref>Boden, T., and Andres, B. 2017, National CO2 Emissions from Fossil-Fuel Burning, Cement Manufacture, and Gas Flaring: 1751-2014, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory.</ref>

GCAM can be considered as a process model for CO2 emissions and reductions. CO2 emissions change over time as fuel consumption in GCAM endogenously changes. Application of Carbon Capture and Storage (CCS) is explicitly considered as separate technological options for a number of processes, such as electricity generation and fertilizer manufacturing. GCAM, in effect, produces a Marginal Abatement Curve for CO2 as a carbon-price is applied within the model. Documentation for CO2 emissions can be found [http://jgcri.github.io/gcam-doc/emissions.html#co2-emissions here].

== Non-CO2 GHG Emissions ==
The non-CO2 greenhouse gases include methane (CH4), nitrous oxide (N2O) and fluorinated gases. These emissions, ''E'', are modeled for any given technology in time period ''t'' as:<math display="block">E_{t}=A_{t}*F_{t0}*(1-MAC(Cprice_{t})) </math>where:
{| class="wikitable"
|-
|F
|Emissions factor: base-year emissions per unit activity
|-
|A
|Activity level (e.g., output of a technology)
|-
|MAC
|Marginal Abatement Cost Curve
|-
|Cprice
|Carbon Price
|}
Non-CO2 GHG emissions are proportional to the activity except for any reductions in emission intensity due to the MAC curve. As noted above, the MAC curves are assigned to a wide variety of technologies, mapped directly from EPA 2019<ref>US EPA, 2019, Global Non-CO2 Greenhouse Gas Emission Projection & Mitigation Potential Report. United States Environmental Protection Agency, Office of Atmospheric Programs.</ref>(Ou et al. 2021).<ref> Ou, Y., Roney, C., Alsalam, J., et al. 2021. Deep mitigation of CO2 and non-CO2 greenhouse gases toward 1.5 °C and 2 °C futures. Nature Communications 12. doi:10.1038/s41467-021-26509-z</ref> Under a carbon policy, emissions are reduced by an amount determined by the MAC curve. Documentation for non CO2 emissions can be found [http://jgcri.github.io/gcam-doc/emissions.html#non-co2-ghg-emissions here].

=== Fluorinated Gases ===
Most fluorinated gas emissions are linked either to the industrial sector as a whole (e.g., semiconductor-related F-gas emissions are driven by growth in the “industry” sector), or population and GDP (e.g., fire extinguishers). As those drivers change, emissions will change. Additionally, we include abatement options based on EPA MAC curves. Documentation for fluorinated gas emissions can be found [http://jgcri.github.io/gcam-doc/emissions.html#fluorinated-gases here].

Other end-use - GCAM

2022-06-21T22:01:19Z

Matthew Binsted:

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== Energy for Water ==
==== System boundaries ====
The specific system boundaries are explained in Kyle et al. (2016),<ref>Kyle, P., Johnson, N., Davies, E., Bijl, D.L., Mouratiadou, I., Bevione, M., Drouet, L., Fujimori, S., Liu, Y., and Hejazi, M. 2016. Setting the system boundaries of “energy for water” for integrated modeling. *Environmental Science & Technology 50(17), 8930-8931.</ref> and are set so as to include all energy for activities whose primary output is water, and to exclude from this domain production technologies that use both energy and water as inputs to produce some other good. The system boundaries of “energy-for-water” (EFW) consist of the following activities:
* Water abstraction
* Water treatment
* Water distribution
* Wastewater treatment

Within the following sectors:
* Desalinated water supply
* Irrigated crop production
* Industrial manufacturing
* Municipal water supply

==== Modeling Energy-for-Water ====

The modeling approach is documented in Kyle et al. (2021),<ref>Kyle, P., Hejazi, M., Kim, S., Patel, P., Graham, N., and Liu, Y. 2021. Assessing the future of global energy-for-water. Environmental Research Letters 16(2), 024031.</ref> and consists of the following steps:
* Estimation of water flow volumes of EFW processes and sectors
* Multiplication of water flow volumes by assumed energy intensities
* Adjustment of historical energy consumption in the commercial and industrial sectors to accommodate explicitly represented EFW

See the official documentation section on energy for water [https://jgcri.github.io/gcam-doc/demand_energy.html#energy-for-water here], with additional details found [https://jgcri.github.io/gcam-doc/details_energy.html#energy-for-water here].

Residential and commercial sectors - GCAM

2022-06-21T21:42:59Z

Matthew Binsted:

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GCAM disaggregates the building sector into residential and commercial sectors and models three aggregate services (heating, cooling, and other). Within each region, each type of building and each service starts with a different mix of fuels supplying energy. The future evolution of building energy use is shaped by changes in (1) floorspace, (2) the level of building service per unit of floorspace, and (3) fuel and technology choices by consumers. Residential floorspace depends on population, income, population density, and exogenously estimated parameters. Commercial floorspace depends on population, income, the average price of energy services, and exogenously specified satiation levels. Note that GCAM also includes the option to specify [https://jgcri.github.io/gcam-doc/details_energy.html#optional-exogenous-floorspace floorspace exogenously]. The level of building service demands per unit of floorspace depend on climate, building shell conductivity, affordability, and satiation levels. The approach used in the buildings sector is documented in Clarke et al. 2018,<ref>Clarke, L., Eom, J., Hodson Marten, E., et al. 2018. Effects of long-term climate change on global building energy expenditures. Energy Economics 72, pp. 667-677.</ref> which has a focus on heating and cooling service and energy demands. Within building services, the structures and functional forms are similar to any other GCAM sector, described in [https://jgcri.github.io/gcam-doc/en_technologies.html Energy Technologies]. See the section on [https://jgcri.github.io/gcam-doc/demand_energy.html#buildings buildings] for more details.

Liquid fuels - GCAM

2022-06-21T21:12:07Z

Matthew Binsted:

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== Refining ==
The refining sector, or liquid fuels production sector, explicitly tracks all energy inputs, emissions, and costs involved with converting primary energy forms into liquid fuels. Liquid fuels include gasoline, diesel, kerosene, ethanol and many other liquid hydrocarbon fuels; for the full mapping see [http://jgcri.github.io/gcam-doc/details_inputs.html#mapping-the-iea-energy-balances Mapping the IEA Energy Balances]. The refining sector includes subsectors of oil refining, biomass liquids, gas to liquids, and coal to liquids, each of which are described below. Each of these four subsectors is available starting in the first future time period, and the capital stocks of refineries are explicitly tracked. Click on the headings for links to the corresponding section in the documentation, and see the documentation sections on [https://jgcri.github.io/gcam-doc/supply_energy.html#refining refining] and [https://jgcri.github.io/gcam-doc/details_energy.html#refining refining details].

=== [https://jgcri.github.io/gcam-doc/supply_energy.html#oil-refining Oil Refining] ===
The oil refining subsector accounts for the vast majority of the historical output of the refining sector, globally and in all regions. Each region is assigned a single production technology for oil refining; this technology does not differentiate between conventional and unconventional oil, whose competition is explicitly modeled upstream of the refining sector. In a typical region, the oil refining technology consumes three energy inputs: crude oil, natural gas, and electricity. The coefficients of the oil refining production technology reflect whole-process inputs and liquid fuel outputs; there is no explicit tracking of the production and on-site use of intermediate products such as refinery gas (still gas). Electricity produced at refineries (both the fuel inputs and electricity outputs) is modeled in the electricity and/or industrial energy use sectors, as the IEA Energy Balances (IEA 2019)<ref>International Energy Agency, 2019, Energy Balances of OECD Countries 1960-2017 and Energy Balances of Non-OECD Countries 1971-2017, International Energy Agency, Paris, France.</ref> do not disaggregate autoproducer electric power plants at refineries from elsewhere. There is no oil refining technology option with CO2 capture and storage (CCS) considered.

=== [https://jgcri.github.io/gcam-doc/supply_energy.html#biomass-liquids Biomass Liquids] ===
The biomass liquids subsector includes up to eight technologies in each region, with a global total of 11 production technologies. The biomass liquids technologies include up to four “first-generation” biofuels in each region, defined as biofuels produced from agricultural crops that are also used as food, animal feed, or other modeled uses (described in the [https://jgcri.github.io/gcam-doc/land.html land module]). The model tracks secondary feed outputs of first generation biofuel production, as DDGS (dried distillers grains and solubles) from ethanol production, and as feedcakes from biodiesel production. Second-generation technologies consume the “biomass” or “biomassOil” commodities, which include purpose-grown bioenergy crops, as well as residues from forestry and agriculture, and municipal and industrial wastes. Starting in 2020, second-generation biofuels (cellulosic ethanol and Fischer-Tropsch syn-fuels) are introduced, each with three levels of CCS: none, level 1, and level 2. The first CCS level generally consists of relatively pure and high-concentration CO2 sources (e.g., from gasifiers or fermenters), which have relatively low capture and compression costs. The second CCS level includes a broader set of sources (e.g., post-combustion emissions), and incurs higher costs but has a higher CO2 removal fraction.

=== [https://jgcri.github.io/gcam-doc/supply_energy.html#coal-to-liquids Coal to Liquids] ===
The majority of the world’s coal to liquids production is in South Africa (IEA 2012),<ref>International Energy Agency, 2011, Energy Balances of OECD Countries 1960-2010 and Energy Balances of Non-OECD Countries 1971-2010, International Energy Agency, Paris, France.</ref> but the technology is available to all regions in GCAM starting in the first future time period. Note that the CO2 emissions intensity is substantially higher than all other liquid fuel production technologies, due to high process energy intensities, and high primary fuel carbon contents. Where crude oil refining emits about 5.5 kg of CO2 per GJ of fuels produced, coal to liquids emits over 130 kg of CO2 per GJ of fuel produced. The upstream emissions from fuel production by this pathway are substantially higher than the “tailpipe” emissions from combustion of the fuels produced (about 70 kg CO2 per GJ). As with biomass liquids, two different production technologies with CCS are represented, with costs and CO2 removal fractions based on Dooley and Dahowski (2009).<ref>Dooley, J.J., and Dahowski, R.T. 2009. Large-scale U.S. unconventional fuels production and the role of carbon dioxide capture and storage technologies in reducing their greenhouse gas emissions. Energy Procedia 1(1), pp. 4225-4232.</ref>

=== [https://jgcri.github.io/gcam-doc/supply_energy.html#gas-to-liquids Gas to Liquids] ===
While a minor contributor to liquid fuels production globally (about 0.1%; (IEA 2012),<ref>International Energy Agency, 2011, Energy Balances of OECD Countries 1960-2010 and Energy Balances of Non-OECD Countries 1971-2010, International Energy Agency, Paris, France.</ref> gas to liquids has received increased attention in recent years, with several large-scale plants completed in the last decade (Glebova 2013),<ref>Glebova, O. 2013. Gas to Liquids: Historical Development and Future Prospects, Report NG 80, Oxford Institute for Energy Studies.</ref> and others in various stages of planning and construction (Enerdata 2014).<ref>Enerdata, 2016. The Future of Gas-to-Liquid (GTL) Industry.</ref> Because of the relatively low carbon content of natural gas, and whole-process energy efficiency ratings typically about 60%, the net CO2 emissions from the process are about 20 kg CO2 per GJ of fuel, significantly lower than coal to liquids. There is only one production technology represented in GCAM, with no CCS option available.

Gaseous fuels - GCAM

2022-06-21T20:54:05Z

Matthew Binsted:

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== Gas Processing ==
The three subsectors of the gas processing sector, and the downstream sectors are described below and in the [https://jgcri.github.io/gcam-doc/supply_energy.html#gas-processing gas processing] documentation section. See [https://jgcri.github.io/gcam-doc/details_energy.html#gas-processing gas processing details] for an overview of the structure. Click on each heading to bring you to the corresponding section in the documentation.

=== [http://jgcri.github.io/gcam-doc/supply_energy.html#natural-gas Natural Gas] ===
Natural gas accounts for almost 99% of the gaseous fuel production represented in GCAM’s calibration year (2015). The natural gas commodity in GCAM includes all gaseous fuels produced at gas wells, the gaseous co-products from oil production, and gas produced from coal mines and coal seams. The natural gas commodity excludes natural gas liquids, and it excludes gas that is vented, flared, or re-injected. Further information is available in [https://jgcri.github.io/gcam-doc/details_inputs.html#mapping-the-iea-energy-balances Mapping the IEA Energy Balances] and IEA (2011).<ref>International Energy Agency, 2011, Energy Balances of OECD Countries: Documentation for Beyond 2020 Files, International Energy Agency, Paris, France.</ref> In the gas processing sector, the natural gas technology is assigned an input-output coefficient of 1, as natural gas plant fuel is not a disaggregated flow in the IEA energy balances.

=== [http://jgcri.github.io/gcam-doc/supply_energy.html#coal-gasification Coal Gasification] ===
The GCAM coal gasification technology in historical years represents gas works gas, or town gas, that is produced from coal. It does not include blast furnace gas, coke oven gas, and other coal-derived gaseous fuels that are by-products of other activities, and typically consumed on-site. Many regions produced no coal gas in 2010. In future periods, the technology represents a broader suite of coal gasification processes that are capable of producing a commodity that competes for market share with natural gas. See Linden et al. 1976<ref>Linden, H.R., Bodle, W.W., Lee, B.S., and Vyas, K.C. 1976. Production of high-btu gas from coal. Annual Reviews of Energy 1, pp. 65-86.</ref> for a review of technologies for producing pipeline-grade gaseous fuels from coal.

=== [http://jgcri.github.io/gcam-doc/supply_energy.html#biomass-gasification Biomass Gasification] ===
In historical years, biomass gasification, or biogas, is considered to be gases captured from landfills, sludge, and agricultural wastes, that are used to provide heat and power. As with coal gasification, in future periods, biomass gasification is intended to represent a suite of processes that convert biomass feedstocks into pipeline-grade gaseous fuels that can be used by a variety of end users. For a technical description see Zwart et al. 2006.<ref>Zwart, R., Boerrigter, H., Deurwaarder, E.P., van der Meijden, C.M., and van Paasen, S.V.B. 2006. Production of Synthetic Natural Gas (SNG) from Biomass: Development and operation of an integrated bio-SNG system. Report ECN-E-06-018, Energy Research Centre of the Netherlands.</ref>

=== [http://jgcri.github.io/gcam-doc/supply_energy.html#gas-pipeline-delivered-gas-and-wholesale-gas Gas Pipeline, Delivered Gas, and Wholesale Gas] ===
The gas pipeline sector explicitly represents the energy consumed by compressors for transmission and distribution of natural gas. Delivered gas and wholesale gas are differentiated in their consumers and therefore cost mark-ups; delivered gas refers to gas used by the buildings and transportation sectors, whereas wholesale gas is used by industrial and energy sector consumers. The historical input-output coefficient of the gas pipeline sector in any region is estimated as the sum of reported pipeline energy consumption, delivered gas, and wholesale gas, divided by the sum of delivered gas and wholesale gas.

== [http://jgcri.github.io/gcam-doc/supply_energy.html#hydrogen Hydrogen] ==
Hydrogen is represented as a commodity in future time periods that is available for various energy and industrial processes. Hydrogen is not treated as a fuel in the IEA Energy Balances,<ref>International Energy Agency, 2019, Energy Balances of OECD Countries 1960-2017 and Energy Balances of Non-OECD Countries 1971-2017, International Energy Agency, Paris, France.</ref> or most other energy statistics. As such, the representation excludes the on-site production and use of hydrogen at oil refineries, ammonia plants, and other present-day industrial facilities. The representation of hydrogen in GCAM includes 10 “central” production technologies, as well as 2 “forecourt” (i.e. on-site) production technologies, which may have higher costs due to the economies of scale and higher capacity factors of central production, but the forecourt technologies avoid the costs and energy requirements of distribution. The hydrogen distribution representation differentiates a range of hydrogen commodities whose costs largely reflect the various temperatures and pressures at which hydrogen is transported and stored for different end-use applications. Production technology costs and energy intensities are from the U.S. Department of Energy’s Hydrogen Analysis (H2A) models (NREL 2018),<ref>National Renewable Energy Laboratory, 2018, H2A: Hydrogen Analysis Production Models, National Renewable Energy Laboratory.</ref> and the distribution costs and energy intensities are from Argonne’s Hydrogen Delivery Scenario Analysis Model (HDSAM).<ref>Argonne National Laboratory, 2015, Hydrogen delivery scenario analysis model (HDSAM), Argonne National Laboratory.</ref> See [https://jgcri.github.io/gcam-doc/details_energy.html#hydrogen hydrogen details] for more information.

Bioenergy - GCAM

2022-06-21T20:23:14Z

Matthew Binsted:

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== Biomass ==
While most of the effort in modeling biomass supply is in the agriculture and land use component, there is a renewable resource represented in the energy system, that generally refers to municipal and industrial wastes that can be used for energy purposes. The supply curves use the same functional form as printed in the [https://jgcri.github.io/gcam-doc/supply_energy.html#wind wind section], and the specific quantities are documented in Gregg and Smith (2010).<ref>Gregg, J.S., and Smith, S.J. Global and regional potential for bioenergy from agricultural and forestry residue biomass. Mitigation and Adaptation Strategies for Global Change 15(3), pp 241-262.</ref> Unlike other resources, the waste biomass supply curve is assumed to grow with GDP, as prescribed by the exogenous supply elasticity of GDP, or “gdpSupplyElast”. See GCAM's [https://jgcri.github.io/gcam-doc/supply_energy.html#biomass biomass] and [https://jgcri.github.io/gcam-doc/details_energy.html#biomass-liquids biomass liquids] sections for more details.

== Traditional biomass ==
Traditional biomass in GCAM is defined as the IEA’s “primary solid biomass” product consumed by the residential sector, in selected regions where it is considered to be an important part of the energy system. The largest consumers of traditional biomass in 2010 were China, India, and Western Africa. The specific energy goods involved include firewood, agricultural residues, animal dung, and others; no effort is made to disaggregate the category into these constituent parts, or to link the production volumes with the agriculture and land use module. See GCAM's [https://jgcri.github.io/gcam-doc/supply_energy.html#traditional-biomass traditional biomass] section.

Model Documentation - GCAM

2022-06-21T20:15:13Z

Matthew Binsted:

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GCAM is a global model that represents the behavior of, and interactions between five systems: the energy system, water, agriculture and land use, the economy, and the climate. GCAM has been under development for over 30 years. Work began in 1980 with the work first documented in 1982 in working papers (Edmonds and Reilly, 1982a,b,c)<ref>Edmonds, J. and J. Reilly. 1982a. “Global energy and CO2 to the year 2050,” IEA/ORAU Working Paper Contribution No. 82-6.</ref> <ref>Edmonds, J. and J. Reilly. 1982b. “Global energy production and use to the year 2050,” IEA/ORAU Working Paper Contribution No. 82-7.</ref>
<ref>Edmonds, J. and J. Reilly. 1982c. An introduction to the use of the IEA/ORAU, Long-term, global energy model,” IEA/ORAU Working Paper Contribution No. 82-9.</ref> and the first peer-reviewed publications in 1983 (Edmonds and Reilly, 1983a,b,c)<ref>Edmonds, J. and J. Reilly. 1983a. “Global Energy and CO2 to the Year 2050,” The Energy Journal, 4(3):21-47.</ref><ref>Edmonds, J. and J. Reilly. 1983b. “A Long-Term, Global, Energy-Economic Model of Carbon Dioxide Release From Fossil Fuel Use,” Energy Economics, 5(2):74-88.</ref><ref>Edmonds, J. and J. Reilly. 1983c. “Global Energy Production and Use to the Year 2050,” Energy, 8(6):419-32.</ref>. A this point, the model was known as the Edmonds-Reilly (and subsequently the Edmonds-Reilly-Barnes) model. The model was renamed MiniCAM in the mid-1990s, the model code was re-written in object-oriented C++ (Kim et al. 2006)<ref>Kim, S.H., J. Edmonds, J. Lurz, S. J. Smith, and M. Wise (2006) The ObjECTS Framework for Integrated Assessment: Hybrid Modeling of Transportation. The Energy Journal 27(Special Issue 2): pp 63-91.</ref> and renamed to GCAM in the mid-2000s. The first coupling to a carbon cycle model was published in Edmonds et al. (1984)<ref>Edmonds, J., J. Reilly, J.R. Trabalka and D.E. Reichle. 1984. An Analysis of Possible Future Atmospheric Retention of Fossil Fuel CO2. TR013, DOE/OR/21400-1. National Technical Information Service, U.S. Department of Commerce, Springfield Virginia 22161.</ref>. The first use of GCAM (MiniCAM at the time) in conjunction with a Monte Carlo uncertainty analysis was published in Reilly et al. (1987)<ref>Reilly, J.M., Edmonds, J.A., Gardner, R.H., and Brenkert, A.L. 1987. “Uncertainty Analysis of the IEA/ORAU CO2 Emissions Model,” The Energy Journal, 8(3):1-29. Response Strategies Working Group, Intergovernmental Panel on Climate Change. 1990. Emissions Scenarios.</ref>.

Throughout its lifetime, GCAM has evolved in response to the need to address an expanding set of science and assessment questions. The original question that the model was developed to address was the magnitude of mid-21st-century global emissions of fossil fuel CO2. Over time GCAM has expanded its scope to include a wider set of energy producing, transforming, and using technologies, emissions of non-CO2 greenhouse gases, agriculture and land use, water supplies and demands, and physical Earth systems. GCAM has been used to produce scenarios for national and international assessments ranging from the very first IPCC scenarios (Response Strategies Working Group, 1990)<ref>Reilly, J.M., Edmonds, J.A., Gardner, R.H., and Brenkert, A.L. 1987. “Uncertainty Analysis of the IEA/ORAU CO2 Emissions Model,” The Energy Journal, 8(3):1-29. Response Strategies Working Group, Intergovernmental Panel on Climate Change. 1990. Emissions Scenarios.</ref> through the present Shared Socioeconomic Pathways (Calvin et al., 2017)<ref>Calvin, K., B. Bond-Lamberty, L. Clarke, J. Edmonds, J. Eom, C. Hartin, S. Kim, P. Kyle, R. Link, R. Moss, H. McJeon, P. Patel, S. Smith, S. Waldhoff and M. Wise (2017). “The SSP4: A world of deepening inequality.” Global Environmental Change 42: 284-296.</ref>. GCAM is increasingly being used in multi-model, multi-scale analysis, in which it is either soft- or hard-coupled to other models with different focuses and often greater resolution in key sectors. For example, a range of downscaling tools have been developed for use with GCAM to be able to land and water outputs at a grid resolution. Similarly, it has been coupled to a state of the art Earth system model (Collins, et al., 2015)<ref>Collins, William D., Anthony P. Craig, John E. Truesdale, A. V. Di Vittorio, Andrew D. Jones, Benjamin Bond-Lamberty, Katherine V. Calvin, James A. Edmonds, Allison M. Thomson, Benjamine Bond-Lamberty, Pralit Patel, Sonny H. Kim, Peter E. Thornton, Jiafu Mao, Xiaoying Shi, Louise P. Chini, and George C. Hurtt. “The integrated Earth system model version 1: formulation and functionality.” Geoscientific Model Development 8, no. 7 (2015): 2203-2219.</ref>. Hundreds of papers have been published in peer-reviewed journals using GCAM over its lifetime and the GCAM system continues to be an important international tool for scientific inquiry. GCAM is also a community model being used by researchers across the globe, creating a shared global research enterprise. GCAM can be run on Windows, Linux, Mac, and high-performance computing systems.

The official documentation for GCAM can be found [http://jgcri.github.io/gcam-doc/index.html here].

Mathematical model description - GCAM

2022-06-21T14:06:59Z

Matthew Binsted:

{{ModelDocumentationTemplate
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The GCAM documentation has a description of the key equations for each system, liked below.
* [http://jgcri.github.io/gcam-doc/choice.html Logit Choice Function]
* [http://jgcri.github.io/gcam-doc/economy.html#equations Economy Equations]
* [http://jgcri.github.io/gcam-doc/demand_energy.html#equations Energy Demand Equations]
* [http://jgcri.github.io/gcam-doc/supply_energy.html#equations Energy Supply Equations]
* [http://jgcri.github.io/gcam-doc/land.html#equations Land Equations]
* [http://jgcri.github.io/gcam-doc/supply_land.html#equations Land Supply Equations]
* [http://jgcri.github.io/gcam-doc/demand_land.html#equations Land Demand Equations]
* [http://jgcri.github.io/gcam-doc/demand_water.html#equations Water Demand Equations]
* [http://jgcri.github.io/gcam-doc/emissions.html#equations Non-CO2 Emissions Equations]

Mathematical model description - GCAM

2022-06-21T14:05:39Z

Matthew Binsted:

Mathematical model description - GCAM

2022-06-21T13:54:20Z

Matthew Binsted: Creating equations page

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Test

Data - GCAM

2022-06-17T17:55:22Z

Matthew Binsted:

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The [https://github.com/JGCRI/gcamdata GCAM Data System] combines and reconciles a wide range of different data sets, and systematically incorporates a range of future assumptions. The output of the data system is an XML dataset with historical and base-year data for calibrating the model along with assumptions about future trajectories such as GDP, population, and technology. It includes the necessary information for representing energy, water, land, and the economic system. The GCAM Data System is largely constructed in R, but accommodates inputs in a range of different formats. Creating new scenarios does not require the use of the GCAM data system. New, “add on” xml files can be created to overwrite key future scenario assumptions such as population, economic activity, and technology cost and performance, among others. See [https://jgcri.github.io/gcam-doc/overview.html#overview-of-gcam-computational-components Overview of GCAM Computational Components] and the [https://github.com/JGCRI/gcamdata gcamdata GitHub repository] for more details.

Appendices - GCAM

2022-06-17T17:53:56Z

Matthew Binsted:

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Some useful GCAM guides include:

* [http://jgcri.github.io/gcam-doc/index.html GCAM Documentation] (main documentation page)
* [https://jgcri.github.io/gcam-doc/user-guide.html How to Get Started Running GCAM] (user guide)
* [http://jgcri.github.io/gcam-doc/gcam-build.html How to Set Up and Build] (build instructions)
* [http://www.globalchange.umd.edu/data/gcam/GCAM_tutorial_2019.pdf GCAM tutorial] (presentation)
* [https://gcims.pnnl.gov/community GCAM Video Tutorials]
* [https://github.com/JGCRI/gcam-core/releases GCAM Releases]

To download GCAM, and for questions, issues, and discussions, please visit the [https://github.com/jgcri/gcam-core gcam-core GitHub Repository].

Appendices - GCAM

2022-06-17T17:52:12Z

Matthew Binsted:

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Some useful GCAM guides include:

* [http://jgcri.github.io/gcam-doc/index.html GCAM Documentation] (main documentation page)
* [https://jgcri.github.io/gcam-doc/user-guide.html How to Get Started Running GCAM] (user guide)
* [http://jgcri.github.io/gcam-doc/gcam-build.html How to Set Up and Build] (build instructions)
* [http://www.globalchange.umd.edu/data/gcam/GCAM_tutorial_2019.pdf GCAM tutorial] (presentation)
* [https://gcims.pnnl.gov/community GCAM Video Tutorials]
* [https://github.com/JGCRI/gcam-core/releases GCAM Releases]

To download GCAM, and for questions, issues, and discussions, please visit GCAM's [https://github.com/jgcri/gcam-core GCAM's GitHub Repository].

Water - GCAM

2022-06-17T17:46:10Z

Matthew Binsted:

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== Supply of Water ==
Three distinct sources of fresh water are modeled, renewable water (surface and ground), non-renewable groundwater, and desalinated water. Renewable water is water that is replenished naturally by surface runoff and subsurface infiltration and release. Non-renewable groundwater is fossil groundwater resources or groundwater where abstraction exceeds recharge. Renewable water and non-renewable groundwater are separately modeled for each basin. Desalinated water is available as an additional source of freshwater within each basin and for alternative end-use demands for water. See [http://jgcri.github.io/gcam-doc/supply_water.html Supply of Water] for a full description.

== Demand for Water ==
Water demand is calculated for six major sectors: agriculture, electricity generation, industrial manufacturing, primary energy production, livestock, and municipal uses. For each sector, up to four types of water demand are represented. Types of water include
* water withdrawals: water diverted or withdrawn from a surface water or groundwater source ([https://jgcri.github.io/gcam-doc/demand_water.html#vickers2001 Vickers 2001]).
* water consumption: water use that permanently withdraws water from its source; water that is no longer available because it has evaporated, been transpired by plants, incorporated into products or crops, consumed by people or livestock, or otherwise removed from the immediate water environment ([https://jgcri.github.io/gcam-doc/demand_water.html#vickers2001 Vickers 2001]).
* biophysical water consumption: total water required for crop evapo-transpiration; the sum of “blue” and “green” water in [https://jgcri.github.io/gcam-doc/demand_water.html#mekonnen2011 Mekonnen and Hoekstra 2011].
* seawater: water from the oceans, including brackish estuaries, that is withdrawn for cooling thermo-electric power plants, or used in primary energy production.

For more information, see [http://jgcri.github.io/gcam-doc/demand_water.html Demand for Water].

== Water Module Details ==
For more information on water in GCAM including on basin-to-region and basin-to-sector mappings and water markets, visit the [https://jgcri.github.io/gcam-doc/details_water.html detailed water] page.

Water - GCAM

2022-06-17T17:45:22Z

Matthew Binsted:

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== Supply of Water ==
Three distinct sources of fresh water are modeled, renewable water (surface and ground), non-renewable groundwater, and desalinated water. Renewable water is water that is replenished naturally by surface runoff and subsurface infiltration and release. Non-renewable groundwater is fossil groundwater resources or groundwater where abstraction exceeds recharge. Renewable water and non-renewable groundwater are separately modeled for each basin. Desalinated water is available as an additional source of freshwater within each basin and for alternative end-use demands for water. See [http://jgcri.github.io/gcam-doc/supply_water.html Supply of Water] for a full description.

== Demand for Water ==
Water demand is calculated for six major sectors: agriculture, electricity generation, industrial manufacturing, primary energy production, livestock, and municipal uses. For each sector, up to four types of water demand are represented. Types of water include
* water withdrawals: water diverted or withdrawn from a surface water or groundwater source ([https://jgcri.github.io/gcam-doc/demand_water.html#vickers2001 Vickers 2001]).
* water consumption: water use that permanently withdraws water from its source; water that is no longer available because it has evaporated, been transpired by plants, incorporated into products or crops, consumed by people or livestock, or otherwise removed from the immediate water environment ([https://jgcri.github.io/gcam-doc/demand_water.html#vickers2001 Vickers 2001]).
* biophysical water consumption: total water required for crop evapo-transpiration; the sum of “blue” and “green” water in [https://jgcri.github.io/gcam-doc/demand_water.html#mekonnen2011 Mekonnen and Hoekstra 2011]
* seawater: water from the oceans, including brackish estuaries, that is withdrawn for cooling thermo-electric power plants, or used in primary energy production.

For more information, see [http://jgcri.github.io/gcam-doc/demand_water.html Demand for Water].

== Water Module Details ==
For more information on water in GCAM including on basin-to-region and basin-to-sector mappings and water markets, visit the [https://jgcri.github.io/gcam-doc/details_water.html detailed water] page.

Water - GCAM

2022-06-17T17:42:49Z

Matthew Binsted:

{{ModelDocumentationTemplate
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== Supply of Water ==
Three distinct sources of fresh water are modeled, renewable water (surface and ground), non-renewable groundwater, and desalinated water. Renewable water is water that is replenished naturally by surface runoff and subsurface infiltration and release. Non-renewable groundwater is fossil groundwater resources or groundwater where abstraction exceeds recharge. Renewable water and non-renewable groundwater are separately modeled for each basin. Desalinated water is available as an additional source of freshwater within each basin and for alternative end-use demands for water. See [http://jgcri.github.io/gcam-doc/supply_water.html Supply of Water] for a full description.

== Demand for Water ==
Water demand is calculated for six major sectors: agriculture, electricity generation, industrial manufacturing, primary energy production, livestock, and municipal uses. For each sector, up to four types of water demand are represented. Types of water include
* water withdrawals: water diverted or withdrawn from a surface water or groundwater source (Vickers 2001).
* water consumption: water use that permanently withdraws water from its source; water that is no longer available because it has evaporated, been transpired by plants, incorporated into products or crops, consumed by people or livestock, or otherwise removed from the immediate water environment (Vickers 2001).
* biophysical water consumption: total water required for crop evapo-transpiration; the sum of “blue” and “green” water in Mekonnen and Hoekstra 2011
* seawater: water from the oceans, including brackish estuaries, that is withdrawn for cooling thermo-electric power plants, or used in primary energy production.

For more information, see [http://jgcri.github.io/gcam-doc/demand_water.html Demand for Water].

== Water Module Details ==
For more information on water in GCAM including on basin-to-region and basin-to-sector mappings and water markets, visit the [https://jgcri.github.io/gcam-doc/details_water.html detailed water] page.

Non-climate sustainability dimension - GCAM

2022-06-17T17:32:47Z

Matthew Binsted:

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GCAM produces a range of output variables that can be used to inform non-climate sustainability. These include indicators such as the price of agricultural commodities, production of local air pollutants, ocean pH, land use and land cover, and energy access. See [https://www.nature.com/articles/s41558-017-0039-z Iyer, et al. (2018)] for a discussion of this topic.

Climate - GCAM

2022-06-17T17:30:03Z

Matthew Binsted:

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Hector v2.0 is the default climate model ([http://jgcri.github.io/gcam-doc/hector.html#references Hartin et al., 2015]) within GCAM.

Hector, an open-source, object-oriented, reduced-form global climate carbon-cycle model, is written in C++. This model runs essentially instantaneously while still representing the most critical global-scale earth system processes. Hector has a three-part main carbon cycle: a one-pool atmosphere, three-pool land, and 4-pool ocean. The model’s terrestrial carbon cycle includes primary production and respiration fluxes, accommodating arbitrary geographic divisions into, e.g., ecological biomes or political units. Hector actively solves the inorganic carbon system in the surface ocean, directly calculating air– sea fluxes of carbon and ocean pH. Hector reproduces the global historical trends of atmospheric [CO2], radiative forcing, and surface temperatures. The model simulates all four Representative Concentration Pathways (RCPs) with equivalent rates of change of key variables over time compared to current observations, MAGICC, and models from CMIP5 ([http://jgcri.github.io/gcam-doc/hector.html#references Hartin et al., 2015]). Hector’s flexibility, open-source nature, and modular design facilitates a broad range of research in various areas.

Currently the GCAM sectors interact with Hector via their emissions. At every time step, emissions from GCAM are passed to Hector. Hector converts these emissions to concentrations when necessary, and calculates the associated radiative forcing, as well as the response of the climate system (e.g., temperature, carbon-fluxes, etc.). See [http://jgcri.github.io/gcam-doc/hector.html Hector] for more details.

Carbon dioxide removal (CDR) options - GCAM

2022-06-17T17:26:51Z

Matthew Binsted:

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Carbon dioxide removal options in GCAM include Carbon Capture and Storage (CCS), bioenergy with CCS, reforestation, afforestation, and [https://jgcri.github.io/gcam-doc/demand_energy.html#direct-air-capture-for-carbon-dioxide-removal direct air capture]. See the section on [http://jgcri.github.io/gcam-doc/emissions.html#co2-emissions CO2 emissions] for more details.

Pollutants and non-GHG forcing agents - GCAM

2022-06-17T17:22:15Z

Matthew Binsted:

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== Air Pollutant Emissions ==
Air pollutant emissions (E) such as sulfur dioxide (SO2) and nitrogen oxides (NOx) are modeled as

<math>E_{t}=A_{t}*EF_{t0}*(1-EmCtrl(pcGDP_{t}))</math>

where A is activity level, EF is emissions factor, and EmCtrl is a function that represents decreasing emissions intensity as per-capita income increases:

<math>EmCtrl_{t}=1-\frac{1}{1+\frac{(pcGDP_{t}-pcGDP_{t0})}{steepness}}</math>

where ''pcGDP'' stands for the per-capita GDP, and ''steepness'' is an exogenous constant, specific to each technology and pollutant species, that governs the degree to which changes in per-capita GDP will be translated to emissions controls. The purpose here is to capture the general global trend of increasing pollutant controls over time, but does not capture regional and technological heterogeneity. See the documentation's section on [http://jgcri.github.io/gcam-doc/emissions.html#air-pollutant-emissions air pollution].

GHGs - GCAM

2022-06-17T16:53:26Z

Matthew Binsted:

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== CO2 Emissions ==
GCAM endogenously estimates CO2 fossil-fuel related emissions based on fossil fuel consumption and global emission factors by fuel (oil, unconventional oil, natural gas, and coal). These emission factors are consistent with global emissions by fuel from the CDIAC global inventory ([http://jgcri.github.io/gcam-doc/emissions.html#cdiac2017 CDIAC 2017]).

GCAM can be considered as a process model for CO2 emissions and reductions. CO2 emissions change over time as fuel consumption in GCAM endogenously changes. Application of Carbon Capture and Storage (CCS) is explicitly considered as separate technological options for a number of processes, such as electricity generation and fertilizer manufacturing. GCAM, in effect, produces a Marginal Abatement Curve for CO2 as a carbon-price is applied within the model. Documentation for CO2 emissions can be found [http://jgcri.github.io/gcam-doc/emissions.html#co2-emissions here].

== Non-CO2 GHG Emissions ==
The non-CO2 greenhouse gases include methane (CH4), nitrous oxide (N2O) and fluorinated gases. These emissions, ''E'', are modeled for any given technology in time period ''t'' as:<math display="block">E_{t}=A_{t}*F_{t0}*(1-MAC(Cprice_{t})) </math>where:
{| class="wikitable"
|-
|F
|Emissions factor: base-year emissions per unit activity
|-
|A
|Activity level (e.g., output of a technology)
|-
|MAC
|Marginal Abatement Cost Curve
|-
|Cprice
|Carbon Price
|}
Non-CO2 GHG emissions are proportional to the activity except for any reductions in emission intensity due to the MAC curve. As noted above, the MAC curves are assigned to a wide variety of technologies, mapped directly from [http://jgcri.github.io/gcam-doc/emissions.html#epa2019 EPA 2019]([http://jgcri.github.io/gcam-doc/emissions.html#ou2021a Ou et al. 2021]). Under a carbon policy, emissions are reduced by an amount determined by the MAC curve. Documentation for non CO2 emissions can be found [http://jgcri.github.io/gcam-doc/emissions.html#non-co2-ghg-emissions here].

=== Fluorinated Gases ===
Most fluorinated gas emissions are linked either to the industrial sector as a whole (e.g., semiconductor-related F-gas emissions are driven by growth in the “industry” sector), or population and GDP (e.g., fire extinguishers). As those drivers change, emissions will change. Additionally, we include abatement options based on EPA MAC curves. Documentation for fluorinated gas emissions can be found [http://jgcri.github.io/gcam-doc/emissions.html#fluorinated-gases here].

GHGs - GCAM

2022-06-17T16:52:22Z

Matthew Binsted:

{{ModelDocumentationTemplate
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== CO2 Emissions ==
GCAM endogenously estimates CO2 fossil-fuel related emissions based on fossil fuel consumption and global emission factors by fuel (oil, unconventional oil, natural gas, and coal). These emission factors are consistent with global emissions by fuel from the CDIAC global inventory ([http://jgcri.github.io/gcam-doc/emissions.html#cdiac2017 CDIAC 2017]).

GCAM can be considered as a process model for CO2 emissions and reductions. CO2 emissions change over time as fuel consumption in GCAM endogenously changes. Application of Carbon Capture and Storage (CCS) is explicitly considered as separate technological options for a number of processes, such as electricity generation and fertilizer manufacturing. GCAM, in effect, produces a Marginal Abatement Curve for CO2 as a carbon-price is applied within the model. Documentation for CO2 emissions can be found [http://jgcri.github.io/gcam-doc/emissions.html#co2-emissions here].

== Non-CO2 GHG Emissions ==
The non-CO2 greenhouse gases include methane (CH4), nitrous oxide (N2O) and fluorinated gases. These emissions, ''E'', are modeled for any given technology in time period ''t'' as:<math display="block">E_{t}=A_{t}*F_{t0}*(1-MAC(Cprice_{t})) </math>where:
{| class="wikitable"
|-
|F
|Emissions factor: base-year emissions per unit activity
|-
|A
|Activity level (e.g., output of a technology)
|-
|MAC
|Marginal Abatement Cost Curve
|-
|Cprice
|Carbon Price
|}
Non-CO2 GHG emissions are proportional to the activity except for any reductions in emission intensity due to the MAC curve. As noted above, the MAC curves are assigned to a wide variety of technologies, mapped directly from [http://jgcri.github.io/gcam-doc/emissions.html#epa2019 EPA 2013]([http://jgcri.github.io/gcam-doc/emissions.html#ou2021a Ou et al. 2021]). Under a carbon policy, emissions are reduced by an amount determined by the MAC curve. Documentation for non CO2 emissions can be found [http://jgcri.github.io/gcam-doc/emissions.html#non-co2-ghg-emissions here].

=== Fluorinated Gases ===
Most fluorinated gas emissions are linked either to the industrial sector as a whole (e.g., semiconductor-related F-gas emissions are driven by growth in the “industry” sector), or population and GDP (e.g., fire extinguishers). As those drivers change, emissions will change. Additionally, we include abatement options based on EPA MAC curves. Documentation for fluorinated gas emissions can be found [http://jgcri.github.io/gcam-doc/emissions.html#fluorinated-gases here].

GHGs - GCAM

2022-06-17T16:51:04Z

Matthew Binsted:

{{ModelDocumentationTemplate
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== CO2 Emissions ==
GCAM endogenously estimates CO2 fossil-fuel related emissions based on fossil fuel consumption and global emission factors by fuel (oil, unconventional oil, natural gas, and coal). These emission factors are consistent with global emissions by fuel from the CDIAC global inventory ([http://jgcri.github.io/gcam-doc/emissions.html#cdiac2017 CDIAC 2017]).

GCAM can be considered as a process model for CO2 emissions and reductions. CO2 emissions change over time as fuel consumption in GCAM endogenously changes. Application of Carbon Capture and Storage (CCS) is explicitly considered as separate technological options for a number of processes, such as electricity generation and fertilizer manufacturing. GCAM, in effect, produces a Marginal Abatement Curve for CO2 as a carbon-price is applied within the model. Documentation for CO2 emissions can be found [http://jgcri.github.io/gcam-doc/emissions.html#co2-emissions here].

== Non-CO2 GHG Emissions ==
The non-CO2 greenhouse gases include methane (CH4), nitrous oxide (N2O) and fluorinated gases. These emissions, ''E'', are modeled for any given technology in time period ''t'' as:<math display="block">E_{t}=A_{t}*F_{t0}*(1-MAC(Cprice_{t})) </math>where:
{| class="wikitable"
|-
|F
|Emissions factor: base-year emissions per unit activity
|-
|A
|Activity level (e.g., output of a technology)
|-
|MAC
|Marginal Abatement Cost Curve
|-
|Cprice
|Carbon Price
|}
Non-CO2 GHG emissions are proportional to the activity except for any reductions in emission intensity due to the MAC curve. As noted above, the MAC curves are assigned to a wide variety of technologies, mapped directly from [http://jgcri.github.io/gcam-doc/emissions.html#epa2019 EPA 2013](http://jgcri.github.io/gcam-doc/emissions.html#ou2021a [Ou et al. 2021]). Under a carbon policy, emissions are reduced by an amount determined by the MAC curve. Documentation for non CO2 emissions can be found [http://jgcri.github.io/gcam-doc/emissions.html#non-co2-ghg-emissions here].

=== Fluorinated Gases ===
Most fluorinated gas emissions are linked either to the industrial sector as a whole (e.g., semiconductor-related F-gas emissions are driven by growth in the “industry” sector), or population and GDP (e.g., fire extinguishers). As those drivers change, emissions will change. Additionally, we include abatement options based on EPA MAC curves. Documentation for fluorinated gas emissions can be found [http://jgcri.github.io/gcam-doc/emissions.html#fluorinated-gases here].

Emissions - GCAM

2022-06-17T16:44:08Z

Matthew Binsted:

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GCAM projects emissions of a suite of greenhouse gases (GHGs) and air pollutants:

CO2, CH4, N2O, CF4, C2F6, SF6, HFC23, HFC32, HFC43-10mee, HFC125, HFC134a, HFC143a, HFC152a, HFC227ea, HFC236fa, HFC245fa, HFC365mfc, SO2, BC, OC, CO, VOCs, NOx, NH3

Future emissions are determined by the evolution of drivers (such as energy consumption, land-use, and population), technology mix, and abatement measures. How this is represented in GCAM varies by emission type. More details can be found in the documentation's section on [http://jgcri.github.io/gcam-doc/emissions.html emissions] and [https://jgcri.github.io/gcam-doc/details_emissions.html emissions details].

Land-use - GCAM

2022-06-17T16:39:13Z

Matthew Binsted:

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Land use and land cover is determined within the larger GCAM modeling structure. The agriculture and land-use components of GCAM are coupled in code with other GCAM model components. Agricultural production, land use and land cover are determined for each of the 384 subregions based on land characteristics, technology availability, policy, and aggregate demand for goods and services produced on the land. The 384 subregions are determined by subdividing each of GCAM’s 32 global geo-political regions into the region's major water basins. Within each of these subregions, land is categorized into approximately a dozen types based on cover and use. Some of these types, such as tundra and desert, are not considered arable. Among arable land types, further divisions are made for lands historically in non-commercial uses such as forests and grasslands as well as commercial forestlands and croplands. A description of the treatment of agriculture and land use can be found in the documentation's section on [https://jgcri.github.io/gcam-doc/land.html land], as well as in more detail in the [https://jgcri.github.io/gcam-doc/details_land.html detailed land] section. Information on land supply can be found at the [https://jgcri.github.io/gcam-doc/supply_land.html Supply of Food, Feed, and Forestry] page, and demand at the [https://jgcri.github.io/gcam-doc/demand_land.html Demand for food, forestry, etc] page. Land inputs can be found at the [http://jgcri.github.io/gcam-doc/inputs_land.html External Inputs to the Land Model] page, and outputs at the [http://jgcri.github.io/gcam-doc/outputs_land.html Outputs from the Land Model] page.

Technological change in land-use - GCAM

2022-06-17T16:31:42Z

Matthew Binsted:

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In general, the technologies available at the investment margin are prescribed by the user as input assumptions, while the technologies deployed are determined in the model. See the land-use section [https://jgcri.github.io/gcam-doc/land.html here].

Agricultural demand - GCAM

2022-06-17T16:30:30Z

Matthew Binsted:

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The demand for goods and services produced on the land is determined by a set of simple demand equations that employ income and price elasticities. See the GCAM documentation's section on [https://jgcri.github.io/gcam-doc/demand_land.html land demand].