GCAM: Difference between revisions
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|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. | |||
|Concept=GCAM allows users to explore what-if scenarios, quantifying the implications of possible future conditions. These outputs are not predictions of the future; they are a way of analyzing the potential impacts of different assumptions about future conditions. GCAM reads in external “scenario assumptions” about key drivers (e.g., population, economic activity, technology, and policies) and then assesses the implications of these assumptions on key scientific or decision-relevant outcomes (e.g., commodity prices, energy use, land use, water use, emissions, and concentrations). | |||
Developing and quantifying a single set of scenario assumptions is the most common way that a model such as GCAM is used to explore scientific and assessment questions. However, another class of question that GCAM has taken up is the systematic representation of uncertainty. As early as the 1980s, GCAM was used to map the implications of uncertain key input assumptions and parameters into implied distributions of outputs, such as greenhouse gas emissions, energy use, energy prices, and trade patterns. A range of techniques has been employed using GCAM to explore the potential range of future outcomes. Techniques include scenarios analysis, sensitivity analysis, and Monte Carlo simulations. Exploring and understanding the role of uncertainty in shaping events remains an important research use of GCAM. | |||
|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=2010 | |||
|TimeSteps=5-year (default), minimum time step is 1-year | |||
|Horizon=2100 | |||
|Nr=32 (default) | |||
|Region=Africa_Eastern; Africa_Northern; Africa_Southern; Africa_Western; Argentina; Australia_NZ; Brazil; Canada; Central America and the Caribbean; Central Asia; China; Colombia; EU-12; EU-15; Europe_Eastern; European Free Trade Association; Europe_Non_EU; India; Indonesia; Japan; Mexico Middle East; Pakistan; Russia; South Africa; South America_Northern; South America_Southern; South Asia; Southeast Asia; South Korea; Taiwan; USA; | |||
|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. | |||
|PolicyImplementation=GCAM can be used to understand the implication of interactions between assumptions about the implications of interactions between inputs (population, labor productivity, technology availability and performance, resource availability, and policies) with energy, economy, land-use and land cover, water, atmosphere and climate. It tracks physical flows of energy production, transformation, trade, and use, agricultural production, trade and consumption, land use, land cover, and the sources and disposition of water resources. A key feature is that the system is coupled in code and solved as a reconciled system. | |||
}} | |||
{{Socio-economicTemplate}} | {{Socio-economicTemplate}} | ||
{{Macro-economyTemplate}} | {{Macro-economyTemplate}} | ||
Revision as of 22:00, 20 February 2020
The reference card is a clearly defined description of model features. The numerous options have been organized into a limited amount of default and model specific (non default) options. In addition some features are described by a short clarifying text.
Legend:
- not implemented
- implemented
- implemented (not default option)
A page refresh may be needed after modifying data.
About
Name and version
GCAM 7.0
Institution
Pacific Northwest National Laboratory, Joint Global Change Research Institute (PNNL, JGCRI), USA, https://www.pnnl.gov/projects/jgcri.
Documentation
GCAM documentation consists of a referencecard and detailed model documentation
Process state
under review
Model scope and methods
Model documentation: Model_scope_and_methods - GCAM
Model type
- Integrated assessment model
- Energy system model
- CGE
- CBA-integrated assessment model
Geographical scope
- Global
- Regional
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.
Solution concept
- Partial equilibrium (price elastic demand)
- Partial equilibrium (fixed demand)
- General equilibrium (closed economy)
- GCAM solves all energy, water, and land markets simultaneously
Solution horizon
- Recursive dynamic (myopic)
- Intertemporal optimization (foresight)
Solution method
- Simulation
- Optimization
- 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.
Temporal dimension
Base year:2015, time steps:5-year (default), minimum time step is 1-year, horizon: 2100
Spatial dimension
Number of regions:32 (default)
- 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
Time discounting type
- Discount rate exogenous
- Discount rate endogenous
Policies
- 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-economic drivers
Model documentation: Socio-economic drivers - GCAM
Population
- Yes (exogenous)
- Yes (endogenous)
Population age structure
- Yes (exogenous)
- Yes (endogenous)
Education level
- Yes (exogenous)
- Yes (endogenous)
Urbanization rate
- Yes (exogenous)
- Yes (endogenous)
GDP
- Yes (exogenous)
- Yes (endogenous)
Income distribution
- Yes (exogenous)
- Yes (endogenous)
Employment rate
- Yes (exogenous)
- Yes (endogenous)
Labor productivity
- Yes (exogenous)
- Yes (endogenous)
Total factor productivity
- Yes (exogenous)
- Yes (endogenous)
Autonomous energy efficiency improvements
- Yes (exogenous)
- Yes (endogenous)
Macro-economy
Model documentation: Macro-economy - GCAM
Economic sector
Industry
- Yes (physical)
- Yes (economic)
- Yes (physical & economic)
Energy
- Yes (physical)
- Yes (economic)
- Yes (physical & economic)
Transportation
- Yes (physical)
- Yes (economic)
- Yes (physical & economic)
Residential and commercial
- Yes (physical)
- Yes (economic)
- Yes (physical & economic)
Agriculture
- Yes (physical)
- Yes (economic)
- Yes (physical & economic)
Forestry
- Yes (physical)
- Yes (economic)
- Yes (physical & economic)
Macro-economy
Trade
- Coal
- Oil
- Gas
- Uranium
- Electricity
- Bioenergy crops
- Food crops
- Capital
- Emissions permits
- Non-energy goods
Cost measures
- GDP loss
- Welfare loss
- Consumption loss
- Area under MAC
- Energy system cost mark-up
Categorization by group
- Income
- Urban - rural
- Technology adoption
- Age
- Gender
- Education level
- Household size
Institutional and political factors
- 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
- Constraints on cross country financial transfers
Resource use
Coal
- Yes (fixed)
- Yes (supply curve)
- Yes (process model)
Conventional Oil
- Yes (fixed)
- Yes (supply curve)
- Yes (process model)
Unconventional Oil
- Yes (fixed)
- Yes (supply curve)
- Yes (process model)
Conventional Gas
- Yes (fixed)
- Yes (supply curve)
- Yes (process model)
Unconventional Gas
- Yes (fixed)
- Yes (supply curve)
- Yes (process model)
Uranium
- Yes (fixed)
- Yes (supply curve)
- Yes (process model)
Bioenergy
- Yes (fixed)
- Yes (supply curve)
- Yes (process model)
Water
- Yes (fixed)
- Yes (supply curve)
- Yes (process model)
Raw Materials
- Yes (fixed)
- Yes (supply curve)
- Yes (process model)
Land
- Yes (fixed)
- Yes (supply curve)
- Yes (process model)
Technological change
Energy conversion technologies
- No technological change
- Exogenous technological change
- Endogenous technological change
Energy End-use
- No technological change
- Exogenous technological change
- Endogenous technological change
Material Use
- No technological change
- Exogenous technological change
- Endogenous technological change
Agriculture (tc)
- No technological change
- Exogenous technological change
- Endogenous technological change
Energy
Model documentation: Energy - GCAM
Energy technology substitution
Energy technology choice
- No discrete technology choices
- Logit choice model
- Production function
- Linear choice (lowest cost)
- Lowest cost with adjustment penalties
Energy technology substitutability
- Mostly high substitutability
- Mostly low substitutability
- Mixed high and low substitutability
Energy technology deployment
- Expansion and decline constraints
- System integration constraints
Energy
Electricity technologies
- 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
- Ocean power
Hydrogen production
- 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
Refined liquids
- 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
Refined gases
- 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
Heat generation
- Coal heat
- Natural gas heat
- Oil heat
- Biomass heat
- Geothermal heat
- Solarthermal heat
- CHP (coupled heat and power)
Grid Infra Structure
Electricity
- Yes (aggregate)
- Yes (spatially explicit)
Gas
- Yes (aggregate)
- Yes (spatially explicit)
Heat
- Yes (aggregate)
- Yes (spatially explicit)
CO2
- Yes (aggregate)
- Yes (spatially explicit)
Hydrogen
- Yes (aggregate)
- Yes (spatially explicit)
Energy end-use technologies
Passenger transportation
- Passenger trains
- Buses
- Light Duty Vehicles (LDVs)
- Electric LDVs
- Hydrogen LDVs
- Hybrid LDVs
- Gasoline LDVs
- Diesel LDVs
- Passenger aircrafts
- CNG Buses
- CNG Three-wheelers
- Diesel Three-wheelers
- Electric Buses
- Electric Three-wheelers
- LPG/CNG LDVs
Freight transportation
- Freight trains
- Heavy duty vehicles
- Freight aircrafts
- Freight ships
Industry
- Steel production
- Aluminium production
- Cement production
- Petrochemical production
- Paper production
- Plastics production
- Pulp production
Residential and commercial
- Space heating
- Space cooling
- Cooking
- Refrigeration
- Washing
- Lighting
Land-use
Model documentation: Land-use - GCAM
Land cover
- Cropland
- Cropland irrigated
- Cropland food crops
- Cropland feed crops
- Cropland energy crops
- Forest
- Managed forest
- Natural forest
- Pasture
- Shrubland
- Built-up area
Agriculture and forestry demands
- 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
Agricultural commodities
- Wheat
- Rice
- Other coarse grains
- Oilseeds
- Sugar crops
- Ruminant meat
- Non-ruminant meat and eggs
- Dairy products
Template:OtherResourcesTemplate
Emission, climate and impacts
Model documentation: Emissions - GCAM, Climate - GCAM, Non-climate sustainability dimension - GCAM
Greenhouse gases
- 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
Pollutants
- 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
Climate indicators
- 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
- Radiative Forcing (Land Albedo) - Yes (exogenous)
Carbon dioxide removal
- Bioenergy with CCS
- Reforestation
- Afforestation
- Soil carbon enhancement
- Direct air capture
- Enhanced weathering
Climate change impacts
- Agriculture
- Energy supply
- Energy demand
- Economic output
- Built capital
- Inequality
Co-Linkages
- Energy security: Fossil fuel imports & exports (region)
- Energy access: Household energy consumption
- Air pollution & health: Source-based aerosol emissions
- Air pollution & health: Health impacts of air Pollution
- Food access
- Water availability
- Biodiversity
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.
Note: 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.