# Snapshot of - GCAM

Archive of GCAM, version: 5.3

# Reference card - GCAM

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)

Name and version

GCAM 5.3

Institution

Pacific Northwest National Laboratory, Joint Global Change Research Institute (PNNL, JGCRI), USA, http://www.globalchange.umd.edu.

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)

1. USA
3. Mexico
4. Australia_NZ
5. Japan
6. South Korea
7. EU-12
8. EU-15
10. Europe_Non_EU
11. Europe_Eastern
12. Russia
13. China
14. Taiwan
15. Central Asia
16. South Asia
17. Southeast Asia
18. Indonesia
19. India
20. Pakistan
21. Middle East
22. Africa_Eastern
23. Africa_Northern
24. Africa_Southern
25. Africa_Western
26. South Africa
27. Argentina
28. Brazil
29. Central America and Caribbean
30. Colombia
31. South America_Northern
32. South America_Southern

Note: 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.

Time discounting type

• Discount rate exogenous
• Discount rate endogenous

Policies

• Emission tax
• Emission pricing
• 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

• 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
• 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
• Other electrical uses
• Water heating

## 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

## Emission, climate and impacts

Model documentation: Emissions - GCAMClimate - GCAMNon-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
• 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

• 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

# Model Documentation - GCAM

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)[1] [2] [3] and the first peer-reviewed publications in 1983 (Edmonds and Reilly, 1983a,b,c)[4][5][6]. 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)[7] and renamed to GCAM in the mid-2000s. The first coupling to a carbon cycle model was published in Edmonds et al. (1984)[8]. The first use of GCAM (MiniCAM at the time) in conjunction with a Monte Carlo uncertainty analysis was published in Reilly et al. (1987)[9].

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)[9] through the present Shared Socioeconomic Pathways (Calvin et al., 2017)[10]. 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)[11]. 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 here.

# 1) Model scope and methods - GCAM

The Global Change Analysis Model (GCAM) is a global model that represents the behavior of, and interactions between five systems: energy, water, agriculture and land use, economy, and 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 those associated with response strategies to address important environmental questions. GCAM is a community model stewarded by The Joint Global Change Research Institute (JGCRI). Full documentation for GCAM can be found here.

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. See GCAM Model Overview for an overview of the model and its capabilities.

# 1.1) Model concept, solver and details - GCAM

## Overview

Supplied with input information from the GCAM Data System, the GCAM Core is the heart of the dynamic character of GCAM. GCAM takes in a set of assumptions and then processes those assumptions to create a full scenario of prices, energy and other transformations, and commodity and other flows across regions and into the future. GCAM represents five different interacting and interconnected systems. The interactions between these different systems all take place within the GCAM core; that is, they are not modeled as independent modules, but as one integrated whole.

The core operating principle for GCAM is that of market equilibrium. Representative agents in GCAM use information on prices, as well as other information that might be relevant, and make decisions about the allocation of resources. These representative agents exist throughout the model, representing, for example, regional electricity sectors, regional refining sectors, regional energy demand sectors, and land users who have to allocate land among competing crops within any given land region. Markets are the means by which these representative agents interact with one another. Agents indicate their intended supply and/or demand for goods and services in the markets. GCAM solves for a set of market prices so that supplies and demands are balanced in all these markets across the model. See the overview for more details.

## Solver

At each time step, GCAM searches for a vector of prices that cause all markets to be cleared and all consistency conditions to be satisfied. The mapping from input prices to output market disequilibria is a vector function ${\textstyle {\vec {y}}=F({\vec {p}})}$. The GCAM solver is responsible for finding the root of this equation; that is, the point at which ${\displaystyle F({\vec {p}})=0}$ .

GCAM has several solver algorithms at its disposal. The solver algorithms can be combined so that several of them are used in sequence. The mix of algorithms can be varied from one model timestep to the next and can be customized for markets that require special treatment. Additionally, each solver algorithm has several adjustable parameters that are user configurable. For more information on the solver, see the GCAM solver page.

## Economic Choice

Most of the economic activities represented in GCAM present us with a choice among several ways to produce the end result of the activity. Examples of these choices include choosing between different fuels or feed stocks, between different technologies, and between transportation modes. In some cases the choice is between different uses of a limited resource, such as when we allocate land area to different uses. In each of these cases we must allocate the total activity to the available alternatives.

Choice in GCAM is based on a single numerical value that orders the alternatives by preference. Generically, we call this the choice indicator, p. In practice the choice indicator is either cost or profit rate, though other indicators are possible in principle. In cases where multiple factors influence a choice, such as passenger transportation (where faster modes are more desirable), the additional factors are converted into a cost penalty and added to the basic cost to produce a single indicator that incorporates all of the relevant factors. Economic choice is described in more detail here.

### Choice Functions

A function that takes as input a vector of indicators and returns a vector of market shares for the corresponding choice alternatives is called a choice function. Choice functions reflect that the single best choice does not necessarily capture the entire market. A variety of factors not captured in the model, such as individual preferences, local variations in cost, and simple happenstance cause some of the market to go to alternatives that, based on their indicator alone, are theoretically inferior choices.

GCAM provides a flexible system for specifying choice functions at runtime on a sector-by-sector basis. Choice functions are represented in the code by classes that implement the IDiscreteChoice interface. Two such classes, the Logit and the Modified Logit are currently provided. Descriptions of these classes and a comparison of the two can be found in the choice functions section of the documentation.

# 1.3) Temporal dimension - GCAM

The release version of GCAM is typically operated in five-year time steps with 2015 as the final calibration year. However, the model has flexibility to be operated at different temporal resolutions through user-defined parameters. See Overview of Integrated Dynamics in the GCAM Core.

# 1.4) Spatial dimension - GCAM

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 Overview of Integrated Dynamics in the GCAM Core.

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

# 1.5) Policy - GCAM

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 policy section.

# 2) Socio-economic drivers - GCAM

The socioeconomic components of GCAM set 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. 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. See the economy section for more information on inputs, outputs, and the modeling approach used.

# 2.1) Population - GCAM

GCAM’s inputs include information on population for each of GCAM’s energy-economic regions. GCAM requires globally consistent data sets for each of its historical model periods, currently 1990, 2005, 2010 and 2015, to initialize the model. Each scenario requires assumptions about population for future time periods. See the socioeconomic inputs section and the economy section for more information.

• Population: The number of people living in each GCAM region in the benchmark and projection years

Historical population and observed GDP are used to calibrate a GCAM simulation using data from 1990, 2005, 2010, and 2015. Prognostic values for population and GDP per capita growth rates are provided by the user, though a default set is provided in the GCAM data base. Alternative assumptions associated with the SSPs are also implemented in the GCAM implementation of the Shared-Socioeconomic Pathways.

# 2.2) Economic activity - GCAM

GCAM’s inputs include information on the rate of per capita income growth for each of GCAM’s energy-economic regions. GCAM requires globally consistent data sets for each of its historical model periods, currently 1990, 2005, 2010 and 2015, to initialize the model. Each scenario requires assumptions about per capita GDP growth for future time periods.

• GDP Per Capita Growth: The annual average rate of growth for per capita GDP over each time step in the projection. Time steps are 5 years by default.

The macro-economic module takes population and GDP per capita growth to produce overall GDP in each GCAM energy-economic region. See the socioeconomic inputs section and the economy section for more information.

Historical population and observed GDP are used to calibrate a GCAM simulation using data from 1990, 2005, 2010, and 2015. Prognostic values for population and GDP per capita growth rates are provided by the user, though a default set is provided in the GCAM data base. Alternative assumptions associated with the SSPs are also implemented in the GCAM implementation of the Shared-Socioeconomic Pathways.

# 3) Macro-economy - GCAM

Details on GCAM's economy sector can be found in the economic inputs and economy section of the official documentation.

International trade in most commodities in GCAM is done by one of two methods: (1) Heckscher-Ohlin (single global markets), or (2) 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 trade details and trade outputs for more details.

# 4) Energy - GCAM

The overall structure of the energy system can be thought of as consisting of three main elements: energy resources, energy transformation, and final energy demands. It also tracks international trade in energy commodities. Consistent with the overall structure of GCAM, all the different elements of GCAM interact through market prices and physical flows of, for example, electricity. Technology choices are made based on prices using discrete choice methods. GCAM's energy system is described in detail in the following documentation sections:

# 4.1) Energy resource endowments - GCAM

GCAM's energy system is comprised of both exhaustible and renewable resources. Exhaustible resources are modeled using graded resource supply curves and include oil, unconventional oil, natural gas, coal, and uranium. Renewable resources quantities are always indicated in terms of annual flows, and include wind, geothermal, solar, hydropower, and biomass. See resources, and resource details for more information.

# 4.1.1) Fossil energy resources - GCAM

GCAM models depletable resources (oil, unconventional oil, natural gas, coal, and uranium) using graded resource supply curves. The fossil resources are produced from these supply curves using a “Resource / Reserve” model. In this approach as the market price of the resource increases, we look up the supply curve to determine the additional quantity available and move that quantity of “resource” into a “reserve”. We assume production of that reserve over some well / mine lifetime appropriate for each fuel. Technical change can be applied to reduce the extraction cost of the “resource” in future years. See depletable resources for a full description and examples.

# 4.1.2) Uranium and other fissile resources - GCAM

GCAM models depletable resources (oil, unconventional oil, natural gas, coal, and uranium) using graded resource supply curves. The fossil resources are produced from these supply curves using a “Resource / Reserve” model. In this approach as the market price of the resource increases, we look up the supply curve to determine the additional quantity available and move that quantity of “resource” into a “reserve”. We assume production of that reserve over some well / mine lifetime appropriate for each fuel. Technical change can be applied to reduce the extraction cost of the “resource” in future years. See depletable resources for a full description and examples.

# 4.1.3) Bioenergy - GCAM

## 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 wind section, and the specific quantities are documented in Gregg and Smith (2010).[12] 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 biomass and biomass liquids sections for more details.

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 traditional biomass section.

# 4.1.4) Non-biomass renewables - GCAM

GCAM’s non-biomass renewable resources include onshore wind, offshore wind, solar, geothermal, and hydropower. In contrast to the depletable resources, whose cumulative stocks are explicitly tracked, renewable resource quantities in GCAM are always indicated in terms of annual flows. Wind and solar are considered as options for producing electricity or hydrogen, while geothermal and hydropower are only considered as options for producing electricity. None of these resources are traded between regions.

In general, the costs of producing electricity from renewable energy forms consist of the sum of the resource costs described in the renewable resources section, the technology costs, and in some cases, backup-related costs. The latter two components to the costs are documented in the electricity section of the documentation.

# 4.2) Energy conversion - GCAM

Broadly, the energy transformation sectors in GCAM consist of all supplysectors between the energy resources and the final demands, where the latter are identified by the final-energy keywords “buildings”, “industry”, or “transportation”. Energy transformation sectors consume energy goods which are supplied either by resources or other energy transformation sectors, and they produce energy goods which are consumed either by other energy transformation sectors or by final demand sectors. This category is also considered to include a number of “pass-through” supplysectors whose purpose is explicit tracking of cost mark-ups and efficiency losses in the inter-sectoral transportation of energy goods. The main energy transformation sectors highlighted in the documentation are electricity, refining, gas processing, hydrogen production, and district services. For more detail on these sectors, see GCAM's section on energy transformation, and energy transformation details.

In energy transformation sectors, the output unit and input unit are EJ (per year), the price unit is 1975\$ per GJ of output, and the subsector nest is used for competition between different fuels (or feedstocks). The competition between subsectors takes place according to a calibrated logit sharing function, detailed in choice function. Within the subsectors, there may be multiple competing technologies, where technologies typically represent either different efficiency levels, and/or the application of carbon dioxide capture and storage (CCS). The parameters relevant for technologies in GCAM are identified and explained in energy technologies.

# 4.2.1) Electricity - GCAM

The GCAM electricity sector models the conversion of primary fuels (e.g., coal, gas, oil, bioenergy) to electricity. For most fuels, GCAM includes several different technology options (e.g., pulverized coal, coal IGCC, etc.). Individual technologies compete for market share based on their technological characteristics (conversion efficiency in the production of products from inputs), and cost of inputs and price of outputs. The cost of a technology in any period depends on (1) its exogenously specified non-energy cost, (2) its endogenously calculated fuel cost, and (3) any cost of emissions, as determined by the climate policy. The first term, non-energy cost, represents capital, fixed and variable O&M costs incurred over the lifetime of the equipment (except for fuel or electricity costs). For electricity technologies, GCAM reads in each of these terms and computes the levelized cost of energy within the model. For example, the non-energy cost of coal-fired power plant is calculated as the sum of overnight capital cost (amortized using a capital recovery factor and converted to dollars per unit of energy output by applying a capacity factor), fixed and variable operations and maintenance costs. The second term, fuel or electricity cost, depends on the specified efficiency of the technology, which determines the amount of fuel or electricity required to produce each unit of output, as well as the cost of the fuel or electricity. For details and a schematic showing the electricity nesting structure, see GCAM's sections on electricity and electricity details

# 4.2.2) Heat - GCAM

Heat is included as a final energy carrier in the IEA Energy Balances, and is intended to represent heat sold to third parties. That is, the use of heat and/or steam produced on-site at buildings and factories is simply reported as the energy consumption used to produce the heat and/or steam.

In most regions in GCAM, heat is not explicitly represented as an energy commodity; instead, the reported fuel inputs to heat plants are assigned directly to the end-use sectors that consume the heat (buildings and industry). Combined heat and power (CHP) is included as a technology option, but is located within the industrial energy use sector, and no inter-sectoral flow of heat is represented. However, in several regions where purchased heat accounts for a large share of the final energy use, GCAM does include a representation of district heat production, with four competing technology options: liquid fuels, natural gas, biomass, and coal. See sections on district services and district services details for more information.

# 4.2.3) Gaseous fuels - GCAM

## Gas Processing

The three subsectors of the gas processing sector, and the downstream sectors are described below and in the gas processing documentation section. See gas processing details for an overview of the structure. Click on each heading to bring you to the corresponding section in the documentation.

### 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 Mapping the IEA Energy Balances and IEA (2011).[13] 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.

### 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[14] for a review of technologies for producing pipeline-grade gaseous fuels from coal.

### 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.[15]

### 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.

## 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,[16] 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),[17] and the distribution costs and energy intensities are from Argonne’s Hydrogen Delivery Scenario Analysis Model (HDSAM).[18] See hydrogen details for more information.

# 4.2.4) Liquid fuels - GCAM

## 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 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 refining and refining details.

### 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)[19] 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.

### 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 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.

### Coal to Liquids

The majority of the world’s coal to liquids production is in South Africa (IEA 2012),[20] 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).[21]

### Gas to Liquids

While a minor contributor to liquid fuels production globally (about 0.1%; (IEA 2012),[20]) gas to liquids has received increased attention in recent years, with several large-scale plants completed in the last decade (Glebova 2013),[22] and others in various stages of planning and construction (Enerdata 2014).[23] 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.

# 4.2.5) Solid fuels - GCAM

GCAM tracks two solid fuels: coal and biomass. Each can be liquefied, gasified, used in hydrogen production, or used directly in the industry or buildings end-use sectors. For a description of coal usage, see the depletable resources section. For biomass, see the bioenergy section above, or in the official documentation.

# 4.2.6) Grid, pipelines and other infrastructure - GCAM

### 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. See the gas pipeline section in the documentation.

# 4.3) Energy end-use - GCAM

End-use sectors in GCAM include buildings (residential and commercial), industry, and transportation.

# 4.3.1) Transport - GCAM

The transportation sector in GCAM is subdivided into four final demands: long-distance passenger air travel, (other) passenger travel, international freight shipping, and (other) freight. The transportation sector excludes energy consumption and materials moved via pipeline transport (but see gas supply system). Energy used by mobile mining, agricultural, industrial, and construction equipment is similarly not considered as transportation energy use, unless used on roadways and for the primary purpose of moving passengers and/or freight.

# 4.3.2) Residential and commercial sectors - GCAM

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 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,[24] 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 Energy Technologies. See the section on buildings for more details.

# 4.3.3) Industrial sector - GCAM

## Industry

Nine detailed industrial sectors are modeled in GCAM. These include six manufacturing sectors (Iron & Steel, Chemicals, Aluminum, Cement, Fertilizer, and Other Industry) and three non-manufacturing sectors (Construction, Mining energy use, and Agricultural energy use). IEA energy balances are used to calibrate the sectoral energy consumption (except in Cement and Fertilizer where historical energy use is estimated bottom-up). Sectoral outputs such as physical commodity flows are calibrated based on historical data from different industrial associations. For each sector, the future industrial output growth is driven by GDP, income elasticities, and price elasticities. The current industry representation does not consider global trade. Output of the detailed industry sectors is represented in physical outputs (Mt) and/or generic terms (EJ of energy services). See the official documentation's industry section.

### Iron and Steel

The Iron and Steel sector in GCAM consists of three distinct subsectors: Basic Oxygen Furnace (BOF), Electric Arc Furnace with scrap (EAF), and EAF with Direct Reduced Iron (DRI). Each subsector includes several competing technologies, such as fossil fuels w/ & w/o CCS, electricity, hydrogen, and biomass. See iron and steel for more information.

### Chemical

The chemicals sector represents the chemicals and petrochemicals industry, which is the largest industrial consumer of oil and gas. The chemicals sector is disaggregated into chemicals energy use and feedstocks. See chemicals for more information.

### Aluminum

The aluminum production in GCAM involves two main steps: (1) alumina refining, to refine bauxite ore into alumina, and (2) aluminum smelting, to convert alumina to aluminum. Alumina refining has multiple competing technologies, such as coal, refined liquids, gas, and biomass with and without CCS. Aluminum smelting uses alumina as an input and consumes electricity. See aluminum for more information.

### Construction

The construction sector includes energy use and feedstocks for construction of buildings, roads, railways, utility projects, and other civil engineering projects, as classified in the IEA energy balances (CONSTRUC and NECONSTRUC flow codes). Historical and base year construction energy use and feedstocks are calibrated using IEA energy balances. See construction for more information.

### Mining Energy Use

In GCAM, mining energy use includes mining of metal ores and other materials such as stone, sand, clay, peat, and chemical/fertilizer minerals, as classified in the IEA energy balances (MINING flow). To better represent technology competition and fuel substitution, mining energy use is also disaggregated into mobile and stationary uses, in similar fashion to construction energy use described in the construction section above. See mining for more information.

### Agricultural Energy Use

Agricultural Energy use includes energy use to operate machinery and equipment, and for heating, cooling, and power in buildings. Refined liquids currently make up about half of agricultural energy consumption, and electricity about a quarter. To better represent technology competition and fuel substitution, agricultural energy use is also disaggregated into mobile and stationary uses, with hydrogen and battery-electric mobile technologies introduced in future periods. See agricultural energy use for more information.

### Cement

GCAM includes a physical representation of the manufacture of cement, that tracks both the fuel- and limestone-derived emissions of CO2. Production volumes are indicated in Mt of cement; input-output coefficients of heat and electricity are indicated in GJ per kg of cement, and the input-output coefficient of limestone is unitless. See cement for more information.

### Nitrogen Fertilizer

The representation of nitrogenous fertilizers (“N fertilizer”), indicated in Mt of fixed N in synthetic fertilizers, includes both the specific production technologies for transforming various feedstocks into N fertilizer, as well as the demands for the commodity in the agricultural sectors. Nitrogenous fertilizers manufactured for non-agricultural purposes are excluded from the commodity modeled in GCAM. See N fertilizer for more information.

### Other Industry

The remaining industrial sectors are collectively modeled as “Other industry”, and represented as a consumer of generic energy services and feedstocks. Within “Other industry” there is cost-based competition between fuels, but with a low elasticity of substitution, as the specific uses of the energy are not specified. Cogeneration of electricity is tracked, and represented as a separate technology option for each fuel consumed by “Other industry” (other than electricity). Output of aggregate industrial sectors is represented in generic terms.

# 4.3.4) Other end-use - GCAM

## Energy for Water

#### System boundaries

The specific system boundaries are explained in Kyle et al. (2016),[25] 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),[26] 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 here, with additional details found here.

# 4.4) Energy demand - GCAM

Energy demand in GCAM is described in the documentation’s Demand for Energy section

# 4.5) Technological change in energy - GCAM

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. For information on parameters and functional forms found within technologies in GCAM’s energy system, see Energy Technologies.

# 5) Land-use - GCAM

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 land, as well as in more detail in the detailed land section. Information on land supply can be found at the Supply of Food, Feed, and Forestry page, and demand at the Demand for food, forestry, etc page. Land inputs can be found at the External Inputs to the Land Model page, and outputs at the Outputs from the Land Model page.

# 5.1) Agriculture - GCAM

Agriculture in GCAM is described in the GCAM documentation’s land sections, linked in the Land-use section above.

# 5.2) Forestry - GCAM

Forestry in GCAM is described in the GCAM documentation’s land sections, linked in the Land-use section above.

# 5.3) Land-use change - GCAM

Land use in GCAM is determined endogenously in each period. Land-use change is determined by the land history and its state in the current period. See the description of the GCAM land-use determination in the land sections linked above, as well as in the land economic modeling approach page. Land-use change emissions are described here.

# 5.4) Bioenergy land-use - GCAM

Bioenergy in GCAM is described in the GCAM documentation’s land sections, linked in the Land-use section above.

# 5.6) Agricultural demand - GCAM

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 land demand.

# 5.7) Technological change in land-use - GCAM

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 here.

# 6) Emissions - GCAM

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 emissions and emissions details.

# 6.1) GHGs - GCAM

## 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).[27]

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 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:

${\displaystyle E_{t}=A_{t}*F_{t0}*(1-MAC(Cprice_{t}))}$
where:

 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[28] (Ou et al. 2021).[29] Under a carbon policy, emissions are reduced by an amount determined by the MAC curve. Documentation for non CO2 emissions can be found 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 here.

# 6.2) Pollutants and non-GHG forcing agents - GCAM

## Air Pollutant Emissions

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

${\displaystyle E_{t}=A_{t}*EF_{t0}*(1-EmCtrl(pcGDP_{t}))}$

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

${\displaystyle EmCtrl_{t}=1-{\frac {1}{1+{\frac {(pcGDP_{t}-pcGDP_{t0})}{steepness}}}}}$

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 air pollution.

# 6.3) Carbon dioxide removal (CDR) options - GCAM

Carbon dioxide removal options in GCAM include Carbon Capture and Storage (CCS), bioenergy with CCS, reforestation, afforestation, and direct air capture. See the section on CO2 emissions for more details.

# 7) Climate - GCAM

Hector v2.0 is the default climate model (Hartin et al., 2015)[30] 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),[31] and models from CMIP5.[30] 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 Hector for more details.

# 7.1) Modelling of climate indicators - GCAM

GCAM climate indicators are produced by the Hector Model. Indicators produced by GCAM include radiative forcing and CO2 equivalent (CO2e) emissions. CO2e emissions are obtained by taking the emission outputs of radiatively active gases of GCAM and weighting them by Global Warming Potential (GWP) coefficients.

# 8) Non-climate sustainability dimension - GCAM

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 Iyer, et al. (2018) for a discussion of this topic.

# 8.1) Air pollution and health - GCAM

## Air Pollutant Emissions

Air pollutant emissions (E) such as sulfur dioxide (SOs) and nitrogen oxides (NOx) are modeled as

${\displaystyle E_{t}=A_{t}*EF_{t0}*(1-EmCtrl(pcGDP_{t}))}$

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

${\displaystyle EmCtrl_{t}=1-{\frac {1}{1+{\frac {(pcGDP_{t}-pcGDP_{t0})}{steepness}}}}}$

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 air pollution.

# 8.2) Water - GCAM

## 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 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).[32]
• 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).[32]
• biophysical water consumption: total water required for crop evapo-transpiration; the sum of “blue” and “green” water in Mekonnen and Hoekstra 2011.[33]
• seawater: water from the oceans, including brackish estuaries, that is withdrawn for cooling thermo-electric power plants, or used in primary energy production.

## 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 detailed water page.

# 9) Appendices - GCAM

Some useful GCAM guides include:

# 9.1) Mathematical model description - GCAM

The GCAM documentation has a description of the key equations for each system, liked below.

The 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 Overview of GCAM Computational Components and the gcamdata GitHub repository for more details.

# 10) References - GCAM

1. Edmonds, J. and J. Reilly. 1982a. “Global energy and CO2 to the year 2050,” IEA/ORAU Working Paper Contribution No. 82-6.
2. Edmonds, J. and J. Reilly. 1982b. “Global energy production and use to the year 2050,” IEA/ORAU Working Paper Contribution No. 82-7.
3. 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.
4. Edmonds, J. and J. Reilly. 1983a. “Global Energy and CO2 to the Year 2050,” The Energy Journal, 4(3):21-47.
5. 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.
6. Edmonds, J. and J. Reilly. 1983c. “Global Energy Production and Use to the Year 2050,” Energy, 8(6):419-32.
7. 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.
8. 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.
9. 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.
10. 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.
11. 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.
12. 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.
13. International Energy Agency, 2011, Energy Balances of OECD Countries: Documentation for Beyond 2020 Files, International Energy Agency, Paris, France.
14. 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.
15. 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.
16. 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.
17. National Renewable Energy Laboratory, 2018, H2A: Hydrogen Analysis Production Models, National Renewable Energy Laboratory.
18. Argonne National Laboratory, 2015, Hydrogen delivery scenario analysis model (HDSAM), Argonne National Laboratory.
19. 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.
20. 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.
21. 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.
22. Glebova, O. 2013. Gas to Liquids: Historical Development and Future Prospects, Report NG 80, Oxford Institute for Energy Studies.
23. Enerdata, 2016. The Future of Gas-to-Liquid (GTL) Industry.
24. 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.
25. 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.
26. 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.
27. 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.
28. US EPA, 2019, Global Non-CO2 Greenhouse Gas Emission Projection & Mitigation Potential Report. United States Environmental Protection Agency, Office of Atmospheric Programs.
29. 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
30. 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.
31. Wigley, T. M. (2008), MAGICC/SENGEN 5.3: User manual (version 2), edited, p. 80, NCAR, Boulder CO.
32. Vickers, A. 2001. Handbook of Water Use and Conservation. WaterPlow Press, Amherst, MA, USA.
33. 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.