GCAM v6 Documentation: Emissions

Documentation for GCAM
The Global Change Analysis Model

View the Project on GitHub JGCRI/gcam-doc



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.

Table of Contents

Inputs to the Module

Table 1: Inputs to the Module

Name Resolution Unit Source
Emissions data by sector for NonCO2 (Described in detailed in initialization section below) country, sector,fuel,gas, year \(Tg\) Exogenous
Activity data from GCAM by sector By region, year, sector, fuel \(EJ\) Endogenous
Marginal abatement cost (MAC) assumptions By region, sector, year \(Unitless\) Exogenous
Energy production (for emissions driven by production) By region, technology, year EJ/yr Energy Supply Module
Energy consumption (for emissions driven by consumption) By region, technology, year EJ/yr Energy Demand Module
Agricultural production By GLU, technology, year Mt/yr Land Supply Module
Land use and land use change By GLU, type, year thous km3 Land Module


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).

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. The GCAM, in effect, produces a Marginal Abatement Curve for CO2 as a carbon-price is applied within the model.

CO2 emissions from limestone used in cement production are also estimated. Limestone consumption has one global emissions factor, however, each region’s IO coefficient (limestone / cement) is calibrated to return CDIAC estimates (CDIAC 2017). (CO2 from fuel consumed in producing limestone is estimated in the same manner as other fuel consumption.)

CO2 emissions from gas flaring are not currently included in GCAM.

CO2 Emissions From Land-Use and Land-Cover Change (LULCC)

Land-Use and Land-Cover Change emissions are tracked separately. See Carbon Emissions.

Non-CO2 Emissions Overview

We summarize here some general points common to non-CO2 emissions in GCAM


Data sources

Note that there are some calibration year differences between CEDS and GCAM.

Modeling approach

Modeling non-CO2 abatement at the process level would require too much detail for the scales at which GCAM operates. We, therefore, use parameterized functions for future emissions controls (for air pollutants) and Marginal Abatement Cost (MAC) curves (for GHGs) to change emission factors over time. The emissions controls, which reduce emissions factors as a function of per-capita GDP in each region and time period, are based on the general understanding that pollutant control technologies are deployed as incomes rise (e.g., Smith et al. 2005, although the functional form used in GCAM 5 is different than that in this reference). The MAC curves for GHGs are mapped directly to GCAM’s technologies from the EPA’s 2019 report on non-CO2 greenhouse gas mitigation (EPA 2019).

Note that technology shifts still play a role, since emission factors can differ between technologies.

Energy System Drivers
Agriculture and Land-Use Drivers
Naming Conventions

There are some naming conventions for a few emission species/sectors within GCAM that are useful to note.


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:



F Emissions factor: base-year emissions per unit activity
A Activity level (e.g., output of a technology)
MAC Marginal Abatement Cost Curve
Eprice Emissions Price

MAC-related parameters:

XML Tag Description
tech-change annual improvement of reduction potential defined in MAC curve
mac-phase-in-time MAC phase-in periods (see description below)

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 (Ou et al. 2021). Under a carbon policy, emissions are reduced by an amount determined by the MAC curve.

The default set-up is that MAC curves use the scenario’s carbon price (if any). The non-CO2 GHG MACs are an exogenous input, and are read in as the percent of emissions abated as a function of the emissions prices. Note that they are read in with explicit cost points (i.e., piece-wise linear form), with no underlying equation describing the percentage of abatement as a function of the carbon price.

The parameter technological change (tech-change) represents annual improvement of reduction potential at all prices (essentially shifting the entire MAC curve to the right). For non-CO2 GHGs, MAC is typically defined in the initial available year (typically 2025), while tech.change starts from the next modeling period (i.e. 2030) to adjust the base-year MAC. tech-change from 2030 to 2050 is backward calculated from the maximum mitigation potential in EPA 2019, and tech.change after 2050 is assumed to be the average of pre-2050 tech.change (since current EPA mitigation report only contains mitigation potential till 2050) (Ou et al. 2021).

\(R_{t_2, p}=R_{t_1, p}*(1+TC_{t_2})^{t_2-t_1}\) where \(R_{t_1, p}\) and \(R_{t_2, p}\) represent MAC reduction in year \(t_1\) and \(t_2\) at emission price \(p\), respectively. \(TC_{t_2}\) is the tech.change in \(t_2\).

In general, MAC curve is an aggregated representation of physical changes (retrofit, technology upgrade, process optimization), and in real world, these changes take time to finish. If omitting these “time costs” or other institutional barriers, the first MAC year (as well as the carbon price year) would lead to a dramatic yet sometimes unrealistic MAC-driven emission reductions. mac-phase-in-time offers users an option to make additional adjustment for the existing MAC reductions due to factors other than “zero-cost” emission reductions and technological changes. A primary purpose is to smoothly phase in MACs in early modeling periods, so mac-phase-in-time can be applied to make the reduction in those first several modeling periods more realistic; A second purpose is to allow users to explore scenarios when different regions have different MAC phase-in periods. The default mac-phase-in-time is 25 years.

Below-zero (i.e. “no cost”) MAC mitigation (e.g. MAC reduction percentage is > 0 at zero emission price) are applied in the reference case and phased in over several decades. (This can be turned off by setting the no-zero-cost-reductions option to 1 within a MAC curve.) Here, below-zero MAC mitigation is assumed to be unaffected by tech-change, despite that it is not necessarily unreasonable in general to think that technological change would impact below-zero options, as well.

Note that a species-specific emissions market can also be specified using advanced options, described below.

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.

SF6 emissions from electric transformers scale with electricity consumption. HFC134a from cooling (e.g., air conditioners) scale with air conditioner electricity consumption. For these emissions we also make additional exogenous adjustments to emissions factors in future periods in developing regions to reflect their continued transition from CFCs to HFCs.

Air Pollutant Emissions

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


where EmCtrl is a function that represents decreasing emissions intensity as per-capita income increases:


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.

Note that the GCAM implementation of the SSP scenarios used a different approach, incorporating region-, sector-, and fuel-specific pollutant emission factor pathways (Calvin et al. 2017, Rao et al. 2017).

Policy options

Users are allowed to selectively specify both new vintage specific emissions factors and existing vintage retrofits for technologies when calculating emissions. Users can also specify a GDP level at which emissions controls are applied to the model.

Users can also specify marginal abatement cost curves (MACC) for emissions in the model. These can be specified by the user directly or the user can also select to apply pre-calculated MACC curves from the EPA to the GCAM sectors. Note that the EPA sectors were harmonized with the GCAM sectors when calculating these MACC curves. MACC are applied with technological change applied over time and there are different levels of change applied for the different SSPs.

There are three main policy approaches that can be applied in GCAM to reduce emissions of CO2 or other greenhouse gases: a fixed carbon or GHG price, 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, potentially decreasing its competitiveness relative to other fuels. The three policy approaches are described below.

Linked Emission Markets

Emissions prices of different GHGs can be linked together for a multi-gas policy using the linked-ghg-policy object. For example, in the default linked_ghg_policy.xml file in the GCAM release, all non-CO2 GHGs are linked to the market for CO2. Also, see example

The parameter price-adjust is used to convert prices and demand-adjust is used to convert demand units (typically to convert to a common units of carbon equivalents using the individual gasses Global Warming Potential). These can be changed by year as well in order to, for example, phase in a gas into the policy.

Setting price-adjust to zero means that there is no economic feedback for the price of this GHG. MAC curves, however, will still operate under the default set-up (whereby MAC curves are driven by CO2 prices). This can be changed separately for energy/industrial/urban CH4, agricultural CH4 (CH4_AGR), and CH4 from agricultural waste burning (CH4_AWB), Land Use Change CO2 emissions (e.g. CO2_LUC).

Note that you must first create a policy by reading in a <ghgpolicy> object in your configuration before attempting to read in a linked GHG policy that needs to link to it.

This flexibility allows CO2-only, CO2-equivalent, or non-CO2 markets/constraints for various baskets of emissions as needed.

Note that the GCAM default set-up includes economic feedbacks for methane and nitrous oxide. This is an idealized assumption, but might not happen in real-world policies. For example, in many current systems agricultural emissions are offsets only e.g., they get paid to reduce emissions, but are not charged for any remaining emissions. (So to simulate this type of policy, price-adjust would be set to zero).

Markets For non-CO2 Emission Species

Markets in GCAM can be set for any emission species. (e.g., CH4 -only market, NOx market, etc.)

Note that it generally does not make sense to set up an emissions market unless the model has a direct way to reduce emissions. For instance, you’ve added relevant MAC curves, or explicit technology options with differing emissions rates. For example, in Shi et al. (2017) US electricity sector SO2 and NOx markets were used to represent current policies that cap emissions in certain states. MAC curves for existing power plants were added to allow emissions to change in response to market prices.

XML inputs within the MAC curve that will be needed to set-up new markets are:

XML Tag Description
market-name Name of market from which the price used by the MAC curve will be obtained (default = “CO2”)
mac-price-conversion Value to multiply market price by to convert to unit expected by the MAC curve (for example, converting from $/tC to $/tCO2eq) (default = 1)
Note mac-price-conversion can also be set to -1, which is a flag to turn off all use of the MAC curve. This is useful for sensitivity studies.
zero-cost-phase-in-time Number of years over which to phase-in “below-zero” MAC curve reductions (default = 25 years)

Insights and Intuition

NonCO2 GHG abatement

In summary, nonCO2 GHG emissions can be controlled by three mechanisms. First, changes in activity (phasing out of carbon-intensive fuels due to climate policy) will reduce non-CO2 GHG emissions (e.g. fugitive CH4 from natural gas production). Second, for emission sources without explicit representation of the underlying activity, emission reductions are calculated off of MAC curves that are parametrized to abatement technologies and abatement levels. While decarbonization-driven fuel switching mainly reduces non-CO2 emissions from fuel extraction and end use, targeted non-CO2 mitigation measures can significantly reduce fluorinated gas emissions from industrial processes and cooling sectors. (Ou et al. 2021). Finally, carbon prices can be directly passed to nonCO2 GHGs, see example.


For information on using markets for non-CO2 emissions see the markets for non-CO2 section of the polices page.

Additional Non-CO2 Emission Options

Emission objects can be added/changed via user input in any time period. New parameters (such as emission factors) overwrite any previous values. Note that emission control objects will be copied forward. For vintaged technologies (e.g. electric generation, road transport), any new emission object will be applied to new vintages. Old vintages will retain the previously read-in emission characteristics.

This also means that GHG objects can be removed or overwritten after a given year by reading in a new GHG object for that gas. This also applies to GHG control objects. Reading in a new control object, for example, in 2020 for an electric generation technology will negate the effect of a previously read in control object of the same name for that vintage and future vintages. This can, for example, represent a transition from some emissions control regime applied to older vintages to a regime defined by new source performance standards.

In addition to the GDP control object, a linear-control object is also available that allows a user to specify that an emission factor will linearly change over time to a user-defined value over a specified time period. The parameters controlling the linear-control object are:

XML Tag Description
end-year Year by which emission factor should reach specified value
start-year (Optional) Start year after which EF should begin to decline. (defaults to final calibration year)
final-emissions-coefficient Emissions coefficient that should be set by end-year (and every year thereafter)
allow-ef-increase (optional) Allow emission factors to increase from their start-year value (default to false)

Emissions Control Drop Folder

There is a method in the GCAM data system that allows users to selectively specify both new vintage emissions factors and existing vintage retrofits (base-year or future) for technologies, replacing the current smooth GDP control for those technologies, without changing any model code. The user just needs to place any number of CSV input files into a folder, and the system will generate XML input files that will use this new data to replace any previous emission factors and/or GDP control functions. Two options are available. The first, and main, option is to specify a New Source Performance Standard (NSPS) for a technology, which is the emission factor that newly built technologies should have (often specified by air pollution control regulations). The NSPS is functionally applied as an emission factor. The second option is to specify retrofits (only applicable to technologies with a lifetime > 1 period). Retrofits are implemented as a linear-control object such that the emissions factor linearly declines from its value in a specified start year to its end year, for the specified technology vintage. The code automatically removes the existing GDP control object for any technology for which either a NSPS or retrofit is specified.

Both retrofits and NSPS can be specified by either a specific GCAM region, GCAM meta-region, or all regions. These can also be specified to occur at a specific year (most useful for countries with near-term policies in place) or at a user-specified per capita GDP level.

The system will generate two sets of output .xml files. One for core GCAM inputs (that are now a standard part of the configuration file) and a separate set of “user” input files which are intended for changes that are specific to a project, but not part of the GCAM core model.

For instructions on how to implement this, see the README in the emissions user_emission_controls folder in the data system.

IAMC Reference Card

Greenhouse gases


Carbon dioxide removal


[Calvin et al. 2017] Calvin, K., Bond-Lamberty, B., Clarke, L., et al. 2017. The SSP4: a world of deepening inequality. Global Environmental Change 42: 284-296. doi:10.1016/j.gloenvcha.2016.06.010. Link

[CDIAC 2017] 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. Link

[EDGAR 2011] Joint Research Centre. 2011. EDGAR - Emissions Database for Global Atmospheric Research: Global Emissions EDGAR v4.2. doi:10.2904/EDGARv4.2. Link [EPA 2011] US EPA, 2011, 2011 National Emissions Inventory (NEI) Data. United States Environmental Protection Agency, Office of Air Quality Planning and Standards. Link

[EPA 2019] US EPA, 2019, Global Non-CO2 Greenhouse Gas Emission Projection & Mitigation Potential Report. United States Environmental Protection Agency, Office of Atmospheric Programs. Link

[Lamarque et al. 2010] Lamarque, J.F., Bond, T. C., Eyring, V., et al. 2010. Historical (1850-2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application, Atmospheric Chemistry and Physics 10(15): 7017-7039. doi:10.5194/acp-10-7017-2010. Link

[Rao et al. 2017] Rao, S., Klimont, Z., Smith, S., et al. 2017. Future air pollution int he Shared Socio-economic Pathways. Global Environmental Change 42: 246-358. doi:10.1016/j.gloenvcha.2016.05.012. Link

[Ou et al. 2021] 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 Link

[Smith et al. 2005] Smith, S.J., Pitcher, H., and Wigley, T. 2005. “Future Sulfur Dioxide Emissions” Climatic Change 3: 267-318. doi: 10.1007/s10584-005-6887-y. Link

[Hoesly et al. 2018]Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O’Rourke, P. R., and Zhang, Q.: Historical (1750-2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369-408, Link, 2018