GCAM v5.4 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


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)

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

[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

[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

[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