| title | layout | prev | next | gcam-version |
|---|---|---|---|---|
Emissions |
index |
supply_energy.html |
outputs_emissions.html |
v5.4 |
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 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 | Exogenous | |
| Activity data from GCAM by sector | By region, year, sector, fuel | Endogenous | |
| Marginal abatement cost (MAC) assumptions | By region, sector, year | 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 |
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.
Land-Use and Land-Cover Change emissions are tracked separately. See Carbon Emissions.
We summarize here some general points common to non-CO2 emissions in GCAM
-
Non-CO2 emissions, both GHGs & air pollutants in GCAM are initialized from the CEDS inventory (Hoesly et al 2018). Standard CEDS output datasets are used for input, although the emissions by GCAM regions are also generated as prebuilt data. Only the pre-built data should be distributed publicly (to parallel with the IEA energy data). The data covers emissions for all GCAM regions from 1970 to 2019. Only anthropogenic emissions (including open burning) are processed.
-
The CEDS inventory do not contain emissions for grasslands, forest fires, deforestation and agricultural waste burning on fields. The data for these categories of emissions were added from the GFED LULC data set(as used in CMIP6).
-
CEDS does not have a breakdown of emissions for road transport by mode. Therefore, GAINS emission factors by transport sectors (passenger, freight) and fuels to supplement the CEDS road emissions and derive emissions by different modes using emissions factors from the GAINS data set.
-
Additional information on fluorinated gases is from the 2019 EPA Global Non-CO2 Greenhouse Gas Emission Projection & Mitigation Potential Report.
Note that there are some calibration year differences between CEDS and GCAM.
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.
- Emissions in the energy system can be driven by input (e.g., fuel consumed by a particular technology) or output (e.g., fuel or service produced by a particular technology).
- Emissions information is technology-specific. As a result, different technologies that produce the same output can have different emissions per unit of activity.
- For most gases and species, we explicitly model the drivers for the emissions. However, for some (e.g., HFCs from fire extinguishers), the future emissions scale directly with population and/or GDP.
- Emissions in the agriculture and land use system can be driven by output (e.g., for crop production) or land area (e.g., for forest fires).
- Emissions are modeled at the level of agricultural technologies: region, water basin, crop type, and irrigation and management level. Note however that the underlying inventory data is far coarser, typically only available by sector and country.
There are some naming conventions for a few emission species/sectors within GCAM that are useful to note.
- Emissions from most agricultural activities are suffixed with "_AGR", except for burning of agricultural waste on fields (AWB), which are suffixed with "_AWB". (This allows us to separate emissions from these distinct processes from the same technology.) These are in addition to emissions named without a suffix. So to obtain total emissions, the three variants of a given species should be added (e.g., CH4 emissions is the sum of CH4, CH4"_AGR", and CH4"_AWB".)
- Sulfur dioxide (SO2) emissions are currently differentiated by four metaregions (SO2_1,SO2_2,SO2_3,SO2_4). These are kept separate for purposes of feeding the emissions information to the climate model; for emissions estimation, these different categories should be added to obtain global totals.
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:
$$ E_{t}=A_{t}F_{t0}(1-MAC(Eprice_{t}))) $$
where:
F |
Emissions factor: base-year emissions per unit activity |
A |
Activity level (e.g., output of a technology) |
MAC |
Marginal Abatement Cost Curve |
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
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.
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 such as sulfur dioxide (SO2) and nitrogen oxides (NOx) are modeled as:
$$ E_{t}=A_{t}EF_{t0}(1-EmCtrl(pcGDP_{t})) $$
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).
For information on using markets for non-CO2 emissions see the markets for non-CO2 section of the polices page.
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) |
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 energy
- NH3 other
Carbon dioxide removal
- Bioenergy with CCS
- Reforestation
- Afforestation
- Soil carbon enhancement
- Direct air capture
- Enhanced weathering
[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