Documentation for GCAM
The Global Change Analysis Model
View the Project on GitHub JGCRI/gcam-doc
GCAM’s demand inputs include information on consumption and prices in the historical period in order to calibrate model parameters. Additional parameters related to income and price elasticities are needed for modeling future periods. GCAM requires demand data to be globally consistent with supply data for each of its historical model periods as it solves for market equilibrium in these years as it does for future years. These inputs are required for each region and historical year.
Table 1: External inputs used for demand of energy1
| Name | Description | Type | Source | Resolution | Unit |
|---|---|---|---|---|---|
| Historical demand for energy | Demand for energy in the historical period; used for initialization/calibration of GCAM | External data | IEA | Specified by demand, fuel, country, and year | ktoe and GWh |
| Historical demand for floorspace | Demand for floorspace in the historical period; used for initialization/calibration of GCAM | External data | IEA, Odyssee, Other | Specified by country, and year | BM2 and m2/pers |
| Price elasticity of demand | Elasticity determining how demand responds to changes in price | Assumption | Specified by demand | unitless | |
| Value of time in transit multiplier | Factor multiplied by the wage rate to determine the value of time in transit, used in the transportation module | Assumption | Specified by demand | unitless | |
| Cost | Cost of production | Assumption | Specified by technology and year | 1975$/kg or 1975$/GJ | |
| Default input-output coefficients | Default amount of input required per unit of output produced; can be overwritten by region-specific information derived from historical data | Assumption | Specified by technology and year | Various (e.g., GJ per kg, GJ per GJ) | |
| Default efficiencies | Default amount of output produced per unit of input; can be overwritten by region-specific information derived from historical data | Assumption | Specified by technology and year | Various (e.g., GJ per kg, GJ per GJ) | |
| CO2 capture rates | Fraction of CO2 captured in CCS technologies. | Assumption | Specified by technology and year | unitless | |
| Retirement rules | For vintaged technologies, GCAM requires the user to specify the lifetime, and the parameters required for phased and profit-based shutdown. | Assumption | Specified by technology and year | Years (for lifetime), unitless for others | |
| Logit exponents | GCAM requires the user to specify the logit exponents that determine the substitutability between technologies. | Assumption | Specified by sector and subsector | N/A | |
| Share weight interpolation rules | These rules dictate how share weights (GCAM’s calibration parameter) are specified in future years. | Assumption | Specified by sector and subsector | N/A | |
| Fuel preference elasticity | Elasticity dictating how share weights change with GDP per capita | Assumption | Specified by technology and year | unitless | |
| Residential floorspace parameters | Estimated parameters for residential floorspace demand | Analysis/Assumption | Specified by region | m2/pers and unitless | |
| Satiation levels | Assumed satiation values for commerical floorspace and building energy services | Assumption | Specified by demand, service, and region | m2/pers and EJ/pers | |
| Income elasticity of demand | Elasticity determining how demand responds to changes in per capita output for industry and cement | Assumption | Specified by demand | unitless | |
| Energy intensities | Energy intensity for energy-for-water processes (desalination, abstraction, treatment, distribution, wastewater treatment) | External data | Liu et al. 2016 | Global | GJ per \(m^3\) |
| Desalinated water production | Water produced through desalination, used to estimate energy-for-water | External data | FAO Aquastat | By nation | \(km^3\) per year |
| Shares of wastewater treated | Shares of wastewater treated, used to estimate energy-for-water | External data | Liu et al. 2016 | By nation | Unitless |
| Non-renewable groundwater supply curves - electricity inputs | Electricity inputs to groundwater production | External data | Superwell | 20 grades per geopolitical region and GLU | GJ per \(m^3\) |
| Historical non-CO2 emissions | Historical emissions of non-CO2 | External data | CEDS v_2021_04_21 |
Specified by country, technology, gas, and year | Various |
Note that for the Shared Socioeconomic Pathways (SSPs), different inputs are used for some variables. See SSPs for more information.
Throughout GCAM, the number in the name of assumption file indicates to which sector the file applies. Files with A32 in the name are assumptions for industry, A321 indicates cement, and A322 indicates fertilizer. Files with A44 in the name are assumptions for buildings. Files with A54 in the name are assumptions for transportation.
GCAM uses IEA energy balances as a source for historical energy supply and demand. IEA data are proprietary and thus are not provided in the GCAM data repository. Instead, we provide all of the R code used to process the IEA data so that the user can replicate the processing if they purchase the IEA data. In addition, we provide aggregated data after it has undergone processing so that GCAM input files can be created and used by the user community.
Price elasticity of demand is specified in A32.demand.csv, and A54.demand.csv. Income elasticities of demand for industry and cement are specified in A32.inc_elas_output.csv and A321.inc_elas_output.csv.
Costs are specified in A32.globaltech_cost.csv, A321.globaltech_cost.csv, A44.cost_efficiency.csv
Efficiencies are specified in A32.globaltech_eff.csv, A44.cost_efficiency.csv, and A44.tech_eff_mult_RG3.csv.
Coefficients are specified in A32.globaltech_coef.csv, A321.globaltech_coef.csv, A322.globaltech_coef.csv
CO2 capture rates for cement are specified in A321.globaltech_co2capture.csv. Capture rates for fertilizer are specified in A322.globaltech_co2capture.csv
Retirement rules are specified in A322.globaltech_retirement.csv, A44.cost_efficiency.csv
Logit exponents are specified in A32.sector.csv, A32.subsector_logit.csv, A321.subsector_logit.csv, A322.subsector_logit.csv, A44.sector.csv, A44.subsector_logit.csv, A54.sector.csv, and A54.tranSubsector_logit.csv.
Share weight interpolation rules are specified in A32.subsector_shrwt.csv, A32.subsector_interp.csv, A321.subsector_shrwt.csv, A321.subsector_interp.csv, A322.subsector_shrwt.csv, A322.subsector_interp.csv, A44.subsector_shrwt.csv, A44.subsector_interp.csv, A54.globaltranTech_shrwt_revised.csv, A54.globaltranTech_interp_revised.csv, A54.tranSubsector_shrwt_revised.csv, A54.tranSubsector_interp_revised.csv. For each sector, the file that ends _interp specifies the rule (e.g., fixed, linear) and the file that ends _shrwt indicates the value to interpolate to (if needed).
Fuel preference elasticities are specified in A32.fuelprefElasticity.csv, A44.fuelprefElasticity.csv.
Multipliers used to determine the value of time in transit are specified in A54.tranSubsector_VOTT_revised.csv.
The parameters (a,b,c) for the estimation of residential floorspace demand are calculated within the model DS (LA144.building_det_flsp.R) and saved in L144.flsp_param.
The econometric analysis is developed using different global floorspace data sources, which are used for floorspace calibration (e.g., IEA, Odyssee).
Considering the subnational data availability for the US, and its different behaviour in terms of residential floorspace demand (higher observed floorpace than other regions with similar per capita income or population density), parameters for the US are different from the global values, and have been estimated outside the model and are included in the constants.R file (to ensure everything is consistent when/if the GCAM-USA module is disabled).
Satiation levels for energy services are specified in A44.demand_satiation_mult.csv. For commercial floorspace, the satiation values are specified in A44.satiation_flsp.csv.
Historical non-CO2 emissions information is provided in the GCAM release as “pre-built” data aggregated to GCAM regions, technologies, and fuels. Users that want to build using CEDS raw data, for example to build for different regional aggregations, will need to generate CEDS data using the open-source CEDS system and place the resulting emissions data by country, fuel, and sector within the CEDS folder.
Table 2: External inputs used for demand of water 2
| Name | Description | Type | Source | Resolution | Unit |
|---|---|---|---|---|---|
| Agriculture water coefficients | Water coefficients for agricultural commodities, including blue (irrigation) and green (rain) water, includes data for a single year circa 2000 | External data set | Mekonnen and Hoekstra | Crop, country, water type (blue, green) | \(m^3\) per ton |
| Industrial manufacturing water coefficients | Water coefficients for industrial manufacturing for 1995 | External data set | Vassolo and Döll 2005 | Continent and water type (withdrawals, consumption) | \(Mm^3\) per year |
| Livestock water coefficients | Water coefficients for drinking and the servicing of livestock commodities, includes data for the period 1996-2005 | Mekonnen, M. M., & Hoekstra, A. Y. (2010). The green, blue and grey water footprint of farm animals and animal products. Volume 2: Appendices | External data set | livestock type | liters per head per day |
| Electricity cooling system shares | Histroical shares of cooling system types associated with power plants aggregated to GCAM3 regions | UCS and Schakel Inventories | External data set | GCAM3 region, power plant type, cooling system type, water type (fresh, seawater), and year | Unitless |
| Electricity water coefficients | Water withdrawal and consumption coefficients for power plants and cooling system types | External data set | Macknick et al., 2011 | fuel, power plant type, cooling system type, water type (fresh, seawater) | \(m^3\) per MWh |
| Primary energy water coefficients | Water coefficients for the consumption of water during the process of mining primary energy fuel sources | Maheu, A. (2009). Energy choices and their impacts on demand for water resources: An assessment of current and projected water consumption in global energy production. Unisféra. | External data set | global, fuel, mining technology, water type (consumption) | \(m^3\) per TJ |
| Municipal water withdrawals | Water withdrawal values for municipalities include data, as reported, from 1987 to 2017 | FAO Aquastat | External data set | GCAM region, year | \(km^3\) |
| Municipal water use efficiency | Water efficiency values for municipalities | Shiklomanov 2000 | Continent | Percent | |
| Municipal water cost | Price per unit of water delivered to municipalities | International Benchmarking Network for Water and Sanitation Utilities (IBNET) | External data set | Country | USD per \(km^3\) |
Note that for the Shared Socioeconomic Pathways (SSPs), different inputs are used for some variables. See SSPs for more information.
The raw data used for agricultural water coefficients is provided in Mekonnen_Hoekstra_Rep47_A2.csv. Note that these water demand estimates are built up from gridded (for 18 crops) and nation-level (for the remaining ~150 crops) “water footprint” estimates of Mekonnen and Hoekstra 2011.
The data specifying manufacturing water coefficients is specified in Vassolo_mfg_water.csv. Note that this data is derived from the Vassolo and Döll 2005 global inventory of manufacturing and electric power water demands for a base year of 1995. The manufacturing water demands of each country are multiplied by an exogenous ratio of self-supply to total industrial withdrawals (about 0.8; this comes from US-specific data in Kenny et al. 2009), and extrapolated to all historical years assuming a fixed ratio between industrial electricity and water demands. The values estimated from this bottom-up calculation are limited to a maximum of 85% of the corresponding nation and year’s estimate of industrial water withdrawals in FAO Aquastat.
Table 3: External inputs used for demand of food, feed, and forestry 3
| Name | Description | Type | Source | Resolution | Unit |
|---|---|---|---|---|---|
| Historical demand for crops | Demand for agricultural commodities in the historical period; used for initialization/calibration of GCAM | External data | FAO | Specified by crop, use, country, and year | tons |
| Historical demand for livestock | Demand for livestock commodities in the historical period; used for initialization/calibration of GCAM | External data | FAO | Specified by crop, use, country, and year | tons |
| Historical demand for forest | Demand for forest products in the historical period; used for initialization/calibration of GCAM | External data | FAO | Specified by country and year | m3 |
| Income and price elasticity | Income and price elasticity of demand (for non-food, non-feed demand) | Assumption | Specified by demand | unitless | |
| Food demand parameters | Set of 11 parameters required for the food demand model | External data | Ambrosia | unitless | |
| Logit exponents | Share parameters dictating substitution between different commodities | Assumption | Specified by type demand | unitless |
Note that for the Shared Socioeconomic Pathways (SSPs), different inputs are used for some variables. See SSPs for more information.
Historical demand for agricultural commodities is provided in separate files for food, feed, export, and import.
Historical demand for livestock commodities is provided in separate files for food, feed, export, and import.
Historical data for forest demand is determined by production, export, and import data.
Price and income elasticity are specified in A_demand_supplysector.csv.
Logit exponents are specified in A_demand_supplysector.csv and A_demand_subsector.csv.
Parameters needed for the food demand module are determined by a separate model, Ambrosia, and read into GCAM. The GCAM input files are A_demand_food_nonstaples.csv and A_demand_food_staples.csv.
[Davies et al. 2013] Davies, E.G.R., Kyle, P., and Edmonds, J. 2013. An integrated assessment of global and regional water demands for electricity generation to 2095. Advances in Water Resources 52(3), pp 296-313. Link
[FAO Aquastat] FAO. 2016. AQUASTAT Main Database, Food and Agriculture Organization of the United Nations (FAO). Link
[FAOSTAT] FAO. 2016. FAOSTAT Statistics Database, Food and Agriculture Organization of the United Nations (FAO). Link
[Hejazi et al. 2014] Hejazi, M., J. Edmonds, L. Clarke, P. Kyle, E. Davies, V. Chaturvedi, M. Wise, P. Patel, J. Eom, K. Calvin, R. Moss, and S. Kim. 2014. Long-term global water projections using six socioeconomic scenarios in an integrated assessment modeling framework. Technological Forecasting and Social Change 13, pp 112-123. Link
[Kenny et al. 2009] Kenny, J., N. Barber, S. Hutson, K. Linsey, J. Lovelace, M. Maupin. Estimated use of water in the United States in 2005 Circular 1344, U.S. Geological Survey, U.S. Department of the Interior, Reston, Virginia. Link
[Liu et al. 2016] Liu, Y., Hejazi, M., Kyle, P., Kim, S., Davies, E., Miralles, D., Teuling, A., He, Y., and Niyogi, D. 2016. Global and Regional Evaluation of Energy for Water. Environmental Science & Technology 50(17), 9736-9745. Link
[Mekonnen and Hoekstra 2011] 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. Link
[Vassolo and Döll 2005] Vassolo, S., and Döll, P. 2005. Global-scale gridded estimates of thermoelectric power and manufacturing water use. Water Resources Research 41, W04010. Link