Drivers/Model drivers

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Model drivers

The assumptions of direct model drivers are used in setting the parameter values in IMAGE 3.3. The start values are estimated from the literature and data, and future changes in values are inferred from the narratives and scenario drivers. The resulting parameter values are used as input for different parts of IMAGE 3.3. A list of the most important model drivers, their source and their use in the Framework overview is given in the table of drivers below.

Example of a model driver: technological change in agriculture

The management factor (MF) describes the actual yield per crop group and region as a proportion of the maximum potential yield. This maximum potential yield is estimated taking into account inhomogeneous soil and climate data across grid cells. The MF for the period up to 2005 is estimated as part of the IMAGE calibration procedure, using FAO statistics on actual crop yields and crop areas (FAO, 2013a). The start year for the MF is subsequently taken as a point of departure for future projections.

Guidance for future development of yield changes is provided by expert projections, such as FAO projections up to 2030 and 2050 (Bruinsma, 2003; Alexandratos and Bruinsma, 2012).The FAO trends are used as exogenous technical development in the MAGNET model, and subsequently adjusted to reflect the relative shortage of suitable land, as part of the model calculation (Component Agricultural economy). The combinations of production volumes and land areas from MAGNET are adopted as future MF projections into the future in IMAGE.

Future technological change is dependent on the storyline and needs to be consistent with other scenario drivers. For instance, strong economic growth is typically facilitated by rapid technology development and deployment, rising wages and a labour shift from primary production (agriculture) to secondary (industry) and tertiary (services) sectors. These developments foster more advanced management and technology in agriculture. In order to reflect different trends in exogenous yield increase, FAO trends are combined with projections of economic growth to develop scenario-specific trends of yield changes in multiple—baseline studies, like for the SSPs. Because the MF is such a decisive factor in future net agricultural land area, careful consideration of uncertainties is warranted.

Driver DescriptionSourceUse
Population projections
Population Number of people per region. UN
future: exogenous input
Population - grid Number of people per gridcell (using downscaling). UN
own downscaling
Urban population fraction Urban/rural split of population. UN
future: exogenous input
Economic development
Capital supply Capital available to replace depreciated stock and expand the stock to support economic growth. GTAP database
GDP per capita Gross Domestic Product per capita, measured as the market value of all goods and services produced in a region in a year, and is used in the IMAGE framework as a generic indicator of economic activity. World Bank database
future: exogenous input
GDP per capita - grid Scaled down GDP per capita from country to grid level, based on population density. Own downscaling
GINI coefficient Measure of income disparity in a population. If all have the same income, GINI equals 1. The lower the GINI, the wider the gap between the lowest and highest income groups. World Bank database
future: scenario assumptions
Labour supply Effective supply of labour input to support economic activities, taking into account the participation rate of age cohorts. Demographics: population by age cohort times participation rate, corrected for skill level
Private consumption Private consumption reflects expenditure on private household consumption. It is used in IMAGE as a driver of energy. The World Bank
future: exogenous input
Sector value added Value Added for economic sectors: Industry (IVA), Services (SVA) and Agriculture (AVA). These variables are used in IMAGE to indicate economic activity. Various sources
Timber demand Demand for roundwood and pulpwood per region. FAO
future: exogenous input or own calculation
Trade regimes, tariffs and barriers
Trade policy Assumed changes in market and non-market instruments that influence trade flows, subject to WTO rules and country and region regulation. Own assumptions
Trade restriction Trade tariffs and barriers limiting trade in energy carriers (in energy submodel). Own assumptions
Environmental and other policies
Adaptation level Level of adaptation to climate change , defined as the share of climate change damage avoided by adaptation. This level is be calculated by the model to minimise adaptation costs and residual damage, or set by the user. own assumpions, optimisation
Air pollution policy Air pollution policies set to reach emission reduction targets, represented in the model in the form of energy carrier and sector specific emission factors. EDGAR database
future based on own assumptions
Biofuel policy Policies to foster the use of biofuels in transport, such as financial incentives and biofuel mandates and obligations. Literature and own assumptions
Climate target Climate target, defined in terms of concentration levels, radiative forcing, temperature targets, or cumulative emissions. Own assumptions
Domestic climate policy Planned and/or implemented national climate and energy policies, such as taxes, feed-in tariffs, renewable targets, efficiency standards, that affect projected emission reduction. Own assumptions
Energy policy Policy to achieve energy system objectives, such as energy security and energy access. Own assumptions
Equity principles General concepts of distributive justice or fairness used in effort sharing approaches. Three key equity principles are: Responsibility (historical contribution to warming); capability (ability to pay for mitigation); and equality (equal emissions allowances per capita). Expert judgement
Protected area - grid Map of protected nature areas, limiting use of this area. WDPA database
own assumptions, derived from polygon maps
Taxes and other additional costs Taxes on energy use, and other additional costs IEA
future: scenario assumptions
Technological change in the energy system
Energy efficiency technology Model assumptions determining future development of energy efficiency. Own assumptions
Energy intensity parameters Set of parameters determining the energy use per unit of economic activity (in absence of technical energy efficiency improvements). IEA
future: own calculations and scenario assumptions
Learning rate Determines the rate of technology development in learning equations. Literature and scenario assumptions
Technology development of energy conversion Learning curves and exogenous learning that determine technology development. Scenario assumption based on various sources
Technology development of energy supply Learning curves and exogenous learning that determine technology development. Scenario assumptions based on various sources
Energy and land resources
Built-up area - grid Urban built-up area per grid cell, excluded from all biophysical modelling in IMAGE, increasing over time as a function of urban population and a country- and scenario-specific urban density curve. Klein Goldewijk et al., 2010
Energy resources Volume of energy resource per carrier, region and supply cost class (determines depletion dynamics). Mulders et al., 2006, Rogner, 1997
Technological change in agriculture and forestry
Animal productivity Effective production of livestock commodities per animal per year. FAOSTAT database
future: scenario assumptions
Feed conversion Measure of an animal's efficiency in converting feed mass into the desired output such as meat and milk (for cattle, poultry, pigs, sheep and goats). FAOSTAT database
future: scenario assumptions
Fertiliser use efficiency Ratio of fertiliser uptake by a crop to fertiliser applied. FAOSTAT database
future: scenario assumptions
Bouwman et al., 2013b
Forest plantation demand Demand for forest plantation area. FAOSTAT database
future: scenario assumptions
Fraction of selective logging The fraction of forest harvested in a grid, in clear cutting, selective cutting, wood plantations and additional deforestation. Fraction of selective cut determines the fraction of timber harvested by selective cutting of trees in semi-natural and natural forest. Own estimates, based on FAO
Harvest efficiency Fraction of harvested wood used as product, the remainder being left as residues. Specified per biomass pool and forestry management type. Various sources
Increase in irrigated area - grid Increase in irrigated area, often based on external projections (e.g., FAO). FAO, IIASA
Own assumptions for spatial allocation
Irrigation conveyance efficiency Ratio of water supplied to the irrigated field to the quantity withdrawn from the water source, determining the quantity of water lost during transport. This parameter is defined at country level. PIK
Rohwer et al., 2007
    Irrigation project efficiency Ratio of quantity of irrigation water required by the crop (based on soil moisture deficits) to the quantity withdrawn from rivers, lakes, reservoirs or other sources. This parameter is given at country level. PIK
    Rohwer et al., 2007
      Livestock rations Determines the feed requirements per feed type (food crops; crop residues; grass and fodder; animal products; scavenging), specified per animal type and production system (extensive/intensive/backyard/intermediate/intensive/broiler/laying hens). Own estimates
      Bouwman et al., 2005, Lassaletta et al., 2019
      Manure spreading fraction Fraction of manure produced in staples that is spread on agricultural areas. Own estimates
      Bouwman et al., 2013b
      Production system mix Livestock production is distributed over two systems for dairy and beef production (intensive: mixed and industrial; extensive: pastoral grazing), and to three systems for pigs (backyard, intermediate, intensive) and poultry (backyard, boilers, laying hens) with specific intensities, rations and feed conversion ratios. Own estimates
      Bouwman et al., 2005, Lassaletta et al., 2019
      Technological change (crops and livestocks) Increase in productivity in crop production (yield/ha) and livestock production (carcass weight, offtake rate). FAO
      own estimates
      Lifestyle parameters
      Lifestyle parameters Lifestyle parameters influence the relationship between economic activities and demand for energy. IEA
      based on calibration
      Preferences Non-price factors determining market shares, such as preferences, environmental policies, infrastructure and strategic considerations, used for model calibration. Calibration, future: scenario assumptions