Model Documentation - REMIND-MAgPIE

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Model Documentation - REMIND-MAgPIE

Corresponding documentation
Previous versions
Model information
Model link
Institution Potsdam Institut für Klimafolgenforschung (PIK), Germany,
Solution concept General equilibrium (closed economy)MAgPIE: partial equilibrium model of the agricultural sector;
Solution method OptimizationMAgPIE: cost minimization;

This documentation describes the REMIND-MAgPIE framework coupling the energy-economy model REMIND and the agricultural production model MAgPIE.


The Integrated Assessment Model REMIND (REgional Model of Investment and Development) represents the future evolution of the world economies with a special focus on the development of the energy sector and the implications for our world climate. Given a set of population, technology, policy and climate constraints, the goal of REMIND is to find the welfare-optimal mix of investments in the economy and the energy sectors of each model region. It also accounts for regional trade characteristics on goods, energy fuels, and emissions allowances. All greenhouse gas emissions due to human activities are represented in the model.

REMIND is an energy-economy general equilibrium model linking a macro-economic growth model with a bottom-up engineering-based energy system model. It covers twelve world regions, differentiates various energy carriers and technologies and represents the dynamics of economic growth and international trade.

A Ramsey-type growth model with perfect foresight serves as a macro-economic core projecting growth, savings and investments, factor incomes, energy and material demand. The macro-economic production factors are capital, labor, and final energy. A nested production function with constant elasticity of substitution determines the final energy demand. REMIND uses economic output for investments in the macro-economic capital stock as well as for consumption, trade, and energy system expenditures.

The energy system representation differentiates between a variety of fossil, biogenic, nuclear and renewable energy resources. More than 50 technologies are available for the conversion of primary energy into secondary energy carriers as well as for the distribution of secondary energy carriers into final energy. The macro-economic core and the energy system part are hard-linked via the final energy demand and the costs incurred by the energy system. Economic activity results in demand for final energy in different sectors (transport, industry, buildings..) and of different type (electric and non-electric).

The model accounts for crucial drivers of energy system inertia and path dependencies by representing full capacity vintage structure, technological learning of emergent new technologies, as well as adjustment costs for rapid upscaling of new technologies. The emissions of greenhouse gases (GHGs) and air pollutants are largely represented by source and linked to activities in the energy-economic system. Several energy sector policies are represented explicitly, including energy-sector fuel taxes and consumer subsidies.

Further reading:

  • REMIND code on GitHub:
  • REMIND documentation (version 2.1.3):
  • REMIND2.1 paper:


The Model of Agricultural Production and its Impact on the Environment (MAgPIE) is a global land use allocation model, which is connected to the grid-based dynamic vegetation model LPJmL, with a spatial resolution of 0.5°x0.5°. It takes regional economic conditions such as demand for agricultural commodities, technological development and production costs as well as spatially explicit data on potential crop yields, land and water constraints (from LPJmL) into account. Based on these, the model derives specific land use patterns, yields and total costs of agricultural production for each grid cell. The objective function of the land use model is to minimize total cost of production for a given amount of regional food and bioenergy demand. Regional food energy demand is defined for an exogenously given population in 10 food energy categories, based on regional diets. Future trends in food demand are derived from a cross-country regression analysis, based on future scenarios on GDP and population growth.

Food and feed energy for the demand categories can be produced by 20 cropping activities and 3 livestock activities. Feed for livestock is produced as a mixture of crops, crop residuals, processing byproducts, green fodder produced on crop land, and pasture. Variable inputs of production are labour, chemicals, and other capital (all measured in US$). Costs of production are derived from the Global Trade Analysis Project (GTAP) Database. The model can endogenously decide to acquire yield-increasing technological change at additional costs. The costs for technological change for each economic region are based on its level of agricultural development, measured as agricultural land-use intensity. These costs grow with further investment in technological change. The use of technological change is either triggered by a better cost-effectiveness compared to other investments or as a response to resource constraints, such as land scarcity.

For future projections the model works on 5-10 year a time steps of 10 years in a recursive dynamic mode. The link between two consecutive periods is established through the land-use pattern. The optimized land-use pattern from one period is taken as the initial land constraint in the next. If necessary, additional land from non-agricultural areas can be converted into cropland at additional costs. Potential crop yields for MAgPIE are originally computed with LPJmL at a 0.5° resolution, as weighted average of irrigated and non-irrigated production, if part of the grid cell is equipped for irrigation according to the global map of irrigated areas. In case of purely rain-fed production, no additional water is required, but yields are generally lower than under irrigation. If a certain area share is irrigated, additional water for agriculture is taken from available water discharge in the grid cell. Each cell of the geographic grid is assigned to 1 of 120 economic world regions: CAZ (Canada, Australia and New Zealand; CHA (China); EUR (European Union); IND (India); JPN (Japan); LAM (Latin America); MEA (Middle East and north Africa); NEU (non-EU member states); OAS (other Asia); REF (reforming countries); SSA (Sub-Saharan Africa); USA (United States) The regions are initially characterized by data for the year 1995 on population, gross domestic product (GDP), food energy demand, average production costs for different production activities, and current self-sufficiency ratios for food. Land-conversion activities provide for potential expansion and shifts of agricultural land in specific locations. For the base year 1995, total agricultural land is constrained to the area currently used within each grid cell, according to the dataset of as extended by. Cropland can be converted into rangeland, and vice versa. If additional land is required for fulfilling demand, this can be taken from the pool of non-agricultural land at additional costs. These land-conversion costs force the model to utilize available cropland and rangeland first, and land conversion will become relevant only if land becomes scarce in a certain location or if the marginal cost reductions by producing crops on converted land outweigh the costs of conversion.

Further reading:


For some questions, REMIND and MAgPIE are soft coupled to provide a detailed answer. From a climate protection perspective, two aspects of the land-use sector are of particular interest: the supply of biomass that can be used for energy production (possibly with carbon capture and storage – CCS) and the total emissions of the land-use sector. Changing crucial parameters in REMIND (such as the climate target or the availability of technologies or resources) can have significant impact on GHG prices and bioenergy demand. Thus, REMIND and MAgPIE can be run in an iterative soft-coupled mode, where REMIND updates MAgPIE's assumptions regarding bioenergy demand and GHG prices, and MAgPIE, in turn, updates REMIND's assumptions regarding bioenergy prices and land-use emissions and agricultural production costs. The iteration is continued until changes between iterations become negligible. The resulting scenarios are consistent regarding the price and quantity of bioenergy and GHG emissions.