Models, data & tools

GLOBIOM/G4M consists of two interlinked models, GLOBIOM and G4M. The Global Biosphere Management Model (GLOBIOM) is a global recursive dynamic partial equilibrium model of the forest and agricultural sectors. The model is based on a bottom-up approach where the supply side of the model is built-up from the bottom (land cover, land use, management systems) to the top (production/markets). The agricultural and forest productivity is modelled spatially explicit through links to biophysical models (crop models, forest growth models, etc.) for different production systems. G4M is a forest sector model and estimates the impact of forestry activities (afforestation, deforestation, residue harvest and forest management) on biomass and carbon stocks. The model incorporates empirical forest growth functions for major tree species groups and is spatially explicit. G4M relies on information for wood prices, land rents and wood demand from GLOBIOM. GLOBIOM/G4M together cover all UNFCCC land use categories of relevance for CO2 emissions.

The GEM-E3 model is a multi-regional, multi-sectoral, recursive dynamic computable general equilibrium (CGE) model which provides details on the macro-economy and its interaction with the environment and the energy system. It is an empirical, large scale model, written entirely in structural form. GEM-E3 allows for a consistent comparative analysis of policy scenarios since it ensures that in all scenarios, the economic system remains in general equilibrium. In addition, it incorporates micro-economic mechanisms and institutional features within a consistent macro-economic framework and avoids the representation of behaviour in reduced form. Particularly valuable are the insights the model provides regarding the distributional aspects of long-term structural adjustments. The GEM-E3 model is extensively used as a tool of policy analysis and impact assessment.

D-PLACE is a recursive dynamic, global, multi-sector Computable General Equilibrium (CGE) model, based on GTAP (Global Trade Analysis Project) dataset. In a current setting, the model distinguishes 20 industries/commodities (including energy intensive and trade exposed industries, such as production of refined oil products and coke, chemicals, non-metallic mineral products, paper and pulp, iron and steel, and non-ferrous metals), and 19 regions (country groups or individual countries), including 9 EU regions, and 10 non-EU regions. In its core, d-PLACE follows standard formulations, with nested Leontief-CES (Constant Elasticity of Substitution) production functions, marginal cost pricing and bilateral trade based on the Armington assumption. The model contains detailed information on GHG emission structure and implements emission reduction targets in the EU ETS (European Union Emissions Trading System), as well as separate country-level targets for the non-ETS sectors in the EU. Emission pricing is used as an instrument to facilitate emission reductions. The model includes a few explicit technologies of emission abatement, including hydrogen and CCS. Emission abatement in energy, agriculture and transport model is calibrated with the output of sectoral models.

RICE50+ is a multi-regional Integrated Assessment Model for Assessment and Cost-Benefit Analyses based on calibrated mitigation and climate impact costs for over 50 regions and countries. The model is a modular, highly regionalised, and flexible model built upon the DICE model, with calibrated abaetment costs, a variety of regional climate and impact functions. It includes in a modular fashion features such as within-country inequality, Solar Radiation Management, Carbon Dioxide Removal, and Natural Capital, among others. The modeling framework considers 57 (and up to 160) independent countries/regions. The dynamics of economic growth, greenhouse gases (GHG) emissions, emissions mitigation costs, and economic impacts due to climate change follow the well-known integrated assessment model DICE. The regional representation is consistent with the finest granularity at which data, especially marginal abatement costs curves (MACC), is available. Socioeconomic drivers, including population and economics growth at the country level, come from the five Shared Socioeconomic Pathways (SSP).

MIMOSA is an open-source cost-benefit Integrated Assessment Model. It uses a Cobb-Douglas production function and has baseline emissions, total factor productivity and population calibrated on the Shared Socio-Economic Pathways (SSPs). Mitigation occurs through a Marginal Abatement Cost (MAC) curve, which has been calibrated to reproduce the mitigation costs from the IPCC 6th Assessment Report WGIII scenario database. Each region uses the same global Marginal Abatement Cost (MAC) curve, but with a region-specific scaling factor calibrated using SSP2 MAC curves from the IMAGE model. This scaling factor is obtained by comparing the carbon price per region required to reach 75% CO2 reduction in 2050 compared to baseline, relative to the world average. The regional differences in marginal abatement costs stay constant over time: a region that has relatively cheap mitigation options now is assumed to keep relatively cheap marginal mitigation costs throughout the century.