The GAINS model (Greenhouse gas-Air pollution Interactions and Synergies) model

Integrated assessment of cost-effective control strategies


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GAINS explores cost-effective multi-pollutant emission control strategies that meet environmental objectives on air quality impacts (on human health and ecosystems) and greenhouse gases.

Model characteristics

The GAINS model is an integrated assessment model that brings together information on the sources and impacts of air pollutant and greenhouse gas emissions and their interactions. GAINS is an extension of the earlier RAINS (Regional Air Pollution Information and Simulation) model, which addressed air pollution aspects only. GAINS brings together data on economic development, the structure, control potential and costs of emission sources, the formation and dispersion of pollutants in the atmosphere and an assessment of environmental impacts of pollution. GAINS addresses air pollution impacts on human health from fine particulate matter and ground-level ozone, vegetation damage caused by ground-level ozone, the acidification of terrestrial and aquatic ecosystems and excess nitrogen deposition) of soils, in addition to the mitigation of greenhouse gas emissions. GAINS describes the inter­relations between these multiple effects and the range of pollutants (SO2, NOx, PM, NMVOC, NH3, CO2, CH4, N2O, F-gases) that contribute to these effects at the European scale.

Typical features and results

GAINS assesses, for each of the 43 countries in Europe, more than 1000 measures to control the emissions to the atmosphere. It computes the atmospheric dispersion of pollutants and analyzes the costs and environmental impacts of pollution control strategies. In its optimization mode, GAINS identifies the least-cost balance of emission control measures across pollutants, economic sectors and countries that meet user-specified air quality and climate targets.

Data sources

Input from other EC4MACS models

Output to other EC4MACS models


A detailed description of the GAINS model can be found in Amann et al. (2011) and

Developed by

International Institute for Applied Systems Analysis (IIASA)