The GAINS Optimization Module: Identifying Cost-effective Measures for Improving Air Quality and Short-term Climate Forcing

Wagner, F. ORCID: https://orcid.org/0000-0003-3429-2374, Heyes, C. ORCID: https://orcid.org/0000-0001-5254-493X, Klimont, Z. ORCID: https://orcid.org/0000-0003-2630-198X, & Schoepp, W. ORCID: https://orcid.org/0000-0001-5990-423X (2013). The GAINS Optimization Module: Identifying Cost-effective Measures for Improving Air Quality and Short-term Climate Forcing. IIASA Interim Report. IIASA, Laxenburg, Austria: IR-13-001

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Abstract

This document describes the optimization framework of the GAINS model as used for the development of cost-effective air pollution control scenarios for Europe. We put particular emphasis on the methodology for finding cost-effective control strategies that address both environmental impact indicators related to air pollution, and the radiative forcing of (some of) these pollutants. The GAINS multi-pollutant multi-effect framework lends itself for analysing synergies and trade-offs between different objectives and for quantifying cost implications.

In this document we describe various formal aspects of the optimization, including the dimension of the solution space, nature and use of decision variables and their relation to relevant functions, such as cost, emissions and environmental impact indicators. We illustrate standard optimization configurations that are used to calculate commonly used scenarios. We introduce the gap closure procedure that allows to set targets that are guaranteed to be feasible and which at the same time respect the need to distribute environmental benefits evenly, as far as possible, between countries.

We further illustrate, for selected ambition levels, the trade-off between reductions in environmental impact indicators and radiative forcings. Within certain ranges, these trade-offs in terms of physical effects can be compensated by changing to a more costly control strategy. The cost for compensation can systematically be calculated, and very specific recommendations can be made in terms of measures in different countries.

Unlike in multi-criteria optimization the current formulation of the GAINS optimization makes very explicit the distinction between environmental objectives and control costs. Thus, judgements about the relative value of various environmental benefits are not hidden in some model assumption but need to be made explicit and open in view of the results. In this way, GAINS can be used to aid policy makers to contemplate policy options with the required flexibility, without losing sight of cost-effectiveness considerations.

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