Presenter/Author Information

Stefan Reis
Steffen Nitter
Rainer Friedrich

Keywords

genetic algorithms, optimisation, environment, air quality, climate change

Start Date

1-7-2004 12:00 AM

Description

In this paper, crucial aspects of the implications and the complexity of interconnected multipollutantmulti-effect assessments of both air pollution control strategies and the closely related reduction ofGreenhouse Gas (GHG) emissions will be discussed. The main aims of the work described here are to identifythe core problems which occur when trying to apply current state-of-the-art methodology to conductintegrated assessments – in this context, cost-benefit assessment (CBA) as well as cost-effectivenessassessment (CEA) – using sophisticated computer models and propose solutions to the problems identified.The approaches described will display the integrated use of databases, efficient Genetic Algorithms (GA) andalready existing software tools and models in a unified model framework. The first part of the paper discussesthe need for new developments in one particular field of Integrated Assessment Models (IAMs), the use of(typically) country-specific single pollutant abatement cost curves, which have been applied in a large numberof modelling approaches with the aim to find cost effective solutions for given air quality targets. However,research conducted to find such cost effective solutions for the non-linear problem of tropospheric ozoneabatement (dealing with two primary pollutants and their rather complex relationship to form troposphericozone) identified basic problems of cost-curve based approaches even in this two-pollutant case. Theapproach discussed here solves the key problems identified, making extensive use of databases in order toprovide fast and high quality model input for CEA and CBA. In addition to that, the application of GeneticAlgorithms will be discussed as a means to address extremely complex, vast solution spaces which are typicalfor the tasks IAMs are set to solve nowadays. In the final part of the paper, diversity increasing operators andmethods to increase the performance of the GA to find optima are described and first results of extensivemodel runs are discussed.

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Jul 1st, 12:00 AM

Application of Genetic Algorithms for the Optimisation of Multi-Pollutant Multi-Effect Problems

In this paper, crucial aspects of the implications and the complexity of interconnected multipollutantmulti-effect assessments of both air pollution control strategies and the closely related reduction ofGreenhouse Gas (GHG) emissions will be discussed. The main aims of the work described here are to identifythe core problems which occur when trying to apply current state-of-the-art methodology to conductintegrated assessments – in this context, cost-benefit assessment (CBA) as well as cost-effectivenessassessment (CEA) – using sophisticated computer models and propose solutions to the problems identified.The approaches described will display the integrated use of databases, efficient Genetic Algorithms (GA) andalready existing software tools and models in a unified model framework. The first part of the paper discussesthe need for new developments in one particular field of Integrated Assessment Models (IAMs), the use of(typically) country-specific single pollutant abatement cost curves, which have been applied in a large numberof modelling approaches with the aim to find cost effective solutions for given air quality targets. However,research conducted to find such cost effective solutions for the non-linear problem of tropospheric ozoneabatement (dealing with two primary pollutants and their rather complex relationship to form troposphericozone) identified basic problems of cost-curve based approaches even in this two-pollutant case. Theapproach discussed here solves the key problems identified, making extensive use of databases in order toprovide fast and high quality model input for CEA and CBA. In addition to that, the application of GeneticAlgorithms will be discussed as a means to address extremely complex, vast solution spaces which are typicalfor the tasks IAMs are set to solve nowadays. In the final part of the paper, diversity increasing operators andmethods to increase the performance of the GA to find optima are described and first results of extensivemodel runs are discussed.