Innovative approaches in integrated assessment modelling of European air pollution control strategies - Implications of dealing with multi-pollutant multi-effect problems

In this paper, crucial aspects of the implications and the complexity of interconnected multi-pollutant multi-effect assessments of both air pollution control strategies and the closely related reduction of greenhouse gas emissions will be discussed. The main aims of the work described here are to identify the core problems which occur when trying to apply current state-of-the-art methodology to conduct integrated assessments - in this context, cost-benefit assessment (CBA) as well as cost-effectiveness assessment (CEA) - using sophisticated computer models and propose solutions to the problems identified. The approaches described will display the integrated use of databases, efficient algorithms and already existing software tools and models in a unified model framework. The first part of the paper discusses the need for new developments in one particular field of Integrated Assessment Models (IAMs), which is the use of (typically) country-specific single pollutant abatement cost curves, which have been applied in a large number of 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 ozone abatement (dealing with two primary pollutants and their rather complex relationship to form tropospheric ozone, [see] [Friedrich, R., Reis, S. (Eds.), 2000. Tropospheric Ozone Abatement - Developing Efficient Strategies for the Reduction of Ozone Precursor Emissions in Europe. Springer Publishers] identified basic problems of cost curve based approaches even in this two-pollutant case. The approach discussed here promises to solve the key problems identified, making extensive use of databases in order to provide fast and high quality model input for CEA and CBA. In addition to that, the application of Genetic Algorithms will be discussed as a means to address extremely complex, vast solution spaces which are typical for the tasks IAMs are set to solve nowadays. As the application of the model in extensive assessment studies is currently under way, it is yet too early for a full evaluation of lessons learned. However, initial tests of performance and behaviour have shown robust and promising results.