Composite indicators in energy and environmental modeling

Abstract

Energy and environmental (E&E) modeling is useful to decision makers dealing with complex E&E issues in making rational decisions. Among the wide spectrum of E&E modeling techniques, the construction of various E&E related composite indicators has recently received much attention. This thesis aims to address some key methodological issues related to applying data envelopment analysis (DEA) and multiple criteria decision analysis (MCDA) to construct E&E related composite indicators. In DEA, we discuss different environmental DEA technologies and present their radial implementations in developing environmental performance index. A non-radial DEA approach and two slacks-based efficiency measures for modeling environmental performance are also proposed. In MCDA, based on the "minimum information loss" concept, we propose the Shannon-Spearman measure for comparing alternative MCDA aggregation methods in constructing composite indicators, and give an information-theoretical aggregation approach to constructing composite indicators. In virtue of the ideas of DEA and MCDA, we finally present a linear programming approach to constructing composite indicators.