Environmental Efficiency Model Based on Data Envelopment Analysis and Its Application to Environmentally Sustainable Transport Policies

To realize environmentally sustainable transport (EST), it becomes more and more important to reduce the environmental load from the transport sector while maintaining the level of mobility. Because mobility policies often conflict with environmental ones, policy decision makers need to find a way to solve the exclusiveness between these two policies. This study attempts to apply the concept of ecoefficiency, originally proposed by the World Business Council for Sustainable Development, to tackle the dilemma between urban mobility and environmental load in cities in both developed and developing countries. This study proposes a new model, the environmental efficiency (EE) model, which expands the concept of the data envelopment analysis cost-efficiency model. This new model aims to measure the efficiency of energy consumption at a given level of mobility in each transport system by incorporating some feasible conditions. In addition, the model can find multiple sets of frontier cities that are the most efficient among homogeneously developed cities. Consequently, the proposed method contributes to providing each city with a feasible transport energy consumption goal. Moreover, a panel analysis was carried out to examine temporal changes in environmental efficiency. Finally, the effects of some EST policies implemented to raise efficiency are simulated on the basis of the EE model, including modal shifts in a city, technological innovations to improve the intensity of energy consumption in cities, and the emissions trading scheme between cities in developing and developed countries. The method is confirmed to be a useful tool for the establishment of more plausible targets for transport energy-saving policies.


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  • Accession Number: 01152397
  • Record Type: Publication
  • ISBN: 9780309142939
  • Report/Paper Numbers: 10-3830
  • Files: TRIS, TRB
  • Created Date: Jan 25 2010 11:57AM