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Multi-criteria analysis of policies for implementing clean energy vehicles in China

Li, C, Negnevitsky, M ORCID: 0000-0002-5130-419X, Wang, X ORCID: 0000-0003-4293-7523, Yue, WL and Zou, X 2019 , 'Multi-criteria analysis of policies for implementing clean energy vehicles in China' , Energy Policy, vol. 129 , pp. 826-840 , doi: 10.1016/j.enpol.2019.03.002.

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Abstract

To resolve socioeconomic and environmental issues caused by vehicular emissions, the Chinese government has developed a series of policies for promoting clean energy vehicles (CEVs), which can be powered by electricity, gas, ethanol or methanol. Effective implementation of these policies requires a comprehensive evaluation of CEVs. Decision-makers need to take into account multiple criteria such as energy performance, energy cost, vehicular emission, market acceptance and energy security. This paper proposes a decision support model, which applies multi-criteria analysis to prioritize CEVs existing and to be launched on the Chinese market. Government officials, academic researchers and industrial executives are interviewed to select and rank criteria for optimizing decision-making using the Analytic Hierarchy Process and the VIKOR optimization techniques. Thirty-five experts have been interviewed for prioritizing four categories of CEVs including electric, gas, methanol and ethanol vehicles from both the national and provincial perspectives. Results demonstrate that electric vehicles represent the highest ranking, followed by gas, methanol and ethanol vehicles. The proposed model has been validated using statistical data and existing government policies. The proposed multi-criteria analysis can be used for advising decision-makers in the area of clean energy vehicles.

Item Type: Article
Authors/Creators:Li, C and Negnevitsky, M and Wang, X and Yue, WL and Zou, X
Keywords: Clean energy vehicles, multi-criteria analysis, analytic hierarchy process, VIKOR
Journal or Publication Title: Energy Policy
Publisher: Elsevier Sci Ltd
ISSN: 0301-4215
DOI / ID Number: 10.1016/j.enpol.2019.03.002
Copyright Information:

© 2019 Elsevier Ltd. All rights reserved.

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