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Prospective assessment of methanol vehicles in China using FANP-SWOT analysis

Li, C, Negnevitsky, M ORCID: 0000-0002-5130-419X and Wang, X ORCID: 0000-0003-4293-7523 2020 , 'Prospective assessment of methanol vehicles in China using FANP-SWOT analysis' , Transport Policy, vol. 96 , pp. 60-75 , doi:

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China has been at the global forefront of alternative fuel vehicle development, including methanol vehicles, in a bid to achieve economic benefits and improve environmental outcomes for the nation. Methanol vehicle pilot projects completed in ten Chinese cities have demonstrated that methanol vehicles can reduce costs and emissions. In considering whether methanol vehicles should be deployed more widely in China, there is a role for bespoke decision-making methods as an aid for assessing the prospects for such an expansion and developing the strategies needed for implementation. In this paper, a FANP (fuzzy analytic network process) - SWOT (strengths, weaknesses, opportunities, and threats) method was proposed and applied to assess the prospects for methanol vehicles in China. Based on the proposed method, an evaluation criteria system that includes 12 criteria from social, technological, economic, environmental, and political fields was established. Each criterion was weighted based on interviews with 16 experts who have extensive experience and expertise related to policy development for methanol vehicles in China. As a result, 10 strategies for the further implementation of methanol vehicles in China were proposed and prioritized. The results form the basis for six proposed policy implications for the further deployment of methanol vehicles. Policies for developing core technologies for methanol vehicles to achieve socioeconomical and environmental benefits are given the highest priority.

Item Type: Article
Authors/Creators:Li, C and Negnevitsky, M and Wang, X
Keywords: methanol vehicles, fuzzy analytic network process, SWOT analysis
Journal or Publication Title: Transport Policy
Publisher: Elsevier Sci Ltd
ISSN: 0967-070X
DOI / ID Number:
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© 2020 Elsevier Ltd. All rights reserved

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