Using an Entropy-GRA, TOPSIS, and PCA Method to Evaluate the Competitiveness of AFVs – The China Case

  • Feng Xue Southwest Jiaotong University, Chengdu, China
  • Qian Huang Southwest Jiaotong University, Chengdu, China
  • Chuanlei He Southwest Jiaotong University, Chengdu, China
  • Bharat Pathak Southwest Jiaotong University, Chengdu, China
Keywords: alternative fuel vehicle industry, comprehensive evaluation of competitiveness, entropy weighting method, correlation analysis

Abstract

With the increase in severe environmental problems associated with fossil fuel vehicles, the development of Alternative Fuel Vehicles (AFVs) has led to their promotion and use in Chinese provinces and cities. The comprehensive evaluation of competitiveness of the AFV industry in Chinese cities is beneficial to analyse the effects and relationships of different factors to promote the sustainable development of the AFV industry and guide the growth paths of the cities. An industrial competitiveness evaluation index system is established based on the characteristics of AFVs, and the development of the AFV industry in ten typical cities in China is comprehensively evaluated based on the Grey Relative Analysis (GRA) Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) and Principal Component Analysis (PCA) methods. To evaluate the results, the entropy weighting method is used for the weight distribution, and the industrial competitiveness rankings of ten cities are obtained by the entropy-GRA, TOPSIS, PCA (EGTP) method. The results show that Beijing is ranked first, followed by Shanghai, and Qingdao is ranked last. By analysing the correlation between the evaluation methods and indicators, it is found that EGTP has a high correlation with the other three evaluation methods, which proves the rationality of the weighted linear combination of GRA and the other three methods. Indices C5 (pure electric car proportion) and C13 (average concentration of PM2.5) were outliers due to the small number of samples.

References

Creutzig F, Jochem P, Edelenbosch OY, Mattauch L, van Vuuren DP, McCollum D, Minx J. Transport: A roadblock to climate change mitigation? Science. 2015;350: 911-912.

Ministry of Ecology and Environment of the People’s Republic of China. 2017 China Ecological Environment Status Bulletin Background Material; 2018. Available from: http://www.mee.gov.cn/gkml/sthjbgw/qt/201805/t20180531_442212.htm

Sengupta S, Cohan DS . Fuel cycle emissions and life cycle costs of alternative fuel vehicle policy options for the City of Houston municipal fleet. Transportation Research Part D: Transport and Environment. 2017;54: 160-171.

Hackbarth A, Madlener R. Consumer preferences for alternative fuel vehicles: A discrete choice analysis. Transportation Research Part D: Transport and Environment. 2013;25: 5-17.

Jenn A, Azevedo I, Michalek J. Alternative-fuel-vehicle policy interactions increase U.S. greenhouse gas emissions. Transportation Research Part A: Policy and Practice. 2019;124: 396-407.

Zhang Y, Qi D, Jiang W, et al. Optimal allocation of changing station for electric vehicle based on queuing theory. Promet – Traffic&Transportation. 2016;28(5): 497-505.

Ruan X-J, Shi R-L. Study on the evaluation of competitiveness of new energy automobile industry based on grey correlation model. Mathematics in Practice and Theory. 2016; 46(21):72-79.

Xie W-H, Zeng D-C. Empirical study on competitiveness evaluation of new energy automobile industry in Guangdong province based on the new diamond model. Science and Technology Management Research. 2019;9: 6-61.

Yan S-G. Assessment of Beijing’s new energy industry based on AHP-FCE comprehensive evaluation. Science and Technology Management Research. 2017;7: 93-97.

The State Council of the People’s Republic of China. Guiding Opinions on Accelerating the Promotion and Application of New Energy Vehicles by the State Council of the People’s Republic of China; 2014. Available from: http://www.gov.cn/zhengce/content/2014-07/21/content_8936.htm

Tang B-J, Wang X-Y, Wei Y-M et al. Analysis and Prospect of China's New Energy Automobile Industry

Development Level. Beijing Institute of Technology Energy and Environmental Policy Research Center; 2019. Available from: http://ceep.bit.edu.cn/docs/2019-01/20190114103247617774.pdf

Chan J, Tong T. Multi-criteria material selections and end-of-life product strategy: Grey relational analysis approach. Materials & Design. 2007;28(5): 1539-1546.

Kuo T. A modified TOPSIS with a different ranking index. European Journal of Operational Research. 2016;260(1): 152-160.

Abdi H, Williams LJ, Valentin D. Multiple factor analysis: Principal component analysis for multitable and multiblock data sets. Wiley Interdisciplinary Reviews: Computational Statistics. 2013;5(2): 149-179.

Wang Q, Wu C, Sun Y. Evaluating corporate social responsibility of airlines using entropy weight and grey relation analysis. Journal of Air Transport Management. 2015;42: 55-62.

Industrial Economy Research Department of Development Research Center of the State Council. China automobile industry development report. Beijing: Social Sciences Academic Press; 2018.

China Traffic Yearbook Editorial Committee. China traffic yearbook. Beijing: Yearbook of China Transportation & Communication; 2018.

Published
2020-10-05
How to Cite
1.
Xue F, Huang Q, He C, Pathak B. Using an Entropy-GRA, TOPSIS, and PCA Method to Evaluate the Competitiveness of AFVs – The China Case. PROMET [Internet]. 2020Oct.5 [cited 2020Oct.20];32(5):655-66. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/3417
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Articles