IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i8p3150-d345305.html
   My bibliography  Save this article

Risk Assessment of New Energy Vehicle Supply Chain Based on Variable Weight Theory and Cloud Model: A Case Study in China

Author

Listed:
  • Qingyou Yan

    (School of Economic & Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy & Low Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Meijuan Zhang

    (School of Economic & Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy & Low Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Wei Li

    (School of Economic & Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy & Low Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Guangyu Qin

    (School of Economic & Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy & Low Carbon Development, North China Electric Power University, Beijing 102206, China)

Abstract

In order to protect the environment and reduce energy consumption, new energy vehicles have begun to be vigorously promoted in various countries. In recent years, the rise of intelligent technology has had a great impact on the supply chain of new energy vehicles, which, coupled with the complexity of the supply chain itself, puts it at great risk. Therefore, it is quite indispensable to evaluate the risk of the new energy vehicle supply chain. This paper assesses the risks faced by China’s new energy vehicle supply chain in this period of technological transformation. First of all, this paper establishes an evaluation criteria system of 16 sub-criterion related to three dimensions: the market risk, operational risk, and the environmental risk. Then, variable weight theory is proposed to modify the constant weight obtained by the fuzzy analytic hierarchy process (FAHP). Finally, a risk assessment of China’s new energy vehicle supply chain is carried out by combining the variable weight and the cloud model. This method can effectively explain the randomness of matters, and avoid the influence of value abnormality on the criteria system. The results show that China’s new energy vehicle supply chain is at a high level. Through the identification of risk factors, mainly referring to the low clustering risk, technical level risk and information transparency risk, this paper can provide a risk prevention reference for corresponding enterprises.

Suggested Citation

  • Qingyou Yan & Meijuan Zhang & Wei Li & Guangyu Qin, 2020. "Risk Assessment of New Energy Vehicle Supply Chain Based on Variable Weight Theory and Cloud Model: A Case Study in China," Sustainability, MDPI, vol. 12(8), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3150-:d:345305
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/8/3150/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/8/3150/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chun-Hung Chiu & Tsan-Ming Choi, 2016. "Supply chain risk analysis with mean-variance models: a technical review," Annals of Operations Research, Springer, vol. 240(2), pages 489-507, May.
    2. Jian Huang & Mingming Leng & Liping Liang & Jian Liu, 2013. "Promoting electric automobiles: supply chain analysis under a government’s subsidy incentive scheme," IISE Transactions, Taylor & Francis Journals, vol. 45(8), pages 826-844.
    3. Masiero, Gilmar & Ogasavara, Mario Henrique & Jussani, Ailton Conde & Risso, Marcelo Luiz, 2017. "The global value chain of electric vehicles: A review of the Japanese, South Korean and Brazilian cases," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 290-296.
    4. Luo, Chunlin & Leng, Mingming & Huang, Jian & Liang, Liping, 2014. "Supply chain analysis under a price-discount incentive scheme for electric vehicles," European Journal of Operational Research, Elsevier, vol. 235(1), pages 329-333.
    5. Muhammad Junaid & Ye Xue & Muzzammil Wasim Syed & Ji Zu Li & Muhammad Ziaullah, 2019. "A Neutrosophic AHP and TOPSIS Framework for Supply Chain Risk Assessment in Automotive Industry of Pakistan," Sustainability, MDPI, vol. 12(1), pages 1-26, December.
    6. Kannan Govindan & Martin Brandt Jepsen, 2016. "Supplier risk assessment based on trapezoidal intuitionistic fuzzy numbers and ELECTRE TRI-C: a case illustration involving service suppliers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(2), pages 339-376, February.
    7. Noori, Mehdi & Tatari, Omer, 2016. "Development of an agent-based model for regional market penetration projections of electric vehicles in the United States," Energy, Elsevier, vol. 96(C), pages 215-230.
    8. Munir, Manal & Jajja, Muhammad Shakeel Sadiq & Chatha, Kamran Ali & Farooq, Sami, 2020. "Supply chain risk management and operational performance: The enabling role of supply chain integration," International Journal of Production Economics, Elsevier, vol. 227(C).
    9. Han, Liu & Wang, Shanyong & Zhao, Dingtao & Li, Jun, 2017. "The intention to adopt electric vehicles: Driven by functional and non-functional values," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 185-197.
    10. Anna Corinna Cagliano & Alberto De Marco & Sabrina Grimaldi & Carlo Rafele, 2012. "An integrated approach to supply chain risk analysis," Journal of Risk Research, Taylor & Francis Journals, vol. 15(7), pages 817-840, August.
    11. Ata, Mustafa & Erenoğlu, Ayşe Kübra & Şengör, İbrahim & Erdinç, Ozan & Taşcıkaraoğlu, Akın & Catalão, João P.S., 2019. "Optimal operation of a multi-energy system considering renewable energy sources stochasticity and impacts of electric vehicles," Energy, Elsevier, vol. 186(C).
    12. Shafie-khah, M. & Neyestani, N. & Damavandi, M.Y. & Gil, F.A.S. & Catalão, J.P.S., 2016. "Economic and technical aspects of plug-in electric vehicles in electricity markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1168-1177.
    13. Yoram Wind & Thomas L. Saaty, 1980. "Marketing Applications of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 26(7), pages 641-658, July.
    14. Xu Hao & Yan Zhou & Hewu Wang & Minggao Ouyang, 2020. "Plug-in electric vehicles in China and the USA: a technology and market comparison," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(3), pages 329-353, March.
    15. Giannakis, Mihalis & Papadopoulos, Thanos, 2016. "Supply chain sustainability: A risk management approach," International Journal of Production Economics, Elsevier, vol. 171(P4), pages 455-470.
    16. Aqlan, Faisal & Lam, Sarah S., 2015. "A fuzzy-based integrated framework for supply chain risk assessment," International Journal of Production Economics, Elsevier, vol. 161(C), pages 54-63.
    17. Olivier Lavastre & A. Gunasekaran & Alain Spalanzani, 2012. "Supply chain risk management in french companies," Post-Print halshs-00740450, HAL.
    18. Qingyou Yan & Guangyu Qin & Meijuan Zhang & Bowen Xiao, 2019. "Research on Real Purchasing Behavior Analysis of Electric Cars in Beijing Based on Structural Equation Modeling and Multinomial Logit Model," Sustainability, MDPI, vol. 11(20), pages 1-15, October.
    19. Wu, Yunna & Jia, Weibing & Li, Lingwenying & Song, Zixin & Xu, Chuanbo & Liu, Fangtong, 2019. "Risk assessment of electric vehicle supply chain based on fuzzy synthetic evaluation," Energy, Elsevier, vol. 182(C), pages 397-411.
    20. Stokes, Leah C. & Breetz, Hanna L., 2018. "Politics in the U.S. energy transition: Case studies of solar, wind, biofuels and electric vehicles policy," Energy Policy, Elsevier, vol. 113(C), pages 76-86.
    21. Ji, Shou-feng & Zhao, Dan & Luo, Rong-juan, 2019. "Evolutionary game analysis on local governments and manufacturers' behavioral strategies: Impact of phasing out subsidies for new energy vehicles," Energy, Elsevier, vol. 189(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Meixia Pan & Wanming Chen & Shengyuan Wang & Xiaolan Wu, 2022. "The Influence of Low Carbon Emission Engine on the Life Cycle of Automotive Products: A Case Study of Three-Cylinder Models in the Chinese Market," Energies, MDPI, vol. 15(18), pages 1-15, September.
    2. Wanming Chen & Shengyuan Wang & Xiaolan Wu, 2022. "Growth Mechanism and Synchronization Effect of China’s New Energy Vehicle Enterprises: An Empirical Analysis Based on Moving Logistic and Kuramoto Model," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    3. Chen, Yinuo & Xie, Shuyi & Tian, Zhigang, 2022. "Risk assessment of buried gas pipelines based on improved cloud-variable weight theory," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    4. Nishat Alam Choudhary & Shalabh Singh & Tobias Schoenherr & M. Ramkumar, 2023. "Risk assessment in supply chains: a state-of-the-art review of methodologies and their applications," Annals of Operations Research, Springer, vol. 322(2), pages 565-607, March.
    5. Shengyuan Wang, 2022. "Exploring the Sustainability of China’s New Energy Vehicle Development: Fresh Evidence from Population Symbiosis," Sustainability, MDPI, vol. 14(17), pages 1-21, August.
    6. Jianxue Chai & Lihui Zhang & Meng Yang & Qingyun Nie & Lei Nie, 2020. "Investigation on the Coupling Coordination Relationship between Electric Power Green Development and Ecological Civilization Construction in China: A Case Study of Beijing," Sustainability, MDPI, vol. 12(21), pages 1-29, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nishat Alam Choudhary & Shalabh Singh & Tobias Schoenherr & M. Ramkumar, 2023. "Risk assessment in supply chains: a state-of-the-art review of methodologies and their applications," Annals of Operations Research, Springer, vol. 322(2), pages 565-607, March.
    2. Zhou, Min & Long, Piao & Kong, Nan & Zhao, Lindu & Jia, Fu & Campy, Kathryn S., 2021. "Characterizing the motivational mechanism behind taxi driver’s adoption of electric vehicles for living: Insights from China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 134-152.
    3. Shafiee, Mohammad & Zare-Mehrjerdi, Yahia & Govindan, Kannan & Dastgoshade, Sohaib, 2022. "A causality analysis of risks to perishable product supply chain networks during the COVID-19 outbreak era: An extended DEMATEL method under Pythagorean fuzzy environment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    4. Hao, Jun & Li, Jianping & Wu, Dengsheng & Sun, Xiaolei, 2020. "Portfolio optimisation of material purchase considering supply risk – A multi-objective programming model," International Journal of Production Economics, Elsevier, vol. 230(C).
    5. Sun, Ya-Fang & Zhang, Yue-Jun & Su, Bin, 2022. "Impact of government subsidy on the optimal R&D and advertising investment in the cooperative supply chain of new energy vehicles," Energy Policy, Elsevier, vol. 164(C).
    6. Qazi, Abroon & Dickson, Alex & Quigley, John & Gaudenzi, Barbara, 2018. "Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks," International Journal of Production Economics, Elsevier, vol. 196(C), pages 24-42.
    7. Yurong Chen & Juan Zhang, 2024. "Impact of Consumer Subsidy Considering Subsidy Threshold on New Energy Vehicle Firms and Environmental Benefits," Sustainability, MDPI, vol. 16(6), pages 1-22, March.
    8. Silvia Carpitella & Ilyas Mzougui & Joaquín Izquierdo, 2022. "Multi-criteria risk classification to enhance complex supply networks performance," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 769-785, September.
    9. Fan, Zhi-Ping & Cao, Yue & Huang, Chun-Yong & Li, Yongli, 2020. "Pricing strategies of domestic and imported electric vehicle manufacturers and the design of government subsidy and tariff policies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    10. Hatem Elleuch & Wafik Hachicha & Habib Chabchoub, 2014. "A combined approach for supply chain risk management: description and application to a real hospital pharmaceutical case study," Journal of Risk Research, Taylor & Francis Journals, vol. 17(5), pages 641-663, May.
    11. Juan Zhang & Ziyue Wang & Huiju Zhao, 2020. "The Impact of Consumer Subsidy on Green Technology Innovations for Vehicles and Environmental Impact," IJERPH, MDPI, vol. 17(20), pages 1-24, October.
    12. Kiunke, Theresa & Gemignani, Natalia & Malheiro, Pedro & Brudermann, Thomas, 2022. "Key factors influencing onshore wind energy development: A case study from the German North Sea region," Energy Policy, Elsevier, vol. 165(C).
    13. Cheng, Fei & Chen, Tong & Chen, Qiao, 2022. "Cost-reducing strategy or emission-reducing strategy? The choice of low-carbon decisions under price threshold subsidy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    14. Yoo, Seung Ho & Choi, Thomas Y. & Sheu, Jiuh-Biing, 2021. "Electric vehicles and product–service platforms: Now and in future," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    15. Huang, Weixiang & Zhu, Han, 2021. "Performance evaluation and improvement for ZEV credit regulation in a competitive environment," Omega, Elsevier, vol. 102(C).
    16. Manik Chandra Das & Abanish Pandey & Arun Kumar Mahato & Rajnish Kumar Singh, 2019. "Comparative performance of electric vehicles using evaluation of mixed data," OPSEARCH, Springer;Operational Research Society of India, vol. 56(3), pages 1067-1090, September.
    17. Aysu Göçer & Yao Henry Jin & Stanley E. Fawcett, 2019. "How Does the Contingent Sustainability–Risk–Cost Relationship Affect the Viability of CSR? An Emerging Economy Perspective," Sustainability, MDPI, vol. 11(19), pages 1-25, September.
    18. Li, Yina & Liang, Chenchen & Ye, Fei & Zhao, Xiande, 2023. "Designing government subsidy schemes to promote the electric vehicle industry: A system dynamics model perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 167(C).
    19. Jing Peng & Zhiping Lin, 2022. "Eco-Innovation in New Energy Vehicle Supply Chains under Government Subsidies," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    20. Chih-Hung Hsu & Ru-Yue Yu & An-Yuan Chang & Wan-Ling Liu & An-Ching Sun, 2022. "Applying Integrated QFD-MCDM Approach to Strengthen Supply Chain Agility for Mitigating Sustainable Risks," Mathematics, MDPI, vol. 10(4), pages 1-41, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3150-:d:345305. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.