Evolution Features and Robustness of Global Photovoltaic Trade Network

Jianxiong Xiao, Chao Xiong, Wei Deng and Guihai Yu ()
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Jianxiong Xiao: Department of Information Engineering, Guiyang Institute of Information Science and Technology, Guiyang 550025, China
Chao Xiong: School of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang 550025, China
Wei Deng: School of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang 550025, China
Guihai Yu: School of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang 550025, China

Sustainability, 2022, vol. 14, issue 21, 1-18

Abstract: Photovoltaic industry trade has become a global trade activity, and a wide range of photovoltaic trade relations have been formed between countries. In order to further strengthen and balance trade relations, this paper analyzes global photovoltaic (PV) trade from the perspective of complex networks. We employ network indicators and the cascading process of risk propagation to analyze the evolution features and the vulnerability of the PV trade network. Firstly, we establish the global PV trade networks from 2000 to 2021 based on the PV trade flow between countries. We then explore evolution features and analyze the influencing factors of the trade network structure. Finally, we simulate the cascading process of risk propagation on the trade network based on an improved bootstrap percolation model. The evolution features reveal the following three results: (1) the scale of global PV trade continues to grow, and the participation of some countries has increased significantly; (2) the global PV trade network has small-world characteristics, and the related products have high circulation efficiency; and (3) the global PV trade network has a core-periphery structure, while a few countries drive most of the trade. China, Germany, and the U.S. are the top PV traders. Some Asian countries, such as Vietnam, are gradually increasing their share of the market. The QAP regression analysis shows that the gaps in GDP and electricity access rate are the biggest facilitating and hindering factors in the global PV trade, respectively. The simulation results show that the global PV trade network is vulnerable to targeted risk and is robust to randomness risk.

Keywords: global photovoltaic trade network; evolution features; QAP regression analysis; bootstrap percolation model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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