Abstract
China plays an important role in the global trade of photovoltaic products, and the RCEP agreement provides more opportunities and possibilities for China. This paper develops an improved trade gravity model with BP neural networks to estimate trade potentials, and the following conclusions are obtained: (1) The BP neural network is a more effective estimation method than traditional pooled regression, fixed effects, and random effects, and the combination of multiple neural networks for prediction can lead to higher robustness and accuracy. (2) The potential of China’s trade in PV products to RCEP countries is relatively mature, but the scale of trade in PV products between China and Japan and other countries may still be further expanded. (3) China’s trade potential for different regions in the RCEP agreement changed historically in different processes, with China’s trade potential for the Oceania region declining, while its trade potential for the East and Southeast Asia region increased in recent years.
Funder
National Social Science Foundation of China
Humanities and Social Science Project of China’s Ministry of Education
Natural Science Foundation of Guangdong
Basic and Applied Basic Research Project of Guangzhou
Guangdong University of Foreign Studies, Guangdong-Hong Kong-Macao Greater Bay Area Accounting and Economic Development Research Center Project
Guangzhou International Trade Centre Research Base Project
Guangdong University of Foreign Studies, African Research Institute Project
Guangdong University of Foreign Studies, Asia-Pacific Security and Economic and Political Cooperation Research Centre Project
Guangdong Postgraduate Education Innovation Project
Philosophy and Social Science Development Planning Project of Guangzhou
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
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