Spatio-temporal evolution and scenario analysis of end-recycling governance in China: based on GM-SVR-BP combined forecasting model

ABSTRACT

End-recycling governance is the core link for achieving high-quality development of circular economy. This study uses data of 30 provinces in China from 2011 to 2021 as research samples, constructs a comprehensive evaluation index system, and analyzes the achievements and progress of end-recycling governance in China over the years. Based on scenario analysis, a GM-SVR-BP combined model is further used to explore the development trends of end-recycling governance performance in 30 provinces in China from 2022 to 2030. Research finding: (1) The end-recycling governance performance of Chinese provinces presents a dynamic upward trend, but overall, it is low. (2) The barycenter of end-recycling governance in China migrated to the southeast, and the transfer speed was stable, showing a northeast-southwest pattern. There are pronounced differences among different regions. (3) There are apparent spatial autocorrelation characteristics in China’s end-recycling governance. The overall spatial agglomeration degree showed a weakening trend during the observation period. (4) The development trend of end-recycling governance performance in various regions in China during 2022–2030 follows the differentiation characteristics of the observation period in this study. Among them, technological breakthrough is one of the effective ways to improve the performance of end-recycling governance. The effect of the improvement of emotional level on the performance of end-recycling governance showed apparent regional heterogeneity. This study provides a clear and reliable decision-making basis for promoting the improvement of regional end-recycling governance performance and system construction, and has certain reference value for promoting national and regional resource conservation and environmental protection.

KEYWORDS:

• End-recycling governance performance
• evaluation index system
• Spatio-temporal evolution
• scenario analysis
• combined forecasting model

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15567036.2024.2327602

Additional information

Funding

The work was supported by the National Social Science Fund of China [23FJYB029] and the Innovation Strategy Research Plan Project of Fujian Province [2023R0050].

Notes on contributors

Chuang Li

Chuang Li is a professor and doctoral supervisor at the School of Business Administration of Jimei University. He graduated from Shanghai Jiaotong University with a doctor’s degree. His main research interests include resource and environmental management, sustainable development, and public green behavior.

Guoliang Zhang

Guoliang Zhang is a postgraduate at Jimei University. His main research interests include regional circular economy, renewable energy, and green development.

Liping Wang

Liping Wang is a professor and doctoral supervisor at the Finance and Economics College of Jimei University. She graduated from Donghua University with a doctor’s degree. Her main research interests include environmental economics, green finance, and energy policy.