Trend Analysis of the Impact of Ecological Governance on Industrial Structural Upgrading under the Dual Carbon Target

Siqing You, Chaoyu Zhang, Han Zhao, Hongli Zhou (), Zican Li, Jiayi Xu and Yan Meng
Additional contact information
Siqing You: School of Information, Beijing Wuzi University, Beijing 101100, China
Chaoyu Zhang: School of Information, Beijing Wuzi University, Beijing 101100, China
Han Zhao: School of Information, Beijing Wuzi University, Beijing 101100, China
Hongli Zhou: School of Information, Beijing Wuzi University, Beijing 101100, China
Zican Li: School of Information, Beijing Wuzi University, Beijing 101100, China
Jiayi Xu: School of Information, Beijing Wuzi University, Beijing 101100, China
Yan Meng: School of Information, Beijing Wuzi University, Beijing 101100, China

Sustainability, 2023, vol. 15, issue 15, 1-15

Abstract: The Chinese government faces significant challenges in achieving the goals of carbon peaking and carbon neutrality (dual carbon targets), particularly in the realms of implementing a low-carbon economy and achieving ecological balance. In order to assist the Chinese government in formulating more effective ecological governance policies, this paper focuses on 288 cities in China and proposes a predictive model combining gray forecasting, Backpropagation Neural Network, and threshold effect testing to forecast yearly ecological governance intensity. Under the premise of examining the predictive effect, fixed effects testing and threshold regression analysis were conducted to assess the future intensity of ecological governance. The empirical research results reveal that the increasing intensity of future ecological governance has a promoting effect on China’s upgrading of industrial structure, but this effect gradually diminishes. On the contrary, there is significant potential for optimizing industry’s internal structure. Efforts should be directed towards intensified governance, emphasizing energy-saving and emission reduction in high-carbon industries, and promoting environmentally and economically beneficial models. Our research provides a widely applicable method for studying the trend of research as it pertains to government decision-making effectiveness and valuable insights for governments to make more informed decisions in the pursuit of sustainable development.

Keywords: ecological governance; industrial structure; low carbonization; Backpropagation Neural Network; trend analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/15/11775/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/15/11775/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:15:p:11775-:d:1207150

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().