Liu Fan, Zhang Cuixia, Zhang Yingyan, Liu Hongjun
Business School, Suzhou University, Suzhou, 234000, China.
School of Economics, Anhui University, Hefei, 230000, China.
Environ Sci Pollut Res Int. 2023 Jan;30(3):7655-7670. doi: 10.1007/s11356-022-22699-1. Epub 2022 Aug 31.
Industrial ecological efficiency is regarded as an urgent challenge that affects the development of ecological civilization and environmental governance. Here, we propose a data-driven approach to measure and promote regional industrial ecological efficiency. We collected data related to regional industrial development and used the Data Envelopment Analysis-Banker Charnes and Cooper (DEA-BCC) model to measure regional industrial ecological efficiency from a static perspective. The Malmquist index model was then used to measure regional industrial ecological efficiency from a dynamic perspective. In addition, we used a Tobit regression model to identify the factors affecting regional industrial ecological efficiency. Through a case study of regional industrial ecological efficiency, we demonstrate the specific application of the proposed data-driven approach. This study provides a new and effective tool for improving industrial ecological efficiency at a regional scale. This method can help enterprises and local governments improve industrial ecological efficiency, coordinate the relationship between industrial economic growth and the ecological environment, and boost regional efforts to achieve carbon peaking and carbon neutralization goals.
产业生态效率被视为影响生态文明发展和环境治理的一项紧迫挑战。在此，我们提出一种数据驱动的方法来衡量和提升区域产业生态效率。我们收集了与区域产业发展相关的数据，并使用数据包络分析-班克-查恩斯-库珀（DEA-BCC）模型从静态角度衡量区域产业生态效率。随后使用曼奎斯特指数模型从动态角度衡量区域产业生态效率。此外，我们使用托宾回归模型来识别影响区域产业生态效率的因素。通过对区域产业生态效率的案例研究，我们展示了所提出的数据驱动方法的具体应用。本研究为在区域层面提高产业生态效率提供了一种新的有效工具。该方法有助于企业和地方政府提高产业生态效率，协调产业经济增长与生态环境之间的关系，并推动区域实现碳达峰和碳中和目标的努力。
