School of Economics and Management, Inner Mongolia University, Hohhot, 010021, China.
School of Environment, Tsinghua University, Beijing, 100084, China.
Environ Sci Pollut Res Int. 2023 Apr;30(18):53904-53919. doi: 10.1007/s11356-023-25894-w. Epub 2023 Mar 4.
Continuous and rapid economic development has brought about excessive resource consumption and environmental pollution. Therefore, it is particularly essential to coordinate economic, resource, and environmental factors to achieve sustainable development. This paper develops a new data envelopment analysis (DEA) method that can be used for multi-level complex system evaluation (MCSE-DEA) to reveal the inter-provincial green development efficiency (GDE) in China from 2010 to 2018. Moreover, the Tobit model is applied to explore the influencing factors of GDE. We found that (i) the MCSE-DEA model tends to have lower efficiency scores than the traditional P-DEA (panel data envelopment analysis) model, and the top three provinces are Shanghai, Tianjin, and Fujian; (ii) the efficiency shows an increasing trend during the whole study period. The southeast region and the Middle Yangtze River region have the highest efficiency values, reaching 1.09, while the northwest region ranks last with an average efficiency value of 0.66. Shanghai performs the best, while Ningxia performs the worst, with efficiency values of 1.43 and 0.58, respectively; (iii) the provinces with lower efficiency values mainly come from economically underdeveloped remote regions, which can be attributed to issues of water consumption (WC) and energy consumption (EC). Moreover, there are much room for improvement in solid waste emissions (SW) and soot and industrial dust emissions (SD); (iv) the environmental investment, R&D investment, and economic development level can significantly improve GDE, while industrial structure, urbanization level, and energy consumption have inhibiting effects.
持续快速的经济发展带来了过度的资源消耗和环境污染。因此，协调经济、资源和环境因素以实现可持续发展尤为重要。本文开发了一种新的数据包络分析（DEA）方法，可用于多层次复杂系统评价（MCSE-DEA），以揭示 2010 年至 2018 年中国各省份的绿色发展效率（GDE）。此外，还应用 Tobit 模型探讨了 GDE 的影响因素。结果表明：（i）MCSE-DEA 模型的效率得分往往低于传统的 P-DEA（面板数据包络分析）模型，效率排名前三的省份是上海、天津和福建；（ii）整体来看，效率呈上升趋势。东南地区和长江中游地区的效率值最高，达到 1.09，而西北地区的效率值最低，平均为 0.66。上海的表现最好，宁夏的表现最差，效率值分别为 1.43 和 0.58；（iii）效率值较低的省份主要来自经济欠发达的偏远地区，这可以归因于耗水量（WC）和能源消耗（EC）问题。此外，在固体废物排放（SW）和烟尘及工业粉尘排放（SD）方面还有很大的改进空间；（iv）环境投资、研发投资和经济发展水平可以显著提高 GDE，而产业结构、城市化水平和能源消耗则具有抑制作用。
