Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China.
Institute of Green Development for the Yellow River Drainage Basin, Lanzhou University, Lanzhou 730000, China.
Int J Environ Res Public Health. 2020 May 15;17(10):3456. doi: 10.3390/ijerph17103456.
Eco-efficiency enhancement is an inherent requirement of green development and an important indicator of high-quality development in general. It aims to achieve the coordinated development of nature, the economy, and society. Therefore, eco-efficiency measurements should focus on not only total factor input, but also process analysis. Based on the "full world" model in ecological economic theory, this study constructed a theoretical framework for a composite economic-environmental-social system that reflects human welfare and sustainability. To this end, using network data envelopment analysis (DEA), this study established a staged eco-efficiency evaluation model that uses economic, environmental, and social factors to measure the overall and staged eco-efficiency of China's provinces from 2003 to 2016 and analyze its spatiotemporal characteristics. A geographically weighted regression (GWR) model was also used to analyze the influencing factors of eco-efficiency changes and the spatial differentiation in their effect intensity. The findings were as follows: (1) China's overall eco-efficiency is still at a low level. It varies significantly from region to region, and only three regions are at the frontier of production. The eastern region has the highest eco-efficiency, followed by the central region, and the gap between the central and western regions has gradually narrowed. In terms of staged efficiency, the level of eco-efficiency in the production stage is less than in the environmental governance stage, which is less than that in the social input stage. (2) In terms of the efficiency of each stage, the efficiency level of the production stage showed a downward trend throughout the entire process, and the decline in the central and western regions was more obvious. The social input stage and the environmental governance stage both showed upward trends. The social input stage showed a higher level, and the increase was relatively flat during the period of study. Efficiency continued to rise during the environmental governance stage from 2003 to 2010 and rose overall, but with some fluctuations from 2011 to 2016. (3) Geographically weighted regression showed that the effects of the influencing factors on eco-efficiency had obvious spatial heterogeneity. The factors affecting overall, production stage, and social input eco-efficiency were, in order of effect intensity from high to low, economic growth level, marketization level, and social input level. In terms of environmental governance, social input level had the greatest impact, followed by economic growth; marketization level did not show a significant impact.
生态效率提升是绿色发展的内在要求，也是高质量发展的重要指标。它旨在实现自然、经济和社会的协调发展。因此，生态效率的衡量应该不仅关注总要素投入，还应该关注过程分析。基于生态经济理论中的“全世界”模型，本研究构建了一个反映人类福利和可持续性的综合经济-环境-社会系统的理论框架。为此，本研究使用网络数据包络分析（DEA），建立了一个分阶段的生态效率评价模型，该模型使用经济、环境和社会因素来衡量中国各省 2003 年至 2016 年的整体和分阶段生态效率，并分析其时空特征。还使用地理加权回归（GWR）模型分析了生态效率变化的影响因素及其影响强度的空间差异。结果如下：（1）中国的整体生态效率仍处于较低水平。地区间差异显著，仅有三个地区处于生产前沿。东部地区的生态效率最高，其次是中部地区，中西部分化差距逐渐缩小。分阶段效率方面，生产阶段的生态效率水平低于环境治理阶段，低于社会投入阶段。（2）从各阶段效率来看，生产阶段的效率水平呈下降趋势，且中部和西部地区下降更为明显。社会投入阶段和环境治理阶段均呈上升趋势。社会投入阶段的效率水平较高，研究期间上升较为平缓。环境治理阶段的效率从 2003 年到 2010 年持续上升，总体上升，但 2011 年至 2016 年有所波动。（3）地理加权回归表明，影响因素对生态效率的影响具有明显的空间异质性。影响整体、生产阶段和社会投入生态效率的因素，按影响强度从高到低排列，依次为经济增长水平、市场化水平和社会投入水平。在环境治理方面，社会投入水平的影响最大，其次是经济增长；市场化水平没有表现出显著的影响。
