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基于有效性和效率的中国生态福利绩效测度改进方法。

A Effectiveness-and Efficiency-Based Improved Approach for Measuring Ecological Well-Being Performance in China.

机构信息

School of Engineering Management and Real Estate, Henan University of Economics and Law, Zhengzhou 450000, China.

School of Management, Center for Energy, Environment & Economy Research, Zhengzhou University, Zhengzhou 450000, China.

出版信息

Int J Environ Res Public Health. 2023 Jan 22;20(3):2024. doi: 10.3390/ijerph20032024.

DOI:10.3390/ijerph20032024
PMID:36767390
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9915347/
Abstract

Finding solutions to the challenges posed by China's urbanization is an urgent, pressing global concern. An effective approach for evaluating the ecological well-being performance (EWP) is a guideline for improvement. Most previous studies have focused on the evaluation of EWP efficiency without considering the effectiveness of the EWP, which may mislead the practice of improving the EWP. This paper proposed a bi-dimensional effectiveness and efficiency perspective evaluation of the EWP for pursuing sustainable development goals. The Ecological Consumption Index and the Human Development Index are selected to evaluate indicators for the EWP. The entropy method, line-weighted method, and four-quadrant evaluation framework are used to disclose EWP effectiveness. A Super SBM model and the DEA moving split-windows analysis method are applied to calculate the EWP efficiency. Data from 30 provinces in China for the period of 1997 to 2019 have been collected for empirical study to demonstrate the effectiveness of the proposed method. The main findings of the case study are: (1) The ECI and HDI increased during the study period, while the annual average value of the EWP efficiency among 30 provinces in China has decreased with fluctuation; (2) provinces in southern China and Chongqing have a low level of ECI and demonstrate good performance in the HDI; and (3) most developed regions, such as Beijing, Shanghai, and Guangdong, have not presented the best EWPs. The results of this study can provide a basis for understanding the EWP in China so as to formulate targeted sustainable-development strategies.

摘要

解决中国城市化所带来的挑战是全球亟待解决的问题。评估生态福利绩效(EWP)的有效方法是改进的指导方针。大多数先前的研究都集中在评估 EWP 效率上,而没有考虑 EWP 的有效性,这可能会误导改进 EWP 的实践。本文提出了一种从二维视角评估 EWP 的有效性和效率,以追求可持续发展目标。选择生态消费指数和人类发展指数来评估 EWP 的指标。利用熵值法、线权法和四象限评价框架来揭示 EWP 的有效性。应用超 SBM 模型和 DEA 移动分窗分析方法来计算 EWP 的效率。收集了 1997 年至 2019 年中国 30 个省份的数据进行实证研究,以验证所提出方法的有效性。案例研究的主要发现是:(1)研究期间 ECI 和 HDI 增加,而中国 30 个省份的 EWP 效率的年平均值呈下降趋势且波动较大;(2)中国南方省份和重庆的 ECI 水平较低,HDI 表现良好;(3)大多数发达地区,如北京、上海和广东,并未呈现出最佳的 EWP。本研究的结果可以为了解中国的 EWP 提供基础,从而制定有针对性的可持续发展战略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/9bdfe826728c/ijerph-20-02024-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/5bd8239acad0/ijerph-20-02024-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/c4d3fa451186/ijerph-20-02024-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/4219444cacea/ijerph-20-02024-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/7b6f14cfeced/ijerph-20-02024-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/ce23bff25829/ijerph-20-02024-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/294af84aaf29/ijerph-20-02024-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/b1951326069d/ijerph-20-02024-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/ada2310da280/ijerph-20-02024-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/9bdfe826728c/ijerph-20-02024-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/5bd8239acad0/ijerph-20-02024-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/c4d3fa451186/ijerph-20-02024-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/4219444cacea/ijerph-20-02024-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/7b6f14cfeced/ijerph-20-02024-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/ce23bff25829/ijerph-20-02024-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/294af84aaf29/ijerph-20-02024-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/b1951326069d/ijerph-20-02024-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/ada2310da280/ijerph-20-02024-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5579/9915347/9bdfe826728c/ijerph-20-02024-g009.jpg

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