Ecological Well-Being Performance Evaluation of Chinese Major Node Cities along the Belt and Road

Author:

Bian Jing,Lan Feng,Hui Zhao,Bai Jiamin,Wang Yuanping

Abstract

Under the constraints of resources and the environment, improving the urban ecological well-being performance (EWP) is a fundamental requirement and inevitable choice for urban ecological civilization construction and sustainable development. In this paper, 36 Chinese major node cities along the Belt and Road were selected as the research area, and an EWP evaluation index system was constructed. The two-stage Super Network Slack-based measure (Super-NSBM) model was used to evaluate the static EWP from 2011 to 2018, and the Malmquist–Luenberger productivity index was used to evaluate the dynamic EWP. It was found that: (1) The EWP value of 36 Chinese major node cities along the Belt and Road from 2011 to 2018 did not reach effectiveness, with Sanya, Shenzhen, and Haikou being the top three performers. (2) In terms of two-stage efficiency, the ecological economic efficiency in the first stage was significantly lower than the economic well-being efficiency in the second stage, which indicated that the low ecological economic efficiency was the main reason for the low average value of the EWP. (3) From the dynamic analysis results, the Malmquist–Luenberger productivity index experienced a fluctuating upward trend, and the technical change was the main factor for the improvement in the EWP. Finally, policy recommendations were proposed based on the above findings. This study will contribute to the sustainable development of Chinese major node cities along the Belt and Road, and can provide a reference for other Belt and Road regions.

Funder

Natural Science Basic Research Program of Shaanxi

Special Scientific Research Program of Shaanxi Provincial Department of Education

National Natural Science Foundation of China

Chongqing Municipal Education Commission

Publisher

MDPI AG

Subject

Nature and Landscape Conservation,Ecology,Global and Planetary Change

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