Green development evaluation of China’s Yangtze River Economic Belt based on hierarchical clustering and composite ecosystem index system

Ting Yang and Kaile Zhou ()
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Ting Yang: Anhui Medical University
Kaile Zhou: Hefei University of Technology

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2024, vol. 26, issue 8, No 83, 21197-21216

Abstract: Abstract Green development evaluation is an important way for understanding the development status of a region. This study focuses on the green development evaluation of China’s Yangtze River Economic Belt, which includes 11 provinces across the eastern, central, and western China. The population and gross domestic product of this area both exceed 40% of the national total. We first established a city-level green development evaluation index system, which includes indicators in the ecological, economic and social dimensions, based on complex ecosystem theory. There are two sub-dimensions under each dimension, and there are totally 21 indicators at the bottom layer. Then, we collected relevant data to measure the 21 indicators in three dimensions of the 110 cities in the Yangtze River Economic Belt area in 2017. Finally, the 110 cities were divided into 6 groups with hierarchical clustering. The green development level and characteristics of each group of cities were compared and analyzed. The results show that the green development levels of cities in China’s Yangtze River Economic Belt are quite different and have different characteristics. In particular, some cities are facing dual pressures in the economy and the ecology. This study can support formulating more targeted green development policies and regional collaboration policies for the Yangtze River Economic Belt area. It also has important reference value for the green development evaluation of other regions.

Keywords: Yangtze River Economic Belt; Green development; Hierarchical clustering; Complex ecosystem (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10668-023-03524-2

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