Study on Driving Factors and Spatiotemporal Differentiation of Eco-Environmental Quality in Jianghuai River Basin of China

Hong Cai, Xueqing Ma, Pengyu Chen and Yanlong Guo ()
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Hong Cai: Anhui Cultural Tourism Innovative Development Research Institute, Anhui Jianzhu University, Hefei 203106, China
Xueqing Ma: Social Innovation Design Research Center, Anhui University, Hefei 203106, China
Pengyu Chen: Social Innovation Design Research Center, Anhui University, Hefei 203106, China
Yanlong Guo: Social Innovation Design Research Center, Anhui University, Hefei 203106, China

Sustainability, 2024, vol. 16, issue 11, 1-18

Abstract: For an in-depth analysis of the ecosystems of the Jianghuai Valley, this study utilized municipal data from 2017 to 2021. In addition, this study established an index scale evaluation system for the quality of the ecological environment in the Jianghuai Valley. This system encompasses five critical dimensions: drivers, pressures, states, impacts, and responses, in accordance with the DPSIR model. The entropy-weighted TOPSIS method combined with the gray correlation method was used to assess the ecological status of each region of the Jianghuai Valley at different time periods and the driving factors affecting the ecological quality of the Jianghuai Valley. Our study yields several key conclusions. First, it was observed that the ecological environment within the Jianghuai Valley showed a continuous upward bias in inter-annual variability. Second, there exists variation in ecological environment quality among the eleven urban areas within the Jianghuai Valley, highlighting regional disparities. Third, among the eleven urban areas in the Jianghuai Valley, Anqing has the best ecological quality, and Huainan has the worst ecological performance. Fourth, the ecological environment quality within the Jianghuai Valley demonstrates an aggregated pattern. From west to east, this pattern is delineated by distinct areas: one marked by excellent ecological environment quality, another exhibiting average ecological environment quality, followed by a zone characterized by good ecological environment quality, and finally, an area with poor ecological environment. Fifth, our analysis reveals that Q9 (indicating the percentage of excellent air days) and Q13 (denoting the annual average temperature) have a pronounced correlation with the Jianghuai Valley’s ecological quality. Conversely, Q3, which pertains to the rate of natural population growth, had the lowest relevance to the ecological quality of the Jianghuai Valley.

Keywords: Jianghuai Valley; ecological environment; entropy-weighted TOPSIS; gray correlation method; spatio-temporal differentiation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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