Navigating Ecological–Economic Interactions: Spatiotemporal Dynamics and Drivers in the Lower Reaches of the Jinsha River

Zhongyun Ni, Yinbing Zhao (), Jingjing Liu, Yongjun Li, Xiaojiang Xia and Yang Zhang
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Zhongyun Ni: College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China
Yinbing Zhao: College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China
Jingjing Liu: College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China
Yongjun Li: College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China
Xiaojiang Xia: College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China
Yang Zhang: College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China

Land, 2024, vol. 13, issue 12, 1-59

Abstract: The lower reaches of the Jinsha River, serving as a vital ecological barrier in southwestern China and playing a crucial role in advancing targeted poverty alleviation efforts, remain underexplored in terms of the coupling between ecological and economic development, creating a gap in understanding the region’s sustainable development potential. This study combines the remote sensing ecological index (RSEI) derived from MODIS data and the biodiversity richness index (BRI) based on land use data to create the ecological environment index (EEI) using a weighted approach. It also develops the economic development index (EDI) from economic data using the entropy weight method. By integrating the EEI and EDI, the study calculates key metrics, including the ecological–economic coupling degree (EECD), coupling coordination degree (EECCD), and relative development degree (EERDD), and examines their spatiotemporal changes from 2000 to 2020. Additionally, the study applies a geographic detector model to identify the spatial drivers of the EEI, an obstacle factor diagnosis model to pinpoint the main barriers to EDI, and a neural network model to uncover the underlying forces shaping EECCD. The results indicate that: (I) From 2000 to 2020, the overall improvement rate of the ecological and economic subsystems was greater than that of the ecological–economic coupling system. The entire region is still in the Running-In Stage, and the coordination level has been upgraded from near imbalance to marginal coordination. About 85% of the counties’ EERDDs are still in the EDI Behind EEI Stage. (II) The structural composition of the EEI shows a pattern of low Dry Hot Valley Area and high in other areas, mainly driven by natural factors, although human activities had a notable impact on these interactions. (III) Originating from an impact model primarily driven by economic factors and supplemented by ecological factors, both EDI and EECCD exhibit a pattern of high in the south and low in the north, with improvements spreading northward from the urban area of Kunming. The development gradient differences between 24 poverty-stricken counties and 16 non-poverty-stricken counties have been reduced. (IV) For the six types of ecological–economic coupling development zones, it is essential to adopt localized approaches tailored to the differences in resource and environmental characteristics and development stages. Key efforts should focus on enhancing ecological protection and restoration, increasing financial support, implementing ecological compensation mechanisms, and promoting innovative models for sustainable development.

Keywords: ecological environment index; economic development index; coupling coordination degree; geographic detector; obstacle recognition; neural network model; natural and anthropogenic drivers; development gradient; targeted poverty alleviation (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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