Navigating Ecological–Economic Interactions: Spatiotemporal Dynamics and Drivers in the Lower Reaches of the Jinsha River
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...
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2024-12-01
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| author | Zhongyun Ni Yinbing Zhao Jingjing Liu Yongjun Li Xiaojiang Xia Yang Zhang |
| author_facet | Zhongyun Ni Yinbing Zhao Jingjing Liu Yongjun Li Xiaojiang Xia Yang Zhang |
| author_sort | Zhongyun Ni |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bfa4629695fa483e856864307c3daab9 |
| institution | DOAJ |
| issn | 2073-445X |
| language | English |
| publishDate | 2024-12-01 |
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| spelling | doaj-art-bfa4629695fa483e856864307c3daab92025-08-20T02:39:40ZengMDPI AGLand2073-445X2024-12-011312215910.3390/land13122159Navigating Ecological–Economic Interactions: Spatiotemporal Dynamics and Drivers in the Lower Reaches of the Jinsha RiverZhongyun Ni0Yinbing Zhao1Jingjing Liu2Yongjun Li3Xiaojiang Xia4Yang Zhang5College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, ChinaCollege of Geography and Planning, Chengdu University of Technology, Chengdu 610059, ChinaCollege of Geography and Planning, Chengdu University of Technology, Chengdu 610059, ChinaCollege of Geography and Planning, Chengdu University of Technology, Chengdu 610059, ChinaCollege of Geography and Planning, Chengdu University of Technology, Chengdu 610059, ChinaCollege of Geography and Planning, Chengdu University of Technology, Chengdu 610059, ChinaThe 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.https://www.mdpi.com/2073-445X/13/12/2159ecological environment indexeconomic development indexcoupling coordination degreegeographic detectorobstacle recognitionneural network model |
| spellingShingle | Zhongyun Ni Yinbing Zhao Jingjing Liu Yongjun Li Xiaojiang Xia Yang Zhang Navigating Ecological–Economic Interactions: Spatiotemporal Dynamics and Drivers in the Lower Reaches of the Jinsha River Land ecological environment index economic development index coupling coordination degree geographic detector obstacle recognition neural network model |
| title | Navigating Ecological–Economic Interactions: Spatiotemporal Dynamics and Drivers in the Lower Reaches of the Jinsha River |
| title_full | Navigating Ecological–Economic Interactions: Spatiotemporal Dynamics and Drivers in the Lower Reaches of the Jinsha River |
| title_fullStr | Navigating Ecological–Economic Interactions: Spatiotemporal Dynamics and Drivers in the Lower Reaches of the Jinsha River |
| title_full_unstemmed | Navigating Ecological–Economic Interactions: Spatiotemporal Dynamics and Drivers in the Lower Reaches of the Jinsha River |
| title_short | Navigating Ecological–Economic Interactions: Spatiotemporal Dynamics and Drivers in the Lower Reaches of the Jinsha River |
| title_sort | navigating ecological economic interactions spatiotemporal dynamics and drivers in the lower reaches of the jinsha river |
| topic | ecological environment index economic development index coupling coordination degree geographic detector obstacle recognition neural network model |
| url | https://www.mdpi.com/2073-445X/13/12/2159 |
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