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<p>Balancing economic-oriented (Eno-SDGs) and ecological-oriented SDGs (Elo-SDGs) in underdeveloped mountains remains challenging due to a lack of spatially explicit understanding of their coupling mechanisms and drivers. Taking the Dabie Mountains as a case, we integrated multi-source remote sensing and socio-economic data within an analytical framework that quantifies SDG scores, identifies dominant drivers via a geographical detector, and diagnoses coupling coordination degree (CCD). Key findings: (1) Socio-economic factors dominate SDG outcomes, but driver regimes shift spatially-population and agricultural factors control low-level development zones, whereas industrial and construction land factors prevail in high-level zones. (2) Factor interactions evolve from socio-economic-natural synergies in low-level zones to socio-economic internal synergies in mid-to-high level zones. (3) Land-dominant zones exhibit the highest CCD volatility (CV = 0.24), identifying land use change as the most sensitive factor affecting eco-economic coupling. Spatially, Eno-SDGs lag in the resource-rich southwest, while Elo-SDGs lag in the development-pressured north and east. We conclude that spatial heterogeneity of eco-economic coupling is governed by the “land-population-industry” system, and propose a differentiated zoning governance strategy tailored to the identified dominant factors.</p>

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Spatially Explicit Diagnosis of Eco-Economic Coupling and Its Drivers: A GIS and Remote Sensing-Based Framework for Zoned Sustainability Governance in the Dabie Mountains

  • 期刊论文

  • 发表日期：

    2026年5月9日

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  • Caixia Wei,
  • Huan Li,
  • Fang Liu,
  • Yali Zhang,
  • Shuo Qi,
  • Yuxing Zhang,
  • Zongran Han,
  • Hongli Zhang,
  • Siqi Lu,
  • Heli Lu,
  • Thanasis Kizos

摘要

Balancing economic-oriented (Eno-SDGs) and ecological-oriented SDGs (Elo-SDGs) in underdeveloped mountains remains challenging due to a lack of spatially explicit understanding of their coupling mechanisms and drivers. Taking the Dabie Mountains as a case, we integrated multi-source remote sensing and socio-economic data within an analytical framework that quantifies SDG scores, identifies dominant drivers via a geographical detector, and diagnoses coupling coordination degree (CCD). Key findings: (1) Socio-economic factors dominate SDG outcomes, but driver regimes shift spatially-population and agricultural factors control low-level development zones, whereas industrial and construction land factors prevail in high-level zones. (2) Factor interactions evolve from socio-economic-natural synergies in low-level zones to socio-economic internal synergies in mid-to-high level zones. (3) Land-dominant zones exhibit the highest CCD volatility (CV = 0.24), identifying land use change as the most sensitive factor affecting eco-economic coupling. Spatially, Eno-SDGs lag in the resource-rich southwest, while Elo-SDGs lag in the development-pressured north and east. We conclude that spatial heterogeneity of eco-economic coupling is governed by the “land-population-industry” system, and propose a differentiated zoning governance strategy tailored to the identified dominant factors.