Author
Listed:
- Linglin Zhao
(College of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550025, China
Guizhou Provincial Key Laboratory of Intelligent Processing and Application of Remote Sensing Big Data, Guiyang 550025, China
These authors contributed equally to this work and should be considered co-first authors.)
- Man Li
(College of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550025, China
Guizhou Provincial Key Laboratory of Intelligent Processing and Application of Remote Sensing Big Data, Guiyang 550025, China
These authors contributed equally to this work and should be considered co-first authors.)
- Guangbin Yang
(College of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550025, China
Guizhou Provincial Key Laboratory of Intelligent Processing and Application of Remote Sensing Big Data, Guiyang 550025, China)
- Ou Deng
(College of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550025, China
Guizhou Provincial Key Laboratory of Intelligent Processing and Application of Remote Sensing Big Data, Guiyang 550025, China)
Abstract
Climate regulation ecosystem services (CRESs) play a crucial role in maintaining ecological balance and promoting regional sustainability. Previous studies have primarily focused on the total volume or per-unit-area quantity of CRESs, with limited attention given to their underlying driving mechanisms. This neglect overlooks their multidimensional attributes and dynamic complexity. Such simplifications often overlook the multidimensional attributes and dynamic complexity inherent in these services. Therefore, this study introduces a multidimensional evaluation framework to reveal the characteristic of the spatiotemporal evolution of CRESs. By integrating a multiscale geographically weighted regression (MGWR) model, the intensity and effective distance of theireffects are quantitatively identified, thereby providing a scientific and refined cognitive foundation for regional sustainable development. The results showed the following: (1) Between 2002 and 2022, CRESs in Guizhou Province showed an upward trend, with 64% of counties experiencing positive trends, whereas 51% of counties remained below average in terms of output and efficiency. (2) The spatial pattern of CRESs varied significantly, with stabilization in hotspots, improvement in coldspots, and the highest proportion of “A progress zones” in the east (45%). (3) Vegetation cover and annual precipitation were the two mainpositive factors that most strongly influenced the intensity of the CRESs, with values of 1.494 and 1.196, respectively; GDP had the most significant negative effect, with a value of −0.189; and population density had the largest range of effects, with a bandwidth of 1629. (4) Except for annual rainfall and aspect, the remaining eight influencingfactors, including population density, GDP, altitude, NPP, vegetation cover, annual temperature, and annual humidity, had positive and negative bidirectional effects on CRESs. Overall, this study emphasizes the need for differentiated, sustainability-oriented management strategies to better integrate ecosystem service evaluations into regional planning and sustainable policy development.
Suggested Citation
Linglin Zhao & Man Li & Guangbin Yang & Ou Deng, 2025.
"Assessing Climate Regulation Ecosystem Services for Sustainable Management: A Multidimensional Framework to Inform Regional Pathways,"
Sustainability, MDPI, vol. 17(24), pages 1-23, December.
Handle:
RePEc:gam:jsusta:v:17:y:2025:i:24:p:10918-:d:1811963
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