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Updated: Jan 3, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Spatiotemporal patterns and drivers of ecosystem service supply and demand across the conterminous United States: A
Xiao Sun1, Huajun Tang1, Peng Yang1
1Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Abstract:
Land-use and land-cover changes associated with urbanization have significantly influenced biodiversity and ecosystem functions, as well as the supply and demand of ecosystem services (ESs). Assessing ESs and exploring their drivers are critical for regional land-use planning and ecological sustainability. In this study, the supply-demand matrix approach was used to quantify ES supply, demand, and their gap at multiple scales across the conterminous United States from 1940 to 2011. A new integrated measurement framework was proposed to offset ES deficits by identifying an optimal land-use conversion strategy. We focused on exploring the scale and spatial effects of the impacts of various drivers on ESs using ordination and regression analysis. The results showed that the expansion of developed land led to decreased ES supply and increased ES demand during the past seven decades, generating growing ES deficits at different scales, especially in highly urbanized metropolitan areas. To alleviate or offset ES deficits, promoting the intensive utilization of developed land and converting cropland, pasture, and barren land into forests would be the optimal land use strategies. Moreover, the drivers of ESs exhibited not only scale dependence but also spatial heterogeneity. The smaller the scale, the more diverse the drivers. The natural and socioeconomic drivers explained less variation at the metropolitan scale than at the state scale. Economic factors were key drivers for ESs at the state scale, while social factors were key drivers at the metropolitan scale. The regression coefficients for the drivers of ESs in the geographically weighted regression (GWR) model showed remarkable spatial heterogeneity. The GWR coefficients might have important implications for decision making in ES management. Localized and efficient land-use strategies and management policies are needed to reduce the ecological footprints of urban areas and thus achieve regional sustainability.
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