Big Geodata Technology: Carbon Supply–Demand Balance Analysis of Ecological Service Systems

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Abstract
Amid intensifying global climate change and accelerating urbanization, maintaining a balance between carbon emission reduction has become essential for achieving sustainable development. This research investigates the spatiotemporal evolution and driving mechanisms of carbon sequestration services in the ecological development zone of northern Guangdong, China. By integrating Big Geodata technology with the InVEST model, the study quantitatively evaluates both the supply and demand dimensions of carbon sequestration services using land-use, nighttime light, and socioeconomic data. Carbon storage capacities were estimated for different land-use types (including cropland, forest, grassland, water body, built-up land, and undeveloped land), while carbon emissions were spatially distributed based on nighttime light intensity, providing a holistic perspective on the regional carbon budget. The findings indicate significant spatial heterogeneity: the western region exhibits an average carbon sequestration capacity approximately 20% higher than the eastern region, due to extensive forest and grassland coverage, whereas urban areas exhibit higher carbon demand coupled with insufficient supply. Through an analysis of land-use transfer matrices and contribution assessment, land-use transformations, particularly the conversion of ecological land to urban built-up areas, were quantitatively identified as the primary factor disrupting the regional carbon balance. This study proposes actionable territorial spatial planning strategies, such as prioritizing ecological conservation in high-carbon-supply areas and promoting low-carbon urban renewal in high-demand zones, directly derived from the spatial mismatch patterns revealed by the InVEST model outputs. These insights contribute significantly to regional sustainable development practices and global climate governance.
Funding Information
  • Guangdong Science and Technology (2024A0505050031)
  • Double Hundred, Thousand, and Ten Thousand Project (323A0404)

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