Nighttime Light Data and Spatial Modeling for Land-Use Carbon Emissions: Insights from Northeast China

Abstract

Carbon emissions are a critical global issue requiring detailed spatial analyses to support regional carbon peak and neutrality strategies. This study investigates the spatiotemporal evolution and spatial differentiation of county-level land-use carbon emissions (CELU) in the Changchun-Jilin-Tumen (CJT) region from 2012 to 2021 by integrating land use data, nighttime light imagery, and socio-economic statistics with the Optimal Parameter Geodetector (OPGD) model. The analysis identifies key drivers of spatial emission variability, including construction land proportion (q-value: 0.8882), land area per capita (q-value: 0.7609), and urbanization rate (q-value: 0.5875), underscoring the significant role of land-use patterns and urbanization. Results show a 21.2% increase in CELU, from 67,594.46×10⁴ t in 2012 to 81,942.35×10⁴ t in 2021, with emissions concentrated in industrially active and urbanized western and southern counties, while forest-rich central and eastern counties exhibit lower emissions. Using the Grey Model (GM (1,1)), the study forecasts that CELU will rise from 78,484.364×10⁴ t in 2022 to 88,985.198×10⁴ t by 2030, reflecting a 14% increase over the forecast period. This trajectory highlights the misalignment between current trends and the region's goals of creating a "low-carbon industrial zone" and "livable cities," emphasizing the need for transitioning to renewable energy, optimizing industrial structures, and implementing sustainable land-use practices such as brownfield redevelopment and ecological land protection. By combining advanced remote sensing with nonlinear spatial analysis, this study offers a replicable high-resolution framework for understanding carbon emission drivers and spatial patterns, providing actionable insights for refining carbon reduction strategies and achieving sustainable development goals at both national and global scales.