공간통계 기법을 활용한 산림 탄소흡수량의 공간 패턴 분석: 사회·경제적 변수를 중심으로

This study aims to analyze the carbon sequestration of districts across the nation and the impact of socioeconomic factors using spatial statistical techniques. Initially, we assessed the spatial autocorrelation of carbon sequestration using Global Moran's I and Local Moran's I. The results revealed that population density, GRDP (Gross Regional Domestic Product), per capita GRDP, and the proportions of primary and secondary industries significantly influence carbon sequestration. Notably, regions such as Gangwon Special Self-Governing Province, North Gyeongsang Province, and North Jeolla Special Self-Governing Province were identified as hotspots with high carbon sequestration rates, while the metropolitan area including Seoul and Gyeonggi Province was classified as cold spots with low carbon sequestration. Comparisons among the OLS (Ordinary Least Squares), GWR (Geographically Weighted Regression), and MGWR (Multi-scale Geographically Weighted Regression) models showed that GWR effectively explains spatial heterogeneity. This research provides essential data and insights that can contribute to policy formulation for carbon neutrality goals. Such analyses are crucial for informing policies aimed at mitigating climate change and promoting sustainable development.