A Three-Stage Super-Efficient SBM-DEA Analysis on Spatial Differentiation of Land Use Carbon Emission and Regional Efficiency in Shanxi Province, China

Ahui Chen, Huan Duan, Kaiming Li (), Hanqi Shi and Dengrui Liang
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Ahui Chen: Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
Huan Duan: Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
Kaiming Li: Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
Hanqi Shi: Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
Dengrui Liang: Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China

Sustainability, 2025, vol. 17, issue 20, 1-39

Abstract: Achieving carbon peaking and neutrality is critical for global sustainability efforts and addressing climate change, yet improving land use carbon emission efficiency (LUCE) remains a challenge, especially in resource-dependent regions like Shanxi Province. Existing studies often overlook the spatial heterogeneity of LUCE and the mechanisms behind its driving factors. This study assesses LUCE disparities and explores low-carbon land use pathways in Shanxi to support its sustainable transition. Based on county-level land use data from 1990 to 2022, carbon emissions were estimated, and LUCE was measured using a three-stage super-efficient SBM-DEA model, with stochastic frontier analysis (SFA) to control for external noise. eXtreme Gradient Boosting (XGBoost) with SHAP values was used to identify key socio-economic and environmental drivers. The results show the following: (1) emissions rose 2.46-fold, mainly due to expanding construction land and shrinking cultivated land, with hotspots in Taiyuan, Jinzhong, and Linfen; (2) LUCE improved due to gains in technical and scale efficiency, while pure technical efficiency stayed stable; (3) urbanization and government intervention promoted LUCE, whereas higher per capita GDP constrained it; and (4) population density, economic growth, urbanization, and green technology were the dominant, interacting drivers of land use carbon emissions. This study integrates LUCE assessment with interpretable machine learning, demonstrating a framework that links efficiency evaluation with driver analysis. The findings provide critical insights for formulating regionally adaptive low-carbon land use policies, which are essential for achieving ecological sustainability and supporting the sustainable development of resource-based regions.

Keywords: sustainability; land use change; carbon emissions; carbon emission efficiency; three-stage sbm-dea model; xgboost-shap regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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