Work

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

Year: 2025

Type: article

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.

Source: Sustainability

DOI: 10.3390/su17209086

Authors: Ahui Chen, Huan Duan, Kaiming Li, Hanqi Shi, Dengrui Liang

Institutions: Lanzhou Jiaotong University, Lanzhou City University, Longnan Normal University

Language: English

Topic: Efficiency Analysis Using DEA

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,896