A Comprehensive Overview Based on Data Envelopment Analysis (DEA): An Approach Towards Green Economy Development and Sustainability

Abstract This study evaluates the efficiency of green economy development (GED) in Northwest China by integrating Data Envelopment Analysis (DEA) and Tobit regression models. Spatial heterogeneity was assessed using global and local Moran’s I indices from 2016 to 2022. Results revealed significant regional disparities, with mean GED efficiency scores ranked as Northern Xinjiang (0.8815) > Southern Xinjiang (0.7656) > Whole Xinjiang (0.7607) > Eastern Xinjiang (0.4711). Tobit regression analysis identified key influencing factors (general public budget expenditure and tourism revenue exhibited negative correlations with GED efficiency). In contrast, student enrollment and total import–export volume showed positive associations. These findings underscore the critical role of human capital development and trade openness in enhancing GED efficiency. The DEA-Tobit framework provides a robust methodological tool for policymakers to formulate targeted strategies in resource-dependent regions, offering scalable insights for similar economies globally. Highlights DEA-Tobit modeling with spatial autocorrelation to assess GED efficiency Prioritize human capital investment and trade facilitation to boost GED efficiency DEA-Tobit framework serves as a versatile template for efficiency analysis Re-evaluate budget allocation strategies to avoid inefficiencies in public spending