Evaluation of the environmental efficiency of China's power generation industry considering carbon emissions and air pollution: An improved three-stage SBM-SE-DEA model

Abstract

Abstract Evaluating and enhancing the environmental efficiency of the power generation industry is an effective approach for addressing the challenges of climate change and environmental pollution. Considering the influence of external environmental factors and stochastic factors, this paper proposes an improved three-stage slack-based measure with superefficiency data envelopment analysis (SBM-SE-DEA) model to evaluate the environmental efficiency of the power generation industry in China’s 30 provincial regions during 2015–2021. The model integrates three-stage DEA model, SBM-DEA model, and SE-DEA model while accounting for undesirable outputs such as carbon emissions and air pollutants. The results show that (1) regions with a high proportion of renewable energy generation demonstrate the best environmental efficiency when considering the environmental constraints from carbon emissions and air pollution. However, the results of the first stage are evidently overestimated due to the influence of external environmental factors. (2) Rational adjustments in the economic development level, power structure, and industrial structure play a positive role in improving environmental efficiency. However, improving resource endowment does not yield the expected results. Additionally, provinces with higher electricity outputs often bear greater pressure from environmental pollution. (3) The environmental efficiency in the third stage exhibited a stable trend driven by internal factors. However, except for the Northeast and Central-South regions, most regions still experienced overestimation of environmental efficiency in the first stage. Thus, optimizing the power generation structure, promoting industrial restructuring, and strengthening interregional cooperation and coordination are imperative.