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

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 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article 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air pollution: An improved three-stage SBM-SE-DEA model Shanglei Chai, Qiang Li, Siyuan Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3863064/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version 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. Environmental Policy environmental efficiency three-stage SBM-SE-DEA model power generation industry carbon emission air pollution Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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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\u0026rsquo;s 30 provincial regions during 2015\u0026ndash;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. 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