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Energy efficiency assessment of RCEP member states: A three-stage slack based measurement DEA with undesirable outputs

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  • Zhou, Sheng
  • Xu, Zhiwei

Abstract

Improving the energy efficiency of economies is the core of promoting global sustainable development. At present, improving energy efficiency is a common problem faced by all countries. In this paper, the Three-stage DEA model improved by radial slack based measure (SBM) model is used to evaluate the energy efficiency and influencing factors of Regional Comprehensive Economic Partnership (RCEP) members. Meanwhile, the dynamic changes of energy efficiency of each country are further explored by the Malmquist index model. The empirical analysis shows that the energy efficiency of China, Japan, and Australia are all 1, which is at the forefront of efficiency, while the energy efficiency of Vietnam is the lowest, which is 0.136. China's technological progress change index is 1.013, leading other countries. The results of the study are helpful to understand the trend of energy efficiency changes in various countries, to transform their energy efficiency advantages into the common advantages of each member state, and to improve the comprehensive governance capacity of energy efficiency of each country.

Suggested Citation

  • Zhou, Sheng & Xu, Zhiwei, 2022. "Energy efficiency assessment of RCEP member states: A three-stage slack based measurement DEA with undesirable outputs," Energy, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:energy:v:253:y:2022:i:c:s0360544222010738
    DOI: 10.1016/j.energy.2022.124170
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    as
    1. Li, Ke & Lin, Boqiang, 2016. "Impact of energy conservation policies on the green productivity in China’s manufacturing sector: Evidence from a three-stage DEA model," Applied Energy, Elsevier, vol. 168(C), pages 351-363.
    2. Lv, Yulan & Chen, Wei & Cheng, Jianquan, 2020. "Effects of urbanization on energy efficiency in China: New evidence from short run and long run efficiency models," Energy Policy, Elsevier, vol. 147(C).
    3. Chen, Yu & Lin, Boqiang, 2021. "Understanding the green total factor energy efficiency gap between regional manufacturing—insight from infrastructure development," Energy, Elsevier, vol. 237(C).
    4. R. W. Goldsmith, 1966. "The Uses Of National Balance Sheets," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 12(2), pages 95-133, June.
    5. Li, Hongkuan & He, Haiyan & Shan, Jiefei & Cai, Jingjing, 2019. "Innovation efficiency of semiconductor industry in China: A new framework based on generalized three-stage DEA analysis," Socio-Economic Planning Sciences, Elsevier, vol. 66(C), pages 136-148.
    6. Zhang, H. & Fan, L.W. & Zhou, P., 2020. "Handling heterogeneity in frontier modeling of city-level energy efficiency: The case of China," Applied Energy, Elsevier, vol. 279(C).
    7. Dariush Khezrimotlagh & Yao Chen, 2018. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 217-234, Springer.
    8. Popkova, Elena G. & Sergi, Bruno S., 2021. "Energy efficiency in leading emerging and developed countries," Energy, Elsevier, vol. 221(C).
    9. Guerrini, Andrea & Romano, Giulia & Leardini, Chiara, 2018. "Economies of scale and density in the Italian water industry: A stochastic frontier approach," Utilities Policy, Elsevier, vol. 52(C), pages 103-111.
    10. Xu, Jia & Wei, Jiuchang & Zhao, Dingtao, 2016. "Influence of social media on operational efficiency of national scenic spots in china based on three-stage DEA model," International Journal of Information Management, Elsevier, vol. 36(3), pages 374-388.
    11. H. Fried & C. Lovell & S. Schmidt & S. Yaisawarng, 2002. "Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 17(1), pages 157-174, January.
    12. Yao, Xing & Yasmeen, Rizwana & Hussain, Jamal & Hassan Shah, Wasi Ul, 2021. "The repercussions of financial development and corruption on energy efficiency and ecological footprint: Evidence from BRICS and next 11 countries," Energy, Elsevier, vol. 223(C).
    13. Yuda, Michio, 2016. "Inefficiencies in the Japanese National Health Insurance system: A stochastic frontier approach," Journal of Asian Economics, Elsevier, vol. 42(C), pages 65-77.
    14. Zeng, Yuan & Guo, Waiying & Wang, Hongmei & Zhang, Fengbin, 2020. "A two-stage evaluation and optimization method for renewable energy development based on data envelopment analysis," Applied Energy, Elsevier, vol. 262(C).
    15. Tan, Xiujie & Choi, Yongrok & Wang, Banban & Huang, Xiaoqi, 2020. "Does China's carbon regulatory policy improve total factor carbon efficiency? A fixed-effect panel stochastic frontier analysis," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    16. Kaoru Tone, 2011. "Slacks-Based Measure of Efficiency," International Series in Operations Research & Management Science, in: William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), Handbook on Data Envelopment Analysis, chapter 0, pages 195-209, Springer.
    17. Lee, Hiro & Itakura, Ken, 2018. "The welfare and sectoral adjustment effects of mega-regional trade agreements on ASEAN countries," Journal of Asian Economics, Elsevier, vol. 55(C), pages 20-32.
    18. Li, Chunding & Wang, Jing & Whalley, John, 2016. "Impact of mega trade deals on China: A computational general equilibrium analysis," Economic Modelling, Elsevier, vol. 57(C), pages 13-25.
    19. Qu, Chenyao & Shao, Jun & Shi, Zhenkai, 2020. "Does financial agglomeration promote the increase of energy efficiency in China?," Energy Policy, Elsevier, vol. 146(C).
    20. Li, Qiaomin, 2017. "Analyzing Effects of the Regional Comprehensive Economic Partnership on FDI in a Firm Heterogeneity CGE Framework," Conference papers 330179, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    21. Chen, Zhenling & Zhang, Xiaoling & Ni, Guohua, 2020. "Decomposing capacity utilization under carbon dioxide emissions reduction constraints in data envelopment analysis: An application to Chinese regions," Energy Policy, Elsevier, vol. 139(C).
    22. Li, Qiaomin & Scollay, Robert & Gilbert, John, 2017. "Analyzing the effects of the Regional Comprehensive Economic Partnership on FDI in a CGE framework with firm heterogeneity," Economic Modelling, Elsevier, vol. 67(C), pages 409-420.
    23. Klein, Nadja & Herwartz, Helmut & Kneib, Thomas, 2020. "Modelling regional patterns of inefficiency: A Bayesian approach to geoadditive panel stochastic frontier analysis with an application to cereal production in England and Wales," Journal of Econometrics, Elsevier, vol. 214(2), pages 513-539.
    24. Kuhn, Lena & Balezentis, Tomas & Hou, Lingling & Wang, Dan, 2020. "Technical and environmental efficiency of livestock farms in China: A slacks-based DEA approach," China Economic Review, Elsevier, vol. 62(C).
    25. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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