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Carbon Dioxide Emission-Reduction Efficiency in China’s New Energy Vehicle Sector Toward Sustainable Development: Evidence from a Three-Stage Super-Slacks Based-Measure Data Envelopment Analysis Model

Author

Listed:
  • Liying Zheng

    (Business School, Hohai University, Nanjing 211100, China)

  • Fangjuan Zhan

    (Business School, Hohai University, Nanjing 211100, China)

  • Fangrong Ren

    (College of Economics and Management, Nanjing Forestry University, Nanjing 210037, China)

Abstract

This research evaluates the carbon dioxide emission-reduction efficiency of new energy vehicles (NEVs) in China from 2018 to 2023 by applying a three-stage super-SBM data envelopment analysis (DEA) model that incorporates undesirable outputs. This model offers significant advantages over traditional DEA models, as it effectively disentangles the influences of external environmental factors and stochastic noise, thereby providing a more accurate and robust assessment of true efficiency. Its super-efficiency characteristic also allows for effective ranking of all decision-making units (DMUs) on the efficiency frontier. The empirical findings reveal several key insights. (1) The NEV industry’s carbon-reduction efficiency in China between 2018 and 2023 displayed an upward trend accompanied by pronounced fluctuations. Its mean super-efficiency score was 0.353, indicating substantial scope for improvements in scale efficiency. (2) Significant interprovincial disparities in efficiency appear. Unbalanced coordination between production and consumption in provinces such as Shaanxi, Beijing, and Liaoning has produced correspondingly high or low efficiency values. (3) Although accelerated urbanization has reduced the capital and labor inputs required by the NEV industry and has raised energy consumption, the net effect enhances carbon-reduction efficiency. Household consumption levels and technological advancement exerts divergent effects on efficiency. The former negatively relates to efficiency, whereas the latter is positively associated.

Suggested Citation

  • Liying Zheng & Fangjuan Zhan & Fangrong Ren, 2025. "Carbon Dioxide Emission-Reduction Efficiency in China’s New Energy Vehicle Sector Toward Sustainable Development: Evidence from a Three-Stage Super-Slacks Based-Measure Data Envelopment Analysis Model," Sustainability, MDPI, vol. 17(16), pages 1-26, August.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:16:p:7440-:d:1726389
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    1. Apostolaki-Iosifidou, Elpiniki & Codani, Paul & Kempton, Willett, 2017. "Measurement of power loss during electric vehicle charging and discharging," Energy, Elsevier, vol. 127(C), pages 730-742.
    2. Zhibo Zhao & Jiamin Ren & Zheng Liu, 2023. "How Does Urbanization Affect Carbon Emission Performance? Evidence from 282 Cities in China," Sustainability, MDPI, vol. 15(21), pages 1-20, October.
    3. Yahong Jiang & Qunqi Wu & Min Li & Yulei Gu & Jun Yang, 2023. "What Is Affecting the Popularity of New Energy Vehicles? A Systematic Review Based on the Public Perspective," Sustainability, MDPI, vol. 15(18), pages 1-29, September.
    4. Wu, Peng & Wang, Yiqing & Chiu, Yung-ho & Li, Ying & Lin, Tai-Yu, 2019. "Production efficiency and geographical location of Chinese coal enterprises - undesirable EBM DEA," Resources Policy, Elsevier, vol. 64(C).
    5. Hongchen Li & Huijun Qi & Hongjian Cao & Li Yuan, 2022. "Industrial Policy and Technological Innovation of New Energy Vehicle Industry in China," Energies, MDPI, vol. 15(24), pages 1-17, December.
    6. 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.
    7. Shiqi Ou & Rujie Yu & Zhenhong Lin & Huanhuan Ren & Xin He & Steven Przesmitzki & Jessey Bouchard, 2020. "Intensity and daily pattern of passenger vehicle use by region and class in China: estimation and implications for energy use and electrification," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(3), pages 307-327, March.
    8. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    9. Zhang, Ming & Song, Yan & Li, Peng & Li, Huanan, 2016. "Study on affecting factors of residential energy consumption in urban and rural Jiangsu," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 330-337.
    10. Li, Rongrong & Wang, Qiang & Li, Lejia & Hu, Sailan, 2023. "Do natural resource rent and corruption governance reshape the environmental Kuznets curve for ecological footprint? Evidence from 158 countries," Resources Policy, Elsevier, vol. 85(PB).
    11. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    12. Fare, Rolf & Shawna Grosskopf & Mary Norris & Zhongyang Zhang, 1994. "Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries," American Economic Review, American Economic Association, vol. 84(1), pages 66-83, March.
    13. Liu, Xiaoxiao & Niu, Qian & Dong, Shuli & Zhong, Shuiying, 2023. "How does renewable energy consumption affect carbon emission intensity? Temporal-spatial impact analysis in China," Energy, Elsevier, vol. 284(C).
    14. Chen, Huanyu & Yi, Jizheng & Chen, Aibin & Peng, Duanxiang & Yang, Jieqiong, 2023. "Green technology innovation and CO2 emission in China: Evidence from a spatial-temporal analysis and a nonlinear spatial durbin model," Energy Policy, Elsevier, vol. 172(C).
    15. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    16. Gaopeng Jiang & Rui Jin & Cuijie Lu & Menglong Gao & Jie Li, 2024. "Provincial Coal Flow Efficiency of China Quantified by Three-Stage Data-Envelopment Analysis," Sustainability, MDPI, vol. 16(11), pages 1-21, May.
    17. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "A survey of data envelopment analysis in energy and environmental studies," European Journal of Operational Research, Elsevier, vol. 189(1), pages 1-18, August.
    18. Wang, Qiang & Hu, Sailan & Li, Rongrong, 2024. "Could information and communication technology (ICT) reduce carbon emissions? The role of trade openness and financial development," Telecommunications Policy, Elsevier, vol. 48(3).
    19. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    20. Qiao, Qinyu & Zhao, Fuquan & Liu, Zongwei & He, Xin & Hao, Han, 2019. "Life cycle greenhouse gas emissions of Electric Vehicles in China: Combining the vehicle cycle and fuel cycle," Energy, Elsevier, vol. 177(C), pages 222-233.
    21. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, March.
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