IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i9p5395-d804873.html
   My bibliography  Save this article

A Study of Carbon Emission Efficiency in Chinese Provinces Based on a Three-Stage SBM-Undesirable Model and an LSTM Model

Author

Listed:
  • Huayong Niu

    (International Business School, Beijing Foreign Studies University, Beijing 100089, China)

  • Zhishuo Zhang

    (International Business School, Beijing Foreign Studies University, Beijing 100089, China)

  • Yao Xiao

    (International Business School, Beijing Foreign Studies University, Beijing 100089, China)

  • Manting Luo

    (International Business School, Beijing Foreign Studies University, Beijing 100089, China)

  • Yumeng Chen

    (Liaoning Banking and Insurance Regulatory Bureau, Shenyang 110013, China)

Abstract

As a major carbon-emitting country, there is an urgent need for China to reduce carbon emissions. Studying the carbon emission efficiency of each province helps us to learn about the characteristics and evolution of regional carbon emissions, which is important for proposing effective and targeted measures to achieve the carbon peaking and carbon neutrality goals. This paper measures the carbon emission efficiency of 30 Chinese provinces from 2006 to 2019 based on a three-stage SBM-undesirable model and explores external drivers using stochastic frontier models. The results of the SBM-undesirable model show that the inter-provincial carbon emission efficiency is unevenly distributed and shows a big difference. From the results of the stochastic frontier model analysis, external drivers such as the intensity of finance in environmental protection, the level of economic development, the industrial structure, the level of urbanization, the degree of openness and the level of science as well as technology innovation all have an impact on the emission efficiency. In terms of LSTM model prediction, the model shows an excellent fitting effect, which provides a possible path for carbon emission efficiency prediction. Finally, based on the empirical results and the actual situation of each province in China, this paper proposes relevant feasible suggestions.

Suggested Citation

  • Huayong Niu & Zhishuo Zhang & Yao Xiao & Manting Luo & Yumeng Chen, 2022. "A Study of Carbon Emission Efficiency in Chinese Provinces Based on a Three-Stage SBM-Undesirable Model and an LSTM Model," IJERPH, MDPI, vol. 19(9), pages 1-19, April.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:9:p:5395-:d:804873
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/9/5395/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/9/5395/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mielnik, Otavio & Goldemberg, Jose, 1999. "Communication The evolution of the "carbonization index" in developing countries," Energy Policy, Elsevier, vol. 27(5), pages 307-308, May.
    2. 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.
    3. Shrestha, Ram M. & Timilsina, Govinda R., 1996. "Factors affecting CO2 intensities of power sector in Asia: A Divisia decomposition analysis," Energy Economics, Elsevier, vol. 18(4), pages 283-293, October.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Reinhard, Stijn & Knox Lovell, C. A. & Thijssen, Geert J., 2000. "Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA," European Journal of Operational Research, Elsevier, vol. 121(2), pages 287-303, March.
    6. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    7. 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.
    8. 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.
    9. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "Measuring environmental performance under different environmental DEA technologies," Energy Economics, Elsevier, vol. 30(1), pages 1-14, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pan Jiang & Mengyue Li & Yuting Zhao & Xiujuan Gong & Ruifeng Jin & Yuhan Zhang & Xue Li & Liang Liu, 2022. "Does Environmental Regulation Improve Carbon Emission Efficiency? Inspection of Panel Data from Inter-Provincial Provinces in China," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    2. Huayong Niu & Zhishuo Zhang & Manting Luo, 2022. "Evaluation and Prediction of Low-Carbon Economic Efficiency in China, Japan and South Korea: Based on DEA and Machine Learning," IJERPH, MDPI, vol. 19(19), pages 1-28, October.
    3. Mei Zhang & Hanye Zhang & Yun Deng & Chuanqi Yi, 2024. "Effects of Conservation Tillage on Agricultural Green Total Factor Productivity in Black Soil Region: Evidence from Heilongjiang Province, China," Land, MDPI, vol. 13(8), pages 1-23, August.
    4. Min Zhou & Hua Zhang & Zixuan Zhang & Hanxiaoxue Sun, 2023. "Digital Financial Inclusion, Cultivated Land Transfer and Cultivated Land Green Utilization Efficiency: An Empirical Study from China," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    5. Elahi, Ehsan & Zhu, Min & Khalid, Zainab & Wei, Kezhen, 2024. "An empirical analysis of carbon emission efficiency in food production across the Yangtze River basin: Towards sustainable agricultural development and carbon neutrality," Agricultural Systems, Elsevier, vol. 218(C).
    6. Rongbang Xu & Fujie Yang & Sanmang Wu & Qinwen Xue, 2024. "Spatio-Temporal Evolution and Drivers of Carbon Emission Efficiency in China’s Iron and Steel Industry," Sustainability, MDPI, vol. 16(12), pages 1-22, June.
    7. Jih-Shong Wu, 2023. "Measuring Economic Development and Carbon Dioxide Emissions Inefficiency," SAGE Open, , vol. 13(1), pages 21582440231, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    2. Avkiran, Necmi K. & Rowlands, Terry, 2008. "How to better identify the true managerial performance: State of the art using DEA," Omega, Elsevier, vol. 36(2), pages 317-324, April.
    3. Vaninsky, Alexander, 2010. "Prospective national and regional environmental performance: Boundary estimations using a combined data envelopment – stochastic frontier analysis approach," Energy, Elsevier, vol. 35(9), pages 3657-3665.
    4. Hall, Maximilian J.B. & Kenjegalieva, Karligash A. & Simper, Richard, 2012. "Environmental factors affecting Hong Kong banking: A post-Asian financial crisis efficiency analysis," Global Finance Journal, Elsevier, vol. 23(3), pages 184-201.
    5. Mai, Nhat Chi, 2015. "Efficiency of the banking system in Vietnam under financial liberalization," OSF Preprints qsf6d, Center for Open Science.
    6. Chiu, Yung-Ho & Chen, Yu-Chuan, 2009. "The analysis of Taiwanese bank efficiency: Incorporating both external environment risk and internal risk," Economic Modelling, Elsevier, vol. 26(2), pages 456-463, March.
    7. da Cruz, Nuno Ferreira & Marques, Rui Cunha, 2014. "Revisiting the determinants of local government performance," Omega, Elsevier, vol. 44(C), pages 91-103.
    8. Liangen Zeng & Haiyan Lu & Yenping Liu & Yang Zhou & Haoyu Hu, 2019. "Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015," Energies, MDPI, vol. 12(16), pages 1-21, August.
    9. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499, March.
    10. Chin‐wei Huang & Hsiao‐Yin Chen, 2023. "Using nonradial metafrontier data envelopment analysis to evaluate the metatechnology and metafactor ratios for the Taiwanese hotel industry," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(4), pages 1904-1919, June.
    11. Avkiran, Necmi K., 2006. "Developing foreign bank efficiency models for DEA grounded in finance theory," Socio-Economic Planning Sciences, Elsevier, vol. 40(4), pages 275-296, December.
    12. Pengyu Ren & Zhaoxia Liu, 2021. "Efficiency Evaluation of China’s Public Sports Services: A Three-Stage DEA Model," IJERPH, MDPI, vol. 18(20), pages 1-12, October.
    13. Aparicio, Juan & Kapelko, Magdalena, 2019. "Accounting for slacks to measure dynamic inefficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 278(2), pages 463-471.
    14. César Salazar & Roberto Cárdenas-Retamal & Marcela Jaime, 2023. "Environmental efficiency in the salmon industry—an exploratory analysis around the 2007 ISA virus outbreak and subsequent regulations in Chile," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(8), pages 8107-8135, August.
    15. Alizadeh, Reza & Gharizadeh Beiragh, Ramin & Soltanisehat, Leili & Soltanzadeh, Elham & Lund, Peter D., 2020. "Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach," Energy Economics, Elsevier, vol. 91(C).
    16. Nicole Adler & Georg Hirte & Shravana Kumar & Hans-Martin Niemeier, 2022. "The impact of specialization, ownership, competition and regulation on efficiency: a case study of Indian seaports," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 24(3), pages 507-536, September.
    17. Wang, Qian & Ren, Shuming, 2022. "Evaluation of green technology innovation efficiency in a regional context: A dynamic network slacks-based measuring approach," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    18. Behrouz Arabi & Susila Munisamy Doraisamy & Ali Emrouznejad & Alireza Khoshroo, 2017. "Eco-efficiency measurement and material balance principle: an application in power plants Malmquist Luenberger Index," Annals of Operations Research, Springer, vol. 255(1), pages 221-239, August.
    19. Chien-Ming Chen & Magali A. Delmas & Marvin B. Lieberman, 2015. "Production frontier methodologies and efficiency as a performance measure in strategic management research," Strategic Management Journal, Wiley Blackwell, vol. 36(1), pages 19-36, January.
    20. Wu, Tai-Hsi & Chen, Ming-Shiun & Yeh, Jin-Yii, 2010. "Measuring the performance of police forces in Taiwan using data envelopment analysis," Evaluation and Program Planning, Elsevier, vol. 33(3), pages 246-254, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:9:p:5395-:d:804873. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.