IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v148y2025ics0140988325004517.html

The impact of China's manufacturing industry transfer on the comprehensive efficiency of pollution and carbon emissions reduction: An empirical analysis using a spatial panel model

Author

Listed:
  • Shao, Changhua
  • Lv, Chengchao
  • Ning, Jiajun
  • Gao, Yuan
  • Cui, Yue

Abstract

This study applies the DEA-SBM model to assess the comprehensive efficiency of pollution and carbon emission reduction across thirty provincial-level administrative regions in China from 2012 to 2021. It explores the evolving trends of this efficiency at both national and regional levels, including the eastern, central, western, and northeastern areas. The manufacturing industry in China is segmented into labor-intensive, capital-intensive, and technology-intensive categories. A spatial matrix is devised to empirically investigate the spatial spillover effects of these industrial transfers on the comprehensive efficiency of pollution and carbon emission reduction, utilizing panel data. The findings reveal a disparity in the comprehensive efficiency of carbon reduction among different regions within the study period, with the eastern region demonstrating superior performance compared to the central, western, and northeastern regions. However, an overall upward trend is observed at both national and regional scales. The study identifies a positive spatiotemporal correlation in the comprehensive efficiency of pollution and carbon emission reduction across China. Regression analyses using the spatial Durbin model indicate that the relocation of labor-intensive industries negatively affects the improvement of comprehensive efficiency in related areas, whereas the transfer of capital-intensive industries enhances it. Conversely, the impact of technology-intensive industry transfers on comprehensive efficiency appears negligible. Further analysis of the econometric model's regression results highlights significant indirect and total effects of shifts from labor-intensive and capital-intensive industries. Specifically, the former exerts a negative influence on the comprehensive efficiency of pollution and carbon emission reduction, while the latter promotes its enhancement.

Suggested Citation

  • Shao, Changhua & Lv, Chengchao & Ning, Jiajun & Gao, Yuan & Cui, Yue, 2025. "The impact of China's manufacturing industry transfer on the comprehensive efficiency of pollution and carbon emissions reduction: An empirical analysis using a spatial panel model," Energy Economics, Elsevier, vol. 148(C).
  • Handle: RePEc:eee:eneeco:v:148:y:2025:i:c:s0140988325004517
    DOI: 10.1016/j.eneco.2025.108624
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988325004517
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2025.108624?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Ben Kheder, Sonia & Zugravu, Natalia, 2012. "Environmental regulation and French firms location abroad: An economic geography model in an international comparative study," Ecological Economics, Elsevier, vol. 77(C), pages 48-61.
    2. Dong-hyun Oh, 2010. "A global Malmquist-Luenberger productivity index," Journal of Productivity Analysis, Springer, vol. 34(3), pages 183-197, December.
    3. Bing Wang & Yifan Wang & Yuqing Zhao, 2021. "Collaborative Governance Mechanism of Climate Change and Air Pollution: Evidence from China," Sustainability, MDPI, vol. 13(12), pages 1-16, June.
    4. Xiaoli Shi & Ying Chen & Qianju Cheng, 2022. "Environmental Regulation, Environmental Knowledge Spillover, and Regional Economic Growth in China: An Empirical Test Based on the Spatial Durbin Model," Sustainability, MDPI, vol. 14(21), pages 1-23, November.
    5. Alwyn Young, 2003. "Gold into Base Metals: Productivity Growth in the People's Republic of China during the Reform Period," Journal of Political Economy, University of Chicago Press, vol. 111(6), pages 1220-1261, December.
    6. Zahra, Samia & Fatima, Syeda Noreen, 2024. "Do energy diversification and green growth transition help to achieve the target of carbon neutrality? Testing the validity of the EKC hypothesis under the prism of green growth," Applied Energy, Elsevier, vol. 373(C).
    7. S. A. Montzka & E. J. Dlugokencky & J. H. Butler, 2011. "Non-CO2 greenhouse gases and climate change," Nature, Nature, vol. 476(7358), pages 43-50, August.
    8. Wang, Maria & Kuusi, Tero, 2024. "Trade flows, carbon leakage, and the EU Emissions Trading System," Energy Economics, Elsevier, vol. 134(C).
    9. Wang, Chengwei & Miao, Wang & Lu, Miaomiao, 2022. "Evolution of the Chinese industrial structure: A social network perspective," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    10. Zhang, Yijun & Li, Xiaoping & Song, Yi & Jiang, Feitao, 2021. "Can green industrial policy improve total factor productivity? Firm-level evidence from China," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 51-62.
    11. Tone, Kaoru & Tsutsui, Miki, 2010. "Dynamic DEA: A slacks-based measure approach," Omega, Elsevier, vol. 38(3-4), pages 145-156, June.
    12. Lee, Chien-Chiang & He, Zhi-Wen & Yuan, Zihao, 2023. "A pathway to sustainable development: Digitization and green productivity," Energy Economics, Elsevier, vol. 124(C).
    13. Lv, Chengchao & Shao, Changhua & Lee, Chien-Chiang, 2021. "Green technology innovation and financial development: Do environmental regulation and innovation output matter?," Energy Economics, Elsevier, vol. 98(C).
    14. Zhao, Xiaoli & Yin, Haitao, 2011. "Industrial relocation and energy consumption: Evidence from China," Energy Policy, Elsevier, vol. 39(5), pages 2944-2956, May.
    15. Lee, Chien-Chiang & Hussain, Jafar & Mu, Xian, 2024. "Renewable energy and carbon-neutral gaming: A holistic approach to sustainable electricity," Energy, Elsevier, vol. 297(C).
    16. Mielnik, Otavio & Goldemberg, Jose, 2002. "Foreign direct investment and decoupling between energy and gross domestic product in developing countries," Energy Policy, Elsevier, vol. 30(2), pages 87-89, January.
    17. Zhang, Zengkai & Guo, Ju'e & Hewings, Geoffrey J.D., 2014. "The effects of direct trade within China on regional and national CO2 emissions," Energy Economics, Elsevier, vol. 46(C), pages 161-175.
    18. 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.
    19. Zhu, Qingyuan & Xu, Chengzhen & Lee, Chien-Chiang, 2024. "Trade-induced carbon-economic inequality within China: Measurement, sources, and determinants," Energy Economics, Elsevier, vol. 136(C).
    20. Monteforte, Fabio, 2020. "Structural change, the push-pull hypothesis and the Spanish labour market," Economic Modelling, Elsevier, vol. 86(C), pages 148-169.
    21. Thomas, V.J. & Sharma, Seema & Jain, Sudhir K., 2011. "Using patents and publications to assess R&D efficiency in the states of the USA," World Patent Information, Elsevier, vol. 33(1), pages 4-10, March.
    Full references (including those not matched with items on IDEAS)

    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. Min Wang & Meng Ji & Xiaofen Wu & Kexin Deng & Xiaodong Jing, 2023. "Analysis on Evaluation and Spatial-Temporal Evolution of Port Cluster Eco-Efficiency: Case Study from the Yangtze River Delta in China," Sustainability, MDPI, vol. 15(10), pages 1-16, May.
    2. Shuming Ren & Lianqing Li & Yueqi Han & Yu Hao & Haitao Wu, 2022. "The emerging driving force of inclusive green growth: Does digital economy agglomeration work?," Business Strategy and the Environment, Wiley Blackwell, vol. 31(4), pages 1656-1678, May.
    3. 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).
    4. Xiang Ji & Jiasen Sun & Qunwei Wang & Qianqian Yuan, 2019. "Revealing Energy Over-Consumption and Pollutant Over-Emission Behind GDP: A New Multi-criteria Sustainable Measure," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1391-1421, December.
    5. Shihong Zeng & Gen Li & Shaomin Wu & Zhanfeng Dong, 2022. "The Impact of Green Technology Innovation on Carbon Emissions in the Context of Carbon Neutrality in China: Evidence from Spatial Spillover and Nonlinear Effect Analysis," IJERPH, MDPI, vol. 19(2), pages 1-25, January.
    6. Jie Tao & Weidong Cao & Yebing Fang & Yujie Liu & Xueyan Wang & Haipeng Wei, 2022. "Spatiotemporal Differences and Spatial Spillovers of China’s Green Manufacturing under Environmental Regulation," IJERPH, MDPI, vol. 19(19), pages 1-20, September.
    7. Guoqun Ma & Xiaopeng Dai & Yuxi Luo, 2023. "The Effect of Farmland Transfer on Agricultural Green Total Factor Productivity: Evidence from Rural China," IJERPH, MDPI, vol. 20(3), pages 1-15, January.
    8. Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2021. "Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    9. Mohammad Nourani & Qian Long Kweh & Evelyn Shyamala Devadason & V.G.R. Chandran, 2020. "A decomposition analysis of managerial efficiency for the insurance companies: A data envelopment analysis approach," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(6), pages 885-901, September.
    10. Mohammad Tavassoli & Mahsa Ghandehari & Masoud Taherinia, 2023. "Rang-adjusted measure: modelling and computational aspects from internal and external perspectives for network DEA," Operational Research, Springer, vol. 23(4), pages 1-34, December.
    11. Yongqi Feng & Haolin Zhang & Yung-ho Chiu & Tzu-Han Chang, 2021. "Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage meta-frontier dynamic network DEA model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3091-3129, April.
    12. Yu-Chuan Chen & Yung-Ho Chiu & Tzu-Han Chang & Tai-Yu Lin, 2023. "Sustainable Development, Government Efficiency, and People’s Happiness," Journal of Happiness Studies, Springer, vol. 24(4), pages 1549-1578, April.
    13. Qian Wang & Zhuoya Du & Boyu Wang & Yung‐ho Chiu & Tzu‐Han Chang, 2022. "Environmental regulation and foreign direct investment attractiveness: Evidence from China provinces," Review of Development Economics, Wiley Blackwell, vol. 26(2), pages 899-917, May.
    14. Gómez-Calvet, Roberto & Conesa, David & Gómez-Calvet, Ana Rosa & Tortosa-Ausina, Emili, 2014. "Energy efficiency in the European Union: What can be learned from the joint application of directional distance functions and slacks-based measures?," Applied Energy, Elsevier, vol. 132(C), pages 137-154.
    15. Chen, Kuan-Chen & Lin, Sun-Yuan & Yu, Ming-Miin, 2022. "Exploring the efficiency of hospital and pharmacy utilizations in Taiwan: An application of dynamic network data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    16. Zhijiang Li & Decai Tang & Mang Han & Brandon J. Bethel, 2018. "Comprehensive Evaluation of Regional Sustainable Development Based on Data Envelopment Analysis," Sustainability, MDPI, vol. 10(11), pages 1-18, October.
    17. Chunhua Xin & Xiufeng Lai, 2022. "Does the Environmental Information Disclosure Promote the High-Quality Development of China’s Resource-Based Cities?," Sustainability, MDPI, vol. 14(11), pages 1-26, May.
    18. Zhen Shi & Fengping Wu & Huinan Huang & Xinrui Sun & Lina Zhang, 2019. "Comparing Economics, Environmental Pollution and Health Efficiency in China," IJERPH, MDPI, vol. 16(23), pages 1-30, December.
    19. Dai, Zhifeng & Zhu, Haoyang, 2024. "Climate policy uncertainty and urban green total factor productivity: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    20. Onder Belgin, 2024. "Efficiency Analysis of EU and Non-EU R&D Investor Firms on Matched Samples," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 13601-13621, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:eneeco:v:148:y:2025:i:c:s0140988325004517. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    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.