IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v26y2024i6d10.1007_s10668-023-03273-2.html
   My bibliography  Save this article

Mechanism analysis of the influence of intelligent manufacturing on carbon emission intensity: evidence from cross country and industry

Author

Listed:
  • Wei Geng

    (Tianjin University of Finance and Economics)

  • Xiaoqian Liu

    (Tianjin University of Finance and Economics)

  • Xianchun Liao

    (University of Jinan
    Research Center for Shandong Longshan Green Economy)

Abstract

How to reduce carbon emission intensity is a common challenge facing in various countries, particularly in developing countries. We identify three literature gaps: theoretical framework of a novel perspective of intelligent manufacturing (IM) influencing carbon intensity, empirical tests by solving endogeneity with cross country and industry data to answer why IM influences carbon intensity, and mediation analysis to explain how IM influences carbon intensity. By applying the World Input–Output Database (WIOD) and environmental account database with panel data of 13 manufacturing sectors in 39 economies from 2000 to 2011, we reveal that IM has a significant reduction in carbon intensity after considering endogeneity and robust check. Our mechanism test demonstrates that energy consumption structure and position in global value chains (GVCs) are main channels. Heterogeneity analysis reveals that developing countries have larger carbon restraining influence by IM. The contributions of this study are: First, this study investigates restraining influence on carbon intensity from novel perspective of IM, which enriches theoretical analysis. Second, this study takes advantage of cross country and industry data to test and adopts Generalized Method of Moments (GMM) model to solve endogeneity, which answers why IM influences carbon intensity. Further, this paper performs heterogeneity analysis and explores the mediating effects played by improving energy consumption structure and enhancing position in global value chains (GVCs), which answers how IM influences carbon intensity.

Suggested Citation

  • Wei Geng & Xiaoqian Liu & Xianchun Liao, 2024. "Mechanism analysis of the influence of intelligent manufacturing on carbon emission intensity: evidence from cross country and industry," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(6), pages 15777-15801, June.
  • Handle: RePEc:spr:endesu:v:26:y:2024:i:6:d:10.1007_s10668-023-03273-2
    DOI: 10.1007/s10668-023-03273-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-023-03273-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10668-023-03273-2?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Nabernegg, Stefan & Bednar-Friedl, Birgit & Muñoz, Pablo & Titz, Michaela & Vogel, Johanna, 2019. "National Policies for Global Emission Reductions: Effectiveness of Carbon Emission Reductions in International Supply Chains," Ecological Economics, Elsevier, vol. 158(C), pages 146-157.
    2. Daron Acemoglu & Philippe Aghion & Leonardo Bursztyn & David Hemous, 2012. "The Environment and Directed Technical Change," American Economic Review, American Economic Association, vol. 102(1), pages 131-166, February.
    3. Su, Bin & Ang, B.W., 2015. "Multiplicative decomposition of aggregate carbon intensity change using input–output analysis," Applied Energy, Elsevier, vol. 154(C), pages 13-20.
    4. Guy Michaels & Ashwini Natraj & John Van Reenen, 2010. "Has ICT Polarized Skill Demand? Evidence from Eleven Countries over 25 years," NBER Working Papers 16138, National Bureau of Economic Research, Inc.
    5. Zong, Yi & Gu, Guoda, 2022. "The threshold effect of manufacturing Servitization on carbon emission: An empirical analysis based on multinational panel data," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 353-364.
    6. Li, Yaya & Zhang, Yuru & Pan, An & Han, Minchun & Veglianti, Eleonora, 2022. "Carbon emission reduction effects of industrial robot applications: Heterogeneity characteristics and influencing mechanisms," Technology in Society, Elsevier, vol. 70(C).
    7. Yang, Xue & Su, Bin, 2019. "Impacts of international export on global and regional carbon intensity," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    8. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    9. Yaya Li & Yuru Zhang & An Pan & Minchun Han & Eleonora Veglianti, 2022. "Carbon emission reduction effects of industrial robot applications: Heterogeneity characteristics and influencing mechanisms," Post-Print hal-04522085, HAL.
    10. Lene Kromann & Nikolaj Malchow-Møller & Jan Rose Skaksen & Anders Sørensen, 2020. "Automation and productivity—a cross-country, cross-industry comparison," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 29(2), pages 265-287.
    11. Curtis, E. Mark & Lee, Jonathan M., 2019. "When do environmental regulations backfire? Onsite industrial electricity generation, energy efficiency and policy instruments," Journal of Environmental Economics and Management, Elsevier, vol. 96(C), pages 174-194.
    12. Xie, Rui & Fang, Jiayu & Liu, Cenjie, 2017. "The effects of transportation infrastructure on urban carbon emissions," Applied Energy, Elsevier, vol. 196(C), pages 199-207.
    13. Torkayesh, Ali Ebadi & Alizadeh, Reza & Soltanisehat, Leili & Torkayesh, Sajjad Ebadi & Lund, Peter D., 2022. "A comparative assessment of air quality across European countries using an integrated decision support model," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    14. Tang, Yiding & Zhu, Shujin & Luo, Yan & Duan, Wenjing, 2022. "Input servitization, global value chain, and carbon mitigation: An input-output perspective of global manufacturing industry," Economic Modelling, Elsevier, vol. 117(C).
    15. Luo, Yusen & Lu, Zhengnan & Long, Xingle, 2020. "Heterogeneous effects of endogenous and foreign innovation on CO2 emissions stochastic convergence across China," Energy Economics, Elsevier, vol. 91(C).
    16. Guy Michaels & Ashwini Natraj & John Van Reenen, 2014. "Has ICT Polarized Skill Demand? Evidence from Eleven Countries over Twenty-Five Years," The Review of Economics and Statistics, MIT Press, vol. 96(1), pages 60-77, March.
    17. Yu, Chunjiao & Luo, Zhechong, 2018. "What are China's real gains within global value chains? Measuring domestic value added in China's exports of manufactures," China Economic Review, Elsevier, vol. 47(C), pages 263-273.
    18. Jiansuo Pei & Bo Meng & Fei Wang & Jinjun Xue & Zhongxiu Zhao, 2018. "Production Sharing, Demand Spillovers And Co2 Emissions: The Case Of Chinese Regions In Global Value Chains," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 63(02), pages 275-293, March.
    19. Sun, Chuanwang & Li, Zhi & Ma, Tiemeng & He, Runyong, 2019. "Carbon efficiency and international specialization position: Evidence from global value chain position index of manufacture," Energy Policy, Elsevier, vol. 128(C), pages 235-242.
    20. Alizadeh, Reza & Soltanisehat, Leili & Lund, Peter D. & Zamanisabzi, Hamed, 2020. "Improving renewable energy policy planning and decision-making through a hybrid MCDM method," Energy Policy, Elsevier, vol. 137(C).
    21. Dong, Kangyin & Hochman, Gal & Zhang, Yaqing & Sun, Renjin & Li, Hui & Liao, Hua, 2018. "CO2 emissions, economic and population growth, and renewable energy: Empirical evidence across regions," Energy Economics, Elsevier, vol. 75(C), pages 180-192.
    22. Robert Koopman & William Powers & Zhi Wang & Shang-Jin Wei, 2010. "Give Credit Where Credit Is Due: Tracing Value Added in Global Production Chains," NBER Working Papers 16426, National Bureau of Economic Research, Inc.
    23. Morakinyo Adetutu & Anthony Glass & Karligash Kenjegalieva & Robin Sickles, 2015. "The effects of efficiency and TFP growth on pollution in Europe: a multistage spatial analysis," Journal of Productivity Analysis, Springer, vol. 43(3), pages 307-326, June.
    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. Yi Wang & Shuo Fan, 2025. "Does the Integrated Development of High-End, Intelligent, and Green Manufacturing in China Influence Regional Dual Control of Carbon Emissions?—An Analysis Based on Impact Mechanisms and Spatial Effec," Sustainability, MDPI, vol. 17(8), pages 1-24, April.
    2. Zhu, Huayou & Bao, Weiping & Yu, Guojun, 2024. "How can intelligent manufacturing lead enterprise low-carbon transformation? Based on China's intelligent manufacturing demonstration projects," Energy, Elsevier, vol. 313(C).

    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. Ye, Chusheng & Ye, Qin & Shi, Xunpeng & Sun, Yongping, 2020. "Technology gap, global value chain and carbon intensity: Evidence from global manufacturing industries," Energy Policy, Elsevier, vol. 137(C).
    2. Xing Zhao & Sasa Yang, 2023. "Does Intelligence Improve the Efficiency of Technological Innovation?," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 14(4), pages 3671-3695, December.
    3. Shi, Qiaoling & Zhao, Yuhuan & Qian, Zhiling & Zheng, Lu & Wang, Song, 2022. "Global value chains participation and carbon emissions: Evidence from Belt and Road countries," Applied Energy, Elsevier, vol. 310(C).
    4. Wang, En-Ze & Lee, Chien-Chiang & Li, Yaya, 2022. "Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries," Energy Economics, Elsevier, vol. 105(C).
    5. Wang, Jianlong & Wang, Weilong & Liu, Yong & Wu, Haitao, 2023. "Can industrial robots reduce carbon emissions? Based on the perspective of energy rebound effect and labor factor flow in China," Technology in Society, Elsevier, vol. 72(C).
    6. Han, Wang-Zhe & Zhang, Yi-Ming, 2024. "Carbon reduction effect of industrial robots: Breaking the impasse for carbon emissions and development," Innovation and Green Development, Elsevier, vol. 3(3).
    7. David Autor & Anna Salomons, 2018. "Is Automation Labor Share–Displacing? Productivity Growth, Employment, and the Labor Share," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 49(1 (Spring), pages 1-87.
    8. Lionel Fontagné & Ariell Reshef & Gianluca Santoni & Giulio Vannelli, 2024. "Automation, global value chains and functional specialization," Review of International Economics, Wiley Blackwell, vol. 32(2), pages 662-691, May.
    9. Antonio Cabrales & Penélope Hernández & Angel Sánchez, 2020. "Robots, labor markets, and universal basic income," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-8, December.
    10. Cortes, Matias & Lerche, Adrian & Schönberg, Uta & Tschopp, Jeanne, 2023. "Technological Change, Firm Heterogeneity and Wage Inequality," IZA Discussion Papers 16070, Institute of Labor Economics (IZA).
    11. Johannes Lehmann & Michael Beckmann, 2024. "Digital technologies and performance incentives: Evidence from businesses in the Swiss economy," Papers 2412.12780, arXiv.org.
    12. Wenjuan Lu & Shenya Mao & Xinfeng Qiu & Chenjing Yan, 2025. "Industrial Robots and Environmental Pollution: Evidence From Chinese Cities," Journal of International Development, John Wiley & Sons, Ltd., vol. 37(3), pages 820-833, April.
    13. Lewandowski, Piotr & Keister, Roma & Hardy, Wojciech & Górka, Szymon, 2020. "Ageing of routine jobs in Europe," Economic Systems, Elsevier, vol. 44(4).
    14. Clément Bosquet & Paul Maarek & Elliot Moiteaux, 2021. "Routine-biased technological change and wages by education level: Occupational downgrading and displacement effects," Working Papers hal-03270715, HAL.
    15. Wu, Ziqi & Xiao, Yi & Zhang, Jian, 2022. "Labor mobility and corporate investment—Evidence from a Quasi-natural experiment in China," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 1110-1129.
    16. Tan, Joanne, 2024. "Multidimensional heterogeneity and matching in a frictional labor market — An application to polarization," Labour Economics, Elsevier, vol. 90(C).
    17. Qiao, Xiaole & He, Yang & Du, Qiang, 2025. "How does the urban digital economy drive labor allocation in China?—A perspective of factor mobility between digital and non-digital enterprises," Economic Analysis and Policy, Elsevier, vol. 85(C), pages 1159-1175.
    18. Maarek, Paul & Moiteaux, Elliot, 2021. "Polarization, employment and the minimum wage: Evidence from European local labor markets," Labour Economics, Elsevier, vol. 73(C).
    19. Lorenz, Hanno & Stephany, Fabian, 2018. "Back to the future: Changing job profiles in the digital age," Working Papers 13, Agenda Austria.
    20. Barth, Erling & Bryson, Alex & Dale-Olsen, Harald, 2022. "Creative Disruption: Technology Innovation, Labour Demand and the Pandemic," IZA Discussion Papers 15762, Institute of Labor Economics (IZA).

    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:spr:endesu:v:26:y:2024:i:6:d:10.1007_s10668-023-03273-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.