IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i20p5034-d1495770.html
   My bibliography  Save this article

The Impact of Industrial Robots on Green Total Factor Energy Efficiency: Empirical Evidence from Chinese Cities

Author

Listed:
  • Chuanyue Zhao

    (College of Mathematics and System Science, Xinjiang University, Urumqi 830046, China)

  • Zhishuang Zhu

    (Faculty of Economics, Shanxi University of Finance and Economics, Taiyuan 030006, China)

  • Yujuan Wang

    (Faculty of Business Administration, Shanxi University of Finance and Economics, Taiyuan 030006, China)

  • Junhong Du

    (College of Mathematics and System Science, Xinjiang University, Urumqi 830046, China)

Abstract

Improving energy utilization efficiency is a crucial means to achieve energy conservation, emission reduction, and green development. At present, to establish a high-quality development framework and satisfy the growing need for a better life among all its people, China must steadfastly pursue the path of green development. Although China’s substantial economic scale and achievements in ecological civilization construction provide favorable conditions for green transformation, there remains a significant gap compared to developed countries in the application of green and clean technologies. Confronted with technological bottlenecks, leveraging emerging technologies such as industrial robots from the new round of scientific and technological revolutions to improve the green total factor energy efficiency (GTFEE) is of critical importance to China’s green development. This study explores the potential impact of industrial robots on enhancing China’s GTFEE. It begins by reviewing the current research landscape in this field, highlighting its shortcomings, and theorizing potential impact pathways of industrial robots. Subsequently, the paper analyzes data from 2010 to 2019 on the usage of industrial robots and GTFEE across 276 cities at the prefectural level or above in China. Through empirical regression models that incorporate control variables and interaction terms, the study investigates the specific impacts of industrial robots on energy efficiency and their mechanisms of action. The results indicate that industrial robots significantly enhance the GTFEE of Chinese cities, especially in the Northeastern region. Industrial robots notably improve the GTFEE in resource-based cities, old industrial bases, and low-carbon pilot cities. Additionally, robots indirectly boost GTFEE by increasing labor productivity. Enhanced levels of green innovation and environmental regulations also positively moderate the effectiveness of industrial robots in improving energy efficiency. The findings of this research can assist local government agencies in coordinating and implementing policies that are conducive to green development, making better use of industrial robots to serve the people, and are of significant importance for promoting the transformation of China’s economy and society towards high-quality development.

Suggested Citation

  • Chuanyue Zhao & Zhishuang Zhu & Yujuan Wang & Junhong Du, 2024. "The Impact of Industrial Robots on Green Total Factor Energy Efficiency: Empirical Evidence from Chinese Cities," Energies, MDPI, vol. 17(20), pages 1-24, October.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:20:p:5034-:d:1495770
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/20/5034/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/20/5034/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Du, Junhong & He, Jiajia & Yang, Jing & Chen, Xiaohong, 2024. "How industrial robots affect labor income share in task model: Evidence from Chinese A-share listed companies," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    2. 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).
    3. Daron Acemoglu, 2002. "Directed Technical Change," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 69(4), pages 781-809.
    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. Dongya Koh & Raül Santaeulàlia-Llopis, 2017. "Countercyclical Elasticity of Substitution," Working Papers 946, Barcelona School of Economics.
    2. Chu, Angus C. & Cozzi, Guido & Furukawa, Yuichi, 2016. "Unions, innovation and cross-country wage inequality," Journal of Economic Dynamics and Control, Elsevier, vol. 64(C), pages 104-118.
    3. Ottaviano, Gianmarco & Peri, Giovanni, 2008. "Immigration and National Wages: Clarifying the Theory and the Empirics," CEPR Discussion Papers 6916, C.E.P.R. Discussion Papers.
    4. Patrick Mellacher, 2021. "Growth, Inequality and Declining Business Dynamism in a Unified Schumpeter Mark I + II Model," Papers 2111.09407, arXiv.org, revised Nov 2023.
    5. Nicholas Bloom & Tarek Alexander Hassan & Aakash Kalyani & Josh Lerner & Ahmed Tahoun, 2021. "The diffusion of disruptive technologies," CEP Discussion Papers dp1798, Centre for Economic Performance, LSE.
    6. Fan, Fei & Dai, Shangze & Yang, Bo & Ke, Haiqian, 2023. "Urban density, directed technological change, and carbon intensity: An empirical study based on Chinese cities," Technology in Society, Elsevier, vol. 72(C).
    7. Martin, Ralf, 2009. "Why is the US so energy intensive? Evidence from US multinationals in the UK," LSE Research Online Documents on Economics 28703, London School of Economics and Political Science, LSE Library.
    8. David Hémous & Morten Olsen, 2022. "The Rise of the Machines: Automation, Horizontal Innovation, and Income Inequality," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(1), pages 179-223, January.
    9. Battisti, Michele & Gatto, Massimo Del & Parmeter, Christopher F., 2022. "Skill-biased technical change and labor market inefficiency," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    10. Voosholz, Frauke, 2014. "The influence of different production functions on modeling resource extraction and economic growth," CAWM Discussion Papers 72, University of Münster, Münster Center for Economic Policy (MEP).
    11. Foellmi, Reto & Wuergler, Tobias & Zweimüller, Josef, 2014. "The macroeconomics of Model T," Journal of Economic Theory, Elsevier, vol. 153(C), pages 617-647.
    12. Areendam Chanda & Beatrice Farkas, 2009. "Technology-Skill Complementarity and International TFP Differences," DEGIT Conference Papers c014_028, DEGIT, Dynamics, Economic Growth, and International Trade.
    13. Benabou, Roland, 2005. "Inequality, Technology and the Social Contract," Handbook of Economic Growth, in: Philippe Aghion & Steven Durlauf (ed.), Handbook of Economic Growth, edition 1, volume 1, chapter 25, pages 1595-1638, Elsevier.
    14. Markus Brueckner & Ngo Van Long & Joaquin L. Vespignani, 2020. "Non-Gravity Trade," Globalization Institute Working Papers 388, Federal Reserve Bank of Dallas.
    15. Oscar Afonso & Pedro Neves & Maria Thompson, 2014. "The skill premium and economic growth with costly investment, complementarities and international technological-knowledge diffusion," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 23(6), pages 878-905, September.
    16. Francesco Lamperti & Giovanni Dosi & Mauro Napoletano & Andrea Roventini & Alessandro Sapio, 2018. "And then he wasn't a she : Climate change and green transitions in an agent-based integrated assessment model," Working Papers hal-03443464, HAL.
    17. Zhangsheng Liu & Liuqingqing Yang & Liqin Fan, 2021. "Induced Effect of Environmental Regulation on Green Innovation: Evidence from the Increasing-Block Pricing Scheme," IJERPH, MDPI, vol. 18(5), pages 1-15, March.
    18. Lin, Boqiang & Xu, Chongchong, 2024. "The effects of industrial robots on firm energy intensity: From the perspective of technological innovation and electrification," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    19. Ryuzo Sato & Tamaki Morita, 2009. "Quantity Or Quality: The Impact Of Labour Saving Innovation On Us And Japanese Growth Rates, 1960–2004," The Japanese Economic Review, Japanese Economic Association, vol. 60(4), pages 407-434, December.
    20. Donatella Gatti, 2021. "Protecting Natural and Social Resources: A political economy approach," CEPN Working Papers hal-03374129, HAL.

    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:jeners:v:17:y:2024:i:20:p:5034-:d:1495770. 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.