IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i9p4989-d546024.html
   My bibliography  Save this article

Intelligence and Green Total Factor Productivity Based on China’s Province-Level Manufacturing Data

Author

Listed:
  • Yining Zhang

    (Business School, University of Shanghai for Science and Technology, Shanghai 200093, China)

  • Zhong Wu

    (Business School, University of Shanghai for Science and Technology, Shanghai 200093, China)

Abstract

The application of intelligent technology has an important impact on the green total factor productivity of China’s manufacturing industry. Based on the provincial panel data of China’s manufacturing industry from 2008 to 2017, this article uses the Malmquist–Luenburger (ML) model to measure the green total factor productivity of China’s manufacturing industry, and further constructs an empirical model to analyze the impact mechanism of intelligence on green total factor productivity. The results show that intelligence can increase the green total factor productivity of the manufacturing industry. At the same time, mechanism analysis shows that intelligence can affect manufacturing green total factor productivity by improving technical efficiency. However, the effect of intelligence on the technological progress of the manufacturing industry is not significant. In addition, the impact of intelligence has regional heterogeneity. It has significantly promoted the green total factor productivity in the eastern and central regions of China, while its role in the western region is not obvious. The research in this article confirms that intelligence has a significant positive impact on the green total factor productivity of the manufacturing industry, and can provide suggestion for the current further promotion of the deep integration of intelligence and the green development of the manufacturing industry to achieve the strategic goal of industrial upgrading.

Suggested Citation

  • Yining Zhang & Zhong Wu, 2021. "Intelligence and Green Total Factor Productivity Based on China’s Province-Level Manufacturing Data," Sustainability, MDPI, vol. 13(9), pages 1-16, April.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4989-:d:546024
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/9/4989/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/9/4989/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chang, Tzu-Pu & Hu, Jin-Li, 2010. "Total-factor energy productivity growth, technical progress, and efficiency change: An empirical study of China," Applied Energy, Elsevier, vol. 87(10), pages 3262-3270, October.
    2. Izabela Nielsen & Sani Majumder & Eryk Szwarc & Subrata Saha, 2020. "Impact of Strategic Cooperation under Competition on Green Product Manufacturing," Sustainability, MDPI, vol. 12(24), pages 1-28, December.
    3. Georg Graetz & Guy Michaels, 2018. "Robots at Work," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 753-768, December.
    4. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation, and Work," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 197-236, National Bureau of Economic Research, Inc.
    5. Lene Kromann & Nikolaj Malchow-Møller & Jan Rose Skaksen & Anders Sørensen, 2020. "Automation and productivity—a cross-country, cross-industry comparison [Computing inequality: have computers changed the labor market?]," Industrial and Corporate Change, Oxford University Press, vol. 29(2), pages 265-287.
    6. Jiang, Yufan & Wang, Hongyan & Liu, Zuankuo, 2021. "The impact of the free trade zone on green total factor productivity ——evidence from the shanghai pilot free trade zone," Energy Policy, Elsevier, vol. 148(PB).
    7. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation and Work," Boston University - Department of Economics - Working Papers Series dp-298, Boston University - Department of Economics.
    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. He Zhao & Qin Heng Zhao & Beata Ślusarczyk, 2019. "Sustainability and Digitalization of Corporate Management Based on Augmented/Virtual Reality Tools Usage: China and Other World IT Companies’ Experience," Sustainability, MDPI, vol. 11(17), pages 1-17, August.
    10. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    11. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2018. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 23-57, National Bureau of Economic Research, Inc.
    12. Wu, Haitao & Hao, Yu & Ren, Siyu, 2020. "How do environmental regulation and environmental decentralization affect green total factor energy efficiency: Evidence from China," Energy Economics, Elsevier, vol. 91(C).
    13. Cieślik Andrzej & Michałek Jan Jakub & Nasadiuk Iryna, 2017. "The Regional Heterogeneity of Productivity Determinants: Evidence from Ukrainian Firms," Miscellanea Geographica. Regional Studies on Development, Sciendo, vol. 21(1), pages 44-50, March.
    14. Georg Graetz & Guy Michaels, 2015. "Robots at work: the impact on productivity and jobs," CentrePiece - The magazine for economic performance 447, Centre for Economic Performance, LSE.
    15. William D. Nordhaus, 2021. "Are We Approaching an Economic Singularity? Information Technology and the Future of Economic Growth," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(1), pages 299-332, January.
    16. Simon Commander & Rupert Harrison & Naercio Menezes-Filho, 2011. "ICT and Productivity in Developing Countries: New Firm-Level Evidence from Brazil and India," The Review of Economics and Statistics, MIT Press, vol. 93(2), pages 528-541, May.
    17. Daron Acemoglu & Pascual Restrepo, 2017. "Robots and Jobs: Evidence from US Labor Markets," Boston University - Department of Economics - Working Papers Series dp-297, Boston University - Department of Economics.
    18. Kevin J. Stiroh, 2002. "Are ICT Spillovers Driving the New Economy?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 48(1), pages 33-57, March.
    19. Hong Li & Jianbo Hu & Wei Zhang, 2018. "Regional Differences between the Rate of Change of CO2 Emission Intensity of Chinese Provinces and Implications for Sustainable Development," Sustainable Development, John Wiley & Sons, Ltd., vol. 26(4), pages 321-336, July.
    20. Gustavo Adler & Mr. Romain A Duval & Davide Furceri & Ksenia Koloskova & Mr. Marcos Poplawski Ribeiro, 2017. "Gone with the Headwinds: Global Productivity," IMF Staff Discussion Notes 2017/004, International Monetary Fund.
    21. Gustavo Adler & Romain A Duval & Davide Furceri & Sinem Kılıç Çelik & Ksenia Koloskova & Marcos Poplawski Ribeiro, 2017. "Gone with the Headwinds; Global Productivity," IMF Staff Discussion Notes 17/04, International Monetary Fund.
    22. Shiying Hou & Liangrong Song, 2021. "Market Integration and Regional Green Total Factor Productivity: Evidence from China’s Province-Level Data," Sustainability, MDPI, vol. 13(2), pages 1-19, January.
    23. Zhang, Chuanguo & Liu, Cong, 2015. "The impact of ICT industry on CO2 emissions: A regional analysis in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 12-19.
    24. Pittman, Russell W, 1983. "Multilateral Productivity Comparisons with Undesirable Outputs," Economic Journal, Royal Economic Society, vol. 93(372), pages 883-891, December.
    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. Xi Zhang & Rui Li & Jinglei Zhang, 2022. "Understanding the Green Total Factor Productivity of Manufacturing Industry in China: Analysis Based on the Super-SBM Model with Undesirable Outputs," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
    2. Haisheng Chen & Dingqing Ni & Shuiping Zhu & Ying Ying & Manhong Shen, 2022. "Does the National Credit Demonstration Policy Affect Urban Green Economy Efficiency? Evidence from the Yangtze River Delta Region of China," IJERPH, MDPI, vol. 19(16), pages 1-20, August.
    3. Haisheng Chen & Shuiping Zhu & Jianjun Sun & Kaiyang Zhong & Manhong Shen & Xiaoli Wang, 2022. "A Study of the Spatial Structure and Regional Interaction of Agricultural Green Total Factor Productivity in China Based on SNA and VAR Methods," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
    4. Jiekuan Zhang & Yan Zhang, 2023. "Examining the effects of economic growth pressure on green total factor productivity: evidence from China," Economic Change and Restructuring, Springer, vol. 56(6), pages 4309-4337, December.
    5. Batara Surya & Agus Salim & Seri Suriani & Firman Menne & Emil Salim Rasyidi, 2021. "Economic Growth and Development of a Minapolitan Area Based on the Utilization of Renewable Energy, Takalar Regency, South Sulawesi, Indonesia," International Journal of Energy Economics and Policy, Econjournals, vol. 11(5), pages 255-274.
    6. Yuxin Fang & Hongjun Cao & Jihui Sun, 2022. "Impact of Artificial Intelligence on Regional Green Development under China’s Environmental Decentralization System—Based on Spatial Durbin Model and Threshold Effect," IJERPH, MDPI, vol. 19(22), pages 1-27, November.
    7. Haoran Yang & Yaoben Lin & Yang Hu & Xueqing Liu & Qun Wu, 2022. "Influence Mechanism of Industrial Agglomeration and Technological Innovation on Land Granting on Green Total Factor Productivity," Sustainability, MDPI, vol. 14(6), pages 1-17, March.
    8. Jorge Ariel Franco-López, Julián Alberto Uribe-Gómez, Sebastián Agudelo-Vallejo, 2021. "Factores clave en la evaluación de la productividad: estudio de caso," Revista CEA, Instituto Tecnológico Metropolitano, vol. 7(15), pages 1-26, September.
    9. Yu Mao & Yonglin Li & Deyi Xu & Yaqi Wu & Jinhua Cheng, 2022. "Spatial-Temporal Evolution of Total Factor Productivity in Logistics Industry of the Yangtze River Economic Belt, China," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
    10. Lipeng Sun & Nur Ashikin Mohd Saat, 2023. "How Does Intelligent Manufacturing Affect the ESG Performance of Manufacturing Firms? Evidence from China," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    11. Wang, Jianda & Wang, Kun & Dong, Kangyin & Zhang, Shiqiu, 2023. "Assessing the role of financial development in natural resource utilization efficiency: Does artificial intelligence technology matter?," Resources Policy, Elsevier, vol. 85(PA).

    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. Venturini, Francesco, 2022. "Intelligent technologies and productivity spillovers: Evidence from the Fourth Industrial Revolution," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 220-243.
    2. Xueyuan Gao & Hua Feng, 2023. "AI-Driven Productivity Gains: Artificial Intelligence and Firm Productivity," Sustainability, MDPI, vol. 15(11), pages 1-21, June.
    3. Geiger, Niels & Prettner, Klaus & Schwarzer, Johannes A., 2018. "Automatisierung, Wachstum und Ungleichheit," Hohenheim Discussion Papers in Business, Economics and Social Sciences 13-2018, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    4. Jongwanich, Juthathip & Kohpaiboon, Archanun & Obashi, Ayako, 2022. "Technological advancement, import penetration and labour markets: Evidence from Thailand," World Development, Elsevier, vol. 151(C).
    5. Wang, Huijuan & Ding, Lin & Guan, Rong & Xia, Yan, 2020. "Effects of advancing internet technology on Chinese employment: a spatial study of inter-industry spillovers," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    6. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
    7. Ana L. ABELIANSKY & Eda ALGUR & David E. BLOOM & Klaus PRETTNER, 2020. "The future of work: Meeting the global challenges of demographic change and automation," International Labour Review, International Labour Organization, vol. 159(3), pages 285-306, September.
    8. Gregory, Terry & Salomons, Anna & Zierahn, Ulrich, 2016. "Racing With or Against the Machine? Evidence from Europe," VfS Annual Conference 2016 (Augsburg): Demographic Change 145843, Verein für Socialpolitik / German Economic Association.
    9. Barbieri, Laura & Mussida, Chiara & Piva, Mariacristina & Vivarelli, Marco, 2019. "Testing the employment and skill impact of new technologies: A survey and some methodological issues," MERIT Working Papers 2019-032, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    10. Filippo Bertani & Marco Raberto & Andrea Teglio, 2020. "The productivity and unemployment effects of the digital transformation: an empirical and modelling assessment," Review of Evolutionary Political Economy, Springer, vol. 1(3), pages 329-355, November.
    11. Liu, Liang & Yang, Kun & Fujii, Hidemichi & Liu, Jun, 2021. "Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 276-293.
    12. Ben Vermeulen & Jan Kesselhut & Andreas Pyka & Pier Paolo Saviotti, 2018. "The Impact of Automation on Employment: Just the Usual Structural Change?," Sustainability, MDPI, vol. 10(5), pages 1-27, May.
    13. Alonso, Cristian & Berg, Andrew & Kothari, Siddharth & Papageorgiou, Chris & Rehman, Sidra, 2022. "Will the AI revolution cause a great divergence?," Journal of Monetary Economics, Elsevier, vol. 127(C), pages 18-37.
    14. Ryosuke Shimizu & Shohei Momoda, 2020. "Does Automation Technology increase Wage?," KIER Working Papers 1039, Kyoto University, Institute of Economic Research.
    15. Kanit Sangsubhan & Kumpon Pornpattanapaisankul & Pisacha Kambuya, 2023. "Automation and Productivity: Evidence from Thai Manufacturing Firms," PIER Discussion Papers 199, Puey Ungphakorn Institute for Economic Research.
    16. Camiña, Ester & Díaz-Chao, Ángel & Torrent-Sellens, Joan, 2020. "Automation technologies: Long-term effects for Spanish industrial firms," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    17. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    18. Heyman, Fredrik & Olsson, Martin, 2022. "Long-Run Effects of Technological Change: The Impact of Automation and Robots on Intergenerational Mobility," Working Paper Series 1451, Research Institute of Industrial Economics, revised 29 Jun 2023.
    19. Colombo, Emilio & Mercorio, Fabio & Mezzanzanica, Mario, 2019. "AI meets labor market: Exploring the link between automation and skills," Information Economics and Policy, Elsevier, vol. 47(C), pages 27-37.
    20. Alejandro Micco, 2019. "The Impact of Automation in Developed Countries," Working Papers wp480, University of Chile, Department of Economics.

    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:jsusta:v:13:y:2021:i:9:p:4989-:d:546024. 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.