IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v365y2024ics0306261924006287.html
   My bibliography  Save this article

Enhancing energy-environmental performance through industrial intelligence: Insights from Chinese prefectural-level cities

Author

Listed:
  • Lin, Boqiang
  • Xu, Chongchong

Abstract

Industrial intelligence optimizes resource allocation and enhances productivity, but discussions on its potential to empower green growth are inadequate. Utilizing panel data for 279 Chinese cities from 2008 to 2019, this study investigates the effect of industrial intelligence on urban energy-environmental performance. The findings reveal that industrial intelligence enhances urban energy-environmental performance. Technological innovation, industrial agglomeration and labor upgrading constitute critical conduits for reaping such benefits. Heterogeneity analysis demonstrates that industrial intelligence has more pronounced effects in non-resource-based cities, non-old industrial base cities, big cities and megacities. Our findings impart valuable insights to guide policymaking for expediting China's industrial intelligence advancement and facilitating sustainable urban industrial development.

Suggested Citation

  • Lin, Boqiang & Xu, Chongchong, 2024. "Enhancing energy-environmental performance through industrial intelligence: Insights from Chinese prefectural-level cities," Applied Energy, Elsevier, vol. 365(C).
  • Handle: RePEc:eee:appene:v:365:y:2024:i:c:s0306261924006287
    DOI: 10.1016/j.apenergy.2024.123245
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924006287
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2024.123245?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. Rubashkina, Yana & Galeotti, Marzio & Verdolini, Elena, 2015. "Environmental regulation and competitiveness: Empirical evidence on the Porter Hypothesis from European manufacturing sectors," Energy Policy, Elsevier, vol. 83(C), pages 288-300.
    2. Liu, Jun & Liu, Liang & Qian, Yu & Song, Shunfeng, 2022. "The effect of artificial intelligence on carbon intensity: Evidence from China's industrial sector," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    3. Cheng, Lu & Mi, Zhifu & Sudmant, Andrew & Coffman, D'Maris, 2022. "Bigger cities better climate? Results from an analysis of urban areas in China," Energy Economics, Elsevier, vol. 107(C).
    4. Liu, Jun & Chang, Huihong & Forrest, Jeffrey Yi-Lin & Yang, Baohua, 2020. "Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    5. Daron Acemoglu & Pascual Restrepo, 2018. "Low-Skill and High-Skill Automation," Journal of Human Capital, University of Chicago Press, vol. 12(2), pages 204-232.
    6. 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).
    7. Huzhou Zhu & Bin Sang & Chunyuan Zhang & Lin Guo, 2023. "Have Industrial Robots Improved Pollution Reduction? A Theoretical Approach and Empirical Analysis," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 31(4), pages 153-172, July.
    8. Ouyang, Xiaoling & Li, Qiong & Du, Kerui, 2020. "How does environmental regulation promote technological innovations in the industrial sector? Evidence from Chinese provincial panel data," Energy Policy, Elsevier, vol. 139(C).
    9. Krugman, Paul, 1991. "Increasing Returns and Economic Geography," Journal of Political Economy, University of Chicago Press, vol. 99(3), pages 483-499, June.
    10. Joseph S. Shapiro & Reed Walker, 2018. "Why Is Pollution from US Manufacturing Declining? The Roles of Environmental Regulation, Productivity, and Trade," American Economic Review, American Economic Association, vol. 108(12), pages 3814-3854, December.
    11. Ariel J. Binder & John Bound, 2019. "The Declining Labor Market Prospects of Less-Educated Men," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 163-190, Spring.
    12. David H. Autor & David Dorn, 2013. "The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market," American Economic Review, American Economic Association, vol. 103(5), pages 1553-1597, August.
    13. 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.
    14. Haitao Wu & Ruohan Zhong & Zhen Wang & Yuanfeng Qu & Xiaodong Yang & Yu Hao, 2024. "How Does Industrial Intellectualization Affect Energy Intensity? Evidence from China," The Energy Journal, , vol. 45(2), pages 49-70, March.
    15. 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.
    16. Rammer, Christian & Fernández, Gastón P. & Czarnitzki, Dirk, 2022. "Artificial intelligence and industrial innovation: Evidence from German firm-level data," Research Policy, Elsevier, vol. 51(7).
    17. Korinek, Anton & Stiglitz, Joseph, 2021. "Artificial Intelligence, Globalization, and Strategies for Economic Development," CEPR Discussion Papers 15772, C.E.P.R. Discussion Papers.
    18. repec:ags:aaea22:335879 is not listed on IDEAS
    19. Fang, Jiayu & Tang, Xue & Xie, Rui & Han, Feng, 2020. "The effect of manufacturing agglomerations on smog pollution," Structural Change and Economic Dynamics, Elsevier, vol. 54(C), pages 92-101.
    20. Wang, Ting & Zhang, Yi & Liu, Chun, 2024. "Robot adoption and employment adjustment: Firm-level evidence from China," China Economic Review, Elsevier, vol. 84(C).
    21. 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.
    22. Du, Mengfan & Zhang, Yue-Jun, 2023. "The impact of producer services agglomeration on green economic development: Evidence from 278 Chinese cities," Energy Economics, Elsevier, vol. 124(C).
    23. Gan, Jiawu & Liu, Lihua & Qiao, Gang & Zhang, Qin, 2023. "The role of robot adoption in green innovation: Evidence from China," Economic Modelling, Elsevier, vol. 119(C).
    24. Li, Quan & Chen, Huimin & Chen, Yang & Xiao, Tong & Wang, Li, 2023. "Digital economy, financing constraints, and corporate innovation," Pacific-Basin Finance Journal, Elsevier, vol. 80(C).
    25. Li, Wencong & Yang, Xingquan & Yin, Xingqiang, 2024. "Digital transformation and labor upgrading," Pacific-Basin Finance Journal, Elsevier, vol. 83(C).
    26. Lin, Boqiang & Du, Kerui, 2015. "Energy and CO2 emissions performance in China's regional economies: Do market-oriented reforms matter?," Energy Policy, Elsevier, vol. 78(C), pages 113-124.
    27. Huang, Hongyun & Mbanyele, William & Fan, Shuangshuang & Zhao, Xin, 2022. "Digital financial inclusion and energy-environment performance: What can learn from China," Structural Change and Economic Dynamics, Elsevier, vol. 63(C), pages 342-366.
    28. Huang, Geng & He, Ling-Yun & Lin, Xi, 2022. "Robot adoption and energy performance: Evidence from Chinese industrial firms," Energy Economics, Elsevier, vol. 107(C).
    29. Haochang Yang & Faming Zhang & Yixin He, 2021. "Exploring the effect of producer services and manufacturing industrial co-agglomeration on the ecological environment pollution control in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(11), pages 16119-16144, November.
    30. Zhang, Ning & Kong, Fanbin & Choi, Yongrok & Zhou, P., 2014. "The effect of size-control policy on unified energy and carbon efficiency for Chinese fossil fuel power plants," Energy Policy, Elsevier, vol. 70(C), pages 193-200.
    31. Wang, Mei & Xu, Mi & Ma, Shaojun, 2021. "The effect of the spatial heterogeneity of human capital structure on regional green total factor productivity," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 427-441.
    32. Lund, Henrik & Østergaard, Poul Alberg & Connolly, David & Mathiesen, Brian Vad, 2017. "Smart energy and smart energy systems," Energy, Elsevier, vol. 137(C), pages 556-565.
    33. Yang, Haochang & Li, Lianshui & Liu, Yaobin, 2022. "The effect of manufacturing intelligence on green innovation performance in China," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    34. Jiang, Zhengyu & Zhang, Xinyi & Zhao, Yingzhi & Li, Chengming & Wang, Zeyu, 2023. "The impact of urban digital transformation on resource sustainability: Evidence from a quasi-natural experiment in China," Resources Policy, Elsevier, vol. 85(PA).
    35. Du, Zhili & Wang, Yao, 2022. "Does energy-saving and emission reduction policy affects carbon reduction performance? A quasi-experimental evidence in China," Applied Energy, Elsevier, vol. 324(C).
    36. Xie, Rui & Wei, Dihan & Han, Feng & Lu, Yue & Fang, Jiayu & Liu, Yu & Wang, Junfeng, 2019. "The effect of traffic density on smog pollution: Evidence from Chinese cities," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 421-427.
    37. Werner Antweiler & Brian R. Copeland & M. Scott Taylor, 2001. "Is Free Trade Good for the Environment?," American Economic Review, American Economic Association, vol. 91(4), pages 877-908, September.
    38. Wang, Linhui & Wang, Hui & Cao, Zhanglu & He, Yongda & Dong, Zhiqing & Wang, Shixiang, 2022. "Can industrial intellectualization reduce carbon emissions? — Empirical evidence from the perspective of carbon total factor productivity in China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    39. 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).
    40. Guo, Qingbin & Wang, Yong & Dong, Xiaobin, 2022. "Effects of smart city construction on energy saving and CO2 emission reduction: Evidence from China," Applied Energy, Elsevier, vol. 313(C).
    41. Hong Cheng & Ruixue Jia & Dandan Li & Hongbin Li, 2019. "The Rise of Robots in China," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 71-88, Spring.
    42. Bai, Ling & Guo, Tianran & Xu, Wei & Liu, Yaobin & Kuang, Ming & Jiang, Lei, 2023. "Effects of digital economy on carbon emission intensity in Chinese cities: A life-cycle theory and the application of non-linear spatial panel smooth transition threshold model," Energy Policy, Elsevier, vol. 183(C).
    43. 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.
    44. Zhou, P. & Ang, B.W. & Wang, H., 2012. "Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach," European Journal of Operational Research, Elsevier, vol. 221(3), pages 625-635.
    45. Lee, Chien-Chiang & Qin, Shuai & Li, Yaya, 2022. "Does industrial robot application promote green technology innovation in the manufacturing industry?," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    46. Ma, Ruiyang & Lin, Boqiang, 2023. "Digitalization and energy-saving and emission reduction in Chinese cities: Synergy between industrialization and digitalization," Applied Energy, Elsevier, vol. 345(C).
    47. Chen, Yang & Cheng, Liang & Lee, Chien-Chiang, 2022. "How does the use of industrial robots affect the ecological footprint? International evidence," Ecological Economics, Elsevier, vol. 198(C).
    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. 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).
    2. Zhou, Wei & Zhuang, Yan & Chen, Yan, 2024. "How does artificial intelligence affect pollutant emissions by improving energy efficiency and developing green technology," Energy Economics, Elsevier, vol. 131(C).
    3. Lee, Chien-Chiang & Yan, Jingyang, 2024. "Will artificial intelligence make energy cleaner? Evidence of nonlinearity," Applied Energy, Elsevier, vol. 363(C).
    4. 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).
    5. Wang, Hua & Liao, Lingtao & Wu, Ji (George), 2023. "Robot adoption and firm's capacity utilization: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    6. Chen, Yang & Cheng, Liang & Lee, Chien-Chiang, 2022. "How does the use of industrial robots affect the ecological footprint? International evidence," Ecological Economics, Elsevier, vol. 198(C).
    7. Ke An & Yike Shan & Sheng Shi, 2022. "Impact of Industrial Intelligence on Total Factor Productivity," Sustainability, MDPI, vol. 14(21), pages 1-21, November.
    8. Zhou, Zhongsheng & Li, Zhuo & Du, Shanzhong & Cao, June, 2024. "Robot adoption and enterprise R&D manipulation: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    9. Lin, Boqiang & Xu, Chongchong, 2024. "Reaping green dividend: The effect of China's urban new energy transition strategy on green economic performance," Energy, Elsevier, vol. 286(C).
    10. Zhang, Xinchun & Sun, Murong & Liu, Jianxu & Xu, Aijia, 2024. "The nexus between industrial robot and employment in China: The effects of technology substitution and technology creation," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    11. Genz, Sabrina & Schnabel, Claus, 2021. "Digging into the digital divide: Workers' exposure to digitalization and its consequences for individual employment," Discussion Papers 118, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Labour and Regional Economics.
    12. Yin, Zi Hui & Zeng, Wei Ping, 2023. "The effects of industrial intelligence on China's energy intensity: The role of technology absorptive capacity," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    13. Kunkel, S. & Neuhäusler, P. & Matthess, M. & Dachrodt, M.F., 2023. "Industry 4.0 and energy in manufacturing sectors in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    14. Lee, Chien-Chiang & Qin, Shuai & Li, Yaya, 2022. "Does industrial robot application promote green technology innovation in the manufacturing industry?," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    15. Lee, Chien-Chiang & Yan, Jingyang & Wang, Fuhao, 2024. "Impact of population aging on food security in the context of artificial intelligence: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    16. Zhang, Dongyang, 2024. "The pathway to curb greenwashing in sustainable growth: The role of artificial intelligence," Energy Economics, Elsevier, vol. 133(C).
    17. Cheng, Can & Luo, Jiayu & Zhu, Chun & Zhang, Shangfeng, 2024. "Artificial intelligence and the skill premium: A numerical analysis of theoretical models," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    18. Yanying Wang & Qingyang Wu, 2024. "Robots, firm relocation, and air pollution: unveiling the unintended spatial spillover effects of emerging technology," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-17, December.
    19. Li, Jianjun & Wu, Zhouyi & Yu, Kaijia & Zhao, Wei, 2024. "The effect of industrial robot adoption on firm value: Evidence from China," Finance Research Letters, Elsevier, vol. 60(C).
    20. Ding, Tao & Li, Jiangyuan & Shi, Xing & Li, Xuhui & Chen, Ya, 2023. "Is artificial intelligence associated with carbon emissions reduction? Case of China," Resources Policy, Elsevier, vol. 85(PB).

    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:appene:v:365:y:2024:i:c:s0306261924006287. 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/wps/find/journaldescription.cws_home/405891/description#description .

    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.