IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v85y2023ipbs0301420723006037.html
   My bibliography  Save this article

Is artificial intelligence associated with carbon emissions reduction? Case of China

Author

Listed:
  • Ding, Tao
  • Li, Jiangyuan
  • Shi, Xing
  • Li, Xuhui
  • Chen, Ya

Abstract

With the rapid development of modern information technology, the widespread application of artificial intelligence (AI) technology has had an increasingly critical impact on China's industrial development in recent years. However, few research focuses on the impact of AI development on China's carbon emissions (CEs). Using a data set of 30 Chinese provinces during the period 2006–2019, this study investigates the impact of AI development on CEs and explore potential influencing mechanisms via spatial Durbin model (SDM). It shows that AI development effectively contributes to CEs reduction and the reduction effect remains consistent over varied spatial weights. In terms of the mechanism analysis, technique and structure effects of AI reduce CEs. The heterogeneity analysis reveals that AI reduces CEs through spatial spillover effect with the effect being stronger in central and western China. Finally, based on the findings of this study, some recommendations are provided to promote the development of AI in China and the reduction of CEs.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:jrpoli:v:85:y:2023:i:pb:s0301420723006037
    DOI: 10.1016/j.resourpol.2023.103892
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420723006037
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.resourpol.2023.103892?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. Tao Ding & Jiangyuan Li & Xing Shi & Huaqing Wu, 2021. "Driving Forces of Water Intensity in China’s Industrial Sector: A Global Meta-Frontier Production–Theoretical Decomposition Analysis," Water Economics and Policy (WEP), World Scientific Publishing Co. Pte. Ltd., vol. 7(03), pages 1-25, July.
    2. Choi, Yongrok & Zhang, Ning & Zhou, P., 2012. "Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure," Applied Energy, Elsevier, vol. 98(C), pages 198-208.
    3. Hong, Qianqian & Cui, Linhao & Hong, Penghui, 2022. "The impact of carbon emissions trading on energy efficiency: Evidence from quasi-experiment in China's carbon emissions trading pilot," Energy Economics, Elsevier, vol. 110(C).
    4. Wang, Yongpei & Li, Jun, 2019. "Spatial spillover effect of non-fossil fuel power generation on carbon dioxide emissions across China's provinces," Renewable Energy, Elsevier, vol. 136(C), pages 317-330.
    5. 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).
    6. Li, Zhiguo & Wang, Jie, 2022. "Spatial spillover effect of carbon emission trading on carbon emission reduction: Empirical data from pilot regions in China," Energy, Elsevier, vol. 251(C).
    7. Zhang, Wei & Liu, Xuemeng & Wang, Die & Zhou, Jianping, 2022. "Digital economy and carbon emission performance: Evidence at China's city level," Energy Policy, Elsevier, vol. 165(C).
    8. Xie, Mengmeng & Ding, Lin & Xia, Yan & Guo, Jianfeng & Pan, Jiaofeng & Wang, Huijuan, 2021. "Does artificial intelligence affect the pattern of skill demand? Evidence from Chinese manufacturing firms," Economic Modelling, Elsevier, vol. 96(C), pages 295-309.
    9. Zhao, Congyu & Wang, Kun & Dong, Xiucheng & Dong, Kangyin, 2022. "Is smart transportation associated with reduced carbon emissions? The case of China," Energy Economics, Elsevier, vol. 105(C).
    10. 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).
    11. Hongda Liu & Pinbo Yao & Xiaoxia Wang & Jialiang Huang & Liying Yu, 2021. "Research on the Peer Behavior of Local Government Green Governance Based on SECI Expansion Model," Land, MDPI, vol. 10(5), pages 1-26, May.
    12. 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.
    13. 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.
    14. Liu, Jian & Yang, Qingshan & Ou, Suhua & Liu, Jie, 2022. "Factor decomposition and the decoupling effect of carbon emissions in China's manufacturing high-emission subsectors," Energy, Elsevier, vol. 248(C).
    15. Qu, Chenyao & Shao, Jun & Shi, Zhenkai, 2020. "Does financial agglomeration promote the increase of energy efficiency in China?," Energy Policy, Elsevier, vol. 146(C).
    16. Hao Lv & Beibei Shi & Nan Li & Rong Kang, 2022. "Intelligent Manufacturing and Carbon Emissions Reduction: Evidence from the Use of Industrial Robots in China," IJERPH, MDPI, vol. 19(23), pages 1-20, November.
    17. Shan, Yuli & Liu, Jianghua & Liu, Zhu & Xu, Xinwanghao & Shao, Shuai & Wang, Peng & Guan, Dabo, 2016. "New provincial CO2 emission inventories in China based on apparent energy consumption data and updated emission factors," Applied Energy, Elsevier, vol. 184(C), pages 742-750.
    18. Zhao, Jun & Jiang, Qingzhe & Dong, Xiucheng & Dong, Kangyin & Jiang, Hongdian, 2022. "How does industrial structure adjustment reduce CO2 emissions? Spatial and mediation effects analysis for China," Energy Economics, Elsevier, vol. 105(C).
    19. Zhang, Cheng & Zhou, Xinxin & Zhou, Bo & Zhao, Ziwei, 2022. "Impacts of a mega sporting event on local carbon emissions: A case of the 2014 Nanjing Youth Olympics," China Economic Review, Elsevier, vol. 73(C).
    20. 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).
    21. Ma, Hongmei & Gao, Qian & Li, Xiuzhen & Zhang, Yun, 2022. "AI development and employment skill structure: A case study of China," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 242-254.
    22. Wang, Xuliang & Xu, Lulu & Ye, Qin & He, Shi & Liu, Yi, 2022. "How does services agglomeration affect the energy efficiency of the service sector? Evidence from China," Energy Economics, Elsevier, vol. 112(C).
    23. 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).
    24. J. Paul Elhorst, 2014. "Matlab Software for Spatial Panels," International Regional Science Review, , vol. 37(3), pages 389-405, July.
    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. Ding, Tao & Li, Hao & Tan, Ruipeng & Zhao, Xin, 2023. "How does geopolitical risk affect carbon emissions?: An empirical study from the perspective of mineral resources extraction in OECD countries," Resources Policy, Elsevier, vol. 85(PB).

    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. Genghua Tang & Hongxun Mai, 2022. "How Does Manufacturing Intelligentization Influence Innovation in China from a Nonlinear Perspective and Economic Servitization Background?," Sustainability, MDPI, vol. 14(21), pages 1-16, October.
    2. Wang, Xuliang & Xu, Lulu & Ye, Qin & He, Shi & Liu, Yi, 2022. "How does services agglomeration affect the energy efficiency of the service sector? Evidence from China," Energy Economics, Elsevier, vol. 112(C).
    3. Xianpu Xu & Yuchen Song, 2023. "Is There a Conflict between Automation and Environment? Implications of Artificial Intelligence for Carbon Emissions in China," Sustainability, MDPI, vol. 15(16), pages 1-22, August.
    4. Yang Shen & Zhihong Yang, 2023. "Chasing Green: The Synergistic Effect of Industrial Intelligence on Pollution Control and Carbon Reduction and Its Mechanisms," Sustainability, MDPI, vol. 15(8), pages 1-22, April.
    5. Wei Qian & Yongsheng Wang, 2022. "How Do Rising Labor Costs Affect Green Total Factor Productivity? Based on the Industrial Intelligence Perspective," Sustainability, MDPI, vol. 14(20), pages 1-19, October.
    6. Yuxi Chen & Mengting Zhang & Chencheng Wang & Xin Lin & Zhijie Zhang, 2023. "High-Tech Industrial Agglomeration, Government Intervention and Regional Energy Efficiency: Based on the Perspective of the Spatial Spillover Effect and Panel Threshold Effect," Sustainability, MDPI, vol. 15(7), pages 1-29, April.
    7. Da Gao & Chang Liu & Xinyan Wei & Yang Liu, 2023. "Can River Chief System Policy Improve Enterprises’ Energy Efficiency? Evidence from China," IJERPH, MDPI, vol. 20(4), pages 1-17, February.
    8. Wanlin Yu & Jinlong Luo, 2022. "Impact on Carbon Intensity of Carbon Emission Trading—Evidence from a Pilot Program in 281 Cities in China," IJERPH, MDPI, vol. 19(19), pages 1-19, September.
    9. Kangni Lyu & Shuwang Yang & Kun Zheng & Yao Zhang, 2023. "How Does the Digital Economy Affect Carbon Emission Efficiency? Evidence from Energy Consumption and Industrial Value Chain," Energies, MDPI, vol. 16(2), pages 1-20, January.
    10. Xian’En Wang & Shimeng Wang & Xipan Wang & Wenbo Li & Junnian Song & Haiyan Duan & Shuo Wang, 2019. "The Assessment of Carbon Performance under the Region-Sector Perspective based on the Nonparametric Estimation: A Case Study of the Northern Province in China," Sustainability, MDPI, vol. 11(21), pages 1-23, October.
    11. Jun Liu & Yu Qian & Yuanjun Yang & Zhidan Yang, 2022. "Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China," IJERPH, MDPI, vol. 19(4), pages 1-18, February.
    12. Yue‐Jun Zhang & Jing‐Yue Liu & Richard T. Woodward, 2023. "Has Chinese Certified Emission Reduction trading reduced rural poverty in China?," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 67(3), pages 438-458, July.
    13. Huaxi Yuan & Longhui Zou & Xiangyong Luo & Yidai Feng, 2022. "How Does Manufacturing Agglomeration Affect Green Development? A Spatial and Nonlinear Perspective," IJERPH, MDPI, vol. 19(16), pages 1-23, August.
    14. Zhang, Guo-Xing & Yang, Yang & Su, Bin & Nie, Yan & Duan, Hong-Bo, 2023. "Electricity production, power generation structure, and air pollution: A monthly data analysis for 279 cities in China (2015–2019)," Energy Economics, Elsevier, vol. 120(C).
    15. Na Yu & Jianghua Chen & Lei Cheng, 2022. "Evolutionary Game Analysis of Carbon Emission Reduction between Government and Enterprises under Carbon Quota Trading Policy," IJERPH, MDPI, vol. 19(14), pages 1-22, July.
    16. Teng, Xiangyu & Zhuang, Weiwei & Liu, Fan-peng & Chang, Tzu-han & Chiu, Yung-ho, 2023. "China's path of carbon neutralization to develop green energy and improve energy efficiency," Renewable Energy, Elsevier, vol. 206(C), pages 397-408.
    17. Yang Liu & Ruochan Xiong & Shigong Lv & Da Gao, 2022. "The Impact of Digital Finance on Green Total Factor Energy Efficiency: Evidence at China’s City Level," Energies, MDPI, vol. 15(15), pages 1-17, July.
    18. Li, Chengming & Xu, Yang & Zheng, Hao & Wang, Zeyu & Han, Haiting & Zeng, Liangen, 2023. "Artificial intelligence, resource reallocation, and corporate innovation efficiency: Evidence from China's listed companies," Resources Policy, Elsevier, vol. 81(C).
    19. Luo, Yusen & Lu, Zhengnan & Wu, Chao, 2023. "Can internet development accelerate the green innovation efficiency convergence: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    20. Cheng, Xiaoqiang & Yao, Dingjun & Qian, Yuanyuan & Wang, Bin & Zhang, Deliang, 2023. "How does fintech influence carbon emissions: Evidence from China's prefecture-level cities," International Review of Financial Analysis, Elsevier, vol. 87(C).

    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:jrpoli:v:85:y:2023:i:pb:s0301420723006037. 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/inca/30467 .

    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.