IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i12p5591-d1681349.html

The Impact of Urban Digital Intelligence Transformation on Corporate Carbon Performance: Evidence from China

Author

Listed:
  • Zhen Wang

    (School of Economics, Lanzhou University, Lanzhou 730000, China
    These authors contributed equally to this work.)

  • Hongwen Jia

    (School of Economics, Lanzhou University, Lanzhou 730000, China
    These authors contributed equally to this work.)

  • Jiale Wu

    (School of Economics, Lanzhou University, Lanzhou 730000, China)

Abstract

In response to urban digital intelligence transformation (DIT) and the rising global emphasis on corporate carbon performance (CP), this study leverages the “National New-Generation AI Innovation Development Pilot Zones” (NAIPZs) as a quasi-natural experiment. Utilizing an unbalanced panel of A-share listed firms from China’s Shanghai and Shenzhen stock exchanges between 2010 and 2022, this study employs a multi-period Difference-in-Differences (DID) model combined with propensity score matching (PSM-DID) to examine how urban DIT affects corporate CP and its underlying mechanisms. The results indicate that the policy significantly enhances corporate CP, with robustness confirmed through parallel trend, placebo, and PSM-DID tests. Heterogeneity analysis shows stronger effects for non-state-owned enterprises, high-pollution industries, and large enterprises. Mechanism analysis reveals that green technological innovation and R&D expenditure are key drivers of improved CP. The study concludes with policy suggestions including tailored regulation, the development of innovation platforms, strengthened R&D support, and the implementation of monitoring systems to better harness AI technologies for improving corporate carbon performance.

Suggested Citation

  • Zhen Wang & Hongwen Jia & Jiale Wu, 2025. "The Impact of Urban Digital Intelligence Transformation on Corporate Carbon Performance: Evidence from China," Sustainability, MDPI, vol. 17(12), pages 1-26, June.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:12:p:5591-:d:1681349
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/12/5591/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/12/5591/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhu, Bangzhu & Xu, Chenxin & Wang, Ping & Zhang, Lin, 2022. "How does internal carbon pricing affect corporate environmental performance?," Journal of Business Research, Elsevier, vol. 145(C), pages 65-77.
    2. Doğan, Buhari & Chu, Lan Khanh & Ghosh, Sudeshna & Diep Truong, Huong Hoang & Balsalobre-Lorente, Daniel, 2022. "How environmental taxes and carbon emissions are related in the G7 economies?," Renewable Energy, Elsevier, vol. 187(C), pages 645-656.
    3. Jiban Khuntia & Terence J. V. Saldanha & Sunil Mithas & V. Sambamurthy, 2018. "Information Technology and Sustainability: Evidence from an Emerging Economy," Production and Operations Management, Production and Operations Management Society, vol. 27(4), pages 756-773, April.
    4. Zhang, Rui & Sharma, Rajesh & Tan, Zhixiong & Kautish, Pradeep, 2022. "Do export diversification and stock market development drive carbon intensity? The role of renewable energy solutions in top carbon emitter countries," Renewable Energy, Elsevier, vol. 185(C), pages 1318-1328.
    5. Neves, Sónia Almeida & Marques, António Cardoso & Patrício, Margarida, 2020. "Determinants of CO2 emissions in European Union countries: Does environmental regulation reduce environmental pollution?," Economic Analysis and Policy, Elsevier, vol. 68(C), pages 114-125.
    6. Zheng, Huanyu & Song, Malin & Shen, Zhiyang, 2021. "The evolution of renewable energy and its impact on carbon reduction in China," Energy, Elsevier, vol. 237(C).
    7. Ping Chen & Jiawei Gao & Zheng Ji & Han Liang & Yu Peng, 2022. "Do Artificial Intelligence Applications Affect Carbon Emission Performance?—Evidence from Panel Data Analysis of Chinese Cities," Energies, MDPI, vol. 15(15), pages 1-16, August.
    8. Lara Waltersmann & Steffen Kiemel & Julian Stuhlsatz & Alexander Sauer & Robert Miehe, 2021. "Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review," Sustainability, MDPI, vol. 13(12), pages 1-26, June.
    9. Pan, Junyu & Du, Lizhao & Wu, Haitao & Liu, Xiaoqian, 2024. "Does environmental law enforcement supervision improve corporate carbon reduction performance? Evidence from environmental protection interview," Energy Economics, Elsevier, vol. 132(C).
    10. Huang, Yujie & Liu, Shucheng & Gan, Jiawu & Liu, Baoliu & Wu, Yuxi, 2024. "How does the construction of new generation of national AI innovative development pilot zones drive enterprise ESG development? Empirical evidence from China," Energy Economics, Elsevier, vol. 140(C).
    11. Benzidia, Smail & Makaoui, Naouel & Bentahar, Omar, 2021. "The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    12. Li, Da-yuan & Liu, Juan, 2014. "Dynamic capabilities, environmental dynamism, and competitive advantage: Evidence from China," Journal of Business Research, Elsevier, vol. 67(1), pages 2793-2799.
    13. Gao, Qiang & Cheng, Changming & Sun, Guanglin, 2023. "Big data application, factor allocation, and green innovation in Chinese manufacturing enterprises," Technological Forecasting and Social Change, Elsevier, vol. 192(C).
    14. Smaïl Benzidia & Naouel Makaoui & Omar Bentahar, 2021. "The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance," Post-Print hal-03028127, HAL.
    15. Li, Pei & Lu, Yi & Wang, Jin, 2016. "Does flattening government improve economic performance? Evidence from China," Journal of Development Economics, Elsevier, vol. 123(C), pages 18-37.
    16. Li, Zhenghui & Huang, Zimei & Su, Yaya, 2023. "New media environment, environmental regulation and corporate green technology innovation:Evidence from China," Energy Economics, Elsevier, vol. 119(C).
    17. Paul M. Romer, 1994. "The Origins of Endogenous Growth," Journal of Economic Perspectives, American Economic Association, vol. 8(1), pages 3-22, Winter.
    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. Shiheng Xie & Jiaqi Ji & Yiran Zhang & Shuping Wang, 2025. "How Does Industrial Intelligence Enhance Green Total Factor Productivity in China? The Substitution Effect of Environmental Regulation," Sustainability, MDPI, vol. 17(17), pages 1-31, September.
    2. Oesterreich, Thuy Duong & Anton, Eduard & Teuteberg, Frank & Dwivedi, Yogesh K, 2022. "The role of the social and technical factors in creating business value from big data analytics: A meta-analysis," Journal of Business Research, Elsevier, vol. 153(C), pages 128-149.
    3. Chotia, Varun & Khoualdi, Kamel & Broccardo, Laura & Yaqub, Muhammad Zafar, 2025. "The role of cyber security and digital transformation in gaining competitive advantage through Strategic Management Accounting," Technology in Society, Elsevier, vol. 81(C).
    4. Li, Longda, 2024. "The environmental spillovers of buyers' digital transformation: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    5. Wang, Meiling & Liu, Zichen & Zhou, Bingxuan, 2025. "Towards carbon neutrality: The impact of energy right trading policy on carbon performance of manufacturing enterprises," Energy, Elsevier, vol. 323(C).
    6. Chand Bhatt, Priyanka & Kumar, Vimal & Lu, Tzu-Chuen & Daim, Tugrul, 2021. "Technology convergence assessment: Case of blockchain within the IR 4.0 platform," Technology in Society, Elsevier, vol. 67(C).
    7. Chiarello, Filippo & Fantoni, Gualtiero & Hogarth, Terence & Giordano, Vito & Baltina, Liga & Spada, Irene, 2021. "Towards ESCO 4.0 – Is the European classification of skills in line with Industry 4.0? A text mining approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    8. Mohammad Mousa Mousa & Heyam Abdulrahman Al Moosa & Issam Naim Ayyash & Fandi Omeish & Imed Zaiem & Thamer Alzahrani & Samiha Mjahed Hammami & Ahmad M. Zamil, 2025. "Big Data Analytics as a Driver for Sustainable Performance: The Role of Green Supply Chain Management in Advancing Circular Economy in Saudi Arabian Pharmaceutical Companies," Sustainability, MDPI, vol. 17(14), pages 1-24, July.
    9. Kaviya Sri Suthagar & Umakanta Mishra, 2025. "Sustainable green circular economic model with controllable waste and emission in healthcare system," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(4), pages 8767-8809, April.
    10. Lin Wu & Jimmy Huang & Miao Wang & Ajay Kumar, 2024. "Unleashing supply chain agility : Leveraging data network effects for digital transformation," Post-Print hal-04850421, HAL.
    11. Li, Lixu & Liu, Yaoqi & Jin, Yong & Cheng, T.C. Edwin & Zhang, Qianjun, 2024. "Generative AI-enabled supply chain management: The critical role of coordination and dynamism," International Journal of Production Economics, Elsevier, vol. 277(C).
    12. Shaker Salem Abuzawida & Ahmad Bassam Alzubi & Kolawole Iyiola, 2023. "Sustainable Supply Chain Practices: An Empirical Investigation from the Manufacturing Industry," Sustainability, MDPI, vol. 15(19), pages 1-24, September.
    13. Akram, Rabia & Li, Qiyuan & Srivastava, Mohit & Zheng, Yulu & Irfan, Muhammad, 2024. "Nexus between green technology innovation and climate policy uncertainty: Unleashing the role of artificial intelligence in an emerging economy," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    14. Wang, Linhui & Chen, Qi & Dong, Zhiqing & Cheng, Lu, 2024. "The role of industrial intelligence in peaking carbon emissions in China," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    15. Jolanta Słoniec & Monika Kulisz & Marta Małecka-Dobrogowska & Zhadyra Konurbayeva & Łukasz Sobaszek, 2025. "Awareness of the Impact of IT/AI on Energy Consumption in Enterprises: A Machine Learning-Based Modelling Towards a Sustainable Digital Transformation," Energies, MDPI, vol. 18(21), pages 1-24, October.
    16. Ionica Oncioiu & Diana Andreea Mândricel & Mihaela Hortensia Hojda, 2025. "Artificial Intelligence-Enabled Digital Transformation in Circular Logistics: A Structural Equation Model of Organizational, Technological, and Environmental Drivers," Logistics, MDPI, vol. 9(3), pages 1-28, August.
    17. Ahmadova, Gozal & Delgado-Márquez, Blanca L. & Pedauga, Luis E. & Leyva-de la Hiz, Dante I., 2022. "Too good to be true: The inverted U-shaped relationship between home-country digitalization and environmental performance," Ecological Economics, Elsevier, vol. 196(C).
    18. Benzidia, Smaïl & Makaoui, Naouel & Subramanian, Nachiappan, 2021. "Impact of ambidexterity of blockchain technology and social factors on new product development: A supply chain and Industry 4.0 perspective," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    19. Yi Lin & Xin Qi & Lijuan Wang, 2024. "Digital Transformation and Carbon Intensity: Evidence from Chinese Tourism Companies," Sustainability, MDPI, vol. 16(21), pages 1-22, October.
    20. Yang, Yimin & Yi, Chaoqun & Li, Hailing & Dong, Xuesong & Yang, Lulu & Wang, Zilong, 2025. "An analysis on the role of artificial intelligence in green supply chains," Technological Forecasting and Social Change, Elsevier, vol. 217(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:17:y:2025:i:12:p:5591-:d:1681349. 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.