IDEAS home Printed from https://ideas.repec.org/a/eee/streco/v72y2025icp89-102.html
   My bibliography  Save this article

Can big data aggregation help businesses save energy and reduce emissions? Quasi-natural experiment in big data comprehensive test

Author

Listed:
  • Lv, Jingyao
  • Zhao, Zhongxiu
  • Ji, Yongsheng

Abstract

Aggregating big data pieces is critical for increasing enterprise resource allocation efficiency, reducing energy usage, and lowering carbon emissions intensity. This research aims to investigate the impact of big data aggregation on energy efficiency and carbon emission intensity among Chinese enterprises. To this end, it employs primary financial data from Chinese listed companies from 2009 to 2021 and carbon emissions data disclosed in social responsibility reports, sustainable development reports, and environmental reports. The findings revealed that the aggregation of big data elements dramatically reduces the intensity of carbon emissions in firms in the pilot regions. The decrease effect is more effective in economically developed places and regions with higher degrees of digitization, particularly for organizations in high-energy-consuming industries, and it is more robust for small and non-state-owned businesses. The aggregation of big data elements mainly aids firms in pilot regions in lowering energy consumption and emissions by increasing technical innovation and energy usage efficiency. To create a new national competitive advantage, we should actively promote the gradual expansion of the comprehensive pilot zone for big data, advance the in-depth application of big data in environmental governance, and better capitalize on the dividends of big data aggregation.

Suggested Citation

  • Lv, Jingyao & Zhao, Zhongxiu & Ji, Yongsheng, 2025. "Can big data aggregation help businesses save energy and reduce emissions? Quasi-natural experiment in big data comprehensive test," Structural Change and Economic Dynamics, Elsevier, vol. 72(C), pages 89-102.
    • Handle: RePEc:eee:streco:v:72:y:2025:i:c:p:89-102
    DOI: 10.1016/j.strueco.2024.12.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0954349X24001784
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.strueco.2024.12.003?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. Mehrab Nodehi & Abbas Assari Arani & Vahid Mohamad Taghvaee, 2022. "Sustainability spillover effects and partnership between East Asia & Pacific versus North America: interactions of social, environment and economy," Letters in Spatial and Resource Sciences, Springer, vol. 15(3), pages 311-339, December.
    2. Sarah Giest, 2017. "Big data analytics for mitigating carbon emissions in smart cities: opportunities and challenges," European Planning Studies, Taylor & Francis Journals, vol. 25(6), pages 941-957, June.
    3. 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.
    4. Zhang, Yiren & Ran, Congjing, 2023. "Effect of digital economy on air pollution in China? New evidence from the “National Big Data Comprehensive Pilot Area” policy," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 986-1004.
    5. Hamdi, Helmi & Sbia, Rashid & Shahbaz, Muhammad, 2014. "The nexus between electricity consumption and economic growth in Bahrain," Economic Modelling, Elsevier, vol. 38(C), pages 227-237.
    6. Jun Gao & Ning Xu & Ju Zhou, 2023. "Does Digital Transformation Contribute to Corporate Carbon Emissions Reduction? Empirical Evidence from China," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    7. Guan, Jialin & Xu, Huijuan & Huo, Da & Hua, Yechun & Wang, Yunfeng, 2021. "Economic policy uncertainty and corporate innovation: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    8. Olatunji Abdul Shobande, 2021. "Decomposing the Persistent and Transitory Effect of Information and Communication Technology on Environmental Impacts Assessment in Africa: Evidence from Mundlak Specification," Sustainability, MDPI, vol. 13(9), pages 1-12, April.
    9. André Hanelt & René Bohnsack & David Marz & Cláudia Antunes Marante, 2021. "A Systematic Review of the Literature on Digital Transformation: Insights and Implications for Strategy and Organizational Change," Journal of Management Studies, Wiley Blackwell, vol. 58(5), pages 1159-1197, July.
    10. Bolton, Patrick & Kacperczyk, Marcin, 2021. "Do investors care about carbon risk?," Journal of Financial Economics, Elsevier, vol. 142(2), pages 517-549.
    11. Dapeng Liang & Jianjun Liu & Mengting Liu & Jiayin Sun, 2024. "Does information infrastructure and technological infrastructure reduce carbon dioxide emissions in the context of sustainable development? Examining spatial spillover effect," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(3), pages 1599-1615, June.
    12. Roland W. Scholz & Eric J. Bartelsman & Sarah Diefenbach & Lude Franke & Arnim Grunwald & Dirk Helbing & Richard Hill & Lorenz Hilty & Mattias Höjer & Stefan Klauser & Christian Montag & Peter Parycek, 2018. "Unintended Side Effects of the Digital Transition: European Scientists’ Messages from a Proposition-Based Expert Round Table," Sustainability, MDPI, vol. 10(6), pages 1-48, June.
    13. Jiahao Zhang & Fusheng Liang & Peng Gao, 2024. "Can big data reduce urban environmental pollution? Evidence from China’s digital technology experimental zone," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-20, October.
    14. Cui, Xin & Wang, Chunfeng & Liao, Jing & Fang, Zhenming & Cheng, Feiyang, 2021. "Economic policy uncertainty exposure and corporate innovation investment: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    15. Lei Shi & Shan Gao & Airong Xu & Kexin Zheng & Yuanpeng Ji & Xianlei Dong & Lizhi Xing, 2023. "Influence of Enterprise’s Factor Inputs and Co-Opetition Relationships to Its Innovation Output," Sustainability, MDPI, vol. 15(1), pages 1-23, January.
    16. Caihong Yang, 2023. "Digital economy drives regional industrial structure upgrading: Empirical evidence from China’s comprehensive big data pilot zone policy," PLOS ONE, Public Library of Science, vol. 18(12), pages 1-25, December.
    17. Gao, Jing & Zhang, Wanfei & Guan, Tao & Feng, Qiuhong & Mardani, Abbas, 2023. "The effect of manufacturing agent heterogeneity on enterprise innovation performance and competitive advantage in the era of digital transformation," Journal of Business Research, Elsevier, vol. 155(PA).
    18. Daria Gritsenko & Jon Aaen & Bent Flyvbjerg, 2024. "Rethinking Digitalization and Climate: Don't Predict, Mitigate," Papers 2407.15016, arXiv.org.
    19. Zuoyufan Sheng & Chengpeng Zhu & Mo Chen, 2024. "Exploring the Impact of the Digital Economy on Green Total Factor Productivity—Evidence from Chinese Cities," Sustainability, MDPI, vol. 16(7), pages 1-13, March.
    20. Bent Flyvbjerg & Alexander Budzier & Jong Seok Lee & Mark Keil & Daniel Lunn & Dirk W. Bester, 2022. "The Empirical Reality of IT Project Cost Overruns: Discovering A Power-Law Distribution," Papers 2210.01573, arXiv.org.
    21. Rudra P. Pradhan & Mak B. Arvin & Mahendhiran Nair & Sara E. Bennett, 2020. "Sustainable economic growth in the European Union: The role of ICT, venture capital, and innovation," Review of Financial Economics, John Wiley & Sons, vol. 38(1), pages 34-62, January.
    22. Jiangying Wei & Xiuwu Zhang, 2023. "The Role of Big Data in Promoting Green Development: Based on the Quasi-Natural Experiment of the Big Data Experimental Zone," IJERPH, MDPI, vol. 20(5), pages 1-18, February.
    23. Jiangang Huang & Xinya Chen & Xing Zhao, 2024. "How Digital Technology Reduces Carbon Emissions: From the Perspective of Green Innovation, Industry Upgrading, and Energy Transition," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(4), pages 19294-19326, December.
    24. Kivimaa, Paula & Kern, Florian, 2016. "Creative destruction or mere niche support? Innovation policy mixes for sustainability transitions," Research Policy, Elsevier, vol. 45(1), pages 205-217.
    25. Wang, Huizong & Hao, Yulong & Fu, Qiang, 2024. "Data factor agglomeration and urban green finance: A quasi-natural experiment based on the National Big Data Comprehensive Pilot Zone," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    26. Guo, Bingnan & Wang, Yu & Zhang, Hao & Liang, Chunyan & Feng, Yu & Hu, Feng, 2023. "Impact of the digital economy on high-quality urban economic development: Evidence from Chinese cities," Economic Modelling, Elsevier, vol. 120(C).
    27. El-Kassar, Abdul-Nasser & Singh, Sanjay Kumar, 2019. "Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 483-498.
    28. Chen, Shiyi, 2015. "Environmental pollution emissions, regional productivity growth and ecological economic development in China," China Economic Review, Elsevier, vol. 35(C), pages 171-182.
    29. Ruying Chen & Lanyu Wu, 2024. "Calculation and analysis of the efficiency of resource allocation for technological innovation in China," PLOS ONE, Public Library of Science, vol. 19(8), pages 1-20, August.
    30. Zhang, Guiyang & Tang, Chaoying, 2017. "How could firm's internal R&D collaboration bring more innovation?," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 299-308.
    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. Zhu, Qing & Ma, Dan & He, Xin, 2023. "Digital transformation and firms' pollution emissions," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    2. Yao, Shouyu & Pan, Yuying & Sensoy, Ahmet & Uddin, Gazi Salah & Cheng, Feiyang, 2021. "Green credit policy and firm performance: What we learn from China," Energy Economics, Elsevier, vol. 101(C).
    3. Martinsson, Gustav & Sajtos, László & Strömberg, Per & Thomann, Christian, 2022. "Carbon Pricing and Firm-Level CO2 Abatement: Evidence from a Quarter of a Century-Long Panel," Misum Working Paper Series 2022-10, Stockholm School of Economics, Mistra Center for Sustainable Markets (Misum).
    4. Xie, Xuejing & Gong, Yukai & Cheng, Le, 2025. "Managerial myopia and carbon emission: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
    5. Cui, Xin & Wang, Chunfeng & Sensoy, Ahmet & Liao, Jing & Xie, Xiaochen, 2023. "Economic policy uncertainty and green innovation: Evidence from China," Economic Modelling, Elsevier, vol. 118(C).
    6. Jia, Shaoqing & Yang, Liuyong & Zhou, Fangzhao, 2022. "Geopolitical risk and corporate innovation: Evidence from China," Journal of Multinational Financial Management, Elsevier, vol. 66(C).
    7. Bo Li & Jing Liu & Qian Liu & Muhammad Mohiuddin, 2022. "The Effects of Broadband Infrastructure on Carbon Emission Efficiency of Resource-Based Cities in China: A Quasi-Natural Experiment from the “Broadband China” Pilot Policy," IJERPH, MDPI, vol. 19(11), pages 1-27, May.
    8. Wan, Qunchao & Chen, Jin & Yao, Zhu & Yuan, Ling, 2022. "Preferential tax policy and R&D personnel flow for technological innovation efficiency of China's high-tech industry in an emerging economy," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    9. Maxwell Chukwudi Udeagha & Edwin Muchapondwa, 2023. "Environmental sustainability in South Africa: Understanding the criticality of economic policy uncertainty, fiscal decentralization, and green innovation," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(3), pages 1638-1651, June.
    10. Ye, Jiangfeng & Wan, Qunchao & Li, Ruida & Yao, Zhu & Huang, Dujuan, 2022. "How do R&D agglomeration and economic policy uncertainty affect the innovative performance of Chinese high-tech industry?," Technology in Society, Elsevier, vol. 69(C).
    11. Weilong Wang & Jianlong Wang & Shaersaikai Wulaer & Bing Chen & Xiaodong Yang, 2021. "The Effect of Innovative Entrepreneurial Vitality on Economic Resilience Based on a Spatial Perspective: Economic Policy Uncertainty as a Moderating Variable," Sustainability, MDPI, vol. 13(19), pages 1-23, September.
    12. Mingjun Deng & Xusheng Fang & Ziyan Tian & Wenbing Luo, 2022. "The Impact of Environmental Uncertainty on Corporate Innovation: Evidence from Chinese Listed Companies," Sustainability, MDPI, vol. 14(9), pages 1-19, April.
    13. Shen, Xieyang & Yang, Sijie & Chen, Yulin & Zeng, Jianyu, 2022. "How does economic policy uncertainty influence managers' learning from peers' stock prices? Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 74(C).
    14. Ling-Yun He & Geng Huang, 2020. "Tariff Reduction and Environment: Evidence from CAFTA and Chinese Manufacturing Firms," Sustainability, MDPI, vol. 12(5), pages 1-25, March.
    15. Xu, Yuanbin & Wei, Yuan & Zeng, Xin & Yu, Haiqing & Chen, Hongjie, 2024. "Big data development and labor income share: Evidence from China's national big data comprehensive pilot zones," Economic Analysis and Policy, Elsevier, vol. 84(C), pages 1415-1437.
    16. Zhang, Yijun & Meng, Zhenzhen & Song, Yi, 2023. "Digital transformation and metal enterprise value: Evidence from China," Resources Policy, Elsevier, vol. 87(PB).
    17. Nguyen, Duc Nguyen & Nguyen, Canh Phuc & Dang, Le Phuong Xuan, 2022. "Uncertainty and corporate default risk: Novel evidence from emerging markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 78(C).
    18. Zhu, Bo & Zhao, Yue, 2022. "Carbon risk and the cost of bank loans: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    19. Yituan Liu & Qihang Li & Zheng Zhang, 2022. "Do Smart Cities Restrict the Carbon Emission Intensity of Enterprises? Evidence from a Quasi-Natural Experiment in China," Energies, MDPI, vol. 15(15), pages 1-20, July.
    20. Qiaozhe Guo & Chengxuan Geng & Nengzhi Yao & Lexin Zhao, 2024. "Can digital infrastructure enhance economic efficiency? Evidence from China," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(2), pages 1729-1752, April.

    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:streco:v:72:y:2025:i:c:p:89-102. 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/525148 .

    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.