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The impact of artificial intelligence development on embodied carbon emissions: Perspectives from the production and consumption sides

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  • Luo, Qingfeng
  • Wang, Jingyuan

Abstract

The positive impact of artificial intelligence (AI) on carbon emission issues has been widely discussed, but no research has yet thoroughly examined the specific effects of AI on embedded carbon emissions (ECE). To address this gap, this study examines the impacts and underlying mechanisms of AI development on ECE from both production and consumption perspectives. The results show that AI development significantly reduces carbon emissions on both fronts, with a more noticeable effect on the production side. Specifically, AI has generated significant carbon reduction effects on ECE from both the production and consumption sides by optimizing manufacturing structures, promoting low-carbon energy consumption, and driving green product innovation. Additionally, factors such as strengthened environmental regulations, improved ecological civilization, improved green innovation capacity, and higher renewable energy consumption further strengthen the carbon reduction effects of AI. However, the impact of AI on carbon emissions shows regional heterogeneity. This study offers a new perspective on differentiated carbon governance and equitable allocation of carbon emission responsibilities. It also offers empirical evidence and theoretical insights for policymakers, supporting the effective use of AI technology to achieve carbon reduction goals.

Suggested Citation

  • Luo, Qingfeng & Wang, Jingyuan, 2025. "The impact of artificial intelligence development on embodied carbon emissions: Perspectives from the production and consumption sides," Energy Policy, Elsevier, vol. 199(C).
    • Handle: RePEc:eee:enepol:v:199:y:2025:i:c:s0301421525000424
    DOI: 10.1016/j.enpol.2025.114535
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    as
    1. Chao-Chung Hsu & Bi-Hai Jiang & Chun-Cheng Lin, 2023. "A Survey on Recent Applications of Artificial Intelligence and Optimization for Smart Grids in Smart Manufacturing," Energies, MDPI, vol. 16(22), pages 1-15, November.
    2. Michael Jakob & Robert Marschinski, 2013. "Interpreting trade-related CO2 emission transfers," Nature Climate Change, Nature, vol. 3(1), pages 19-23, January.
    3. Lyu, Hengyu & Ma, Chunai & Arash, Farnoosh, 2024. "Central environmental protection inspection, green technology innovation and carbon intensity of industrial enterprises – Empirical research based on multi-period differences-in-differences model," Energy, Elsevier, vol. 307(C).
    4. Wang, Jianda & Yang, Senmiao & Dong, Kangyin & Nepal, Rabindra, 2024. "Assessing embodied carbon emission and its drivers in China's ICT sector: Multi-regional input-output and structural decomposition analysis," Energy Policy, Elsevier, vol. 186(C).
    5. Gabriela Badareu & Marius Dalian Doran & Mihai Alexandru Firu & Ionuț Marius Croitoru & Nicoleta Mihaela Doran, 2024. "Exploring the Role of Robots and Artificial Intelligence in Advancing Renewable Energy Consumption," Energies, MDPI, vol. 17(17), pages 1-17, September.
    6. Shi, Zhongtuo & Yao, Wei & Li, Zhouping & Zeng, Lingkang & Zhao, Yifan & Zhang, Runfeng & Tang, Yong & Wen, Jinyu, 2020. "Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions," Applied Energy, Elsevier, vol. 278(C).
    7. 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).
    8. Quentin André & Ziv Carmon & Klaus Wertenbroch & Alia Crum & Douglas Frank & William Goldstein & Joel Huber & Leaf Boven & Bernd Weber & Haiyang Yang, 2018. "Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 28-37, March.
    9. 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).
    10. 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.
    11. Ramesh Chandra, 2004. "Adam Smith, Allyn Young, and the Division of Labor," Journal of Economic Issues, Taylor & Francis Journals, vol. 38(3), pages 787-805, September.
    12. Zhou, Xiaoyong & Zhou, Dequn & Wang, Qunwei & Su, Bin, 2020. "Who shapes China's carbon intensity and how? A demand-side decomposition analysis," Energy Economics, Elsevier, vol. 85(C).
    13. Izabela Rojek & Adam Mroziński & Piotr Kotlarz & Marek Macko & Dariusz Mikołajewski, 2023. "AI-Based Computational Model in Sustainable Transformation of Energy Markets," Energies, MDPI, vol. 16(24), pages 1-26, December.
    14. Lee, Chien-Chiang & Yan, Jingyang, 2024. "Will artificial intelligence make energy cleaner? Evidence of nonlinearity," Applied Energy, Elsevier, vol. 363(C).
    15. Chao Ding & Jing Ke & Mark Levine & Nan Zhou, 2024. "Potential of artificial intelligence in reducing energy and carbon emissions of commercial buildings at scale," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    16. Su, Min & Wang, Qiang & Li, Rongrong & Wang, Lili, 2022. "Per capita renewable energy consumption in 116 countries: The effects of urbanization, industrialization, GDP, aging, and trade openness," Energy, Elsevier, vol. 254(PB).
    17. 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).
    18. 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).
    19. Li, Guangpei & Wang, Xiaoyu & Su, Shibin & Su, Yuan, 2019. "How green technological innovation ability influences enterprise competitiveness," Technology in Society, Elsevier, vol. 59(C).
    20. 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.
    21. Huakun Huang & Dingrong Dai & Longtao Guo & Sihui Xue & Huijun Wu, 2023. "AI and Big Data-Empowered Low-Carbon Buildings: Challenges and Prospects," Sustainability, MDPI, vol. 15(16), pages 1-21, August.
    22. Guan, Chunxu & Lin, Boqiang, 2024. "Do consumers concern about energy saving in purchasing energy-efficient home appliances? Evidence from online e-commerce review," Energy Policy, Elsevier, vol. 193(C).
    23. Guo, Jianxin & Zhu, Kaiwei & Cheng, Yonglong, 2024. "Deployment of clean energy technologies towards carbon neutrality under resource constraints," Energy, Elsevier, vol. 295(C).
    24. 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.
    25. 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.
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