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Transforming supply chain operations: Unveiling the path ahead by leveraging artificial intelligence (AI) to drive the shift towards carbon neutrality (CN)

Author

Listed:
  • Zheng, Li
  • Zhou, Rong
  • Singh, Nidhi
  • Yaqub, Muhammad Zafar
  • Badghish, Saeed

Abstract

The negative impact of carbon emissions has received greater attention and is a significant challenge. Recent research has focused on promoting CN and achieving CN goals through the use of advanced techniques. While there is some debate on the use and influence of different technologies, there is a limited exploration of the use of specific technologies, such as AI, in achieving CN, particularly in the supply chain (SC) domain. This research paper examines the intersection between AI-led CN and SC, exploring the multifaceted aspects that define this evolving landscape. This paper aims to unravel the complexities and outcomes surrounding the use of AI-led disruptive technologies to achieve CN in SC operations. The authors conducted a comprehensive review of 47 relevant studies to facilitate a critical synthesis of the literature. Based on an extensive literature review, the study identified various antecedents and consequences of AI-led CN goals, spanning a geographical expanse under the broad themes of Sustainable Energy, Digital and Technological Transformation, Biomass Conversion, Waste Management, and Carbon Forecasting and Accounting in the SC domain. The findings revealed several antecedents to AI-led CN goals, including discussion on energy supply issues, intelligent systems, access to green financial markets, and slag waste optimization, as well as a few consequences of such implementation, such as AI-based methods to monitor slag waste, biomass concerns, and carbon neutrality, among others. The study also developed a framework, highlighting various AI-led CN strategies at the optimization and operational stages, as well as several AI-led innovations and their importance. The study offers a few policy suggestions, such as designing emission reduction policies, encouraging public-private partnerships, developing a regulatory framework to promote the industrial transformation of intelligent buildings, and managing uncertainties related to slag waste, biomass, energy systems, and other SC issues to ensure proper implementation of AI-led CN systems in the SC domain.

Suggested Citation

  • Zheng, Li & Zhou, Rong & Singh, Nidhi & Yaqub, Muhammad Zafar & Badghish, Saeed, 2025. "Transforming supply chain operations: Unveiling the path ahead by leveraging artificial intelligence (AI) to drive the shift towards carbon neutrality (CN)," Technological Forecasting and Social Change, Elsevier, vol. 219(C).
    • Handle: RePEc:eee:tefoso:v:219:y:2025:i:c:s0040162525003117
    DOI: 10.1016/j.techfore.2025.124280
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    1. Xia, Yanfang & Qiu, Yong & Gu, Zhuoyu & Zhang, Liang & Yang, Jiayu, 2026. "Leveraging generative AI and circular innovation for equitable and resilient supply chains: The mediating role of transparency and sustainability-oriented decision empowerment," Technological Forecasting and Social Change, Elsevier, vol. 225(C).

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