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AI-Enabled Digital Twins for Low-Carbon Logistics in Emerging Markets: A Human-Centric Framework for Cold-Chain Energy Efficiency and CBAM-Ready Supply Chains in India “A Human-Centric Framework for Energy-Efficient and Sustainable Cold-Chain Supply Chainsâ€

Author

Listed:
  • Syed Eirfan Atthar

    (Department of Mechanical Engineering Specialisation in Thermal & Fluid, Dibrugarh University, Assam)

Abstract

Cold-chain logistics play a critical role in ensuring food security and pharmaceutical safety in emerging markets; however, they remain among the most energy-intensive and environmentally sensitive segments of modern supply chains. In countries such as India, rapid expansion of cold-chain infrastructure has been accompanied by persistent challenges related to refrigeration energy inefficiency, product spoilage, fragmented decision-making, and limited carbon transparency. At the same time, evolving global sustainability expectations and carbon-linked trade mechanisms are increasing the need for reliable, auditable emission information across logistics operations. This paper proposes a human-centric, AIenabled digital twin framework aimed at improving energy efficiency, reducing carbon intensity, and enhancing sustainability transparency in food and pharmaceutical cold-chain logistics within emerging markets. Adopting a conceptual design-science approach, the study integrates insights from cold-chain engineering, logistics management, artificial intelligence, and sustainability governance. A layered digital twin architecture is developed that combines real-time operational data, energy-aware system modelling, AI-driven optimization, and human-in-the-loop decision support. Illustrative energy and emission calculations are presented to demonstrate potential reductions in refrigeration energy demand, spoilage risk, and operational uncertainty. The proposed framework emphasizes human accountability and explainable AI, ensuring that technological intelligence augments rather than replaces managerial judgment. By linking operational optimization with sustainability reporting and future carbon compliance readiness, the framework offers practical value for logistics managers, exporters, and policymakers. The study contributes a structured pathway for transforming cold-chain logistics into an energy-efficient, transparent, and future-ready system capable of supporting sustainable growth in emerging markets.

Suggested Citation

  • Syed Eirfan Atthar, 2026. "AI-Enabled Digital Twins for Low-Carbon Logistics in Emerging Markets: A Human-Centric Framework for Cold-Chain Energy Efficiency and CBAM-Ready Supply Chains in India “A Human-Centric Framework for Energy-Efficient and Sustainable Cold-Chain Suppl," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 10(19), pages 41-57, February.
    • Handle: RePEc:bcp:journl:v:10:y:2026:i:19:p:41-57
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    References listed on IDEAS

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    1. Wamba, Samuel Fosso & Gunasekaran, Angappa & Akter, Shahriar & Ren, Steven Ji-fan & Dubey, Rameshwar & Childe, Stephen J., 2017. "Big data analytics and firm performance: Effects of dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 356-365.
    2. Mohamed Ben-Daya & Elkafi Hassini & Zied Bahroun, 2019. "Internet of things and supply chain management: a literature review," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 4719-4742, August.
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