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 environmen-tally 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 refriger-ation 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, AI-enabled 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. Il-lustrative 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 opti-mization with sustainability reporting and future carbon compliance readiness, the framework offers practical value for logistics managers, exporters, and policymakers. The study contrib-utes 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.
