AI-Driven Supply Chain Decarbonization: Strategies for Sustainable Carbon Reduction

Supply chains are a primary contributor to global greenhouse gas (GHG) emissions, rendering their decarbonization an essential dimension of sustainable development. Artificial intelligence (AI) provides a transformative pathway by facilitating proactive emission avoidance through operational efficiency, transparency, and resilience, in contrast to post-emission mitigation approaches such as carbon capture. This study explores the potential of AI to support indirect carbon dioxide removal (CDR) via supply chain decarbonization, adopting a comparative case study methodology. Empirical evidence is drawn from Tunisian agri-food, textile, and port logistics sectors, based on multi-source datasets spanning 6–12 months and covering fleet sizes ranging from 40 to 250,000 units. Methodological robustness was ensured through the use of pre-intervention baselines, statistical imputation for missing data (<5%), and validation against 20% out-of-sample test sets. Results indicate that AI-enabled interventions achieved annual avoided emissions between 500 and 1500 tCO 2 and reduced fuel consumption by 12–15%, with sensitivity analyses incorporating ±8–12% error margins. Among the approaches tested, hybrid models integrating operational and strategic layers demonstrated the most pronounced impact, aligning immediate efficiency gains with long-term systemic decarbonization. Furthermore, AI facilitates renewable energy integration, digital twin applications, and compliance with international sustainability frameworks, notably the Paris Agreement and the United Nations Sustainable Development Goals. Nevertheless, challenges related to data quality, computational demands, limited expertise, and organizational resistance constrain scalability. The findings underscore AI’s dual role as a technological enabler and systemic driver of supply chain decarbonization, advancing its positioning within global environmental sustainability transitions.