AI and Optimization: Driving Carbon Efficiency in Supply Chain Networks

The accelerating climate crisis and increasing regulatory pressures have compelled organizations to redesign supply chain networks for enhanced carbon efficiency. This chapter explores how artificial intelligence (AI) and advanced optimization techniques are transforming supply chain decision-making to drive measurable reductions in greenhouse gas emissions while maintaining operational performance. Integrating machine learning, reinforcement learning, predictive analytics, and metaheuristic optimization models, AI-enabled systems facilitate carbon-aware routing, demand forecasting, inventory management, network design, and energy-efficient warehousing. The chapter develops a comprehensive conceptual framework illustrating how AI functions as an intelligent decision layer that simultaneously optimizes cost, service level, and carbon emissions across Scope 1, 2, and 3 activities. Drawing on contemporary case evidence from logistics, manufacturing and retail sectors, the discussion highlights how digital twins, real-time data analytics and autonomous optimization engines enable dynamic carbon monitoring and adaptive supply chain reconfiguration. Furthermore, the chapter examines strategic implications through the lens of resource-based and dynamic capability perspectives, emphasizing AI as a sustainability-enabling capability that enhances resilience and long-term competitiveness. By bridging technological innovation with environmental performance objectives, this work contributes to the growing body of research on green digital transformation and provides practical guidance for practitioners aiming to develop scalable pathways toward achieving net zero supply chain operations.