Applications of AI to low carbon decision support system for global supply chains

Carbon emissions are inherently embedded in the complex and dynamic supply chain, which is driven by operational activities. These climate change implications are intensifying, making it increasingly important to mitigate carbon emissions within supply chains. Specifically, this systematic literature review examines how artificial intelligence is applied to the reduction of carbon emissions, focusing on low-carbon interventions, supply chain performance evaluation, and decision support systems for managing carbon emissions in global supply chains. This study identifies and synthesizes key insights and trends in AI-driven approaches to carbon emissions reductions and supply chain optimization by reviewing a comprehensive array of available literature. A diverse range of AI applications, including optimization algorithms, predictive modeling, and decision support tools, demonstrate promising potential for enhancing efficiency and sustainability within global supply chains. Additionally, this review identifies key challenges in integrating AI for decarbonization. It presents real-world case studies demonstrating how AI-driven approaches have successfully reduced logistics, manufacturing, and energy management emissions. This review aims to synthesize the state of research on AI’s role in enabling sustainable supply chain management and carbon emission reductions and to provide valuable insights for practitioners, policymakers, and researchers interested in leveraging AI in these efforts. Finally, the paper develops and presents a conceptual framework proposed to guide the development of low carbon decision support system for global supply chains using AI.