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This abstract introduces a data-driven approach to managing carbon footprints in global supply chains through the integration of artificial intelligence (AI) algorithms. With the pressing need for sustainable practices, understanding and mitigating carbon emissions throughout the supply chain has become imperative. This study proposes a comprehensive framework that harnesses the power of AI to analyze, optimize, and monitor carbon footprints at various stages of the supply chain. The proposed approach utilizes AI algorithms to gather, process, and analyze vast amounts of data related to carbon emissions, including transportation, manufacturing, and sourcing activities. By leveraging machine learning and optimization techniques, the framework identifies key areas for emission reduction and develops strategies to minimize environmental impact while maintaining operational efficiency. Through real-time monitoring and predictive analytics, this approach enables proactive decision-making, allowing companies to adapt quickly to changing environmental regulations and market dynamics. The integration of AI not only enhances the accuracy and reliability of carbon footprint assessments but also provides insights for continuous improvement and sustainability performance tracking. This research contributes to the advancement of sustainable supply chain management by offering a data-driven approach that empowers organizations to effectively manage their carbon footprints and contribute to a more environmentally conscious global economy.
Huang et al. (Mon,) studied this question.