Artificial Intelligence-Powered Carbon Emissions Forecasting: Implications for Sustainable Supply Chains and Green Finance

This review explores the integration of Artificial Intelligence (AI) in carbon emissions forecasting within the context of sustainable supply chain management (SCM). It examines theoretical frameworks such as Resource-Based Theory (RBT), Strategic Choice Theory (SCT), and Dynamic Capabilities Theory (DCT) to understand how AI technologies can be leveraged to achieve sustainability goals and operational efficiency. The conceptual model for AI-powered emissions forecasting is discussed, highlighting the flow of data from external sources to AI-driven predictive analysis and actionable insights for emission reduction strategies. Challenges such as data availability, algorithmic accuracy, system integration, and ethical concerns, including data privacy and environmental risks, are also addressed. Recommendations for future research emphasize improving data integration, fostering collaboration across sectors, and exploring AI applications in diverse industries. Testing and validating the conceptual model through quantitative methods, scenario-based modeling, and real-time feedback loops are proposed to ensure its practical applicability. Ultimately, AI presents significant potential to drive sustainable practices while balancing environmental goals with financial performance, thus promoting long-term business resilience.