Optimizing AI-based emission reduction efficiency and subsidies in supply chain management: A sensitivity-based approach with duopoly game dynamics

This study aims to fill an essential gap in the integration of supply chain management (SCM) dynamics by incorporating three key elements: emission reduction efficiency (ERE), Artificial intelligence-based emission reduction technology (AI-ERT), and profit maximization. The primary goal of the research is to determine the best methods by which stakeholders of SCM can attain carbon neutrality. This study examines China, which is trying to reduce carbon dioxide CO2 emissions while dealing with issues related to energy system efficiency. Mathematical models embedded with sensitivity-based optimization strategies are used within the experiments. The sensitivity of the indicators was determined using regression analysis, which provided concise information about the effects of production-related variables on ERE for manufacturers and agents. The findings provide optimum values for profit and price decisions, highlighting the best practices for manufacturers and agents. Agents display significant attention to ERE-enhancing approaches, which improve profit and CO2 emissions. Additionally, we evaluate the impact of subsidies on AI-ERT, uncovering significant implications for production quantity, AI-ERT decisions, and profitability. The work addresses the gap of SCM, highlighting the importance of considering AI-ERT, profit maximization, and ERE together. Additionally, using carbon-neutral SCM techniques helps SCM stakeholders achieve economic goals by optimizing profits while simultaneously advancing environmental sustainability.