Analyzing enablers of artificial intelligence for decarbonization: implications for circular supply chains
研究识别了人工智能推动脱碳的15个关键因素，分为环境、组织、制度和技术四类，并发现采用可回收材料、本地化生产和智能制造是前三重要因素，为供应链管理者转向循环供应链提供指导。
Abstract This study comprehensively explores the pivotal position that Artificial Intelligence (AI) enables on the advancement of decarbonization efforts, mainly in the context of Circular Supply Chains (CSCs). Employing a two-stage methodology, this study delves into identifying and analyzing the enablers essential for leveraging AI in the pursuit of decarbonization objectives. In the first stage, a literature review and an exploratory factor analysis are performed to discern the key enablers of AI for decarbonization initiatives. This process resulted in the identification of 15 significant enablers and categorization of enablers into environmental, organizational, institutional, and technological categories. Building upon the findings from the first stage, this study progresses to its second stage, wherein the Grey-Ordinal Priority Approach (G-OPA) is applied to analyze the identified enablers. The results indicate that adopting recyclable materials to enhance the efficiency of supply chains, emphasizing local production for recovery practices through advanced technology, and managing product life-cycle through intelligent and additive manufacturing technologies are the top three enablers. The application of the G-OPA enriches the robustness and comprehensiveness of the analysis, enabling an understanding of the complex interplay among the enablers. By clarifying the key enablers, business planners and designers can migrate from traditional linear supply chains to more sustainable CSCs through the careful implementation of enablers for decarbonization.