Prioritizing interoperable AI-driven solutions for sustainable circular economy in the public sector

Although digital transformation using artificial intelligence (AI) presents numerous opportunities to enhance public services and align them with circular economy (CE) goals, there are various challenges that make such digital integrations tedious and slow. Various technical, organizational and policy factors for AI integration in CE-aligned public governance have been explored in silos and their interrelationships and dependencies are not widely reported. Hence the study identifies upstream/downstream drivers for a sustainable circular economy and examines causal influence of each driver and further identifies boundary-linking drivers. Fifteen prominent factors reported in literature and validated by 102 experts from academia and industry for their cause effect relationship. The causal relationship is identified using Decision-Making Trial and Evaluation Laboratory (DEMATEL) analysis. The findings are reported as a hierarchy of cause-and-effect groups based on their influence in the system. Privacy and Security Concerns, Feedback Loops, Data Interoperability and Training and Digital Literacy are reported as major drivers. The most affected dependent variable Urban Infrastructure Planning, National Goal Alignment, Standardized Data Protocols, AI-based Energy Optimization, Policy Support for AI Adoption, AI in Waste Management and Cross-Agency Data Sharing Capability are reported as effect variables. This study provides a comprehensive framework of causality by mapping interdependencies, revealing the underlying enablers and the downstream operational results. For practitioners, the results of this study, provide an evidence-based, stepwise roadmap that can help in the sequencing of strategic investment and policy deployment for achieving circular economy goals through digital transformations using AI.