How artificial intelligence amplifies decarbonization in urban waste management: Causal evidence from China

Urban waste management systems represent significant greenhouse gas sources while offering substantial mitigation opportunities. Despite growing interest in digital environmental technologies, limited causal evidence exists on the effects of artificial intelligence (AI) on urban waste decarbonization. This study addresses this gap by examining whether AI integration amplifies zero-waste city initiatives' climate benefits. Employing a triple-difference model with panel data from 289 Chinese cities (2000–2022), we leverage China's zero-waste city program as a natural experiment and measure city-level AI integration using a composite index of AI-related infrastructure, enterprise adoption, and patent activity. Results show that cities integrating AI into zero-waste initiatives reduced emissions by 2.77% compared to controls, equivalent to a reduction of 1.45 million metric tons. Mixed-methods investigation identifies four interconnected causal mechanisms: operational efficiency enhancements that lower energy consumption per unit of waste processed, resource flow optimizations that substitute recovered materials for virgin extraction, behavioral interventions that modify stakeholder decision-making patterns, and system coordination capabilities that enable governance integration across policy domains and urban infrastructure. Heterogeneity analysis reveals notable regional and sectoral variations—digital development serves as the dominant moderating factor, old industrial base cities demonstrate unexpectedly strong responsiveness despite legacy challenges, and policy coordination between zero-waste and circular economy frameworks generates synergistic benefits in waste-intensive manufacturing and energy sectors. These findings suggest that strategic AI infrastructure investments, particularly when paired with integrated circular economy governance, can offer high-return decarbonization pathways for traditional industrial regions and inform targeted urban waste management policy design.