AI-Enhanced Assessment Framework for City-Scale Management of Municipal Living Plastic Waste Towards Zero-Waste Cities

Managing municipal living plastic waste (MLPW) entails complex system-level challenges across collection, recycling, and treatment, necessitating simultaneous optimization of resource, environmental, and economic objectives. However, robust assessment is frequently impeded by data scarcity and measurement inaccuracies, which undermine the transferability of evaluation models. To address these limitations, an artificial intelligence (AI)-enhanced evaluation framework is proposed. Baseline material flows were derived directly from field measurements, while machine learning algorithms were employed for independent validation and data imputation, thereby enhancing the credibility of environmental and economic assessments. The framework was applied in a megacity as the case study. Results indicated that MLPW management should focus on upgrading recycling and advancing the substitution of biodegradable plastics while rigorously enforcing source reduction. The recycling/treatment yields a 96.3% reduction in annual emissions by 2060 relative to the baseline. Cumulatively, this optimal trajectory achieves a reduction of 22.22 Mt CO 2 -eq between 2020 and 2060, generating economic benefits of approximately 197.7 billion CNY. Given the current technological conditions, mechanical recycling is identified as the priority pathway, offering superior mitigation potential (emission intensity of about 108 kg CO 2 -eq·t −1 ) and cost-effectiveness (economic return of around 613.9 CNY·t −1 ). By leveraging AI to ensure evaluation completeness and credibility even under data-constrained conditions, this framework offers a transferable tool for providing quantitative evidence to support policy prioritization in zero-waste city initiatives globally.