Data‐Driven Pathways to Circular E‐Waste Management
本研究以加拿大电子废弃物行业为例，利用人工智能、自然语言处理等方法分析200多篇学术文献，揭示循环经济原则融入区域治理的路径，指出国家政策在各省间的差距及社区和青年主导的循环倡议的潜力。
ABSTRACT As the volume and complexity of electronic waste grow worldwide, regional and subnational systems are increasingly tasked with managing the environmental, economic, and social challenges of circular resource recovery. This paper focuses on Canada's e‐waste sector to examine how circular economy ( ce ) principles can be integrated into regional governance frameworks. Drawing on a novel methodology with artificial intelligence (AI), natural language processing (NLP), K ‐means clustering, and theory of change (ToC) modeling, the study synthesizes over 200 academic texts to map transformation pathways toward sustainable e‐waste management. Our findings highlight gaps in national policy across provinces and the underutilized potential of community‐led and youth‐driven circular initiatives.