Waste management–related trust, acceptance, and reputation: A multidisciplinary big data analysis across knowledge domains

Abstract

Addressing global waste management challenges requires understanding not only the technical capabilities of products and technologies but also the factors shaping their development and deployment across the waste hierarchy. Deployment outcomes are strongly influenced by acceptance, reputation, and trust, distinct yet interrelated constructs whose dynamics remain insufficiently understood. Deepening this understanding can enhance stakeholder engagement and improve decision-making in waste management. This study examines waste-to-energy incineration as a representative case to investigate these dynamics across global, regional, and local levels. A multidisciplinary, data-driven approach is applied, combining artificial intelligence, big data analytics, opinion mining, Correspondence Analysis on Generalized Aggregated Lexical Tables, and content classification to assess acceptance, trust, and reputation in multiple knowledge domains. The analysis clarifies these constructs as interwoven but individually influential factors shaping technology deployment and explores their interplay with public perception. A novel method is also introduced for generating indicative reputation scores derived from sentiment analysis. The findings show that AI-enhanced analytical tools, when integrated with established methods, yield valuable insights into stakeholder sentiment and public discourse. These insights can inform more targeted stakeholder engagement and strategic communication in waste management planning. Overall, the study demonstrates the potential of emerging analytical tools to produce timely, structured indicators of trust, acceptance, and reputation, key dimensions for navigating the socio-political challenges of technology deployment in the waste sector.