Resumo

Organic solid Waste(OSW) represents both a major challenge and an opportunity in urban waste management:in 2023,only 0.4% was composted,despite its potential for conversion into agricultural energy inputs.Alternatives such as biomethane production reinforce the role of OSW in the transition to a circular model(ABREMA,2024).Digital technologies(DT) offer capabilities with the potential to address these challenges,such as AI for forecasting and optimization,IoT for monitoring,Big Data for data integration,and blockchain for traceability and reliability.

The literature addresses DT in MSW management in general, without a specific focus on SOR. Studies highlight AI (Ali et al., 2024; Gupta et al., 2024), IoT and Big Data (Shetty et al., 2024), blockchain (Bag et al., 2024; Shetty et al., 2024), and digital twins (Cappelletti; Menato, 2023). However, no research has validated experts' perceptions of the essentiality of these technologies in OSW RL in a given context. This study identifies which DTs are essential in OSW RL, combining a systematic review and the perceptions of Brazilian experts analyzed using the Lawshe method.

DTs have proven useful for MSW management, such as AI combined with machine learning, sensors, and drones to optimize composting processes and predict waste generation (Ali et al., 2024; Gupta et al., 2024). Big Data analyzes large volumes of information, identifying waste patterns and tracking waste flows (Shetty et al., 2024). Blockchain ensures greater traceability and transparency throughout the supply chain, ensuring data reliability (Bag et al., 2024). Digital twins contribute to the longevity of equipment used in waste processing (Capeleti & Menato, 2023).

The research followed four stages: (i) a systematic literature review following the PRISMA method; (ii) defining the respondents' profile and developing the questionnaire; (iii) data collection; and (iv) quantitative content validity analysis using the Lawshe method (1975). In the systematic review, conducted in the Scopus and Web of Science databases with a time frame of 2023–2024, 272 articles were identified. After exclusions, 148 remained for full reading. From the analysis, nine DTs with potential application in OSW RL were identified.

None of the nine functionalities evaluated were validated. However, three presented positive CVRs: AI in waste generation prediction, IoT in traceability, and Big Data in machinery maintenance. Although not validated, the literature recognizes their practical potential but also highlights barriers such as costs, limited infrastructure, and low expert familiarity, which explains the conservative assessment. Three DTs presented negative CVRs, meaning they are not considered essential: AI in decomposition monitoring and composting optimization, and IoT in predictive equipment maintenance.

The contributions are presented in three dimensions. In the theoretical field, a framework with nine DTs was proposed for future research. Methodologically, the use of Lawshe offered an alternative and robust statistical validation. In the practical field, no functionality was validated, possibly due to barriers such as high implementation costs and incipient digital infrastructure. Furthermore, the absence of technology developers on the panel may have limited technical assessment, favoring more conservative positions. However, IoT, Big Data, and AI were considered essential, suggesting areas

Ali, Z. A., Zain, M., Pathan, M. S., & Mooney, P. (2024). Contributions of artificial intelligence for circular economy transition leading toward sustainability: An explorative study in agriculture and food industries of Pakistan; Bag, S., Rahman, M. S., Singh, A., Bryde, D., & Graham, G. (2024). Leveraging digital technology capability for circular economy innovation in the food products supply chain: A mixed-method study. Cappelletti, F., & Menato, S. (2023). Developing a circular business model for machinery lifecycle extension by exploiting tools for digitalization.