## 4 Discussion and Conclusions  
The analysis conducted in this study highlights how the application of advanced technologies such as AI and Digital Twins (DT) represents a strategic element in the transition toward more sustainable models of waste management [55, 56]. The integration of digital solutions across collection, treatment, recycling, and vaporization processes enables significant improvements in operational efficiency, traceability of material flows, and reductions in environmental impacts. Beyond reiterating the key findings, it is crucial to frame them within a broader theoretical and applicative perspective. AI and DT should not be considered merely as technical tools but as enabling infrastructures for socio-technical transformation. Their integration redefines the ontology of waste management by shifting the focus from reactive to anticipatory systems, capable of adapting in real time to environmental and operational conditions. The bibliometric analysis revealed two major research streams. The first focuses on forecasting and managing urban waste, demonstrating that predictive models based on Machine Learning (ML) enable more accurate estimations of waste generation in residential and regional contexts. This improves urban planning and reduces inefficiencies [35, 45, 46]. The second stream emphasizes the design of recycled and sustainable materials, showing how AI is used to optimize the mechanical and environmental properties of recycled aggregates, thus promoting more circular practices [36, 40, 42]. A third area concerns monitoring and environmental impacts, with a particular focus on AI and deep learning applications for real-time environmental analysis, pollutant detection, and risk assessment [41, 47, 51]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Research Area** & **AI/ML Methods and Purpose** & **References** \\ \hline AI for forecasting and managing waste generation & ANN, ANFIS, k-NN, RNN-LSTM—regression techniques to estimate urban and regional waste generation with high temporal accuracy. & [35, 45, 46, 47] \\ \hline AI and ML for recycled materials & MARS, LSSVR, MSTree, ANN + PSO/GA—regression and optimization models for compressive strength, carbonation, and durability prediction. & [37, 38, 39, 40, 41, 50, 55] \\ \hline Environmental monitoring & Random Forest, CNN, geospatial ML—classification and spatial simulation to identify pollution hotspots, heavy metals, and water contamination. & [48, 49, 52, 53] \\ \hline Digitalization and circular economy & Digital Twin (DT), m-IoT, hybrid AI systems—simulation and system modeling for process optimization and lifecycle management. & [43, 50] \\ \hline Deep learning and environmental health & CNN, deep neural networks—anomaly detection and monitoring for SDG-aligned pollution control. & [44, 49] \\ \hline AI and Industrial integration & CRISP-DM, clustering, predictive analytics—data-driven decision support and waste & [51] \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of AI/ML methods by research area. Our elaboration. What emerges is not only a diversification of technological applications but a convergence of methods toward integrated, data-driven governance models. For instance, case studies such as the implementation of AI-based waste prediction models in Ho Chi Minh City ([PERSON] et al., 2021 [35]) or real-time pollution detection using CNN architectures in agricultural areas of Southeast China ([PERSON] et al., 2020 [54]) provide evidence of operational scalability and replicability. In parallel, the digitalization of the circular economy emerges as a rapidly expanding domain. The use of DT in production and industrial environments enables continuous simulation of transformation and recovery processes, with the aim of optimizing resource flows in real time, reducing waste, and increasing systemic efficiency [42; 49; 56]. This approach supports the shift from a linear logic to an intelligent and adaptive management of post-consumer materials.