## 5 Study Limitations  
Despite the integrated approach adopted and the richness of the sources analyzed, this study presents several methodological limitations that should be acknowledged.  
First, the bibliometric analysis was conducted exclusively using the Scopus database, recognized as one of the most authoritative and comprehensive resources in the scientific landscape. However, this choice may have excluded significant contributions from other relevant databases, such as Web of Science or IEEE Xplore, especially in more interdisciplinary topics.  
Second, the selection criteria primarily focused on technical-scientific domains, potentially underrepresenting contributions from social, economic, or political sciences, which could offer complementary perspectives on governance and the real-world implementation of intelligent waste management technologies.  
A further limitation concerns the static nature of the analysis, which provides a snapshot of the literature within a specific timeframe but may not fully capture the rapid and continuous evolution of emerging technologies such as AI, IoT, or DT. Some recent developments may not yet be widely represented in the highly cited scientific literature.  
Finally, it should be noted that this work adopts a conceptual and descriptive approach and does not include empirical validation of the technologies discussed. There is a lack of experimental data or case studies to assess the concrete impact of AI and DT solutions on actual waste management practices in urban or industrial settings.  
To overcome these limitations and build upon the findings of this study, future research could follow several specific directions:  
Conduct empirical case studies focused on the implementation of AI and DT systems in real waste management settings--particularly in smart cities or industrial symbiosis contexts--evaluating cost-effectiveness, scalability, and environmental performance.  
Explore the integration of AI with regulatory frameworks and public policy tools to assess how predictive models and Digital Twins can support governance and decision-making in circular economy strategies.  
Develop comparative analyses of algorithmic performance (e.g., ANN, CNN, RNN, and ANFIS) across different types of waste (municipal, industrial, hazardous) to identify the most effective configurations for specific applications.  
Investigate the ethical, social, and organizational implications of AI and DT adoption in the waste sector, with attention to data governance, user acceptance, and workforce adaptation.  
Expand bibliometric mapping to include emerging fields such as edge computing, blockchain, and generative AI in waste systems, which were outside the scope of this review but are gaining rapid momentum.  
These research paths would not only address the current methodological gaps but also contribute to advancing the theoretical and operational maturity of intelligent waste management systems.  
Conceptualization, R.C., P.C., A.M.T. and R.R.; methodology, R.C.; software, R.C.; validation, R.C., P.C., A.M.T. and R.R.; formal analysis, R.C.; investigation, P.C. and A.M.T.; data curation, R.C.; writing--original draft preparation, R.C.; writing--review and editing, P.C. and R.R.; visualization, R.C., P.C., A.M.T. and R.R.; supervision, P.C., A.M.T. and R.R.; project administration, P.C., A.M.T. and R.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.