## 4 Results and Discussion
### Justification for Including a Framework and Roadmap in a Systematic Review  
A framework in a PRISMA-based systematic review provides structured synthesis. [PERSON], [PERSON] and [PERSON] [41] emphasize frameworks for conceptual synthesis in management research. [PERSON] [68] shows how findings can shape frameworks guiding future research. [PERSON] and [PERSON] [69] discuss bridging theory and practice via structured frameworks. [PERSON] and [PERSON] [70] highlight frameworks' role in knowledge accumulation and practical applicability. The integration of MCDA and AI with robust governance mechanisms enhances transparency, traceability, and operational optimization in reverse supply chains for solid waste. For instance, in the case of plastic waste, the integration of TOPSIS with neural networks (GNNs) has led to significant improvements \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Phase** & **Strategic Action** & **Tools/Methods** & **Responsible Entities** & **Success Indicators** & **References** \\ \hline
**1. Initial Diagnosis** & Mapping RSCSW chains and identifying logistical and regulatory bottlenecks. & SWOT Stakeholder Interviews. & Governments, companies, researchers. & Diagnostic reports and gap analysis. & [PERSON] et al. [53]: SWOT applied to lifecycle of building products; [PERSON] et al. [52]: Identifies critical factors via qualitative/DEMATEL analysis. & [PERSON] et al. [53]: SWOT applied to lifecycle of building products; [PERSON] et al. [52]: Identifies critical factors via qualitative/DEMATEL analysis. & \\ \hline
**2. Definition of Governance Model** & Selecting regulatory approach and defining decision criteria. & Governance models ([PERSON], [PERSON]). & Regulatory agencies, sectoral organizations. & Policy documents and regulatory frameworks. & [PERSON] et al. [53]: Proposes framework for AI governance; [PERSON] et al. [52]: Factors for AI adoption in manufacturing governance. \\ \hline
**3. Implementation of MCDA + AI** & Applying MCDA for strategy prioritization and AI for automation. & TOPSIS, AHP, Neural Networks, Genetic Algorithms. & AI specialists, strategic decision-design-design-design-efficiency. & Reduction in operational costs, increased efficiency. & [PERSON] et al. [45]: GNN + TOPSIS in plastic waste chain; [PERSON] et al. [46]: AI + AHP/VIKOR; [PERSON] [57]: MCDA + genetic algorithms. \\ \hline
**4. Infrastructure Development** & Implementing digital platforms for tracking and optimization. & Blockchain, Big Data, IoT. & Tech companies, companies, municipalities. & Technology adoption rate and data integration levels. & [PERSON] et al. [54]: Simulation for tire recycling; [PERSON] et al. [49]: Agent-based modeling; [PERSON] et al. [58]: AI in routing and emission control. \\ \hline
**5. Continuous Monitoring and Adjustments** & Ongoing assessment of impacts and strategy refinements. & Performance Indicators (KPIs), audits. & Academics, regulators, managers. & Continuous improvement in environmental and economic indicators. & [PERSON] [56]: KPIs for e-waste practices; [PERSON] et al. [60]: Robust optimization with emissions control; [PERSON] et al. [64]: Fuzzy + metaheuristics. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Five-phase roadmap for implementing MCDA + AI in RSCSW governance. in material recovery efficiency [62]. Meanwhile, blockchain-based governance mechanisms have facilitated greater traceability and collaboration among stakeholders. In terms of discussion around this framework, the following can be pointed out:  
* Bridging research and practice: Unlike traditional theoretical models, this framework offers a structured, actionable approach, addressing gaps highlighted by [PERSON] et al. [14]. * Governance integration: While many AI frameworks focus on efficiency, this proposal integrates governance mechanisms such as blockchain-based traceability and policy-driven incentives, ensuring a holistic and practical approach [19].