## 4 Discussion
This reinforces the applicability of the proposed model in similar low-tech contexts. Far from being a limitation, the validation under real conditions, combined with statistically significant results (ANOVA, \(p<0.001\)), demonstrates the model's viability in settings where advanced technologies are rarely applied. Compared to previous works, our case study provides not only conceptual advances but also empirically validated operational impact through an accessible and cost-effective implementation. The modular, scalable, and replicable nature of the methodology was intentionally designed to enable deployment in other sectors and regions, as further discussed. To strengthen the comparative analysis, a detailed evaluation was conducted against the methodology proposed by [24], which is widely recognized for its robustness in industrial DT implementations. Unlike [PERSON]'s approach rooted in system development life cycle (SDLC) principles and primarily aimed at manufacturing environments, our methodology demonstrated superior adaptability, cost-efficiency, and contextual relevance, particularly in composting systems. A comprehensive quantitative analysis, developed as part of a doctoral dissertation, evaluated both methodologies across a broad set of criteria such as scalability, interoperability, implementation costs, and customization capacity. The results revealed that the proposed methodology consistently outperformed [PERSON]'s approach in areas requiring contextual flexibility and cost efficiency. While [PERSON]'s framework demonstrated strong alignment with industrial standards and robust structural design, it showed limitations when applied to specific domains like composting, where adaptability and practical deployment in low-tech environments are essential. This comparative evaluation reinforces the methodological soundness and practical suitability of our proposal for low-tech or decentralized systems where flexibility and economic feasibility are critical. Future research will focus on multi-case studies in fields such as wastewater treatment, precision agriculture, and decentralized manufacturing, where the minimum replication conditions have already been outlined. At this stage, however, the depth, traceability, and robustness of the validation process outweigh the need for multiple study sites. Beyond composting, recent studies in anaerobic digestion [7] and recycling systems [3] highlight the need for intelligent monitoring and process optimization in decentralized waste streams. Our methodology directly addresses these needs through its adaptable architecture and capacity for local deployment, offering a solution that balances technical performance with economic and operational viability. Moreover, in the context of smart cities, ref. [50] emphasizes the importance of classifying DTs based on their lifecycle and integration levels. While their taxonomy is useful for conceptual design, it does not provide an implementation framework. In contrast, the methodology proposed here offers a replicable process tested in real conditions with measurable impact, aligning with smart city objectives such as sustainability, circularity, and resource efficiency. An analysis of potential negative effects reveals that implementing the DTs could increase reliance on complex digital systems, potentially leading to vulnerabilities in the event of technological failures or cyberattacks. Additionally, poorly planned scalability could result in overspending and difficulties maintaining consistency in results. In the case of adaptations to other contexts, it will be crucial to analyze the specificities of the target system to avoid imbalances in the integration of the digital and physical layers. Integration with other waste management systems represents a significant opportunity. DTs can be linked with IoT platforms and geographic information systems (GIS) to improve coordination and decision-making. However, this requires designs that ensure compatibility and real-time data exchange.