## 3 Results  
### Results of Bibliometric Analysis  
The bibliometric analysis performed through VOSviewer produced the network visualization presented in Figure 3.  
The VOSviewer mapping organizes interconnected keywords into three distinct thematic clusters, each represented by a different color for visual clarity. These clusters represent distinct yet interconnected areas of research in the application of artificial intelligence and digital technologies to sustainable waste management. The red cluster, located on the left and central part of the map, is strongly focused on data-driven methodologies and environmental variables. This group includes keywords such as machine learning, support vector machine, deep learning, prediction, data interpretation, and artificial neural network, indicating a clear emphasis on computational tools used to analyze and model complex waste-related phenomena. Other keywords like methane, organic matter, and environmental factors suggest a research focus on the chemical and environmental dimensions of waste treatment, particularly in contexts such as landfill emissions and organic waste decomposition. On the other hand, the green cluster, positioned on the right side of the map, highlights research related to optimization, sustainability, and the circular economy. Key terms such as artificial intelligence, digital twin, circular economy, recycling, zero carbon, and sustainable development goals reflect a growing emphasis on integrating advanced technologies into broader sustainability frameworks. This cluster captures the role of AI and digital simulations in performance assessment, process optimization, and supporting the transition to carbon-neutral waste management strategies. A third blue cluster, positioned toward the upper part of the map, is more concentrated around human-environment interactions. It includes keywords such as air pollution, human, and environmental monitoring, pointing to research that connects waste management with public health concerns and environmental quality. The presence of water supply within the shared areas of the clusters further suggests an interdisciplinary interest in how waste systems intersect with other resource infrastructures. Visually, the red cluster appears dense and positioned toward the center-left of the map, indicating a high level of internal connectivity among keywords related to analytical models and algorithmic processing of waste-related data. In contrast, the green cluster is more dispersed and located on the right side of the visualization, illustrating a broader sys  
Figure 3: Keyword co-occurrence map derived from bibliometric analysis of journal articles and conference proceedings. Our elaboration. temic perspective encompassing sustainability goals and strategic innovation. Meanwhile, the blue cluster, though smaller, adds depth by emphasizing social and environmental monitoring dimensions. Table 3 summarizes the top 10 most frequently co-occurring keywords identified through this bibliometric analysis, reinforcing the conceptual structure illustrated in the visualization. Leading the ranking is prediction (TLS = 128), a term closely aligned with the red cluster, underscoring the prominence of forecasting techniques in waste estimation and emissions modeling. Keywords such as artificial intelligence (TLS = 119) and artificial neural network (TLS = 106) also reflect the dominance of AI-based methods across the literature. These terms, alongside machine learning and environmental management, form the analytical core of the red cluster's emphasis on computational modeling. Notably, recycling (TLS = 94) and waste management (TLS = 90) bridge the red and green clusters, demonstrating the integration of data-driven tools with sustainability-oriented practices.