Cost-Effective AI Solutions for Industrial Waste Recycling and Environmental Performance Monitoring in industrial enterprises
Annotatsiya
The challenges for cost-effective recycling solutions in industrial waste management in Uzbekistan enterprises have been widely examined, and the results highlighted numerous critical determinants involved in environmental performance monitoring. The decision-making frameworks (AHP) based on multi-criteria evaluation play a pivotal function in the systematic assessment of the industrial enterprises, and, by estimating the survival of the parametric models, these frameworks can transform the monitoring process into quantitative predictions or strategic evaluations. The aim of this study was to identify factors and model dynamics related to waste recycling in industrial contexts by analyzing survival probabilities in operations and determinant weights via Analytical Hierarchy Process and parametric survival models. So, in this research, we propose a hybrid approach called Eco-AI based on a decision support method and predictive modelling (survival analysis) integration. The framework provides a cost-effective solution to a set of industrial challenges taking account of the critical indicators for the Uzbekistan enterprises, waste recycling (quantities), and environmental performance (indices). We validated the model using survey and secondary statistics from enterprises/ministries and monitoring data of industrial operations acquired with a structured database. The patterns in the survival functions and weights were significantly different in short-term and long-term projections, respectively, including the economic efficiency, environmental compliance, and technological adoption, which might be related to regulatory frameworks. There were three clusters of determinants in Uzbekistan industries, important for waste efficiency; two clusters with economic drivers, important for policy orientation; and four clusters of performance indicators, important for sustainability goals. Therefore, these findings provide novel insights enhancing our understanding of the decision mechanisms underlying waste recycling in Uzbekistan.