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Sustainable municipal solid waste (MSW) treatment in rapidly urbanizing secondary cities requires evidence-based, district-level prioritization of technologies. We integrate GIS hotspot mapping, Random Forest, and AnyLogic System Dynamics into a decision-support framework and apply it to Shymkent, Kazakhstan (population 1.19 million; ≈301,400 tonnes of MSW in 2025). This is the first application of such a framework to MSW management in a Kazakhstani secondary city and, to our knowledge, the first regional application across Central Asia; the integration concept has prior precedents in Latin American, South Asian, and East Asian metropolitan studies, and the present contribution lies in empirical calibration to a Central Asian upper-middle-income context using 2015–2025 morphological audits, air-quality and soil monitoring, and Sentinel-2 NDVI. Random Forest (n = 80, 9 predictors) achieved R2 = 0.976 ± 0.011 under 5-fold cross-validation; a complementary GroupKFold protocol confirms the model is Shymkent-calibrated while the methodology remains transferable. AnyLogic simulation shows an Infrastructure/Waste-to-Energy pathway reduces the 2030 annual landfilled volume to ≈201 kt, environmental risk by 70%, and methane emissions by 86% (≈270 kt CO2-eq/year) relative to the Inertial baseline. The principal deliverable is a District × Technology × Phase prioritization matrix for sequencing sustainable investment under realistic budget constraints.
Aitimbetova et al. (Mon,) studied this question.