Leveraging advanced data analytics in sustainable Land Use and Green Building Applications in Tashkent Housing Planning

While urban planners and policymakers have increasingly sought to manage sustainable land use in their housing development agendas, their understanding of what eco-efficient urban planning is and what should be prioritized in practice remains fragmented. Within the framework of multi-criteria decision support systems supporting municipal authorities in their response to green infrastructure challenges, we conducted an integrated analysis using AHP, SEM, and conceptual mapping for Tashkent's urban housing sector. We argue a key limitation with planners’ efforts to manage green building integration is that it is a contextually fluid concept for them. To address this gap, the present study conceptualizes the institutional factors and technical variables related to an adaptive planning framework, whereby both quantitative metrics and expert-driven criteria are included. We interviewed 36 urban planning professionals at municipal housing departments in Chilonzor, Yashnobod, and Yunusobod and identified distinct approaches to managing land use sustainability at different levels of planning hierarchies. We were able to achieve a balance between the need for regulatory compliance and the environmental performance targets by combining the output from three different models, each aiming at capturing system complexity, in a multi-layered decision tree. We also identified that misalignment between planners’ assumptions to implementation and what they identify in their real-time spatial assessments, reveals important decision-making gaps that help to improve institutional understanding of policy-driven interventions. The statistical analysis of the proposed SEM model provides a hierarchical explanation of land-use priorities from respondents’ evaluations that lies in national green development standards with an improved understanding of local housing dynamics.

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