Prioritizing Building-Integrated Greenhouse Potential in Accommodation Facilities Using a Spatially Oriented Multi-Criteria Decision Framework: The Case of Istanbul

This study develops a multi-criteria decision-support approach that integrates spatial, functional, and managerial dimensions to assess the suitability potential of accommodation facilities in Istanbul for building-integrated greenhouse applications within the context of sustainable food systems and sustainable tourism. The dataset, comprising 688 lodging facilities affiliated with the Ministry of Culture and Tourism of the Republic of Türkiye, was analyzed in accordance with a hierarchical evaluation framework that includes categories, criteria, and subcriteria. In this context, the distribution of facilities across each criterion was first examined to assess the current situation; subsequently, the criterion weights were determined using the Best–Worst Method (BWM) based on expert opinions, and the Greenhouse Integration Suitability Scores (GISSs) were calculated using the Weighted Linear Cumulative (WLC) method. Facilities were ranked according to their continuous GISSs; under the Jenks Natural Breaks classification, only 13.4% of the sites (92 out of 688) fell into the high and very high classes. The six-dimensional sensitivity analysis showed that facility rankings remained largely stable under different data, weighting, and method scenarios, whereas class boundaries were more sensitive to the classification method used. Spatial statistical analyses revealed that GISS values exhibited significant and positive spatial autocorrelation (Moran’s I = 0.2478, p < 0.001); Beşiktaş, Pendik, and Ümraniye emerged as the top-priority districts. The findings reveal that physical and spatial conditions are the key determinants of greenhouse integration potential, while functional and managerial variables play a significant role in the varying levels of suitability of the facilities. The framework developed extends the assessment of building-integrated greenhouse applications from the individual building scale to the urban scale, offering a transferable decision-support approach for public institutions, planners, and facility managers. In this regard, the study establishes a data-driven and adaptable prioritization tool that can guide decisions on urban food production, sustainable tourism, and investment.