Yucenur, G. NilayMaden, Ayca2025-03-092025-03-0920250360-54421873-6785https://doi.org/10.1016/j.energy.2024.134179https://hdl.handle.net/20.500.12662/4711Photovoltaic agriculture stands out as an innovative approach that integrates traditional agricultural practices with solar energy production. This approach aims to enhance sustainability and efficiency by utilizing agricultural land for both energy generation and food cultivation. With escalating energy demands and growing environmental concerns, the significance of photovoltaic agriculture is increasingly recognized. This study addresses the critical issue of selecting optimal locations for such investments in T & uuml;rkiye. Within the framework of this study, we propose a hierarchical multi-criteria decision-making model comprising 6 primary criteria, 26 subcriteria, and 5 alternative locations. The proposed model is navigated using the F-PIPRECIA and WASPAS methods. In the initial phase of our solution, we apply the F-PIPRECIA method in a fuzzy logic environment to assign weights to the 26 sub-criteria. Subsequently, in the second phase, we employ the WASPAS method to evaluate and rank the five alternative locations. The study's findings indicate that Konya province emerges as the most suitable location for establishing photovoltaic agricultural sites in T & uuml;rkiye. Following Konya, Bal & imath;kesir and Malatya provinces were identified as the second and third most suitable options, respectively. These conclusions stem from a comprehensive assessment of factors encompassing climate, environment, economy, society, and risk. The insights derived from this research offer valuable guidance to private and public institutions, investors, and farmers in T & uuml;rkiye, assisting them in identifying the most favourable photovoltaic agricultural areas tailored to diverse criteria.eninfo:eu-repo/semantics/closedAccessPhotovoltaic agriculturalClean energyLocation selectionF-PIPRECIAWASPASLocation selection for a photovoltaic agricultural with f-PIPRECIA and WASPAS methods: A case studyArticle10.1016/j.energy.2024.1341792-s2.0-85212150685Q1314WOS:001391010700001Q1