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<p>This study maps Syria’s immense solar power potential to create a data-driven roadmap for its post-conflict energy reconstruction. We developed a comprehensive spatial decision-support model using GIS and Multi-Criteria Decision Analysis (MCDA) to identify the most suitable locations for large-scale photovoltaic (PV) farms. The methodology’s core is the systematic integration of eleven geospatial criteria (climatic, topographic, environmental, infrastructural), with relative importance determined through a literature-derived Analytical Hierarchy Process (AHP). A weighted overlay analysis was then performed to synthesize these factors into a detailed, five-level national suitability map, which was subsequently validated against strategic government projects and tested for robustness through sensitivity analysis. The analysis reveals that while a vast 81% of Syria shows moderate to high suitability, and we identified 51 prime zones totaling 4,000&#xa0;km² that are exceptionally well-suited for development. The potential energy yield from these optimal sites alone could surpass Syria’s peak pre-war consumption. This study provides a foundational and scientifically rigorous framework for strategic investment and policy, demonstrating how applied geospatial science can power Syria’s recovery and build a resilient energy future.</p>

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GIS-based spatial modeling and mapping for assessing the suitability of PV solar farm sites in Syria

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月31日

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  • Fadi Chaaban,
  • Assem Khatib,
  • Hanan Darwishe

摘要

This study maps Syria’s immense solar power potential to create a data-driven roadmap for its post-conflict energy reconstruction. We developed a comprehensive spatial decision-support model using GIS and Multi-Criteria Decision Analysis (MCDA) to identify the most suitable locations for large-scale photovoltaic (PV) farms. The methodology’s core is the systematic integration of eleven geospatial criteria (climatic, topographic, environmental, infrastructural), with relative importance determined through a literature-derived Analytical Hierarchy Process (AHP). A weighted overlay analysis was then performed to synthesize these factors into a detailed, five-level national suitability map, which was subsequently validated against strategic government projects and tested for robustness through sensitivity analysis. The analysis reveals that while a vast 81% of Syria shows moderate to high suitability, and we identified 51 prime zones totaling 4,000 km² that are exceptionally well-suited for development. The potential energy yield from these optimal sites alone could surpass Syria’s peak pre-war consumption. This study provides a foundational and scientifically rigorous framework for strategic investment and policy, demonstrating how applied geospatial science can power Syria’s recovery and build a resilient energy future.