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<p>This study develops a scenario-based spatial assessment framework to support integrated planning and deployment of multiple renewable energy power plants, toward achieving sustainable energy objectives. Focusing on the Makran region in Iran, a location with diverse renewable energy resources and strategic geographic relevance, we analyzed the potential for solar, wind, and geothermal energy development. The framework incorporates 22 spatial criteria, including production capacity, installation cost–influencing factors, infrastructure accessibility, demand-influencing factors, and environmental impacts and 16 spatial constraints. Using the ordered weighted averaging (OWA) method, a range of decision-making scenarios from highly pessimistic to highly optimistic was simulated. The results revealed that in the most pessimistic scenario, high-potential areas accounted for 9.14% (solar and wind) and 10.96% (geothermal) of the region. Under the most optimistic scenario, these figures increased to 20.25%, 23.6%, and 30.13%, respectively. This framework demonstrates a transferable, spatially explicit approach to support sustainable energy technology planning and reduce investment risks.</p>

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A scenario-based framework for spatial assessment of multi-source renewable energy parks: a case study of Makran region in Iran

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月27日

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  • Zeinab Sazvar,
  • Saman Nadizadeh Shorabeh,
  • Hamide Mahmoodi,
  • Mohammad Karimi Firozjaei

摘要

This study develops a scenario-based spatial assessment framework to support integrated planning and deployment of multiple renewable energy power plants, toward achieving sustainable energy objectives. Focusing on the Makran region in Iran, a location with diverse renewable energy resources and strategic geographic relevance, we analyzed the potential for solar, wind, and geothermal energy development. The framework incorporates 22 spatial criteria, including production capacity, installation cost–influencing factors, infrastructure accessibility, demand-influencing factors, and environmental impacts and 16 spatial constraints. Using the ordered weighted averaging (OWA) method, a range of decision-making scenarios from highly pessimistic to highly optimistic was simulated. The results revealed that in the most pessimistic scenario, high-potential areas accounted for 9.14% (solar and wind) and 10.96% (geothermal) of the region. Under the most optimistic scenario, these figures increased to 20.25%, 23.6%, and 30.13%, respectively. This framework demonstrates a transferable, spatially explicit approach to support sustainable energy technology planning and reduce investment risks.