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<p>Urbanization and climate change have increased the frequency and severity of stormwater-related challenges, necessitating more effective approaches for selecting green infrastructure (GI) during the planning and design process. Although numerous studies have evaluated the environmental performance of individual GI practices, comparatively limited attention has been devoted to translating performance assessments into practical guidance for selecting and prioritizing GI practices before implementation. To address this gap, this research developed a pre-design GI prioritization framework by integrating local sensitivity analyses with three deterministic performance models. The framework quantified runoff mitigation, ecosystem-service benefits, and 20-year life-cycle costs to establish hierarchical rankings of GI practices under alternative decision-making scenarios. The framework was applied to a 266-ha redevelopment scenario in downtown Lansing, Michigan, USA, and subsequently evaluated through post-design performance prediction. Results consistently identified rain gardens, street planters, infiltration basins, wetlands, and vegetation filter strips as the highest-priority practices under the balanced environmental-economic scenario. Implementation of the resulting GI plan reduced annual runoff volume by 52%, decreased pollutant loading by 9%–15%, and generated substantial carbon sequestration benefits. The study demonstrates how sensitivity-analysis results derived from multiple performance models can be synthesized into a practical decision-support framework for pre-design GI prioritization, providing a transferable method for informing GI planning and resource-allocation decisions.</p>

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A Sensitivity Analysis Framework for Pre-design Green Infrastructure Prioritization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

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  • Dustin Krise,
  • Wonmin Sohn,
  • Jun-Hyun Kim

摘要

Urbanization and climate change have increased the frequency and severity of stormwater-related challenges, necessitating more effective approaches for selecting green infrastructure (GI) during the planning and design process. Although numerous studies have evaluated the environmental performance of individual GI practices, comparatively limited attention has been devoted to translating performance assessments into practical guidance for selecting and prioritizing GI practices before implementation. To address this gap, this research developed a pre-design GI prioritization framework by integrating local sensitivity analyses with three deterministic performance models. The framework quantified runoff mitigation, ecosystem-service benefits, and 20-year life-cycle costs to establish hierarchical rankings of GI practices under alternative decision-making scenarios. The framework was applied to a 266-ha redevelopment scenario in downtown Lansing, Michigan, USA, and subsequently evaluated through post-design performance prediction. Results consistently identified rain gardens, street planters, infiltration basins, wetlands, and vegetation filter strips as the highest-priority practices under the balanced environmental-economic scenario. Implementation of the resulting GI plan reduced annual runoff volume by 52%, decreased pollutant loading by 9%–15%, and generated substantial carbon sequestration benefits. The study demonstrates how sensitivity-analysis results derived from multiple performance models can be synthesized into a practical decision-support framework for pre-design GI prioritization, providing a transferable method for informing GI planning and resource-allocation decisions.