Natural Infrastructure for Health and Environmental Risk Mitigation
健康と環境リスク軽減のための自然インフラ (AI 翻訳)
Mohamed Dardir, Jeffrey Wilson, Umberto Berardi
🤖 gxceed AI 要約
日本語
本論文は、都市の自然インフラ(緑地やクールな表面など)が気候変動による極端な環境リスクを軽減し、公衆衛生と地域経済に利益をもたらすことを示すデータ駆動型の意思決定フレームワークを提案する。統計モデルと微気候シミュレーションを用いて、環境変数と健康アウトカムを予測し、自然インフラの拡大が熱ストレスを11%以上低減するなど実証した。
English
This paper introduces a data-driven decision framework to demonstrate how natural infrastructure (greenery, cool surfaces) mitigates climate risks like heatwaves and flooding while promoting public health and economic co-benefits. Using statistical models and microclimate simulations, it shows that modest increases in natural covers reduce heat stress by over 11% and improve community resilience.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では都市のヒートアイランド対策や豪雨対策として自然インフラが注目されており、本フレームワークは自治体の適応計画やグリーンインフラ政策に貢献できる。
In the global GX context
Globally, this framework supports evidence-based investment in nature-based solutions (NbS) for climate adaptation, aligning with urban resilience goals under frameworks like the Sendai Framework and UNFCCC adaptation.
👥 読者別の含意
🔬研究者:Provides an integrated modeling approach linking environmental variables, health outcomes, and energy use for urban natural infrastructure planning.
🏢実務担当者:City planners and environmental consultants can use the framework to prioritize green retrofitting investments with quantified benefits.
🏛政策担当者:Offers evidence for policies promoting green infrastructure as a cost-effective climate adaptation strategy.
📄 Abstract(原文)
Abstract. Urban microclimates are facing escalating environmental risks associated with climate change, e.g., extreme heat, flooding, and storm events. These risks result in increased healthcare demands and reduced quality of life, especially among vulnerable populations. Previous conduct of natural urban developments had limited perception of their multifaceted benefits, impacting their adoption in municipal planning. This paper introduces a comprehensive, evidence-based and data-driven decision-making framework to demonstrate how natural and green retrofitting features, including urban greenery and cool urban surfaces, can effectively mitigate extreme environmental risks while promoting significant co-benefits for public health and local economy. The method integrates statistical models for community-level data and localized weather measurements, using non-linear regression and microclimate simulations. This modeling approach investigates the impact of applying natural retrofitting features on environmental and community resilience. The model simulates environmental variables (pollutant dispersion, heat exposure intensity, urban flooding, and wind storms), anticipates community health outcomes (emergency department visits, hospitalizations, etc.) and quantifies reductions in energy consumption. The proposed applications revealed that even modest increases in urban natural covers substantially reduce microclimate ambient temperatures, manage wind speeds, control runoff potential, and decrease heat-related health impacts. Key findings show that expanding natural infrastructure controlled ambient temperatures and reduced heat stress during anticipated heatwaves by more than 11%. The proposed application also controlled flooding and storm peak conditions. Anticipated reductions in health risks were also reported as a result of enhanced urban environments. While limitations exist in terms of correlative predictions, limited variables of study, and the availability of health data, this research offers an adaptable model for future studies on community resilience through natural infrastructure. This decision-making framework gives evidence-based insights into the strategic investment in nature-based solutions for healthier, more sustainable, and resilient cities in the face of increasing climate hazards.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1051/e3sconf/202671610010/pdffirst seen 2026-07-04 04:41:54
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