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Carbon footprint trends and cost-emission dynamics of a Saudi tertiary hospital

サウジアラビアの三次医療病院における炭素排出量トレンドとコスト排出ダイナミクス (AI 翻訳)

Hamza Alhamza, Zindzi Kwavayi, Abdulkarim B Sayed, Amira Magdi Youssef, Ahmer Waheed, Modesto Quisteriano, Fadiah Omar Al-Alem

Discover Public Health📚 査読済 / ジャーナル2026-04-19#炭素会計
DOI: 10.1186/s12982-026-01903-4
原典: https://doi.org/10.1186/s12982-026-01903-4
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🤖 gxceed AI 要約

日本語

サウジアラビアの大病院(511床)を対象に、2023年の炭素排出量をScope1~3まで月次で算定。総排出量41.76kt CO2e、うちScope2(電力)49.13%、Scope3(サプライチェーン)48.55%と拮抗。夏季の冷房需要による電力ピークや、冷媒の影響を可視化し、コスト排出効率指標を用いて優先削減項目を特定した。熱乾燥地域の病院向けに、気候に応じた排出管理の枠組みを提案。

English

This study conducts a monthly hybrid carbon footprint assessment for a 511-bed Saudi hospital, finding 2023 emissions of 41.76 kt CO₂e with near parity between Scope 2 (49.13%) and Scope 3 (48.55%). Summer electricity peaks from cooling demand and refrigerant impacts are highlighted, and cost-emission efficiency metrics prioritize grid electricity and refrigerants for reduction. It provides a decision-support framework for climate-responsive healthcare sustainability in hot-arid regions.

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

サウジアラビアの事例だが、月次分解やコスト排出効率指標は日本の病院でも応用可能。特に気候変動適応と医療施設の排出削減を統合する際の参考になる。

In the global GX context

This paper extends hospital carbon accounting with temporal disaggregation and cost-emission metrics, offering novel data from an extreme climate. It contributes to global healthcare decarbonization literature by revealing supply-chain parity in hot-arid settings.

👥 読者別の含意

🔬研究者:Provides a methodology for monthly Scope 1-3 accounting in hospitals, valuable for climate-health research.

🏢実務担当者:Offers cost-emission efficiency metrics to prioritize decarbonization actions (e.g., grid electricity, refrigerants) in hospital operations.

🏛政策担当者:Demonstrates the need for climate-responsive healthcare emission policies, especially in hot-arid regions.

📄 Abstract(原文)

Abstract Introduction Hospitals are both highly vulnerable to climate change and significant contributors to greenhouse gas (GHG) emissions. In hot-arid regions such as Saudi Arabia, conventional annual carbon footprint assessments often obscure seasonal demand patterns and operational inefficiencies critical for effective mitigation planning. Aim This study examined carbon footprint trends and cost-emission dynamics within a large tertiary healthcare facility to generate operationally relevant insights for hospital sustainability in extreme climatic conditions. Methods A hybrid activity-based and spend-based carbon footprint assessment was conducted at Sultan Bin Abdulaziz Humanitarian City (SBAHC), a 511-bed rehabilitation hospital. Monthly emissions were quantified across Scope 1, Scope 2, and selected Scope 3 categories to enable temporal analysis. Cost-emission efficiency metrics were applied to integrate financial expenditure with environmental impact, supporting prioritization of decarbonization interventions. Results SBAHC’s total 2023 carbon footprint was 41.76 kt CO₂e, corresponding to an emission intensity of 262.9 kg CO₂e/m²/year. Scope 2 emissions from grid electricity constituted the largest share (49.13%, 20.52 kt CO₂e), closely followed by Scope 3 emissions (48.55%, 20.28 kt CO₂e), indicating near parity between operational energy use and supply-chain impacts. Electricity emissions exhibited pronounced summer peaks associated with cooling demand, highlighting the influence of climatic stress on hospital energy systems. Cost-emission analysis identified grid electricity and refrigerants as priority leverage points for emissions reduction, diverging from supply-chain-dominated patterns reported in temperate-region hospitals. Conclusion By combining temporal disaggregation with cost-emission efficiency metrics, this study extends conventional hospital carbon accounting and provides a decision-support framework for climate-responsive healthcare sustainability in hot-arid settings.

🔗 Provenance — このレコードを発見したソース

gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。