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Electrification Using Renewable Energy Sources in Relation to the Operational Carbon and Water Footprint in Non-Residential Buildings

非住宅建築物における再生可能エネルギーによる電化と運用炭素・水フットプリントの関係 (AI 翻訳)

Michał Kaczmarczyk, Marta Czapka

Sustainability📚 査読済 / ジャーナル2026-04-07#再生可能エネルギーOrigin: EU
DOI: 10.3390/su18073641
原典: https://doi.org/10.3390/su18073641

🤖 gxceed AI 要約

日本語

本論文は、非住宅建築物（ポーランドの物流施設）を対象に、EPC/監査ベースの簡易スクリーニング手法を用いて、運用エネルギー、炭素、水フットプリントを評価したケーススタディを提示する。PV自家消費率（25/50/75%）の変化に伴う水フットプリントの低減効果を分析し、効率改善とPV導入により一次エネルギー消費量が59%削減されることを示した。運用の効率性と再生可能エネルギーの自家消費最大化が長期的なGXに重要であると結論づけている。

English

This paper presents a case study of a non-residential logistics facility in Poland, using a low-data EPC/audit-based screening workflow to assess operational energy, carbon, and water footprints. It shows that combining efficiency measures with PV integration (≈64 kWp) reduces primary energy demand by 59%, and that the operational water footprint decreases nearly linearly with higher PV self-consumption. The framework supports transparent benchmarking for staged renewable energy integration in building portfolios.

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、非住宅建築物のZEB化や省エネ基準の強化が進む中、運用段階の炭素・水フットプリントを統合的に評価する手法は、SSBJや有報での環境情報開示にも活用可能。本論文の簡易スクリーニング手法は、中小規模の物流施設などでも適用しやすく、実務的なGX推進に寄与する。

In the global GX context

Globally, the paper contributes to the growing need for practical, low-data methods to assess operational carbon and water footprints in buildings, aligning with TCFD/ISSB's emphasis on operational metrics. The integration of water footprint with renewable energy self-consumption offers a novel screening metric that can inform transition finance and portfolio-level decarbonization strategies.

👥 読者別の含意

🔬研究者:Provides a replicable low-data methodology for assessing operational carbon and water footprints in non-residential buildings, useful for further empirical studies.

🏢実務担当者:Offers a practical screening workflow for logistics facility managers to evaluate energy efficiency and PV integration benefits, including water footprint implications.

🏛政策担当者:Demonstrates how EPC/audit data can support staged decarbonization pathways, informing building code revisions and subsidy programs for renewable energy.

📄 Abstract（原文）

Long-term energy sustainability in the built environment depends not only on deploying renewables but also on maintaining high energy efficiency that consistently lowers demand and enables more effective use of low-carbon electricity over time. This paper presents an illustrative case study that demonstrates a low-data, EPC/audit-based screening workflow for assessing operational energy, carbon, and water-related indicators in a non-residential building. An explanatory case study is conducted for a mixed-use logistics facility in Poland (≈610 m2), combining approaches to useful/final/primary energy indicators with operational carbon and water footprints. The operational water footprint is evaluated as a screening metric (L/kWh) applied to the annual electricity balance and tested across PV self-consumption levels (25/50/75%) to reflect the role of energy management and flexibility. The results indicate that an efficiency-oriented modernization pathway supported by PV integration (≈64 kWp; ~57,350 kWh/yr) reduces the primary energy performance indicator EP from 154 to 62.5 kWh/m2·yr, corresponding to a 59% reduction in annual primary energy demand. The operational water footprint indicator decreases nearly linearly with increasing PV self-consumption, demonstrating that long-term benefits depend on sustained efficiency and on maximizing on-site renewable utilization through controls, demand shifting, and/or storage. Overall, the framework supports transparent benchmarking and the development of staged pathways for integrating renewable and low-carbon energy systems into logistics-building portfolios, while maintaining an analytical focus on operational energy, carbon, and water performances.

🔗 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。