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Carbon data and its requirements in infrastructure-related GHG standards

インフラ関連GHG基準における炭素データとその要件 (AI 翻訳)

Jinying Xu, Kristen MacAskill

Environmental Science & Policy📚 査読済 / ジャーナル2024-10-30#炭素会計Origin: Global経営インパクト: 調達リスク対象セクター: construction
DOI: 10.1016/j.envsci.2024.103935
原典: https://doi.org/10.1016/j.envsci.2024.103935

🤖 gxceed AI 要約

日本語

本論文は、分散型インフラにおける炭素データの複雑性と不確実性を指摘し、国際・欧州・英国のGHG基準をテーマ別にレビューする。データカテゴリ、測定方法、データソースの観点から、多くの基準がScope3算定を未要求であることや、ライフサイクル分析が主流であることを明らかにする。標準化されたデータ収集方法論の必要性を提言する。

English

This paper reviews international, European, and British GHG standards for carbon data in distributed infrastructure, highlighting challenges in data collection and uncertainty. It finds that many standards do not require Scope 3 accounting, and that lifecycle analysis dominates embodied carbon measurement. The authors call for standardized data collection methodologies with unified schemes and transparent sharing protocols.

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SSBJ開示基準がScope3算定を求める方向にあり、本レビューはインフラ分野のデータ整備の課題を整理する上で参考になる。特に、ライフサイクル全体での炭素データ管理の標準化は、日本の建設・インフラ企業の開示対応に示唆を与える。

In the global GX context

This review is relevant to global efforts to enhance carbon data quality for infrastructure, especially as ISSB and CSRD push for more rigorous Scope 3 disclosures. It provides a structured overview of existing standards and gaps, supporting the development of robust data collection frameworks.

👥 読者別の含意

🔬研究者:Provides a thematic map of carbon data requirements across standards, useful for identifying research gaps in data standardization.

🏢実務担当者:Helps infrastructure companies understand what data categories and sources are expected by current standards, aiding in compliance and data management.

🏛政策担当者:Highlights the need for standardized carbon data methodologies, informing policy development for GHG accounting in infrastructure.

📄 Abstract(原文)

Accurate carbon data is crucial for informed decision-making to achieve net-zero targets within the next several decades. However, data collection in the infrastructure sector faces significant challenges. Carbon data is either manually collected or extracted from design models, and carbon factors often come from secondary databases with varying boundaries and assumptions. Distributed infrastructure presents complex data management issues throughout its lifecycle, leading to uncertainty in accurately estimating emissions. Despite numerous guidelines and standards emerging since the 1990s, trustworthy data management remains nascent. This paper provides a thematic review of international, European and British standards for carbon data in distributed infrastructure, focusing on data categories, measurement methods, and sources. The standards broadly set out the boundaries of the assessment in terms of emission scopes and categories. While three scopes of emissions are often recognised, many standards do not yet require Scope 3 accounting. Embodied carbon is the current key focus whilst operational carbon is gaining more attention. The lifecycle analysis method is a dominating method for measuring lifecycle embodied emissions. Standards endeavour to direct the user to quality sources of activity data and emission factors; they also emphasise using primary activity data and specific emission factors from reliable sources and accurate measurement methods to enhance data trustworthiness. Developing a standardised carbon data collection methodology with a unified scheme, standard format, clear ontology, streamlined process, and transparent sharing protocol is essential and warrants further research. • Carbon data of distributed infrastructure assets face complexity and uncertainty. • This paper reviews carbon data requirements from standards and guidelines. • The core review themes are: data categories, measurement methods, and data sources. • Standards recommend specific data from trustworthy sources with reliable methods.

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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。