Do ESG Disclosures Capture Systemic Sustainability Risks? Integrating Life Cycle Sustainability Assessment into ESRS-Based Sustainability Accounting
ESG開示はシステム上のサステナビリティリスクを捉えるか?ライフサイクル・サステナビリティ評価をESRSに基づくサステナビリティ会計に統合する (AI 翻訳)
Radosveta Krasteva-Hristova, Biser Krastev
🤖 gxceed AI 要約
日本語
本研究は、ESRSに基づく企業のサステナビリティ開示がライフサイクル視点をどの程度反映しているかを分析し、LCSAを統合した概念モデルを提案する。エンテル、ユニリーバ、シーメンスの3社の2024年開示データを質的に比較分析した結果、いずれも統合度は部分的であり、上流・環境情報は進んでいるが下流・廃棄・社会・経済面での統合が不十分であることが明らかになった。既存開示をライフサイクル対応とLCSA完全統合とに区別し、開示を境界調整問題として捉える新たな視点を提供する。
English
This study examines to what extent ESRS-based sustainability disclosures incorporate a life cycle perspective and proposes a conceptual model integrating Life Cycle Sustainability Assessment (LCSA). Using qualitative comparative content analysis of 2024 disclosures from Enel, Unilever, and Siemens, it finds that none achieve systemically integrated disclosure; all are partially integrated with strong upstream/environmental coverage but weak downstream, social, and economic aspects. The study distinguishes life cycle-related disclosure from full LCSA integration and frames sustainability reporting as a boundary alignment problem.
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
本論文はEUのESRSを対象としているが、日本でもSSBJ基準の実装や統合報告書の充実が進む中、ライフサイクル評価と開示の統合は今後の重要課題である。特に、上流・下流のバランスや複数影響領域の調和は日本企業のサプライチェーン開示にも応用可能な示唆を含む。
In the global GX context
This paper critically evaluates ESRS-based disclosures against life cycle thinking, offering a model that connects LCSA to double materiality and systemic risk. While focused on EU standards, it contributes to the global discourse on disclosure quality, which is relevant for ISSB implementation, CSRD compliance, and SEC climate rules.
👥 読者別の含意
🔬研究者:Provides a conceptual framework for integrating life cycle assessment into sustainability accounting, useful for scholars in ESG disclosure and materiality analysis.
🏢実務担当者:Highlights gaps in life cycle coverage in current ESRS reports, guiding companies on improving value chain disclosure for double materiality.
🏛政策担当者:Suggests regulatory enhancements for ESRS to better capture systemic risks through life cycle integration.
📄 Abstract(原文)
This study addresses two distinct questions: (1) to what extent do ESRS-oriented corporate sustainability disclosures operationalize a life cycle perspective; and (2) how can Life Cycle Sustainability Assessment (LCSA) inform a conceptual model for improving the risk relevance of such disclosures? A theory-driven qualitative comparative content analysis is applied to the 2024 sustainability disclosures of Enel, Unilever, and Siemens AG across six analytical dimensions and four integration categories. Across the 18 company–dimension assessments, none met the criteria for systemically integrated disclosure; all were classified as partially integrated. The three cases show broad but uneven value-chain coverage: upstream and environmental information is more developed than downstream, end-of-life, social, and economic integration. Life cycle methods are used selectively, while cross-dimensional links and cradle-to-grave boundary transparency remain limited. Because the evidence concerns three purposively selected cases, the findings are analytical rather than statistically or sectorally generalizable. The study therefore proposes, rather than validates, an LCSA–ESRS Operationalization Model based on boundary reconfiguration, stage-based indicator mapping, dimensional harmonization, and an accounting translation layer. The model indicates how life cycle evidence could be connected to double materiality, systemic sustainability risks, and decision-useful disclosure. The study contributes by distinguishing life cycle-related disclosure from full LCSA integration and by positioning sustainability reporting as a boundary-alignment problem.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://www.mdpi.com/1911-8074/19/7/521/pdf?version=1783870343first seen 2026-07-16 05:44:12
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録(無料・3キーワードまで)。
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。