Mapping ESG compliance and sustainability pathways in multinational companies: a PC-Mahalanobis analysis
多国籍企業におけるESGコンプライアンスとサステナビリティ経路のマッピング：PC-マハラノビス分析 (AI 翻訳)
Maurizio Pompella, Lorenzo Costantino
🤖 gxceed AI 要約
日本語
本論文は、多国籍企業が新たな規制枠組みや技術的混乱、地政学的懸念の中でESGコンプライアンスをどのように進めているかを調査。主成分分析とマハラノビス距離を組み合わせたフレームワークを用い、2015～2023年のデータからESG活動の3つの潜在次元（環境フットプリント、コンプライアンストレードオフ、財務パフォーマンスとサステナビリティのバランス）を特定。企業のESG行動の多様な軌跡を明らかにし、集約されたESGスコアよりも環境要素に焦点を当てた分解評価の必要性を主張。
English
This paper investigates how multinational companies navigate ESG compliance amid new regulations, technological disruption, and geopolitical concerns. Using a Principal Component-Mahalanobis framework on 2015-2023 data, it identifies three latent dimensions of ESG activity (environmental footprint, compliance trade-offs, and financial-sustainability balance) capturing over 85% of variance. The spatial approach reveals diverse firm trajectories, arguing for disaggregated ESG evaluation focused on environmental components rather than aggregated scores.
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
日本企業にとって、ESGスコアの分解評価の提案は、SSBJや有報での非財務情報開示の質向上に示唆を与える。特に環境要素への焦点は、GX政策と整合的であり、投資家対応にも有用。
In the global GX context
This paper contributes to global ESG disclosure debates by empirically challenging aggregated ESG scores, aligning with ISSB's push for more granular, industry-specific metrics. The PC-Mahalanobis method offers a novel analytical tool for identifying benchmark deviations, relevant for regulators and standard-setters like ISSB and CSRD.
👥 読者別の含意
🔬研究者:Provides a novel PC-Mahalanobis framework for analyzing ESG behavior and benchmarking firm trajectories.
🏢実務担当者:Offers insights for corporate sustainability teams to evaluate their ESG positioning relative to peers and identify areas for improvement.
🏛政策担当者:Supports arguments for disaggregated ESG disclosure requirements, relevant for regulators designing transparency standards.
📄 Abstract（原文）
This paper investigates how multinational companies are navigating the evolving landscape of ESG (Environmental, Social, and Governance) compliance, in the context of new regulatory frameworks, technological disruption, and geopolitical concerns. Combining the theoretical approach with the empirical analysis, the paper explores the influence of Artificial Intelligence, how disclosure dynamics affect ESG reporting, and which transition costs derive from sustainability strategies. At the same time emphasizing how both current geopolitical turbulences and – to some extent – the rapid development of AI may represent a “decelerating” factor for ESG adoption. Building on a Principal Component–Mahalanobis framework, the empirical section maps ESG behaviour across a sample of multinational companies from 2015 to 2023, focusing not just on the quality of ESG conducts but also gauging their deviation from a statistical benchmark. The PCM-A identifies three latent dimensions of ESG activity - environmental footprint, compliance trade-offs, and the balance between financial performance and sustainability alignment - capturing over 85 % of variance. By integrating Mahalanobis distance, our “spatial approach” reveals a varied set of trajectories: some firms exhibit consistent, benchmark-aligned behaviours, while others diverge significantly, and a few of them follow a stable but idiosyncratic path. These results reveal structural and strategic shortcomings underpinning the ESG framework and provide robust arguments for a reconsideration of aggregated ESG scores in favour of more transparent, disaggregated evaluation mechanisms, mostly focused on the Environmental component.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.1016/j.finr.2025.100075first seen 2026-05-05 19:08:30
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