Collaboration or compliance? Unpacking ESG performance and carbon penalties
協力かコンプライアンスか？ESGパフォーマンスと炭素ペナルティの解明 (AI 翻訳)
Manjeevan Seera, Ravichandran K. Subramaniam, Shyamala Dhoraisingam Samuel
🤖 gxceed AI 要約
日本語
本研究は、Fortune 500企業を対象に、政府機関との協力がESGパフォーマンスと炭素ペナルティに与える影響を機械学習で分析。Scope 3排出が炭素フットプリントの大部分を占め、政府との積極的な協力がEPA罰金の減少と環境スコアの向上に関連することを発見。政策立案者は協力プログラムの設計を、企業はサプライチェーン管理の強化を検討すべき。
English
This study uses machine learning to analyze how collaboration with government agencies affects ESG performance and carbon penalties among Fortune 500 firms. It finds that Scope 3 emissions dominate carbon footprints and that active government collaboration is associated with fewer EPA fines and higher environmental scores. Policy implications suggest targeted collaboration programs and improved supply chain management.
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Scope 3排出量の管理はSSBJ開示基準やサプライチェーン排出削減の要であり、本論文の知見は政府との協調的アプローチの有効性を示唆。日本のGX政策（GXリーグ等）における官民連携の設計に示唆を与える。
In the global GX context
This paper contributes to global GX discourse by empirically linking government collaboration to reduced environmental penalties and improved ESG scores, with a focus on Scope 3 emissions. It offers insights for regulators designing compliance programs and for firms managing supply chain emissions under frameworks like ISSB and CSRD.
👥 読者別の含意
🔬研究者:Demonstrates the value of machine learning for capturing non-linear interactions in ESG and carbon penalty research.
🏢実務担当者:Highlights the importance of government collaboration and Scope 3 management for reducing regulatory risk and improving ESG ratings.
🏛政策担当者:Provides evidence that targeted collaboration programs can enhance environmental compliance, suggesting a shift from punitive to cooperative approaches.
📄 Abstract（原文）
This study addresses a key research gap by investigating how collaboration with government agencies influences ESG (Environmental, Social, and Governance) performance and carbon penalty outcomes among Fortune 500 firms, using machine learning techniques. Applying logistic regression and decision tree models to data from 2017 to 2021, we find that Scope 3 emissions dominate corporate carbon footprints and that active government collaboration is associated with fewer EPA fines and higher environmental scores. The machine learning approach allows for capturing complex, non-linear interactions that traditional statistical methods might miss. Policy implications suggest that regulators should design targeted collaboration programmes to improve environmental compliance, while firms should enhance supply chain management to address indirect emissions risks.
🔗 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。