Climate Risk, CEO Risk Preference, and Corporate Greenwashing in High-Emission Industry: A Debiased Machine Learning Approach
気候リスク、CEOのリスク選好と高排出産業における企業のグリーンウォッシング：デバイアスド機械学習アプローチ (AI 翻訳)
Shijie Ma, Jingzhi Hou, Haoran Niu, Hsing Hung Chen
🤖 gxceed AI 要約
日本語
本研究は、中国の高排出産業の上場企業を対象に、デバイアスド機械学習と因果フォレストを用いて、気候リスクがグリーンウォッシングに与える非線形効果を分析。気候圧力が閾値を超えると、企業は実質的な脱炭素ではなく戦略的な情報収益に転じる「防衛的グリーンウォッシング」を明らかにした。デジタル成熟度や資源余裕のある企業ほど巧妙なナラティブを構築し、CEOのリスク選好がこの傾向を増幅する。
English
Using a debiased machine learning framework and causal forest analysis on Chinese high-emission listed firms (2009–2024), this study reveals a 'threshold-trigger' mechanism: once climate pressures exceed firm endurance, companies shift to strategic 'information arbitrage' rather than substantive decarbonization. Firms with higher digital maturity leverage narratives to widen the monitoring gap, and CEO risk preference amplifies strategic decoupling under transition risk. The findings offer micro-level insights for policymakers to combat greenwashing.
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
日本でも気候関連リスクが高まる中、同様の「防衛的グリーンウォッシング」が生じる可能性がある。本研究の閾値メカニズムは、日本企業のSSBJ対応や開示品質向上に示唆を与える。
In the global GX context
This paper provides a novel causal machine learning approach to detect non-linear greenwashing responses to climate stress, relevant for global regulators (e.g., ISSB, SEC) aiming to ensure substantive decarbonization over symbolic compliance. The debiased method offers a template for monitoring corporate climate disclosures worldwide.
👥 読者別の含意
🔬研究者:Demonstrates how debiased ML and causal forest can uncover non-linear greenwashing dynamics, offering a methodological contribution for empirical climate finance research.
🏢実務担当者:Highlights that digital maturity and slack resources may enable sophisticated greenwashing, advising sustainability teams to align narratives with genuine emission reductions.
🏛政策担当者:Provides evidence that climate stress can inadvertently incentivize symbolic compliance, urging regulators to design oversight mechanisms that detect threshold-driven greenwashing.
📄 Abstract（原文）
The transition to a low-carbon economy is the cornerstone of global sustainability, requiring high-emission enterprises to shift from carbon-intensive production to genuine green innovation. However, this study uncovers a significant structural impediment to this transition: the “defensive greenwashing” response to climate stress. Focusing on listed companies in China’s high-emission industries (2009–2024), we employ a Debiased Machine Learning (DML) framework and Causal Forest analysis to capture the non-linear impacts of multi-dimensional climate risks. Our findings reveal a robust “threshold-trigger” mechanism: once climate pressures—whether physical shocks or policy-induced transition risks—exceed corporate endurance levels, firms aggressively pivot toward strategic “information arbitrage” rather than substantive decarbonization. We identify a profound “capability paradox” in sustainability governance, where firms with higher digital maturity and resource slack leverage their technical prowess to “calibrate” sophisticated narratives, thereby widening the monitoring gap and distorting green asset pricing. Furthermore, CEO risk preference acts as a psychological accelerator, amplifying strategic decoupling, particularly under transition-risk-induced uncertainty. By demonstrating how climate stress inadvertently incentivizes symbolic compliance over sustainable transformation, this research offers critical micro-level insights for policymakers. These findings are vital for refining sustainability oversight and ensuring that capital allocation fosters a resilient, equitable transition toward true ecological and economic decoupling.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18105174first seen 2026-06-08 04:34:00
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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。