Climate risk, sectoral technological progress and corporate profitability in the US: an ADL-MIDAS forecasting approach
気候リスク、セクター別技術進歩と企業収益性：ADL-MIDAS予測アプローチによる米国分析 (AI 翻訳)
Kazeem Isah, Sin-Yu Ho
🤖 gxceed AI 要約
日本語
米国の11セクターを対象に、物理的・移行気候リスクが企業収益性に与える影響を分析。グリーンイノベーション（特許）を技術進歩の代理変数とし、ADL-MIDASモデルで短期・中期・長期の予測力を検証。エネルギーや産業など炭素集約型セクターで感応度が高く、情報技術やヘルスケアではグリーンイノベーションが予測精度を向上させることを示した。投資家や政策担当者に実務的示唆を提供。
English
This study examines how physical and transition climate risks affect US sectoral corporate profitability and whether green innovation improves forecast accuracy. Using a mixed-frequency ADL-MIDAS model on 2000Q1-2024Q1 data, it finds significant predictive power of climate risks, especially in carbon-intensive sectors. Green innovation enhances forecasts in innovation-intensive sectors. Results offer practical insights for investors, risk managers, and policymakers.
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
米国データに基づくが、SSBJや有報での気候関連リスク開示が進む日本企業にとっても、セクター別の収益影響評価やグリーンイノベーションの価値評価に示唆を与える。特に、日本企業の海外事業やクロスボーダー投資における気候リスク管理の参考となる。
In the global GX context
This study contributes to the global climate-finance literature by jointly modeling physical and transition risks with green innovation in a mixed-frequency framework. It is relevant for investors and regulators implementing TCFD/ISSB disclosures, as it demonstrates how sector-level technological progress can improve risk-return assessments.
👥 読者別の含意
🔬研究者:Methodology (ADL-MIDAS) and joint modeling of climate risks and innovation offer new avenues for climate-finance research.
🏢実務担当者:Investors and risk managers can use findings to enhance sector-level climate risk assessment and identify innovation-driven hedges.
🏛政策担当者:Regulators may consider the role of green innovation in mitigating climate risk impacts on corporate profitability.
📄 Abstract（原文）
This study examines how physical and transition climate risks affect US sectoral corporate profitability and whether sectoral technological progress, proxied by green innovation, improves the predictability of profits under climate uncertainty. Using a mixed-frequency dataset covering 2000Q1–2024Q1, the analysis combines quarterly corporate profits for 11 US Global Industry Classification Standard sectors with daily physical and transition climate risk indices and quarterly green patent activity. An Autoregressive Distributed Lag–Mixed Data Sampling (ADL-MIDAS) framework is employed to capture the dynamic effects of high-frequency climate risks on quarterly profits across short- (4 quarters), medium- (8 quarters) and long-term (12 quarters) horizons. Out-of-sample forecast performance is evaluated using the Campbell–Thompson and Clark–West tests. The results show that both physical and transition climate risks contain significant predictive information for sectoral profitability, with substantial heterogeneity across industries. Carbon-intensive sectors such as Energy and Industrials exhibit stronger sensitivity, while Financials and Communication Services display comparatively weaker responses. Incorporating green innovation improves forecast accuracy across most sectors, particularly in innovation-intensive industries such as Information Technology, Health Care and Real Estate. This study contributes to the climate–finance literature by jointly integrating climate risks and sectoral technological progress within a mixed-frequency forecasting framework. It demonstrates that accounting for green innovation enhances the predictive performance of climate risk models, offering practical value for investors, risk managers and policymakers concerned with monitoring sectoral exposure to climate-related risks.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1108/jes-12-2025-1081first seen 2026-06-29 08:22:18
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