Climate risk, sectoral technological progress and corporate profitability in the US: an ADL-MIDAS forecasting approach

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.