Measuring and Hedging Carbon Risk with Tradeable Factors: Evidence from ETF Carbon Betas

This paper develops a market-based, implementable framework to measure and hedge carbon risk. We construct a tradeable carbon factor from carbon-allowance ETFs and integrate it into an extended Fama–French model to estimate dynamic carbon betas across a large cross-section of exchange-traded funds (ETFs). Using an ETF sample classified into high- and low-carbon universes, we document economically meaningful cross-sectional differences in exposure to the carbon factor: high-carbon ETFs load more strongly on the factor than low-carbon ETFs. To standardize sector/thematic controls, ETFs are labeled with an LLM-assisted, taxonomy-constrained classifier, ensuring consistent category assignment at scale. We then design portfolios intended to target specific decarbonization scenarios via a simple linear interpolation scheme between the two universes. In implementation, realized carbon betas systematically miss their targets because both sleeves retain positive carbon loadings, producing persistent tracking error. Over the sample period, low-carbon allocations delivered superior risk-adjusted performance, implying a negative carbon premium in-sample, but this outperformance did not translate into effective hedging because target exposures were not achieved. Our findings (i) validate carbon risk as a priced covariation component, and (ii) show that naïve green tilts are insufficient to neutralize carbon exposure. The framework standardizes measurement with observable prices and surfaces practical constraints that matter for portfolio construction and risk management.