Emissions-Robust Portfolios

TL;DR

This paper develops a robust portfolio optimization method that accounts for measurement errors in firm emissions data, significantly reducing emissions without sacrificing returns, and provides insights into the trade-offs and uncertainties involved.

Contribution

It introduces a novel scope-specific penalty operator for emissions data, enabling robust mean-variance and CVaR portfolio strategies with clear economic interpretation.

Findings

Reduces average Scope 1 emissions by 92% in a U.S. large-cap equity portfolio

No statistically significant impact on Sharpe ratio or average returns

Provides a Pareto frontier and sensitivity analysis for return-emissions trade-offs

Abstract

We study portfolio choice when firm-level emissions intensities are measured with error. We introduce a scope-specific penalty operator that rescales asset payoffs as a smooth function of revenue-normalized emissions intensity. Under payoff homogeneity, unit-scale invariance, mixture linearity, and a curvature semigroup axiom, the operator is unique and has the closed form $P^{(m)}_j(r,\lambda)=\bigl(1-\lambda/\lambda_{\max,j}\bigr)^m r$. Combining this operator with norm- and moment-constrained ambiguity sets yields robust mean-variance and CVaR programs with exact linear and second-order cone reformulations and economically interpretable dual variables. In a U.S. large-cap equity universe with monthly rebalancing and uniform transaction costs, the resulting strategy reduces average Scope~1 emissions intensity by roughly 92\% relative to equal weight while exhibiting no statistically detectable reduction in the Sharpe ratio under block-bootstrap inference and no statistically detectable change in average returns under HAC inference. We report the return-emissions Pareto frontier, sensitivity to robustness and turnover constraints, and uncertainty propagation from multiple imputation of emissions disclosures.