Optimal incentive scheme for ESG disclosure

TL;DR

This paper derives optimal incentive schemes for ESG disclosure in a continuous-time setting, balancing incentives and risk-sharing using traded assets and signal loadings.

Contribution

It provides a closed-form solution for optimal contracts considering heterogeneity, risk aversion, and market-neutral regimes in ESG disclosure incentives.

Findings

Optimal contracts balance incentive provision and variance of payouts.

High risk aversion leads to market-neutral, hedge-focused schemes.

Heterogeneity causes sign changes in cross-signal loadings and negative diagonal entries.

Abstract

This paper characterises optimal incentive schemes for ESG disclosure in a continuous-time principal-agent setting. We model a risk-averse principal (e.g., a platform or standard-setter) contracting with a team of heterogeneous agents whose disclosure signals are each correlated with a traded climate risk factor. The optimal contract balances incentive provision against the variance of aggregate payouts by leveraging three instruments: own-signal loading, cross-signal loadings across agents, and hedging tilts on the traded asset. We derive closed-form linear optimal controls in a tractable linear-quadratic-Gaussian framework. When the principal is nearly risk-neutral, the contract uses the traded asset purely to hedge the specific `enforcement risk' generated by high-powered incentives. As the principal's risk aversion increases, the optimal scheme converges to a `market-neutral' regime where aggregate asset exposure is eliminated and the cross-signal structure tightens to an `identity pooling' constraint. We characterise this limit analytically as a constrained quadratic program governed by an M-matrix. In the high-risk-aversion regime, heterogeneity creates genuinely new effects absent under symmetry: the cross-section of S-tilts must change sign (unless degenerate), and an agent's own-signal diagonal can turn negative when that row is too strongly exposed to the common traded factor relative to the rest of the group. The results provide a theoretical foundation for `mixed' compensation structures in Regenerative Finance (ReFi), rationalising the use of both stable payments and volatile governance tokens to optimise risk-sharing.