Optimal incentive scheme for ESG disclosure

2026-04-27

AI Summary

This study investigates the optimal incentive design for ESG disclosure under climate risk and agent heterogeneity. Within a continuous-time principal–agent framework, the authors integrate three instruments—idiosyncratic signal loading, cross-agent signal interactions, and tradable asset hedging—into a linear–quadratic–Gaussian (LQG) model that balances incentive strength against payment variance. Employing LQG control theory, M-matrix-constrained quadratic programming, and continuous-time stochastic optimization, they derive a closed-form linear optimal contract. The analysis reveals that risk aversion shifts incentives from pure hedging toward market-neutral strategies and uncovers novel phenomena arising from heterogeneity, including sign reversals in cross-signal tilts and negative diagonal terms. These findings provide a theoretical foundation for hybrid compensation structures in regenerative finance that combine stable payments with volatile governance tokens.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• ESG disclosure
• incentive scheme
• principal-agent
• risk aversion
• climate risk

Innovation

• optimal incentive design
• ESG disclosure
• linear-quadratic-Gaussian framework
• market-neutral contracting
• heterogeneous agents