Optimal incentive scheme for ESG disclosure
ESG開示のための最適インセンティブ制度 (AI 翻訳)
Imen Ben Tahar, Dylan Possamaï, Xiaolu Tan
🤖 gxceed AI 要約
日本語
本論文は、連続時間のプリンシパル・エージェントモデルを用いて、ESG開示における最適なインセンティブ制度を特徴づける。リスク回避的なプリンシパル（プラットフォームや基準設定主体）が、気候リスク因子と相関する開示シグナルを持つ異質なエージェント群と契約する状況を分析。最適契約は、自己シグナル負荷、交差シグナル負荷、取引資産へのヘッジ傾斜の3つの手段を活用し、インセンティブ提供と総支払いの分散のバランスをとる。高リスク回避体制では、市場中立体制に収束し、異質性が新たな効果を生むことを示す。
English
This paper characterizes optimal incentive schemes for ESG disclosure in a continuous-time principal-agent setting. A risk-averse principal contracts with heterogeneous agents whose disclosure signals correlate with a traded climate risk factor. The optimal contract balances incentives and payout variance using own-signal loading, cross-signal loadings, and hedging tilts. Under high risk aversion, the scheme converges to a market-neutral regime where heterogeneity creates novel effects, such as sign changes in cross-sectional tilts. The results provide a theoretical foundation for mixed compensation structures in Regenerative Finance (ReFi).
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
本論文は、ESG開示のインセンティブ設計に関する理論的基盤を提供し、日本のSSBJや有報における開示品質向上や、投資家対応のための実務に示唆を与える。特に、異質な企業間での開示インセンティブの最適設計は、日本企業の統合報告書やTCFD開示の質的向上に貢献しうる。
In the global GX context
This paper offers a theoretical foundation for designing incentive schemes in ESG disclosure, relevant to global frameworks like ISSB, TCFD, and CSRD. The model's insights on risk-sharing and market-neutral regimes can inform standard-setters and platforms (e.g., CDP) on how to structure disclosure incentives to improve data quality and reduce greenwashing risk.
👥 読者別の含意
🔬研究者:Provides a rigorous theoretical model for optimal ESG disclosure incentives, extending principal-agent theory to climate finance and ReFi contexts.
🏢実務担当者:Offers insights for designing compensation structures (e.g., stable payments vs. governance tokens) to incentivize high-quality ESG disclosure.
🏛政策担当者:Highlights how standard-setters can design disclosure frameworks that balance incentives and risk-sharing, potentially improving market integrity.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- arXiv https://arxiv.org/abs/2604.24344first seen 2026-05-04 11:00:47
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