Stochastic Control of Corporate Abatement Effort Under Carbon Price Uncertainty and Surplus-Allowance Monetization
炭素価格の不確実性と余剰排出枠の現金化の下での企業排出削減努力の確率的制御 (AI 翻訳)
Haichao Yang
🤖 gxceed AI 要約
日本語
本研究は、炭素価格の不確実性下における企業の排出削減意思決定を確率制御モデルとして定式化。炭素価格を幾何ブラウン運動とし、削減容量は努力により蓄積され減耗する。余剰排出枠の現金化が企業の削減インセンティブを維持する上で重要であることを示した。
English
This study formulates a corporate abatement decision problem under carbon price uncertainty as a stochastic control model, modeling carbon price as geometric Brownian motion and abatement capacity as depreciating over time. Results show that partial monetization of surplus allowances significantly increases abatement effort in surplus regions and shifts firm behavior toward active low-carbon investment.
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
日本のGX政策において、炭素価格メカニズムや排出量取引制度（GX-ETS）の設計に示唆を与える。特に余剰排出枠の扱いは、企業の排出削減行動に直結するため、制度設計上の重要な論点である。
In the global GX context
This paper offers a rigorous framework for understanding corporate behavior under carbon pricing with surplus allowance monetization, directly relevant to global carbon markets like the EU ETS and emerging systems. The findings highlight the importance of allowing surplus allowance monetization to maintain abatement incentives.
👥 読者別の含意
🔬研究者:Provides a stochastic control model integrating carbon price uncertainty and endogenous abatement capacity, offering a foundation for further theoretical work on firm-level carbon management.
🏢実務担当者:Corporate sustainability teams can use the model's insights to optimize abatement investment strategies under carbon pricing regimes with allowance trading.
🏛政策担当者:Regulators designing carbon markets should consider that partial monetization of surplus allowances can shift firms from passive compliance to active low-carbon investment.
📄 Abstract（原文）
This study formulates a corporate abatement decision problem under carbon price uncertainty as a continuous-time stochastic control model. To this end, the carbon price is modeled as a geometric Brownian motion, while abatement capacity is accumulated through costly effort and depreciates over time. Specifically, the firm chooses its abatement effort to maximize expected discounted profits while accounting for allowance purchasing costs, compliance-related penalties, abatement costs, and potential revenues from surplus allowances. The paper contributes by integrating stochastic carbon prices, endogenous abatement-capacity accumulation, allowance-shortage/allowance-surplus asymmetry, and surplus allowance monetization into a unified corporate abatement framework. Applying the dynamic programming principle, the associated Hamilton–Jacobi–Bellman equation is derived, and the bounded optimal abatement effort is characterized in feedback form. Since the resulting nonlinear HJB equation generally does not admit a closed-form solution, a finite-difference scheme with damped policy iteration is used for numerical analysis. The results show that optimal abatement effort is strongly state-dependent. Higher carbon prices strengthen abatement incentives in the allowance-shortage region, whereas effort declines sharply after reaching allowance neutrality if surplus allowances cannot be monetized. Moreover, partial monetization of surplus allowances significantly increases abatement effort in the surplus region and can shift firms’ behavior from passive compliance to active low-carbon investment. Overall, these findings suggest that surplus allowance monetization plays an important role in sustaining firms’ abatement incentives under carbon price uncertainty.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/math14111850first seen 2026-05-28 05:07:16 · last seen 2026-06-03 05:06:01
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