Carbon Crediting When Better Measurement Is Not Enough
より良い測定だけでは不十分な炭素クレジット付与 (AI 翻訳)
Daniel Heyen, Frederik Holtel
🤖 gxceed AI 要約
日本語
炭素クレジット制度におけるMRV情報の活用とゲーミング耐性のトレードオフを理論モデルで分析。より正確な測定があっても、操作が可能な場合はクレジット発行ルールの設計が重要で、最適ルールは一般にMRVシグナルを減衰させることを示す。クックストーブのデータによる実証例も提示。
English
This paper models how carbon-crediting authorities should translate imperfect MRV evidence into issued credits when developers can game the signal. The optimal rule typically attenuates project-specific data, balancing statistical accuracy against gaming robustness. An empirical illustration with cookstove credits shows how independent reassessments can inform crediting design.
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
日本ではJCMやJ-クレジットの信頼性向上が課題であり、本論文の理論はクレジット発行ルールの設計に示唆を与える。特に、プロジェクト固有データへの依存度と操作耐性のバランスを考える上で有用。
In the global GX context
As carbon markets expand under Article 6 and VCM integrity initiatives, this paper provides a rigorous framework for designing crediting rules that resist manipulation. It speaks directly to ICVCM and CORSIA debates on MRV and crediting approaches.
👥 読者別の含意
🔬研究者:A rigorous model distinguishing statistical accuracy from gaming robustness, with clear implications for crediting rule design under imperfect measurement.
🏢実務担当者:Project developers and verifiers can gain insight into why crediting rules may attenuate data and how to anticipate regulatory responses.
🏛政策担当者:Offers a formal basis for designing crediting methodologies that balance measurement quality with manipulation resistance in carbon markets.
📄 Abstract（原文）
Carbon-crediting methodologies determine how imperfect monitoring, reporting, and verification (MRV) evidence is translated into issued credits. When project developers can influence measured outcomes, greater reliance on project-specific data improves targeting but also strengthens incentives to manipulate the signal. We develop a model in which a crediting authority commits to a crediting rule while anticipating the project developer's response. The framework distinguishes statistical accuracy from gaming robustness. The optimal rule generally attenuates the MRV signal: greater accuracy and robustness justify stronger reliance on project-specific evidence, whereas higher credit prices and greater heterogeneity in gaming ability call for a flatter rule. Even when manipulation becomes prohibitively difficult, measurement noise alone implies attenuation. We also characterize how market and project conditions affect the relative value of improving accuracy versus robustness. An illustration using project-level data on cookstove carbon credits shows how independent reassessments can inform the framework and highlights the data requirements for empirical implementation.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.65864/95ucdcrjv6first seen 2026-07-31 05:55:50
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