段階的登録下のLandsat時系列を用いたマングローブ再生炭素クレジットの追加性に関する因果推論：永続性リスク過大評価のAI評価
Causal inference on mangrove-restoration carbon-credit additionality from Landsat time series under staggered enrollment: an artificial intelligence evaluation of permanence-risk overstatement (原題)
Neema Mwita Kinyasi, Amina Juma Msuya, Baraka Mwinyi Hamisi
🤖 gxceed AI 要約
日本語
マングローブ再生型炭素クレジットの永続性控除が、段階的登録と未観測のベースライン損失の下で偏りを持つ条件を理論的に導出した研究。潜在アウトカムモデルと隠れマルコフ表現を組み合わせ、登録時期とベースライン脅威の相関が高いほど永続性リスクが過大評価されることを示す。新規フィールドデータなしで既存レジストリに適用できる診断手法を提供する。
English
This paper derives identification conditions under which conventional permanence deductions in mangrove-restoration carbon credits become biased under staggered enrollment and unobserved baseline loss. Combining a potential-outcomes model with a hidden-Markov representation, it shows permanence-risk overstatement rises with correlation between enrollment timing and baseline threat. It offers a registry-applicable diagnostic requiring no new field data.
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
炭素クレジットの品質・追加性・永続性はICVCMやVCMI、日本企業のオフセット調達・移行金融判断に直結する。JCMやブルーカーボン活用を検討する日本企業・発行体にとって、既存レジストリの控除前提を検証する実務的示唆がある。
In the global GX context
Speaks directly to integrity debates around VCM methodologies, ICVCM Core Carbon Principles, and permanence/overstatement risk that underpin credible offset claims under CSRD and net-zero transition plans. The diagnostic is portable to other nature-based registries beyond mangroves.
👥 読者別の含意
🔬研究者:Provides a formal identification framework for additionality and permanence bias in staggered-adoption carbon-credit settings.
🏢実務担当者:Offers a no-new-data diagnostic to stress-test permanence deductions in existing offset registries before claiming credits.
🏛政策担当者:Highlights that permanence deductions are only defensible when enrollment timing is independent of baseline threat, informing registry standards.
📄 Abstract（原文）
Carbon-credit methodologies for mangrove restoration typically assume that credited carbon storage persists for a fixed horizon, yet staggered enrollment and unobserved baseline dynamics make permanence risk difficult to identify from satellite time series alone. The framework combines a potential-outcomes model for staggered adoption with a hidden-Markov representation of unobserved baseline loss, and derives bounds on the bias of the conventional permanence deduction. Analysis of the identification conditions shows that the standard deduction is unbiased only when baseline loss is independent of enrollment timing, an assumption that fails whenever enrollment is targeted at high-threat sites. The derived bounds imply that permanence-risk overstatement is increasing in the correlation between enrollment timing and baseline threat, and that the sign of the bias can be recovered from observable enrollment-date and land-cover-covariate patterns. The contribution is a diagnostic that credit issuers can apply to existing registries without new field data, together with a formal statement of the conditions under which the conventional deduction is defensible.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.23606805first seen 2026-10-25 04:49:57 · last seen 2026-11-06 04:55:12
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