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Dynamic Carbon Credit Evaluation Driven by Power-Carbon Signals: Mechanism Design and Proxy-Based Conceptual Validation

電力-炭素シグナルに基づく動的炭素クレジット評価：メカニズム設計とプロキシベースの概念検証 (AI 翻訳)

Lu Liu, Keran Li, Yaling Liu, Haoheng Qin, Lin Mei, Zhuo Chen

Sustainability📚 査読済 / ジャーナル2026-06-08#炭素会計Origin: CN経営インパクト: 資金調達対象セクター: finance
DOI: 10.3390/su18125845
原典: https://doi.org/10.3390/su18125845

🤖 gxceed AI 要約

日本語

本論文は、電力-炭素シグナル（炭素強度やコンプライアンス記録など）を統合した動的な企業炭素クレジット評価フレームワークを提案する。ベイズAHP-CRITIC重み付け方式により物理的指標とESG開示を組み合わせ、「信用格付け-グリーンラベル」の二元的分類を構築。3,327社のサンプルで81.3%の分類一致率を達成し、財務のみのベースライン（46.8%）や自主的炭素開示モデル（61.4%）を大幅に上回る。さらに、炭素クレジットスコアが将来の財務困難を予測できることを示す。

English

This paper proposes a dynamic corporate carbon credit evaluation framework integrating power-carbon signals (e.g., carbon intensity, compliance records). It combines physical metrics with ESG disclosures via Bayesian AHP-CRITIC weighting to create a dual-dimensional 'Credit Rating–Green Label' classification. Empirical validation on 3,327 firms achieves 81.3% classification consistency, outperforming financial-only (46.8%) and voluntary-disclosure models (61.4%). Panel regression shows carbon credit scores predict future financial distress.

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

このフレームワークは、日本の銀行がSSBJ開示やグリーンローン評価に活用できる可能性がある。特に、物理的シグナルとESG開示の統合は、日本の融資実務における情報非対称性の緩和に寄与する。

In the global GX context

The framework addresses information asymmetry and greenwashing in green credit, relevant to global transition finance and ISSB-aligned disclosure. It demonstrates how physical carbon signals can enhance credit risk assessment, offering a data-driven approach for banks integrating climate considerations.

👥 読者別の含意

🔬研究者:The integration of Bayesian AHP-CRITIC with physical and ESG signals provides a novel methodology for carbon credit scoring that can be extended to other contexts.

🏢実務担当者:Banks can adopt the dynamic credit limit and interest rate adjustment mechanism using carbon credit scores to improve green lending decisions.

🏛政策担当者:The framework supports the design of green credit policies by showing how verifiable physical metrics can complement voluntary disclosures.

📄 Abstract（原文）

In green credit markets, information asymmetry and corporate greenwashing increasingly undermine the efficiency of resource allocation, while traditional assessment models relying on static, self-reported environmental data fail to impose effective constraints. To address this limitation, this paper develops a dynamic corporate carbon credit evaluation framework by integrating multiple sources of physical (hard) signals and embeds it into commercial banks’ credit management systems. Anchored in multi-source power-carbon signals (e.g., carbon intensity and compliance records), the framework integrates verifiable physical metrics with ESG disclosures via a Bayesian AHP–CRITIC weighting scheme to construct a dual-dimensional classification scheme (“Credit Rating–Green Label”). It further embeds carbon credit scores into dynamic adjustments to credit limits and differentiated interest rate pricing, forming an integrated risk management mechanism. Empirically, a stratified validation strategy is adopted. Analysis based on a sample of 3327 firms shows that the proposed framework achieves a classification consistency of 81.3%, significantly outperforming both a financial-only baseline model (46.8%) and models based on voluntary carbon disclosure (61.4%). Ablation studies further confirm that physical (hard) signal indicators contribute substantially to ranking stability. Moreover, panel regression analysis, based on 36,185 firm-year observations from 3327 firms over the period 2000–2023, demonstrates that carbon credit scores have robust predictive power for future financial distress. Overall, the proposed framework offers a sustainable, data-driven approach to green credit risk management.

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