Proposing CarbonLedgerProof: A Cryptographic Traceability Algorithm Linking Asset-Level Emissions Data to Financial Statement Estimates for ESG Assurance and Impairment Testing in the United States
CarbonLedgerProofの提案:資産レベルの排出データと財務諸表情報を結びつける暗号トレーサビリティアルゴリズム - ESG保証と減損テストのための米国における取り組み (AI 翻訳)
Hazel A. Kissi Dankwah
🤖 gxceed AI 要約
日本語
本稿では、資産レベルの排出データと財務諸表情報を暗号技術で結びつけるCarbonLedgerProof(CLP)アルゴリズムを提案。ブロックチェーンとゼロ知識証明を活用し、ESG保証と減損テストの自動化を実現。従来手法よりスケーラビリティとデータ整合性で優れ、検証時間を短縮する。
English
This paper proposes CarbonLedgerProof (CLP), a cryptographic algorithm that links asset-level emissions data to financial statement estimates for ESG assurance and impairment testing. Using blockchain and zero-knowledge proofs, CLP ensures data integrity and scalability, outperforming existing methods in verification time and accuracy.
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SSBJが基準開発中で、排出データと財務情報の連携は重要。本手法は有報や統合報告書におけるデータ保証に応用可能で、今後の開示実務に示唆を与える。
In the global GX context
Globally, ISSB and SEC climate rules require reliable emissions data linked to financials. CLP offers a cryptographic bridge for auditors and regulators, enhancing credibility of ESG disclosures and enabling real-time impairment testing.
👥 読者別の含意
🔬研究者:This paper provides a novel cryptographic framework for integrating carbon data into financial reporting, offering a foundation for further research on automated assurance.
🏢実務担当者:CLP can be adopted by firms to improve the trustworthiness of emissions data in financial statements and streamline impairment testing processes.
🏛政策担当者:The algorithm demonstrates the feasibility of cryptographic verification for climate disclosures, supporting the development of standards that mandate data traceability.
📄 Abstract(原文)
This paper introduces CarbonLedgerProof (CLP), a novel cryptographic traceability algorithm designed to connect asset-level emissions data with financial statement estimates for enhanced Environmental, Social, and Governance (ESG) assurance and impairment testing. The proposed CLP algorithm bridges the gap between carbon emissions reporting and the financial implications of environmental risks, ensuring transparency and traceability across asset portfolios. By integrating blockchain technology and zero-knowledge proofs (ZKPs), CLP offers a secure and efficient way to validate emissions data against financial estimates, addressing challenges in ESG data integrity and providing an automated framework for impairment testing in the context of sustainability. In comparison to existing algorithms such as GreenLedger, CarbonProof, ESG-Chain, and a Traditional Audit (TradAudit) baseline. CLP demonstrates superior performance in terms of scalability, data integrity, and computational efficiency. Through an extensive experimental evaluation, we showcase CLP's ability to significantly reduce verification time and enhance the accuracy of ESG assurance processes. The results indicate that CLP outperforms traditional methods in integrating emissions data into financial systems, offering an innovative approach for real-time emissions monitoring and risk assessment. This paper concludes by proposing CLP as a transformative tool for corporate ESG reporting, with practical implications for financial institutions, auditors, and regulators seeking to streamline the integration of carbon data into decision-making frameworks.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.38124/ijisrt/26feb1119first seen 2026-06-23 06:10:05
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