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

Abstract

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.