Core Insights - The report "ESG Data Assetization: Risks and Governance White Paper (2025)" focuses on the development of ESG data assetization at the intersection of the digital economy and green transformation, providing a comprehensive guide for industry development [1][3]. Group 1: ESG Data Assetization Overview - ESG data assetization involves transforming decentralized, unstructured ESG-related data into digital assets with clear ownership, quantifiable value, and tradability through processes such as collection, cleansing, rights confirmation, evaluation, pricing, and trading [1][27]. - The economic significance of ESG data assetization includes enhancing asset pricing efficiency, attracting long-term capital, fostering green financial innovation, empowering supply chain risk management, and providing data support for government regulation and policy-making [1][28]. Group 2: Policy Environment and International Trends - Domestic ESG information disclosure has transitioned from voluntary guidelines to mandatory regulations, establishing clear compliance boundaries [2][33]. - Internationally, three main frameworks—EU CSRD, US SEC climate rules, and ISSB standards—coexist, showing a trend towards unified disclosure standards while differing in substantive principles and focus [2][34]. Group 3: Technological Support and Implementation Path - The lifecycle of ESG data assetization encompasses five stages: data source aggregation, collection and preprocessing, governance and quality control, analysis and modeling, and service application, with cutting-edge technologies like privacy computing, blockchain, and artificial intelligence playing crucial roles [2][38]. - Privacy computing ensures data is usable but not visible, blockchain guarantees trustworthy data storage and traceability, and AI aids in processing vast amounts of data for value extraction [2][40]. Group 4: Governance Framework - The white paper proposes a multi-level collaborative governance framework based on three core principles: safety, efficiency, and fairness, which includes national data governance committees, top-level legal regulations, industry standards, market constraints, public supervision, and internal controls within enterprises [2][49]. - Effective governance requires a dynamic regulatory technology (RegTech) system that utilizes automated reporting, intelligent monitoring, and penetrative regulation to manage ESG data throughout its lifecycle [2][50]. Group 5: Strategic Outlook and Future Path - The report emphasizes the need for consensus among various stakeholders through technological innovation, regulatory improvement, and collaborative governance to build a trustworthy, inclusive, and sustainable ESG data ecosystem [3][30]. - It highlights the importance of ESG data assetization as a key node in bridging the gap in green finance, addressing challenges such as value measurement, risk assessment, and circulation [3][30].