ESG-as-Code: A Deterministic Rule-Based Framework for Automated ESG Compliance Validation

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a novel architecture for deterministic ESG compliance, bridging computational law and sustainability disclosure.

🏢実務担当者:Offers a methodology to automate ESG compliance checks, potentially reducing manual effort and improving audit trails.

🏛政策担当者:Highlights the importance of rule-level traceability in ESG reporting, informing future regulatory design.

Environmental, Social, and Governance (ESG) compliance has shifted from voluntary best practice to enforceable legal obligation across major global jurisdictions. Frameworks such as the European Union's Corporate Sustainability Reporting Directive (CSRD), the Sustainable Finance Disclosure Regulation (SFDR), the United Kingdom's Sustainability Disclosure Requirements (SDR), and disclosure rules from the U.S. Securities and Exchange Commission (SEC), adopted in 2024 and now under proposed rescission, impose structured reporting duties on a growing range of organizations. Yet dominant compliance methods remain manual, fragmented, and difficult to verify. Static documentation, subjective interpretation, and scoring systems that cannot be traced back to specific legal provisions continue to define current practice. This paper introduces ESG-as-Code, a deterministic rule-based framework designed to address these limitations by converting ESG regulatory obligations into structured rule logic formally specified for machine implementation. Drawing on established principles from Policy-as-Code, Infrastructure-as-Code, and computational law, the framework provides a structured methodology for encoding jurisdictional ESG rules as conditional logic that can be evaluated systematically against organizational data and disclosures.   Central to the framework is a deliberate separation between probabilistic systems used for document interpretation and deterministic engines used for compliance decision-making. This separation preserves full auditability, allowing every compliance outcome to be traced directly to a specific regulatory provision rather than a statistical inference. Determinism in this sense guarantees reproducibility and rule-level traceability, properties that probabilistic scoring systems cannot offer by design. It does not by itself guarantee that a given outcome is legally correct; that additionally depends on the accuracy of the underlying rule encoding, the currency of the regulatory source, and the governance processes surrounding the rule library. The paper argues that reproducibility and traceability are necessary but not sufficient conditions for regulatory defensibility and proposes deterministic rule-based architecture as the foundation on which the remaining conditions can be built.

• semanticscholar https://doi.org/10.63084/cognexus.v2i1.282first seen 2026-08-18 05:06:52