Serverless Carbon Accounting: A Cloud-Native Machine Learning Architecture for Verifying Corporate Environmental Disclosures and Science-Based Emissions Targets

The transition toward sustainable corporate governance relies heavily on the accuracy of environmental disclosures and the adherence to science-based emissions reduction targets. However, traditional carbon accounting methodologies are constrained by low-frequency, self-reported data that is highly susceptible to corporate greenwashing and retrospective manipulation. This paper proposes a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time, serverless machine learning pipeline for dynamic carbon accounting. By deploying asynchronous Python middleware integrated with Natural Language Processing and eXtreme Gradient Boosting algorithms, the proposed system programmatically ingests corporate sustainability reports and cross-references them against high-frequency supply chain telemetry. The system translates these inputs into a dynamic Disclosure Integrity Score, instantly identifying discrepancies between stated climate goals and actual operational emissions. Preliminary architectural evaluations demonstrate that decoupling data ingestion from the empirical verification engine significantly reduces the latency of ESG auditing, providing financial accountants and institutional investors with a deterministic, highly scalable tool for verifying corporate climate commitments.