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Carbon Accounting Automation Through Machine Learning and Natural Language Processing: Reducing Compliance Costs and Enhancing Data Quality in Enterprise ESG Reporting
Internal Auditor, Energy Sector, Swift Oil Limited, Lagos, Nigeria.
Review
International Journal of Science and Research Archive, 2021, 04(01), 490–501.
Article DOI: 10.30574/ijsra.2021.4.1.0213
Publication history:
Received on 13 November 2021; revised on 26 December 2021; accepted on 29 December 2021
Abstract:
As enterprises face intensifying regulatory and stakeholder pressure for transparent Environmental, Social, and Governance (ESG) reporting, the complexity and cost of manual carbon accounting have become major barriers to credible disclosure. Traditional methods reliant on spreadsheets, fragmented data systems, and human interpretation struggle to keep pace with expanding compliance obligations under frameworks such as the GHG Protocol, ISO 14064, and IFRS S2. This paper explores the transformative potential of Machine Learning (ML) and Natural Language Processing (NLP) in automating carbon accounting workflows, reducing compliance costs, and enhancing the accuracy, timeliness, and reliability of reported emissions data. The study begins by situating automation within the broader digital transformation of ESG data management, highlighting persistent inefficiencies in data extraction, normalization, and audit traceability. It then narrows to propose an AI-driven architecture that integrates ML-based anomaly detection, NLP-enabled document parsing, and predictive emissions modeling. The framework streamlines Scope 1–3 data collection from unstructured enterprise sources such as invoices, sensor feeds, and supplier reports, while continuously learning from historical patterns to refine emission factor assignments. By embedding assurance-ready metadata tagging and automated materiality thresholds, the system supports both compliance verification and real-time decision-making. Empirical insights suggest that automation can reduce reporting cycles by up to 40% and improve data quality metrics by over 30%, enabling more responsive carbon risk management. The paper concludes that integrating ML and NLP into carbon accounting processes represents a paradigm shift transforming ESG reporting from a reactive compliance burden into a proactive instrument for strategic decarbonization and sustainable value creation.
Keywords:
Carbon accounting automation; Machine learning; Natural language processing; ESG reporting; Compliance efficiency; Data quality enhancement
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Copyright © 2021 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
