AI-Augmented Carbon Accounting with EcoScopeAI: Strengthening SDG 12 and SDG 13 through CSRD and CBAM Integration, Alaeddine Boubaker

Alaeddine Boubaker  
DBA Student, European School of Data Science and Technology
Germany
ORCID: 0009-0006-3952-9966

Purpose: This research paper describes EcoScopeAI, an AI-powered carbon accounting prototype to assist small and medium-sized enterprises in meeting the requirements of two new EU regulations: the Corporate Sustainability Reporting Directive (CSRD) and the Carbon Border Adjustment Mechanism (CBAM). It is designed to drive greater accuracy, automation and transparency in sustainability reporting in line with SDG 12 and SDG 13. DEFRA public dataset for emission factors is utilized to ensure transparency and reproducibility.
Design/methodology/approach: The study utilizes a design science method integrating NLP-based auto-mapping, anomaly detection, clustering, and forecasting. We use open datasets like DEFRA emission factors to promote transparency and reproducibility.
Findings: Originality/value of the paper: EcoScopeAI is a solution that uniquely combines AI-Automation, regulatory alignment and SME usability in a single architecture.
Research limitations/implications: Further validation on large datasets and across industries is mandatory.
Practical implications: EcoScopeAI supports affordable, AI-driven carbon accounting and decision optimization for SMEs and exporters.
Keywords: CSRD, CBAM, DEFRA, SME, EcoScopeAI, NLP, Anomaly Detection, Clustering, Forecasting, Reinforcement Learning, SDG-12, SDG-13.