Comprehensive Evaluation Model for Improving Carbon Accounting Accuracy in Corporate Sustainability Programs

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🔬研究者:Provides an integrated framework combining ML and carbon accounting that can be empirically tested across industries.

🏢実務担当者:Offers a methodology to strengthen Scope 3 data collection, verification processes, and disclosure quality.

🏛政策担当者:Illustrates how ML can support regulatory compliance and accurate emissions reporting, informing assurance standards.

Accurate carbon accounting is central to the credibility and effectiveness of corporate sustainability programs, yet many organizations continue to rely on fragmented methodologies that overlook data quality, boundary inconsistencies, and dynamic emission factors. This study introduces a comprehensive evaluation model designed to improve carbon accounting accuracy by integrating standardized measurement protocols, advanced analytics, and multi-layer verification processes. The model incorporates primary activity data, real-time operational metrics, supply chain inputs, and region-specific emission coefficients to generate more precise carbon footprints across Scope 1, Scope 2, and Scope 3 emissions. By applying machine learning–enhanced anomaly detection and uncertainty quantification, the framework identifies data gaps, flags inconsistent reporting patterns, and optimizes emission estimates through iterative refinement. Additionally, the model embeds a structured materiality assessment that prioritizes high-impact emission sources and ensures alignment with global reporting standards such as the GHG Protocol, ISSB guidelines, and emerging regulatory requirements. Case evaluations demonstrate that organizations applying the proposed model achieve significant improvements in data completeness, emission factor accuracy, and cross-functional accountability. The model also supports scenario analysis, enabling companies to assess the carbon implications of operational changes, procurement strategies, and energy transition initiatives. Findings indicate that integrating digital tools, supplier engagement mechanisms, and automated verification enhances transparency and reduces the risk of underreporting or misclassification. The study contributes an adaptable, data-driven methodology suitable for diverse industries seeking to strengthen carbon disclosure, meet regulatory expectations, and accelerate progress toward net-zero commitments. Ultimately, the evaluation model offers a robust foundation for continuous improvement in corporate carbon management by unifying data governance, analytical rigor, and sustainability performance monitoring. Furthermore, the model facilitates integration with enterprise resource planning systems and carbon management platforms, enabling automated data ingestion and real-time performance tracking. By incorporating stakeholder feedback loops, third-party verification pathways, and sector-specific benchmarks, the framework enhances organizational readiness for mandatory disclosures and climate-risk assessments. The expanded evaluation model also provides decision-makers with actionable insights for prioritizing decarbonization initiatives, optimizing resource allocation, and improving long-term resilience. Overall, the approach strengthens corporate sustainability governance by embedding accuracy, transparency, and adaptability into every stage of the carbon accounting lifecycle for enhanced environmental accountability overall nationwide.

• openalex https://doi.org/10.5281/zenodo.21684233first seen 2026-08-02 17:01:07 · last seen 2026-08-02 17:01:08