Conceptualization of Artificial Intelligence Use for GHG Scope 3 Emissions Measurement, Reporting, Monitoring, and Assurance: A Critical Systems Perspective

This article provides a conceptual and exploratory examination of Scope 3 greenhouse gas (GHG) emissions, focusing on the complexities associated with their nature, measurement, reporting, and verification. It examines the emerging role of artificial intelligence (AI) in addressing these complexities, particularly considering the fragmented, opaque, and often inaccessible nature of Scope 3 data. The paper introduces Critical Systems Thinking (CST) as a foundational framework for considering the practicality of utilization of AI in this context. CST emphasizes three key principles: critical awareness of assumptions and contexts, emancipation through attention to power dynamics and continuous improvement, and methodological pluralism, to engage with complexity through diverse analytical approaches. Due to the complex nature of GHG emissions reporting and assurance, AI application for this purpose remains limited. While Scope 3 reporting has made progress in certain sectors and regions, overall maturity remains uneven—particularly in developing and emerging markets. Although AI applications in Scope 3 reporting are still at an early stage, they hold significant potential to enhance both reporting quality and assurance processes. A key factor that needs to be addressed in the future utilization of AI for Scope 3 emissions reporting and assurance is the integration of CST into the development and implementation of AI tools. This paper proposes such integration as a necessary step forward. At present, there are substantial gaps in Scope 3 emissions measurement and reporting due to the inherently highly complex, distributed, and fragmented nature of value chain emissions. This gap poses risks to data quality and consistency, which in turn can hinder the implementation of reporting legislation and informed decision making by management and stakeholders. Systemic fragmentation, power asymmetries in data access, and methodological inconsistencies present substantial challenges to traditional forms of validation. Rather than offering a predictive model or finalized solution, the paper aims to lay a conceptual foundation for future empirical research and highlights the importance of systems-based approaches in advancing the credibility and utility of Scope 3 GHG disclosures. This is a key limitation relating to this paper, as it mainly focuses on the CST framework and the potential incapacities of artificial intelligence in relation to the implementation of CST, rather than applications of CST, as they are limited at present.