Unveiling the Impact of Information Vagueness on Carbon Emission Inventories using Fuzzy Sets
de Oliveira, J.A.P. Wanke, P. Antunes, J. Tan, Yong
de Oliveira, J.A.P.
Wanke, P.
Antunes, J.
Tan, Yong
Publication Date
2025
End of Embargo
Supervisor
Rights
© 2025 Published by Elsevier. This work is distributed under the Creative Commons CC-BY license (https://creativecommons.org/licenses/by/4.0/)
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2025-06-13
Institution
Department
Awarded
Embargo end date
Collections
Additional title
Abstract
Carbon emissions reporting has become essential to pursue corporate sustainability. Several market regulators have established guidelines on disclosures of greenhouse gas emissions. However, significant challenges persist due to the uncertainty and vagueness in emissions data, particularly in the context of Scope 3 emissions from the supply chain. This uncertainty hinders effective decision-making and compromises the comparability of emissions data across companies, leading to a lack of accountability, inefficient regulatory frameworks, and delayed action on climate change. Without developing proper management approaches to address this vagueness urgently, companies, investors and policymakers risk making misguided decisions that can significantly hinder global sustainability goals that need to be quickly addressed. This study introduces a Two-Dimensional Fuzzy-Monte Carlo (2DFMC) framework that integrates Monte Carlo simulations to model variability with Type-2 Fuzzy Sets (T2FS), which capture higher-order uncertainty inherent in carbon emissions data. We tested the model for Brazilian companies that release emission data through GHG Protocol. By combining these methodologies, the 2DFMC model addresses both aleatory (randomness) and epistemic (vagueness) uncertainty, providing a more robust tool for evaluating carbon performance, especially for Scope 3 emissions. Our results show that the 2DFMC approach improves the accuracy and reliability of emissions assessments, helping companies better manage data uncertainty and ensuring more trustworthy carbon disclosures. The 2DFMC framework offers critical practical implications for companies and regulators: it enhances environmental accountability, improves the comparability of emissions disclosures, and provides actionable insights for better-informed decision-making. By addressing the methodological gap in managing data uncertainty, this study offers a significant step forward in improving carbon reporting practices and helping both firms and policymakers respond more effectively to climate change.
Version
Published version
Citation
de Oliveira JAP, Wanke P, Antunes J and Tan, Y (2025) Unveiling the Impact of Information Vagueness on Carbon Emission Inventories using Fuzzy Sets. Energy Economics. Accepted for publication.
Link to publisher’s version
Link to published version
Link to Version of Record
Type
Article