This study aims to address a critical gap in the literature by examining the incorporation of uncertainty in carbon emission measurement using the Greenhouse Gas (GHG) Protocol methodology across all three scopes. By comprehensively considering the various dimensions of CO₂ emissions within the context of organizational activities, this research makes a significant contribution to the existing body of knowledge.

Challenges such as data quality issues and a high prevalence of missing values are addressed using information entropy, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and an Artificial Neural Network (ANN) to analyze contextual variables. The results, based on a sample of data from 56 companies across 18 sectors and 13 Brazilian states between 2017 and 2019, reveal that Scope 3 emissions exhibit the highest levels of information entropy.

Furthermore, the study highlights the crucial role of public policy in increasing the availability of GHG emissions data, which in turn positively influences policy-making practices. By demonstrating the potential for a virtuous cycle between greater information availability and improved policy outcomes, this research underscores the importance of addressing uncertainty in carbon emission measurement to promote effective climate change mitigation strategies.

Researcher: Peter Wanke et al.
Brazilian School of Public and Business Administration (FGV EBAPE)

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