Confidence in Greenhouse Gas Emission Estimation: A Case Study of Formaldehyde Manufacturing

Ernesto C. Marujo (), José R. U. C. Almeida, Luiz F. L. Souza, Alan R. S. P. Costa, Paulo C. G. Miranda, Arthur A. Covatti, Solange G. Holschuch and Potira M. S. Melo
Additional contact information
Ernesto C. Marujo: Departament of Fundamental Sciences, Instituto Tecnologico de Aeronautica, Sao Jose dos Campos 12228-900, SP, Brazil
José R. U. C. Almeida: Copenor, Companhia Petroquímica do Nordeste, Camacari 42816-200, BA, Brazil
Luiz F. L. Souza: Copenor, Companhia Petroquímica do Nordeste, Camacari 42816-200, BA, Brazil
Alan R. S. P. Costa: Copenor, Companhia Petroquímica do Nordeste, Camacari 42816-200, BA, Brazil
Paulo C. G. Miranda: DEEP Brasil Informação e Tecnologia S/A, Sao Jose dos Campos 12243-380, SP, Brazil
Arthur A. Covatti: DEEP Brasil Informação e Tecnologia S/A, Sao Jose dos Campos 12243-380, SP, Brazil
Solange G. Holschuch: DEEP Brasil Informação e Tecnologia S/A, Sao Jose dos Campos 12243-380, SP, Brazil
Potira M. S. Melo: DEEP Brasil Informação e Tecnologia S/A, Sao Jose dos Campos 12243-380, SP, Brazil

Sustainability, 2023, vol. 15, issue 24, 1-13

Abstract: In this article, we discuss the uncertainties involved in the models and in the measurements necessary to estimate the emissions of greenhouse gases (GHG) in a chemical industry. When these uncertainties cannot be neglected and some measurements exhibit correlations with others, estimating the final emission is not a trivial task. Even if we intend to determine a simple point estimate for the mean emissions, we will need to use the average values of the measurements as well as information about their uncertainties and correlations in complex computations. To solve this problem, we propose a Monte Carlo method to estimate the mean and confidence interval of CO 2 emissions in the context of uncertainties and correlations. We validated our approach through a case study involving a traditional chemical company in Brazil. Our results indicated that previously, there was an overestimation of the emission because the company did not consider uncertainties and correlations. The overestimation was modest since the parameters involved present relatively little uncertainty, but the bias effect was clear. This research has demonstrated the importance of accounting for uncertainties and correlations in emission estimates, providing a practical framework for analyses in industrial settings.

Keywords: GHG inventory; propagation of emission uncertainties; uncertainties and correlation of emissions (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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