IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i24p16578-d1294663.html
   My bibliography  Save this article

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

Author

Listed:
  • Ernesto C. Marujo

    (Departament of Fundamental Sciences, Instituto Tecnologico de Aeronautica, Sao Jose dos Campos 12228-900, SP, Brazil
    DEEP Brasil Informação e Tecnologia S/A, Sao Jose dos Campos 12243-380, 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)

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.

Suggested Citation

  • 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 & Potira M. S. Melo, 2023. "Confidence in Greenhouse Gas Emission Estimation: A Case Study of Formaldehyde Manufacturing," Sustainability, MDPI, vol. 15(24), pages 1-13, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:24:p:16578-:d:1294663
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/24/16578/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/24/16578/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Myroslava Lesiv & Andriy Bun & Matthias Jonas, 2014. "Analysis of change in relative uncertainty in GHG emissions from stationary sources for the EU 15," Climatic Change, Springer, vol. 124(3), pages 505-518, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ozturk, Fatma & Keles, Melek & Evrendilek, Fatih, 2016. "Quantifying rates and drivers of change in long-term sector- and country-specific trends of carbon dioxide-equivalent greenhouse gas emissions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 823-831.
    2. Konstantinaviciute, Inga & Bobinaite, Viktorija, 2015. "Comparative analysis of carbon dioxide emission factors for energy industries in European Union countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 603-612.
    3. Khrystyna Boychuk & Rostyslav Bun, 2014. "Regional spatial inventories (cadastres) of GHG emissions in the Energy sector: Accounting for uncertainty," Climatic Change, Springer, vol. 124(3), pages 561-574, June.
    4. Mathieu Fortin, 2021. "Comparison of uncertainty quantification techniques for national greenhouse gas inventories," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 26(2), pages 1-20, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:24:p:16578-:d:1294663. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.