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Estimating CO(2) emissions from emergency-supply transport: The case of COVID-19 vaccine global air transport

The environmental cost of disaster-related emergency supplies is significant. However, little research has been conducted on the estimation of emergency-supply transportation-related carbon emissions. This study created an “emergency supply emission estimation methodology” (ESEEM). The CO(2) emissio...

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Autores principales: Sajid, Muhammad Jawad, Ali, Ghaffar, Santibanez Gonzalez, Ernesto D.R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8810292/
https://www.ncbi.nlm.nih.gov/pubmed/35132298
http://dx.doi.org/10.1016/j.jclepro.2022.130716
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author Sajid, Muhammad Jawad
Ali, Ghaffar
Santibanez Gonzalez, Ernesto D.R.
author_facet Sajid, Muhammad Jawad
Ali, Ghaffar
Santibanez Gonzalez, Ernesto D.R.
author_sort Sajid, Muhammad Jawad
collection PubMed
description The environmental cost of disaster-related emergency supplies is significant. However, little research has been conducted on the estimation of emergency-supply transportation-related carbon emissions. This study created an “emergency supply emission estimation methodology” (ESEEM). The CO(2) emissions from the global air dispatch of COVID-19 vaccines were estimated using two hypothetical scenarios of one dose per capita and additional doses secured. The robustness of the model was tested with the Monte Carlo Simulation method (MCM) based one-sample t-test. The model was validated using the “Expression of Uncertainty in Measurement (GUM)” and GUM's MCM approaches. The results showed that to dispatch at least one dose of the COVID-19 vaccine to 7.8 billion people, nearly 8000 Boeing 747 flights will be needed, releasing approximately 8.1 ± 0.30 metric kilotons (kt) of CO(2). As countries secure additional doses, these figures will increase to 14,912 flights and about 15 ± 0.48 kt of CO(2). According to the variance-based sensitivity analysis, the total number of doses (population), technology, and wealth play a significant role in determining CO(2) emissions across nations. Thus, wealthy nations' long-term population reduction efforts, technological advancements, and mitigation efforts can benefit the environment as a whole and the CO(2) burdens associated with current COVID-19 and any future disasters' emergency-supply transportation.
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spelling pubmed-88102922022-02-03 Estimating CO(2) emissions from emergency-supply transport: The case of COVID-19 vaccine global air transport Sajid, Muhammad Jawad Ali, Ghaffar Santibanez Gonzalez, Ernesto D.R. J Clean Prod Article The environmental cost of disaster-related emergency supplies is significant. However, little research has been conducted on the estimation of emergency-supply transportation-related carbon emissions. This study created an “emergency supply emission estimation methodology” (ESEEM). The CO(2) emissions from the global air dispatch of COVID-19 vaccines were estimated using two hypothetical scenarios of one dose per capita and additional doses secured. The robustness of the model was tested with the Monte Carlo Simulation method (MCM) based one-sample t-test. The model was validated using the “Expression of Uncertainty in Measurement (GUM)” and GUM's MCM approaches. The results showed that to dispatch at least one dose of the COVID-19 vaccine to 7.8 billion people, nearly 8000 Boeing 747 flights will be needed, releasing approximately 8.1 ± 0.30 metric kilotons (kt) of CO(2). As countries secure additional doses, these figures will increase to 14,912 flights and about 15 ± 0.48 kt of CO(2). According to the variance-based sensitivity analysis, the total number of doses (population), technology, and wealth play a significant role in determining CO(2) emissions across nations. Thus, wealthy nations' long-term population reduction efforts, technological advancements, and mitigation efforts can benefit the environment as a whole and the CO(2) burdens associated with current COVID-19 and any future disasters' emergency-supply transportation. Elsevier Ltd. 2022-03-15 2022-02-03 /pmc/articles/PMC8810292/ /pubmed/35132298 http://dx.doi.org/10.1016/j.jclepro.2022.130716 Text en © 2022 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Sajid, Muhammad Jawad
Ali, Ghaffar
Santibanez Gonzalez, Ernesto D.R.
Estimating CO(2) emissions from emergency-supply transport: The case of COVID-19 vaccine global air transport
title Estimating CO(2) emissions from emergency-supply transport: The case of COVID-19 vaccine global air transport
title_full Estimating CO(2) emissions from emergency-supply transport: The case of COVID-19 vaccine global air transport
title_fullStr Estimating CO(2) emissions from emergency-supply transport: The case of COVID-19 vaccine global air transport
title_full_unstemmed Estimating CO(2) emissions from emergency-supply transport: The case of COVID-19 vaccine global air transport
title_short Estimating CO(2) emissions from emergency-supply transport: The case of COVID-19 vaccine global air transport
title_sort estimating co(2) emissions from emergency-supply transport: the case of covid-19 vaccine global air transport
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8810292/
https://www.ncbi.nlm.nih.gov/pubmed/35132298
http://dx.doi.org/10.1016/j.jclepro.2022.130716
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