Markov chain-based impact analysis of the pandemic Covid-19 outbreak on global primary energy consumption mix

© 2024. The Author(s)..

The historic evolution of global primary energy consumption (GPEC) mix, comprising of fossil (liquid petroleum, gaseous and coal fuels) and non-fossil (nuclear, hydro and other renewables) energy sources while highlighting the impact of the novel corona virus 2019 pandemic outbreak, has been examined through this study. GPEC data of 2005-2021 has been taken from the annually published reports by British Petroleum. The equilibrium state, a property of the classical predictive modeling based on Markov chain, is employed as an investigative tool. The pandemic outbreak has proved to be a blessing in disguise for global energy sector through, at least temporarily, reducing the burden on environment in terms of reducing demand for fossil energy sources. Some significant long term impacts of the pandemic occurred in second and third years (2021 and 2022) after its outbreak in 2019 rather than in first year (2020) like the penetration of other energy sources along with hydro and renewable ones in GPEC. Novelty of this research lies within the application of the equilibrium state feature of compositional Markov chain based prediction upon GPEC mix. The analysis into the past trends suggests the advancement towards a better global energy future comprising of cleaner fossil resources (mainly natural gas), along with nuclear, hydro and renewable ones in the long run.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:14

Enthalten in:

Scientific reports - 14(2024), 1 vom: 24. Apr., Seite 9449

Sprache:

Englisch

Beteiligte Personen:

Ahmad, Hussaan [VerfasserIn]
Liaqat, Rehan [VerfasserIn]
Alhussein, Musaed [VerfasserIn]
Muqeet, Hafiz Abdul [VerfasserIn]
Aurangzeb, Khursheed [VerfasserIn]
Ashraf, Hafiz Muhammad [VerfasserIn]

Links:

Volltext

Themen:

COVID-19 pandemic
Equilibrium state
Fossil Fuels
Global primary energy consumption
Journal Article
Markov chain
Predictive modeling
Research Support, Non-U.S. Gov't

Anmerkungen:

Date Completed 25.04.2024

Date Revised 27.04.2024

published: Electronic

Citation Status MEDLINE

doi:

10.1038/s41598-024-60125-3

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM371476666