Artikel
Change-point detection in CO2 emission-energy consumption nexus using a recursive Bayesian estimation approach
This article focuses on the synthesis of conditional dependence structure of recursive Bayesian estimation of dynamic state space models with time-varying parameters using a newly modified recursive Bayesian algorithm. The results of empirical applications to climate data from Nigeria reveals that the relationship between energy consumption and carbon dioxide emission in Nigeria reached the lowest peak in the late 1980s and the highest peak in early 2000. For South Africa, the slope trajectory of the model descended to the lowest in the mid-1990s and attained the highest peak in early 2000. These changepoints can be attributed to the economic growth, regime changes, anthropogenic activities, vehicular emissions, population growth and industrial revolution in these countries. These results have implications on climate change prediction and global warming in both countries, and also shows that recursive Bayesian dynamic model with time-varying parameters is suitable for statistical inference in climate change and policy analysis.
- Language
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Englisch
- Bibliographic citation
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Journal: Statistics in Transition New Series ; ISSN: 2450-0291 ; Volume: 21 ; Year: 2020 ; Issue: 1 ; Pages: 123-136 ; New York: Exeley
- Subject
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dynamic model
Bayesian inference
CO2
climate change
energy
- Event
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Geistige Schöpfung
- (who)
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Awe, Olushina Olawale
Adepoju, Abosede Adedayo
- Event
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Veröffentlichung
- (who)
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Exeley
- (where)
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New York
- (when)
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2020
- DOI
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doi:10.21307/stattrans-2020-007
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Awe, Olushina Olawale
- Adepoju, Abosede Adedayo
- Exeley
Time of origin
- 2020
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