ABSTRACT
In this paper, we aimed to investigate the relationship between energy consumption and carbon dioxide (CO2) emission quantities. To explain the dependence structure, we employed asymmetric Archimedean copulas and determined the best fitting multi-parameter Archimedean copula based on the λ–function. A goodness-of-fit improvement can be obtained by using concordance invariant and tail dependence preserving transforms. The association of CO2 emission quantities and energy consumption is characterized by the transformed convex sum of the Gumbel and Clayton copula, indicating evidence of joint extreme occurrences when both of the variables increase or decrease in the industrial sector. Also, the Kendall hazard scenario approach was used to evaluate the probability of extreme events. The results provide valuable information for researchers who model changes in CO2 emission quantities with energy consumption.
Abbreviations
U.S. | = | United States |
ASEAN | = | Association of Southeast Asian nations |
FDI | = | Foregin direct investment |
EIA | = | The U.S. energy Information Administration |
MMmt | = | Million metric tons |
Tmt | = | Trillion metric tons |
Btu | = | British thermal units |
TCDE | = | The total amount of CO2 emission |
TEC | = | Total energy consumption |
GCCS | = | Convex sum of Gumbel and Clayton copula |
GCCS – T | = | Transformed convex sum of the Gumbel and Clayton copula |
CvM | = | Cramér-von Mises |
Nomenclature
CO2 | = | Carbon dioxide |
= | Archimedean copula generator function | |
= | Archimedean copula lambda function | |
= |
| |
= |
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= | Kendall’s tau coefficient | |
= | Upper tail dependence | |
= | Lower tail dependence | |
= | Kendall distribution function | |
= | Empirical estimate of Kendall distribution function |
Acknowledgments
We thank the anonymous referees and the editor for their helpful suggestions which improved the presentation of the paper.