Yazarlar (1) |
![]() Kırşehir Ahi Evran Üniversitesi, Türkiye |
Özet |
Energy intensity refers to energy consumption per unit of GDP. Therefore, a decline in energy intensity implies that either more output is produced with the same amount of energy use or less energy is used to produce the same output level. Considering that fossil energy dominates the energy mix in the US and that fossil energy use causes many environmental problems, it is crucial to determine the levels of energy intensities of the states in the US and whether they tend to converge over time. This study aims to analyze the possible convergence patterns in energy intensity for the states in the US over the period 2000-2021. Accordingly, the paper first examines β-convergence and then investigates stochastic convergence. This study diverges from prior research in two respects. First, the study gives state-specific findings. Second, one of the unit root tests employed in this work considers structural breaks, whilst the other is based on machine learning methodologies. The findings indicate that the convergence hypothesis is validated solely for Alaska, Oklahoma, Texas, and Vermont, suggesting there is minimal evidence supporting energy intensity convergence among the states. This study contends that to decrease energy intensity in the US, it is crucial to provide greater support for environmental innovations, particularly in states with high energy intensities. |
Anahtar Kelimeler |
Convergence analysis | energy intensity convergence | stochastic convergence | β-convergence |
Makale Türü | Özgün Makale |
Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
Dergi Adı | Applied Economics Letters |
Dergi ISSN | 1350-4851 |
Dergi Tarandığı Indeksler | SSCI |
Dergi Grubu | Q3 |
Makale Dili | İngilizce |
Basım Tarihi | 01-2025 |
Sayı | 1 |
Doi Numarası | 10.1080/13504851.2024.2449547 |
Makale Linki | https://www.tandfonline.com/doi/abs/10.1080/13504851.2024.2449547 |