The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
The impact of energy depletion and renewable energy on CO2 emissions in Thailand: Fresh evidence from the novel dynamic ARDL simulation
Abstract
Abstract Thailand's Nationally Determined Contribution (NDC) intends to minimize CO2 emissions by 20–25%. Similarly, to focus on achieving the Paris Agreement's long-term target of remaining well below 2 °C, aggressive mitigation steps are necessary beyond 2030. Given the importance, the study examines the impact of energy depletion rate, renewable energy consumption, depletion rate of non-renewable energy, and GDP on CO2 emissions in Thailand from 1980 to 2018. The research using a novel dynamic ARDL simulations model [1] and frequency domain causality (FDC) test. The empirical outcomes indicate that the pace of depletion has a significant adverse impact on CO2 emissions both in the long run and short run. Additionally, we found that renewable energy has a negative and statistically significant impact on CO2 emissions in the short run. However, the depletion rate of non-renewable energy and GDP revealed a positive and statistically substantial effects on CO2 emissions in the short and long run. Also, the FDC test confirmed the short, medium, and long-run causality among DR, RE, DRNRE, and CO2 emission. The findings show that without a radical shift in Thailand's economic environment and energy infrastructure, the nation will have to face high costs in decreasing its CO2 emission.
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1 retracted
Abbasi, K. R., & Adedoyin, F. F. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-020-12217-6 · Cited by 164
Self-citationRetractedCo-integration and error correction: representation, estimation and testing
Engle, R. F., & Granger, C. W. J. (1987) · Econometrica · doi:10.2307/1913236 · Cited by 32,007
Distribution of the Estimators for Autoregressive Time Series with a Unit Root
Dickey, D. A., & Fuller, W. A. (1979) · Journal of the American Statistical Association · doi:10.1080/01621459.1979.10482531 · Cited by 23,211
Bounds testing approaches to the analysis of level relationships
Pesaran, M. H., Shin, Y., & Smith, R. J. (2001) · Journal of Applied Econometrics · doi:10.1002/jae.616 · Cited by 20,385
Testing for a unit root in time series regression
PHILLIPS, P. C. B., & PERRON, P. (1988) · Biometrika · doi:10.1093/biomet/75.2.335 · Cited by 19,155
STATISTICAL ANALYSIS OF COINTEGRATION VECTORS
Johansen, S. (1988) · Journal of Economic Dynamics and Control · doi:10.1016/0165-1889(88)90041-3 · Cited by 17,027
Johansen, S., & Juselius, K. (1990) · Oxford Bulletin of Economics and Statistics · doi:10.1111/j.1468-0084.1990.mp52002003.x · Cited by 14,051
Testing the null hypothesis of stationarity against the alternative of a unit root
Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992) · Journal of Econometrics · doi:10.1016/0304-4076(92)90104-y · Cited by 12,633
Distribution of the Estimators for Autoregressive Time Series With a Unit Root
Dickey, D. A., & Fuller, W. A. (1979) · Journal of the American Statistical Association · doi:10.2307/2286348 · Cited by 9,106
Pooled Mean Group Estimation of Dynamic Heterogeneous Panels
Pesaran, M. H., Shin, Y., & Smith, R. P. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10474156 · Cited by 6,683
Cited by
Awan, A., Abbasi, K. R., Rej, S., Bandyopadhyay, A., & Lv, K. (2022) · Renewable Energy · doi:10.1016/j.renene.2022.03.017 · Cited by 284
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Metadata
- Authors
- Abbasi, K. R., Adedoyin, F. F., Abbas, J., & Hussain, K.
- Venue
- Renewable Energy
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 180
- Pages
- 1439-1450
- ISSN
- 0960-1481 (ISSN-L)
- OpenAlex
- W3196815358
- Cited by
- 312
- References
- 54
- FWCI
- 48.26
- Type
- journal article
- Language
- en
- Open access
- closed
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- Abbasi, K. R., Adedoyin, F. F., Abbas, J., & Hussain, K. (2021). The impact of energy depletion and renewable energy on CO2 emissions in Thailand: Fresh evidence from the novel dynamic ARDL simulation. Renewable Energy. https://doi.org/10.1016/j.renene.2021.08.078
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