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Estimation performance comparison of machine learning approaches and time series econometric models: evidence from the effect of sector-based energy consumption on CO 2 emissions in the USA.
- Source :
-
Environmental science and pollution research international [Environ Sci Pollut Res Int] 2023 Apr; Vol. 30 (18), pp. 52576-52592. Date of Electronic Publication: 2023 Feb 25. - Publication Year :
- 2023
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Abstract
- By considering the existence of two separate analysis families and the usage of different data frequencies, this study aims to examine the effect of method choice, data frequency, and sector-based energy consumption on carbon dioxide (CO <subscript>2</subscript> ) emissions by performing machine learning (ML) algorithms and time series econometric (TS) models simultaneously. In this situation, the study examines the United States (USA), considers sector-based energy consumption indicators as explanatory variables, uses monthly and yearly data between January 1973 and December 2021, estimates CO <subscript>2</subscript> emissions, and compares the estimation performance of the models. The empirical findings reveal that (i) the ML algorithms outperform the TS models based on R <superscript>2</superscript> and goodness of fit criteria; (ii) the estimation performance of the models increases with the high-frequency (i.e., monthly) data; (iii) the ML algorithms perform much better in case of high-frequency usage; (iv) some thresholds identify the effects of the sector-based energy consumption indicators on the CO <subscript>2</subscript> emissions; (v) electric power and transportation sectors are the most important sectors in the estimation of the CO <subscript>2</subscript> emissions for monthly and yearly data, respectively. Hence, the study provides to help the understanding role of method choice, data frequency, and sector-based energy consumption for the estimation of CO <subscript>2</subscript> emissions. Based on the results, this study proposes that US policymakers should consider the ML algorithms, use higher-frequency data, and include sector-based energy consumption indicators to have a better estimation of CO <subscript>2</subscript> emissions.<br /> (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Details
- Language :
- English
- ISSN :
- 1614-7499
- Volume :
- 30
- Issue :
- 18
- Database :
- MEDLINE
- Journal :
- Environmental science and pollution research international
- Publication Type :
- Academic Journal
- Accession number :
- 36829097
- Full Text :
- https://doi.org/10.1007/s11356-023-26050-0