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    Rights statement: This is the author’s version of a work that was accepted for publication in Energy Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy Economics, 70, 2018 DOI: 10.1016/j.eneco.2018.01.004

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Coal overcapacity in China: Multiscale analysis and prediction

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Coal overcapacity in China: Multiscale analysis and prediction. / Wang, Delu; Wang, Yadong; Song, Xuefeng et al.
In: Energy Economics, Vol. 70, 02.2018, p. 244-257.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Wang, D, Wang, Y, Song, X & Liu, Y 2018, 'Coal overcapacity in China: Multiscale analysis and prediction', Energy Economics, vol. 70, pp. 244-257. https://doi.org/10.1016/j.eneco.2018.01.004

APA

Wang, D., Wang, Y., Song, X., & Liu, Y. (2018). Coal overcapacity in China: Multiscale analysis and prediction. Energy Economics, 70, 244-257. https://doi.org/10.1016/j.eneco.2018.01.004

Vancouver

Wang D, Wang Y, Song X, Liu Y. Coal overcapacity in China: Multiscale analysis and prediction. Energy Economics. 2018 Feb;70:244-257. Epub 2018 Jan 6. doi: 10.1016/j.eneco.2018.01.004

Author

Wang, Delu ; Wang, Yadong ; Song, Xuefeng et al. / Coal overcapacity in China : Multiscale analysis and prediction. In: Energy Economics. 2018 ; Vol. 70. pp. 244-257.

Bibtex

@article{630c65378cd5469ba56a39214e65e1ac,
title = "Coal overcapacity in China: Multiscale analysis and prediction",
abstract = "Gaining a quantitative understanding of the causes of coal overcapacity and accurately predicting it are important for both government agencies and coal enterprises. Following the decomposition-reconstruction-prediction concept, a combined Ensemble Empirical Mode Decomposition-Least Square Support Vector Machine-Autoregressive Integrated Moving Average (EEMD-LSSVM-ARIMA) model is proposed for quantitatively analyzing and forecasting coal overcapacity in China. The results show that the main causes of coal overcapacity in China include insufficient demand, market failure, and institutional distortion. Institutional distortion, with an influence degree of 73.75%, is the most fundamental and influential factor. From 2017 to 2019, the scale of coal overcapacity in China will reach between 1.721and 1.819 billion tons, suggesting that coal overcapacity will remain a serious problem. The rate of coal overcapacity caused by insufficient demand will fluctuate slightly, while coal overcapacity caused by market failure will trend downward, but the impact of institutional distortion on coal overcapacity will be exacerbated. A statistical analysis demonstrates that the EEMD-LSSVM-ARIMA model significantly outperformed other widely developed baselines (e.g. ARIMA, LSSVM, EEMD-ARIMA, and EEMD-LSSVM).",
keywords = "Coal industry, Overcapacity, Multiscale analysis, Forecasting, Integrated model",
author = "Delu Wang and Yadong Wang and Xuefeng Song and Yun Liu",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Energy Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy Economics, 70, 2018 DOI: 10.1016/j.eneco.2018.01.004",
year = "2018",
month = feb,
doi = "10.1016/j.eneco.2018.01.004",
language = "English",
volume = "70",
pages = "244--257",
journal = "Energy Economics",
issn = "0140-9883",
publisher = "Elsevier Science B.V.",

}

RIS

TY - JOUR

T1 - Coal overcapacity in China

T2 - Multiscale analysis and prediction

AU - Wang, Delu

AU - Wang, Yadong

AU - Song, Xuefeng

AU - Liu, Yun

N1 - This is the author’s version of a work that was accepted for publication in Energy Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy Economics, 70, 2018 DOI: 10.1016/j.eneco.2018.01.004

PY - 2018/2

Y1 - 2018/2

N2 - Gaining a quantitative understanding of the causes of coal overcapacity and accurately predicting it are important for both government agencies and coal enterprises. Following the decomposition-reconstruction-prediction concept, a combined Ensemble Empirical Mode Decomposition-Least Square Support Vector Machine-Autoregressive Integrated Moving Average (EEMD-LSSVM-ARIMA) model is proposed for quantitatively analyzing and forecasting coal overcapacity in China. The results show that the main causes of coal overcapacity in China include insufficient demand, market failure, and institutional distortion. Institutional distortion, with an influence degree of 73.75%, is the most fundamental and influential factor. From 2017 to 2019, the scale of coal overcapacity in China will reach between 1.721and 1.819 billion tons, suggesting that coal overcapacity will remain a serious problem. The rate of coal overcapacity caused by insufficient demand will fluctuate slightly, while coal overcapacity caused by market failure will trend downward, but the impact of institutional distortion on coal overcapacity will be exacerbated. A statistical analysis demonstrates that the EEMD-LSSVM-ARIMA model significantly outperformed other widely developed baselines (e.g. ARIMA, LSSVM, EEMD-ARIMA, and EEMD-LSSVM).

AB - Gaining a quantitative understanding of the causes of coal overcapacity and accurately predicting it are important for both government agencies and coal enterprises. Following the decomposition-reconstruction-prediction concept, a combined Ensemble Empirical Mode Decomposition-Least Square Support Vector Machine-Autoregressive Integrated Moving Average (EEMD-LSSVM-ARIMA) model is proposed for quantitatively analyzing and forecasting coal overcapacity in China. The results show that the main causes of coal overcapacity in China include insufficient demand, market failure, and institutional distortion. Institutional distortion, with an influence degree of 73.75%, is the most fundamental and influential factor. From 2017 to 2019, the scale of coal overcapacity in China will reach between 1.721and 1.819 billion tons, suggesting that coal overcapacity will remain a serious problem. The rate of coal overcapacity caused by insufficient demand will fluctuate slightly, while coal overcapacity caused by market failure will trend downward, but the impact of institutional distortion on coal overcapacity will be exacerbated. A statistical analysis demonstrates that the EEMD-LSSVM-ARIMA model significantly outperformed other widely developed baselines (e.g. ARIMA, LSSVM, EEMD-ARIMA, and EEMD-LSSVM).

KW - Coal industry

KW - Overcapacity

KW - Multiscale analysis

KW - Forecasting

KW - Integrated model

U2 - 10.1016/j.eneco.2018.01.004

DO - 10.1016/j.eneco.2018.01.004

M3 - Journal article

VL - 70

SP - 244

EP - 257

JO - Energy Economics

JF - Energy Economics

SN - 0140-9883

ER -