Home > Research > Publications & Outputs > Coal overcapacity in China


Text available via DOI:

View graph of relations

Coal overcapacity in China: Multiscale analysis and prediction

Research output: Contribution to journalJournal article

  • Delu Wang
  • Yadong Wang
  • Xuefeng Song
  • Yun Liu
<mark>Journal publication date</mark>02/2018
<mark>Journal</mark>Energy Economics
Number of pages14
Pages (from-to)244-257
Early online date6/01/18
<mark>Original language</mark>English


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).

Bibliographic note

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