Has China’s coal consumption already peaked? A demand-side analysis based on hybrid prediction models
Qiao-Mei Liang and
Energy, 2018, vol. 162, issue C, 272-281
To deal simultaneously with the environmental problems caused by the current high-intensity exploitation and extensive use of coal resources, it is necessary to perform a scientific prediction of the trend and, especially, the peak of China’s coal demand. Based on the historical data on coal consumption and four primary factors (economic growth, energy structure, investment, and industrial structure) during the period of 1981–2015, this study established a hybrid model for coal demand prediction, using particle swarm optimization and cointegration methods. According to the prediction results combined with the actual statistics, in the business-as-usual scenario, China’s coal demand had peaked in 2014, then a downward trend started with an average annual decline rate of 5.85% for 2016 to 2020. However, future coal demand will keep increasing in the pessimism scenario. And in the optimism scenario, coal demand will decline much faster than the business-as-usual scenario. Sensitive analysis on four influential factors shows that coal demand is more sensitive to changes in investment and industrial structure, and more emphasis should be put on the supply and the demand side of coal industry.
Keywords: Coal demand; Hybrid model; Peak; Forecast; China (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:162:y:2018:i:c:p:272-281
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().