Forecasting China's total energy demand and its structure using ADL-MIDAS model

Yongda He and Boqiang Lin ()

Energy, 2018, vol. 151, issue C, 420-429

Abstract: Forecasting total energy demand and its structure is the basis for energy planning and industrial policy formulation. However, existing research on the forecast of energy structure remains inadequate. This study aims at constructing an ADL–MIDAS model to identify the optimal model to forecast China's energy demand and its structure, and offer a reasonable judgement on future carbon emission and energy scenarios in China and other developing countries. Thus, this study adopts mixed frequency data for quarterly GDP, quarterly added value, and annual energy demand of various industries to construct an ADL–MIDAS model. Then, the optimal model to forecast China's energy demand is selected from various model combinations that employ different weight functions and forecasting methods. The model forecasts China's total energy demand and its structure as proposed in the 13th Five Year Plan. The in-sample prediction results show that, in the optimal model, the smallest prediction error is 0.02%, while the largest of the four future periods is 2%, indicating that the ADL–MIDAS model is effective in forecasting energy demand. Further, the forecast results suggest that, by 2020, China's total energy demand will reach approximately 4.65 billion tonnes of standard coal equivalent; the demand for coal, natural gas, and non-fossil fuel will be 57%, 7.6%, and 18%, respectively, contingent on economic growth conditions. Given these forecast results, the energy planning targets set under the 13th Five Year Plan are attainable. However, in the case of natural gas demand, considerable marketing is required to promote its use.

Keywords: Energy demand; Energy structure; Forecast; Mixed frequency data; ADL–MIDAS model (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544218304766
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:151:y:2018:i:c:p:420-429

DOI: 10.1016/j.energy.2018.03.067

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 Catherine Liu ().