Why did the historical energy forecasting succeed or fail? A case study on IEA's projection
Hua Liao (),
Jia-Wei Cai,
Dong-Wei Yang and
Yi-Ming Wei
Technological Forecasting and Social Change, 2016, vol. 107, issue C, 90-96
Abstract:
Medium-to-long term energy prediction plays a widely-acknowledged role in guiding national energy strategy and policy but could also lead to serious economic and social chaos when poorly executed. A consequent issue may be the effectiveness of these predictions, and sources that errors can be traced back to. The International Energy Agency (IEA) has published its annual World Energy Outlook (WEO) concerning energy demand based on its long term world energy model (WEM) under specific assumptions towards uncertainties such as population, macroeconomy, energy price and technology. Unfortunately, some of its predictions succeeded while others failed. We in this paper attempt to decompose the leading source of these errors quantitatively. Results suggest that GDP acts as the leading source of demand forecasting errors while fuel price comes thereafter, which requires extra attention in forecasting. Gas, among all fuel types witness the most biased projections. Ignoring the catch-up effect of acquiring rapid economic growth in developing countries such as China will lead to huge mistake in predicting global energy demand. Finally, asymmetric cost of under- and over-estimation of GDP suggests a potentially less conservative stance in the future.
Keywords: Energy demand; Medium-to-long term prediction; Forecast error; Social development (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162516300154
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:tefoso:v:107:y:2016:i:c:p:90-96
DOI: 10.1016/j.techfore.2016.03.026
Access Statistics for this article
Technological Forecasting and Social Change is currently edited by Fred Phillips
More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().