Forecasting China’s regional energy demand by 2030: A Bayesian approach

Xiao-Chen Yuan, Xun Sun, Weigang Zhao, Zhifu Mi, Bing Wang and Yi-Ming Wei

Resources, Conservation & Recycling, 2017, vol. 127, issue C, 85-95

Abstract: China has been the largest energy consumer in the world, and its future energy demand is of concern to policy makers. With the data from 30 provinces during 1995–2012, this study employs a hierarchical Bayesian approach to present the probabilistic forecasts of energy demand at the provincial and national levels. The results show that the hierarchical Bayesian approach is effective for energy forecasting by taking model uncertainty, regional heterogeneity, and cross-sectional dependence into account. The eastern and central areas would peak their energy demand in all the scenarios, while the western area would continue to increase its demand in the high growth scenario. For the country as a whole, the maximum energy demand could appear before 2030, reaching 4.97/5.25 billion tons of standard coal equivalent in the low/high growth scenario. However, rapid economic development would keep national energy demand growing. The proposed Bayesian model also serves as an input for the development of effective energy policies. The analysis suggests that most western provinces still have great potential for energy intensity reduction. The energy-intensive industries should be cut down to improve energy efficiency, and the development of renewable energy is essential.

Keywords: Energy demand; Model uncertainty; Bayesian; Forecast (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:recore:v:127:y:2017:i:c:p:85-95

DOI: 10.1016/j.resconrec.2017.08.016

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