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

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

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    67 Citations (Scopus)

    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.

    Original languageEnglish
    Pages (from-to)85-95
    Number of pages11
    JournalResources, Conservation and Recycling
    Volume127
    DOIs
    Publication statusPublished - 2017

    Keywords

    • Bayesian
    • Energy demand
    • Forecast
    • Model uncertainty

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