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Identifying the determinants of energy intensity in China: A Bayesian averaging approach

Dayong Zhang (), Hong Cao and Yi-Ming Wei ()

Applied Energy, 2016, vol. 168, issue C, 672-682

Abstract: Facing serious energy constraints and environmental challenges, policy makers in China have set up clear targets to reduce energy intensity in order to secure a sustainable economic growth; however, it is unclear in theory what the determining forces are. The empirical evidence, although intensively discussed in the literature, also remain divided in opinion. This paper contributes to the existing literature full of heated debates using a Bayesian averaging approach to identify robust determinants of energy intensity in China. Using provincial level data, key contributors that help explain the level of energy intensity across China are found. By ranking the relative importance of explanatory variables according to their posterior inclusion probabilities, this study can also offer support to policy makers in designing intensity reduction policies. It is suggested that policies should focus on those robust and relatively more important factors such as fiscal expenditure, infrastructure and economic structure.

Keywords: Energy intensity; Extreme bound analysis; Bayesian averaging; Sensitivity analysis; China (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1016/j.apenergy.2016.01.134

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