Identifying the determinants of energy intensity in China: A Bayesian averaging approach. (15th April 2016)

• Record Type: Journal Article
• Authors: Zhang, Dayong; Cao, Hong; Wei, Yi-Ming
• Is Part Of: Applied energy. Volume 168(2016)
• Page Start: 672
• Page End: 682
• Publication Date: 2016-04-15
• Subjects: Energy intensity -- Extreme bound analysis -- Bayesian averaging -- Sensitivity analysis -- China; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2016.01.134
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Bayesian averaging approach is used to find determinants of China's energy intensity. This method can reduce potential subjectivity of empirical model building. The relative importance of each key determining factor can be identified. Policy makers should have priorities and focus on relatively more important factors. 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.