Spatial econometric analysis of China’s province-level industrial carbon productivity and its influencing factors

Ruyin Long, Tianxiang Shao and Hong Chen

Applied Energy, 2016, vol. 166, issue C, 210-219

Abstract: This study measured the industrial carbon productivity of 30 provinces in China from 2005 to 2012 and examined the space–time characteristics and the main factors of China’s industrial carbon productivity using Moran’s I index and spatial panel data models. The empirical results indicate that there is significant positive spatial dependence and clustering characteristics in China’s province-level industrial carbon productivity. The spatial dependence may create biased estimated parameters in an ordinary least squares framework; according to the analysis of our spatial panel models, industrial energy efficiency, the opening degree, technological progress, and the industrial scale structure have significantly positive effects on industrial carbon productivity whereas per-capita GDP, the industrial energy consumption structure, and the industrial ownership structure exert a negative effect on industrial carbon productivity.

Keywords: Industrial carbon productivity; Spatial dependence; Spatial panel data models; Moran’s I index; China (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (52)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:166:y:2016:i:c:p:210-219

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DOI: 10.1016/j.apenergy.2015.09.100

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