『Abstract
　A large amount of literature on China's energy intensity seldom considers the regional differences of energy intensity inside China and the spatial effects. Based on spatial statistics methods, this paper explores the regional imbalance of China's provincial energy intensity and the spatially correlation of energy intensity among provinces. Using spatial panel data models, this paper finds that GDP per capita, transportation infrastructure, the level of marketization, and scientific and technological input significantly reduce the energy intensity; the ratio of heavy industries to total industries and the ratio of coal consumption to total energy consumption significantly expand the energy intensity; meanwhile, the coefficient of the ratio of export to GDP is not significant. Then, the spillover and convergence of China's regional energy intensity have been tested. The results indicate that the spillover effect between the eastern and western China is remarkable, and there exist absolute β-convergence of provincial energy intensity. moreover, GDP per capita, transportation infrastructure, the level of marketization and scientific & technological input are conductive to conditional convergence after the spatial effects are controlled. According to the empirical results, this paper proposes some policy suggestions on reducing China's energy intensity.

Keywords: Energy intensity; Spatial panel data model; China』

1. Introduction
2. Relevant literature review
3. The spatial statistics of China's provincial energy intensity
　3.1. Data sources and data processing
　3.2. The overall distribution of China's provincial energy intensity
　3.3. The spatial correlation of China's provincial energy intensity
　　3.3.1. The choice of spatial weight matrix
　　3.3.2. The global spatial correlation of regional energy intensity
　　3.3.3. Local spatial autocorrelation
4. Variables and model setting
　4.1. The setting of basin model
　4.2. The explanatory variables
5. The estimation results of influential factors of China's regional energy intensity
　5.1. The full sample estimation results
6. Spatial spillover and convergence mechanism
　6.1. The spatial spillover effects of energy intensity
　6.2. The convergence of China's provincial energy intensity
7. Conclusions and policy suggestions
Acknowledgments
Appendix A. The Moran Scatterplot of China's Provincial Energy Intensity (1988-2007)
Appendix B
References