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China has the highest level of energy consumption in the world with comparatively low-level energy efficiency. Moreover, energy intensity varies greatly in the different provinces. It is necessary to find out the differences of influencing factors in various provinces in order to improve energy utilization while reducing the energy efficiency lags. Based on the panel data from 1995-2017, this paper investigates the driving factors of energy intensity through the spatial Durbin model. Then, in consideration of the inconsistency of the explanatory variables in different regions, the GWR model was established. The empirical results show that six factors have different impacts on local and surrounding areas in general. And the impact of six factors changed in research years as it was shown to be very different through the spatial distribution map. 30 provinces were finally divided into 7 groups according to various key impacts. Consequently, the government should take the differences of impacts in various provinces into account to formulate policies in reducing energy intensity.

Differences Among Influencing Factors of China’s Provincial Energy Intensity: Empirical Analysis from a Geographically Weighted Regression Model