Journal of Ecology and Rural Environment ›› 2016, Vol. 32 ›› Issue (2): 270-276.doi: 10.11934/j.issn.1673-4831.2016.02.015

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ESDA-GWR Analysis of Spatial Pattern of Carbon Emission From Private Cars and Its Influencing Factors in China

PAN Jing-hu   

  1. College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
  • Received:2015-03-04 Online:2016-03-25 Published:2016-04-01


A study was carried out on spatial differentiation pattern, general trend, spatial heterogeneity and driving factors of carbon emission from private cars in 312 cities of prefecture or above level in 2012, using the exploratory spatial data analysis (ESDA) and the geographic weight regression (GWR) model. Results show that spatial difference in carbon emission from private cars is significant between the cities, displaying a general trend of Central China> East China> West China, and Central China> North China> South China, in terms of total volume of carbon emission, East China >West China, and North China> South China in terms of per capita carbon emission, and East China> West China, and South China> North China in terms of carbon emissions per unit area of land. GWR analysis shows that the driving factor of the per capita carbon emission from private cars also varied between cities of prefecture-or above levels in China. Per capita saving deposit is the major factor affecting the per capita carbon emissions from private cars, and the latter is closely related to the former. Per capita gross domestic product (GDP) comes the second, also has a positive relationship with carbon emission and then is followed by disposable income of urban residents, per capita living space, urbanization rate, density of the road network and Engel's coefficient. Per capital annual expenditure of urban residents on consumption has the least influence. The influencing factors of carbon emission from private cars in the cities of prefecture or above levels in China show a clear latitudinal zonality or longitudinal zonality.

Key words: carbon emission from private cars, ESDA, GWR, spatial heterogeneity, China

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