| It is an essential premise for NOx reduction to analyze the social and economic influencing mechanism.In China,provinces have different nature source endowment and development orientation,and they are also closly related to economic,culture and population.So there are obvious spatial characteristics for NOx generation and its influencing mechanism between different provinces.Therefore,this study made use of the Geographically Weighted Regression(GWR)to explore the spatial characteristics of influencing mechanism,which could consider both the spatial autocorrelation and spatial heterogeneity.In order to explore the spatial distribution characteristics of NO_x generation from energy consumption,this study made a spatial autocorrelation test.According to the results,the NO_x generation in all years showed obvious spatial agglomeration.Especially,the hot spot(high-high value area)was the Shandong-Jiangsu area.This study made use of OLS to select influencing factors.The infuening factors were divided into two kinds,the first kind was direct facor,which means the enrgy related factors.And the second kind was indirect factors,which included population factors and economic factors.And according to the test of OLS,there is spatial heterogeneity between every influencing factor and provincial NO_x genenration from energy consumption.Therefore,this study analyzed the influencing mechansim of NO_x generation by GWR.The results indicated:The provincial influencing mechanism showed both spatial heterogeneity and spatial agglomeration.Firstly,from the view of provincial spatial heterogeneity:In 2005 and 2010,energy intensity,GDP per capita,and population scale had more obvious provincial spatial heterogeneity.But in 2015,population scale factor had the smallest spatial heterogeneity,while the economic factors had the biggest spatial heterogeneity.This indicated that the economic factors should be fouced on the NO_x reduction policies for different regions.Secondly,from the view of spatial agglomeration of each influencing factor for NO_x generation from energy consumtion.For energy intensity,the regions with high value were focused on the west-east pattern of our country in 2005 and 2010,but the Yangtze River Delta,the Pearl River Delta and parts of Central regions were with the low value.The NO_x generation of Xinjiang,Gansu,Qinghai,ShaanXi were more sensitive for thermal power scale.The consumption rates of natural gas made more NO_x reduction in Northeast China.And population scale effect made the most negative contribution for NO_x reduction,and the regression coefficients of north were bigger than the south.The regression coefficients about urban population density showed the spatial distribution contrary to Hu Huanyong Line.And the spatial distribution characteristics of GDP per capita was the same with the consumption rate of natural gas factor.Actual use of foreign investment factor made positive contributions to NO_x reduction in 2005and 2010,but played negative roles in the most provinces of western country for NO_x reduction in 2015.As the increase of the proportion of the third industry added value,the NO_x generation were reduced in 2015,and showed a declining trend from east to west.Thirdly,from the view of regional spatial heterogeneity:in 2005,the NO_x generation from energy consumption of northeast three provinces and western region were closly related to the population scale fators,while the GDP per capita was the most important factor for Jing-jin-ji and Southeast coastal area.In 2015,the population and GDP per capita made the mian contributons to the NO_x generation of northeast three provinces and Jing-jin-ji region,while the other regions were related to population sacle and energy intensity.In addition,this paper summarized the policy list of NO_x reduction,and the results showed the NO_x reduction in China were focused on emission scale control,however it was poor in the NO_x reduction methods for different regions.Therefore,this paper proposed countermeasures for NO_x reduction for different regions in China. |