江西省雾霾污染的区域差异及其驱动因素研究
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摘要
针对江西省雾霾污染的整体状况和区域差异,采用描述统计分析和泰尔指数进行分析和测算,并在已有的经济和环境理论的基础上,借助面板数据回归模型对江西省雾霾污染的驱动因素进行定位和分析。研究表明,江西省雾霾污染呈波动上升的发展趋势,且具有春夏季节低、秋冬季节高的特征;江西省雾霾污染的区域差异明显,主要原因是各区域内的城市经济发展不平衡;选取的影响因素经筛选后都回归显著,经济发展将带动人们收入提升,并且推动产业升级和产业结构调整,从而将减少雾霾污染。此外,多乘坐公交车少开私家车出行,减少能源消耗,降低能源结构中的煤炭消费比重等都有利于降低雾霾污染。
In view of the overall situation and regional difference of fog and haze pollution in Jiangxi Province,this paper uses the statistical analysis and the Tel index to analyze and calculate the fog and haze pollution. On the basis of the existing economic and environmental theories,the driving factors of the haze pollution in Jiangxi province are located and analyzed with the help of panel data regression model. The study shows that the fog and haze pollution in Jiangxi shows a trend of fluctuating and rising trend,which has the characteristics of low in spring and summer season and high in autumn and winter season. The regional difference of fog and haze pollution in Jiangxi is obvious,the main reason is that the urban economic development is unbalance in each region; the selected influence factors are all significant,and the economic development will bring the promotion of people's income,and promote industrial upgrading and industrial structure adjustment,which will reduce fog and haze pollution. In addition,more bus travel and less private cars,reduction of energy consumption and the proportion of coal consumption in the energy structure will help to reduce the fog pollution.
In view of the overall situation and regional difference of fog and haze pollution in Jiangxi Province,this paper uses the statistical analysis and the Tel index to analyze and calculate the fog and haze pollution. On the basis of the existing economic and environmental theories,the driving factors of the haze pollution in Jiangxi province are located and analyzed with the help of panel data regression model. The study shows that the fog and haze pollution in Jiangxi shows a trend of fluctuating and rising trend,which has the characteristics of low in spring and summer season and high in autumn and winter season. The regional difference of fog and haze pollution in Jiangxi is obvious,the main reason is that the urban economic development is unbalance in each region; the selected influence factors are all significant,and the economic development will bring the promotion of people's income,and promote industrial upgrading and industrial structure adjustment,which will reduce fog and haze pollution. In addition,more bus travel and less private cars,reduction of energy consumption and the proportion of coal consumption in the energy structure will help to reduce the fog pollution.
引文
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[20]马忠玉,肖宏伟.中国区域PM2. 5影响因素空间分异研究[J].山西财经大学学报,2017,35(5):14-26.
[2] Salvador P,Artíano B,Viana M M,et al. Spatial and temporal variations in PM10 and PM2. 5 across Madrid metropolitan area in 1999-2008[J]. Procedia Environmental Sciences,2011,4(10):198-208.
[3] Mikuka P,KubátkováN,Kˇru。mal K,et al. Seasonal variability of monosaccharide anhydrides,esin acids,methoxyphenols and saccharides in PM2. 5 in Brno,the Czech Republic[J]. Atmospheric Pollution Research,2017,8(03):576-586.
[4] Lanzaco B L,Olcese L E,Querol X,et al. Analysis of PM2. 5 in Córdoba,Argentina under the effects of the El Nio southern oscillation[J]. Atmospheric Environment,2017,171(10):49-58.
[5] Cesari D,Benedetto De G E,Bonasoni P,et al. Seasonal variability of PM2. 5 and PM10 composition and sources in an urban background site in Southern Italy[J]. Science of the Total Environment,2017,612(08):202-213.
[6] Lindmark M. An EKC-Pattern in Historical Perspective:Carbon Dioxide Emissions,Technology,Fuel Prices and Growth in Sweden 1870-1997[J]. Ecological Economics,2002,42(01):333-347.
[7] Poon J P H,Casas I,He C. The Impact of Energy,Transport,and Trade on Air Pollution in China[J]. Eurasian Geographyand Economics,2006,47(05):568-584.
[8] Kotchenruther R A. The effects of marine vessel fuel sulfur regulations on ambient PM 2. 5,along the west coast of the U. S[J]. Atmospheric Environment,2015,103(03):121-128.
[9] Requia W J,Dalumpines R,Adams M D,et al. Modeling spatial patterns of link-based PM2. 5 emissions and subsequent human exposure in a large canadian metropolitan area[J]. Atmospheric Environment,2017,158(03):172-180.
[10]PokornáP,Schwarz J,Krejci R,et al. Comparison of PM2. 5,chemical composition and sources at a rural background site in Central Europe between 1993/1994/1995and 2009/2010:Effect of legislative regulations and economic transformation on the air quality[J]. Environmental Pollution,2018,241(06):841-851.
[11]高歌. 1961-2005年中国霾日气候特征及变化分析[J].地理学报,2008,63(7):761-768.
[12]马丽梅,张晓.中国雾霾污染的空间效应及经济、能源结构影响[J].中国工业经济,2014,(4):19-31.
[13]潘竟虎,张文,李俊峰,等.中国大范围雾霾期间主要城市空气污染物分布特征[J].生态学杂志,2014,33(12):3423-3431.
[14]蔡海亚,徐盈之,孙文远.中国雾霾污染强度的地区差异与收敛性研究———基于省际面板数据的实证检验[J].山西财经大学学报,2017,39(3):1-14.
[15]刘炳杰,彭晓敏,李继红.基于LUR模型的中国PM2. 5时空变化分析[J].环境科学,2018,39(12):5296-5307.
[16]郝新东,刘菲.我国PM2. 5污染与煤炭消费关系的面板数据分析[J].生产力研究,2013,(2):118-119+127.
[17]陈开琦,杨红梅.发展经济与雾霾治理的平衡机制[J].社会科学研究,2015,(06):42-48.
[18]齐园,张永安.北京三次产业演变与PM2. 5排放的动态关系研究[J].中国人口·资源与环境,2015,25(7):15-23.
[19]吴建南,秦朝,张攀.雾霾污染的影响因素:基于中国监测城市PM2. 5浓度的实证研究[J].行政论坛,2016,(1):62-66.
[20]马忠玉,肖宏伟.中国区域PM2. 5影响因素空间分异研究[J].山西财经大学学报,2017,35(5):14-26.