中国城市环境污染监管水平的空间演化特征与影响因素

基于中国2010—2016年的地级市污染监管水平数据,采用空间自相关、位序-规模法则,空间计量模型等地学方法,分析中国地级城市污染监管水平的时空分异特征和影响因素及其空间溢出效应。结果表明：① 2010—2016年中国污染监管水平整体上呈现上升趋势且区域性和集聚性特征明显,东南部地区为稳定的高值集聚地区,中西部地区为稳定的低值集聚地区。② 中国城市污染监管水平属于次位型分布,监管规模分布的分散趋势大于集中趋势。③ 城市的人口密度、经济发展水平、第二产业占比等对城市污染监管水平有显著的正向影响,城市规模对污染监管水平存在负向的影响。④ 城市经济发展水平和城市人口密度对污染监管水平起到显著的直接效应;第二产业占比、二氧化硫排放量等起到了显著的间接效应。

Spatial evolution and influencing factors of urban environmental pollution supervision level in China

Based on the data on China's 2010-2016 prefecture-level city pollution supervision level, The Rank-Size rule is used to analyze the scale distribution of urban environmental pollution supervision level in China. Using spatial autocorrelation, spatial econometric model and other geoscience research methods, we adopt the quantitative analysis to examine the regional differences, spatial and temporal distribution patterns, influencing factors and spatial spillover effects of urban pollution regulation in China. Four conclusions are listed below: (1) From 2010 to 2016, China's pollution supervision level showed an overall upward trend, and its regional agglomeration was obvious. There was a significant spatial autocorrelation in the pollution supervision level. Among them, the Yangtze River Delta and the Pearl River Delta were relatively stable high-value gathering areas of pollution control, and in recent years extended to the eastern Shandong Peninsula urban agglomeration, while the central and western regions were stable low-value gathering areas, indicating that the pollution supervision level had obvious regional aggregation. (2) The level of urban pollution regulation in China presents a sub-type distribution. The cities with high pollution supervision level are mainly distributed in the eastern and central regions. The scale of pollution supervision between high-order cities is gradually narrowing, and the distribution among cities is relatively well distributed. The dispersion trend is more obvious than the concentration trend. (3) Considering the complexity of the influencing factors, the spatial measurement model is used to analyze the influencing factors in the selection of many research methods. The urban population density, economic development level and proportion of secondary industry output value have a significant positive impact on urban pollution supervision level, whereas urban scale has a negative impact on pollution supervision level. (4) Urban economic development, population density, openness, and industrial structure have a significant direct effect on the spillover effect of urban pollution supervision level, while the ratio of the output value of the secondary industry and sulphur dioxide emissions have a significant indirect effect on the pollution supervision level, that is, the promotion of the secondary industry in the region is conducive to that of pollution supervision in neighboring cities