集中供暖对中国空气污染影响的实证研究Empirical Analysis of the Effect of Central Heating on Air Pollution in China
李金珂,曹静
摘要(Abstract)：
近年来关于集中供暖对空气污染和健康影响的话题备受关注,同时不断有人呼吁南方也推出集中供暖,因此精确估计集中供暖对中国大气环境的影响至关重要。本文利用全国230个城市的空气污染和气象数据,采用断点回归的方法,研究集中供暖对多种空气污染物浓度的影响。研究结果发现:1)集中供暖的开始直接导致多种空气污染物浓度显著上升,其中PM2.5、PM10、SO_2、NO_2以及CO分别上升14.148μg/m~3、15.788μg/m~3、18.626μg/m~3、9.863μg/m~3和0.221mg/m~3,分别为非供暖期均值的26.79%、14.73%、74.10%、30.45%和20.85%,该结果均在5%水平上显著;2)集中供暖的结束使得空气污染物浓度平缓降低,但未带来显著的非连续变化;3)在改变带宽以及拟合方法的情况下估计结果也较为稳健。实证结果表明,中国的集中供暖显著加剧了空气污染水平,造成中国空气污染的首要污染物PM2.5浓度在供暖开始后增加将近三成,SO_2浓度增加超过70%。因此在优化集中供暖相关政策时,应综合考虑其环境成本。
关键词(KeyWords)： 集中供暖;空气污染;断点回归;PM2.5
基金项目(Foundation): 国家自然科学基金项目(71173130和71422013);; 清华大学自主科研计划的资助
作者(Author): 李金珂,曹静
DOI: 10.16513/j.cnki.cje.2017.04.006
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- (1)例如冬天更易发生逆温天气,该天气阻碍了空气污染向高空的扩散,进而污染物更容易积聚。
- (1)因为污染数据的监测点基本都在城市地区。
- (2)该数据为非平衡面板数据,主要原因有二:一是空气污染监测点每年都有增加,二是存在部分监测点污染数据或气象数据的缺失。
- (1)改变天气变量多次项的阶数不会造成估计系数大小和方向的明显变化。
- (1)改变天气变量多次项的阶数不会造成估计系数大小和方向的明显变化。因为篇幅限制,相应结果未放入正文。
- (2)对应的是回归方程为式(1),相应估计结果即为参数β的点估计。
- (1)对应的估计结果即为参数β的点估计。
- (1)对应的是各种污染物浓度作为自变量对回归方程为式(1)的估计。对应的估计结果即为参数β的点估计。
- (1)我们还做了改变拟合方法(由局部线性拟合改为局部多项式拟合)以及改变控制变量的稳健性检验,结果也较为稳健。因为篇幅限制,相应结果未放入正文。