| 摘要: |
| 针对长期存在的空气污染问题，提出将空气质量的污染指标和治理指标相结合来进行分析，在一定程度上对改善我国的空气质量有一定的现实意义；以2008—2019年我国31个主要城市的面板数据为基础，从分位数回归的角度将空气质量污染指标(NO2)、经济增长(人均GDP)以及公共交通(年末实有公共汽电车运营数)对我国空气质量的影响程度进行分析；结果表明：NO2在低分位点时对空气质量产生的负向影响较大，在高分位点时较低，与之相反，人均GDP在高分位点时与空气质量的负相关程度大于低分位点，而公共交通利用率的增加对我国空气质量的改善有着正向的推动作用，且在高分位点的正向影响大于低分位点。 |
| 关键词: 空气质量 面板数据 固定效应回归模型 分位数回归 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Empirical Study on Influencing Factors of Air Quality in China |
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HU Min, YANG Yi-ping
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School of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing 400067, China
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| Abstract: |
| In view of long-standing air pollution problem, this paper proposes to conduct air pollution analysis by the combination of air quality pollution index and air pollution control index, which is of real significance to improving air quality of China to certain extent. Based on the panel data of 31 main cities of China during 2008—2019, from the perspective of quantile regression, this paper analyzes the impact of air quality pollution index (NO2), economic growth (per capita GDP) and public transportation (the number of bus and trolley bus operations at the end of the year) on air quality situation in China. The results show that NO2 has bigger negative impact on air quality at low quantile and has lower impact on air quality at high quantile, on the contrary, however, at high quantile, the degree of the negative correlation between per capita GDP and air quality is bigger than that at low quantile, furthermore, the increase of public transportation utilization plays positively boosting role in the improvement of air quality in China, and the positive impact at high quantile is bigger than that at low quantile. |
| Key words: air quality panel data fixed effect regression model quantile regression |
