热带地理

热带地理 ›› 2020, Vol. 40 ›› Issue (1): 74-87.doi: 10.13284/j.cnki.rddl.003180

• 论文 • 上一篇    下一篇

基于制度背景与经济活动交互效应的广东省PM2.5污染驱动因素分析

黄永源1, 朱晟君1(), 王少剑2   

  1. 1. 北京大学 城市与环境学院,北京 100871
    2. 中山大学 地理科学与规划学院,广东省城市化与地理环境空间模拟重点实验室,广州 510275
  • 收稿日期:2019-04-18 修回日期:2019-08-12 出版日期:2020-01-10 发布日期:2020-02-24
  • 通讯作者: 朱晟君 E-mail:zhus@pku.edu.cn
  • 作者简介:黄永源（1996—）,男,广东东莞人,硕士研究生,主要研究方向为经济地理与产业动态,（E-mail）huangyy2018@pku.edu.cn。 。
  • 基金资助:
    国家自然科学基金面上项目(41971154);国家自然科学基金青年科学基金项目(41701115);国家自然科学基金重点项目(41731278)

Driving Force behind PM2.5 Pollution in Guangdong Province Based on the Interaction Effect of Institutional Background and Socioeconomic Activities

Huang Yongyuan1, Zhu Shengjun1(), Wang Shaojian2   

  1. 1. College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
    2. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
  • Received:2019-04-18 Revised:2019-08-12 Online:2020-01-10 Published:2020-02-24
  • Contact: Zhu Shengjun E-mail:zhus@pku.edu.cn

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摘要：

为探讨社会经济活动与制度背景的交互效应对PM2.5污染的影响,文章选取经济转型的前沿阵地——广东进行了实证分析。基于遥感影像获取的PM2.5质量浓度数据,运用空间马尔科夫链和空间计量模型,定量刻画了广东省1998—2015年PM2.5质量浓度的时空演变特征,并对不同制度背景下社会经济活动对PM2.5的影响进行了量化分析。结果表明：广东省地级市PM2.5质量浓度呈现先上升后缓慢下降的过程,PM2.5质量浓度形成“以珠江口为核心”的圈层空间结构,呈现“以佛山、广州和东莞为核心,先扩散、后集中”的空间演变特征。空间马尔科夫链结果表明：PM2.5质量浓度演变存在显著的空间交互性。若与PM2.5质量浓度高的区域为邻,则该区域PM2.5质量浓度增大的概率将会变大;而与PM2.5质量浓度低的区域为邻,该区域的PM2.5质量浓度的变化则不会受到明显的影响。社会经济因素和制度背景的交互项表明：高市场化水平能够削弱工业生产对PM2.5的正效应。分权化过程促进了工业生产对PM2.5污染的正效应;同时,显著降低了污染密集型工业生产的排放。全球化进程通过引进清洁技术促进技术进步从而降低PM2.5的污染。此外,环境规制通过降低工业生产排放和倒逼技术进步达到降霾效果。

关键词: PM2.5污染, 空间计量模型, 制度背景, 经济活动, 空间溢出效应, 广东省

Abstract:

In recent years, the driving force behind PM2.5 concentrations has been extensively investigated. Most existing literature has focused on the socioeconomic impact factors that contribute to PM2.5 concentrations; however, the importance of institutional background is ignored. During the period of economic transformation, marketization, decentralization, and globalization were the three important institutional processes in China; these processes were considered as the key factors that contributed to pollution. Therefore, the existing literature separately focuses on the socioeconomic activities and institutional background. However, the interaction effect of these factors on air pollution remains understudied. Therefore, the interaction effect of socioeconomic activities and institutional background on PM2.5 pollution in Guangdong Province, which is the frontier of China’s economic reforms and transformation, must be investigated. Using the PM2.5 concentration data obtained from remote sensing images, spatial Markov chain analysis was performed herein using a spatial econometric model to analyze the spatial- temporal pattern of PM2.5 concentrations and its driving factors. Following results were obtained: 1) the PM2.5 concentrations in the Guangdong Province first increased and then decreased. Over the past 18 years, the PM2.5 concentrations have increased by an average of 55.47%. PM2.5 concentrations in the seven prefecture-level cities in the Pearl River Delta region were within the first-level standard of 35 μg/m 3 according to the Ambient Air Quality Standards. 2) The PM2.5 concentrations showed a spatial distribution structure circling around the Pearl River Estuary and presented a diffusion pattern, followed by concentrating spatial-temporal evolving trend whose variation core is situated in Foshan, Guangzhou, and Dongguan cities. Spatial Markov chain analysis result identified a spatial spillover effect of PM2.5 concentrations in Guangdong Province. If a region is adjacent to the regions with high PM2.5 concentrations, then the probability of PM2.5 concentrations increasing in that region will be high. However, if a region is adjacent to the regions with low PM2.5 concentrations, the probability of PM2.5 concentrations decreasing in that region will be high. 3) Based on the Spatial Lag Model, we examined the interaction effect of institutional background and socioeconomic activities on PM2.5 concentrations after controlling the spatial spillover effect. Empirical results indicate the existence of an environment Kuznets curve during the economic development in Guangdong Province. Overall, pollution-intensive industrial production is one of the most important factors promoting PM2.5 emissions; energy-related technological improvement can significantly lower the PM2.5 concentrations. Marketization has a positive relation with PM2.5 concentrations because the ratio of non-state owned industry rose remarkably and reached 96.8% in 2015; however, regions with a high degree of marketization can weaken the positive effects of industrial production on PM2.5 concentrations. The reason is that a better market order has more stringent environmental standards and requires enterprises to have higher resource-utilization efficiency, which enable to emit relatively few pollutants during the industrial production. Excessive amounts of PM2.5 pollutants can be generated via industrial production during decentralization and the PM2.5 discharge from pollution-intensive industrial production can be reduced because of the “race-to-the-bottom” effect. PM2.5 pollution was reduced because of the introduction of clean technologies during globalization. Based on environmental regulations and global market demands, PM2.5 concentration can be reduced by reducing industrial emissions and incentivizing technological progress.

Key words: PM2.5 pollution, spatial econometric model, institutional background, economic activity, spatial spillover effect, Guangdong Province

中图分类号: 

  • X513