School of Business, Suzhou University of Science and Technology, Suzhou, Jiangsu Province, China.
College of Management and Economics, Tianjin University, Tianjin, China.
PLoS One. 2022 Jan 11;17(1):e0262444. doi: 10.1371/journal.pone.0262444. eCollection 2022.
The complex correlation between regions caused by the externality of air pollution increases the difficulty of its governance. Therefore, analysis of the spatio-temporal network of air pollution (STN-AP) holds great significance for the cross-regional coordinated governance of air pollution. Although the spatio-temporal distribution of air pollution has been analyzed, the structural characteristics of the STN-AP still need to be clarified. The STN-AP in the Yangtze River Delta urban agglomeration (YRDUA) is constructed based on the improved gravity model and is visualized by UCINET with data from 2012 to 2019. Then, its overall-individual-clustering characteristics are analyzed through social network analysis (SNA) method. The results show that the STN-AP in the YRDUA was overall stable, and the correlation level gradually improved. The centrality of every individual city is different in the STN-AP, which reveals the different state of their interactive mechanism. The STN-AP could be subdivided into the receptive block, overflow block, bidirectional block and intermediary block. Shanghai, Suzhou, Hangzhou and Wuxi could be key cities with an all above degree centrality, betweenness centrality and closeness centrality and located in the overflow block of the STN-AP. This showed that these cities had a greater impact on the STN-AP and caused a more pronounced air pollution spillovers. The influencing factors of the spatial correlation of air pollution are further determined through the quadratic assignment procedure (QAP) method. Among all factors, geographical proximity has the strongest impact and deserves to be paid attention in order to prevent the cross-regional overflow of air pollution. Furthermore, several suggestions are proposed to promote coordinated governance of air pollution in the YRDUA.
空气污染的外部性导致区域间的复杂关联增加了其治理的难度。因此,分析空气污染的时空网络(STN-AP)对于跨区域协同治理空气污染具有重要意义。虽然已经分析了空气污染的时空分布,但仍需要阐明 STN-AP 的结构特征。基于改进的引力模型构建了长三角城市群(YRDUA)的 STN-AP,并利用 2012 年至 2019 年的数据,通过 UCINET 进行可视化。然后,通过社会网络分析(SNA)方法分析其整体-个体-聚类特征。结果表明,YRDUA 的 STN-AP 整体稳定,相关性水平逐渐提高。每个城市在 STN-AP 中的中心度不同,揭示了它们相互作用机制的不同状态。STN-AP 可以细分为接受块、溢出块、双向块和中介块。上海、苏州、杭州和无锡的程度中心度、中间中心度和接近中心度均较高,位于 STN-AP 的溢出块中,这表明这些城市对 STN-AP 的影响更大,导致更明显的空气污染溢出。通过二次分配程序(QAP)方法进一步确定了空气污染空间相关性的影响因素。在所有因素中,地理邻近性的影响最大,值得关注,以防止跨区域空气污染的溢出。此外,还提出了一些建议,以促进长三角地区的空气污染协同治理。