Locational determinants of emissions from pollution-intensive firms in urban areas.
Locational determinants of emissions from pollution-intensive firms in urban areas.
复制标题
城市地区污染密集型企业排放的地点决定因素。
DOI：
10.1371/journal.pone.0125348
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Zhang L
中科院分区：
文献类型：
--
作者：
Zhou M;Tan S;Guo M;Zhang L
工业污染仍然是世界上许多地区面临的最严峻挑战之一。描述工业污染的决定因素应提供重要的管理意义。不幸的是，由于缺乏高质量的数据，很少有研究使用地理方法系统地检查了位置决定因素。本文旨在通过访问中国湖州市717家污染密集型企业的污染源普查数据来填补这一空白。该数据记录了湖州市717家污染密集型企业的废水和固体废物排放量。采用空间探索性分析方法，分析了城市废弃物排放的空间依赖性和局部集聚性。结果表明，城市废弃物排放在空间上呈现显著的正相关关系。高-高热点区总体上向城市边界集中，低-低热点区向太湖方向集中。通过空间回归确定了它们的位置决定因素。特别是靠近城市边界和县公路的企业，容易产生更多的废物排放。靠近货物转运站或太湖的企业更有可能产生较低的废物排放。人口稠密的地区更有可能产生固体废物排放。河流附近的企业表现出更高的废水排放量。此外，控制变量（企业规模、所有权、经营时间和行业类型）也有显著影响。本方法可适用于其他领域，并进一步为工业污染控制实践提供参考。我们的研究提高了对城市地区污染密集型企业排放决定因素的认识。
Industrial pollution has remained as one of the most daunting challenges for many regions around the world. Characterizing the determinants of industrial pollution should provide important management implications. Unfortunately, due to the absence of high-quality data, rather few studies have systematically examined the locational determinants using a geographical approach. This paper aimed to fill the gap by accessing the pollution source census dataset, which recorded the quantity of discharged wastes (waste water and solid waste) from 717 pollution-intensive firms within Huzhou City, China. Spatial exploratory analysis was applied to analyze the spatial dependency and local clusters of waste emissions. Results demonstrated that waste emissions presented significantly positive autocorrelation in space. The high-high hotspots generally concentrated towards the city boundary, while the low-low clusters approached the Taihu Lake. Their locational determinants were identified by spatial regression. In particular, firms near the city boundary and county road were prone to discharge more wastes. Lower waste emissions were more likely to be observed from firms with high proximity to freight transfer stations or the Taihu Lake. Dense populous districts saw more likelihood of solid waste emissions. Firms in the neighborhood of rivers exhibited higher waste water emissions. Besides, the control variables (firm size, ownership, operation time and industrial type) also exerted significant influence. The present methodology can be applicable to other areas, and further inform the industrial pollution control practices. Our study advanced the knowledge of determinants of emissions from pollution-intensive firms in urban areas.
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