环境管制对佛山市污染密集型企业空间格局变化的影响——基于2004年、2008年、2013年经济普查数据的实证

以佛山市2004年、2008年和2013年3次经济普查数据库中的污染密集型企业为研究对象,采用格数据空间回归模型,研究了佛山市污染密集型企业空间格局变化特征,并探究了环境管制对污染密集型企业及不同行业的企业空间格局变化的影响。研究发现:① 环境管制严格程度的变化使其从影响企业区位变动次要因素逐渐成为推动企业空间格局变化的重要因素,说明了环境管制变化会引起其对污染密集型企业区位变动作用程度的变化。② 在城市内部尺度,地方政府权衡污染行业整治利弊后,针对不同区域不同行业制定的差异化环境管制力度和模式,形成了不同行业空间格局响应的差异。研究结果对理解环境管制影响污染行业空间格局变化提供更微观的视角,对环境管制政策实现企业更替和产业升级具有一定启示,也对政府产业和环境政策制定具有借鉴意义。

The Impact of Environmental Regulation on the Spatial Pattern Evolution of Pollution-intensive Enterprises in Foshan City: An Empirical Study Based on 2004 to 2013 Economic Census Data

Little attention has been paid attention to the intra-urban scale of spatial distribution of pollution-intensive enterprises (PIEs). Most previous studies have examined the macro-middle scale impact of environmental regulation (ER) on the location changes of pollution-intensive industries (PIIs). Foshan City, in Guangdong Province, China, provides an excellent location-based case study regarding ER effect on PIEs. From the Foshan’s experience, we can exam how ER affects the spatial pattern of PIEs location within a metropolitan area. And in the Foshan study, it improves the air quality and other environmental factors by adherence to decades of environmental controls. Thus, this article employs the data of National Economic Census of 2004, 2008 and 2013 and analyzes the PIEs in Foshan City according to industry codes of Industrial Classification for National Economic Activities. Using kernel density estimation and spatial autoregression model, investigators explore the evolution of spatial distribution of PIEs in Foshan City in 2004, 2008 and 2013. Moreover, the article emphasizes the influence of dynamic ER on the location changes of PIEs at the incorporated municipal district, or urban level. In addition, we selected 4 typical firms in 4 industry sectors, so as to examine the spatial responses of different PIIs operating under ER controls. This study’s findings indicate that PIEs first concentrate in the periphery of the central city, then spread into the suburban areas which is consistent with the trend of suburbanization and borderization. Also ER has gradually become an important factor affecting relocation of PIEs at the municipal level. However, the sign of ER coefficient in the model is not static, but rather reversed as environmental policies being stricter. In detail, new businesses preferred areas with lax ER and under weak environmental controls. In contrast, enterprises tend to enter areas with stringent ER and agglomeration effect as environmental controls becoming stricter. We attribute this shift to improvement of industry awareness of environmental protection and more environmental investment to achieve higher environmental standards. Furthermore, we find the spatial response of different industries under ER differs, and there is industry heterogeneity when ER affects the spatial distribution evolution of PIEs. We attribute this pattern to difference in industry characteristics and pollution situations. These findings indicate that the governments could design proper regulations for each industry after they have weighted comprehensive interests such as consequences business relocation. The differences in environmental policies have made differentiated spatial patterns in 4 different PIEs. This article aims to complement research on the impact of ER on PIEs on the intra-urban scale by examining, in detail, the spatial pattern evolution of PIEs at the municipal level. Our results provide a finer geographic perspective for understanding the location changes of PIIs, and offer policy implications for governments to achieve business replacement and industry upgrading.