Spatiotemporal Variations of Water Pollution-Intensive Enterprises and Influencing Factors in China’s “Two Control Zone” Policy Using Spatial Statistics and Spatial Autoregressive Models
The management of water environments is an important public policy issue. With accelerated industrialization, China’s water quality has deteriorated to one of the lowest levels in the world. Against this background, analyzing the spatial agglomeration characteristics and driving mechanisms of water...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/2772091 |
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| author | Fuqiang Dai Xia Zhang Qing Li Hao Liu |
| author_facet | Fuqiang Dai Xia Zhang Qing Li Hao Liu |
| author_sort | Fuqiang Dai |
| collection | DOAJ |
| description | The management of water environments is an important public policy issue. With accelerated industrialization, China’s water quality has deteriorated to one of the lowest levels in the world. Against this background, analyzing the spatial agglomeration characteristics and driving mechanisms of water pollution-intensive enterprises (WPIEs) can help local governments formulate regulations to better manage pollution by enterprises. This study used nearest-neighbor index analysis, global spatial autocorrelation analysis, hot spot analysis, and spatial regression to investigate spatial distribution changes in WPIEs. Further, a spatial autoregressive (SAR) model was used to examine the effects of enterprise agglomeration and environmental regulation on changes in the quantity of WPIEs in the Yangtze River Economic Belt (YREB) from 1998 to 2012. The results showed that the overall quantity of WPIEs in the YREB grew significantly, though growth in the downstream regions gradually slowed from 2005 to 2012. The distribution of WPIEs showed obvious spatial agglomeration characteristics, gradually expanding from a single-core pattern to a multicore one. Enterprise agglomeration and environmental regulation had scale effects on the spatial distribution of WPIEs, yet the effect of environmental regulation was not significant. However, other factors influencing the spatial distribution of WPIEs are complex, and the influence of the boundary effect was found to be more significant. |
| format | Article |
| id | doaj-art-0cccb97d7534476499ed73a74ad15885 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-0cccb97d7534476499ed73a74ad158852025-02-03T05:45:29ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/2772091Spatiotemporal Variations of Water Pollution-Intensive Enterprises and Influencing Factors in China’s “Two Control Zone” Policy Using Spatial Statistics and Spatial Autoregressive ModelsFuqiang Dai0Xia Zhang1Qing Li2Hao Liu3School of Public AdministrationSchool of EconomicsSchool of EconomicsResearch Centre for Economy of Upper Reaches of the Yangtze RiverThe management of water environments is an important public policy issue. With accelerated industrialization, China’s water quality has deteriorated to one of the lowest levels in the world. Against this background, analyzing the spatial agglomeration characteristics and driving mechanisms of water pollution-intensive enterprises (WPIEs) can help local governments formulate regulations to better manage pollution by enterprises. This study used nearest-neighbor index analysis, global spatial autocorrelation analysis, hot spot analysis, and spatial regression to investigate spatial distribution changes in WPIEs. Further, a spatial autoregressive (SAR) model was used to examine the effects of enterprise agglomeration and environmental regulation on changes in the quantity of WPIEs in the Yangtze River Economic Belt (YREB) from 1998 to 2012. The results showed that the overall quantity of WPIEs in the YREB grew significantly, though growth in the downstream regions gradually slowed from 2005 to 2012. The distribution of WPIEs showed obvious spatial agglomeration characteristics, gradually expanding from a single-core pattern to a multicore one. Enterprise agglomeration and environmental regulation had scale effects on the spatial distribution of WPIEs, yet the effect of environmental regulation was not significant. However, other factors influencing the spatial distribution of WPIEs are complex, and the influence of the boundary effect was found to be more significant.http://dx.doi.org/10.1155/2022/2772091 |
| spellingShingle | Fuqiang Dai Xia Zhang Qing Li Hao Liu Spatiotemporal Variations of Water Pollution-Intensive Enterprises and Influencing Factors in China’s “Two Control Zone” Policy Using Spatial Statistics and Spatial Autoregressive Models Discrete Dynamics in Nature and Society |
| title | Spatiotemporal Variations of Water Pollution-Intensive Enterprises and Influencing Factors in China’s “Two Control Zone” Policy Using Spatial Statistics and Spatial Autoregressive Models |
| title_full | Spatiotemporal Variations of Water Pollution-Intensive Enterprises and Influencing Factors in China’s “Two Control Zone” Policy Using Spatial Statistics and Spatial Autoregressive Models |
| title_fullStr | Spatiotemporal Variations of Water Pollution-Intensive Enterprises and Influencing Factors in China’s “Two Control Zone” Policy Using Spatial Statistics and Spatial Autoregressive Models |
| title_full_unstemmed | Spatiotemporal Variations of Water Pollution-Intensive Enterprises and Influencing Factors in China’s “Two Control Zone” Policy Using Spatial Statistics and Spatial Autoregressive Models |
| title_short | Spatiotemporal Variations of Water Pollution-Intensive Enterprises and Influencing Factors in China’s “Two Control Zone” Policy Using Spatial Statistics and Spatial Autoregressive Models |
| title_sort | spatiotemporal variations of water pollution intensive enterprises and influencing factors in china s two control zone policy using spatial statistics and spatial autoregressive models |
| url | http://dx.doi.org/10.1155/2022/2772091 |
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