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<p>The accelerating pace of industrialization and urbanization in China has led to severe air pollution challenges with significant public health implications. In response, Chinese authorities have implemented stricter emission controls for high-polluting industries. Resource-based enterprises–characterized by high energy consumption and emissions–typically operate in geographic clusters, potentially concentrating environmental impacts. However, such clustering may simultaneously facilitate regulatory oversight and environmental technology diffusion, creating a complex relationship with air quality outcomes. This research examined the spatial relationship between resource-based enterprise agglomeration and air pollution using comprehensive microdata from 542,076 resource-based enterprises across China (2003–2022). By integrating enterprise geographical coordinates with high-resolution raster-PM2.5 data and employing DBSCAN algorithm combined with Herfindahl index measurements, the study provides granular insights into this environmental-economic relationship. The findings reveal a nuanced inverted U-shaped relationship between resource-based enterprise agglomeration and air pollution levels. This suggests that initial clustering increases pollution until reaching a threshold, after which additional agglomeration may facilitate environmental improvements. Environmental regulation strength moderates this relationship–stringent policies flatten the inverted U-curve, effectively mitigating pollution impacts from enterprise concentration. Industrial composition significantly influences these dynamics. Higher concentrations of resource extraction enterprises steepen the inverted U-shaped curve, intensifying pollution effects from agglomeration. However, resource manufacturing and supply industries demonstrate no significant moderating effects on this relationship. Regional analysis identifies substantial heterogeneity across China. Eastern regions display more pronounced pollution effects from enterprise agglomeration compared to central and western areas, likely reflecting differences in industrial density, economic development stages, and environmental governance capacities. Our findings demonstrate the applicability of enterprise spatial agglomeration measurements using DBSCAN algorithm and Herfindahl index in environmental impact assessment. The results provide concrete policy implications for environmental governance, calling for differentiated policies instead of a one-size-fits-all approach. This includes implementing stricter environmental standards in high-impact zones like eastern regions and mining-concentrated areas, and strengthening pollution control in regions where enterprise concentration approaches the curve’s critical threshold.</p>

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How spatial clustering of resource-based enterprises affects air pollution: micro evidence from China

  • 期刊论文

  • 发表日期：

    2025年9月11日

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  • Jialu Ren,
  • Yong Zhou

摘要

The accelerating pace of industrialization and urbanization in China has led to severe air pollution challenges with significant public health implications. In response, Chinese authorities have implemented stricter emission controls for high-polluting industries. Resource-based enterprises–characterized by high energy consumption and emissions–typically operate in geographic clusters, potentially concentrating environmental impacts. However, such clustering may simultaneously facilitate regulatory oversight and environmental technology diffusion, creating a complex relationship with air quality outcomes. This research examined the spatial relationship between resource-based enterprise agglomeration and air pollution using comprehensive microdata from 542,076 resource-based enterprises across China (2003–2022). By integrating enterprise geographical coordinates with high-resolution raster-PM2.5 data and employing DBSCAN algorithm combined with Herfindahl index measurements, the study provides granular insights into this environmental-economic relationship. The findings reveal a nuanced inverted U-shaped relationship between resource-based enterprise agglomeration and air pollution levels. This suggests that initial clustering increases pollution until reaching a threshold, after which additional agglomeration may facilitate environmental improvements. Environmental regulation strength moderates this relationship–stringent policies flatten the inverted U-curve, effectively mitigating pollution impacts from enterprise concentration. Industrial composition significantly influences these dynamics. Higher concentrations of resource extraction enterprises steepen the inverted U-shaped curve, intensifying pollution effects from agglomeration. However, resource manufacturing and supply industries demonstrate no significant moderating effects on this relationship. Regional analysis identifies substantial heterogeneity across China. Eastern regions display more pronounced pollution effects from enterprise agglomeration compared to central and western areas, likely reflecting differences in industrial density, economic development stages, and environmental governance capacities. Our findings demonstrate the applicability of enterprise spatial agglomeration measurements using DBSCAN algorithm and Herfindahl index in environmental impact assessment. The results provide concrete policy implications for environmental governance, calling for differentiated policies instead of a one-size-fits-all approach. This includes implementing stricter environmental standards in high-impact zones like eastern regions and mining-concentrated areas, and strengthening pollution control in regions where enterprise concentration approaches the curve’s critical threshold.