Regional heterogeneity in water pollution risk-economic development dynamics: a grid-scale analysis and impact factor exploration in China

Understanding water quality-economy relationships is essential for sustainable environmental governance. Yet most studies remain at macro scales, failing to capture localized nonlinear dynamics and spatial heterogeneity. This study addresses this gap by integrating high-resolution water quality and economic data with an Environmental Kuznets Curve model across 3,854 grid cells at a 0.5° resolution in China. To reveal hidden patterns, we further classify water quality -economy relationships into four distinct types. Results demonstrate that grid-level models substantially enhance explanatory power compared with provincial analyses. Alarmingly, 67.8% of provinces and 56.9% of grid cells show worsening trajectories, with only 18.5% improving. Moreover, 35.4% of grids fall into “High-Risk Rising EKCs” and 21.5% into “Safe Rising EKCs”. Vulnerability is greatest in areas with high population density, low per capita GDP, and limited sewage treatment capacity. By combining fine-scale spatial modeling with relationship-type classification, this study provides an innovative framework for analyzing localized environment-economy interactions, offering actionable insights for region-specific management and advancing the broader understanding of sustainable development pathways.