Spatial Pattern and Associations of Wildlife Smuggling in China: An Analysis Based on a Spatial Econometric Model

Wildlife trafficking poses substantial risks to biodiversity, ecosystem stability, and public health. Using panel data for 31 Chinese provinces, this study applies global spatial diagnostics, the spatial gravity model, and a two-way fixed-effects spatial Durbin model (SDM) to investigate the spatial distribution and socioeconomic drivers of wildlife smuggling. Global Moran’s I confirms significant positive spatial autocorrelation, while the gravity analysis reveals clear spatial disparities: High-incidence clusters are concentrated in coastal and southern border provinces integrated into international trade and logistics networks, whereas inland and northwestern regions exhibit relatively low levels. Regression results indicate that wildlife smuggling is positively associated with regional GDP, while it is negatively associated with government intervention. Protected-area coverage exhibits a positive association with smuggling before 2019 but turns negative and statistically insignificant after 2019, suggesting a possible shift in enforcement and monitoring dynamics during the postpandemic period, social consumption, digital infrastructure, and terrain ruggedness exert negative effects; industrial structure shows a marginally positive impact. Moreover, spillover estimates reveal that government intervention, industrial structure, and protected-area coverage in neighboring provinces generate significant positive displacement effects, suggesting that conservation expansion or stricter enforcement in one province may shift smuggling activities to surrounding regions. By contrast, spillovers from digital infrastructure and social consumption are negative but statistically insignificant. Heterogeneity results show that the influence of socioeconomic and institutional factors on wildlife smuggling varies significantly before and after the COVID-19 pandemic, with notable shifts in smuggling patterns and enforcement effectiveness in the post-2019 period. These findings highlight the importance of cross-regional coordination, digital governance tools, green industrial transformation, and international cooperation to effectively curb wildlife trafficking and strengthen biodiversity protection.