Abstract
This paper studies the emissions of SO2 and COD in China using fine-scale, countylevel data. Using a widely used spatial autocorrelation index, Moran’s I statistics, we first estimate the spatial autocorrelations of SO2 and COD emissions. Distinct patterns of spatial concentration are identified. To investigate the driving forces of emissions, we then use spatial econometric models, including a spatial error model (SEM) and a spatial lag model (SLM), to evaluate the effects of variables that reflect level of economic development, population density, and industrial structure. Our results show that these explanatory variables are highly correlated with the level of SO2 and COD emissions, though their impacts on SO2 and COD vary. Compared to ordinary least square regression, the advantages of SLM and SEM are demonstrated as they effectively reveal the existence and significance of spatial dependence. The SEM, in particular, is chosen over the SLM as the role of spatial correlation is stronger in the error model than in the lag model. Based on the research results, we present some preliminary policy recommendations, especially for those high–high cluster regions that face significant environmental degradation and challenge.
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