To gain a greater understanding of the spatial spillover effect of greenhouse gas emissions and their influencing factors, this paper provides a spatial analysis of four gas pollutants (CO 2 emissions, SO 2 emissions, NO x emissions, and dust emissions). Focusing on China, the paper also explores whether the four gas pollutants are influenced by the emissions of neighboring regions and other possible sources. The paper uses a global spatial autocorrelation analysis, local spatial association

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... lysis and spatial lag model for empirical work. The results suggest that CO 2 , SO 2 , and NO x emissions show significant positive results for both the spatial correlation and space cluster effect in provincial space distribution.CO 2 and NO x emissions have a significant positive spillover effect, while the SO 2 emissions' spatial spillover effect is positive but not significant. Economic growth and urbanization are the key determinants of CO 2 , dust, and NO x emissions, while energy efficiency and industrialization do not appear to play a role. This raises questions about the method of examining the spatial relationship between gas pollution, economic growth and urbanization in the future. regional spatial analysis is rather limited. The existing studies on China's regional emissions mostly focus on CO 2 emissions. Few studies have examined the aforementioned types of GHGs from a provincial perspective. In terms of methodology, few studies have conducted a spatial correlation. China is suffering from air pollution and is becoming the center of the increasing concerns regarding GHG emissions; therefore, this study aims to address the regional disparity and discuss implications. This paper analyzes the regional GHG emissions pattern and correlated effects for policy decision. The policy implications would improve the efficiency of the policy implementation. With the above background, this empirically-grounded study examines the relationship between economic factors and air pollutants (CO 2 , SO 2 , NO x and dust) through a space analysis and further discusses the regional disparity and effects. In this paper, we introduce a spatial vector to the GHG emissions ordinary least squares estimation model and compare the errors and predicting accuracy of the two models. The objective of this study is to find an approach with the spatial factor and provide applicable policy meanings by considering the spatial correlations. The research in this paper is structured as follows. First, this paper examines CO 2 , SO 2 , NO x , and dust spatial relationships by employing a global spatial autocorrelation method using Moran's I index to find whether the four gas emissions are positive correlated with the neighboring province's emissions. Additionally, a local indicator of spatial association (LISA) method is used to examine the GHGs' clustering effect. Finally, based on the above test, the first model is the ordinary least squares estimation model, and the second model is the spatial lag model, which was developed by adding the spatial dimension. We compare the four gas pollutants emissions determinants, which significantly affect the CO 2 , SO 2 , NO x emissions and dust in a spatially correlated condition. The remainder of the paper is structured as follows: Section 2 provides a theoretical background and suggests hypotheses; Section 3 discusses the model for the spatial econometric methodology and data; Section 4 discusses the data; Section 5 provides results, and Section 6 concludes. Table 8. Comparison of Factors Influencing GHG Emissions.