Socioeconomic Drivers of Environmental Pollution in China: A Spatial Econometric Analysis

Abstract

This paper studies the environmental pollution and its impacts in China using prefecture-level cities and municipalities data. Moran’sI, the widely used spatial autocorrelation index, provides a fairly strong pattern of spatial clustering of environmental pollution and suggests a fairly high stability of the positive spatial correlation. To investigate the driving forces of environmental pollution and explore the relationship between fiscal decentralization, economic growth, and environmental pollution, spatial Durbin model is used for this analysis. The result shows that fiscal decentralization of local unit plays a significant role in promoting the environmental pollution and the feedback effect of fiscal decentralization on environmental pollution is also positive, though it is not significant. The relationship of GDP per capita and environmental pollution shows inverted U-shaped curve. Due to the scale effect of secondary industry, the higher the level of secondary industry development in a unit is, the easier it is to attract the secondary industry in adjacent units, which mitigates the environmental pollution in adjacent units. Densely populated areas tend to deteriorate local environment, but environmental regulation in densely populated areas is often tighter than other areas, which reduces environmental pollution to a certain extent.

A Dynamic Spatial Panel of Subnational GHG Emissions: Environmental Effectiveness of Emissions Taxes in Spanish Regions

“…Our model also shows that a region's specialisation in industrial activities (measured by INDGDP and DENS) favours GHG emissions, which is consistent with theoretical expectations, and with the results in the literature [32,[34][35][36]. However, the negative incidence of the DENS*INDW interaction seems to indicate that when industrial congestion phenomena appears, part of the activity eventually shifts to other regions [54].…”

“…According to the model selection procedure proposed by Elhorst [38] and usually used in the literature [11,[30][31][32][33][34], based on the test for absence of spatial autocorrelation in the error term (LM error), presence of spatial autocorrelation in the lagged dependent variable (LM lag) and no general spatial autocorrelation (LM SAC), which can be seen in Table A4 of the Appendix A, the right model for explaining GHG emissions in the Spanish regions would be the dynamic spatial Durbin model (dynamic SDM); in other words, we would have to take into account spatial autocorrelation in the endogenous variable and also in the explanatory variables. Additionally, taking into account that each region has its own characteristics, many of which are unobservable and usually unchanging over time, we will use fixed effects.…”

“…The spatial correlation coefficient (θ), also positive and significant, demonstrates the existence of a contemporaneous global spatial spillover effect in emissions, of a magnitude (0.268) similar to the value predicted by the Pesaran test (0.324), which would indicate that the regions close to other high-polluting regions will very probably be high polluters, too. Liu et al [34] and Zhang et al [36] also found evidence that the behaviour of carbon emissions in neighbouring provinces of China would influence each other rather than be independent of each other. Moreover, the value of our coefficient θ is within the parameters (0.248-0.326) obtained by Zhang et al [36].…”

“…First, based on the literature on the economic geography theory, we have included a group of variables intended to show the importance of industry, one of the most polluting sectors, in each region. These variables are, on one hand, the level of industrial production (INDGDP) and the number of industries per square kilometre, or industrial density (DENS), which should favour the level of emissions; and on the other, the interaction of DENS with the weight of industry in regional production (INDW), intended to capture the degree of industrial congestion in the region [32,34]. A negative sign for this interaction could suggest a high concentration, and even congestion, of the industrial sector, which could lead to the displacement of industries to other regions.…”

“…The relationship of these inputs with emissions may be complementary or substitutional, but it would be reasonable for a greater investment in technology to help reduce emissions. Third, based on the literature on the Kuznets curve [32,34], we have included the square of regional industrial production (INDGDP 2 ). Finally, as is usual in the literature on emissions, we have included the elderly population (AGED) as a proxy for environmental awareness, which is lower in that age group; and also for the lower dynamism and less industrial activity in regions with an elderly population.…”