Evaluating Economic Growth, Industrial Structure, and Water Quality of the Xiangjiang River Basin in China Based on a Spatial Econometric Approach

Economic and Sustainability Inequalities and Water Consumption of European Union Countries

Water scarcity is becoming a global concern for many reasons as its consumption increases. This research aimed to analyze sustainability inequalities in the water consumption of EU countries. Descriptive statistics using data for four AQUASTAT periods (2002, 2007, 2012, and 2017), and quotients for the AQUASTAT 2017 period, were calculated using a proposed econometric model. The main results were that countries with high GPD and population showed high water stress and total water withdrawal. Countries with lower industry-value-added-to-GDP quotients were among those with higher industrial water use efficiency, while low water-services-use-efficiency quotients were associated with high services value added to GDP. Suggestions for policymakers are provided and formula application guidelines for regional-level comparisons are described.

The Spatiotemporal Dynamics and Socioeconomic Factors of SO2 Emissions in China: A Dynamic Spatial Econometric Design

With the great strides of China’s economic development, air pollution has become the norm that is a cause of broad adverse influence in society. The spatiotemporal patterns of sulfur dioxide (SO2) emissions are a prerequisite and an inherent characteristic for SO2 emissions to peak in China. By exploratory spatial data analysis (ESDA) and econometric approaches, this study explores the spatiotemporal characteristics of SO2 emissions and reveals how the socioeconomic determinants influence the emissions in China’s 30 provinces from 1995 to 2015. The study first identifies the overall space- and time-trend of regional SO2 emissions and then visualizes the spatiotemporal nexus between SO2 emissions and socioeconomic determinants through the ESDA method. The determinants’ impacts on the space–time variation of emissions are also confirmed and quantified through the dynamic spatial panel data model that controls for both spatial and temporal dependence, thus enabling the analysis to distinguish between the determinants’ long- and short-term spatial effects and leading to richer and novel empirical findings. The study emphasizes close spatiotemporal relationships between SO2 emissions and the socioeconomic determinants. China’s SO2 emissions variation is the multifaceted result of urbanization, foreign direct investment, industrial structure change, technological progress, and population in the short run, and it is highlighted that, in the long run, the emissions are profoundly affected by industrial structure and technology.