The Spatial-Temporal Matching Characteristics of Water Resources and Socio-Economic Development Factors: A Case Study of Guangdong Province

Abstract

The spatial-temporal mismatch of water resources and socio-economic development in rapidly urbanized regions has been the focus of water resource management, and is one of the main limitations to sustainable development goals (SDGs). Guangdong Province is represented by a highly developed economy and society, and has been undergoing disproportionately rapid population growth during the past two decades. The uneven distribution and spatial mismatch of water resources have aggravated the contradictions between water supply demand. In this paper, we evaluate the matching degree of water resources and social economic elements, detect the spatial-temporal changing pattern of the matching degree, and reveal the changing mechanism using the combined methods of the Thiel index, the space–time Gini coefficient, and the Lorenz curve. The results show that (1) the temporal matching is relatively balanced and reasonable, while the spatial mismatch is prominent and deteriorating constantly, especially the connection between the amount of water and GDP; (2) the water volume pressure is mainly concentrated in the Peral River Delta and eastern and western Guangdong, while water consumption stress is relatively lower in northeastern Guangdong; and (3) the water volume inequality is dominated by an intercity difference and is primarily caused by regional differences. Based on the results, policy implications, such as the implementation of integrated water resource management plans, investment in the development of alternative water sources, as well as enhanced public education and the establishment of a water saving society, have been provided to alleviate the mismatch issue between water elements and socio-economic parameters, and to push the realization of water-related SDGs.

Spatial and Temporal Characterization of Mine Water Inrush Accidents in China, 2014–2022

“…The Thiel index has been widely used in evaluating the fairness of water resource allocation [17], the analysis of the spatial variability of resources [18], the assessment of the differences in the distribution of natural disasters [19], the degree of spatial differentiation of agricultural pollution [20], etc. The Thiel index is decomposable and can be decomposed by intra-regional and inter-regional differences, thus measuring the contribution of intraregional and inter-regional differences to the total differences [21].…”