Geographically Weighted Regression-Based Predictions of Water–Soil–Energy Nexus Solutions in Île-de-France

Abstract

Due to global urbanization, urban areas are encountering many environmental, social, and economic challenges. Different solutions have been proposed and implemented, such as nature-based solutions and green and blue infrastructure. Taking into consideration exogenous factors that are associated with these solutions is a crucial question to assess their possible effects. This study examines the possible explanatory factors and their evolution until the year 2054 of several solutions in the Île-de-France region: wastewater heat-recovery, surface geothermal energy, and heat-mitigation capacities of zones. This investigation is performed by a series of statistical models, namely the ordinary least squares (OLS) and the geographically weighted regressions (GWR), integrated within a geographic information system. The main driving factors were identified as land use/land cover and population distribution. The results show that GWR models capture a large part of spatial autocorrelation. Apropos of prediction results, areas with low, medium, and high potential for implementing specific solutions are determined. Furthermore, the implementation capacities of solutions are compared with the demand depicted as the need for slowing down the effects of surface urban heat islands and the dependence on fossil energy. Moreover, the heat mitigation capacities are not at all times distinctively linked to human activities. Further investigations are needed to discover the remaining possible reasons, particularly air quality, water, vegetation, and climate change.

Keywords

• water–soil–energy nexus
• nature-based solutions
• green and blue infrastructure
• heat recovery
• surface geothermal energy
• geographically weighted regression