Towards Sustainable Energy–Water–Environment Nexus System Considering the Interactions between Climatic, Social and Economic Factors: A Case Study of Fujian, China

Abstract

This study develops a factorial Bayesian least-squares support vector machine-based energy–water–environment nexus system optimization (i.e., FBL–EWEO) model. FBL–EWEO can provide dependable predictions for electricity demand, quantify the interactions among different factors, and present optimal system planning strategies. The application to Fujian Province is driven by three global climate models (i.e., GCMs) under two SSPs, as well as two levels of economic and social factors’ growth rates. Results revealed in the planning horizon: (1) Fujian would encounter rainy and warming trends (e.g., [2.17645, 4.51247] mm/year of precipitation and [0.0072, 0.0073] °C/year of mean temperature); (2) economic, social, and climatic factors contribute 62.30%, 35.50%, and 1.47% to electricity demand variations; (3) electricity demand would grow with time (increase by [64.21, 74.79]%); (4) the ratio of new energy power would rise to [70.84, 73.53]%; (5) authorities should focus on photovoltaic and wind power plants construction (their proportions increase from [0.81, 1.83]% to [9.14, 9.56]%, [1.33, 4.16]% to [11.44, 15.58]%, respectively); and (6) air pollutants/CO2 emissions would averagely decline [51.97, 53.90]%, and water consumption would decrease [41.77%, 42.25]%. Findings provide technical support to sustainable development.