Affiliation:
1. Department of Communication, Xiamen University of Technology, Xiamen 361024, China
2. School of Environmental Science and Engineering, Xiamen University of Technology, Xiamen 361024, 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.
Funder
Fujian provincial social science planning project
Xiamen University of Technology
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
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