Energy-Related CO2 Emissions Forecasting Using an Improved LSSVM Model Optimized by Whale Optimization Algorithm

Abstract

Accurate and reliable forecasting on energy-related carbon dioxide (CO2) emissions is of great significance for climate policy decision making and energy planning. Due to the complicated nonlinear relationships of CO2 emissions with its driving forces, the accurate forecasting for CO2 emissions is a tedious work, which is an important issue worth studying. In this study, a novel CO2 emissions prediction method is proposed which employs the latest nature-enlightened optimization method, named the Whale optimization algorithm (WOA), to search the optimized values of two parameters of LSSVM (least squares support vector machine), namely the WOA-LSSVM model. Meanwhile, the driving forces of CO2 emissions including GDP (gross domestic product), energy consumption and population are chosen to be the import variables of the proposed WOA-LSSVM method. Taking China’s CO2 emissions as an instance, the effectiveness of WOA-LSSVM-based CO2 emissions forecasting is verified. The comparative analysis results indicate that the WOA-LSSVM model is significantly superior to other selected models, namely FOA (fruit fly optimization algorithm)-LSSVM, LSSVM, and OLS (ordinary least square) models in terms of CO2 emissions forecasting. The proposed WOA-LSSVM model has the potential to effectively improve the accuracy of CO2 emissions forecasting. Meanwhile, as a new nature-enlightened heuristic optimization algorithm, the WOA has the prospect for wide application.

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