China’s carbon dioxide emission forecast based on improved marine predator algorithm and multi-kernel support vector regression

Abstract

Global warming has constituted a major global problem. Carbon dioxide emissions from the burning of fossil fuels are the main cause of global warming. Therefore, carbon dioxide emission forecasting has attracted widespread attention. Aiming at the problem of carbon dioxide emissions forecasting, this paper proposes a new hybrid forecasting model of carbon dioxide emissions, which combines the marine predator algorithm (MPA) and multi-kernel support vector regression. For further strengthening the prediction accuracy, a novel variant of MPA is proposed, called EGMPA, which introduces the elite opposition-based learning strategy and the golden sine algorithm into MPA. Algorithm test results show that EGMPA can effectively improve the convergence speed and optimization accuracy. The carbon dioxide emission data of China from 1965 to 2020 are taken as the research objects. Root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the performance of the proposed model. The proposed multi-kernel support vector regression model is used to forecast China's carbon dioxide emissions during the "14th Five-Year Plan" period. The results show that the proposed model has RMSE of 37.43 Mt, MAE of 30.63 Mt, and MAPE of 0.32%, which significantly improves the prediction accuracy and can accurately and effectively predict China's carbon dioxide emissions. During the "14th Five-Year Plan" period, China's carbon dioxide emissions will continue to show an increasing trend, but the growth rate will slow down significantly.

A Review of Macroscopic Carbon Emission Prediction Model Based on Machine Learning

“…Particle swarm optimization algorithm [22,42,48], genetic algorithm [16,69], Improved particle swarm optimization algorithm based on noninertial weight coefficient [49,50,70,71] SVM Particle swarm optimization algorithm [29,[72][73][74], Firefly Algorithm [26], FCS Algorithm [28], chicken swarm optimization algorithm [19], Fruit Fly Algorithm [53], Lion Optimizer [75], genetic algorithm [55,75], Grey Wolf Optimizer [76], Shuffled Frog Leaping Algorithm [51], Ocean Predator Algorithm [77], Bacterial Foraging Optimization Algorithm [52], Whale Optimization Algorithm [78], sparrow search algorithm [57], Gaussian perturbation bat algorithm [54], Butterfly Optimization Algorithm [19], Salp Swarm Algorithm [79] LSTM Bilstm [80], Attention-LSTM [81], sparrow search algorithm [31] RF…”

Towards greener futures: SVR-based CO2 prediction model boosted by SCMSSA algorithm

“…Notably, the results demonstrate the superior accuracy of the SVR-SHO model, achieving a correlation coefficient (R) of 0.94 in flux pressure prediction. Quin et al introduced a new Marine Predator Algorithm (MPA) that addresses the drawbacks of poor convergence accuracy and the propensity for standard MPA to enter a local optimum state by combining it with the Golden sine algorithm with Elite opposition-based learning (EGMPA) [ 25 ]. Additionally, the authors introduced a novel multi-kernel support vector regression that solves the selection of variables problem by using multiple kernel functions.…”