Improving carbon dioxide emission predictions through a hybrid model utilising an advanced sparrow search algorithm

The dramatic increase in carbon dioxide emissions is a major cause of global warming and climate change, posing a serious threat to human development and profoundly affecting the global ecosystem. Currently, carbon dioxide emissions prediction studies rely heavily on a large amount of data support, and the accuracy of predictions is greatly reduced when data are scarce. In addition, the inherent uncertainty, volatility, and complexity of CO2 emission data further exacerbate the challenge of accurate prediction. To address these issues, a novel hybrid model for CO2 emission prediction is proposed in this paper. A feature screening method is designed for effective and reliable feature selection from the perspective of algorithm stability, which can improve the prediction performance. In order to accurately predict periodic sequences with limited training samples, a least squares support vector machine is employed in this paper. In addition, the parameters of the prediction model are optimised using the improved sparrow search algorithm and enhanced by Sin chaos mapping, adaptive inertia weights and Cauchy-Gauss variables. An empirical study is conducted using Chinese carbon emission data as a case study, and the validity and superiority of the proposed model are verified through comparative experiments. The results show that the improved SSA has stronger global optimisation capability and faster convergence speed. In addition, in terms of prediction results, the hybrid model has the best consistency with the actual data, which significantly improves the prediction accuracy.