Title: Research on regional low-carbon economy analysis model based on improved SVM

Authors: Na Xiao; Yan Liu

Addresses: Department of Finance and Accounting, Inner Mongolia Honder College of Arts and Sciences, Hohhot, 010070, China ' Department of Finance and Accounting, Inner Mongolia Honder College of Arts and Sciences, Hohhot, 010070, China

Abstract: In order to further grasp the level of low-carbon economic development in the region and provide feasible suggestions for regional development, this paper proposes a regional low-carbon economy analysis and prediction model based on multiple strategies improved sparrow search algorithm optimised SVM (ISSA-SVM). Where, support vector machine (SVM) is used as the basic prediction method, and sparrow search optimisation algorithm improved based on multi strategy and combine with PLS component extraction is introduced to optimise the parameters of SVM and further enhance its predictive performance. Simulation results show that the designed prediction model based on ISSA-SVM has good prediction performance. Furthermore, the prediction error rate of this model has always been stable below 2.5%, and its precision is high. Therefore, the proposed model can be used to analyse and predict the development level of low-carbon economy in practice, which has certain feasibility.

Keywords: low-carbon economy; support vector machine; SVM; sparrow search algorithm; SSA; prediction model.

DOI: 10.1504/IJBIC.2026.155143

International Journal of Bio-Inspired Computation, 2026 Vol.27 No.4, pp.258 - 267

Received: 23 Jan 2024
Accepted: 11 Jan 2025
Published online: 28 Jul 2026 *

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