Title: Peak carbon emission prediction of tourist attractions based on fuzzy support vector machine

Authors: Xiumei Feng

Addresses: Department of Management, Northeast Petroleum University, Qinhuangdao, 066000, China

Abstract: In order to address the issues of low stability, low sensitivity, and low accuracy in traditional peak carbon emission prediction methods, a peak carbon emission prediction of tourist attractions based on fuzzy support vector machine is proposed. Tourist attraction carbon emission data is collected, and various factors such as average emission, total emission, and growth rate are obtained through statistical analysis. By combining Pearson correlation coefficient and information gain, the interrelationships between various factors are determined, clarifying the key influencing factors of tourist attraction carbon emissions. Key influencing factors are taken as input vectors, and carbon emission peaks are taken as output vectors to construct an optimised fuzzy support vector machine. The experimental results demonstrate that this method has high stability, sensitivity, and accuracy, enabling precise prediction of tourist attraction carbon emission peaks. This study is of great significance for promoting environmental protection, optimising resource utilisation, and guiding policy formulation.

Keywords: fuzzy support vector machine; tourist attractions; carbon emissions; prediction; Pearson correlation coefficient.

DOI: 10.1504/IJEP.2024.142553

International Journal of Environment and Pollution, 2024 Vol.74 No.1/2/3/4, pp.66 - 78

Received: 20 Dec 2023
Accepted: 13 May 2024
Published online: 08 Nov 2024 *

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