Title: Convergence analysis of urban green traffic carbon emission based on grey prediction model

Authors: Lede Niu; Mei Pan; Liran Xiong

Addresses: School of Tourism and Geographical Sciences, Yunnan Normal University, Kunming 650500, China ' School of Tourism and Geographical Sciences, Yunnan Normal University, Kunming 650500, China ' School of Tourism and Geographical Sciences, Yunnan Normal University, Kunming 650500, Yunnan, China; Research Centre for the Opening of Southwest China and Frontier Security, Yunnan Normal University, Kunming, Yunnan, China

Abstract: In order to overcome the big error of convergence test results of traditional methods, a convergence analysis method of urban green traffic carbon emission based on grey prediction model is proposed. The carbon emission data of three major areas of urban green traffic in recent years are collected, and the urban green traffic carbon emission is estimated according to the data collection results, and the spatial characteristics of the obtained urban green traffic carbon emission data are analysed data processing, including differentiation processing and clustering processing, based on the grey prediction model to build a convergence analysis model of urban green traffic carbon emissions, using the model to carry out convergence analysis of urban green traffic carbon emissions. The experimental results show that the standard error of the convergence test results of the proposed method is smaller than traditional methods, which verifies the effectiveness of the proposed method.

Keywords: grey prediction model; urban green traffic; carbon emissions; convergence analysis.

DOI: 10.1504/IJGEI.2020.111180

International Journal of Global Energy Issues, 2020 Vol.42 No.5/6, pp.285 - 301

Received: 31 Dec 2019
Accepted: 24 Apr 2020

Published online: 26 Oct 2020 *

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