Prediction of Carbon Emission of the Transportation Sector in Jiangsu Province-Regression Prediction Model Based on GA-SVM

Zhenggang Huo (), Xiaoting Zha, Mengyao Lu, Tianqi Ma and Zhichao Lu
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Zhenggang Huo: College of Civil Science and Engineering, Yangzhou University, Yangzhou 225127, China
Xiaoting Zha: College of Civil Science and Engineering, Yangzhou University, Yangzhou 225127, China
Mengyao Lu: College of Civil Science and Engineering, Yangzhou University, Yangzhou 225127, China
Tianqi Ma: College of Civil Science and Engineering, Yangzhou University, Yangzhou 225127, China
Zhichao Lu: College of Civil Science and Engineering, Yangzhou University, Yangzhou 225127, China

Sustainability, 2023, vol. 15, issue 4, 1-15

Abstract: To meet the twin carbon goals of “carbon peak” and “carbon neutrality”, it is crucial to make scientific predictions about carbon emissions in the transportation sector. The following eight factors were chosen as effect indicators: population size, GDP per capita, civil vehicle ownership, passenger and freight turnover, urbanization rate, industry structure, and carbon emission intensity. Based on the pertinent data from 2002 to 2020, a support vector machine model, improved by a genetic algorithm (GA-SVM), was created to predict the carbon peak time under three distinct scenarios. The penalty factor c and kernel function parameter g of the support vector machine model were each optimized using a genetic algorithm, a particle swarm algorithm, and a whale optimization algorithm. The results indicate that the genetic algorithm vector machine prediction model outperforms the particle swarm algorithm vector machine model and the whale optimization vector machine. As a result, the model integrating the support vector machine and genetic algorithm can more precisely predict carbon emissions and the peak time for carbon in Jiangsu province.

Keywords: carbon peak; carbon emissions; genetic algorithm; support vector machine; regression prediction model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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