Spatio-temporal heterogeneity and scenario prediction of influencing factors of transportation carbon emissions in the Yangtze River Economic Belt, China

Abstract

Reducing carbon emissions in the transportation sector is a crucial aspect of China achieving its carbon peak and carbon neutrality goals. This study investigates the spatiotemporal differentiation characteristics of carbon emissions from transportation in the Yangtze River Economic Belt. Using the Geographically and Temporally Weighted Regression(GTWR) model to reveal the spatio-temporal heterogeneity of factors influencing transportation carbon emissions. Additionally, the Support Vector Regression(SVR) is trained to predict the carbon emissions reduction potential of transportation under different scenarios. The results showed that: From 2000 to 2021, the transportation emissions of the Yangtze River economic belt showed an overall upward trend. The high carbon emission regions are Jiangsu Province, Shanghai, Zhejiang Province and Hubei Province, and the emission center is located in Hubei Province. The total population, urbanization rate, per capita GDP, carbon emission intensity, passenger turnover volume, and civilian vehicle ownership all have a positive effect on transportation carbon emissions, while energy structure has a negative impact. Moreover, the influence of each factor exhibits significant spatial heterogeneity. Under three scenarios: baseline, low-carbon scenario I, and low-carbon scenario II, transportation carbon emissions in the Yangtze River Economic Belt are projected to peak by 2030. With the application of clean energy and a reduction in population size, low carbon scenario II demonstrates greater potential for carbon emission reduction, with a projected value of 88.552 million tons by 2032.

Keywords

• transportation carbon emissions
• geographically and temporally weighted regression
• support vector regression
• Yangtze River Economic Belt