Spatiotemporal Heterogeneities of Carbon Emissions and Driving Factors of Railway Sector in China
Identifying the characteristics of carbon emissions and driving forces of the railway sector is essential for formulating effective measures to develop a green and low-carbon railway industry. This study systematically evaluated the direct and indirect carbon emissions from 2016 to 2021 generated by the railway sector of China, and analyzed the spatiotemporal dynamic changes of the carbon emissions. On this basis, by adopting the LMDI model, the key factors affecting the carbon emissions of railway sector were discerned. Moreover, the variations in the dominant factors of the carbon emissions over time, and the spatial heterogeneities in the dominant factors of the carbon emissions of the 18 railway bureaus, were analyzed. The results show that: 1) During the periods from 2016 to 2021, the carbon emissions of China's railway sector showed an overall upward trend, increasing from 57.7486 million tons to 64.2084 million tons, by 11.2%. The Shanghai Bureau, Beijing Bureau, Zhengzhou Bureau, Chengdu Bureau and Guangzhou Bureau substantially contributed to the increases of railway carbon emissions. In spatial, the carbon emissions of the 18 railway bureaus were characterized by lower emissions in the west and higher emissions in the east, mainly due to the regional differences in the socio-economic development, industrial structure and population density; 2) During 2016 to 2021, the decline in energy consumption intensity reduced the carbon emissions of the railway sector by 18.8654 million tons, while the changes in carbon emission intensity, economic benefits of per unit passenger and freight turnover, and operating capacity led to an increase of a sum of 25.3252 million tons of carbon emissions. When decomposing the contributions of each factor by sub-periods, it can be found that the impacts of these factors on the carbon emissions changed over time. Only the factor of carbon emission intensity showed a promoting effect in all sub-periods, the other three factors, as energy consumption intensity, economic benefits of per unit passenger and freight turnover, and operating capacity, had a conversion between promoting and inhibiting effects. 3) The dominant factors of carbon emissions across the 18 railway bureaus exhibited spatial heterogeneity. For instance, operating capacity was the main promoting factor for bureaus like Taiyuan, Beijing, Lanzhou, Nanning, Hohhot, Urumqi, and Qinghai-Tibet, while energy consumption intensity was the main inhibiting factor. For Shanghai, Kunming, Wuhan, Chengdu, Xi'an, Zhengzhou, Jinan, Shenyang, Nanchang, and Guangzhou, economic benefits per unit turnover was the main promoting factor, with energy consumption intensity as the main inhibiting factor. For Harbin, energy consumption intensity was the main promoting factor, while economic benefits per unit turnover was the main inhibiting factor. 4) The railway sector can reduce carbon emissions by optimizing transport organization to reduce empty car rates, optimizing energy structure, and retrofitting infrastructure for energy efficiency, while implementing differentiated emission reduction strategies tailored to each bureau's characteristics.