Key Takeaways & Executive Findings
- •• • Carbon emissions from China's railway sector increased by 11.2% from 57.7486 million tons in 2016 to 64.2084 million tons in 2021, with indirect emissions from electricity rising from 68.8% to 78.8% of total, underscoring the need for grid decarbonization. • • Energy consumption intensity decline cut emissions by 18.8654 million tons, but carbon emission intensity, economic benefits per unit turnover, and operating capacity collectively added 25.3252 million tons, indicating that efficiency gains were offset by growth in activity and economic factors. • • Spatially, emissions were higher in eastern China; Shanghai, Beijing, Guangzhou, and Chengdu bureaus had the highest emissions (555.71, 513.90, 458.78, and 427.49 million tons respectively), reflecting regional economic disparities. • • Dominant driving factors varied across 18 bureaus: operating capacity was the main promoter for 7 bureaus (e.g., Taiyuan, Beijing), while economic benefits per unit turnover was the main promoter for 10 bureaus (e.g., Shanghai, Guangzhou), and Harbin was uniquely driven by energy consumption intensity, highlighting the need for tailored mitigation strategies.
Abstract
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.
1. Introduction
The railway sector, a cornerstone of China's transportation infrastructure, faces mounting pressure to align with national carbon peak and carbon neutrality goals. Despite its reputation as a relatively low-carbon mode of transport, the sector's absolute emissions have risen due to increased demand and operational intensity. Previous studies often treated the railway system as a monolithic entity, overlooking the substantial regional disparities in economic development, energy mix, and operational characteristics across China's 18 railway bureaus. This oversight has led to generic mitigation recommendations that fail to address the specific drivers of emissions in each region, thereby limiting the effectiveness of policy interventions.
This study bridges that gap by conducting a comprehensive spatiotemporal analysis of carbon emissions from China's railway sector from 2016 to 2021, employing the Logarithmic Mean Divisia Index (LMDI) decomposition to disentangle the contributions of carbon emission intensity, energy consumption intensity, economic benefits per unit turnover, and operating capacity. By examining both temporal trends and spatial heterogeneity across the 18 bureaus, this research identifies which factors dominate in different contexts, enabling the design of targeted, region-specific emission reduction strategies. The findings not only quantify the sector's emission trajectory but also provide a granular understanding of the underlying drivers, offering actionable insights for policymakers and railway operators seeking to balance economic growth with environmental sustainability.
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LI Min, ZHU Zhiyao, LIU Qin, DU Pengbo, XIONG Xin, SUN Jiazhen, WANG Yinsheng (2026). Spatiotemporal Heterogeneities of Carbon Emissions and Driving Factors of Railway Sector in China. Journal of Environmental Engineering Technology. https://doi.org/10.13205/j.hjgc.202605023
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Frequently Asked Questions
What is the relative contribution of indirect emissions from electricity consumption to total railway carbon emissions, and how has this evolved from 2016 to 2021?
Indirect emissions from electricity consumption increased from 68.8% to 78.8% of total emissions over the study period, reflecting the sector's large-scale electrification. This shift underscores the importance of decarbonizing the power grid to achieve meaningful emission reductions in the railway sector.
How did the four driving factors (carbon emission intensity, energy consumption intensity, economic benefits per unit turnover, and operating capacity) influence the overall change in carbon emissions, and were their effects consistent over time?
Energy consumption intensity had a suppressing effect, reducing emissions by 18.8654 million tons, while the other three factors collectively increased emissions by 25.3252 million tons. Carbon emission intensity consistently promoted emissions across all sub-periods, whereas energy consumption intensity and economic benefits per unit turnover shifted from suppressing to promoting after 2019, and operating capacity promoted emissions except during 2019-2020.
Which railway bureaus exhibited the highest carbon emissions, and what were the dominant driving factors for these high-emission regions?
The Shanghai, Beijing, Guangzhou, and Chengdu bureaus had the highest emissions (555.71, 513.90, 458.78, and 427.49 million tons, respectively). For Shanghai and Guangzhou, economic benefits per unit turnover was the main promoting factor, while for Beijing, operating capacity was the main promoter. This indicates that high-emission regions are driven by different factors, necessitating tailored mitigation approaches.
What specific measures does the study recommend for reducing carbon emissions in the railway sector, and how do they address the identified driving factors?
The study recommends optimizing railway transport organization to reduce empty car rates, optimizing the energy structure (e.g., increasing renewable energy share), and retrofitting infrastructure for energy efficiency. These measures directly target energy consumption intensity and carbon emission intensity, which are key levers for emission reduction. Additionally, differentiated strategies are advised based on each bureau's dominant factors, such as focusing on operational efficiency for bureaus where operating capacity is the main driver.
How does the spatial heterogeneity in dominant factors across railway bureaus inform the design of national versus regional emission reduction policies?
The heterogeneity indicates that a one-size-fits-all policy would be ineffective. For example, in bureaus where operating capacity is the main promoter (e.g., Taiyuan, Beijing), policies should focus on improving operational efficiency and reducing empty runs. In contrast, for bureaus where economic benefits per unit turnover is the main promoter (e.g., Shanghai, Guangzhou), policies should target pricing and economic incentives to decouple revenue from emissions. This suggests that national policies should provide a framework while allowing regional flexibility to address local drivers.
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