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Article

Driving Mechanism and Energy Conservation Strategy for China’s Railway Passenger Stations Towards Carbon Neutrality

by
Yintao Lu
1,2,3,
Bo Hu
1,2,3,
Shengming Qiu
1,2,3,
Shuchang Liu
1,2,3,
Jiayan Wang
1,2,3,
Jiashuai Zhao
1,2,3 and
Hong Yao
1,2,3,*
1
School of Environment, Beijing Jiaotong University, Beijing 100044, China
2
Intelligent Environment Research Center, Beijing Jiaotong University, Beijing 100081, China
3
Engineering Research Center of Clean and Low-Carbon Technology for Intelligent Transportation, Beijing Jiaotong University, Beijing 100091, China
*
Author to whom correspondence should be addressed.
Energies 2025, 18(11), 2768; https://doi.org/10.3390/en18112768
Submission received: 25 April 2025 / Revised: 21 May 2025 / Accepted: 23 May 2025 / Published: 26 May 2025
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Abstract

:
As critical hubs for long-distance transportation, railway passenger stations (RPSs) significantly influence energy conservation and CO2 mitigation. This study investigates the spatiotemporal patterns and driving factors of CO2 emissions across 247 Chinese RPSs (2014–2023), proposing region-specific decarbonization strategies. The key findings include: (1) Emissions increased universally during 2014–2023, with severe cold zones and developed cities hosting the most high-emission RPSs; (2) purchased thermal energy dominated the emissions in severe cold/cold zones, while purchased electricity prevailed in other zones; (3) the heating area (HA) was a primary emission driver, whereas the percentage of lighting energy consumption (PLEC) served as a key constraint, as shown by correlation and PCA analyses; (4) CO2 emissions in severe cold zones exhibited strong correlations with heating-related factors, whereas emissions in other zones were predominantly linked to energy structure-related factors. These findings provide region-specific, actionable strategies to support CO2 emission reduction planning for RPSs.

1. Introduction

In 2023, China’s total CO2 emissions amounted to 12.6 billion tons of CO2, with the transportation sector accounting for approximately 10% [1,2]. Consequently, the transportation sector has emerged as a crucial focus for emission control, following the industrial sector in the quest to achieve the “dual carbon” goals. As critical transportation hubs, by the end of 2022, China had built 1842 railway passenger stations (RPSs), among which 1189 were high-speed RPSs. Owing to their expansive public spaces, high passenger density, and stringent comfort requirements, RPSs are confronted with significant decarbonization challenges.
Railway systems are regarded as the transportation infrastructure with the greatest potential for emissions reduction in China [3]. The operational phase of the railway industry contributes over 80% of its lifecycle CO2 emissions, making it the dominant stage [4]. Among these emissions, those related to RPSs are the second-largest contributors, following traction power supply [5]. This highlights the critical need for research focusing on emission reduction strategies, specifically for RPSs. As large-scale public buildings are distributed across various regions, the energy consumption and CO2 emissions of RPSs are significantly influenced by spatial constraints [6]. Therefore, studying the CO2 emission characteristics of buildings across different zones serves as the foundation for the subsequent formulation of emission reduction strategies. Yin et al. analyzed the spatial patterns of emission intensity in China’s construction sector using geostatistical methods [7], while Xiang et al. explored the spatiotemporal evolution of public building emissions through spatial statistics [8]. Additionally, Gan et al. demonstrated that the emission intensity of public buildings was influenced by energy structures and climate zones, resulting in regional disparities [9]. However, studies on the regional emission characteristics of RPSs—a specific type of public building—remain limited. Moreover, most existing research did not adequately address the impact of the variations in thermal performance across different climate zones on buildings’ CO2 emissions.
Additionally, RPSs embody dual characteristics, both as architectural structures and transportation hubs, making it essential to investigate emission-related issues from the perspectives of both the building and transportation sectors. The Logarithmic Mean Divisia Index (LMDI) decomposition model serves as a vital analytical tool for elucidating the driving factors within these domains. Feng et al. and Wang et al. employed this model to assess the influence of various factors, including the per capita floor area, population size, CO2 emission coefficients, energy intensity, and urbanization rates on public building emissions [10,11]. Hao et al. considered the building emission factors and industrial structures as principal determinants of emissions [12]. However, the LMDI model is constrained by its limited factor selection and its difficulty in accommodating interdependencies among variables, which results in a lack of absolute quantitative analysis [13]. In contrast, statistical methods provide greater flexibility for analyzing complex and short-term datasets. For instance, Liu identified the areas of structure, temperature, and humidity as significant emission factors of hospitals through statistical analysis [14]. Similarly, Huo et al. demonstrated correlations among the geographic proximity, economic development, energy intensity, and CO2 emissions of buildings [15]. Furthermore, Du et al. identified that population, urbanization rate, per capita GDP, green building indices, and industrial structure exhibit spatial correlations and heterogeneities in their effects on CO2 emissions of public buildings [16].
As the primary focus of analyses related to emission characteristics and driving factors, mitigation strategies have attracted considerable scholarly attention. Moreover, research on prediction and emission reduction strategies often employs the Kaya-LMDI decomposition method, as seen in studies focused on CO2 emission pathways for buildings in Fujian and potential pathways for emission reduction in China’s waterway transportation sector [17,18]. However, this research approach still faces limitations inherent to the LMDI method. To address these limitations, various scenario settings and the low emission analysis platform (LEAP) model are frequently employed to forecast CO2 emission trends for buildings [19,20], public transportation [21,22], and other sectors, thereby providing a research foundation for emission reduction strategies. However, these studies largely overlooked climatic disparities between northern and southern regions, thereby limiting their regional applicability. Due to the significant variations in energy consumption of public buildings across different climate zones, regionally differentiated emission reduction strategies should be advocated [23,24,25].
Current research on CO2 emissions from RPSs primarily focuses on the identification of emission sources during subgrade engineering construction phases [26], emission accounting methods for the entire life cycle and operational phase [27], fuel substitution-based mitigation pathways [28], and sustainability assessments of stations [29]. However, studies addressing CO2 emission characteristics during the operational phase are still limited. Furthermore, research on emission reduction strategies during the operational phase of railways also demonstrated a limited consideration of the differences in regional climates and energy structures, resulting in the restricted applicability of those reduction strategies.
To address these gaps, this study establishes the first national framework to analyze the spatiotemporal CO2 emission mechanisms across 247 large and medium-sized RPSs in China through the three following innovative dimensions: (1) the spatiotemporal CO2 emission characteristics of RPSs, (2) the influencing factors of RPSs CO2 emissions, and (3) decarbonization strategies in different zones. The findings are anticipated to offer quantitative insights, region-specific and actionable policy recommendations, and empirical support for establishing scientifically sound emission reduction targets to further low-carbon development in RPS operations in alignment with China’s “dual carbon” goals.

2. Materials and Methods

2.1. Data Sources and Collection

This study collected data from 247 RPSs, covering various parameters such as passenger flow (PF), refrigeration area (RA), heating area (HA), heating duration (HD), electricity emission factor (EEF), total building area (TBA), total electrical equipment power (TEEP), proportion of refrigeration energy consumption (PREC), proportion of thermal energy consumption (PTEC), proportion of lighting energy consumption (PLEC), and proportion of elevators’ energy consumption (PEEC). Additional parameters, including years of opening and operation (YOO), years of renovation or extension (YRE), and designed maximum number of gathered passengers (DMNGP), were also recorded. Energy consumption data involving coal, gasoline, electricity, and thermal energy were gathered for the period from 2014 to 2023. While HD data were collected from municipal statistical yearbooks, all other data were acquired through on-site surveys of the RPSs. The geographical distribution of the RPSs is depicted in Figure 1. Notably, large- and medium-sized RPSs are primarily concentrated in eastern, central, and northern China, whereas smaller RPSs are predominantly located in plateau regions.
Table 1 shows the regional distribution and grade of the RPSs. According to China’s national standard GB 50176-2016 (Thermal Design Code for Civil Buildings) [30], RPSs are categorized into five climate zones: severe cold (37 RPSs), cold (58 RPSs), hot summer and cold winter (111 RPSs), hot summer and warm winter (32 RPSs), and mild (9 RPSs). Due to data limitations and similarities in thermal performance, RPSs in the mild zone were combined with those in the hot summer and warm winter zone for subsequent analysis. Additionally, RPSs were classified by grade following the National Railway Station Classification Standards as special-class (42 RPSs), first-class (95 RPSs), second-class (62 RPSs), third-class (38 RPSs), and fourth-class (10 RPSs).

2.2. Analytical Methods

2.2.1. Calculation Method of CO2 Emissions

CO2 emissions are generally calculated using the emission factor method, which is based on energy consumption data [31]. In this study, CO2 emissions from RPSs are classified into direct emissions from fossil fuel combustion and indirect emissions arising from electricity and thermal energy consumption. The calculation methodology is outlined in Equation (1).
Est = Ef + Ee + Eh
where Est is the total CO2 emissions of the RPSs (tCO2), Ef is the direct CO2 emissions from fossil fuel combustion at the station (tCO2), Ee is the indirect CO2 emissions from purchased electricity consumption (tCO2), and Eh is the indirect CO2 emissions from purchased thermal energy consumption (tCO2).
The calculation method of CO2 emissions from fossil fuel combustion in stations is shown in Equation (2).
Ef = Σ(CONm × Fm)
where m is the type of fossil fuel, CONm is the consumption of fossil fuel m (t/104m3), and Fm is the CO2 emissions factor of the combustion of fossil fuel m (tCO2/t or tCO2/104m3). Among them, the calculation method of the CO2 emissions factor of fossil fuel m is shown in Equation (3).
F m = N m   C m   ×   O m   ×   44 12
where Nm is the average net calorific value (kJ/kg or kJ/m3), Cm is the carbon content per unit calorific value (tC/TJ), and Om is the carbon oxidation rate (%).
The calculation method of the CO2 emissions from the consumption of purchased electricity in passenger stations is shown in Equation (4).
Ee = Elp × EFe
where Elp is the purchased electricity amount of the RPSs (MWh), and EFe represents the electricity emission factor for an individual province in which the RPSs are situated (tCO2/MWh).
The calculation method of the CO2 emissions from the consumption of purchased thermal energy in RPSs is shown in Equation (5).
Eh = Thp × EFh
where Thp is the purchased thermal energy of the station (GJ), and EFh is the thermal energy CO2 emissions factor (tCO2/GJ), with a value of 0.11 tCO2/GJ.
The average lower heating values were obtained from the General Rules for Comprehensive Energy Consumption Calculation [32]. Data regarding the carbon content per unit of heating value and carbon oxidation rates were sourced from the Guidelines for Provincial Greenhouse Gas Inventories (Trial) [33]. The calculated CO2 emission factors for fossil fuels are summarized in Table 2. The heat emission factor was derived from the Accounting and Reporting Guidelines for Greenhouse Gas Emissions of Chinese Power Generation Enterprises (Trial) [34]. Given the extensive regional scope of this study and the considerable disparities in electricity generation sources across different zones, provincial electricity emission factors (Table 3) were utilized for the calculations. These factors were obtained from the 2022 Provincial CO2 Emission Factors for Electricity [35].

2.2.2. Data Processing Method

Correlation analysis and PCA of the influencing factors in RPSs were conducted using SPSS 27, while data visualization and graphical plotting were carried out using ArcGis 10.8.1 and OriginPro 2021.

3. Results and Discussion

3.1. Temporal Characteristics of Carbon Emissions for RPSs

Figure 2 illustrates the variations in CO2 emissions across different climate zones through a heat map. Figure 2 illustrates the CO2 emissions of RPSs in four different climate zones using a color gradient that transitions from blue (indicating the lowest emissions) to white and ultimately to red (representing the highest emissions). The number of large- and medium-sized stations experienced annual fluctuations, driven by continuous renovations, expansions, and the establishment of new stations, while the station count in 2023 was used as the reference benchmark in this study. CO2 emissions from RPSs that were not classified as large- or medium-sized prior to any renovations, expansions, or new constructions were marked as missing data. Furthermore, given that there are fewer RPSs in the mild zone and the hot summer and warm winter zone compared to the other three thermal zones, their data were combined into a single heat map to enhance clarity.
From a temporal perspective, in the cold zone, the total carbon emissions of RPSs in 2023 decreased by 8.31% compared to 2014. However, in the severe cold zone, hot summer and cold winter zone, and hot summer and warm winter zones, the total carbon emissions in 2023 increased by 107.7%, 26.32%, and 64.23%, respectively, compared to 2014. Specifically, CO2 emissions in all climate zones generally increased from 2014 to 2019, which was primarily due to the rapid development of high-speed railways and the resulting surge in PF. CO2 emissions across various thermal zones generally decreased between 2020 and 2022, mainly due to a drop in PF caused by the pandemic [36], which created irregularities in the emission patterns. When these three years of unusual data were removed, the emission trends showed considerable fluctuations over time. We defined a declining trend in CO2 emissions at RPSs if there was a continuous decrease for two years and it exceeded 20%. The result showed that among the 30 RPSs in the severe cold zones, 20% exhibited a decreasing trend in CO2 emissions; among the 36 RPSs in the cold zones, 17% showed a decreasing trend in CO2 emissions; among the 42 RPSs in the hot summer and cold winter zone, 14% presented a decreasing trend; none of the 26 RPSs in the hot summer and warm winter zones showed a decreasing trend; and among the five RPSs in mild zones, only one station had a decreasing trend in CO2 emissions.
Although the CO2 emissions from the RPSs in the hot summer and cold winter zone, the hot summer and warm winter zones, and the mild zones were relatively low, they were still in an upward or stable state. The decline of CO2 emissions in the severe cold and cold zones may be attributed to changes in the energy structure brought about by clean heating renovations, such as the replacement of coal-fired boilers with gas alternatives. As demonstrated by Deng et al. [37], these strategies effectively lowered emissions while maintaining energy efficiency.

3.2. Spatial Characteristic of Carbon Emissions for RPSs

Figure 3 illustrates the spatial distribution of CO2 emissions from RPSs in 2023, with the stacked bar charts representing the contributions from different energy sources to enhance the visualization of spatial patterns. The results revealed that high-emission stations were predominantly concentrated in severe cold zones and developed cities. Notably, 37 RPSs located in severe cold zones—accounting for only 15% of all RPSs—contributed to 30.1% of the annual CO2 emissions. Furthermore, purchased thermal energy emerged as the dominant emission source in severe cold and cold zones, while purchased electricity played a primary role in other zones. Table 4 illustrates the average CO2 emissions from RPSs in various climate zones, ranked as follows: severe cold zones with CO2 emissions of 15,236 tons, cold zones with CO2 emissions of 9461 tons, as well as the hot summer and cold winter zone with CO2 emissions of 6227 tons, hot summer and warm winter zones with CO2 emissions of 5013 tons, and the mild zone with CO2 emissions of 3054 tons. There was a noticeable decrease in CO2 emissions from the northern severe cold zones to the southern mild zone, suggesting a decline as temperatures increase. Southern China’s elevated summer temperatures induced higher cooling loads in RPSs compared to the northern regions, though the associated CO2 emissions showed limited spatial disparity due to lower EEFs in the southern power grids [38]. Conversely, milder winter conditions significantly reduce heating-related energy demands and corresponding emissions in the southern RPSs [39].
In the same climate zone, there were also significant differences in CO2 emissions across various RPSs. These differences were closely associated with factors like the size of the RPSs and DMNGP. To enhance the understanding of emission characteristics, an analysis of emissions based on station classification within each climate zone was performed (Figure 4). Significant variations were shown among stations of different classifications within the same climate zone. In the severe cold zones, special-class, first-class, second-class, and third-class stations had average emissions of 30,531 tCO2, 13,025 tCO2, 10,569 tCO2, and 5557 tCO2, respectively. In the cold zones, the corresponding average values were 18,669 tCO2, 8762 tCO2, 7237 tCO2, and 1953 tCO2, respectively. Stations in the hot summer and cold winter zone displayed averages of 12,635 tCO2 (special-class), 3291 tCO2 (first-class), 3310 tCO2 (second-class), 2220 tCO2 (third-class), and 1914 tCO2 (fourth-class). In the hot summer and warm winter and mild zones, the average emissions for the special-class, first-class, second-class, third-class, and fourth-class stations were 12,251 tCO2, 2887 tCO2, 1363 tCO2, 3617 tCO2, and 1758 tCO2, respectively.
In summary, CO2 emissions were generally lower at RPSs with lower grades within the same climate zone. However, in the hot summer and warm winter and mild zones, third-class stations showed higher emissions than the first-class and second-class stations. This unusual situation was a result of varying operating conditions at different stations. For instance, second-class FS Station ceased operations in late 2023 because of expansion, while third-class ZH Station had a greater PF and RA than most first-class and second-class RPSs, leading to 4221 tCO2 emissions, which have higher than usual values for its classification.
The energy structure of RPSs plays a crucial role in determining CO2 emissions [40]. It is essential to examine the energy-related emission profiles of individual RPSs to develop customized strategies for energy conservation and emission reduction. The sources of CO2 emissions at RPSs mainly consist of fuel oil, natural gas, electricity purchased from the grid, purchased thermal energy, and clean fuels such as alcohol-based fuels and biomass fuels. Figure 4 illustrates the composition of energy consumption related to CO2 emissions in different climate zones. In general, the emissions across all five climate zones exhibited similar patterns, with purchased electricity and thermal energy being the main contributors, together accounting for more than 98% of total emissions. This finding was consistent with supporting Wu’s conclusion that heating, ventilation, and air conditioning (HVAC) systems are the leading sources of emissions at RPSs [41]. However, the order of secondary energy sources differed among the climate zones.
Detailed quantitative contributions are provided in Figure 5. In severe cold zones, the order of energy consumption was as follows: purchased thermal energy, purchased electricity, natural gas, fuel oil, clean fuels, and liquefied petroleum gas (LPG), with purchased thermal energy accounting for 74.75% of the CO2 emissions. In cold zones, the emissions were mainly from purchased electricity, followed by purchased thermal energy, natural gas, clean fuels, and fuel oil, of which purchased electricity and purchased thermal energy were responsible for 63.82% and 35.09% of the emissions, respectively. For the hot summer and cold winter zone, the ranking order was purchased electricity, purchased thermal energy, natural gas, fuel oil, and clean fuels, with purchased electricity making up 89.67% of the emissions. Conversely, in RPSs in the hot summer and warm winter and mild zones, the focus was on purchased electricity, natural gas, fuel oil, and clean fuels, with a staggering 99.6% of emissions attributed to purchased electricity.
Furthermore, natural gas was the primary contributor to CO2 emissions at stations XN and SYB, both located in the severe cold zones, accounting for 74.48% and 47.42% of their total emissions, respectively. This was due to XN Station’s dependence on natural gas for localized heating through combined heat and power systems and gas boilers, given its limited centralized heating coverage [42]. SYB Station employs a hybrid heating system that integrates gas boiler-based centralized heating with central air-conditioning-assisted heating to enhance economic efficiency and energy performance [43,44]. A comparison of heating-related emission intensities among stations with similar HDs (Table 5) indicated that XN and SYB, both using natural gas, had the lowest intensities (0.06 and 0.17 tCO2/m2, respectively). Other stations, such as HG, TL, HEBB, and SYN, have higher carbon emission intensities due to the use of coal-fired boilers for heating, exceeding that of SYB Station by more than twice. Notably, XN’s greater reliance on natural gas showed more significant emission reduction benefits. These results highlighted the essential role of cleaner heating technologies in reducing emissions in severe cold and cold zones.
In cold zones, BDH Station consumes more alcohol-based fuel (31.62%) than fossil fuels, while the DH and JQN stations mainly use liquefied natural gas (LNG) with proportions of 65.22% and 73.36%, respectively. In the hot summer and cold winter zone, stations NJ, WX, and HZ obtained 21.45%, 36.99%, and 42.26% of their CO2 emissions from natural gas while relying solely on purchased electricity for other energy needs. These examples illustrate the emission reduction potential of integrated heating systems, which can promote the use of clean energy in RPSs [45]. To align with national regulations, such as the State Council’s “Air Pollution Prevention and Control Action Plan” (Guofa [2013] No. 37) and the Ministry of Ecology and Environment’s “Implementation Rules for Air Pollution Control in Beijing-Tianjin-Hebei and Surrounding Areas” (Huanfa [2013] No. 104) [46,47], RPSs have gradually shifted from coal-fired boilers to cleaner options, such as ground-source/air-source heat pumps, LNG/compressed natural gas (CNG), alcohol-based fuels, and biomass fuels, especially in areas without centralized heating or gas pipelines [48,49]. These RPSs indicated the significant emission reduction advantages of clean fuels in enhancing air quality.
The critical contribution of clean energy sources like natural gas, alcohol-based fuels, and biomass fuels to reducing carbon emissions at RPSs is evident. Furthermore, a smaller share of CO2 emissions from purchased thermal energy and a higher degree of electrification led to reduced emissions at these RPSs. This suggested that modifying the energy mix played a vital role in promoting CO2 emission reductions. Consequently, enhancing the electrification of RPSs and advancing new energy use through integrated TES technology are key trends for energy efficiency improvement and emission reduction in these RPSs.

3.3. CO2 Emissions’ Influencing Factors and Reduction Strategies

3.3.1. Influencing Factors

Given the differences in population mobility and spatial layout between RPSs and other public buildings, this study focused exclusively on 14 intrinsic factors that are directly related to RPS operations. Given the large sample size, the Kolmogorov–Smirnov (K-S) test was utilized to assess the normality of the data. As the data exhibited deviations from normal distribution, Spearman’s correlation coefficient method was employed to examine the relationships between these intrinsic factors and CO2 emissions (Table 6).

Positive Factors

All positively correlated factors demonstrated statistically significant associations with CO2 emissions in RPSs at the p < 0.01 level. Among these factors, the values of the correlation coefficients, ranked from the largest to the smallest, were as follows: RA (0.666), HA (0.657), TBA (0.595), PF (0.518), PTEC (0.441), DMNGP (0.412), TEEP (0.409), EEF (0.382), and HD (0.358). All these positive factors can be divided into the following four categories: area-related, population-related, electricity-related, and heating-related. RPSs, characterized by their large and open spaces with uneven temperature distributions, require substantial energy for refrigeration and heating to ensure comfort, resulting in higher emissions as the building areas increase. For instance, FT Station has a TBA of 441,250 m2, an RA of 322,000 m2, and a HA of 302,522 m2, while BJ Station covers a TBA of 165,030 m2, with an RA and HA of 80,030 m2 and 83,530 m2, respectively. Notably, FT Station exhibits 15 times higher heating energy consumption, 1.9 times greater refrigeration energy demand, and 5.78 times higher CO2 emissions compared to BJ Station. Population-related factors, such as a larger PF and higher DMNGP at major transport hubs, demand larger spaces for efficient operation, which further contributes to increased emissions. Moreover, RPSs require extensive energy to maintain lighting, HVAC, and vertical transportation systems (e.g., elevators and escalators). Larger PFs would intensify these demands. Meanwhile, passenger activities generate indirect CO2 emissions through supporting infrastructure: a high PF drives the 24/7 operation of shops and restaurants, which rely on refrigeration, cooking equipment, and lighting. A representative example is the comparison between CC and JL Stations in 2023. CC Station recorded a PF of 50,063,770 with a DMNGP of 16,000, while JL Station recorded a PF of 14,460,658 with a DMNGP of 8000. Notably, the CO2 emissions of CC Station were 1.73 times higher than those of JL Station. Electricity consumption is a significant contributor to CO2 emissions, and over half (59.7%) of the total carbon footprint in RPSs was attributable to electricity usage during the 2023 monitoring period. Two key electricity-related factors are TEEP and EEF. The increasing power demand for equipment leads to higher CO2 emissions, underscoring the importance of energy-efficient devices [50]. Research by Bernardo et al. has shown that improving end-use efficiency could significantly reduce the carbon footprints of railways [51]. On the other hand, EEF varies significantly based on the energy sources [52,53]. For instance, Hebei Province, which relies heavily on coal-fired power (with only 25% clean energy usage [54]), has the highest EEF with 0.7252 kgCO2/kWh, which is in stark contrast to hydropower-rich Yunnan with an EEF of 0.1073 kgCO2/kWh and renewable-heavy Qinghai with an EEF of 0.1567 kgCO2/kWh [55]. Additionally, the HD has a positive correlation (0.358) with CO2 emissions, as longer heating periods increase the energy consumption for heating. However, the design and performance of heating systems are closely related to the climate and energy conditions of the region, presenting opportunities for low-carbon upgrades tailored to local climates [56].

Negative Factors

PREC, PLEC, and PEEC showed significant negative correlations with the CO2 emissions of RPSs, with correlation coefficients of −0.316, −0.431, and −0.134, respectively, while YOO and YRE exhibited negligible correlations. These negative factors can be divided into two categories: factors related to refrigeration and factors related to other electrical equipment. However, these two factors ultimately stem from issues of energy structure. RPSs located in regions with a greater number of hot days and correspondingly less demand for heating were characterized by a higher proportion of energy allocated to cooling. As refrigeration is often achieved through electricity consumption [57], which is also the primary source of energy for lighting and elevators, the energy sources used for these functions significantly influence CO2 emissions at the station level. Although PREC, PLEC, and PEEC exerted negative impacts on CO2 emissions in RPSs, these components collectively constitute TEEP. Notably, TEEP exhibited a positive correlation with CO2 emissions in RPSs. Consequently, the relative proportions of PREC, PLEC, and PEEC within TEEP indirectly exerted a positive influence on station-level CO2 emissions.

PCA Analysis

To further evaluate the impact of the driving factors on CO2 emissions in RPSs across different climate zones, PCA was conducted on the CO2 emission drivers. Following Kaiser’s criterion, principal components with eigenvalues exceeding 1 were retained, and a biplot was plotted (Figure 6).
According to the analysis of inter-variable relationships derived from the PCA, the small angles between PF, DMNGP, TBA, RA, HA, and TEEP indicated strong positive correlations among these variables, reflecting the synergistic growth of station scale and power demand for equipment. Similarly, the small angles between PEEC, PREC, and PLEC suggested significant positive correlations, as these variables collectively represent the energy consumption proportions of electrical systems. The small angle between YOO and these energy consumption variables implied that older RPSs may exhibit elevated energy consumption proportions due to outdated, less efficient equipment. Furthermore, the minimal angles among HD, HA, and PTEC highlighted a strong positive correlation, indicating that colder climates intensified both heating demand and associated energy consumption. Notably, the EEF showed a small angle with PTEC but a near-180° angle with PEEC and similar factors. This suggests that a higher EEF (potentially linked to high-energy-consuming equipment, such as heat pumps) may drive an increased PTEC while inversely reducing the proportion of energy allocated to other systems, such as PEEC.
In addition, Principal Component 1 (PC1) and Principal Component 2 (PC2) explained 28.3% and 20.7% of the total variance, respectively. PC1 exhibited strong loadings from HD, PTEC, and HA, suggesting its representation of energy structure characteristics. In contrast, PC2 was predominantly associated with PREC, PEEC, PLEC, and TEEP, reflecting energy efficiency characteristics. Notably, RPSs in severe cold zones clustered along PC1, RPSs in mild zones clustered along PC2, while those in other zones dispersed among PC1 and PC2, demonstrating a distinct spatial differentiation of CO2 emission drivers across climate zones.
In severe cold zones, the CO2 emissions of RPSs exhibited a positive correlation with PC1, whereas in the mild zones, they showed a pronounced positive correlation with PC2, while the emissions in other zones demonstrated correlations with both PC1 and PC2. This suggested that the energy structure dominates CO2 emissions in severe cold zones, energy efficiency prevailed in mild zones, and both factors held equal importance in other climate zones.

3.3.2. The Strategies of Energy Conservation and Emission Reduction

Severe Cold Zones

As mentioned in 3.3.1, the CO2 emissions of RPSs in severe cold zones were significantly associated with factors that included EEF, HD, and PTEC. The measures of energy conservation and emission reduction proposed for different influencing factors were as follows:
The EEF in RPSs could be reduced by increasing clean energy integration within their power mix, thereby achieving lower emissions under equivalent electricity consumption [58,59]. Two synergistic approaches are recommended: (1) photovoltaic panels installed on station rooftops and canopy structures, combined with energy storage systems, enable a localized utilization of renewable electricity, thereby decreasing reliance on grid power [60], and (2) distributed photovoltaic generation could make full use of the solar resource along with high-speed railways, thereby reducing the energy consumption of power supply systems [61]. The HHHT railway station reduced carbon emissions by incorporating 840 amorphous silicon photovoltaic panels into the platform canopy.
Regarding HD, three measures were suggested: (1) dynamically adjust heating schedules based on real-time meteorological forecasts to prevent premature activation of heating systems; (2) utilize Building Information Modeling (BIM) to delineate functional zones (e.g., waiting areas, offices), enabling occupancy schedule-based differentiated heating [62]; and (3) employ TES technology in carbon reduction through enhanced renewable energy utilization and industrial waste heat recovery [63].
From the perspective of PTEC, adopting air-source heat pumps (ASHPs) to replace coal-fired boilers could achieve a coefficient of performance (COP) of 2.5–3.5, which reduces energy consumption by over 50% compared to conventional electric heating. A practical demonstration in inner Mongolia achieved a 28% carbon reduction via a solar-air-source heat pump heating system coupled with a sand-based thermal storage floor [64]. Concurrently, cascaded waste heat utilization could be implemented by recovering occupant-generated thermal energy and equipment waste heat from preheating fresh air or water supply systems, thereby optimizing energy efficiency across multiple operational stages.

Mild Zones

CO2 emissions of RPSs in mild zones were significantly associated with factors that included PREC, PLEC, PEEC, and TEEP. The measures of energy conservation and emission reduction proposed for different influencing factors are as follows:
In the case of PREC, the implementation of a hybrid air-cooling and evaporative-cooling system significantly improved cooling efficiency, achieving a more than 20% enhancement in the COP [65]. This integrated approach demonstrated considerable potential for reducing energy consumption in mild zones.
For PLEC implementation, a dual approach was recommended: (1) the installation of tubular daylight guidance devices in high-occupancy zones, such as waiting halls, to replace daytime artificial lighting with natural illumination [66], and (2) the employment of LED networks equipped with illuminance sensor and zonal dimming controls to enable demand-driven lighting operations, thereby achieving cumulative energy savings of up to 50% compared to conventional systems [67].
For PEEC, due to the high PF in RPSs, frequent elevator usage leads to significant energy consumption. Effectively utilizing regenerative braking energy from elevators is a critical approach to reducing energy demands. By installing regenerative energy recovery systems, elevators could feed the energy generated during elevator braking back into the power grid or reuse it for other equipment, thereby significantly lowering the CO2 emissions associated with elevator operations [68]. This approach could be further enhanced by integrating AI-driven predictive scheduling algorithms that analyze the PF patterns to optimize elevator group control strategies [69], thereby minimizing idle operations and reducing redundant energy consumption. The HZX railway station promoted energy efficiency and reduced emissions by utilizing smart lighting and elevator scheduling systems.
For TEEP optimization, deploying variable frequency drives (VFDs) on HVAC units and pumps was a good measure, which could dynamically adjust the power output in response to real-time load demands, thereby minimizing idle energy consumption [70]. Concurrently, upgrading inefficient motors and transformers to ultra-high-efficiency models further enhanced system-wide energy performance to reduce TEEP.

Other Climate Zones

The CO2 emissions of RPSs in other zones were also closely related to the aforementioned seven factors; thus, the combined implementation of these strategies could effectively achieve emission reduction objectives.
In summary, addressing the challenges related to heating and electricity systems is critical for achieving decarbonization targets in RPSs. These findings provide actionable insights for region-specific pathways in RPSs.

4. Conclusions

This study systematically analyzed the CO2 emission patterns in 247 RPSs across China, revealing critical insights into the spatiotemporal characteristics, influencing factors, and decarbonization pathways. The key findings are summarized as follows:
(1)
Although carbon emissions from all railway stations decreased during the pandemic, they are still increasing overall. In severe cold regions, over 20% of RPSs demonstrated a notable decrease in carbon emissions, which is considerably greater than in other climate zones.
(2)
High-emission RPSs are concentrated in areas with severe cold climates and developed cities. In addition, CO2 emissions tend to decline from regions with severe cold to those with milder climates. Within the same climate zone, CO2 emissions of RPSs generally decline with lower station classes. However, in the hot summer and warm winter zone, the third-class stations surpassed first- and second-class stations in emissions due to an elevated PF and RA.
(3)
In severe cold zones, the carbon emissions from RPSs are primarily due to purchased thermal energy, which constitutes 74.75% of the total emissions. In cold zones, emissions are derived from both purchased thermal energy (63.82%) and electric power (35.09%). Other regions mainly rely on purchased electricity, which makes up 89.67% to 99.6% of their emissions. Implementing clean heating solutions, such as gas-heat pump hybrids, has successfully lowered emissions in certain RPSs.
Carbon emissions in RPSs showed a positive correlation with factors such as PF, HA, RA, HD, PTEC, EEF, TBA, and TEEP while exhibiting a negative correlation with PLEC, PREC, and PEEC. The primary factors influencing CO2 emissions varied by region: energy structure was the main driver in extremely cold areas, energy efficiency played a significant role in moderate climates, and a mix of influences was observed in other regions. It is recommended that the goals of energy conservation and emission reduction that are set by RPSs can be successfully met by applying customized strategies in various climate zones.

Author Contributions

Y.L.: Methodology, Investigation, and Writing—Original Draft; B.H.: Data Curation and Writing—Original Draft; S.Q.: Visualization and Investigation; S.L.: Data analysis; J.W.: Software and Validation; J.Z.: Writing—Review and Editing; H.Y. (Corresponding Author): Conceptualization, Funding Acquisition, Resources, Supervision, and Writing—Review and Editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China [grant number U2268208].

Data Availability Statement

The data created and examined in this research cannot be accessed publicly because of confidentiality agreements with the railway authorities involved. Nevertheless, anonymized portions of the data, such as combined emission factors, classifications of stations, and energy usage patterns specific to different climate zones, can be obtained from the corresponding author upon a reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Distribution map of RPSs in the study area.
Figure 1. Distribution map of RPSs in the study area.
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Figure 2. The heat map of carbon emissions from RPSs across various climate zones from 2014 to 2023. (a) Severe cold zone; (b) cold zone; (c) hot summer and cold winter zone; and (d) hot summer and warm winter zone and mild zone. In graph (d), the x-axis indicates the mild zone, while the y-axis indicates the hot summer and warm winter zone.
Figure 2. The heat map of carbon emissions from RPSs across various climate zones from 2014 to 2023. (a) Severe cold zone; (b) cold zone; (c) hot summer and cold winter zone; and (d) hot summer and warm winter zone and mild zone. In graph (d), the x-axis indicates the mild zone, while the y-axis indicates the hot summer and warm winter zone.
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Figure 3. Distribution map of CO2 emissions from RPSs in 2023.
Figure 3. Distribution map of CO2 emissions from RPSs in 2023.
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Figure 4. The statistical analysis results of CO2 emissions from RPSs of various classifications within the same climate zone: (a) severe cold zone; (b) cold zone; (c) hot summer and cold winter zone; and (d) hot summer and warm winter zone and mild zone.
Figure 4. The statistical analysis results of CO2 emissions from RPSs of various classifications within the same climate zone: (a) severe cold zone; (b) cold zone; (c) hot summer and cold winter zone; and (d) hot summer and warm winter zone and mild zone.
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Figure 5. The chord diagram for the proportion of energy consumption in RPSs across various climate zones: (a) severe cold zone; (b) cold zone; (c) hot summer and cold winter zone; and (d) hot summer and warm winter zone and mild zone.
Figure 5. The chord diagram for the proportion of energy consumption in RPSs across various climate zones: (a) severe cold zone; (b) cold zone; (c) hot summer and cold winter zone; and (d) hot summer and warm winter zone and mild zone.
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Figure 6. PCA biplot analysis of CO2 emission drivers in RPSs.
Figure 6. PCA biplot analysis of CO2 emission drivers in RPSs.
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Table 1. The regional distribution and grade of RPSs.
Table 1. The regional distribution and grade of RPSs.
Zone NameNumber of RPSsStation GradeNumber of RPSs
Severe cold37Special-class42
Cold58First-class95
Hot summer and cold winter111Second-class62
Hot summer and warm winter32Third-class38
Mild9Fourth-class10
Table 2. CO2 emission factors of fuels.
Table 2. CO2 emission factors of fuels.
FuelAverage Net
Calorific Value
(kJ/kg) or (kJ/m3)		Carbon Content per Unit Calorific Value
(tC/TJ)		Carbon
Oxidation Rate
%		CO2
Emissions Factor
(tCO2/t) or (tCO2/104m3)
Raw Coal20,93426.37941.90
Coke28,47029.50932.86
Crude Oil41,86820.10983.02
Gasoline43,12418.90982.93
Kerosene43,12419.60983.04
Diesel Fuel42,70520.20983.10
Cleaned Coal26,37726.37982.50
Fuel Oil41,86821.10983.17
Municipal Gas16,74713.58990.83
LPG50,24217.20983.11
Oil Field Gas38,93115.309921.62
Gas Field Gas35,54415.309919.74
Table 3. Provincial electricity emission factors.
Table 3. Provincial electricity emission factors.
ProvinceEmission Factor (kgCO2/kWh)ProvinceEmission Factor (kgCO2/kWh)ProvinceEmission Factor (kgCO2/kWh)
Beijing0.5580Jiangsu0.5978Hubei0.4364
Tianjin0.7041Zhejiang0.5153Hunan0.4900
Hebei0.7252Anhui0.6782Guangdong0.4403
Shanxi0.7096Fujian0.4092Guangxi0.4044
Liaoning0.5626Jiangxi0.5752Hainan0.4184
Jilin0.4932Shandong0.6410Chongqing0.5227
Shanghai0.5849Henan0.6058Sichuan0.1404
Guizhou0.4989Gansu0.4722Xinjiang0.6231
Yunnan0.1073Qinghai0.1567Heilongjiang0.5368
Shanxi0.6558Ningxia0.6423Neimenggu0.6849
Table 4. Descriptive statistics of CO2 emissions from RPSs.
Table 4. Descriptive statistics of CO2 emissions from RPSs.
Zone NameMaximum Value (tCO2)Minimum Value (tCO2)Mean Value (tCO2)Median Value (tCO2)
Severe cold62,186186624615,236
Cold32,76556154029461
Hot summer and cold winter37,54635839356227
Hot summer and warm winter42,70039622835013
Mild997956426023054
Table 5. Comparison of carbon emission intensity associated with heating.
Table 5. Comparison of carbon emission intensity associated with heating.
Station NameCO2 Emissions for Heating
(t)		HD
(days)		HA
(m2)		Carbon Emission Intensity (tCO2/m2)
XN284818247,0500.06
HG472718211,9980.39
TL677418217,2280.39
HEBX16,18018267,4210.24
HEBB207318251160.41
SYB979315058,1240.17
SYN54,650150131,7760.41
Table 6. Spearman’s correlation analysis for influencing factors.
Table 6. Spearman’s correlation analysis for influencing factors.
Influencing FactorsCorrelation Coefficient
(Significance)
PF0.518 ** (<0.001)
RA0.666 ** (<0.001)
HA0.657 ** (<0.001)
HD0.358 ** (<0.001)
EEF0.382 ** (<0.001)
TBA0.595 ** (<0.001)
TEEP0.409 ** (<0.001)
PREC−0.316 ** (<0.001)
PLEC−0.431 ** (<0.001)
PTEC0.441 ** (<0.001)
PEEC−0.134 * (0.034)
YOO−0.031 (0.621)
YRE−0.084 (0.330)
DMNGP0.412 ** (<0.001)
Note: ** and * represent the correlation significance at the 0.01 level (two-tailed) and 0.05 level (two-tailed), respectively.
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Lu, Y.; Hu, B.; Qiu, S.; Liu, S.; Wang, J.; Zhao, J.; Yao, H. Driving Mechanism and Energy Conservation Strategy for China’s Railway Passenger Stations Towards Carbon Neutrality. Energies 2025, 18, 2768. https://doi.org/10.3390/en18112768

AMA Style

Lu Y, Hu B, Qiu S, Liu S, Wang J, Zhao J, Yao H. Driving Mechanism and Energy Conservation Strategy for China’s Railway Passenger Stations Towards Carbon Neutrality. Energies. 2025; 18(11):2768. https://doi.org/10.3390/en18112768

Chicago/Turabian Style

Lu, Yintao, Bo Hu, Shengming Qiu, Shuchang Liu, Jiayan Wang, Jiashuai Zhao, and Hong Yao. 2025. "Driving Mechanism and Energy Conservation Strategy for China’s Railway Passenger Stations Towards Carbon Neutrality" Energies 18, no. 11: 2768. https://doi.org/10.3390/en18112768

APA Style

Lu, Y., Hu, B., Qiu, S., Liu, S., Wang, J., Zhao, J., & Yao, H. (2025). Driving Mechanism and Energy Conservation Strategy for China’s Railway Passenger Stations Towards Carbon Neutrality. Energies, 18(11), 2768. https://doi.org/10.3390/en18112768

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