Introduction

High-speed railway (HSR), as crucial city-to-city transport infrastructure, provides efficient mobility in modern society1,2. As a living lab for global infrastructure development, China has built the largest HSR system3. This development offered essential commuting services and enhanced national economies4, but it accelerated material consumption and led to greenhouse gas (GHG) emissions5. Environmental analyses of railway (including ordinary railway and HSR) have traditionally focused on reducing operational emissions6,7, verifying the transport benefits by carrying 8% global passengers and 7% freight but representing 2% of transport energy demand8,9,10. Indeed, there are tradeoffs between the pre-use and in-use emissions whereby advancing HSR speed may require more energy-intensive construction materials11,12. Therefore, understanding the upstream and downstream HSR material metabolism and GHG emissions are crucial.

China determines to extend HSR with an “eight verticals and eight horizontals” network by 203513. This is mainly accelerated by the rapid urbanization rate, the increasing wealth development, and the growing demands for intercity commuting14,15. As such, considerable construction materials are required, resulting in sustainability challenges in coming decades16,17. HSR construction technologies have advanced in the past decade18. But the environmental impacts behind technological advances are still not thoroughly evaluated, especially for the embodied emissions across material metabolism. These trends present substantial challenges in comprehensively understanding the HSR material metabolism and GHG emissions for its ambition of “CO2 peaking before 2030 and neutrality before 2060”19.

Material metabolism provides a systematic view in understanding the material flows (material inflows and outflows in defined system boundary), the material stocks (materials that staying in the system over a certain period time), and the overall development patterns of urban built environment20. These studies have been evaluated on buildings21,22 and construction materials (e.g., steel23 and cement24), but researches in HSR systems are largely missing. The current depiction of HSR material metabolism either aims at a single HSR line6, with only a handful efforts on the national scale25, or limits at stock estimations26, leaving the comprehensive understanding of material metabolism at the national level and across lines less discussed. Notably, the historical patterns and the stock disparities among HSR lines are less depicted due mainly to insufficient data on material composition indicators and spatial distributions20. Thus, insights about future HSR material consumption and strategy toward sustainable resource use are not enriched. Further, HSR is encouraged as a green passenger transport model in the operational phase7. But, the HSR carbon replacement value (CRV), implying the GHG emissions that would be required for HSR construction with current technologies and materials27, is less discussed. The emerging big data technology in urban science offers opportunities to address such gaps28,29, but has not yet to fully utilize to HSR studies. To address these gaps in the context of rapid HSR development in China, our analysis pursues the following research questions: (1) What are the development patterns and material metabolism characteristics in China’s HSR? (2) What are the environmental impacts of GHG emissions in Chinese HSR development?

First, we conduct bottom-up material flow and stock modeling with geographical data to characterize the spatiotemporal development patterns of the Chinese HSR. Second, material metabolism wise, we illustrate the stock diversities across material mass, composition, and end-use components among various HSR lines and speeds. The contributions of construction technology advance in reducing material consumption are highlighted. Additionally, we observe the spatially balanced material distributions across Chinese provinces and the material flow characteristics across HSR metabolism. Third, regarding the greenhouse gas emissions, we reveal the carbon replacement value in HSR construction and the operational GHG emissions in HSR operation. By comparing HSR infrastructure with road and aviation systems, we demonstrate the emission mitigation opportunities of HSR, present a comprehensive emission assessment across Chinese passenger transportation infrastructures, and outline the transition effects in green transport development.

Results and discussion

Development patterns and cumulative material stocks

Figure 1a shows the spatiotemporal trajectory of HSR development in China. The HSR experienced rapid growth patterns and reached from 1039 km length and 24 stations in 2008 to 38,914 km and 1064 stations with over 150 lines in 2020. The development boom occurred during 2010 to 2015, with 39% increase annually in HSR length, followed by a growth rate of 11% in the past five years. The current Chinse HSR length already accounted for 60% of the global length and ten times larger than the second-largest country (Spain)3. The rapid growth trends will hold for decades according to the national transportation plan for reaching “eight vertical and eight horizontal” networks by 2035 (Supplementary Fig. S8).

Fig. 1: Development of HSR networks and material stocks in China.
figure 1

a The spatial distribution of HSR network in 2008 and 2020. HSR lines are marked in bule and HSR stations are labeled with green dots. b The distribution patterns HSR material stocks from different end-use components and HSR lines. c Composition of material stocks in HSR bridge and subgrade.

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Stock patterns wise, the cumulative amount was 0.13  Gt in 2008. This mass had dramatically increased by 28 times and added up to 3.65 Gt in 2020 (mean value, with a minimum of 3.60 Gt and a maximum of 3.71 Gt) (Fig.1b), which is larger than global subway stocks in 2020 (2.5 Gt)30 and global in-use plastic stocks (3.2 Gt) in 201431. Remarkably, this stock will continuously add up to 4.9 Gt by 2035. Temporally, HSR became the latest way of traveling across most since the late 2000s in China, with an annual stock growth of 71 Mt in 2008. Afterward, it quadrupled to 342 Mt annually from 2011 to 2015, equivalent to Vienna’s building stocks (380 Mt)32 and five-time higher than Odense’s built environment stocks (67 Mt)33. The net addition slightly decreased to 277 Mt per year from 2016 to 2020. Material stocks expansion in China could be attributed to: (1) HSR system provide efficient solutions for satisfying soaring commuting demands34; (2) technology innovations in HSR construction facilitated construction speed, such as the continuous seamless rail35, unballasted track36, large-span tunnel and bridge37, and safety monitoring system38; and (3) China’s infrastructure-based development strategy accelerated HSR construction, especially for the “Four-Trillion Stimulus” investment in 2008 against the global financial crisis39.

As for material typologies, nonmetallic minerals, represented by gravel (1756 Mt or 48%), sand (891 Mt or 24%), and cement (560 Mt or 15%), are responsible for 88% of total in-use HSR stocks in 2020 (Supplementary Fig. S8 and Table S25). In terms of end-use components, bridge and subgrade contribute most, represented by 37.2% (1.36 Gt) and 34.4% (1.26 Gt) (Fig.1b). Accordingly, we further investigate features with construction materials stocked in HSR bridge and subgrade, namely bridge stocks and subgrade stocks. We found that the majority amounts of bridge stocks are located in box girder (39%). Slope protection (50%) and railway subgrade (33%) dominated the subgrade stocks (Fig. 1c and Supplementary Fig. S9 and Table S26). Notably, since 2008, bridge (17%) and tunnel (1%) material stocks increased over two (34%) and eleven times (14%) respectively, deriving from the growing bridge-tunnel ratio (68% in 2020) that concerning farmland protection in China.

Diverse characteristics for the stock distribution across end-use components are identified among HSR lines. For example, a length of 1120 km bridges are constructed on the Beijing-Shanghai HSR line (totally 1318 km), resulting in a large share of bridge stocks (70%). As for the Hainan East line constructed on a flat island with 9% bridge-tunnel ratio, subgrade stocks account for 56%. By contrast, in the mountainous areas of southwest China, the tunnel stocks induce 52% among the Chongqing-Lichuan line (Fig.1b).

Evolution of high-speed railway stocks with soaring speed and technology advances

We further identify that construction technology innovations could offset the material demands of soaring HSR speed. In China, HSR stock density, indicating the accumulation of construction materials in one meter length of HSR, is falling off (110 t/m in 2008 to 92 t/m in 2020) with increasing HSR speed (240 km/h on average in 2008 and 276 km/h in 2020) (Fig. 2 and Supplementary Table S28). Material stocks embedded in 350 km/h HSR trains increased from 9% in 2008 to 50% in 2011. However, the Wenzhou HSR collision in 2011 prompted Chinese government to slow down HSR operational speed40, and the material stocks of 350 km/h lines fluctuated to 30% afterward. In 2020, stocks across HSR speeds are 871 Mt (200 km/h), 1320 Mt (250 km/h), 253 Mt (300 km/h), and 1127 Mt (350 km/h). According to the national HSR design code, it requires the minimum small radius curves, the at least subgrade surface width, and the lowest tunnel clearance sectional area with various operational speeds41 (Supplementary Tables S29–S32). Therefore, the higher HSR speed of 350 km/h accelerates material consumption in steel rail (increased by 17%), railway subgrade (3%), and tunnel (5%) compared with 200 km/h lines (Fig. 2).

Fig. 2: Evolution of HSR material stock density with increasing HSR speed and developing technologies.
figure 2

The left figure demonstrates the dynamics between HSR material stock density and average HSR speed from 2008 to 2020. The right figure shows the material stock density across HSR steel rail, rail subgrade, tunnel, bridge, and track base.

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Construction technology innovations contribute notably to descending HSR material densities. For example, box girders adopted in the Chinese HSR bridge developed from a 24-m span (weighted 600 t) to 32-m (900 t) and 40-m (1000 t)42 over the years. The utilization of 40-m box girders saves 20% to 39% of material input compared with 32-m and 24-m spans. If 40-m span box girders could be upscale to national HSR bridge constructions, an amount of 292 Mt nature resources are evitable, highlighting the significance of long-span box girder innovations. Track wise, ballastless track (e.g., CRTS I, II, and III slab tack, and CRTS I double-block tack), offering smoother travel, enhanced safety, and longer service time43, reduces 26% materials compared with ballasted track (3.5 t/m versus 4.7 t/m). Thus, upscaling ballastless tracks could mitigate aggregate consumption in HSR construction (Fig. 2). The adaptation of long-span bridges and ballastless tracks in faster speed HSR witnesses less material dependency, with diminishing densities among 300 km/h and 350 km/h speed lines (averagely 88 kt/m) with 250 km/h (93 kt/m) and 200 km/h (96 kt/m) speed lines. Nevertheless, the faster HSR lines emitted 10 to 50% more carbon than 250 km/h and 200 km/h speed trains44,45. Investigating the operational environmental impacts of HSR is thus fundamental.

Stock gaps across Chinese provinces

HSR is claimed to have transformative effects on regional economies. The construction of Chinese HSR brings cities and regions closer and decreases transport costs46. Spatially, material distributions gaps across Chinese provinces are becoming more narrowed. In 2010, regions with the most extensive stocks were distributed in the East (39.6%) and Central (21.5%), while relatively lower amounts were found in western China (4.3% in the Southwest and 2.8% in the Northwest). In order to break through the uneven distribution between East and West China, the government launched Great Western Development. Consequently, accumulated HSR stocks in the western regions increased from 39 Mt (7%) in 2010 to 840 Mt (22.5%) in 2020, which will continually grow to 1283 Mt (26.4%) in 2035 (Fig. 3a–c). The Gini coefficient of accumulated HSR stocks also decreases from 0.56 in 2010 to 0.29 in 2035. Similar trends are identified with the per area and the per capita stocks Gini coefficient that decreased from 0.68 and 0.62 to 0.39 and 0.33 during 2010 to 2035 (Supplementary Fig. S11). Nevertheless, the Hu-Line, separating China into two halves, divided HSR stocks with 9.7% in the Northwest and 90.3% in the Southeast in 2020 (Supplementary Fig. S12), suggesting that efforts are required to transit from speed efficiency to further equity47.

Fig. 3: Evolution of HSR material stocks across provincial regions.
figure 3

The cumulative HSR material stocks across provincial regions in (a) 2010, (b) 2020, and (c) 2035. The cumulative HSR material stocks per area across provincial regions in (d) 2010, (e) 2020, and (f) 2035. The cumulative HSR material stocks per cap across provincial regions in (g) 2010, (h) 2020, and (i) 2035.

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In terms of the per area stock, indicating the accessibility of HSR infrastructure to residents, the western regions are still lagging far behind. For example, in 2020, per area stock in Shanghai (2082 t/m2) is approximately 60 and 44 times larger than Qinghai (35 t/m2) and Xinjiang (47 t/m2) (Fig. 3e). Lower per area stock provinces are manifested in the West and North China. This situation highlights the importance of increasing HSR infrastructure, especially for HSR stations, to meet commuting demands. As for per capita HSR stocks, Hainan (6453 t/cap) and Gansu (5844 t/cap) represented the most considerable volume in 2020, while Ningxia (409 t/cap) and Shanghai (531 t/cap) are the lowest (Fig. 3g–i). This result could derive from the advanced HSR development in Hainan, as well as the low population density (together with 4% of China) but the ample land area (17% of China) in Gansu. For Ningxia province, the lack of HSR infrastructure contributes to lower per capita stocks. While in Shanghai, lower per capita stocks are attributed to the large population. Thus, optimized HSR timetables with large-space HSR station design are essential for avoiding over-crowded in megacities like Shanghai.

These characteristics of HSR stocks and their development trajectories (e.g., growth speed and spatial acceleration), combined with national socioeconomics, planning strategies, and construction technology advances, can be used in forecasting HSR development and supporting green policy in a long run. For instance, instead of forecasting future resource demands in HSR construction based on the design code, we can account for the identified stock results of induvial lines and stations buildings with similar conditions (e.g., line length, bridge and tunnel ratio, geographical location, construction methods). Simultaneously, the detailed depict of historical HSR stock growth with spatial resolution could help urban planners and policy makers to identify areas and cities that require more HSR lines and stations to address inequalities and avoid socioeconomic difficulties48. Finally, all the national HSR results provide a platform for multi-stakeholders (e.g., governments, researchers, and public) to integrate the resource considerations in HSR planning, especially with consideration of the rapid development speed in China.

Material flows embedded in Chinese high-speed railway

The Chinese metabolism database distinguishes seven dominating material production typologies, six construction processes, and nine end-use sectors. Therefore, it maps material flows onto 7 × 6 × 9 = 378 bilateral relationships (Fig. 4 and Supplementary Tables S36–S39). Driven by rapid HSR development, 1744 Mt of gravel, 896 Mt of sand, and 561 Mt of cement are manufactured. As for the construction process, considerable materials are demanded in concrete engineering (totaling 1413 Mt of gravel, sand, cement, steel, fly ash, and water). And only 54 Mt of steel is used for steel track. China continuously extends national HSR and exports to other countries49. Hence, the material flow results allow for predicting the specific resource demands for building new HSR system in China and the globe. For example, massive gravel and sand are utilized in HSR construction, alerting aggregate crises such as coastline erosion50 and ecosystem degradation51. Reducing aggregate consumption through better HSR design, avoiding surplus development, aggregate substitutes are thus imperative16. The metallic materials, in which steel (133 Mt), aluminum (1.3 Mt), and copper (1.6 Mt) are the primary three types but amount to a small share (3.7%) in production, present valuable urban mining when these HSR stocks are detached52,53,54.

Fig. 4: The material cycle of Chinese high-speed rail by 2020.
figure 4

All stocks and flows of HSR related materials are expressed in the figure. The material flows from temporary and formwork processes to recycling and construction waste are zoomed in.

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The longevity of in-use HSR stocks (designed for 100 years) led to merely an amount of 26.3 Mt of waste generated by 2020, mainly deriving from temporary facilities (i.e., equipment plants and worker barracks) in the construction stage. Only a minor amount (0.68 Mt) of steel from formwork projects was recycled or down-cycled, while the majority of HSR wastes (e.g., 24 Mt of concrete products) were landfilled or backfilled as road base55. Although HSR maintenance did not involve structure replacement or cause substantial material turnovers26, we constantly observed HSR maintenance in railway rubber pad and unballasted track base. Consequently, potential strategies to facilitate maintenance waste management, guided by the circular economy principle56, are required.

The estimated data of inflows (resource demand), outflows (waste generation), and material stocks across HSR construction processes and end-use sectors could be directly used for smart resource management with raw material supply, construction waste, and urban mining activities. For example, the material cycle could facilitate efficient HSR resources mining by providing information related to the location, typologies, quantity, and quality of secondary resources. Further, with spatially refined HSR maps and metabolism results, the logistics optimization could be further developed for relevant stakeholders to minimize transportation cost in material supply and construction waste management.

Greenhouse gas emissions and decarbonization strategies of passenger transport in China

In order to understand the comprehensive environmental impacts of resource demand and GHG emissions of passenger transport in China, we compare the material stocks, carbon replacement value (CRV), and operational GHG emissions across Chinese passenger transport infrastructures, including HSR, road, and aviation. The overall transport infrastructure stocks added up to 85 Gt in 2020, almost achieving global resource extraction in 2017 (92 Gt)57. Likewise, the transport infrastructure of roads and airports multiplied during the last decade, from 3.3 million kilometers length road and 173 airports to 4.9 million kilometers and 241 airports. Remarkably, road material stocks reached 81 Gt in 2020, illustrating over 20 and 150 times larger than HSR (3.7 Gt) and aviation (534 Mt) stocks (Fig. 5a).

Fig. 5: Comparisons of material stocks, carbon replacement value, operational emissions, and associated passenger turnover volume efficiency across HSR, road, and aviation.
figure 5

The cumulative (a) material stocks, (b) carbon replacement value, and (c) operational emissions across HSR, road, and aviation in 2010 and 2020; the relations between passenger turnover volume (measured in passenger*kilometer, pkm) with (d) material stocks, (e) carbon replacement value, and (f) operational emissions across HSR, road, and aviation from 2008 to 2020. Note: the uncertainties with max, mean, and min values of operational emissions across HSR, road, and aviation are shown in Fig. 5c.

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CRV is adopted to approximate the GHG emissions generated if current technologies and materials replace existing infrastructure27. We estimate the CRV emissions of transport infrastructure in 2020 is 2.5 Gt, equaling to the CO2 emissions of the cement industry in 2014 (2.4 Gt)55. Between HSR and road, we find large stock gaps and narrowed CRV differences from 2010 to 2020. The disparities could derive from the utilization of energy-intensive materials, such as steel and cement, that account for 96% of HSR CRV (Fig. 5b) in 2020. Regarding passenger turnovers, HSR and aviation turnover increased about seven and two times from 2010 to 2020. At the same time, road transport decreased five times. We thus find the upward operational emissions of HSR and aviation, as well as the downward emissions of road. In 2020, intercity passenger transport accounted for 143 Mt of operational GHG emissions, or 0.4% of global CO2 emissions from fuel combustion58. Aviation contributed to 71 Mt emissions by using aircraft fuel, and HSR and road transportation emitted 31 and 40 Mt GHG from electricity and fossil fuels consumption (Fig. 5c).

As transport infrastructures provide passengers with commuting services59, we further compare the environmental impacts by passenger turnover volume among road, HSR, and aviation systems. Road infrastructure requires much greater construction materials per passenger kilometer (175 kg per passenger*km) than HSR (8 kg per passenger*km) and aviation (0.8 kg per passenger*km) in 2020 (Fig. 5d). Simultaneously, as for CRV, HSR demands a large but decreasing amount from 19 kg per passenger*km in 2008 to 2 kg per passenger*km in 2020 (Fig. 5e). As for operational emissions per passenger, aviation (112 g per passenger*km) is larger than the road (87 g per passenger*km) and HSR (65 g per passenger*km) (Fig.5f). It is worthy of note that, in the case of the COVID-19 pandemic60, the passenger turnover volume of aviation and road transport both declined twice in China, and passenger turnover volume in HSR fell marginally (Supplementary Table S19 and S20). Therefore, more apparent increasing trends of operational emissions per passenger are identified in road and aviation.

The wave of HSR development will cause increasing environmental impacts. Our findings, correspondingly, help to identify critical socioeconomic, technological, and planning strategies to support relevant industry stakeholders and policymakers in mitigating GHG emissions and resource consumptions. Firstly, energy intensive materials present big environmental challenges in HSR construction61. For example, cement (560 Mt or 15%) and steel (132 Mt or 4%) are responsible for only 19% of total in-use HSR stocks in 2020, but account for 96% of the total CRV (49% of cement and 47% of steel). Thus, harnessing emerging low-carbon technologies in steel production (such as hydrogen-based and electrolysis-based options) are effective approaches52,62. In terms of cement, carbon capture and storage (CCS) technologies63 and carbon uptake effects64 should be emphasized.

Further, in the case of decreasing resource consumption, potentials are identified in HSR aggregate utilization for alleviating sand and gravel crisis. Aggregate substitute materials in HSR construction, such as manufactured sand and stone, desalted sea sand, and bio-based materials16,65, need to be addressed. Bedsides, technological development of construction innovations, such as large-span box-girder and ballastless track, should be further promoted for saving construction resources.

Last, understanding the evolution of HSR metabolism can help quantify the climate change impacts throughout its life cycle in both operational (operational emissions due to energy use in HSR train) and embodied (carbon replacement values for the construction of HSR infrastructure) views. For instance, Chang et al.6 have approximated the embodied emissions and operational emissions of Beijing-Shijiazhuang HSR lines of 9.2 Mt GHG emissions. This study, with the results of GHG emissions among 152 HSR lines at the nation scale, could provide comprehensive view for establishing urban climate strategies from a more holistic perspective and tailored direction in emission reduction. Further, by comparing the CRV and operational emission in national HSR, road, and aviation infrastructures, we concluded that mitigating operational transport emissions could urge cleaner electricity production for HSR66, road transport electrification67, and power-to-liquid fuel utilization in aviation68. Finally, floating prices and intelligent logistical systems are necessary for enhancing HSR passenger ridership and improving energy efficiency69,70, especially for traveling in off-seasons and pandemics71.

Methods

Data sources and collection

High-speed railway implies the train speed larger than 200 km/h, and we have collected the HSR dataset from 2008 to 2020 with various sources. The national yearly HSR length is obtained from the national statistic year book72. The attributes of 152 individual lines, including HSR name, length, bridge-tunnel ratio, track length, tunnel length, bridge length, culvert length, speed, geo location, and opening date, are mainly obtained from individual construction companies. For example, we collected the information of Beijing-Shanghai HSR line from its official website (Beijing-Shanghai High Speed Railway Company Limited, http://www.cr-jh.cn/index) and relevant reports (such as the bridge-tunnel ratio of Beijing-Shanghai HSR line is retrieved from an official report in the website of the State Council of the People’s Republic of China, http://www.gov.cn/jrzg/2008-10/01/content_1110292.htm). On-site surveys (frequently conducted in Shandong, Anhui, Sichuan, and Henan provinces) and interviews with HSR constructors and contractors are proceeded to supplement the inaccessible data. See Supplementary Data 1 and Supplementary Note 1.1 of the HSR line database. The HSR station name and attributes, including station volume and class, are retrieved from official website and interviews with employees. For example, we collected the information of Zhengzhou East Station from Zhengzhou Public Transport Group Company Limited (https://www.zhengzhoubus.com/zhuye.html). See Supplementary Data 2 and Supplementary Note 1.2 of the database with an amount of 1064 HSR stations. The national and provincial socioeconomics (i.e., population and GDP) and land area are mainly explored from statistical yearbook72. The detailed data of socio-economic characteristics at Chinese province levels are listed in Supplementary Note 1.3.

We used the web-crawling approach, developed in our previous study73, to access HSR geographical distributions (mainly from Baidu map, the largest online map portal in China). To validate the obtained HSR maps, we developed the rectification method by Li et al.74, carefully checked the HSR station points along each HSR line for geo-reference, and rectified the distortions of retrieved HSR maps (see Supplementary Figs. S5–S9).

In particular, we have labor-intensively compiled the HSR MCIs database from bill of quantities covering more than 50 HSR construction projects (i.e., Zhengzhou-Xi’an HSR in West China, Shijiazhang-Jinan HSR in East China, Hainan Roundabout HSR in South China, Beijing-Zhangjiakou in North China, and Wuhan-Daye in central China). Over 400 material use processes are found in each HSR construction project (see a snapshot of the HSR bill of quantities in Fig. S3). These construction materials are further aggregated into seven end-use HSR components (i.e., HSR bridge, subgrade, tunnel, track, station, culvert, and electrification, power, communication, and signal (EPCS) system). In the end, the materials utilized in HSR construction are classified into 19 typologies, as detailed in Supplementary Table S2 to S12. The road and aviation MCIs databases were also retrieved from the Chinese local construction bills of quantities (see Supplementary Tables S13–S17).

Bottom-up estimation of HSR stocks and flows

A bottom-up HSR stocks accounting method, as shown below in Eq. (1), is then used for calculating the different types of construction materials that are stocked across HSR components. We integrated the physical size (e.g., the bridge, subgrade, tunnel, track, culvert, and EPCS system length, and the station building floor area) and material composition indicators (MCIs) for each type of HSR competent to obtain the material stock.

$$M{S}_{m,i}=\mathop{\sum }\limits_{m,i}(P{S}_{i}\times MC{I}_{m,i})$$
(1)

where PSi is the physical size of HSR components i (measured in km or m2), and MCIm,i (measured in kg/km or kg/m2) is the composition intensity of material typology m in HSR component i. We have considered 19 construction materials that used in HSR, including aluminum, asphalt, brick, cement, ceramics, copper, fly ash, geotextile, gravel, polished granite, non-woven fabric, polyethylene board, PVC, rubber, sand, steel, timber, water and water-reducing admixture. All these 19 construction materials are further grouped into 7 types of bulk materials according to stock mass, namely cement, fly ash, gravel, sand, steel, water and other materials. The Chinese road and aviation stocks were also estimated with the bottom-up model.

With the material flow analysis principle of mass balance, material inflows (for the material demand of new HSR construction) and outflows (as the construction waste) are determined by comparing the differences of estimated HSR material stocks of two consecutive years. The HSR material demand flow is calculated as shown by Eq. (2):

$$\,{MF}\left(t\right)={{MF}}_{s}\left(t,t-1\right)+{{MF}}_{m}\left(t\right)+{{MF}}_{l}\left(t\right)$$
(2)

To calculate the HSR material demand flow \({MF}\left(t\right)\) in year t, three types of data are required: (1) \({{MF}}_{s}\left(t,t-1\right)\) is the differences of HSR material stocks in year t and t-1; (2) \({{MF}}_{m}\left(t\right)\) is maintenance material flow in year t; (3) \({{MF}}_{l}\left(t\right)\) is the material loss in year t (outflows). According to the Code for Design of High-Speed Railway (TB 10621-2014) issued by National Railway Administration of People’s Republic of China41, the main structure of HSR infrastructure are designed to last 100 years, including the HSR subgrade, bridges, and tunnel. Thus, maintenance of \({{MF}}_{m}\left(t\right)\) will not involve structural replacement or cause large material turnover within study period of 2008 to 2035. The material loss is evaluated mainly in on-site construction of temporary facilities (e.g., temporary roads and quarters) and formwork projects (mostly steel).

Spatial analysis of HSR stocks

Based on the Chinese HSR stock estimation and the geographical HSR inventories of lines and stations, we further estimated the cumulative stocks and stock densities at the provincial level. Polyline data of 152 HSR lines and polygon data of 1,064 HSR stations are graphically transformed with stock attributes via bottom-up stock estimation. The provincial HSR stocks across 32 Chinese provincial (excluding Macau and Taiwan) with socioeconomics are split and plotted to identify the spatial stock and density characteristics. The Gini coefficient of total HSR stocks and stock densities are evaluated across the 32 provinces in 2010, 2020, and 2035 to quantify the inequality of in-use stocks among regions. All HSR geospatial data are handled and analyzed with ArcGIS 10.2.

Estimation of environmental impacts of passenger transportation infrastructures

The CRV of HSR materials stocks, implying the volume of GHG emissions required of building specific HSR stocks with current technologies, are calculated based on emission factors of materials and HSR stocks27 (see the detailed CRV estimation in Supplementary Method 1). Specifically, to calculate the CRV of a HSR stocks, two types of data are required: the particular quantity of construction material stocked in HSR component, and the embodied carbon emission factor of construction material. Equation 3 illustrates the bottom-up model to quantify CRV in HSR systems:

$${{CRV}}_{m,i}=\mathop{\sum }\limits_{m,i}({{EF}}_{m}\times {{MS}}_{m,i})$$
(3)

Where \({{CRV}}_{m,i}\) is the total carbon replacement value of material m in HSR end-use component i; \({{EF}}_{{m}}\) represents the emission factor of material m; and \({{MS}}_{m,i}\) refers to the material stocks of material type m in HSR end-use component i. The GHG emission factors, which represent cradle-to-gate requirements for raw material extraction, manufacture, and processing, were obtained from the Chinese Life Cycle Database (CLCD)75 and latest literature for estimating the CRV in HSR stocks. The CRV Chinese road and airport stocks were also estimated with CLCD data and the bottom-up model (see the GHG emission factors in Supplementary Table S18).

The Chinese operational GHG emissions of road, aviation, and HSR transports are evaluated by emission-factor approach, with the passenger turnover volumes (collected from statistics), operational energy, and associated operational emission factors. Equation 4 illustrates the bottom-up model to quantify operational GHG emissions across passenger transport facilities:

$${{OE}}_{t}=\mathop{\sum }\limits_{t}({{OEF}}_{t}\times {{PT}}_{t})$$
(4)

Where OEt is the operational GHG emission of passenger transport typology t; \({{OEF}}_{t}\) represents the operational emission factor of passenger transport t; and \({{PT}}_{t}\) refers to the passenger turnover volume of passenger transport t (see the detailed operational estimations estimation in Supplementary Method 2).

Uncertainties and limitations

While the temporal and spatial HSR stocks and flows estimation we developed represents the best available on the national level of China. It could give a broader scope (e.g., continental or global scales), a more detailed material composition in accordance with each line, and higher spatial resolution in HSR components. Further, our results of HSR stocks and associated emissions rely on the complied HSR physical size, HSR MCIs, and the GHG emission factors. According to the Chinese HSR material metabolism and GHG emission model, critical causes of uncertainties were identified with HSR physical size, MCIs, GHG emission factors, passenger turnover volume. The primary sources of stock and emission uncertainty include the temporal and spatial variations of Chinese HSR MCIs and the GHG emission factors. MCIs are mainly due to spatial construction conditions and temporal construction technological advances. Thus, we considered the parameter varieties of HSR physical size, MCIs, GHG emission factors, passenger turnover volume, and employed the Monte Carlo method to estimate uncertainties in HSR stocks and CRV across years. We ran the Monte Carlo simulation in Crystal Ball software 10,000 times. The width of parameter confidence intervals (such as MCIs, GHG emission factors) is adjusted as 95% to estimate uncertainties in material stocks and associated emissions. The uncertainty results illustrated that the HSR stocks ranges from 3601 Mt to 3713 Mt, with CRV varies from 942 Mt to 1031 Mt. Further, sensitivity analysis of stock estimation shows that the year 2014 (24.9%), 2018 (13.7%), and 2015 (13.5%) contribute most to the uncertainty of material stocks, and the emission factor of cement (77.4%) and steel (10.9%) account most to the CRV uncertainty. Component side, the subgrade (42.5%), track (35.8%), and bridge (15.9%) influence most to the Chinese HSR material stocks (see Supplementary Figs. S24–27). We also assume the operational GHG emission factor and analyze the uncertainties of operational transportation emission (see Supplementary Method 3 and Tables S43, S44).