The growing awareness of sustainable environmental development has led to increased attention from the public and policymakers worldwide regarding ecological issues, including resource depletion, environmental degradation, escalating pollution emissions, and the concerning decline in biodiversity (Wang et al., 2022a; Lee et al., 2023). This attention is particularly prominent in emerging countries. Notably, China, as the largest developing country globally, has experienced a remarkable “double growth” phenomenon since the era of reform and opening up, witnessing continuous stages of high-speed to high-quality economic growth, leveraging its “late-comer advantage” (Wang and Wang, 2021). However, the prevailing development model of conventional industries, characterized by “high input, high energy consumption, and high emissions” has imposed significant pressure on long-term environmental sustainability. Global statistical data on energy usageFootnote 1 (Fig. 1) reinforces China’s position as the foremost energy consumer, accounting for 26.1% of the world’s total energy consumption. It is essential to note that fossil resources, notably coal (57%) and oil (20%), remain the primary sources of fuel in China’s energy consumption. This over-reliance on traditional resources has led to a growing problem of resource depletion within the country. Concurrently, the persistent and elevated levels of pollutant emissions have had detrimental effects on the environmental carrying capacity (Zhang and Lin, 2019). In 2020, China surpassed all nations in carbon emissions, contributing to 30.7% of the global carbon emissions (Fig. 2), with industrial carbon emissions alone accounting for nearly 50% of the total social carbon emissions. The magnitude of these practical dilemmas underscores the importance of promoting ecologically sustainable development in China. The urgency to address these challenges necessitates both initiating a comprehensive and strategic energy transition, shifting towards cleaner and renewable energy sources, while simultaneously implementing robust environmental protection measures.

Fig. 1: Energy consumption in major countries: 2000–2020.
figure 1

Data source: BP World Energy Statistics Yearbook.

Fig. 2: Trends of carbon emissions in major countries: 2000–2020.
figure 2

Data Source: BP World Energy Statistics Yearbook.

As noted by the International Energy Agency, the transport sector stands as the second-largest carbon source worldwide, accounting for 25% of global carbon emissions, and has emerged as a significant driver of environmental degradation (Charabi et al., 2020). Notably, China’s transport sector is widely recognized as the fastest-growing energy consumer (Liu et al., 2021). In light of these concerns, the high-speed railway (HSR) has garnered attention for its critical attributes, including “utilizing eco-friendly natural resources,” “unified central planning,” and “low emissions”, distinguishing it from traditional transportation modes (Sun and Li, 2021; Xu et al., 2022). China, in particular, has witnessed a remarkable expansion of its HSR network since the introduction of its first high-speed railway with speeds exceeding 300 km/h in 2008. As of 2021, China’s HSR system boasts an extensive operating mileage of 40,000 km, connecting 93% of cities with populations exceeding 500,000, establishing the world’s largest and most modern HSR network (Chen et al., 2022). Given this context, it is conceivable that the establishment and operation of HSR may emerge as a primary driver for the low-carbon transformation and development of traditional railways in the future. Against this backdrop, this paper seeks to address critical questions: Does HSR operation significantly contribute to nationwide energy savings and emission reductions? How does HSR promote ecological environmental sustainability? This study aims to shed light on these issues and provide effective solutions to further the understanding and advancement of HSR’s environmental impact, which constitutes the initial motivation of this paper.

Undoubtedly, previous literature on high-speed railways still exhibits imperfections, leaving ample space for subsequent research. One of the primary areas of contention lies in whether the opening of HSR truly facilitates energy conservation and emission mitigation. While some studies suggested that HSR openings optimize transportation structures and foster technological innovation, leading to reduced pollution discharge and enhanced ecological quality (Yang et al., 2019a; Huang and Wang, 2020), others argued that the regional industrial agglomeration and urbanization spurred by modern transportation may be a key driver of carbon pollutant emissions (Gan et al., 2020). Thereby, the environmental effects of high-speed railways are still ambiguous and need further refinement. Secondly, investigations into the influencing mechanisms of HSR operations on environmental sustainability predominantly focus on either energy usage or pollutant emissions, relying heavily on prefecture-level data (Tang et al., 2021; Li and Cheng, 2022). However, environmental degradation often results from the interconnected relationship between excessive energy consumption from fossil fuels and pollutant emissions. In this regard, the role of industrial enterprises, being the main drivers of economic expansion and the primary consumers and producers of fossil fuel resources and carbon emissions, assumes paramount significance in promoting ecological sustainability. Regrettably, few studies have adequately addressed the distinctive attributes of industrial enterprises, necessitating a more refined and objective analysis of HSR’s impact based on their characteristics. Therefore, further research should strive for a comprehensive analysis framework and a robust dataset to enhance the existing knowledge on these topics. Thirdly, the establishment and expansion of the HSR network have significantly reduced spatial and temporal distances between regions. While some studies have explored the impact of HSR openings on carbon pollutants along specific routes (Guo et al., 2020; Zhang et al., 2022c), there remains an inadequate understanding of the broader influence of cities with HSR connections on energy conservation and carbon emission mitigation across entire regions. Specifically, investigating spatial spillover and spatial conduction effects in these areas represents a vital research avenue to comprehensively evaluate the environmental impact of HSR.

Drawing from the considerations outlined above, this paper adopts a quasi-natural experiment approach to examine the impact of high-speed railway operations on environmental sustainability (Sun and Li, 2021; Qin et al., 2023). Leveraging macro-city data from 2003 and 2020 and micro-enterprise data from 2003 and 2012, we employ the traditional and spatial difference-in-difference method to identify the net effects and spatial overflow impacts of HSR on environmental sustainability. Meanwhile, we investigate the mechanism pathways, including technological innovation, elements flows, labor productivity increase, and industrial structure optimization, and analyze their influences on cities with distinct characteristics. This study makes marginal contributions in several aspects: firstly, we empirically explore the connection between HSR openings and ecological environmental sustainability from multiple perspectives, focusing on natural resource consumption and environmental pollutant emissions, using a comprehensive dataset at both macro-city and micro-industrial enterprise levels. Indeed, our comprehensive research thoughts expand and complement the research perspective of existing literature. That is, the empirical framework combines macro and micro perspectives to analyze the environmental effects of the high-speed railways from resource consumption and pollutant emissions, considering the heterogeneity of industrial enterprises in specific industry classifications and enterprise ownership and urban characteristics. Meanwhile, we extend the analysis to the entire region by considering the spatial spillover effects of high-speed railways to overcome the limitations of analysis confined to the local specific regions along the railway line. Secondly, we investigate the potential mechanisms through which HSR openings contribute to environmental sustainability, including technological progress, elements flows (such as capital, labor, and digital information), labor productivity, and industrial transfer and upgrading. This research offers new empirical evidence for related studies in the field. Moreover, we divide the full sample into subsamples based on administrative level, resource endowment, and city size, providing valuable references for understanding heterogeneity and guiding scientifically coordinated high-speed railway network layout in the future. Finally, we present efficient policy suggestions for policymakers in both developing and developed countries, emphasizing the importance of eco-friendly resource utilization and the role of non-policy factors in fostering environmentally benign practices.

The rest of this study is structured as follows (Fig. 3). Section “Literature review and theoretical hypothesis” reviews relative literature and indicates the theoretical hypothesis. Section “Identification strategy and data” depicts empirical strategy and data. The findings of baseline regressions are reported in the section “Empirical analysis and Heterogeneity analysis”, followed by an analysis of mechanical actions and heterogeneity in the sections “Mechanisms test” and “Discussion”, correspondingly. Section “Conclusions and policy implications” remarks crucial highlights and policy recommendations.

Fig. 3
figure 3

Research framework graph.

Literature review and theoretical hypothesis

Literature review

The high-speed railway system stands as a prominent symbol of China’s progress in enhancing its transportation infrastructure. Its origins can be traced back to 1978, marking the commencement of a pivotal phase in Chinese railway construction. Following China’s reform and opening-up policies, Chinese railway construction gradually embraced a scientific development approach, drawing inspiration from Japan’s successful HSR experience (Huang et al., 2018). Later in 1999, a milestone was achieved with the initiation of the Qinhuangdao-Shenyang dedicated passenger railway in 2003, which officially ushered China into the high-speed railway era. During this period, scholarly research predominantly focused on the history, current status, construction conditions, and future planning of railway transportation in China (Saito, 1994; Luguang, 2002; Xue et al., 2002). These studies laid the academic groundwork and provided the essential theoretical foundation for further HSR construction in the country. In 2004, the Chinese government approved the “medium- and long-term railway network planning,” envisioning an extensive HSR network spanning 12,000 km. The blueprint aimed to create a “4 + 4” backbone consisting of four north-south and four east-west HSR lines in China (Li et al., 2021a). During this phase, China actively engaged in collaborations with advanced nations like Japan and Germany, while simultaneously prioritizing the development of domestic technologies. This strategic approach involved an exchange of knowledge and learning experience, fostering growth and innovation in China’s HSR endeavors. Meanwhile, many scholars have gained valuable insights by comparing the current situation and characteristics of HSR systems abroad, facilitating informed decision-making in China’s railway development (Givoni, 2006; Luguang, 2006). For instance, Yan (2004) made pertinent suggestions for establishing the HSR between Beijing and Shanghai, drawing from the experiences of Japan and Germany. The operation of the Beijing-Tianjin high-speed railway line in 2008 marked a significant turning point for China’s transportation infrastructure, ushering in the era of HSR with speeds surpassing 300 km/h (Wu et al., 2014), led to a wide discussion in academia about the nexus between the build and operation of HSR and urban development (Tang et al., 2011). Scholars like Wang et al. (2012) have examined the dynamic impact of HSR on tourism and related industries. Utilizing iso-tourist lines, their research demonstrated that HSR construction significantly facilitated the development and transformation of tourism in China. Besides, Deng et al. (2020) emphasized that HSR operations effectively stimulate economic, social, and cultural growth in cities along the route, meanwhile, the challenge of development imbalances between cities directly connected by HSR and those not served by the network is significant. This observation was corroborated by Hang (2011), whose study similarly indicated that while HSR fosters economic expansion in regions along its route, it can simultaneously have negative repercussions on non-connected cities. The “Belt and Road Initiative” has catalyzed China’s involvement in HSR projects in other countries, resulting in a new era of HSR construction. As a consequence, China has overtaken developed nations like Japan and Germany, establishing itself as the global leader in HSR, namely China boasts the longest mileage and the highest operating speeds of HSR (Niu et al., 2021).

Significantly, it is not hard to find from the history of HSR in China that although the beginning of the construction was late and the initial technical resources were relatively weak, the HSR in China developed miraculously rapidly. Especially since the Beijing-Shanghai railway opened in 2008, China has built an HSR network of “eight vertical and eight horizontal” in just a few decades, covering more than 80% of the cities, which made China become a country with the most advanced HSR technology in the world. Its rapid development has attracted extensive attention from the academic community. Many scholars have compared the construction and evolution of HSR in China with advanced countries such as Japan, Germany, and America, and tried to explore the reasons behind the rapid rise of HSR in China. For example, after comparing the HSR in China with the Japanese Shinkansen, Sone (2018) pointed out that the Japanese Shinkansen was too conservative in the initial construction, which made it difficult to innovate track design and further improve train speed in the future. Meanwhile, the terrain features and track conditions were also key reasons for limiting the speed of HSR in Japan. Whereas, the land of Germany, Britain, and other Western countries was mostly privatized, which leads to high costs in the procedure of construction and hinders the development of HSR in these countries to a certain extent (Campos and De Rus, 2009). What’s more, the small national area also decided that most travel requirements can be accomplished only by cars, so high-cost and high-speed vehicles such as maglev trains are not a viable option in these countries. For the United States, Jiao et al. (2013) indicated that the biggest obstacle to HSR construction is the lack of financial input, a strong and efficient high-speed railway network needs a large amount of infrastructure, which can put pressure on the U.S. finances. In contrast, the HSR technologies in China were drawn on the experience of Japan, Germany, and other developed countries, combining with the special national conditions in China into innovation, which promoted its rapid development (Li et al., 2021a). At the same time, China has a vast territory and abundant human resources, therefore an efficient and extensive network of HSR has become the key to accelerating the transfer of people and driving the growth of neighboring regions. In addition, Chen and Ding (2022) believed the rich human resources in China accelerate the innovation of proprietary technologies and provide technical assistance for the HSR construction, meanwhile, the strong investment of the government has realized the concentration of various resource elements, providing extremely convenient conditions for the rapid rising of HSR.

With the dramatic improvement of HSR in China, its impact on national development has received extensive attention. In addition to the economic benefits (Meng et al., 2018; Liang et al., 2020), more and more scholars have begun to concentrate on the nexus between the running of HSR and technological progress (Dong et al., 2020; Yang et al., 2021b), industrial agglomeration (Shao et al., 2017) and environmental protection (He et al., 2015; Quinn et al., 2019). Especially since the “double carbon” target was put forward, the environmental needs of China’s development are increasingly prominent, and whether HSR construction can achieve the sustainable development goal of energy conservation and emission reduction has been widely concerned. Some scholars believe that the operation of HSR can relieve environmental stress and effectively improve environmental quality (Guo et al., 2020; Li and Cheng, 2022). For instance, Qi and Dauvergne (2022) showed that the “Belt and Road Initiative” vigorously promotes the establishment of HSR such as the Jakarta-Bandung high-speed railway in countries along the route, which not only benefits the rapid economic expansion but also efficiently promotes the development of clean energy and reduced greenhouse gas emissions in these countries. According to the characteristics of resource cities in China, Tang et al. (2021) put forward that the operation of HSR can obviously reduce resource consumption and relieve the carbon emission intensity in cities along the railway, and this effect is more obvious for cities rich in coal resources. Moreover, (Liu et al. 2022b) believed HSR can not only improve the ecological quality of cities with HSR but also have a significant overflow influence on the adjacent cities. Similarly, the study of Jiang et al. (2021) confirms that HSR caused fewer greenhouse gas emissions than other kinds of transportation. In particular, Yang et al. (2019) stated that the construction of the HSR itself would not relieve carbon emissions, but through technology, allocation, and substitution effects. However, every coin has two sides, and the establishment of HSR may also have adverse impacts on the environment. Gan et al. (2020) stated that the running of the HSR is one of the causes of aggravated local carbon emissions, and this promoting effect can be enhanced by the rising number of stations and trains. What’s more, after comparing the impacts of different transportation types on carbon emissions, the study of Gan et al. (2020) confirmed that contrasted with the traditional railway, the operation of HSR can also be seen as the underlying driving factor of urban carbon emissions. Meanwhile, a large amount of energy and resources used in HSR construction was also a crucial factor to increase carbon emissions in the short term (He et al. 2022). Thereby, further exploring the underlying nexus between HSR opening and environmental sustainability is essential under the context of the urgent reality of China’s sustainable development target.

Briefly, numerous scholars have undertaken studies exploring the development of high-speed railways and their impact on environmental quality, laying a solid foundation for the research presented in this paper. However, a considerable portion of the existing research on the nexus between HSR openings and environmental quality remains at the macro level (Liu et al., 2022b; Sun and Li, 2021), with limited studies utilizing microdata from industrial enterprises and analyzing the environmental impacts (Liu et al., 2023), the mechanisms of action need to be further explored at a more granular level. To address this gap, there is an urgent need to conduct a comprehensive analysis of the environmental effects of high-speed railways from a unified macro perspective on pollutant emissions and a micro perspective on energy consumption. Furthermore, a detailed investigation into the potential mechanisms through which high-speed railways influence environmental sustainability is warranted, presenting a more in-depth analysis of the subject matter.

Theoretical hypothesis

As for the impact mechanism of the HSR on environmental sustainability, existing research emphasized that the HSR can increase environmental quality by optimizing transportation structure, alleviating traffic pressure, and reducing exhaust emissions of private cars. As the high-speed railway network in China becomes perfect gradually, its indirect impact mechanisms on sustainability development also become more diversified. Therefore, this study analyzed the indirect effects of the HSR opening from four aspects (Fig. 4), including the labor productivity effect, industrial structure effect, technological innovation effect, and elements flow effect, trying to make up for the deficiency in the mechanism of HSR opening affecting environmental sustainability.

Fig. 4
figure 4

Mechanism of action graph.

Firstly, HSR can enhance environmental quality by increasing the labor production efficiency of enterprises (Cheng et al., 2020). First, HSR construction can expand the market scale and the competition scope of enterprises (Lu and Li, 2022), which urges enterprises to continuously improve management efficiency and labor productivity to gain competitive advantages (Charnoz et al., 2018). Second, HSR had broken the labor market segmentation and expanded the scope of the labor market, so as to alleviate the labor mismatch and stimulate the improvement of the overall productivity within the region (Yan et al., 2022). The improvement in labor productivity can cut the production cost of enterprises and improve production efficiency, which enabled enterprises to spend more money on pollution control and green transformation and effectively reduce their energy consumption. (Zhou and Zhang, 2022). In particular, for enterprises with high pollution and high emissions, the improvement of labor productivity can effectively decrease their fossil energy consumption and improve energy efficiency to reach the purpose of energy saving and emission reduction (Zhang et al., 2022a). Thus, the hypothesis is put forward below:

Hypothesis 1. HSR can reduce fossil energy consumption by improving the labor productivity of enterprises, thus achieving the goal of environmental sustainability.

Secondly, HSR can optimize resource allocation and facilitate the transformation and upgrading of industrial structures, which can reduce the fossil energy consumption and pollutants emissions of industries (Fan et al., 2020; Li and Wang, 2023). The operation of HSR provides the path and direction for the industrial transformation, forming the corresponding economic and industrial belt along the route, which can not only facilitate the agglomeration of different industries but also accelerate the optimization of the industrial structure (Lin et al., 2021). In addition, the operation of HSR can also construct the connection between traditional industrial zones and central cities, which can promote the labor division and the synergetic development of industries close to the route, and stimulate the transformation and updating of traditional industries (Chen and Hall, 2012). The industrial structure adjustment brought by HSR increases the ratio of the tertiary industry, and reduces the number of secondary industries with extensive energy consumption, so as to effectively reduce fossil energy consumption and achieve environmental sustainability (Wang et al., 2022b). Meanwhile, technology and knowledge spillovers brought by industrial optimization and agglomeration were also key factors to accelerate the development and utilization of renewable energy and improve ecological quality (Yang et al., 2020, Solarin et al., 2022). Thus, the hypothesis is proposed as below:

Hypothesis 2. HSR can reduce fossil energy consumption by optimizing industrial structures, thus achieving the goal of environmental sustainability.

Thirdly, HSR can be regarded as an important carrier for production factors flow in space, which can improve the allocation of production factors among regions and stimulate whole economic growth (Berger and Enflo, 2017). The operation of HSR breaks the spatial barriers between different areas which expedite the flow of information, technology, capital, labor, and other elements. The market access brought by it takes a crucial role in optimizing the efficiency of resource allocation in central and adjacent regions (Yang et al., 2019b). Meanwhile, the construction of HSR connects markets in different cities and promoted the free flow of production elements, which can optimize resource allocation efficiency and facilitate the integrated development of regional markets (Li and Cheng, 2022). The optimization of the resource factor structure eases the distortion of the factor and increases the energy utilization efficiency, so as to relieve energy consumption and pollutant discharge of the enterprises (Yang et al., 2021a). Besides, the rational distribution of industrial resources can reallocate the stored resources from areas with low output value and high energy consumption to areas with high output value and low energy consumption, so as to solve environmental problems and mitigate carbon emissions (Wang et al., 2021). Thus, the hypothesis is put forward as below:

Hypothesis 3. HSR can inhibit carbon emissions by promoting element flow, thus achieving the goal of environmental sustainability.

Finally, HSR can facilitate the technological innovation of cities with HSR stations, and accelerate the spillover of innovation, so as to improve the regional environmental pollution control ability (Huang and Wang, 2020). The HSR opening optimizes the accessibility of cities along the route and improves the urban innovation level, which is both represented in the scale and the quality of urban innovation (Yang et al., 2021b). What’s more, the construction of HSR facilitates cross-regional exchanges, learning behaviors, and technical cooperation among different enterprises, which can facilitate the innovation and application of green technologies, so as to increase the environmental efficiency of these cities (Wang et al., 2022c). Technological innovation can also effectively increase resource utilization efficiency and accelerate the innovation and utilization of new energy, thereby reducing carbon emissions and ecological pollution (Yang et al., 2019a). Thus, the hypothesis is proposed as below:

Hypothesis 4. HSR can inhibit carbon emissions by stimulating technological innovation, thus achieving the goal of environmental sustainability.

Identification strategy and data

Model specifications

The multi-period DID strategy and the DDD strategy

As of the end of 2020, nearly 232 cities in China have already operated high-speed railway networks, providing an objective reality for conducting quasi-natural experiments using the difference-in-differences (DID) method. Considering that the opening of high-speed railways varies across different regions and cities in China at multiple time points, this study employs the staggered DID approach instead of the single time-point DID model to conduct an in-depth investigation and analysis of the net effects of high-speed railway operations. The baseline regression model is recognized as follows relying on the Hausman test.

$${\rm {ES}}_{i,t} = \alpha + \beta {\rm {HSR}}_{i,t} + \theta X_{i,t} + \mu _i + \upsilon _t + \varepsilon _{i,t}$$

where ESi,t demonstrates the environmental sustainability of city i in period t, which is represented by energy consumption and carbon emissions. HSRi,t stands for the dummy variable (postt·treati), which is assigned to 1 when city i opens high-speed rail in year t, otherwise, the value is 0. Xi,t reflects the control variables. ui and vt are the region-fixed effects and time-fixed effects, respectively. εi,t represents the random disturbance term. β shows the net effect of high-speed railways opening in prefecture-level cities on carbon emissions.

Moreover, the facts that industries with different types and characteristics may be influenced dissimilarly, this paper further introduces the Difference-in-difference-in-differences strategy (DDD) to explore the consumption of traditional fossil fuels of high-carbon industries, which is estimated as below:

$${\rm {ES}}_{i,t} = \alpha + \beta {\rm {HSR}}_{i,t} \cdot {\rm {Type}}_{i,t} + \theta X_{i,t} + \mu _i + \upsilon _t + \varepsilon _{i,t}$$

where Typei,t is a dummy variable, representing different concerns of industrial enterprise types and characteristics, including high-carbon (HCI), technology-intensive (Tech), capital-intensive (Capi), and human-intensive (Human) industries, property rights (Prop) whether new products have been produced (Np), and whether the city is a pivot city (Pivot).

The spatial DID strategy

Whereas the multi-period DID Model does not take into account the inter-relationship between cities, so as to ignore the spatial transmission effect between the experimental and control group, which is possible to produce biased empirical conclusions. Hence, this paper introduces the spatial DID model (SDID) to deeply analyze the spatial effects of HSR opening in different cities on carbon emissions. The relevant model is expressed as follows:

$${\rm {ES}}_{i,t} = \alpha + \beta {\rm {HSR}}_{i,t} + \varphi \mathop {\sum}\limits_{j = 1}^N {W_{ij}{\rm {HSR}}_{i,t} + \theta X_{i,t} + \lambda \mathop {\sum}\limits_{j = 1}^N {W_{ij}X_{ijt}} + \mu _i + \upsilon _t + \varepsilon _{i,t}}$$

among them, Wij is the spatial weight matrix, namely the spatial geographic adjacency matrix. \(\varphi \mathop {\sum}\nolimits_{j = 1}^N {W_{ij}{\rm {HSR}}_{i,t}}\) stands for the policy overflow effect of the operation of HSR in prefecture-level cities. β is the direct effect and φ represents the average spillover effect. WijXijt is the spatial overflow effects of control variables.

Besides, the policy spillover effect should be decomposed from the view of intra-group and inter-group spillover effects. That is, the former refers to the overflow effect within the treatment group, and the latter represents the overflow effect of the treatment group to the control group. Thus, the spatial weight matrix can be decomposed as follows:

$$W_{ij} = W_{ij}^{\rm {{T,T}}} + W_{ij}^{\rm {{T,NT}}} + W_{ij}^{\rm {{NT,T}}} + W_{ij}^{\rm {{NT,NT}}}$$
$$W_{ij}^{{\rm {TT}}} = D_{it}^{\rm {D}} \cdot W_{ij} \cdot D_{it}^{\rm {D}}$$
$$W_{ij}^{\rm {{T,NT}}} = D_{it}^{\rm {D}} \cdot W_{ij} \cdot D_{it}^{\rm {C}}$$
$$W_{ij}^{\rm {{NT,T}}} = D_{it}^{\rm {C}} \cdot W_{ij} \cdot D_{it}^{\rm {D}}$$
$$W_{ij}^{\rm {{NT,NT}}} = D_{it}^{\rm {C}} \cdot W_{ij} \cdot D_{it}^{\rm {C}}$$

where \(D_{it}^{\rm {D}} = {\rm {diag}}\left( {D_{it}^{\rm {D}}} \right)\), namely the main diagonal of the matrix. \(D_{it}^{\rm {C}} = I - D_{it}^{\rm {D}}\), and I is the identity matrix.

According to the weight decomposition and policy implementation definition, it can be found that \(W_{ij}^{\rm {{T,NT}}}{\rm {HSR}}_{i,t} = 0\) and \(W_{ij}^{\rm {{NT,NT}}}{\rm {HSR}}_{i,t} = 0\), so the final regression model is expressed as below:

$${\rm {ES}}_{i,t} = \alpha + \beta {\rm {HSR}}_{i,t} + \varphi \mathop {\sum}\nolimits_{j = 1}^N {\left( {W_{ij}^{\rm {{T,T}}} + W_{ij}^{\rm {{NT,T}}}} \right)} {\rm {HSR}}_{i,t} + \theta X_{i,t} + \lambda \mathop {\sum}\nolimits_{j = 1}^N {W_{ij}X_{ijt} + \mu _i + \upsilon _t + \varepsilon _{i,t}}$$

where \(W_{ij}^{\rm {{T,T}}}{\rm {HSR}}_{i,t}\) reflects the spatial overflow effect in cities with high-speed railways. \(W_{ij}^{\rm {{NT,T}}}{\rm {HSR}}_{i,t}\) reflects the spatial overflow effect in cities without high-speed railways.

Variables selection

Based on the reality of the over-usage of natural resources and the dilemma of environmental deterioration, this paper tends to verify the influences of HSR opening on environmental sustainability (ES) from the point of view of fossil energy consumption and pollutant emissions. Firstly, in terms of the consumption of fossil fuels, we introduce the coal and fuel oil mining used to measure the usage situation of energy consumption from traditional natural resources, which is denoted by the abbreviated symbols Oil and Coal. The relevant dataset is collected from the China Industrial Enterprise Database. Secondly, high carbon emissions have led to a series of climate problems and severely restricted environmental carrying capacity (Ma et al., 2021). Carbon emissions are closely related to the energy structure and energy efficiency of a country or region, constituting a critical factor influencing regional sustainable development. In the end, we adopt two representative proxies of total carbon emission (TCE) and carbon emissions per capita (PCE) to represent urban carbon emission levels in the context of the top-level design of “double control” in China (Sun and Li, 2021). Based on the research of Zhang et al. (2022b), we uniformly estimate the urban carbon emissions of liquefied petroleum gas, natural gas, and total electricity consumption. Besides, both representative indexes are the natural logarithm. And the detailed calculated equations are shown below:

$${\rm {TCE}}_{i,t} = \lambda _1{\rm {NGas}} + \lambda _2{\rm {LPGas}} + \lambda _3\left( {\kappa \cdot {\rm {Elec}}} \right)$$
$${\rm {PCE}} = {\rm {TCE}}_{i,t}/{\rm {The}}\,{\rm {amount}}\,{\rm {of}}\,{\rm {regional}}\,{\rm {population}}_{i,t}$$

where λ1, λ2 and λ3 refer to the carbon emissions for natural gas, liquefied petroleum gas, and electricity respectively, which are equal to 2.1622 (kg/m3), 3.1013 (kg/kg), and 1.3023 (kg/kWh) correspondingly. κ stands for the proportion of coal-fired electricity generation.

Under the specification of the DID model, it is necessary to introduce two dummy indexes (post and treat) to construct the explanatory variable, namely the running of HSR is represented by the interaction term of two dummy indicators. The post represents the year information, and the treat represents the group information. In detail, post equals 1 at the year and the years after when the high-speed railways are put into operation in a city, and 0 otherwise. treat equals 1 when a city has HSR in the sample period and 0 otherwise.

To avoid ignoring the possible influences of certain hidden factors, a series of control variables are considered in empirical analysis. From the macro-level (Feng and He, 2020; Sun and Ge, 2021; Zhang et al., 2022b), industrial structure (IS) is demonstrated by the share of the output value of the secondary industry to the regional gross domestic production. Infrastructure construction (IC) is calculated by the share of highway mileage to the regional area. High-end talent accumulation (HT) is described by the ratio of the number of students in institutions of higher learning to the number of permanent residents in the region. Fiscal autonomy (FA) is expressed by the share of revenue to the expenditure of the government. Population density (PD) is expressed by the ratio of the permanent population in the region to the administrative area, which is a natural logarithm. Foreign direct investment (FDI) is expressed by the share of foreign direct investment to gross domestic product.

Moreover, from the micro-level (Bai et al., 2022; Zheng et al., 2022a), workforce level (WL) is calculated by the average employee numbers in industrial enterprises. Enterprise age (EA) equals the year from the establishment of the enterprise to the present plus 1. Corporate financing capacity (FC) is reflected by the ratio of corporate interest expenses to total assets. Enterprise scale (ES) equals the natural logarithm of total annual assets. Net asset turnover ratio (NAT) equals the proportion of main business income to total net assets. Net profit margin (NPM) on total assets is calculated by the proportion of net profit to the average balance of total assets.

Data source

The data in this research can be mainly classified into two typesFootnote 2. The first one is the panel dataset of 285 prefecture-level cities in China taken from 2003 to 2020. The data on the opening of HSR in prefecture-level cities by manually sorting out, which is collected from the China National Railway Group (CNRG) website, the National Railway Administration, and railway 12306. Besides, other prefecture-level data is collected from official statistics including China Statistical Yearbook, China City Statistics Yearbook, and China Urban Construction Statistical Yearbook. The second type is the data of micro-enterprises in China taken from 2003 to 2012, which is obtained from the China Industrial Enterprise Database and China Industrial Enterprise Pollution Database. All obtained data are reconfirmed to ensure accuracy and some missing values are interpolated. Some variables are in natural logarithms to eliminate the heteroscedastic problem. Furthermore, all variables are within the rational range and there is no multicollinearity issue in explanatory variables relying on the variance inflation factor test and Pearson correlation coefficient test (supplementary material).

Empirical analysis and heterogeneity analysis

Analysis from micro-scales evidence

Parallel trend test

With regard to the work of Alder et al. (2016), this paper separately introduces the interaction term of the dummy variables three years before and four years after the HSR opening to verify the parallel trend hypothesis. It is clear that in Fig. 5 the estimations of parameters all fall in the 95% confidence intervals in each observation year. Moreover, the estimated coefficients in the years before high-speed railways opened failed to pass the 5% significance test, reflecting there is no pre-correlation between HSR and the usage of fossil fuels. As for the dynamic effect test, it is clear that the consumption of Coal and Oil gradually declined from the year of HSR opening, and this mitigating effect is strengthened over time. The reason for this phenomenon may lie in that the corresponding signals may be released during the construction and planning stage of HSR before it is officially put into operation, although it may take a certain period for the high-speed railway opening to attract labor, capital, technique, and other factors. Briefly, the immediate effect and the long-term emission reduction effect of HSR are evidently established, which are similar to the views of Lin and Jia (2022) and Liu et al. (2022a).

Fig. 5: The dynamic effect of HSR on traditional fossil fuel consumption.
figure 5

A Parallel trend test of coal. B Parallel trend test of oil.

Benchmark results

As the crucial micro subject of the national economy in China, the industrial enterprise takes a fundamental role in balancing the nexus between urban resource consumption and rapid economic expansion (Zheng et al., 2022b). In this regard, we essentially analyze the potential influence of HSR on traditional fossil energy consumption in enterprises based on Eq. (1). The estimations of baseline regression are represented in Table 1. Clearly, the estimated parameters of HSR are negative obviously, namely the operation of HSR can dramatically decrease the consumption of traditional fossil fuels in cities along the line, especially the use of coal. This finding is in line with the highlights of Tang et al. (2021).

Table 1 Enterprise traditional resource consumption.

Moreover, it is undoubted that resource consumption and pollutant emissions are positively correlated in developing nations, yet HSR may devote to strengthening local environmental regulations and squeezing out high-emission industries in the city as a whole, contributing to the low-carbon sustainable development. Based on this, we introduce a vital dummy variable of high-carbon emission industries (HCI) and add the interaction term of HSR and HCI in benchmark regression. As shown in columns (3, 4), it is evident that HSR can dramatically reduce the dependence of high-polluting industries on traditional fossil energy sources. In conclusion, high-speed railways serve as a sustainable and eco-friendly mode of travel, in stark contrast to traditional transportation methods (Yang et al., 2019a). It is conducive to reducing dependence on and consumption of fossil fuels relying on its operational characteristics of using clean energy such as electricity, so as to enhance ecological sustainability in the whole region.

Specifically, the variability in the factor input structure of different industries is also a major reason influencing their reliance on natural resources. That is to say, over-reliance on general factors (e.g. labor, land, capital) of production at the expense of higher factor inputs (e.g. technology, high-quality talents, information) can lead to lock-in at the lower end of the industry and result in over-usage of energy. In this regard, we further classify the secondary industry into three categories, including technology-intensive, capital-intensive, and human-intensive industries refer to Yang et al. (2018), introducing the interaction term of HSR_Tech, HSR_Capi, and HSR_Human to investigate the heterogeneity of HSR opening on saving fossil energy. It can be observed that the operation of HSR has efficiently reduced the consumption of two non-renewable energy sources by three types of industries, especially since this mitigating effect is more pronounced in capital-intensive and human-intensive industries (Lu and Li, 2022). Thereby, the HSR network reshapes the spatial and temporal patterns among regions, the optimization effect of HSR construction on resource mismatch is firstly performed in the human and capital-intensive industries, promoting more inter-industry production factor reallocation and productivity improvement.

In addition, does the mitigating effect of the HSR operation on the consumption of natural resources by companies be differentiated by objective corporate factors? In order to answer this question, we subsequently test the underlying influence of HSR on fossil fuel usage from the perspective of geographical distance, city status, and corporate property. The relevant results are shown in Table 2. Firstly, considering the objective situation that the current spatial pattern of HSR in China still does not achieve full coverage, does the mitigating effect of HSR opening on excessive energy use differ depending on the proximity of the enterprise from the high-speed rail station? Against this background, we further introduce the proxy of HSR_distance into the regression model to test for possible heterogeneity due to due to different shortest straight-line distances. As shown in columns (1, 2), conventional fossil fuel consumption increases by at least 0.001%, for every 1% increase in the distance to the closest HSR station, reflecting the inhibitory effect of HSR has a significant characteristic of distance attenuation (Lu and Li, 2022). Secondly, there is an obvious nexus between the urban development degree and traditional resource demand (Dong et al., 2021), we introduce the dummy variable of whether the industrial enterprise is located in a pivot city (HSR_Pivot) and add an item for its interaction with HSR (Sun and Ge, 2021). As shown in columns (3, 4), it is obvious that HSR can distinctly decrease the usage of fossil fuels in pivot cities along the line, which confirms the green economic attributes of HSR. Indeed, the HSR network is an efficient way to get rid of the pull of economic growth on resource consumption, so as to achieve a strong decoupling between resource depletion and economic growth (Li et al., 2022). Thirdly, industrial innovative performance poses a detrimental effect on the use of non-renewable energy, which may be influenced by internal ownership, we consider the dummy variable of property rights (HSR_Prop) including state-owned enterprises and private enterprises. Columns (5, 6) report that HSR poses significant negative impacts on traditional resource consumption in owned enterprises, which is related to its special feature of being relatively less constrained by talents, technology, and information in the process of high-quality development.

Table 2 Traditional resource consumption: heterogeneity perspective.

In addition, considering the spatio-temporal spillover effect of HSR, this paper introduces the DID approach to space adjacency weight matrix and reports corresponding results in Table 3. As represented in columns (2, 4), the parameters \(W_{ij}^{\rm {{T,T}}}{\rm {HSR}}\) and \(W_{ij}^{\rm {{NT,T}}}{\rm {HSR}}\) are both significantly negative at a 1% statistical level when considering the underlying influences of control variables, and the absolute value of the former is larger. The empirical results confirm the facilitating role of HSR on environmental sustainability through the reduction in fossil consumption. Besides, it is evident that HSR not only poses a negative spatial overflow effect on the fuel resource consumption of cities along the HSR route but also exerts an obvious spatial conduction effect and radiation effect on cities not along the route. Specifically, the intra-group spillover effects of HSR are more significant than the inter-group spillover effects. Indeed, HSR greatly compresses the space-time distance between regions, which emphasizes the evidence in favor of cross-regional cooperative mechanisms for environmental governance, similar to the opinion of Zhang et al. (2022a).

Table 3 The spatial effect of HSR on fuel consumption: SDID approach.

Analysis from macro-scale evidence

From the evidence from macro-scales, the interaction term of the dummy variables of five years before and after high-speed railway opening to identify the parallel trend hypothesis for macro-scales (Fig. 6). It is clear that it passes the parallel trend test, which also proves the long-term emission reduction influence of HSR again that is consistent with the view of Liu et al. (2022a).

Fig. 6: The dynamic effect of HSR on carbon emissions.
figure 6

A Parallel trend test of PCE. B Parallel trend test of TCE.

Besides, the estimations of baseline regression of macro-sales are represented in Table 4. Obviously, the parameters of HSR are distinctly negative when controlling the dual fixed effect of city and year, no matter whether the models consider the control variables. This reveals that the operation of HSR is more possible to alleviate the carbon emissions of cities along the line when compared with other cities, which is in line with the highlights of Chang et al. (2019). Moreover, it is obvious that the strategic position of central cities underestimates the positive effect of HSR opening on urban carbon emission mitigation, by comparing the estimations of full samples and subsamples in columns (5)–(8). This result provides empirical evidence for the importance of differentiated carbon pollution control systems. Moreover, the significant spatial spillover effect and spatial conduction effect are also established based on prefecture-level cities, and the detailed results are represented in Table 5.

Table 4 The effect of HSR on carbon emissions: DID approach.
Table 5 The spatial effect of HSR on carbon emissions: SDID approach.

Robustness checks

PSM-DID estimations

To ensure the causal relationship between HSR and carbon emissions and eliminate the potential issues caused by the selection effect, this study further conducts the propensity-score-matching approach (PSM) according to Feng and Liang (2022). The approach with a 1:2 pairing is employed before the benchmark regression of DID approach, taking the control variables as covariate terms. Obviously, the coefficients of HSR on two carbon emission proxies are still significantly negative, which confirms the results of the benchmark regression, indicating the emission mitigation effect of HSR is robust.

Placebo test

Considering the possibility that the effect of policy evaluation is affected by unobserved urban characteristics and the non-randomness of shocks, this study adopts the placebo test to verify the robustness of highlights. With regard to Liu et al. (2020), this subsection conducts 500 random shocks on 285 sample cities at each time to avoid contamination due to rare events. Meanwhile, the 232 cities are randomly chosen as the experimental group and the policy implementation time is randomly given. The distribution diagram plots of estimation coefficients are generated in Fig. 7. The vertical dashed lines in both figures are the true estimate of columns (5, 7) in Table 1, the values are −0.015 and −0.023, respectively. These true estimate values obviously deviate from the mean value of the 500 random simulations, reflecting the emission-mitigating effect of HSR is not triggered by omitted factors (Table 6).

Fig. 7: The placebo test of carbon emissions.
figure 7

A Placebo test of TCE and B placebo test of PCE.

Table 6 The robustness checks: PSM-DID approach.

Selection bias problem processing and balance test

It is essential to consider whether the opening year of HSR is related to the urban carbon pollution level at the beginning of the research period, so as to avoid the selective bias problem. Referring to Sun and Ge (2021), the detailed model is structured as below:

$${\rm {HSR}}Y_i = \lambda {\rm {CE}}_i^{2003} + \theta X_i^{2003} + \varepsilon _{i,t}$$

among them, HSRYi is the year of the opening of HSR in city i. \({\rm {CE}}_i^{2003}\) refers to the carbon emission level of city i in 2003. \(X_i^{2003}\) represents a bunch of control variables in 2003. εi,t is the random disturbance term.

Meanwhile, it is essential to check whether the treatment and the control group are homogenous prior to the shock of HSR being implemented, thus employ the balance test, and the detailed model is built as follows:

$${\rm {NDV}}_{i,2007} = \alpha + \beta {\rm {group}}_i + \theta X_{i,2007} + \mu _i$$

where NDVi,2007 are observable non-deterministic variables of city i in 2007, including urban financial development level (UFD), marketization degree (MD), urban economic development level (PGDP), per capita real wage (RW), and internet penetration rate (IPR). groupi demonstrates whether the city opened the high-speed railway. Xi,2007 is the same as the above benchmark regression.

As shown in Table 7, it is clear that there is no obvious nexus between urban carbon emissions and the year of the opening of the HSR according to the results in columns (1, 2). Additionally, the estimations also show that the results of the above selection variables on groupi are not significant, ensuring the balance of samples on the premise of adding control variables.

Table 7 The robustness checks: selection effect and balance test.

Other robustness tests

Indeed, the robustness test is also performed from subsequent perspectives, including replacing the measurement of the key variable, replacing samples, and eliminating the effect of interference policy. In detail, this paper uses high-speed railway station points to substitute HSR, excludes samples from the three years before the opening of the HSR under the consideration of the impact of the construction process, and constructs dummy variables of low carbon pilot policy and the implementation of special emission limits for air pollutants. The relevant estimations are reported in Table 8. It is evident that although there exists a slight change in significance levels, the obtained highlights are solid, revealing the emission reduction effect of HSR again.

Table 8 The other robustness checks.

Instrumental variable method

Considering that the selection of cities for the opening of HSR may be impacted by potential endogeneity, this paper introduces the relief degree of the terrain (RDT) to solve endogenous problems relying on the two-stage least squares approach, according to the research of Faber (2014). Obviously, the selection of RDT satisfies the elemental principles, first, the RDT is a critical element that decides the site selection of HSR lines and the cost of construction, which meets the relevant requirement. Second, RDT is an objective natural factor that satisfies the exogenous requirement. Particularly, the interaction term of RDT and the annual number of hackney carriage vehicles licensed is plugged into in panel model, due to the fixed value of RDT. Based on the estimations in Table 9, the value of the F-statistic is 73.597 and larger than the critical value at a 10% confidence interval, revealing the effectiveness of the instrumental variable. Besides, the abatement effects of HSR are confirmed in columns (2, 3), which verify the robustness of highlights in general.

Table 9 The endogenous problem: instrumental variables method.

Heterogeneity analysis

According to the characteristics of city scale, economic level, political status, and transportation accessibility, cities with different administrative levels have different plans for the construction of HSR stations and the running of HSR lines. Therefore, this paper classifies the sample cities into three classes to investigate the heterogeneity of the nexus between HSR and carbon emissions in cities with different administrative levels. Based on columns (1)–(3) of Table 10 (Panel A), the parameters in cities with different tiers are all negative, but it is only significant in third-tier cities. This is mainly because the first and second-tier cities often have relatively high development levels, stable economic structures, and perfect industrial structures, so HSR opening makes it difficult to effectively promote the progress of enterprises in these regions. However, the progress of third-tier cities is generally backward, and resources are insufficient, resources such as technology, talent, and information brought by HSR are more likely to stimulate the transformation and updating of enterprises in these cities and thus complete the purpose of energy conservation and emission mitigation.

Table 10 Empirical results of heterogeneous effects.

Considering that cities with different resource endowments have different dependence on resources during development, the emission-mitigating effect of HSR in these regions may also be different to some extent. According to the National Plan for Sustainable Development of Resource-based Cities (2013–2020), this study classifies samples into resource-based and non-resource-based cities for further analysis. From columns (4, 5), HSR has a more distinct reduction effect on carbon emissions in resource-based cities. This is mainly because that HSR can accelerate the progress of tertiary industry and reduce the dependence of resource-based city development on the development and utilization of coal, oil, and other resources by introducing new technologies and new energy, which can effectively optimize local environmental quality. In addition, this paper also further classifies resource-based cities into four categories, including growing cities, mature cities, declining cities, and regenerating cities. As shown in Table 10 (Panel B), it is clear that HSR is beneficial to easing the pressure on carbon pollution in mature cities, nevertheless, there is no obvious impact on carbon emissions in the other three types of resource-based cities.

The city’s population is also a key factor in HSR construction affecting carbon emissions. According to the population of different cities, this study classifies the cities into megacities and non-megacities to explore the heterogeneity of energy-saving and emission-mitigating effects of HSR. As shown in columns (6, 7), HSR can significantly relieve carbon emissions in non-megacities, while it has the opposite effect in megacities. The possible reasons lie in the following aspects. HSR in non-megacities may make the enterprise resources flood into megacities, which may slow down the development of non-megacities and ease the pressure on the environment. Meanwhile, when the environmental stress in megacities exceeds the city’s capacity to the ability to deal with pollution, it may cause an adverse impact on environmental quality.

Mechanisms test

The impact on enterprise labor productivity

The benchmark regressions in micro-scales affirm the role of HSR in promoting environmental sustainability relies on the mitigation of the over-usage of traditional fossil resources, so its underlying action path is also essential to explore. As mentioned in theoretical analysis, it can be inferred that the enterprise with HSR may hinder the consumption of fossil fuel resources via the channels of labor productivity effect and industrial structure effect.

As for the mechanism test of the labor productivity effect, the per capita industrial sales output value (ISV) and per capita gross industrial output value (GIV) is employed in regressions to measure the labor productivity level of enterprises. The consequences of columns (1)–(4) in Table 11 reflect that HSR can substantially improve production efficiency, especially in high-carbon industries. Moreover, this paper takes GIV as an instance to further investigate the original path and urban differences. And we introduce the dummy variable of whether new products have been produced (HSR_Np) in this subpart. As shown in columns (5, 6), it can be summarized that HSR takes a more important role in accelerating the production efficiency of enterprises that pay more attention to investment in new product innovation and R&D on technical progress, while this facilitation effect is more significant in pivot cities. Briefly, H1 is evidently supported.

Table 11 Mechanism test: labor productivity effect.

The impact on industrial structure effect

Apparently, HSR brings more suitable opportunities for industrial extension and enterprise transfer, meanwhile, its spillover effect and linkage effect may also accelerate the gradient transfer of industries and enterprises. Thus, this paper constructs the indexes of industrial transfer at the industrial and city-wide levels, and the proxies of industrial structure from the rationalization and upgrading perspectives. The estimations are reported in Table 12. First, with regard to Sun and Ge (2021), the change in industry transfer equals the share of industrial output value in GDP (Inov), the share of the output value of urban high-carbon industries (HCI_inov), and the share of employment in high-carbon industries (HCI_emp). Second, the industrial structure is calculated by the three diversified proxies, including Intransfer1 (Dou and Shen, 2014), RIS, and AIS. It is evident that the effects of HSR on industrial transfer have a certain consistency and it exerts significantly positive effects on industrial structure upgrading. Thereby, the viewpoint that HSR can mitigate fossil fuel consumption through the path of industrial restructuring and optimization, thus promoting environmentally friendly development, namely H2 is established.

Table 12 Mechanism test: industrial structure effect.

The impact on urban elements flow

It is undoubted that HSR has significantly improved regional accessibility, which may pose underlying influences on the efficiency of the flow of production elements, relying on expanding the spatial scope and changing the direction of elemental factor flow. Against this background, we investigate the potential influence path from the perspective of capital flow, labor flow, and information flow, respectively. In detail, capital flow is measured in two ways, namely the growth rate of domestic fixed investment (DFI) and FDI. Labor flow is measured by high-end talent accumulation (HT) and human agglomeration level (HA) which is calculated as the share of regional population density to the national population density. Information flow is measured by the digital economy index (DEI) according to Zhao et al. (2020a, 2020b). In Table 13, it can be intuitively found that HSR dramatically increases domestic capital strength for cities along the route, nonetheless, it slightly restrains the attraction to foreign investment. This reflects the reality that HSR still mainly relies on the investment of the central government and the self-raised funds of specific companies, which lacks private capital and foreign direct investment. Moreover, HSR significantly accelerates the rapid transfer of labor and promotes the human capital level, which contributes to the balanced development of the region. Meanwhile, HSR also promotes information exchange and sharing between different regions, reducing information islands and breaking data barriers for economic and social development. Thereby, HSR exogenously promotes the mobility of factors across regions in general, which convincingly supports H3.

Table 13 Mechanism test: elements flow effect.

The impact on urban technological innovation

Technological production factors rely on the convenience of HSR to transfer to the regions along the route, which produces the technological innovation, innovation element agglomeration, and other economic effects of HSR. According to insight, this paper deeply investigates the technological innovation effect of HSR on carbon emissions. With regard to the current literature (Li et al., 2021b; Sun and Ge, 2021), the number of patent applications or patent authorization can be adopted to truly quantify the urban technological innovation output, meanwhile, this paper classifies the total innovation patent proxy (TIP) into two types of the green patent (GIP) and non-green patent (NGIP) under the consideration of energy rebound effect. Table 14 reports that HSR can simulate technological innovation, especially green technologies, which is an efficient way to reduce energy consumption and carbon emissions. Indeed, HSR provides more opportunities for enterprises and industries to cooperate and communicate with each other, accelerating the green and sustainable development of the cities along the route. By contrast, non-green technological innovation as an area of preference is favored by enterprises due to the “energy paradox” (Stucki, 2019), which observably exacerbates carbon emissions. Thereby, technological progress is an efficient path to ease the pressure on carbon emissions, especially green technology development, namely H4 is partially established.

Table 14 Mechanism test: technological innovation effect.


In the benchmark regression analysis, the estimated coefficients of HSR on fossil energy consumption for both coal and oil, as well as the coefficients on carbon emissions, are all significantly negative, demonstrating the positive role of HSR in promoting environmental sustainability. This highlight is supported by micro and macro evidence and is verified by a series of robustness tests. Moreover, in the analysis of the transmission mechanisms, we find that the operation of HSR has accelerated elements flow, promoted technological innovation, increased labor productivity, and promoted industrial structure upgrading, all of which efficiently promote the development of eco-sustainability. And these promoting effects are more pronounced in third-tier cities, non-megacities, and mature resource-based cities. In addition, the spatial spillover effect and spatial conduction effect of HSR are evidently established, and its intra-group spillover effects are more significant than the inter-group spillover effects.

Compared to prior literature, this paper does not independently examine the impact of high-speed railways on energy consumption or environmental pollution, but comprehensively addresses the energy and pollutants from the perspective of environmental sustainability, particularly in exploring potential mechanisms. Besides, when examining the environmental effect of HSR, we not only focus on locally specific regions along the railway line but also extend the analysis to the entire region and consider the spatial overflow effect on the area not along the railway route. Secondly, when analyzing the transmission mechanism, we both consider the microchannels in labor productivity and industrial structure and the macro channels in element flow and technological progress, meanwhile, this paper has refined and expanded upon each mechanism which is distinct from existing studies. For instance, unlike existing research that only focuses on the flow of capital and labor factors (Zhao et al., 2020a; Lin and Jia, 2022), we take a step further by disaggregating capital into international and domestic components, labor into high-end talents and population agglomeration, and introducing digital elements in conjunction with the development context of the digital economy era. Regarding the industrial structure channel, we not only examine the upgrading of industrial structure from the perspectives of industrial rationalization and industrial advancement but also take into account the impact of the opening of HSR on industrial transfer.

Although this research provides empirical evidence for the green attributes of HSR and proposes policy enlightenment for both urban sustainable development and the effective usage of eco-friendly natural resources in China and worldwide, it still has some limitations. On the one hand, there is a need for further improvement in measuring urban carbon emission indicators. In this paper, TCE and PCE are used as representative indicators of carbon emissions, which align with the objective reality of China’s dual-carbon top-level design, but they still provide a relatively broad assessment. Further research may utilize urban carbon emission efficiency for more precise measurements. On the other hand, future research needs to integrate the characteristics of high-speed railway routes and urban cities to analyze the heterogeneity of carbon emission reduction effects in different cities along the high-speed railway lines.

Conclusions and policy implications


Does the opening of the HSR efficiently facilitate environmental sustainability? The answer has practical implications for both achieving win-win goals of economic expansion and eco-friendly development in China, as well as for countries around the world to clarify the nexus between natural resource consumption and environmental degradation and to value renewable energy use. In this study, under the unified analysis framework of macro-city and micro-enterprise scale, utilizing the dataset of 285 cities from 2003 to 2020 and pollution data of industrial enterprises between 2003 and 2016, we treat high-speed railway opening as a quasi-natural experiment to investigate its influence and mechanism on environmental sustainability from the point of view of fossil energy consumption and pollutant emissions. Accordingly, the obtained highlights are concluded as follows.

Firstly, the high-speed railway opening significantly mitigates the consumption of fossil fuel resources and urban carbon emissions from macro and micro-scales, which takes a crucial role in stimulating environmental sustainability in China. And the features of immediate long-term emission reduction of HSR are evidently established. What’s more, the mitigating impact of HSR on resource consumption is more distinct in the high-polluting, capital-intensive, and human-intensive industries, meanwhile, the intensity of the inhibitory effect also varies with the objective characteristics of the enterprise, such as strategic position, property right, and geographical distance. Specifically, the distance attenuation effect of HSR is supported. Furthermore, the spatial spillover effect and spatial conduction effect of HSR opening are established, and its intra-group spillover effects are more significant than the inter-group spillover effects. These key points still hold after dealing with endogeneity issues and a series of robustness checks including parallel trend test, PSM-DID estimations, placebo test, balance test, replacing measurements, replacing samples, and eliminating interference policy.

Secondly, regarding the mechanism of channels, this paper proves that promoting technological innovation, accelerating factor flow, improving labor productivity, and industrial structure upgrading is an important mechanism to steadily promote environmental sustainability and socio-ecological friendly development. Specifically, high-speed railway opening hinders pollutant emissions relying on the green technology progress, the flow of domestic capital, high-quality talents, and digital information, and the increase in labor production efficiency. What’s more, the inhibiting effects of HSR opening on carbon emissions are heterogeneous, which is more pronounced in third-tier cities, non-megacities, and resource-based cities, especially mature cities.

To sum up, the obtained highlights confirm the green attributes of the HSR and also illustrate the importance of using eco-friendly resources to mitigate environmental degradation, which provides a certain reference for environmentally sustainable development both in industrialized and developing nations.

Policy implications

On the basis of the empirical evidence and conclusion of this paper, the following policy implications for government and enterprises are put forward accordingly.

Firstly, governments should actively formulate and implement policies and measures related to renewable energy to promote its development and utilization, thereby fostering sustainable economic and environmental development. The green attributes of high-speed railways underscore the significance of efficient utilization of renewable energy for countries worldwide. On the one hand, developing countries should focus on technology transfer and cooperation, actively introducing advanced eco-friendly resource development and renewable energy technologies. Engaging in technological collaboration with developed countries can promote the growth of domestic green industries and advancements in technology. Additionally, governments should implement eco-environmental tax policies, levying appropriate environmental taxes on high-energy-consuming and high-polluting industries, while providing tax reductions and incentives for eco-friendly industries, encouraging industrial restructuring and the effective use of eco-friendly resources. On the other hand, relevant departments in developed countries should offer patent protection and technical support, and increase investment in green innovation and development, so as to drive progress and innovation in eco-friendly resource and renewable energy technologies. Simultaneously, governments should actively explore market-based trading mechanisms for renewable energy and establish environmental market access mechanisms, granting priority procurement or market access to eco-friendly resources and renewable energy products and services, thereby further enhancing the market share, utilization, and coverage of renewable energy.

Secondly, the national state should equally focus on the important role of non-policy factors in energy saving and emission reduction, especially the green and energy-efficient attributes of the nationwide high-speed railway network. That is, it is essential to carry out targeted high-speed railway construction to promote the effective utilization of ecologically friendly resources and maximize the environmental advantages of high-speed railways. For developing countries, the authorities need to increase the capital input and strengthen the construction of high-speed railways and improve the uneven distribution of high-speed railways to expand its coverage and service range. Meanwhile, it is necessary to pay attention to avoid damage to crucial ecological areas and natural resources during the high-speed railway construction process, relying on importing advanced construction technology and experience. Besides, developing countries should also import and cultivate high-tech talents to enhance the operation and maintenance of the high-speed railway system to meet higher environmental standards, thereby alleviating environmental burdens during high-speed railway operations for the effective utilization of ecologically friendly resources and achieving environmentally friendly development. For developed countries, the authorities should leverage their development advantages to encourage research and innovation and the application of intelligent technology to improve the energy efficiency and environmental performance of the high-speed railway system, providing better technological support for ecologically sustainable development. Moreover, the government needs to make full use of the accessibility of high-speed railways to further strengthen innovation collaboration and sharing among regions and promote the sound and coordinated development of high-speed railways, technological innovation, energy conservation, and emission mitigation. Furthermore, the countries should actively promote environmental education and awareness campaigns, enhancing public understanding of the environmental benefits of high-speed railways, while introducing relevant supporting policies to encourage people to use environmentally friendly modes of transportation to collectively promote the development of an environmentally friendly society.