1. Introduction
Reducing inequality and fostering economic growth, the United Nations’ tenth global sustainable development goal, underscores the importance of equitable transportation systems, particularly rail infrastructure, in promoting regional economic integration [
1,
2]. Addressing inequities in transportation is crucial for enhancing public policy and infrastructure, with growing attention to the equity impacts within this domain [
3,
4].
Over the past few decades, substantial investments in rail transport systems, especially high-speed and intercity railways, have revolutionized intercity travel, adding over 40,000 km of tracks globally [
5,
6,
7]. In China, the development of high-speed and intercity railways with enhanced capacities and speeds aims to overcome the shortcomings of traditional rail networks and meet the extensive travel needs of densely populated areas [
8]. By the end of 2023, China’s high-speed railway network reached a remarkable extent of 45,000 km, making it the fastest-growing transportation system in the country [
9]. Rail transport has been claimed to be the most competitive transport mode, offering short travel times and superior service quality [
10] and providing the additional benefits of enhanced reliability, safety, lower energy consumption, and reduced carbon emissions [
6,
11].
Railway developments have significantly influenced urban economic development by improving intercity accessibility, highlighting the importance of rail transport in national transport and urban economic growth [
8,
12,
13]. Understanding the development and spatial implications of rail transport through comparative analyses across different periods is crucial [
14,
15]. Research suggests that while transport infrastructure can stimulate economic growth and attract foreign investment, its impact varies greatly based on geographical scale and transport modes [
16,
17]. Spatial variations in rail transport leads to uneven service accessibility [
18,
19,
20,
21]. Ensuring transport equity, where all societal groups, especially those in remote and economically disadvantaged areas, have reasonable access to railway services, is essential [
19,
20,
22].
Although equity is essential for sustainable development, the impact of rail transport development on regional economic equity has not been thoroughly studied. Prior research has predominantly focused on single-type rail transit accessibility assessments at specific times using limited methodologies [
18,
23,
24]. In addition, the long-term impact of rail transport development on economic equity remains underexplored. To bridge the gap in understanding the role of rail transport in regional economic equity and provide actionable insights for future infrastructure planning, this study aimed to analyze the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) and explores the impact of rail accessibility on economic equity. This study focuses on balancing development across various regions to promote regional integration, mitigate disparities, and support sustainable development.
Accessibility features from various perspectives are assessed, evaluating the characteristics of accessibility patterns and their impact on regional economic equity owing to rail transport development across different time dimensions. We primarily investigated national railways, focusing on high-speed, intercity, and conventional rails. Indicators were selected to evaluate travel time in different manners, each responding to a distinct concept, and their combined use provides supplementary information regarding accessibility. Door-to-door travel time primarily focuses on the entire journey from origin to destination, weighted average travel time aims to reduce urban transit times, and potential accessibility emphasizes enhancing urban competitiveness or attractiveness by improving accessibility. Rail transport accessibility indicators are estimated to quantify their impact on regional economic equity, capable of quantifying service changes in rail transport accessibility across different regions. The Gini coefficient and Lorenz curve are used to measure economic equity, and the ordinary least squares (OLS) and spatial Durbin model (SDM) models correlate socioeconomic and transportation accessibility variables from 1998 to 2020 in different regions to quantify the equity effects caused by changes in rail transport accessibility. This approach not only calculates the economic impact across four different time periods but also addresses the current gap in using persuasive equity indicators for analysis.
The remainder of this paper is organized as follows.
Section 2 reviews the literature on rail transport accessibility and economic equity.
Section 3 describes the study area and the methods.
Section 4 presents the characteristics of rail accessibility and evaluates its impact on the regional economic equity. Finally,
Section 5 summarizes the major findings and discusses the corresponding policy implications, and contributions of the study.
2. Literature Review
Transportation accessibility is a multifaceted concept that involves the potential for interaction [
25], user friendliness [
26], and overall benefits it brings [
27]. Location-based measures, including distance, connectivity, gravity, and potential-based metrics, are pivotal for evaluating rail accessibility, which emphasizes weighted travel times and considers factors such as gross domestic product (GDP) and population [
28]. Rail infrastructure, particularly high-speed and intercity railways, significantly affects urban economies by reducing travel time and enhancing mobility [
1,
15,
29].
Accessibility metrics have become crucial for assessing the impact of transport infrastructure on regional equity [
20,
22,
30,
31,
32,
33,
34]. Accessibility, based on proximity to economic opportunities, posits that closer destinations are more attractive [
20,
25,
35]. These metrics shed light on how easily desired destinations can be reached from specific location. New rail corridors have shifted the accessibility patterns that affect regional equity [
36]. These changes in accessibility are key indicators of equity across regions [
22].
The development of the rail transport infrastructure has been recognized as a key factor in achieving regional economic equity, particularly in efforts to reduce regional and social disparities and strengthen economic and social cohesion [
37]. Consequently, transport policies focused on improving accessibility through high-quality public infrastructure are essential for fostering regional cohesion [
38]. Assessing the equity impacts of these policies involves examining how their benefits are distributed across different regions to minimize existing spatial disparities in accessibility [
22,
39,
40].
Geographical equity is essential for ensuring fair access to economic activities and services across different population segments. It is divided into horizontal equity (among capable individuals) and vertical equity (focused on disadvantaged groups), which is often associated with socioeconomic aspects [
6,
41,
42,
43]. Economic equity, a key focus in transport equity studies, encompasses the fair and equitable distribution of economic resources, opportunities, and benefits within society [
44].
The Gini coefficient and Lorenz curve are commonly used to measure equity across various indicators [
45,
46]. Economic equity has been assessed using the Gini coefficient, which is related to per capita GDP. Despite China’s 2023 GDP reaching 126 trillion RMB (CNY), making it the world’s second-largest economy, its Gini coefficient was 0.47, surpassing the international alert line of 0.4, indicating a significant wealth disparity. The development of the transportation infrastructure is considered a vital pathway for promoting China’s economic growth [
9,
18]. However, studies on the influence of rail systems on economic equity are insufficient. Railways are crucial for bolstering intercity connectivity and accessibility [
47]. Enhanced rail accessibility fosters spatial and social development, influences land use, drives urban growth, and ultimately contributes to regional economic development and reduces regional disparities [
23,
32,
33,
48,
49,
50].
The primary methods for assessing the economic impacts of transportation infrastructure include cost–benefit analysis (CBA), computable general equilibrium (CGE) models, and econometric analyses [
5,
51]. The CBA approach is predominantly used for pre-assessment, focusing on the marginal benefits of a project’s returns versus its costs, rather than the broader economic impacts [
52]. In contrast, CGE models, while comprehensive and intricate, require detailed databases to evaluate individual infrastructure projects due to their complexity [
53,
54]. Econometric models, such as regression and structural equation models, are commonly employed to quantify the economic impacts of large transportation infrastructure projects, providing a more generalizable approach to understanding these effects in an economic context [
24,
48].
In the new economic geography, transport infrastructure is recognized as an important means of reducing transport costs and promoting economic agglomeration [
55]. Many studies have assessed the impact of rail transport on economic development elucidated its varied impacts [
36,
56,
57]. Rail transport can stimulate growth by facilitating locational choices and increasing housing and land values [
18,
36]. Although rail transport enhances intercity connectivity and urban development, it poses challenges for economic equity. Its effects on economic growth are not always positive, with some regions experiencing exacerbated disparities [
18,
58], particularly the more developed regions with concentrated economic activities [
18,
59,
60,
61]. It also promotes economies in core cities at the potential expense of peripheral areas [
36,
62]. High-speed rail plays a dominant role in the diffusion effect of off-site investments in the Yangtze River Delta region, facilitating the flow of resources from developed to less developed areas, contributing to regional integration, and narrowing urban–rural disparities [
24,
48]. Although existing studies often infer changes in equity by comparing economic indicator trends in different regions, a systematic analysis in this area is still lacking.
3. Data and Methods
3.1. Study Area
The Guangdong–Hong Kong–Macao GBA, situated along China’s southern coast, stands as a central focus in the country’s policy agenda. Boasting a robust economy with a GDP of 11.69 trillion RMB in 2019 and a per capita GDP of 2.54 times the national average, the GBA is a beacon of development and economic vitality within China. This region is pivotal for the construction of a globally influential urban cluster and hub for science and technology innovation, holding a unique strategic position in China’s national development plans.
As of 2020, the GBA’s high-speed rail network extends over 710 km, supplemented by 608 km of intercity railways and 457 km of general-speed railways, cumulatively spanning 1775 km. This extensive rail network makes the GBA an ideal case study for examining the intricacies of rail transport accessibility and its influence on economic equity. This study focused on the GBA, encompassing the Hong Kong and Macao special administrative regions and the major cities of Guangzhou, Shenzhen, Zhuhai, Foshan, Huizhou, Dongguan, Zhongshan, Jiangmen, and Zhaoqing, covering a total area of 56,094.91 km
2 (
Figure 1).
3.2. Data
We employed the 2017 edition of the 1:1,000,000 National Basic Geographic Database of the National Basic Geographic Information Centre. The database was calibrated and verified using the administrative division codes released in July 2020 by the Ministry of Civil Affairs of the People’s Republic of China. The analysis units included 36 district-level units, 12 county-level units, two prefectural-level city units, and two special administrative regions (Hong Kong and Macao), amounting to 52 units in total. The unincorporated areas of Dongguan and Zhongshan, characterized by dispersed streets and towns, were treated as individual citywide units. Given their distinct administrative levels, Hong Kong and Macao are considered separate units without further subdivision.
To ensure the integrity of the study and continuity of comparative analyses, we did not account for changes in the administrative divisions of each unit over time. The evolution of the rail transport network in the GBA over four key years, namely, 1998, 2008, 2014, and 2020, was analyzed. This temporal analysis utilized vectorized data from historical atlases and relevant planning maps, complemented by socioeconomic data such as GDP and population statistics from regional Statistical Yearbooks. For consistency, the data for Hong Kong and Macao were recorded in their local currencies and converted to RMB to align with other cities in the study.
3.3. Model Specification and Variables
3.3.1. Door-to-Door Travel Time Measurement
A door-to-door methodology was adopted to calculate the full-chain travel time between regions via rail, aligning with the methodologies used in prior research [
63]. The total travel time from a starting point in City A to an endpoint in City B consists of the: (1) travel time from a location within City A to the departure station (T
1), (2) intercity rail travel time (T
2), and (3) travel time from the arrival station in City B to the final destination (T
3). Thus, the full-chain travel time equation is:
where DDTT is the door-to-door travel time.
Cost-distance analysis estimates the travel time within the departure (T
1) and destination cities (T
3), whereas rail network analysis calculates the interstation travel time (T
2). The spatial unit for analysis was a 100 × 100 m grid, covering 13,231,100 raster grids across 52 counties. The travel time for each grid was calculated to assess accessibility, and the time cost was attached to each road segment as an attribute according to the route type, calculated as follows:
where
Cost is the travel time (min/100 m) and
V is the restricted travel speed for each type. The speeds for the nonroad types were adapted from existing literature [
11]. Intercity travel times were determined using the average speeds for different rail types in the GBA. The two types of high-speed trains in China operate at 200–250 km/h on upgraded general-speed and lower-standard high-speed railroads or at 300–350 km/h with a more advanced system. Intercity railways typically accommodate speeds of up to 200 km/h, whereas general-speed railroads range from 80 to 120 km/h based on the number of stops. We used these average speeds to model the travel times for different rail services [
63].
To model travel time in a geographic information system (ArcGIS 10.2) environment [
1], two primary methods are employed: cost distance analysis and network analysis, which use vector and raster datasets, respectively. By integrating these methods, a hybrid approach can be applied to calculate regional accessibility, addressing the intersection problem of cost distance analysis in road networks and the low spatial resolution of network analysis. Specifically, the travel time within the starting city (T
1) and the destination city (T
3) is calculated using cost distance analysis of the road network, while the travel time between two stations (T
2) is determined through rail transit network analysis.
3.3.2. Weighted Average Travel Time Measurement
We employed the weighted average travel time metric to assess interregional connectivity. This metric calculates the travel time from one location to all other locations and incorporates the economic and demographic significance of each destination. A destination’s size, which indicates its economic development and urban scale, is typically quantified using GDP and population data [
30]. The weighted average travel time is mathematically represented as:
where
is the accessibility of the location
,
is the travel time to the destination
,
is the size of
, and
denotes the number of study units. Usually, the shortest travel time is used for
and the population size or the total GDP is used for
. In this study,
is expressed as the square root of the product of the population and GDP of each city (Equation (4)),
refers to the GDP of the destination city
, and
represents the population of the destination city
.
3.3.3. Potential Accessibility Measurement
We adopted a potential accessibility measure to gauge the proximity of economic opportunities to various locations. The expression is as follows:
where
is the potential accessibility of location
,
is the travel time between locations
and
, and
α is the distance friction parameter.
3.3.4. Gini Coefficient and Lorenz Curve
We employed the Gini coefficient, derived from the Lorenz curve, to measure disparities in the rail transport supply relative to the population distribution in the Guangdong–Hong Kong–Macao GBA. Originally conceptualized for analyzing wealth distribution [
64], the Gini coefficient serves as an effective tool to evaluate geographic equity in transportation accessibility. In a Lorenz graph, a black dashed line represents a perfectly equitable distribution, while a solid red line represents an uneven wealth distribution scenario, where 70% of the population shares only 25% of the income. Notably, the Lorenz curve is a tool for representation and does not advocate perfect equity.
3.3.5. Econometric Methods
OLS and SDM were employed to assess the effect of railway transport on economic disparities by incorporating demographic and economic variables. The focus was on the proximity effect, in which regional economic equity is influenced by new transit infrastructure.
The primary unit for the econometric analysis was the county. The model evaluates how a county’s economic, demographic, and transportation attributes, along with those of neighboring counties, influence economic equity. Economic equity was gauged using the Gini coefficient of GDP per county. The OLS and SDM were calculated as follows:
where
is an indicator of economic equity measured by the Gini coefficient of GDP for each county
at time
,
represents each county,
represents each observation year,
represent explanatory variables of economic, demographic, and transit characteristics of each county at year
,
is a dummy variable indicating the presence of transit (1 if the transit operates, 0 otherwise),
,
, and
denote the coefficients of the corresponding variables, and
is the error term with a mean of zero.
Based on the OLS model, the SDM incorporates spatial lag matrices. In Equation (7), denotes the spatial lag matrices and , representing the relative locations of counties. and are distributed to represent the origin and destination counties. In particular, and characterize the spatial lags of the explanatory variables, and represents the spatial lag of the dependent variable. The spatial lags reflect the effects of economic, demographic, rail development, and economic equity in neighboring counties. represents the number of counties in the sample. The coefficients , , and estimate the impact of neighboring counties based on the relevant variables. The explanatory variables include GDP per capita, population, proximity to other counties and districts (ADJ_DCM), rail presence, accessibility, and economic potential of rail transport services.
Many studies have established a causal relationship between transportation infrastructure development and economic growth [
18,
28]. A close link has also been established between economic development (GDP) and economic disparities (GDP Gini coefficient) [
18,
65]. Thus, the explanatory variables include per capita GDP, population, ADJ_DCM, presence of rail transport, accessibility, and economic potential of rail transport services.
We employed four indicators to characterize rail transport: coverage area of rail transport, door-to-door travel time, weighted average travel time, and potential accessibility. The coverage of rail transport is represented by the dummy variable
R, which indicates the presence of rail transport services in a county (
Table 1). Door-to-door travel time accessibility, DDTT, measures the full chain travel time between different regions via rail transport [
63,
66,
67]. The accessibility of weighted average travel time, WATT, measures the average of the shortest travel times to reach other regions via rail transport [
18,
21,
24,
68]. Economic potential accessibility, PA, quantifies the sum of the GDP over travel time for accessible counties and districts, offering a metric to access the economic opportunities available within a specific timeframe [
18,
21,
24].
5. Conclusions
We meticulously examined the impact of rail transport development on accessibility and economic equity within the Guangdong–Hong Kong–Macao GBA. Research indicates that the introduction of these networks has substantially improved accessibility within the Guangdong–Hong Kong–Macao GBA, dramatically reducing regional travel times and significantly enhancing intercity attractiveness. This is consistent with the findings of [
1,
22,
36]. In particular, the travel times in Guangzhou’s districts rank among the shortest in the region, highlighting their central role in transportation. Similarly, the districts in Shenzhen experienced significant reductions in travel time. However, despite improvements, travel times in Jiangmen and Zhaoqing, which are located on the eastern and western peripheries of the GBA, continue to lag and remain among the longest in the region. Because Guangzhou and Shenzhen reap substantial benefits from the targeted policies and development strategies, they attract a significant influx of resources. This focus on developing transportation infrastructure in areas adjacent to these cities potentially facilitates economic fairness in neighboring regions.
This study provides a comprehensive analysis of the transformative role of rail transport in shaping the Guangdong–Hong Kong–Macao GBA’s socioeconomic landscape. It highlights the dual-edged nature of rail transport development: serving as a catalyst for regional integration, shrinking travel times, and fostering connectivity, particularly in central urban hubs like Guangzhou and Shenzhen, while simultaneously presenting a challenge by potentially widening the economic divide between well-serviced urban centers and less-connected peripheral regions. These findings underscore the importance of adopting a more holistic and equitable approach to transportation planning and policy formulation. Future strategies must transcend mere infrastructural expansion to include equitable resource distribution and ensure that the benefits of rail transport are uniformly disseminated across the region. This calls for a balanced development paradigm that prioritizes not only the efficiency and expansion of transit networks but also addresses the socioeconomic disparities exacerbated by such developments. In essence, the evolution of the GBA’s rail network should align with the broader goals of sustainable development, promoting not only economic growth, but also social inclusivity and regional cohesion. By doing so, the region can harness the full potential of its transportation infrastructure and serve as a foundation for a more equitable, prosperous, and interconnected future.
Although comprehensive, this study has certain limitations. We included all grid centroids within the research area to ensure a comprehensive analysis of regional accessibility patterns. This approach captures the full spatial variability across the Guangdong–Hong Kong–Macao Greater Bay Area, providing a broad understanding of how rail transport infrastructure impacts accessibility. However, we acknowledge that this methodology does not distinguish between different land use types, such as urban, rural, or natural areas. To address this, future research should integrate high-resolution land use data, allowing for a more nuanced analysis of how land use affects accessibility and economic equity. Additionally, the study relies on traditional data and exclusion of urban subway systems in accessibility modeling. Key recommendations include integrating urban subway systems into accessibility models for a more comprehensive understanding of urban mobility, employing big data to enhance the precision of transport and economic forecasts, and formulating targeted policies to improve connectivity in peripheral regions, such as Jiangmen and Zhaoqing. By aligning these efforts with broader sustainable development goals, the region can better leverage its rail infrastructure for economic growth, social inclusivity, and enhanced regional cohesion, ensuring a prosperous future for all residents.