China's rapid high-speed rail (HSR) expansion has led to the development of numerous HSR new towns, creating significant economic opportunities but also placing strain on resources and the environment. This study addresses the crucial need for accurate assessment of the impact of these towns on urban sustainable development. The unprecedented scale of HSR development in China, with over 40,000 kilometers of high-speed rail and a coverage rate exceeding 94% in cities with populations over one million, makes this a particularly relevant issue. These new towns, built around HSR stations, represent a new urban development model, aiming to integrate efficient public services and eco-friendly technologies. While existing research has examined aspects like transportation accessibility and housing prices, a comprehensive quantitative evaluation of the impact on the coordinated development of urban economic, resource, and environmental systems is lacking. This study aims to fill this gap by analyzing the impact of HSR new towns, categorized as central (near city centers) and peripheral (outskirts), on urban sustainability, considering the challenges and uncertainties involved.

Existing literature primarily focuses on the impact of HSR on transportation accessibility, housing prices, land use, and industrial structure transformation around stations. Some studies evaluate the implementation effects of HSR new town plans and their influencing factors. Big data analysis has also been applied to specific case studies. However, a significant gap exists in quantitative research directly evaluating the impacts of HSR new towns on the coordinated development of urban economic, resource, and environmental systems. While acknowledging the potential benefits of optimizing urban spatial layouts and alleviating pressure on older urban areas, previous research also points to potential drawbacks such as land consumption, increased energy and water demands, and even regional hollowing in inland areas. This underscores the complexity of integrating HSR with urbanization strategies and the need for a comprehensive, quantitative assessment of its impact on sustainability.

This study employs a combination of satellite remote sensing data and spatial econometric methods. Nighttime light data, providing a measure of economic activity, and built-up area data, indicating urban expansion, were extracted from long-term, continuous datasets with a spatial resolution of approximately 500 meters and 30 meters, respectively. A 3-kilometer buffer zone around each HSR new town was used for data extraction. The study constructed two spatial weight matrices: a nested matrix (W1) combining geographic and economic distances, and a geographic distance matrix (W2) to ensure robustness. A dynamic spatial Durbin model (SDM) was used to analyze the impact of HSR new town development (HSRNT) on urban sustainable development (USD). The USD index was constructed using the entropy method, incorporating indicators across economic, resource, and environmental subsystems. Control variables included foreign direct investment (FDI), the ratio of total social retail consumption to GDP (TRSCG), urbanization rate (URB), digital economic development level (DE), and population density (PD). Both static and dynamic SDMs were employed, with the latter preferred based on model fit tests. A fixed effects model was also used to control for time and individual city characteristics. The study further decomposes the effects into short-term and long-term direct, indirect, and total effects using the partial differentiation method.

The study reveals significant growth in both built-up areas and nighttime lights from 2011 to 2021, with nighttime lights exhibiting a much higher growth rate. Development varied geographically, with the eastern region leading, followed by the central and then the western region. Spatial correlation analysis showed a positive spatial correlation between HSRNT development and USD. The dynamic SDM results consistently demonstrate a significant positive effect of HSRNT on USD, with both short-term and long-term positive spatial spillover effects. The long-term impacts are particularly pronounced for neighboring cities. Central-type HSR new towns exhibit a stronger promotional effect on USD than peripheral types, regardless of region. This effect is also evident when considering different city sizes, with extra-large cities showing the strongest response. Further analysis considering population dynamics confirms the stronger positive impact of HSR new towns, especially central-type ones, in cities with net population inflow. The study provides both statistically significant findings and visual representations using maps and graphs to highlight the spatial patterns of development and population changes.

The findings confirm the positive contribution of HSR new towns to urban sustainable development, but highlight the importance of location and type of HSR new town. The stronger impact of central-type towns is attributed to their better connectivity and concentration of economic activities. The spatial spillover effects suggest that HSR new towns can stimulate regional development, although short-term impacts may vary across regions. Regional disparities remain, with the eastern region benefiting the most from HSR development due to its stronger economic base and human resources. The results refute the notion of HSR new towns as "ghost cities" and highlight their role in driving economic growth, particularly in regions with potential for further development and restructuring.

This study provides the first comprehensive analysis of the impact of HSR new towns on urban sustainable development in China. The key contributions are the quantification of the positive effect, the identification of spatial spillover effects, and the differentiation of impacts based on location and city type. Future research could explore the long-term environmental impacts in more detail and investigate the distributional effects of HSR development within cities.

The study relies on nighttime light and built-up area data as proxies for economic activity and urban expansion, which might not fully capture the complexity of sustainable development. The choice of indicators for the USD index could also influence the results. Further research employing more detailed economic and environmental data would enhance the robustness of findings.