The railway sector is the largest branch of the economy in Kazakhstan. Nevertheless, environmental safety issues and the reduction and prevention of harmful impacts are not given sufficient attention. There is very little research on the impact of the railway sector of Kazakhstan on the natural environment, which hinders the further development of the railway sector and thus necessitates the addressal of environment issues. This study aimed to show possible environmentally hazardous sections of the country’s railway infrastructure. The criteria chosen for the analysis were soil cover, water resources, rainfall, protected natural areas, and population. A map of environmentally sensitive areas was created to determine which areas require priority protection from the environmental hazards posed by the country’s railway infrastructure. The map was developed in a GIS environment using the weighted overlay, expert assessment, and snowball methods. Additionally, a model for the Kazakhstan segment of the International Northern Railway Corridor was constructed in this article to identify integral indices that assess the susceptibility of the territory to environmental hazards. The data and results presented in the article can be used to solve current and future environmental issues concerning the country’s railway communications and can be implemented in many practical applications.

The development of railway infrastructure is considered a key driver of vegetation cover transformation, particularly in ecologically sensitive regions. This study aims to quantify the spatio-temporal impact of the Presnogorkovskaya–Zhanaesil railway corridor in Northern Kazakhstan over the period 1985–2024. Using Landsat imagery and a gradient method of comparative analysis with a control area, an innovative coefficient B was developed to assess changes across various vegetation categories. Multiple linear regression was used to determine the influence of natural factors, including precipitation, temperature, and elevation. The results indicate that while some categories (e.g., dense vegetation or wet areas) show consistent degradation near the railway, the observed patterns are also modulated by environmental gradients. Compared to the control area, buffer zones along the railway exhibit an increased presence of degraded land types (≈309 km² vs. ≈72 km² in the control) and a reduction in productive vegetation cover (over 100 km² loss), especially in recent years. The study concludes that the proposed method allows for a differentiated understanding of anthropogenic and natural drivers of vegetation change, offering a replicable approach for assessing the impact of linear infrastructure in other geographical contexts.

Transportation noise is a significant factor affecting public health, yet the spatial assessment of railway noise and its associated consequences remain insufficiently developed in the countries of Central Asia. In this study, based on official technical and operational data provided by JSC NC “Kazakhstan Temir Zholy”, noise propagation in the Shu station was modelled in the NoiseModelling 5.0.1 software using the CNOSSOS-EU method. The number of people exposed to acoustic impact was determined through spatial analysis and a dasymetric method. Within the acoustic exposure zone, 205 residential buildings were identified, home to an estimated 2454 people, including 1767 adults. The potential health impact was assessed using exposure–response functions recommended by the WHO Environmental Noise Guidelines, for high annoyance, high sleep disturbance, and ischemic heart disease. The estimated number of adults with high annoyance was approximately 87 (4.9%), with high sleep disturbance approximately 41 (2.3%), and the population attributable fraction for ischemic heart disease was approximately 0.0225%. Because approximate CNOSSOS-EU values were used for rail roughness and rolling stock, the uncertainty of the results was assessed using a sensitivity analysis with a symmetric ±2 dB shift applied to the calculated noise levels. The proposed workflow is based on open-source software and publicly available data and can be transferred to other railway junctions in Kazakhstan and Central Asia. The proposed framework may also support the integration of environmental-noise and population-health considerations into sustainable railway operation and urban planning.