This study aims to assess the environmental sustainability and carbon footprint of tourism in Bayanaul State National Park (Kazakhstan) using the GSTC criteria and the Protocol on Greenhouse Gas Emissions. As part of the work, surveys and interviews were conducted with representatives of the tourism industry, administration officials and environmental organizations to analyze awareness of the principles of sustainable tourism, existing barriers and opportunities to increase sustainability. The results showed that the main contribution to the carbon footprint (530 tons of CO₂-eq.) is made by indirect emissions (57%) related to procurement and logistics as well as coal heating (20.5%). Based on the expert assessment method, key factors were identified, such as increased energy efficiency, the introduction of renewable energy sources, separate waste collection and recycling, the transparency of financial management and educational programs for tourists and employees. Based on these factors, a model of sustainable tourism management has been developed, adapted to regional peculiarities. This model provides for the active involvement of the state, business and the local community, and introduces mechanisms to reduce the carbon footprint and increase environmental sustainability. The theoretical contribution of the study is the adaptation of GSTC standards to the conditions of Kazakhstan and the development of a sustainable tourism management model that takes into account local characteristics. The results obtained provide a practical basis for reducing the negative impact of tourism on the environment, improving environmental sustainability and creating conditions for further research and the introduction of sustainable practices.

The problem of saving soil resources and their reclamation measures under current climate change conditions attracts the world community’s close attention. It is relevant in the Syr Darya River’s lowlands, where the secondary soil salinization processes have intensified. The demand for robust methods to assess soil salinity is high, and the primary purpose of this study was to develop a quantitative analysis method for soil salinity estimation. We found a correspondence between the sum of salts in a topsoil layer to the Landsat 8 data in the Tasseled cap transformation of the image values. After testing several methods, we built a prediction model. The K-nearest neighborhood (KNN) model with a coefficient of determination equal to 0.96 using selected predictors proved to be the most appropriate for soil salinity assessment. We also performed a quantitative assessment of soil salinity. A significant increase in a salt-affected area and the mean soil sum expressing an intensification of secondary soil salinization from 2018 to 2021 was found. The increasing temperature values, decreasing soil moisture, and agricultural use affect the extension of salt-affected ground areas in the study area. Thus, the soil moisture trend in the Qazaly irrigation zone is negative and declining, with the highest peaks in early spring. The maximum temperature has a mean value of 15.6 °C (minimum = −15.1 °C, maximum = 37.4 °C) with an increasing trend. These parameters are evidence of climate change that also affects soil salinization. PCA transformation of the Landsat-8 satellite images helped to remove redundant spectral information from multiband datasets and map soil salinity more precisely. This approach simultaneously extends mapping opportunities involving visible and invisible bands and results in a smaller dataset.