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Tourism development can both support and strain regional sustainability. Sustainable tourism matters especially in highly urbanized metropolitan areas, where resident mobility and tourist demand jointly use transport systems. This study evaluates transport infrastructure adequacy and quality under tourism pressure in regions containing Russia’s largest urban agglomerations. Because official tourist-flow statistics are available at the regional rather than agglomeration level, the analysis uses an exploratory regional proxy approach. The methods combine comparative analysis, correlation and regression analysis, index analysis, and sensitivity checks. Tourist flows show the strongest statistical associations with absolute indicators of bus infrastructure. Rail transport, especially commuter rail, also shows a stable positive association, which matters for large metropolitan areas and regions with intensive intermunicipal mobility. Overall, tourist flows in the studied regions correlate primarily with the scale of the existing passenger transport system. Therefore, the results represent diagnostic associations rather than causal estimates of tourist transport behavior. The study proposes a comparative index of tourism transport infrastructure adequacy that characterizes how well the selected territories’ transport systems can absorb tourist traffic under data limitations. The index reveals pronounced differentiation among the Moscow, Saint Petersburg, and Kaliningrad cases.

Topic modeling of large news streams is widely used to reconstruct economic and political narratives, which requires coherent topics with low lexical overlap while remaining interpretable to domain experts. We propose TF-SYN-NER-Rel, a structural–semantic term weighting scheme that extends classical TF-IDF by integrating positional, syntactic, factual, and named-entity coefficients derived from morphosyntactic and dependency parses of Russian news texts. The method is embedded into a standard Latent Dirichlet Allocation (LDA) pipeline and evaluated on a large Russian-language news corpus from the online archive of Moskovsky Komsomolets (over 600,000 documents), with political, financial, and sports subsets obtained via dictionary-based expert labeling. For each subset, TF-SYN-NER-Rel is compared with standard TF-IDF under identical LDA settings, and topic quality is assessed using the C_v coherence metric. To assess robustness, we repeat model training across multiple random initializations and report aggregate coherence statistics. Quantitative results show that TF-SYN-NER-Rel improves coherence and yields smoother, more stable coherence curves across the number of topics. Qualitative analysis indicates reduced lexical overlap between topics and clearer separation of event-centered and institutional themes, especially in political and financial news. Overall, the proposed pipeline relies on CPU-based NLP tools and sparse linear algebra, providing a computationally lightweight and interpretable complement to embedding- and LLM-based topic modeling in large-scale news monitoring.

The conceptual foundation of this study is that a country’s exposure to risk when using green bonds as a mechanism for financing sustainable development is shaped by a combination of macroeconomic, market, and social factors. This paper develops and empirically validates a fuzzy-set model to assess national-level risks associated with green financing projects within the Water–Energy–Food (WEF) Nexus in BRICS countries. Building on established theoretical frameworks and empirical evidence, the study conceptualises risk as a function of economic development, the scale of the domestic green bond market, institutional trust, and performance on the Multidimensional Poverty Index (MPI). The study employs fuzzy-set modelling to integrate these heterogeneous indicators into a unified quantitative risk score. This approach enables cross-country comparison and captures the non-linear nature of relationships between socio-economic and institutional factors. The country sample includes Brazil, Russia, India, and China, which have successively chaired the BRICS association between 2021 and 2025, thereby ensuring methodological consistency and representativeness. The empirical results reveal a clear stratification of green-finance risk levels across the four economies: China demonstrates the lowest risk (Y = 0.243), followed by Russia with a below-average risk (Y ≈ 0.41), while India (Y = 0.53) and Brazil (Y = 0.51) exhibit the highest relative risks. These outcomes highlight the critical role of institutional trust and market maturity in reducing financing uncertainty within the WEF nexus. The study contributes to the literature by integrating macroeconomic, social, and institutional indicators into a unified fuzzy-logic model of green-finance risk; offering a transparent methodology for country-level comparison; and providing policy insights for improving the enabling environment for green bond markets in emerging economies.

The global COVID-19 pandemic has led to the self-isolation of people and the transformation of many economic and social processes into an electronic version thus contributing to the digitalization of all spheres. Being part of this environment, enterprises generate information resources to develop their desired image, which may vary according to the factors characterizing the information environment. Information capital is a comprehensive characteristic of an enterprise and determines its effectiveness and sustainability. The purpose of this study is to develop a toolkit that allows one to assess the information capital of an enterprise, reflecting its perception within the digital information environment. It is necessary to develop the methodology for the formation of such tools. As a result, a fuzzy-plural approach has been developed to evaluate the index of external information capital. This model allows us to assess the external information capital and to simulate its changes caused by various kinds of information events. The study of key elements, for example, the stability and tonality indices, index of target perception made it possible to systematize chaotic changes in the external environment and describe them using the Chen-Lee attractor model. The results of this study can be useful for researchers in the field of digital information analysis, in particular for the comparative analysis of enterprises and the assessment of their information capital.

The dynamics of irreversible multidimensional digitalization of production and consumption processes can be described today with a linear-positive or even exponential function. A significant part of the information background of a product, enterprise or brand is formed by their consumers, competitors or partners on the Internet, which considerably increases its accessibility and spread. Such kind of information can be called natural digital information (NDI). Its high market value is counterbalanced by its inhomogeneity and complexity for analysis. The solution to this problem lies in the field of creating automated tools for its subsequent search, aggregation, primary processing, quantification and analysis. The aim of this study is to describe the unique methodological properties of market research based on natural digital information. In order to achieve this aim, this study analyzes the theoretical basis in the field of NDI research, defines the categories of NDI and sources of its formation, describes the key properties of NDI, determines its advantages in comparison with other types of market information, and suggests a basic methodology for conducting typical NDI-based market research. An applied research study was carried out according to the designed methodology to show its advantages, as well as to describe the unique methodological properties of market research based on processing of NDI. The main result of this study is a universal algorithmic model for analyzing NDI in the context of market research, which includes a mechanism for defining and categorizing the digital sources of NDI, a model for forming the key properties of NDI, and basic classes of NDI analytical metrics. The toolkit developed by the authors allows market research to be conducted without direct attraction of research subjects, which results in cost reduction and elimination of the phenomenon of social desirability; this creates the so-called reasoned advertising messages that meet the requests of the target audience, which is proved by the big data that underlie the presented methodology. The developed algorithmic model is universal for analyzing natural digital information, and, with minor adaptations, can be used by any subject conducting market research.

With the exponential growth of textual data, traditional topic modeling methods based on static analysis demonstrate limited effectiveness in tracking the dynamics of thematic content. This research aims to develop a method for quantifying the dynamics of topics within text corpora using a thematic signal (TS) function that accounts for temporal changes and semantic relationships. The proposed method combines associative tokens with original lexical units to reduce thematic entropy and information noise. Approaches employed include topic modeling (LDA), vector representations of texts (TF-IDF, Word2Vec), and time series analysis. The method was tested on a corpus of news texts (5000 documents). Results demonstrated robust identification of semantically meaningful thematic clusters. An inverse relationship was observed between the level of thematic significance and semantic diversity, confirming a reduction in entropy using the proposed method. This approach allows for quantifying topic dynamics, filtering noise, and determining the optimal number of clusters. Future applications include analyzing multilingual data and integration with neural network models. The method shows potential for monitoring information flows and predicting thematic trends.

The aim of the article is to investigate the impact of the new coronavirus infection on the Russian labor market and to suppose the actions to be taken to minimize negative economic consequences. The distinctiveness of this study is the differentiation of the impact of the physical and informational spread of COVID-19. The informational spread of coronavirus is measured through the dynamics of news messages related to the topic of 'coronavirus' in the largest Russian media. The analysis of the average level of wages by type of economic activity, as well as the demand of employers and the number of vacancies, allow testing the hypothesis that the physical and informational spread of coronavirus caused an increase in the number of unemployed, a decrease in average wages in the studied range of economic activities, an increase in supply on the labor market, and a decrease in demand for employees. Another task of the study is to assess the dynamics of related search queries in Yandex (Russian biggest search engine), which can help to reveal the logic in the behavior of the Russian people during the pandemic as well as to understand if the Russian economy, the labor market, and society were prepared for the changes caused by the pandemic. Using a regression modeling methodology, it was found that the influence of the information environment, namely the informational spread of coronavirus, had an even greater impact on studied parameters than the physical spread. A 'delay effect of physical consequences' was discovered. The conclusions obtained showed that in the conditions of wide informatization of society, it is necessary to systematically influence the physical and informational spread of coronavirus to minimize the negative consequences of the pandemic on the labor market.

The global COVID-19 pandemic has caused a transformation of virtually all aspects of the world order today. Due to the introduction of the world quarantine, a considerable share of professional communications has been transformed into a format of distance interaction. As a result, the specific weight of traditional components of the investment attractiveness of a region is steadily going down, because modern business can be built without the need for territorial unity. It should be stated that now the criteria according to which investors decide if they are ready to invest in a region are dynamically transforming. The significance of the following characteristics is increasingly growing: the sustainable development of a region, qualities of the social environment, and consistency of the social infrastructure. Thus, the approaches to evaluating the region's investment attractiveness must be transformed. Moreover, the investment process at the federal level involves the determination of target areas of regional development. Despite the universal significance of innovative development, the region can develop much more dynamically when a complex external environment is formed that complements its development model. Interregional interaction, as well as an integrated approach to innovative development, taking into account not only the momentary effect, but also the qualitative long-term transformation of the region, will significantly increase the return on investment. At the same time, the currently existing methods for assessing the investment attractiveness of the region are usually heuristic in nature and are not universal. The heuristic nature of the existing methods does not allow to completely abstract from the subjectivity of the researcher. Moreover, the existing methods do not take into account the cyclical properties of the innovative development of the region, which lead to the formation of a long-term effect from the transformation of the regional environment. This study is aimed at forming a comprehensive methodology that can be used to evaluate the investment attractiveness of a certain region and conclude about the lines of business that should be developed in it as well as to find ways to increase the region's investment attractiveness. According to the results of the study, a comprehensive methodology was formed to evaluate the region's investment attractiveness. It consists of three key indicators, namely, the level of the region's investment attractiveness, the projected level of the region's investment attractiveness, and the development vector of the region's investment attractiveness. This methodology is based on a set of indicators that consider the status of the economic and social environment of the region, as well as the status of the innovative and ecological environment. The methodology can be used to make multi-dimensional conclusions both about the growth areas responsible for increasing the region's innovative attractiveness and the lines of business that should be developed in the region.

The development of tourism is associated with numerous risks that have a direct and indirect impact on the realization of tourist and recreational potential. In recent years, in addition to internal risks, the importance of external environmental risks (geopolitical and epidemiological) has increased. The COVID-19 pandemic is one of the foremost of these risks, and its effects on the development of regional tourism demands attention. The purpose of the study is to estimate the level of tourist and recreational potential of cross-border regions of the Russian Federation and Kazakhstan, and the possible risks during the COVID-19 pandemic. After the breakup of the USSR, one of the longest land borders in the world was established between Russia and Kazakhstan. The geographical scope of the study includes 12 constituent entities of the Russian Federation and 7 regions of Kazakhstan. Information posted on statistical portals, data from geographical atlases, and specialized websites of the executive authorities were used as the materials for the study. The tourist and recreational potential of the regions of the Russian Federation and Kazakhstan was estimated by the scorecard method, with the assignment of weight coefficients to indicators included in four main clusters: Natural Factors, Cultural and Historical Factors, Social and Economic Factors, and Infrastructure Support of Tourism. Additionally, the experience of studying risks associated with tourism development during the pandemic was summarized. The conclusions reached are indicative of different levels of tourism and recreational potential in cross-border regions of the Russian Federation and Kazakhstan, and the inconsistency of the industry's structure. It was found that the COVID-19 pandemic had increased the number of risks for the realization of tourism and recreational potential, which must be taken into account when making management decisions. The authorities of cross-border regions can use the results of the research to adjust tourism policy under the current restrictions and increased global risks. The application of mechanisms and methods of territorial planning and management will depend on the level of tourism and recreational potential. For regions with high and above-average potential, the emphasis should be on participation in federal projects, the development of cluster initiatives, and the application of a diversification strategy. Regions with medium and low potential should focus on the domestic tourist flow, develop inter-regional cooperation, and focus on the strategy of gaining a competitive advantage.

This study provides a framework for the comparative assessment of the key industry aspects of competitiveness among logistics services and the logistics systems of enterprises in the informational environment. Frequently, the relationships between a consumer and a company created by means of the informational environment determine how the enterprise positions itself in the market. For instance, the evaluation of a company’s representation in the information field is an essential aspect of determining the company’s competitiveness. The study suggests a set of special metrics for measuring the representation of digital components and other aspects of an enterprise’s digital image via data gathering and analysis of the most encountered tokens. The proposed automated analysis algorithm allows companies to examine their image in the digital environment and implement effective decisions. The functionality of the algorithm fosters data collection, helping to form the desired image of the company. Tokens of several thematic groups on social media are collected during the process, and the most significant of them that are valuable for the competitiveness of the enterprise are extracted. The outcome can be used for the tracking of the dynamics of key parameters of an enterprise’s image and for conducting a comparative analysis of the digital image of its competitors.