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The carsharing services have become the necessary component of the life of smart and sustainable city. They meet the numerous requirements put forward by these cities concepts and make life in urban environment cleaner, more comfortable and convenient and better organized. The goal of this research is to determine the factors facilitating the demand for the carsharing services, and on the basis of this analysis to consider the costs structure of these companies. The results are obtained via PLS-SEM analyses implementation in SmartPLS-3.3.7 software. The analysis revealed that such factors as Convenience, Additional Values and Economic (saving factors) have positive impact on growth of demand for carsharing services while New Way of Thinking is insignificant. However, the developed Costs function demonstrate that companies assume the costs of promoting “green” effect of carsharing as important ones. If they reduce these types of costs, it will have positive impact on their efficiency without decrease of demand.

Soil salinity assessment methods based on remote sensing data are a common topic of scientific research. However, the developed methods, as a rule, estimate relatively small areas of the land surface at certain moments of the season, tied to the timing of ground surveys. Considerable variability of weather conditions and the state of the earth surface makes it difficult to assess the salinity level with the help of remote sensing data and to verify it within a year. At the same time, the assessment of salinity on the basis of multiyear data allows reducing the level of seasonal fluctuations to a considerable extent and revealing the statistically stable characteristics of cultivated areas of land surface. Such an approach allows, in our opinion, the processes of mapping the salinity of large areas of cultivated lands to be automated considerably. The authors propose an approach to assess the salinization of cultivated and non-cultivated soils of arid zones on the basis of long-term averaged values of vegetation indices and salinity indices. This approach allows revealing the consistent relationships between the characteristics of spectral indices and salinization parameters. Based on this approach, this paper presents a mapping method including the use of multiyear data and machine learning algorithms to classify soil salinity levels in one of the regions of South Kazakhstan. Verification of the method was carried out by comparing the obtained salinity assessment with the expert data and the results of laboratory tests of soil samples. The percentage of "gross" errors of the method, in other words, errors when the predicted salinity class differs by more than one position compared to the actual one, is 22–28% (accuracy is 0.78–0.72). The obtained results allow recommending the developed method for the assessment of long-term trends of secondary salinization of irrigated arable land in arid areas. [ABSTRACT FROM AUTHOR]

Approximately 50% of the world's uranium is mined in a closed way using underground well leaching. In the process of uranium mining at formation-infiltration deposits, an important role is played by the correct identification of the formation of reservoir oxidation zones (ROZs), within which the uranium content is extremely low and which affect the determination of ore reserves and subsequent mining processes. The currently used methodology for identifying ROZs requires the use of highly skilled labor and resource-intensive studies using neutron fission logging; therefore, it is not always performed. At the same time, the available electrical logging measurements data collected in the process of geophysical well surveys and exploration well data can be effectively used to identify ROZs using machine learning models. This study presents a solution to the problem of detecting ROZs in uranium deposits using ensemble machine learning methods. This method provides an index of weighted harmonic measure (f1_weighted) in the range from 0.72 to 0.93 (XGB classifier), and sufficient stability at different ratios of objects in the input dataset. The obtained results demonstrate the potential for practical use of this method for detecting ROZs in formation-infiltration uranium deposits using ensemble machine learning. [ABSTRACT FROM AUTHOR]

The issue of KPI selection is urgent for sustainable business organization including the financial sector. There are numerous studies devoted to KPIs; however, the European Central Bank (European Central Bank, 2023) insists on the necessity of revising the methodology to improve it and make it based on risk and behavior aspects. This research is an attempt to meet the new vision of the ECB. The authors develop a new approach to the selected KPIs of financial institutions on the basis of risk indicators. The PLS-SEM method was used to construct and test the model, representing the company's compliance function and considering various risk categories. The significant relationship between compliance procedures and risk factors was confirmed by the constructed model. The study results not only meet the ECB's new approach but also contribute to the scientific area of choosing sustainable indicators for sustainable business. Empirical analyses from the study decisively highlighted a pronounced correlation between strict compliance with the established rules and proficient risk management practices. In tandem with the ECB's guiding principles, the conclusions derived from this research not only complement the existing body of knowledge but also offer a novel perspective on the essence of the KPI selection. Conclusively, this research confirms the significance of risk-aware KPIs in guiding financial entities toward a trajectory of sustained excellence and growth. [ABSTRACT FROM AUTHOR]

Risk management is a highly important issue for Fintech companies; moreover, it is very specific and puts forward the serious requirements toward the top management of any financial institution. This study was devoted to specifying the risk factors affecting the finance and capital adequacy of financial institutions. The authors considered the different types of risks in combination, whereas other scholars usually analyze risks in isolation; however, the authors believe that it is necessary to consider their mutual impact. The risks were estimated using the PLS-SEM method in Smart PLS-4 software. The quality of the obtained model is very high according to all indicators. Five hypotheses related to finance and five hypotheses related to capital adequacy were considered. The impact of AML, cyber, and governance risks on capital adequacy was confirmed; the effect of governance and operational risks on finance was also confirmed. Other risks have no impact on finance and capital adequacy. It is interesting that risks associated with staff have no impact on finance and capital adequacy. The findings of this study can be easily applied by any financial institution for risk analysis. Moreover, this study can serve toward a better collaboration of scholars investigating the Fintech activities and practitioners working in this sphere. The authors present a novel approach for enhancing key performance indicators (KPIs) for Fintech companies, proposing utilizing metrics that are derived from the company's specific risks, thereby introducing an innovative method for selecting KPIs based on the inherent risks associated with the Fintech's business model. This model aligns the KPIs with the unique risk profile of the company, fostering a fresh perspective on performance measurement within the Fintech industry. [ABSTRACT FROM AUTHOR]

Contemporary life is closely interconnected with numerous phenomena, which have appeared in our life in recent decades. The concepts of a smart city, digitalization of the economy, and the sharing economy are among them. These factors create new opportunities for businesses operating in modern markets. The article considers the sharing services in digital payment operations for achieving the Key Performance Indicators (KPI) of a smart city. The goal of the research is to determine the costs of sharing economy implementation in the financial sector of a smart city. The study takes the example of Rome's experience. The authors consider KPIs selected by the municipality of Rome as a measure of smart city implementation and their provision by sharing services in financial operations. The authors specify the structure of the costs of shared financial services for a smart city and for Fintech companies operating with open banking, which is followed by the cost functions peculiar to these operations of Fintech companies. The authors demonstrate the point at which a Fintech company starts earning a positive profit on these services via operating leverage. [ABSTRACT FROM AUTHOR]

The article considers the attitude of smart city residents towards the use of web applications in everyday life. It is very important for many stakeholders since it affects the involvement of people in all processes of urban life and contributes to the implementation of the smart city concept. The goal of the research is to study the factors influencing the intention and use of web applications in a smart city. Based on the results of surveying the residents of Riga, the UTA UT model was applied with the employment of partial least squares structural equation modeling in Smart PLS. The traditional constructs of the UTAUT model—Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), as well as Attitude towards the use of Applications (ATA)—had a direct or indirect positive relationship with the intention to use technologies (Behavioral Intention: BI) and/or with usage of these technologies (Use Behavior: UB). Anxiety indirectly via ATA showed a negative effect on UB. The influence of Age, Gender and Education on BI and UB as moderators was also investigated. Only Age as a moderator negatively affected the relationship between FC and PE and SI. The results showed that in order to involve in full scope of the population of Riga in the use of communication technologies and the implementation of the smart city concept, it is necessary to create the appropriate conditions for residents, in particular by teaching people on a permanent basis. Some of the obtained results were different from similar studies' results, which emphasizes that city authorities and other stakeholders should make decisions on the involvement of citizens in smart process based on the local peculiarities, which supports the slogan of smart cities—think globally, act locally. [ABSTRACT FROM AUTHOR]

The accelerated urbanization rate results in the birth of the Smart City concept, presupposing the more efficient use of digital technologies for the benefit of inhabitants and businesses. The idea of digital banking is one of the key issues in a smart city concept. The research should demonstrate that the efficient and effective way to produce such kind of services is to use the fintech companies’ facilities. Many researchers study fintech from a technological point of view, the contribution of fintech to general banking, the level of satisfaction of customers with fintech products. However, there is a gap in studying the economic basis of these companies. The research aims to determine the economic basis for the projects implemented by the fintech company and to determine the source of the efficiency of these companies in financial operations compared to the conventional bank. It has additional importance since the analysis of research devoted to technological solutions demonstrates a certain lacuna in the economic substantiation of such smart solutions. The results of this study are based on the implementation of traditional Cost-Benefit and Total Cost of Ownership analyses and include the developed cost and income functions, calculated Benefit/Cost ratio, and demonstrated the source of efficiency of fintech company compared to a traditional bank. Therefore, the activities of a fintech company in the area of digital banking received the economic ground, and this fact decreases the gap between intuitive comprehension of the necessity of the introduction of smart solutions in practice and the economic substantiation of this process. [ABSTRACT FROM AUTHOR]

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The issue of Energy Efficiency is one of the most urgent for the contemporary world, and one of its components - renewable energy resources - is in the centre of attention. Latvia, being one of the greenest countries in the world, uses renewable energy resources intensively. Nevertheless, use of one of these resources, forests stock (firewood) seems to be quite dangerous for environmental health of the country. The goal of the research is to reveal some of pros and cons of using this resource for energy production. The authors do not set the goal to show all advantages and disadvantages of employing this renewable energy resource, but just to arise the problem of double nature of using the renewable resources. The authors present statistical (correlation and regression) analysis, based on the data for years 2010 to2018. [ABSTRACT FROM AUTHOR]