Finance

The paper observes the dependence of the main macroeconomic indicators in developingcountriesfromthechangeinworldpricesforcrudeoil.Weanalyzed a system of simultaneous equations, which makes it possible to verify some of these hypotheses, and developed the model to forecast the impact of oil prices onbudgetrevenues.Thepracticalsignificanceofthisworkliesinthestructuring of existing knowledge on the impact of oil crisis. The results of this work can be considered confirmation of the hypothesis of the sensitivity of U.S. macroeconomic indicators to the dynamics of oil prices. Outcomes assume stable growth even in the period of shock prices for oil, which is confirmed by the statistics that were used in the model. Deep decarbonization modeling is a trend in industrial facilities that are used by developing countries. The major challenge is the issue of availability that is applicable to the countries that want to utilize this facility in their communities. Industrial modeling toward decarbonization is now a developing mechanism to curb the growing issue of atmospheric pollution. This paper proves the relevance of promoting deep decarbonization applied by the developing countries

The climate has changed significantly under the influence of human behavior. And first of all, this is due to the change in the proportionality and concentration of greenhouse gases in the atmosphere (water vapor, carbon dioxide, methane, ozone, PFC (perfluorocarbons). This paper analyzes the dynamics of greenhouse gas emissions. Climate change has many consequences on human health throughout the world, especially in African countries. The growth of greenhouse gas emissions is viewed as a cause of infectious and non-infectious diseases, negative effects on nutrition, water security and other social disruptions. The global average temperature gradually increases, and the atmospheric CO2 concentration has exceeded 400 ppm due to the intensification of greenhouse effect. The method of energy balance was featured to simulate the trends in Greenhouse Gas Emission Forecast in different sectors until 2030. Through sensitivity analysis, we found that the reduction of anthropogenic CO2 emissions from people (cars and households) would deescalate the consequences of the above trends. Emissions are mostly associated with industries, which can be reduced if local Government will want to achieve the Paris Agreement goal.

The main goal of this study is to evaluate the impact of population mobility on electricity generation in Russian cities in the conditions of the spread of COVID-19, and identify hotspots. Furthermore, the evaluation is also conducted using hybrid fuzzy decision-making modelling. In this context, q-ROF DEMATEL and TOPSIS methods are taken into consideration. Additionally, a comparative evaluation is also performed with the help of Intuitionistic and Pythagorean fuzzy sets. The results are quite similar that allows to conclude that the findings are reliable and coherent. The study proves the hypothesis that human behavior changed during the COVID-19 pandemic, and electricity consumption is declining in major cities around the world. The biggest fall in energy generation was in Moscow and Yekaterinburg. In St. Petersburg and Nizhny Novgorod, the fall in energy generation is no so crucial because these cities have low building density. The study uses Long Short-Term Memory models with many different parameters. The Q-Rung Orthopair Fuzzy Sets model forecasts new COVID-19 using ten parameters. This study identifies factors influencing the spread of COVID-19 based on the theory of "broken windows" and outlines directions in limiting population mobility, which can form the basis of state policy. According to the analysis the air temperature is the variable that most affects this process.

The paper observes the dependence of the main macroeconomic indicators in developing countries from the change in world prices for crude oil. We analyzed a system of simultaneous equations, which makes it possible to verify some of these hypotheses, and developed the model to forecast the impact of oil prices on budget revenues. The practical significance of this work lies in the structuring of existing knowledge on the impact of oil crisis. The results of this work can be considered confirmation of the hypothesis of the sensitivity of U.S. macroeconomic indicators to the dynamics of oil prices. Outcomes assume stable growth even in the period of shock prices for oil, which is confirmed by the statistics that were used in the model. Deep decarbonization modeling is a trend in industrial facilities that are used by developing countries. The major challenge is the issue of availability that is applicable to the countries that want to utilize this facility in their communities. Industrial modeling toward decarbonization is now a developing mechanism to curb the growing issue of atmospheric pollution. This paper proves the relevance of promoting deep decarbonization applied by the developing countries.

The aim of this research paper is a complex assessment of the efficiency of power plants in Russia, the basis of their techno-economic processes and indicators and the development of recommendations for their practical use and improvement. Capital expenses for SWH plants with a drain-down system are 1,4-1,6 times lower than traditional SWHs with circulation systems, and their payback period does not exceed 5-5,7 years. Biogas that is obtained from specialized plants of medium and high-power costs $15,000-60,000 per cubic meter. But the price of biogas produced in low energy power plants is about $80,000-270,000, which is cheaper than natural gas. So, the use of biogas is economically justified at the moment.

Changes in the global energy landscape have increased the importance of research on energy management methods in the power grid. In particular, Demand Side Management (DSM) at consumers is attracting worldwide attention. Distributed generation in intelligent consumers, such as Smart Houses (SH), contributes to the introduction of new distributed generation from the demand side by determining optimal scheduling. However, Fuel-cells (FC) and Battery Energy Storage Systems (BESS) are expensive, and their installation is a significant burden for the demand side. Therefore, this paper proposes a Smart Apartment Building (SAB) model in which multiple distributed power sources are shared by multiple consumers to reduce operation costs and carbon emissions through the implementation of highly efficient operation methods. An important aspect of such a study is to understand the characteristics of the demand side and to propose an operating method that takes into account the preferences of the demand side. In this paper, to provide a variety of options, a Pareto front is generated through multi-objective optimization to reduce the total cost and carbon dioxide emissions of the model. The optimal component capacity is also considered at the same time. A Model Predictive Control (MPC)-based optimization algorithm is then developed to achieve highly efficient operation, thus contributing to the reduction of the two objectives. As a result, in a compromise between the two conflicting objectives, the MPC algorithm successfully reduces operation costs by 44.4% and carbon dioxide emissions by 54.7% compared to the original case.

Smart grid systems help increase RWJ projects (RWJ) so that environmentally friendly energy production can be generated. However, efficient technologies should be implemented to ensure the sustainability of smart grid systems. This study aims to evaluate renewable-friendly smart grid technologies regarding distributed energy investment projects by using a hybrid picture fuzzy rough decision-making approach. Firstly, selected criteria are weighted using the multi stepwise weight assessment ratio analysis (M-SWARA) method based on picture fuzzy rough sets (PFRSs). Subsequently, different renewable-friendly smart grid technologies are ranked with the complex proportional assessment (COPRAS) technique by using PFRSs. It is determined that research and development play the most critical role with respect to the renewable-friendly smart grid technologies for distributed energy investment projects. On the other side, cost is another essential factor for this issue. It is also identified that direct current links are the most important renewable-friendly smart grid technology alternative. Priorities should be given to the development of research and development studies on renewable energies to increase the efficiency of smart grid systems. In this context, private sector companies have a very important role. Similarly, incentives provided by governments to RWJ research and development studies should be increased. Within the scope of these studies, new technologies for RWJ types should be emphasized. In this context, new technologies for all RWJ alternatives should be followed comprehensively. Increasing research and development for such investments will also make smart grid systems more successful.

Solar energy has become an integral part of the economy of developed countries, so it is important to monitor the pace of its development, prospects, as well as the largest companies that produce solar panels since the supply of solar energy in a particular country directly depends on them. e study analyzes the shares of Canadian Solar Inc. and First Solar Inc. e purpose of the study is to study the possibility of forecasting the stock price of solar energy companies using neural networks for the purpose of subsequent investment. e recurrent neural network LSTM is used in the article and this approach is based on complexity theory. Machine learning technologies are now being actively implemented in various sectors of the economy and are considered effective. e program used assigns different significance to the data of the last months and the data for the first months of the 1st year. e first year of the last 5 years of the company's activity is taken as the first year since more distant data no longer have significant significance for the forecast. In the course of the study, a forecast of the stock price of Canadian Solar Inc. and First Solar Inc. for 245 days was obtained. Based on the results obtained, the following conclusions were made: 20 neurons of the network is not enough to make an accurate forecast, but the level of confidence in such a forecast is high enough, neural network forecasts are applicable in investing and are accurate enough to determine medium-and long-term trends, but these forecasts are not applicable for traders. e direction of improving the accuracy of neural network predictions is promising for further research.

Background: The ongoing COVID-19 quarantine restrictions have caused multiple sharp decreases in activities associated with the movement of large masses of people. The economies of regions and cities that are critically dependent on tourist flows related to various segments have suffered. This research aims to provide an economic-mathematical model of smart cities externalities’ impact from the point of view of achieving social and environmental goals Methods: The objective of this study was to develop an algorithm for supporting decision-makers. Methods of mathematical modeling, statistical processing of data received in real-time, as well as methods for finding solutions by expansion into dynamic series are used, and the theory of mathematical games is applied. The theoretical mathematical model presented considers the statistical processing of data provided in real time referring to the performance indicators of megacities. Results: The activities of administrations and governments aimed at maintaining stability over the past two years have been aimed at reducing the negative impact of the pandemic. The prospect of returning to normal conditions is complicated by a number of factors. The proposed approach allows the development of the fundamental basis for making administrative decisions within individual megapolises and in environmental policy on a territory of any scale. The developed mathematical model is abstract by definition and is applied by taking into account specific tasks and criteria. Since the tasks of the administration differ depending on the region and country, the choice of criteria is set individually. Conclusions: During the period of isolation, the volume of services in the Hotel - Restaurant- Catering/Café (HORECA) segment has decreased, and personnel has also been lost. The reduced pressure on public infrastructure and the departure of migrants means that, in the long term, this work cannot be restored within a short period of time.

The advent of fog computing as an extension of cloud-enabling technology has moved from the hub of the internet framework to the unmanned aerial vehicle (UAV)-enabled control and management of drone-based data. The key objectives are to improve the computation and processing of drone-based resource constraints and deliver the data to the outsourced computational node to schedule, process, manage, optimize, and preserve it since they are located close to each other through a wireless sensor network. This collaborative technological approach to robustness is the standard of drone system design and implementation, reducing the computational power, resource consumption, and latency for the application that needs a fast response. However, recent research into fog-enabled drone-based data management and optimization poses a serious challenge in terms of privacy, security, and preservation. On the other hand, Blockchain Hyperledger Technology, which is mostly used in Bitcoin Cryptocurrencies, has been applied in a large number of distributed applications due to the ledger integrity, transparency, trustworthiness, provenance, reliability, availability, protection, and security-related distributed features that are provided. Therefore, the distributed application of drone control has received more attention because of its ability to handle collaborative procedures to capture, schedule, process, optimize, manage, and preserve drone-based data using fog nodes and blockchain hyperledger technology. In this paper, we proposed a collaborative approach using blockchain hyperledger fabric and a metaheuristic-enabled genetic algorithm for fog node management called B-Drone. This approach handles drone-based data collection, scheduling, optimizing, processing, managing, and preservation in a secure manner in the fog node. The protection of each transaction between drone and fog node before exchange is private with the utilization of the hash-encryption (SHA-256) algorithm. The blockchain smart contracts are implemented and deployed to auto-manage all the connectivity and communication protocols between drones and fog nodes in the designed private permissioned network. The simulations of the proposed collaborative approach reduce

Improving people's standard of living has increased their requirements for the environment. Increasing air temperature in urban areas due to urban heat islands (UHI) has been a global concern since industrialization. Apart from suitable facilities and landscapes, a comfortable outdoor thermal environment can improve the efficiency of urban space use. Ensuring outdoor comfort is an integral part of the design agenda where the UHI phenomenon plays a significant role. A study has been conducted on a residential building campus to analyze the effect of these heat island countermeasures (individual and combined) with the help of the simulation tool Grasshopper. A 3D reference model of a small residential campus is developed. The outdoor thermal comfort level is studied for this case, and Universal Thermal Climate Index (UTCI) is evaluated. Further, several UHI mitigation strategies such as wall and roof reflectivity, vegetation, plantation, pavement configuration, and shading are applied to find their effect on the micro-climate and outdoor thermal comfort. Based on the simulation outcomes, urban geometry is identified as the most influential design factor in decreasing the urban heat island effect and outdoor thermal comfort. The study's principal objective is to develop a simulation framework including all mitigation strategies and find the best case for UHI reduction.

This study aims to describe and analyze the paths of social media website users’ interaction with the content of government accounts during the COVID-19 pandemic. The objectives of the study are to describe the roles of government accounts as a tool to support the concept of health communication among users of social media websites and analyze the targeted and untargeted roles that have emerged in this context, describe the roles of government accounts as a tool of government communication and analyze how government institutions employed them during the COVID-19 pandemic, and describe the roles of social media websites as a tool to address the problems and challenges created by the social spacing procedures and solutions provided by these websites to deal with these challenges. This study surveys a sample of previous studies conducted on the roles of government accounts on social media websites during the COVID-19 pandemic. The tools of this study are both analytical and comparative as they analyze and compare the results of previous studies on the interaction paths of internet users. The results indicate that social media websites have been considered a pivotal tool for building a renewable and interactive system. This coincides with developments in the communication environment in which the individual is present within the social pattern, and its depth of impact has been demonstrated, particularly after the repercussions of the spread of the COVID-19 pandemic.

Background: The increasing penetration of smartphones and the Internet in developing countries caused the rise of e-retail. Conversational commerce is highly increasing via interaction through messengers. To extract the benefits of both trends, companies have adopted messenger chatbots. These chatbots use Artificial intelligence and natural language processing to give live responses to the customer and assist online shopping on the messenger interface. This research aims to discover the factors that affect the use of messenger chatbots and their influence on attitude and behavior intention. Methods: The research methodology includes the Technology Acceptance Model (TAM) with the variables of perceived usefulness, perceived ease of use, consumer trust, and anthropomorphism. The authors used an online survey for collecting the responses from 192 respondents and analyzed structural equation modelling. Results: Customer trust has shown the most decisive influence on customer attitude followed by Perceived Usefulness, Perceived Ease of Use. Also, the use of chatbots to make online shopping faster significantly affects the use of messenger chatbots for online shopping in the future. The authors explore various factors resulting in consumers' favor of accepting chatbots as an interface for mcommerce. Conclusions: The findings indicate that organizations should design strategies to improve interaction with the customer by developing messenger chatbots for more trusting conversations. Further Open Peer Review Approval Status AWAITING PEER REVIEW Any reports and responses or comments on the article can be found at the end of the article.

Due to the youth of the cryptocurrency sphere, the logic of interaction between investors, users and protocols is not always precisely defined. Analysis of the impact of ESG on cryptocurrencies proves that the demand for bitcoin network capacity (occupies the main market share) is the main factor in predicting the price of this cryptocurrency and the cryptocurrency market as a whole. The choice of the statistical method of analysis is determined by the purpose of statistically justified determination of the relationship of the data under consideration, and the reliability of the analysis is checked using Fischer and Student tests. In this paper, several innovations are proposed to solve the problem of energy dependence of cryptocurrencies: firstly, the analysis of cryptocurrencies in the paradigm of sustainable development (taking into account the consumption of a huge amount of energy for the functioning of cryptocurrency systems); secondly, feedback logic to explain the interaction of subjects, including the following parties: users, developers, network infrastructure and their interaction; thirdly, statistical analysis with the creation of artificial variables from real data and iterative improvement of the model. This paper proves that sustainable cryptocurrency growth is impossible when viewed from the perspective of "Green Economics" by Molly Scott Cato. The author's approach is relevant compared to other methods of linear transformations for creating artificial variables by selecting data using the VIF test. As a result, several versions of models were obtained using various combinations of the initially proposed factors, on the basis of which the nature of the greatest influence on the price of bitcoin was established in the form of technical factors and energy infrastructure needs.