This paper is focused on the concise review of the specific applications of genetic algorithms in forecasting commodity prices. Genetic algorithms seem relevant in this field for many reasons. For instance, they lack the necessity to assume a certain statistical distribution, and they are efficient in dealing with non-stationary data. Indeed, the latter case is very frequent while forecasting the commodity prices of, for example, crude oil. Moreover, growing interest in their application has been observed recently. In parallel, researchers are also interested in constructing hybrid genetic algorithms (i.e., joining them with other econometric methods). Such an approach helps to reduce each of the individual method flaws and yields promising results. In this article, three groups of commodities are discussed: energy commodities, metals, and agricultural products. The advantages and disadvantages of genetic algorithms and their hybrids are presented, and further conclusions concerning their possible improvements and other future applications are discussed. This article fills a significant literature gap, focusing on particular financial and economic applications. In particular, it combines three important—yet not often jointly discussed—topics: genetic algorithms, their hybrids with other tools, and commodity price forecasting issues.

Around the world, people are becoming more and more worried about how globalization will affect their standard of living. According to the literature, globalization has resulted in the marginalization of the impoverished populations in developing economies and has exacerbated inequality, while the opposite may also be true. The objective of this study is to investigate the impact of globalization on income inequality. The study used two-stage least squares (2SLS) to study the influence of globalization on income inequality in 18 developing countries from 1991–2021. Utilizing the KOF index of globalization, it is determined that globalization, together with its three aspects, has a negative effect on income inequality among developing economies. Evidence demonstrates that the combination of trade openness and foreign direct investment (FDI) plays a significant role in reducing inequality among developing economies. We recommend developing economies actively support globalization in terms of trade and FDI in accordance with the findings. By expanding trade opportunities and opening up markets, globalization can benefit developing nations. This may result in a rise in FDI, the creation of jobs, and technological developments. Governments can contribute to raising the living standards of their inhabitants, lowering rates of poverty, and closing the income gap by promoting globalization. Although the study emphasizes the well-established link between globalization and income inequality, it focuses on the effects of various globalization dimensions, emphasizing the need to comprehend how different dimensions of globalization, namely economic, political, and social globalization affects inequality in developing economies.

This research discovers how international tourism affects the economic growth of selected Asian states, e.g., Bangladesh, China, India, Pakistan, and Sri Lanka, throughout 2001–2019. To attain this objective, we have employed various regression estimation approaches, e.g., Fixed Effect Model (FEM) and Fully Modified Ordinary Least Square (FMOLS) technique. The statistical results of the applied techniques reveal that international tourism activities have a positive and significant effect on the GDP growth rate because such kinds of activities considerably contribute to creating opportunities that lead to hoist economic activities and economic growth. Moreover, an influx of tourism increases tourism activities and operations, which opens further doors to opportunities and generates revenue for the government. Similarly, the GDP per capita has been positively and significantly influenced by international tourism activities. The government and host country should emphasize the activities and operations regarding tourism and should also concentrate on the dynamic role, importance, and sensitivity of tourism operations in under-analyzed economies. This research brings a new arrangement of the variable, which has never been considered in prior literature.

Studying green urban infrastructure is important because of its ecosystem services, contributing to the welfare and comfort of citizens, mitigation of climate changes, and sustainability goals. Urban planning can increase or diminish the performance of ecosystem services. Despite numerous studies on the green infrastructure–services–planning nexus, there are very few concrete planning recommendations. This study aims to provide such recommendations for a broader audience by analyzing the dynamic of open green areas in Polish and Romanian cities, connected with its drivers. A novel approach including mathematical modeling and geostatistical analyses was applied to Urban Atlas and statistical yearbooks data. The results indicated that open green areas were lost and fragmented in all Romanian and Polish cities during 2006–2018. The drivers included urban built-up areas, population and density, the number of building permits, number of new dwellings completed, number of employees, and total length of roads. The study also revealed a tremendous lack of consistent datasets across the countries using the same statistical indicators. Based on the findings, planners should aim to preserve and develop urban greenery and maintain its continuity. City managers should use more research and decision-making policy developers to develop targeted policies and scientists should develop planning manuals.