This study examines the relationship between Russian FDI carried out by large MNCs and investment development path (IDP). Although statistical analysis does not establish a significant relationship between outward FDI and GDP, the behavior of Russian outward FDI contradicts traditional models. Two primary factors contribute to this paradox. First, the complex business environment in Russia, characterized by a combination of both improvements and contradictions, has a significant impact on outward FDI behavior. Secondly, the duality of the Russian economy and society plays a decisive role. This segment resembles a high-income country with ample resources, while most face lower income levels, raising concerns about wealth distribution. Historical factors, including Russia’s transition from a state-controlled to a market-oriented economy, contribute to the internationalization of Russian MNCs. Both state-owned enterprises and privatized firms are influenced by the state, although to varying degrees. Government involvement in international business strategies increases the knowledge and experience of Russian MNCs, but also raises concerns about political influence.

Agricultural productivity has remained central to the gross domestic product (GDP) in Nigeria for several decades. However, the decline in the agricultural sector after the discovery of oil and gas resources is a serious challenge. The government has initiated several policies to rejuvenate agricultural productivity. Little attention has been given to the exploration of policy implementation for fish farming and aquaculture as an integral part of agribusiness in the country. The World Bank asserts that the yearly demand for fish is 3.4 million metric tons (i.e., 40%) is locally produced and the remaining 60% is supplied through importation of fish. Therefore, the primary objective of this paper is to re-assess policy implementation to explore and expand the potential of fish farming in Nigeria to address abject poverty and high unemployment rates. This can be achieved when a shift of attention is given to small- and medium-scale businesses, and consequentially achieve sustainable agribusiness and socio-economic development in the country. This study used library-based research and content analysis as its methodology, wherein secondary data were used to review different aspects that can foster fish farming in the country. The findings from the content analysis of the study demonstrated that in order to achieve domestic production and stop the importation of fish, there is a need for the establishment of nothing less than 400,000 fish farming across the country. The paper highlighted various types and techniques for breeding, rearing, and harvesting fish by strengthening their effectiveness and efficiency. This study emphasized the vital importance of technology, such as reliable energy facilities, solar energy, and solar irrigation, in reducing the cost of diesel in powering generators to maximize fish investment. The limitations of this study are highlighted, and SWOT analysis (i.e., strengths, weaknesses, opportunities, and threats) in fish farming is elaborated. It is suggested that the implementation of policies to support farmers in general and fish farmers in particular, such as the provision of credit loans and other fish feeds for sustainable agribusiness and socio-economic development, occupies a central climax of this research.

This bibliometric review evaluates the research progress and knowledge structure regarding the impact of supporting facilities on halal tourism development. Using the Scopus database and bibliometric analysis with the “bibliometrix” package in R, the study covers the period from 2016 to 2023. The search, employing keywords like “halal tourism,” “facilities,” “infrastructure,” and “local support,” identified 26 relevant publications. The findings highlight a limited body of research, with the Journal of Islamic Marketing being the most active publisher in this area, contributing six articles. Indonesia emerges as a leading contributor to halal tourism research, driven by its significant Muslim population and the economic potential of this niche market. Key facilities, such as mosques, musholla, and high-quality halal food options, are identified as crucial factors influencing Muslim travelers’ destination choices. This review provides a comprehensive overview of the current research landscape on supporting facilities in halal tourism and highlights opportunities for future investigation to further enrich the field.

This study delves into the role of pig farming in advancing Sustainable Development Goal (SDG) 8—Decent work and economic growth in Buffalo City, Eastern Cape. The absence of meaningful employment opportunities and genuine economic progress has remained a significant economic obstacle in South Africa for an extended period. Through a mixed-method approach, the study examines the transformative impact of pig farming as an economic avenue in achieving SDG 8. Through interviews and questionnaires with employed individuals engaged in pig farming in Buffalo City, the study further examines pig farming’s vital role as a source of decent work and economic growth. The study reveals inadequate government support and empowerment for pig farming in Buffalo City despite pig farming’s resilience and potential in mitigating socio-economic vulnerabilities and supporting community’s livelihoods. To enhance pig farming initiatives, this study recommends government’s prioritization of an enabling environment and empowerment measures for the thriving of pig farming in Buffalo City. By facilitating supportive policies and infrastructures, the government can empower locals in Buffalo City to leverage pig farming’s potential in achieving SDG 8.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.