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Water is the key limiting factor in socioeconomic and ecological development, but it is adversely affected by climate change. The novel virtual water (VW) concept and water, energy, food, biodiversity, and human health (WEFBH) nexus approach are powerful tools to assess the sustainability of a region through the lens of climate change. Climate change-related challenges and water are complex and intertwined. This paper analyzed the significant WEFBH sectors using the multicriteria decision-making (MCDM) and analytic hierarchy process (AHP) model. The AHP model demonstrated quantitative relationships among WEFBH nexus sustainability indicators in the Greater Horn of Africa countries. Besides, the net VW imports and water footprints of major staple crops were assessed. The composite WEFBH nexus indices varied from 0.10 to 0.14. The water footprint of crops is increasing period by period. The results also revealed that most countries in the study area are facing WEFBH domains unsustainability due to weak planning or improper management strategies. The strong policy constancy among the WEFBH sector is vital for dissociating the high-water consumption from crop production, energy, environmental, and human health system. Thus, this study enhances insights into the interdependencies, interconnectedness, and interactions of sectors thereby strengthening the coordination, complementarities, and synergies among them. To attain sustainable development, we urgently call all public and private entities to value the amount of VW used in their daily activities and design better policies on the complex WEFBH nexus and future climate change.

Biosurfactant is a biodegradation accelerator that improves bioavailability and facilitates degradation by microorganisms. The study was meant to produce a novel biosurfactant molecule from Bacillussafensis YKS2. An efficient biosurfactant-producing strain, namely, Bacillus safensis YKS2, was selected using hemolytic activity, drop collapsing test, oil spreading test and blue agar plate methods in four oil-degrading strains isolated from a soil sample. Biosurfactant production in the optimization of bacteria culture conditions by RSM is a statistical grouping technique that is analyzed using the AVOVA approach to surface tention. In addition, the study was characterized by UV spectrophotometer FT-IR, HR-SEM, and GC-MS analyses to explain its structural and chemical details. Wastewater treatment was monitored for pH, EC, turbidity, alkalinity, chemical oxygen demand (COD), biochemical oxygen demand (BOD) and dissolved oxygen (DO) in order to justify the efficacy of the biosurfactant during wastewater treatment. The results of the UV spectrophotometer showed absorption at 530 nm, and the FT-IR analyzed carboxylic acids, alcohol and phenols groups, whichthe GC-MS analysis indicated were lipopeptide purified by hexadecanoic andtetradecanoic processes, respectively. The results show that the wastewater removal efficiency of 70% wasachieved within 24 h. In comparison, metagenomics was conducted during the treatment process to identify changes in the microbial load and diversity, which essentially indicatethe biosurfactant performance of the wastewater treatment process. The microbial load in the treated biosurfactant wastewater (84,374 sequences) was greatly decreased compared to untreated wastewater (139,568 sequences). It was concluded that B. safensis YKS2, producing a glycolipid form of biosurfactant, has possible benefits in the remediation of wastewater, and can be used for large-scale processing inbiosurfactant industries.

Artificial Intelligence (AI) is generating new horizons in one of the biggest challenges in the world’s society—poverty. Our goal is to investigate utilities of AI in poverty prediction via finding answers to the following research questions: (1) How many papers on utilities of AI in poverty prediction were published up until March, 2022? (2) Which approach to poverty was applied when AI was used for poverty prediction? (3) Which AI methods were applied for predicting poverty? (4) What data were used for poverty prediction via AI? (5) What are the advantages and disadvantages of the created AI models for poverty prediction? In order to answer these questions, we selected twenty-two papers using appropriate keywords and the exclusion criteria and analyzed their content. The selection process identified that, since 2016, publications on AI applications in poverty prediction began. Results of our research illustrate that, during this relatively short period, the application of AI in predicting poverty experienced a significant progress. Overall, fifty-seven AI methods were applied during the analyzed span, among which the most popular one was random forest. It was revealed that with the adoption of AI tools, the process of poverty prediction has become, from one side, quicker and more accurate and, from another side, more advanced due to the creation and possibility of using different datasets. The originality of this work is that this is the first sophisticated survey of AI applications in poverty prediction.

In Modern era, the Researchers are keenly interested in different areas of green financing projects such as green economics, green trade, green sustainable development activities, green climatic and environment quests, green investment and financial ventures, and green public policy-related topics, respectively. Owing to the lower cost of production for sustainable development, a healthy climate, and a neat environment is needed, this study is structured to build the significant relationship between various green sustainable development projects, the financial effectiveness and performance of PSX and NYSX, respectively. For this purpose, the time series data for 2000–2020 are collected from IFS, WBI, SBP, the Federal Reserve system, S&DP, and the UNDP financial reports. The empirical analysis reveals the insignificant effects of green investment, financial projects, public policies, and social green projects on the financial performance of PSX, whereas the empirical modeling also attests that all the green factors significantly affect the performance of NYSX except the green economic and trading projects and renewable energy green projects, which are insignificant predictors with respect to FIP-NYSX. Moreover, the index for human development insignificantly affects the prediction of FIP-NYSX. The mixed empirical results guide policymakers, the board of PSX and NYSX, and the management of green financing companies to reconsider their policies and objectives with respect to successful green operations and the financial performance of PSX and NYSX.

The paper examines ecotourism in the Aral Sea region of Uzbekistan, an area with a fragile environment that has faced ecological crises and requires careful sustainable development. It looks at the supply side of ecotourism by examining Uzbekistani tour operators’ awareness of benefits derivable from promoting ecotourism in the region, and the local tourism industry’s motives to engage in this development. As a research methodology, the results of an exploratory survey of travel agents and tour operators highlight the policy-making and management interventions required for the more effective promotion and development of ecotourism in the Aral Sea region. The paper analyzes the challenges and opportunities associated with promoting ecotourism activities in the Aral Sea region in pursuit of sustainable regional development, improved livelihood for the local population, employment opportunity and income source creation, and enriched service exports. Key findings from the study show that stakeholders are aware of ecotourism’s value and are motivated to implement ecotourism in the region, but they have limited experience, competence, and international networks to promote and market ecotourism products and services. Local stakeholders have raised the issue that infrastructure development and access to microfinance are their greatest needs from local authorities in Uzbekistan.

The aim of the present research is to assess the impact of factors such as welfare, infrastructure, security, and the environment on inbound tourism as well as to develop its forecast. Six proxy indicators of the above-mentioned factors were selected as variables, namely, welfare (real GDP per capita, life expectancy, consumer price index), infrastructure (passenger transportation volume), security (total recorded crimes), and the environment (CO2 emissions). We used a time series-univariate ARIMA model to forecast the inbound tourism in the Republic of Uzbekistan, and applied the ARDL model to assess the impact of lagged real GDP per capita on inbound tourism in both the short and long terms. The results of our research show that security and welfare significantly affect the inflow of foreign tourists in the country, along with the impact of the COVID-19 pandemic crisis, the effects of which are expected to persist beyond 2026.

Food deserts (FD) have attracted attention after the post-COVID-19 pandemic, primarily due to adverse health and other implications of living in areas designated as food deserts. Most studies have focused on various aspects of the impact of food deserts, including the nutritional and health risks of living in FDs. Spatial integration and analysis of the GIS data in food provide a powerful way to expose the issues of creating deserts and how they change over space and time. This study aims to investigate the socioeconomic factors influencing food deserts using geospatial analyses. Guilford, Bladen, and Rutherford Counties in North Carolina were selected as case studies due to their higher percentage of the population with limited healthy food access. This study used open-source data, such as the USDA’s Crop Land Layer (CDL) land cover maps, census data, and the Food Access Research Atlas. This research provides a geostatistical analysis of FDs based on income/expenditure, population, vehicle, and food aid. The study results generally showed that geospatial technologies are vital for investigating FDs. The results will assist policy makers and other responsible agencies in formulating appropriate intervention policies tailored to meet the demands of these counties.

Agritourism can enhance rural development and is a useful alternative activity to increase farm incomes. It has attracted the attention of agricultural experts and researchers around the world in recent years. As a result of rapid growth worldwide, agritourism has become one of the most significant supplementary income sources for farmers. The main purpose of this study is to identify factors influencing participation of fermer in agritourism in the Samarkand region of Uzbekistan, using scientific studies and statistical data, which indicate that there are great opportunities for agritourism activities. Increasing the economic potential of the rural areas by introducing innovative activities on the Samarkand region’s medium-sized farms (fermers) and ensuring sustainable development are essential. In Samarkand, there are a lot of conditions for the development of tourism, especially agritourism. The study examined all 10 agritourism fermers and 100 randomly selected fermers in Samarkand that do not carry out agritourism activities. Based on this data collection and analysis using a logistic regression model, the results showed several factors influencing the participation of fermers in the Samarkand region in agritourism activities, which was analyzed using the logistic regression model. According to the results of this model, each fermer’s income, employment, fermer’s education, and distance from the city had a significantly positive effect on the fermer’s initiation of agritourism.