This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.

The food insecurity and inadequate management of family farm production is a problem that per-sists today in all corners of the world. Therefore, the purpose of this study was to analyze the socioeconomic and agricultural production management factors associated with food insecurity in rural households in the Machángara river basin in the province Azuay, Ecuador. The information was collected through a survey applied to households that were part of a stratified random sample. Based on this information, the Latin American and Caribbean Household Food Security Measurement Scale (ELCSA) was constructed to estimate food insecurity as a function of socioeconomic factors and agricultural production management, through the application of a Binomial Logit model and an Ordinal Logit model, in the STATA^® 16 program. The results show that head house a married head of household, living in an informal house, having a latrine, producing medicinal or ornamental plants, and the relationship between expenses and income are significant variables that increase the probability of being food insecure. In this way, this research provides timely information to help public policy makers employ effective strategies to benefit rural household that are food vulnerable.

Rural tourism, which offers authentic cultural and nature-based experiences, is increasingly recognized as a vital tool for sustainable development. Ethiopia, with its rich rural landscapes and cultural heritage, holds immense potential for rural tourism, but the sector remains underdeveloped. This study assesses the facilitating conditions and challenges of rural tourism in Ethiopia using a mixed-methods approach. Results indicate that Ethiopia’s economic growth, improved rural infrastructure, large rural population, higher ethnic and religious diversity index, and 11 UNESCO World Heritage Sites provide strong foundations for rural tourism. However, significant challenges such as inadequate infrastructure, limited marketing, restricted access to financing, ethnic conflicts, environmental degradation, and insufficient stakeholder cooperation hinder its growth. To address these barriers, the study proposes a model encompassing strategic investments in infrastructure, enhancing marketing and promotion, access to finance initiatives, conflict resolution strategies, sustainable tourism practices, enhancing stakeholder coordination, and supportive policy frameworks. By employing these strategies, Ethiopia can harness the full potential of its rural tourism sector, contributing to economic development and community well-being while promoting cultural preservation and environmental sustainability. Also, the proposed model is highly applicable to other developing economies that share similar contexts. Besides, given the importance of the seven fundamental pillars of the model, it remains relevant across tourism types like coastal destinations.

This qualitative research aimed to study the effectiveness of the local health constitution in controlling the spread of COVID-19. It reports the role of local communities, government agencies, and healthcare providers in implementing and enforcing local health constitutions and how their engagement can be improved to enhance surveillance. We also reported factors that influence compliance and strategies for improving compliance. We also evaluated the long-term sustainability of local health institutions beyond the pandemic. The population and sample group consisted of key members of the local health constitution teams at the provincial, sub-district, and village levels in the rural area of Ubon Ratchathani. Participants were purposively selected and volunteered to provide information. It included health science professionals, public health volunteers, community leaders, and local government officials, totaling 157 individuals. The study was conducted from December 2022 to September 2023. Our research shows that local health constitutions can better engage and educate communities to actively participate in pandemic surveillance and prevention. This approach is a learning experience for responding to emergencies, such as new infectious diseases that may arise in the future. This simplifies the work of officials, as everyone understands the guidelines for action. Relevant organizations contribute to disease prevention efforts, and there is sustainable improvement in work operations.

Access to clean water and improved sanitation are basic elements of any meaningful discourse in rural development. They are critical challenges for achieving sustainable development over the next decade. This paper seeks to examine the strategies for improving access to clean water and sanitation in Nigerian rural communities. Hypothetically, the paper states that there is no significant relationship between access to clean water and sanitation and the attainment of Sustainable Development Goal 6 in Nigeria. The paper leverages Resilience Theory. The survey research design was adopted, and primary data was obtained from a sample size of 250 respondents, proportionally drawn from the 10 wards in Obanliku local government area of Cross River State. The chi-square statistical technique was to test the hypothesis. The result shows that the calculated value of Chi-square (X²) is 24.4. Since the P-value of 21.03 is less than the level of significance (0.05), the null hypothesis was rejected and the alternate accepted. The study concludes that there is a significant relationship between access to clean water and sanitation and the attainment of Sustainable Development Goal 6 in Cross River State, Nigeria. it recommends the need for more commitment on the part of government and international donor agencies in expanding access to clean water and improved sanitation in Nigeria.