SMEs are characterized by a number of flaws that threaten their survival and counteract them from reaching high levels of growth and development. Access to finance is the primary problem facing these companies in the Moroccan context. Aware of the effective and potential impacts of SMEs on the country as a whole, the Moroccan Government through a variety of actors has mobilized its efforts in a number of ways to support this population of companies. This study assesses the extent to which actors within the Moroccan SMEs’ financing ecosystem align to support these companies and develop their ability to access external financing. Using the MACTOR model, based on an in-depth contextual analysis and expert interviews, our findings suggest that Morocco’s SMEs’ financing ecosystem is skewed, with high levels of convergence between its components.

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.

Providing and using energy efficiently is hampered by concerns about the environment and the unpredictability of fossil fuel prices and quantities. To address these issues, energy planning is a crucial tool. The aim of the study was to prioritize renewable energy options for use in Mae Sariang’s microgrid using an analytical hierarchy process (AHP) to produce electricity. A prioritization exercise involved the use of questionnaire surveys to involve five expert groups with varying backgrounds in Thailand’s renewable energy sector. We looked at five primary criteria. The following four combinations were suggested: (1) Grid + Battery Energy Storage System (BESS); (2) Grid + BESS + Solar Photovoltaic (PV); (3) Grid + Diesel Generator (DG) + PV; and (4) Grid + DG + Hydro + PV. To meet demand for electricity, each option has the capacity to produce at least 6 MW of power. The findings indicated that production (24.7%) is the most significant criterion, closely followed by economics (24.2%), technology (18.5%), social and environmental (18.1%), and structure (14.5%). Option II is strongly advised in terms of economic and structural criteria, while option I has a considerable advantage in terms of production criteria and the impact on society and the environment. The preferences of options I, IV, and III were ranked, with option II being the most preferred choice out of the four.

This article examines the factors influencing sustainable entrepreneurship (SE) in Arab countries, focusing on economic, social, and technological dimensions. Using data from various sources and structural equation modeling, the study explores the relationships between these factors and SE sustainability. The findings reveal that economic factors, such as GDP per capita and foreign direct investment (FDI), positively influence SE sustainability, emphasizing the need for a conducive economic environment. Social factors, measured by Internet usage and the Human Development Index (HDI), also significantly impact SE sustainability, highlighting the importance of access to information and education. However, technological factors like patent applications and high-tech exports did not show a significant positive relationship with SE sustainability, suggesting a minimal direct impact on SE longevity in Arab countries. These insights have implications for policymakers, stressing the importance of fostering economic growth and enhancing social infrastructure to support sustainable entrepreneurial ecosystems. Despite its robust methodology, the study has limitations, such as incomplete data for certain countries, affecting the generalizability of the findings. Future research could explore additional factors influencing SE sustainability, further investigate the role of technology, and expand the geographical scope to include more Arab countries.

This article identifies the role of anti-corruption legislation in sustainable development. The descriptive approach is used to achieve the research objectives, as it is the appropriate scientific method suitable for the nature of the study subject in terms of presenting information related to combating administrative corruption. The results indicate that efforts to combat administrative corruption to achieve sustainable development in countries can only be achieved through strict legislation specialized in combating administrative corruption with the activation of accountability mechanisms in all state departments, bodies, and ministries. The results also demonstrate the alignment of the national and international efforts to reduce the phenomenon of corruption through international agreements and treaties. Towards the end, key conclusions and recommendations are also appropriately incorporated.

This study conducts a comprehensive analysis of the aquaculture industry across 11 coastal regions in eastern China from 2017 to 2021 to assess their adaptability and resilience in the face of climate change. Cluster analysis was employed to examine regional variations in aquaculture adaptation by analyzing data on annual average temperatures, annual extreme high/low temperatures, annual average relative humidity, annual sunshine duration, and total yearly precipitation alongside various aquaculture practices. The findings reveal that southern regions, such as Fujian and Guangdong, demonstrate higher adaptability and resilience due to their stable subtropical climates and advanced aquaculture technologies. In contrast, northern regions like Liaoning and Shandong, characterized by more significant climatic fluctuations, exhibit varying degrees of cluster changes, indicating a continuous need to adjust aquaculture strategies to cope with climatic challenges. Additionally, the study explores the specific impacts of climate change on species selection, disease management, and water resource utilization in aquaculture, emphasizing the importance of developing region-specific strategies. Based on these insights, several strategic recommendations are proposed, including promoting species diversification, enhancing disease monitoring and control, improving water quality management techniques, and urging governmental support for policies and technical guidance to enhance the climate resilience and sustainability of the aquaculture sector. These strategies and recommendations aim to assist the aquaculture industry in addressing future climate challenges and fostering long-term sustainable development.