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.

The objective of this study is to explore the relationship between changing weather conditions and tourism demand in Thailand across five selected provinces: Chonburi (Pattaya), Surat Thani, Phuket, Chiang Mai, and Bangkok. The annual data used in this study from 2012 to 2022. The estimation method is threshold regression (TR). The results indicate that weather conditions proxied by the Temperature Humidity Index (THI) significantly affect tourism demand in these five provinces. Specifically, changes in weather conditions, such as an increase in temperature, generally result in a decrease in tourism demand. However, the impact of weather conditions varies according to each province’s unique characteristics or highlights. For example, tourism demand in Bangkok is not significantly affected by weather conditions. In contrast, provinces that rely heavily on maritime tourism, such as Chonburi (Pattaya), Phuket, and Surat Thani, are notably affected by weather conditions. When the THI in each province rises beyond a certain threshold, the demand for tourism in these provinces by foreign tourists decreases significantly. Furthermore, economic factors, particularly tourists’ income, significantly impact tourism demand. An increase in the income of foreign tourists is associated with a decrease in tourism in Pattaya. This trend possibly occurs because higher-income tourists tend to upgrade their travel destinations from Pattaya to more upscale locations such as Phuket or Surat Thani. For Thai tourists, an increase in income leads to a decrease in domestic tourism, as higher incomes enable more frequent international travel, thereby reducing tourism in the five provinces. Additionally, the study found that the availability and convenience of accommodation and food services are critical factors influencing tourism demand in all the provinces studied.

The cultivation of red chili in East Java, Indonesia, has significant economic and social impacts, necessitating proactive supply chain measures. This research aimed to identify priority risk agents, develop effective risk mitigation, and enhance supply chain resilience using the SCOR model, House of Risk, Interpretative Structural Modelling (ISM), and synthesis analysis. Examining 238 respondents—including farmers, collectors, wholesalers, retailers, home-agroindustries, and experts—the findings highlight farmers’ critical role in supply chain resilience despite risks from crop failures, weather fluctuations, and pest infestations. Simultaneous planting led to market oversupply and price drops, but accurate pricing information facilitated quick market adaptation. Wholesalers influenced pricing dynamics and income levels, impacting farmers directly. To improve resilience, three main strategies were developed through ten key elements: proactive strategies (real-time SCM tracking, Weather Early Warning Systems, risk management team formation, and training), resistance strategies (partnerships, chili stock reserves, storage and drying technologies, GAP implementation, post-harvest management, agricultural insurance, and Fair Profit Sharing Agreements), and recovery and growth strategies (flexible distribution channels and customizable distribution centers). Furthermore, the study delves into the mediating and moderating effects between variables within the model. This research not only addresses a knowledge gap but also provides stakeholders with evidence to consider new strategies to enhance red chili supply resilience.

Humanity is currently facing several global problems, such as global warming, air pollution, water pollution, deforestation, desertification, and land degradation, which are connected to the consequences of negative human activity. One of the possible and effective institutional tools for environmental protection is the environmental education of the general population. It is a relatively well-known and used environmental protection policy tool that governments of all developed countries have in their instrument mix. This qualitative analysis assigned itself the task of investigating whether the ability of environmental education can be affected by certain neuropsychological diseases in addition to thinking about the psychology of environmental education at large. To fulfill this main task, the authors asked themselves the following research questions: 1st—Is pedagogical psychology identical and applicable in the case of environmental education? And 2nd—What effect do some neuropsychological disorders have on the ability of environmental education? Based on the study, analysis, selection, and comparison of current professional scientific works obtained from the research activities of current researches on this topic, it is possible to accept the premise that the psychology of environmental education is basically the same as the general psychology of education and that neuropsychological diseases do indeed affect the ability of environmental education similarly to scholarly education. The main benefit of this qualitative review is the originality of the survey. There are no relevant and credible publications on the chosen topic, i.e., on the influence of selected neuropsychological diseases on the ability of environmental education of the population, to be found in the representative databases. Due to the importance of environmental education of the population, as one of the basic tools of environmental protection, the knowledge gained can gradually be incorporated into the politics, psychology, and didactics of education, to improve the technique of environmental education.

Given the importance of Information Communication Technology (ICT) in stimulating stock market development, many researchers have investigated their influences on the developed markets and high-income economies. The aim of this study is to examine the impact of ICT diffusion on stock market development for a panel of 17 selected emerging countries over the period 1990–2020 and employed the system-generalized method of moments (S-GMM) to test its objective. Three stock market development indicators are also used, namely: stock market capitalization (SMC), stock market total value traded (SMTT), and stock market turnover (SMT). Three ICT indicators are also employed, namely: Fixed telephone subscriptions (FTS), Individuals using the Internet (IUI), and Mobile cellular subscriptions (MCS). Three financial development indicators (deposit money among bank assets (DMB), liquid liabilities (LLB), and private credit by deposit money bank (PCM)) were employed as control variables. In its findings, all selected ICT dynamics positively affect stock market development and its constituents. Secondly, no proof was confirmed in relation to the impact of fixed telephone and stock market development with its elements. Thirdly, evidence of a positive relationship is sparingly apparent in financial development and its components. Fourthly, compared with fixed telephone, internet users more positively and significantly affect stock market development indicators. Policy implications are discussed.