Objective: As the scale and importance of official development assistance (ODA) continue to grow, the need to enhance the effectiveness of ODA policies has become more critical than ever before. In this context, it is essential to systematically classify recipient countries and establish tailored ODA policies based on these classifications. The objective of this study is to identify an appropriate methodology for categorizing developing countries using specific criteria, and to apply it to actual data, providing valuable insights for donor countries in formulating future ODA policies. Design/Methodology/Approach: The data used in this study are the basic statistics on the Sustainable Development Goals (SDGs) published annually in the SDGs Report. The analytical method employed is decision tree analysis. Results: The results indicate that the 167 countries analyzed were classified into 10 distinct nodes. The study further limited the scope to the five nodes representing the most disadvantaged developing countries and suggested future directions for aid policies for each of these nodes.

Educational quality policies are a basic principle that every Peruvian university educational institution pursues in accordance with Law No. 30220, with the objective of training highly competent professionals who contribute to the development of the country. This study to analyzes educational quality policies with the student’s satisfaction of public and private universities in Peru, according to social variables. The study was descriptive-comparative, quantitative, non-experimental, and cross-sectional. One thousand (1000) students from two Peruvian universities, one public (n = 500) and one private (n = 500), were purposively selected by quota using the SERVQUALing instrument. The findings indicate a moderate level of satisfaction reported by 49.2% of participants, with a notable tendency towards high satisfaction observed in 40.9% of respondents. These results suggest that most students perceive that the actual state of service quality policies are in a developmental stage. The results, therefore, indicate that regulatory measures, including university laws, licensing, and accreditation, significantly influence outcomes. These measures are essential for the effective functioning of universities. In addition, the analysis revealed that female and male students at private universities showed higher levels of satisfaction with the educational services offered. It is concluded that educational quality policies in Peru are still being executed, because the implementation of the University Law is in process, according to the satisfaction of the student, this must be improved in central aspects such as optimizing human resources, infrastructure, equipment, curricular plans that differ from the public to the private university, In addition, this should lead to improving and redefining current policies on educational quality and the economic policies that finance the educational service.

The transportation sector in India, which is a vital engine for economic growth, is progressively facing challenges related to climate change. Increased temperature, extreme weather conditions, and rising seas threaten physical infrastructure, service delivery, and the economy. This research examines efforts towards improving the climate resilience of India’s transport sector through policy interventions. Strategies encompass broadening the focus to cover the integration of sustainability, innovative technology deployment, and adaptive infrastructure planning. Multi-sectoral measures are proposed to guarantee longevity, equity and environmental protection. National transport infrastructure will be secured, people will be enabled to move sustainably, and India will take its position in the world economy as a climate-resilient country. Long-term resource management and promoting inclusive governance are critical to agri-transportation systems that can withstand the changing climate.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

The Moroccan economy has undergone significant structural changes since the 1980s. Attracting Foreign Direct Investment (FDI) has been a key strategy for the country’s economic growth and development, particularly in some specific high value-added sectors, such as the automotive supply industry. This paper uses the results of a survey to examine the reasons why multinational enterprises (MNEs) in the automotive supply sector set up in Morocco. Our findings show that proximity to Europe and labor costs and skills are the most important considerations for investing in this sector in Morocco. However, some institutional issues are still of concern to these MNEs.

Research has shown that understanding the fundamental of public support for carbon emission reduction policies may undermine policy formulation and implementation, yet the direction of influence and the transmission mechanism remain unclear. Using data from using data from 1482 questionnaires conducted in Hangzhou, China, this paper has examined a comprehensive model of the factors and paths influencing public support for carbon emission reduction policies, and evaluated the determinants and predictors of policy support regarding individual psychological perceptions, social-contextual perceptions, and perceptions of policy features. The results show that the variables in both the individual psychological perception and social contextual perception dimensions have no significant effect on carbon tax, however, be important constructure in carbon trading; in the policy characteristics perception dimension, both variables have a significant positive effect on both carbon tax and carbon trading, and are also the strongest predictors of policy support for carbon policies. Further evidence suggests that future policies could be more acceptable to residents by strengthening their environmental values, social norms can further arouse residents’ social responsibility to care about climate, and whether the policy is effective or fair to help residents realize the importance of the policy as well as the need for their participation and willingness to dedicate themselves to the mitigation of climate change.