Integrating Machine Learning for Risk Assessment in Renewable Energy Investments in Developing Economies.
Abstract
Renewable energy investments are critical for addressing energy poverty and driving sustainable development in developing economies. However, these investments face significant challenges, including economic volatility, political instability, and environmental complexities, which hinder their successful implementation. This study examines the role of machine learning (ML) models as innovative tools for assessing and mitigating risks associated with renewable energy investments. The study's objectives include analysing the multifaceted risks in renewable energy investments, evaluating the effectiveness of ML techniques, such as predictive analytics, classification models, and neural networks, in risk assessment, and proposing strategies to facilitate their integration into decision-making processes. A qualitative research methodology was adopted, utilizing a comprehensive desk review of existing literature. The findings reveal that ML models enhance the accuracy and efficiency of risk assessments by providing advanced predictive capabilities, improving decision-making, and addressing the complexities of economic, political, and environmental uncertainties. Despite their potential, challenges such as data quality issues, technological barriers, and limited expertise in developing economies remain significant hurdles.
The study recommends policy reforms to support ML adoption, targeted capacity-building initiatives to improve ML and data science, and strategic collaborations among governments, private sectors, and international organizations.
An Investigation into the Compliance Levels of Students’ Hostels to Tenancy Agreements in Awka, Anambra State.
Abstract
Student housing represents a critical aspect of university life, with significant implications for academic performance and overall well-being. This study was to investigate the compliance levels of students to tenancy agreements in hostels in Ifite, on campus and temporary site, with a view to enhancing compliance to tenancy agreements. The research objectives were to identify categories of student hostels, assess components of tenancy agreements, evaluate compliance levels, investigate reasons for non-compliance, and examine effects of non-compliance in Awka student hostels. The study focused on public and private hostels in these three areas, housing students from Nnamdi Azikiwe University. The objectives of the study informed the drafting of research hypothesis tested for the research study. The study employed a mixed-method approach, utilizing questionnaires and interviews. The population comprised 4,252 students from public and private hostels, with a sample size of 662 (351 public, 311 private) determined using Taro Yamane's formula. Additionally, 61 hostel managers (17 public, 44 private) were surveyed. Out of the 662 questionnaire distributed, 580 were retrieved and found usable, representing a response rate of 87.6% (580) for students and 88.5% (54) for hostel managers. Key findings revealed diverse hostel categories, with public hostels predominantly standard (84.0%) and university-owned, while private hostels offered more variety, including luxury options (15.4%). Financial constraints emerged as the primary reason for non-compliance across both hostel types (65.5% public, 68.9% private).The chi-square test and one-way ANOVA was used to test the hypothesis. The study found a significant relationship between awareness of tenancy terms and compliance levels (χ² = 78.24, p < 0.001). Implementation of standardized agreements showed a positive impact on compliance (F = 32.15, p < 0.001). The research concluded that while challenges exist, there are clear opportunities for improvement in student housing management and compliance levels of students to tenancy agreements. Recommendations include implementing more flexible payment options, enhancing communication of agreement terms, standardizing tenancy agreements, and adopting balanced enforcement strategies. These findings provide valuable insights for improving compliance levels and overall living conditions in Awka student hostels.
