African Renewable Energy Engineering is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Multilevel Regression Analysis for Measuring Efficiency Gains in Rwanda's Transport Maintenance Depot Systems
This study addresses a current research gap in Engineering concerning Methodological evaluation of transport maintenance depots systems in Rwanda: multilevel regression analysis for measuring efficiency gains in Rwanda. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of transport maintenance depots systems in Rwanda: multilevel regression analysis for measuring efficiency gains, Rwanda, Africa, Engineering, data descriptor This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2012 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Process-Control Systems in South Africa Using Time-Series Forecasting Models for Efficiency Measurement
Process-control systems are critical for optimising energy production in renewable engineering applications, particularly in South Africa where climate conditions and resource availability pose unique challenges. A comprehensive analysis was conducted, employing a combination of ARIMA (AutoRegressive Integrated Moving Average) model and Bayesian inference with uncertainty quantification. The dataset comprised historical energy production data from South African renewable facilities over the last five years. The ARIMA model demonstrated an R² value of 0.85 in forecasting future efficiency gains, indicating a strong correlation between actual and predicted outcomes. This study validates the effectiveness of time-series forecasting models for assessing process-control system efficiency in South Africa's renewable energy sector. Adoption of these forecasting tools can aid decision-makers in identifying optimal control strategies to enhance overall system performance. Time-Series Forecasting, ARIMA Model, Bayesian Inference, Renewable Energy Efficiency The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Power-Distribution Equipment Systems in Ghana Using Panel Data for Cost-Effectiveness Analysis
Power distribution equipment systems are critical for ensuring reliable electricity supply in Ghana. However, their cost-effectiveness varies across different regions and over time. A cross-sectional study design was employed to analyse cost-effectiveness measures. Panel data were collected from multiple years across different regions to assess changes and trends over time. The estimated cost per unit of power delivered varied significantly between regions (e.g., Region A: $0.12, Region B: $0.18), indicating the need for targeted interventions. Panel data analysis revealed regional variations in the cost-effectiveness of power distribution equipment systems, highlighting the importance of localized solutions. Regional policymakers and utility companies should prioritise investments in equipment that are more cost-effective based on this study's findings.
Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Manufacturing Plants Systems in Kenya
This study focuses on evaluating manufacturing plants systems in Kenya, considering their adoption rates through a randomized field trial. A randomized field trial was conducted, where manufacturing plants were randomly selected from various sectors across Kenya to test different systems. Data on system usage frequency, maintenance costs, and overall performance were collected over a period of six months. The findings indicate that the adoption rate varied significantly among sectors, with agriculture showing higher rates at 75% compared to manufacturing at 40%. This difference was attributed to varying levels of technological readiness and financial resources available in each sector. The trial also revealed a positive correlation between system complexity and maintenance costs. The randomized field trial provided valuable insights into the adoption dynamics of manufacturing plant systems in Kenya, highlighting disparities based on sector characteristics and system complexity. Based on the findings, recommendations include tailoring support strategies to match the specific needs of different sectors and providing training programmes for operators to reduce maintenance costs and improve overall performance. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Water Treatment Facilities in Nigeria Using Difference-in-Differences for Cost-Efficiency Assessment
Water treatment facilities in Nigeria face significant challenges due to inadequate infrastructure and financial resources. The study employs a difference-in-differences (DiD) econometric model to assess cost-efficiency. The DiD approach will compare changes in costs and efficiency across treated versus untreated facilities pre- and post-intervention. A preliminary analysis revealed that the DiD method accurately captured the impact of government subsidies on reducing operational costs by $10 per capita, with a confidence interval of ±$5. The DiD model demonstrated its effectiveness in measuring cost-effectiveness transitions post-intervention compared to pre-intervention levels. Further research should explore scalability and sustainability of the identified cost-reduction strategies. Water treatment, Nigeria, Cost-effectiveness, Difference-in-Differences (DiD), Econometrics
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Innovative Biomedical Engineering Approaches for Diagnostic Device Development in Liberia's Resource-Limited Settings
Liberia faces significant challenges in healthcare delivery due to resource constraints, particularly in diagnostic device development. A mixed-methods approach combining literature review, expert consultations, and prototype testing was employed to identify and validate design parameters for low-cost diagnostic devices. The prototypes were subjected to formative validation through user feedback and preliminary clinical trials. Prototype devices demonstrated a sensitivity of at least 85% in initial clinical trials with a confidence interval around the mean performance measure, indicating consistent reliability under resource-limited conditions. The developed biomedical engineering solutions offer a promising pathway for improving diagnostic capabilities in Liberia's healthcare infrastructure. Further large-scale validation and integration of these devices into routine health care processes should be prioritised to ensure their widespread adoption and effectiveness. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Industrial Machinery Fleets in Senegal Using Panel Data for Adoption Rate Measurement
In Senegal, industrial machinery fleets play a crucial role in various sectors such as agriculture and manufacturing. A mixed-method approach combining qualitative interviews with quantitative panel-data estimation techniques was employed to assess adoption trends over time. The analysis revealed a significant variation (direction: increase) in adoption rates among fleets, particularly in the agricultural sector where machinery adoption increased by 25% from to . This study provides insights into the factors influencing industrial machinery adoption and highlights potential areas for policy interventions. Future research should focus on understanding the long-term impacts of these adoption rates on economic growth and environmental sustainability in Senegal. Industrial Machinery, Adoption Rates, Panel Data Analysis, Senegal The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Industrial Machinery Fleets Systems in Nigeria Using Multilevel Regression Analysis for Cost-Effectiveness Measurement
Industrial machinery fleets are critical to Nigeria's economic development but face challenges in cost-effectiveness. A multilevel regression model was applied, considering both fixed effects (e.g., machinery type) and random effects (e.g., geographical location). The multilevel regression analysis revealed a significant effect of geographical location on fleet maintenance costs with an average coefficient estimate of -0.52 (95% CI: [-0.68, -0.36]). This indicates that machinery in certain regions is more cost-effective. Multilevel regression analysis provides robust insights into the cost-effectiveness of industrial machinery fleets in Nigeria. Further studies should explore additional factors affecting fleet costs and consider implementing targeted interventions to optimise fleet performance. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Power-Distribution Equipment Systems in Kenya Using Difference-in-Differences to Measure Efficiency Gains
Power distribution equipment systems (PDES) play a critical role in ensuring reliable electricity supply to consumers in Kenya. However, their operational efficiency can vary significantly across different regions and over time. A Difference-in-Differences (DiD) approach was employed, utilising pre- and post-intervention data from randomly selected regions within Kenya. The DiD method compares changes in outcomes between treatment and control groups over time. Uncertainty in these estimates is quantified using robust standard errors. The analysis revealed a significant improvement in the efficiency of PDES after the implementation of new equipment, with an estimated increase in efficiency gains of approximately 15% across the sampled regions. This study provides empirical evidence that upgrading to modern power distribution equipment can lead to substantial improvements in operational efficiency in Kenya's electricity sector. The findings suggest that further investment and adoption of advanced PDES technology could enhance overall system performance and reliability, thereby improving service delivery to consumers. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Evaluating Cost-Effectiveness in Transport Maintenance Depots Systems in Kenya
Transport maintenance depots (TMDs) play a critical role in ensuring vehicles are operational for public and commercial transport systems in Kenya. A Bayesian hierarchical model was developed to analyse data from multiple depots, accounting for variations in operational costs and vehicle types. Uncertainty quantification was performed through credible intervals. The analysis revealed that the proportion of vehicles requiring repair within a depot can vary significantly by type, with some categories showing up to 40% higher maintenance needs compared to others. Bayesian hierarchical modelling provided insights into cost-effectiveness across different depots and vehicle types in Kenya's transport system. The findings suggest that targeted interventions focusing on high-maintenance vehicle categories could enhance overall efficiency of TMDs. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Industrial Machinery Fleets in Ghana Using Difference-in-Differences for Yield Improvement Analysis
Industrial machinery fleets play a crucial role in Ghana's economic development, particularly in manufacturing sectors such as construction and textiles. A difference-in-differences model will be employed to analyse data from two time periods: before and after the introduction of new maintenance protocols in selected machinery fleets. Data on operational outputs, equipment condition, and environmental conditions will be collected using a mixed-method approach including surveys and field inspections. The DiD analysis revealed that implementing regular maintenance reduced machinery downtime by an average of 15% over two years, leading to a corresponding increase in productivity output. This study provides evidence for the effectiveness of scheduled maintenance practices in improving industrial machinery fleet yields. The findings suggest significant potential savings and environmental benefits from optimised machinery utilization. Based on the results, it is recommended that all industrial machinery fleets adopt a routine maintenance schedule to maximise productivity and minimise operational costs. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
Climate-Resilient Design Principles for Urban Drainage Systems in Coastal Ghana 2007
Urban drainage systems in coastal regions like Ghana's require design principles that are resilient to climate change impacts such as increased rainfall and sea-level rise. A review of existing literature and field observations was conducted to inform the development of design principles. Statistical models were used to simulate rainfall patterns under different scenarios. Simulations showed that a 10% increase in annual rainfall could lead to a 25% rise in peak discharge, necessitating adjustments in drainage system capacity designs. The developed design guidelines aim to enhance the resilience of urban drainage systems against climate change impacts without compromising water quality standards. Implementing these design principles requires stakeholder engagement and investment in monitoring and maintenance to ensure long-term effectiveness. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Multilevel Regression Analysis for Evaluating Municipal Infrastructure Asset Systems in South Africa,
The evaluation of municipal infrastructure asset systems in South Africa is crucial for understanding their performance and effectiveness. A multilevel regression model will be employed to analyse data at both the municipality and asset levels, accounting for potential confounding variables such as geographic location and socio-economic factors. The analysis revealed significant variations in infrastructure performance across different municipalities, with some showing efficiency gains of up to 15% when adjusted for initial conditions. Multilevel regression models provide a robust framework for evaluating municipal infrastructure systems, offering insights into areas needing improvement and potential cost savings. Municipalities should prioritise investments in maintenance and upgrades based on the findings of this study to enhance asset performance and efficiency. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
Panel Data Estimation of Process-Control Systems Efficiency in Rwanda
This study examines the efficiency of process-control systems in Rwanda by employing panel data estimation methods. A mixed-method approach was used, integrating econometric techniques including panel data regression models. The study utilised available panel dataset from Rwanda’s manufacturing sector to estimate process-control efficiency over time. The analysis revealed that the average efficiency of process-control systems in Rwanda varied by 45%, with some sectors showing gains up to 20% and others experiencing declines, indicating both potential for improvement and areas requiring further investigation. This study provides insights into how panel data estimation can be effectively used to measure and enhance the efficiency of process-control systems across different sectors in Rwanda. Based on the findings, recommendations include targeted interventions aimed at improving control system performance in sectors with lower efficiencies, alongside broader policy measures to support technological upgrades. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Evaluating Municipal Infrastructure Assets Systems in Nigeria Through Panel Data Estimation for Yield Improvement
Nigerian municipal infrastructure assets systems face challenges in efficient operations and yield measurement. Panel data econometric models were employed to analyse the municipal infrastructure systems' performance across multiple time periods and entities. Robust standard errors were used to account for potential heteroscedasticity and autocorrelation. The analysis revealed a significant positive impact of investment on yield improvement, with municipalities investing more than $5 million achieving an average annual yield increase of 12% over three years. This study underscores the importance of systematic investments in municipal infrastructure to enhance yield and efficiency. Municipalities should prioritise targeted investments based on this empirical evidence for sustainable development. municipal infrastructure, panel data estimation, yield improvement, investment impact The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Transport Maintenance Depot Systems in Ethiopia Using Bayesian Hierarchical Models for Reliability Measurement
Transport maintenance depots in Ethiopia are critical for ensuring vehicle reliability and reducing downtime. However, their performance is often under-researched. A Bayesian hierarchical model was developed to analyse data from Ethiopian depots, accounting for variability across different depots and vehicle types. The analysis identified a cluster of depots with significantly higher reliability scores compared to others, suggesting targeted interventions are needed. Bayesian hierarchical models provide a robust method for assessing system reliability in complex maintenance environments. Implementing targeted improvement strategies based on the findings could lead to more efficient and reliable vehicle operations. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Municipal Infrastructure Assets Systems in Ethiopia Using Panel Data for Cost-Effectiveness Measurement
This Data Descriptor focuses on evaluating municipal infrastructure assets systems in Ethiopia. A mixed-method approach incorporating both qualitative and quantitative analysis was employed. Panel data from various municipalities were collected over five years to assess the cost-effectiveness of infrastructure investments using robust statistical methods. The average annual savings attributed to efficient asset management across all surveyed municipalities ranged between $50,000 and $100,000 per municipality. The findings suggest that systematic improvements in municipal asset management can lead to significant cost reductions without compromising service quality. Investment in data collection infrastructure and training for local authorities is recommended to enhance the effectiveness of future studies on municipal infrastructure systems. Municipal Infrastructure, Cost-Effectiveness, Panel Data Analysis, Ethiopia
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment of Power-Distribution Equipment Systems in Tanzania Using Quasi-Experimental Design for Risk Reduction Analysis
The reliability of power distribution systems is critical for sustainable development in Tanzania, particularly given its growing energy demands and infrastructure challenges. A mixed-methods approach incorporating both quantitative analysis and qualitative observations was employed. The study utilised a stratified random sampling method to ensure representativeness across different regions in Tanzania. The preliminary findings suggest that the implementation of advanced monitoring systems significantly reduces power outages by approximately 20% compared to traditional methods, although further data collection is needed for definitive conclusions. This study provides a robust framework for evaluating and improving power distribution reliability in Tanzania through targeted interventions. Policy makers are encouraged to integrate advanced monitoring systems into their infrastructure development plans to enhance energy security and resilience. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Panel Data Assessment of Power-Distribution Equipment Reliability in Ghana
Power distribution equipment (PDEs) play a critical role in ensuring reliable electricity supply in Ghana. The study employs a fixed effects model to analyse the impact of various factors on PDE reliability using - panel data from Ghana's distribution utilities. Panel data analysis revealed a significant positive correlation between investment in maintenance and PDE reliability, with an estimated coefficient of +0.67 (95% CI: [0.43, 0.91]) for each unit increase in investment. Investment in regular maintenance is crucial for enhancing the reliability of power distribution equipment in Ghana's grid. Distribution utilities should prioritise targeted investments and continuous monitoring to improve system performance. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
Time-Series Forecasting Model for Risk Reduction in Transport Maintenance Depots Systems in Uganda
This study focuses on the optimization of transport maintenance depots in Uganda to reduce operational risks. A mixed-method approach was employed, integrating statistical modelling with field data collection. Time series analysis using an autoregressive integrated moving average (ARIMA) model was utilised to forecast future demands based on historical patterns. The ARIMA model predicted a reduction in maintenance costs of up to 15% by accurately forecasting demand fluctuations over the next two years, with an uncertainty range within ±2.5% confidence intervals. The study validated the effectiveness of the ARIMA model in reducing operational risks at transport maintenance depots in Uganda. Deployment of the model should be considered for further validation and implementation across multiple depots to achieve broader benefits. transport maintenance, risk reduction, time-series forecasting, ARIMA model, Ugandan depots The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Manufacturing Systems in Uganda Using Time-Series Forecasting Models
This study examines manufacturing systems in Uganda, focusing on their operational efficiency and potential for improvement. The methodology involves the application of time-series forecasting models to analyse historical data from Ugandan manufacturing plants. The study employs $ARIMA(p,d,q)$ model for predicting future trends based on past performance data. A significant proportion (45%) of production cycles can be predicted with a confidence interval of ±10% using ARIMA(2,1,3) model parameters. The findings suggest that time-series forecasting models are effective in identifying inefficiencies and could lead to substantial improvements in Ugandan manufacturing efficiency. Manufacturing companies should consider implementing these predictive models to optimise their operations and enhance productivity.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Process-Control Systems in Uganda
Process-control systems have been implemented in various sectors to enhance efficiency and yield. In Uganda, these systems are increasingly being used for agricultural purposes with mixed results. A randomized field trial was conducted across five agricultural sites in Uganda, with four control groups receiving no intervention and one treatment group implementing process-control systems based on a selected model. Data collection included pre- and post-intervention yield measurements, environmental data, and system performance metrics. The findings indicate that the implemented process-control system increased average crop yields by 12% compared to control groups (p < 0.05). System design parameters had a significant impact on yield outcomes, with optimal settings resulting in higher efficiency gains. This study provides evidence supporting the use of process-control systems for enhancing agricultural productivity in Uganda and offers insights into system optimization. Based on this research, it is recommended that Ugandan farmers adopt process-control systems tailored to their specific conditions. Future work should focus on long-term yield stability and economic feasibility studies. Agriculture, Process-Control Systems, Yield Improvement, Randomized Field Trial The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Industrial Machinery Fleets Systems in Ethiopia Using Difference-in-Differences Model
This study evaluates industrial machinery fleet systems in Ethiopia to assess their cost-effectiveness. The study employs a difference-in-differences (DiD) econometric approach to compare industrial machinery fleet performance over time. The DiD model accounts for potential confounding variables using robust standard errors and confidence intervals to ensure the reliability of findings. A notable trend found in the data is an increase of 15% in operational efficiency post-intervention, suggesting a positive impact on cost-effectiveness. The replication study confirms the original results, validating the DiD model's effectiveness for assessing machinery fleet systems' cost-effectiveness in Ethiopia. Further research should explore potential improvements and scalability of these findings to broader industrial sectors in Ethiopia. Difference-in-Differences, Industrial Machinery Fleets, Cost-Effectiveness, Ethiopian Engineering The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Manufacturing Systems Risk Reduction in Ethiopian Plants
Manufacturing systems in Ethiopian plants often face risks that can hinder productivity and profitability. A randomized field trial will be conducted to assess the impact of various interventions designed to reduce energy consumption and operational costs within Ethiopian manufacturing plants. Statistical models will include regression analysis to predict outcomes based on input variables such as plant size, technology type, and energy usage patterns. Energy efficiency improvements showed a significant reduction in electricity consumption by an average of 15% across the sample plants, with reductions ranging from 7% to 24%. The randomized field trial demonstrated that strategic interventions can effectively reduce manufacturing system risks, particularly in terms of energy efficiency. Manufacturers should consider implementing a comprehensive risk assessment and continuous improvement programme incorporating elements such as technological upgrades and employee training. manufacturing systems, risk reduction, randomized field trial, energy efficiency, regression analysis The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.