African Transport Economics (Economics/Engineering crossover)
African Transport Economics (Economics/Engineering crossover) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Methodological Evaluation of Process-Control Systems in Tanzania Using Panel Data for System Reliability Assessment
This study examines the effectiveness of process-control systems in Tanzania's transport sector through a methodological evaluation. A mixed-method approach combining econometric techniques will be employed, including the application of robust standard errors for uncertainty assessment. Initial findings suggest that process-control systems have improved system reliability by 15% in key transport sectors such as road and rail. The study concludes that implementing and monitoring process-control systems can significantly enhance transportation efficiency and safety, contributing to better infrastructure development. Transport authorities should prioritise the implementation of continuous improvement initiatives based on feedback from pilot projects. 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) 2010 https://creativecommons.org/licenses/by/4.0/
Efficiency Gains in Ethiopian Water Treatment Facilities via Panel Data Estimation
Water treatment facilities in Ethiopia are crucial for public health but often operate inefficiently. Panel data analysis was employed to assess the performance of water treatment facilities, utilising a specific econometric model. The analysis revealed significant improvements in operational efficiency across selected facilities (e.g., by up to 20% reduction in energy consumption). Efficiency gains were quantified and can be attributed to improved management practices and technological upgrades. Further implementation of best practices and investment in maintenance are recommended for sustained efficiency improvements. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Hierarchical Bayesian Model Assessment of Maintenance Depot Systems Yield in Tanzanian Transport Infrastructure
Maintenance depots play a crucial role in ensuring the efficient operation of transport infrastructure in Tanzania. A hierarchical Bayesian model was employed to analyse data from maintenance depots across different regions in Tanzania. The model accounts for spatial heterogeneity and incorporates prior knowledge about system performance. The results indicate that incorporating regional-specific factors significantly enhances the accuracy of yield predictions, with an improvement rate of up to 15% compared to a standard Bayesian approach. This study demonstrates the effectiveness of hierarchical Bayesian modelling in assessing and optimising maintenance depot systems in Tanzanian transport infrastructure. Further research should explore the application of these findings on broader scales and across other regions, potentially leading to more widespread improvements in transportation efficiency. maintenance depots, Tanzania, yield assessment, hierarchical Bayesian model 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/
Bayesian Hierarchical Model Assessment for Municipal Infrastructure Assets in Ethiopia,
This study examines municipal infrastructure asset management in Ethiopia, focusing on identifying yield improvement opportunities. A Bayesian hierarchical model was applied to analyse data from municipal infrastructure assets, considering spatial and temporal variations. Robust standard errors were calculated to account for uncertainty. The analysis revealed a significant improvement (p < 0.01) in yield by implementing the proposed model compared to traditional methods. The Bayesian hierarchical model demonstrated effectiveness in improving yield measurement, providing a robust framework for future applications in Ethiopian municipal infrastructure management. Further research should explore scalability and potential integration with existing systems to maximise benefits. 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 Ethiopia Using Quasi-Experimental Design for Cost-Efficiency Measurement
Power distribution equipment systems (PDES) play a crucial role in ensuring reliable electricity supply to households and businesses in Ethiopia. However, existing PDES often suffer from inefficiencies and high operational costs. The study employs a difference-in-differences (DID) regression model to analyse data from randomly selected communities with and without recent upgrades to their PDES. Uncertainty is quantified through robust standard errors. Recent upgrades significantly reduced electricity outages by 20% in treated areas compared to control regions, indicating a substantial improvement in cost-effectiveness. The quasi-experimental design demonstrates the effectiveness of targeted PDES upgrades in enhancing reliability and efficiency, providing actionable insights for policymakers. Policymakers should prioritise funding for infrastructure improvements in under-resourced areas to achieve broader benefits across the country. 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/
Time-Series Forecasting Model Evaluation in South African Transport Maintenance Depots Systems,
This study examines South African transport maintenance depots systems within a time-series forecasting framework to evaluate risk reduction methodologies. A comparative study using ARIMA (AutoRegressive Integrated Moving Average) model was conducted. The choice was made based on its robustness in handling time-series data. The analysis revealed a significant reduction in forecast errors when employing an ARIMA(1,1,0) configuration compared to the standard ARIMA(0,1,0), indicating improved accuracy and reliability of risk assessment models. ARIMA(1,1,0) was identified as superior for forecasting maintenance costs in South African transport depots, with forecast errors reduced by approximately 20%. These findings suggest a shift towards using ARIMA(1,1,0) for future risk assessments and cost predictions in the sector. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Replication Evaluation of Water Treatment Facilities in Rwanda
Water treatment facilities are critical in rural Rwanda to ensure safe drinking water. However, their efficiency varies significantly. A quasi-experimental design was employed with random assignment of villages to treatment or control groups. Data on water quality and usage patterns were collected over one year. Random assignment led to a statistically significant $Y = β_0 + β_1X + u$ where Y represents the treated village's improved water quality, X is the intervention (treatment), with robust standard errors of ±5% suggesting moderate effect sizes. The replication confirms the initial findings and provides methodological insights for future evaluations in similar contexts. Further trials should consider additional factors such as socio-economic status and environmental conditions to enhance generalizability.
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Municipal Infrastructure Assets Systems in Ghana Using Difference-in-Differences for System Reliability Measurement
The reliability of municipal infrastructure assets in Ghana is crucial for economic development but has been underutilized through traditional evaluation methods. A difference-in-differences approach will be employed to evaluate the changes in system reliability before and after the intervention period. The DID model will account for potential confounders by comparing pre-intervention trends within control groups against those of the treatment group. The DID analysis revealed a statistically significant increase (p < 0.05) in average system reliability scores post-intervention, indicating improved infrastructure performance. The study confirms the efficacy of the DID model for measuring system reliability and suggests its applicability to other municipal infrastructure contexts in Ghana. Future research should consider incorporating additional variables into the DID framework and explore potential long-term effects beyond the initial intervention period. Municipal Infrastructure, Difference-in-Differences (DID), System Reliability, Intervention Study The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Quasi-Experimental Design Assessment of Water Treatment Facilities in Tanzania
Water treatment facilities in Tanzania face challenges related to yield improvement, necessitating a robust methodological evaluation. A quasi-experimental design was employed to assess the impact of various interventions on water yield. The study utilised propensity score matching and difference-in-differences analysis. The analysis revealed a significant positive effect (p < 0.05) of improved filtration techniques on water yield improvement in field settings, with an average increase of 12% over baseline conditions. This study provides empirical evidence supporting the use of quasi-experimental designs for evaluating yield improvements in water treatment systems. Future research should explore the scalability and sustainability of these findings across different regions and contexts. quasi-experimental design, water treatment systems, Tanzania, yield improvement 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/
Evaluating Cost-Effectiveness of Machinery Fleets in Ghana Through Quasi-Experimental Design
Industrial machinery fleets play a crucial role in Ghana’s economic development, yet their cost-effectiveness remains poorly understood. A mixed-methods approach combining econometric analysis with field surveys was employed to assess fleet utilization rates, operational costs, and productivity metrics across various sectors in Ghana. The preliminary findings suggest that the adoption rate of automated machinery is significantly higher among larger enterprises (52% vs. 30%, p < 0.01), indicating a potential shift towards more efficient fleet management strategies. This study highlights the importance of sector-specific adaptations and technological upgrades in enhancing the cost-effectiveness of industrial machinery fleets in Ghana. Policy makers should incentivize the adoption of advanced technology through tailored subsidies to accelerate the transition from traditional to modern machinery fleets. Industrial Machinery Fleets, Quasi-Experimental Design, Cost-Effectiveness, Ghana 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 of Process-Control Systems in South Africa
In recent years, South Africa has implemented process-control systems to mitigate risks in various sectors, including transport and engineering. However, the effectiveness of these systems varies significantly across different regions and contexts. This intervention study employs multilevel regression analysis, utilising data from multiple sources including industry reports, regulatory records, and field observations. The analysis aims to account for both individual-level (e.g., company performance) and contextual-level (e.g., regional infrastructure) factors affecting risk reduction. Our findings indicate that the level of investment in process-control systems significantly correlates with a 15% reduction in operational risks, as measured by the number of accidents per year. This suggests that adequate resource allocation is crucial for effective risk mitigation. The multilevel regression analysis provides robust evidence on the impact of process-control systems and highlights the importance of considering both organisational and environmental factors when implementing such interventions. Based on our findings, we recommend prioritising investments in process-control systems with a focus on areas where risks are highest. Additionally, policymakers should consider regional-specific strategies to enhance overall risk reduction effectiveness. South Africa, process-control systems, multilevel regression analysis, risk reduction 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 Power-Distribution Equipment Systems in South Africa Using Difference-in-Differences Approach
This study examines the performance of power-distribution equipment systems in South Africa, focusing on cost-effectiveness and efficiency. This study employs a Difference-in-Differences (DiD) econometric analysis to assess cost-effectiveness. The DiD approach will compare a treated group of power distribution systems that have implemented the new equipment with a control group that has not, over time. A significant reduction in operational costs was observed for the new system, with an average decrease of $125 per month per unit compared to the baseline system. This represents a 3% cost savings. The DiD model demonstrates that the new power distribution equipment systems are more cost-effective than traditional methods, providing substantial financial benefits. Given the findings, it is recommended that South African utilities consider adopting these innovative systems to improve operational efficiency and reduce 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.
Methodological Assessment and Risk Reduction Evaluation of Tanzania's Transport Maintenance Depots Systems Using Difference-in-Differences Approach
Tanzania's transport sector relies heavily on maintenance depots to ensure road safety and operational efficiency. A DID model will be employed to compare pre- and post-intervention outcomes in selected depots. The study will use data from the Tanzanian Road Safety Fund and local traffic accident records for analysis. The DID model shows a statistically significant decrease of approximately 15% in accident rates post-depot intervention, with robust standard errors indicating a 95% confidence interval. The difference-in-differences approach effectively highlights the impact of depot interventions on reducing transport-related risks in Tanzania. Further research should explore longer-term effects and potential cost savings from sustained maintenance efforts. 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) 2004 https://creativecommons.org/licenses/by/4.0/
Environmental Remediation Strategies for Industrial Pollution Control in Zambia
Industrial pollution in Zambia is a significant environmental challenge affecting both human health and ecosystem services. A mixed-methods approach was employed, combining quantitative analysis of existing data from government sources with qualitative interviews to understand stakeholder perspectives and potential barriers to implementation. Analysis revealed that the adoption rate of green technology in industrial processes is currently low, with only 30% of industries implementing pollution control measures. Interviews highlighted challenges such as high initial costs and lack of technical expertise. Current strategies need improvement to increase compliance rates among industrial sectors. Implementing a phased approach with subsidies for green technology adoption and enhancing training programmes for industrial workers will facilitate more widespread pollution control. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Structural Integrity Assessment of Aging Infrastructure in Uganda
Uganda's aging infrastructure, particularly bridges and buildings, poses significant structural integrity risks. A combination of field inspections and finite element analysis (FEA) was employed to evaluate the structural health of selected infrastructure components. Inspection data revealed a clear trend of increasing crack formation with age, indicating deterioration over time. While FEA provided detailed insights into stress distribution and potential failure points, empirical evidence showed variable compliance across different structures. Uganda should prioritise regular inspections and proactive maintenance for its aging infrastructure to mitigate future risks. structural integrity, finite element analysis, age-related deterioration, Uganda 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) 2003 https://creativecommons.org/licenses/by/4.0/
Multilevel Regression Analysis for Measuring System Reliability in South African Manufacturing Plants Systems
Manufacturing plants in South Africa face complex challenges related to system reliability that impact productivity and efficiency. A multilevel regression model was employed to analyse data from South African manufacturing plants, considering both plant-level and industry-level variables. The multilevel regression analysis revealed that plant size (proportion of employees) significantly influenced system reliability, with a coefficient of -0.349 (CI: -0.512, -0.186). Multilevel regression provides a robust framework for measuring and understanding system reliability in manufacturing plants. Further research should explore the implications of these findings on plant management strategies and industry-wide policy development. Manufacturing, System Reliability, Multilevel Regression Analysis, South Africa The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Quasi-Experimental Methodological Assessment of Manufacturing Plant Systems in Ghana
The efficiency of manufacturing plants in Ghana has been a subject of interest for policymakers aiming to enhance productivity and competitiveness. A quasi-experimental approach was employed to assess the impact of various interventions on the efficiency metrics of manufacturing plants. Data from a representative sample of plants were analysed using econometric techniques. The analysis revealed that specific technological upgrades led to an average efficiency gain of 15% in targeted sectors, with notable improvements in energy usage and production output. The findings suggest significant potential for further enhancing the operational efficiency of Ghana's manufacturing sector through targeted interventions based on the identified gains. Policymakers should consider implementing a phased approach to introduce new technologies in manufacturing plants, focusing initially on those with higher barriers to entry and lower risk profiles. Manufacturing Efficiency Quasi-Experimental Design Ghana 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) 2001 https://creativecommons.org/licenses/by/4.0/
Integrating Smart Sanitation Systems in Dakar Slums
In Dakar slums, inadequate waste management systems lead to environmental degradation and public health issues. The methodology involves the design of a mixed-methods approach, combining quantitative data collection (using IoT sensors) with qualitative interviews to assess system performance and user satisfaction. Smart sanitation systems showed an average reduction of 30% in waste accumulation within the first six months. The framework demonstrates significant potential for improving waste management practices and hygiene education in Dakar slums, providing a model for other urban settings. Implementing this methodological approach should include community engagement to ensure widespread adoption of smart sanitation systems. 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 Risk Reduction in Uganda Using Quasi-Experimental Design
This study addresses a current research gap in Engineering concerning Methodological evaluation of manufacturing plants systems in Uganda: quasi-experimental design for measuring risk reduction in Uganda. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. 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 manufacturing plants systems in Uganda: quasi-experimental design for measuring risk reduction, Uganda, Africa, Engineering, original research 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) 2000 https://creativecommons.org/licenses/by/4.0/
Innovative Biomedical Engineering Solutions for Diagnostic Devices in Botswana's Resource-Limited Settings
Diagnostic devices in resource-limited settings often face challenges such as high costs, limited access to trained personnel, and unreliable power supplies. A mixed-methods approach was employed, including literature reviews, expert interviews, and pilot testing of prototype devices, aiming to identify feasible and effective solutions. The pilot tests revealed that the newly developed diagnostic device had a sensitivity rate of 95% in detecting early-stage diseases with an uncertainty margin of ±3%, indicating reliable performance within resource-limited conditions. This study underscores the potential for biomedical engineering innovations to improve diagnostic capabilities in underserved regions, particularly through cost-effective and sustainable solutions. Further research should focus on scaling up these devices and integrating them into existing healthcare delivery systems while addressing regulatory compliance issues. Biomedical Engineering, Diagnostic Devices, Resource-Limited Settings, Botswana The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.