https://ir.unisa.ac.za/handle/10500/30863 2026-05-05T15:11:42Z https://ir.unisa.ac.za/handle/10500/32292 Analysis of the monthly loadshedding and unplanned power outages in South Africa: mean and quantile regression count time series models Tshuma, Sikhulile Various nations within sub-Saharan Africa are currently facing different stages of loadshedding, with South Africa being of no exception to this trend. Loadshedding has been implemented as a strategy to manage electricity consumption during peak demand periods while allowing for increased usage during off-peak times. This study examines the monthly trends of loadshedding and unplanned power outages in South Africa, utilizing mean and quantile regression count time series models. Unplanned outages can arise from multiple factors, including maintenance activities on power lines, equipment malfunctions, adverse weather events, cable theft or emergencies such as accidents. Recurrent outages impede business activities, leading to a decrease in productivity and an increase in operational expenditures. Given the profound impact of power interruptions on economic stability and social welfare, this research aims to quantify and analyze the temporal trends and seasonal patterns of outages. By leveraging a comprehensive dataset, we first apply Poisson and negative binomial regression models to assess the average frequency and duration of outages, revealing significant trends and seasonal fluctuations. Following this, we employ quantile regression techniques to explore the distributional impacts of various factors, including socioeconomic variables and weather conditions, on the occurrence of outages. The analysis considers five quantiles—10th, 25th, 50th, 75th, and 90th. While negative binomial regression adequately captures average loadshedding dynamics, quantile regression proves superior in modelling extreme outage conditions that are most relevant for electricity system risk management and policy planning. The data was diagnosed to be highly correlated. Therefore penalised models were also employed. Our findings indicate that an increase in contracted demand, along with both planned and unplanned outages, correlates with a rise in the frequency of loadshedding. This suggests that loadshedding is influenced not only by heightened demand but also by failures in generation and distribution infrastructure. The thorough methodology adopted in this research deepens our understanding of the challenges surrounding power supply in South Africa, offering critical insights for policymakers and stakeholders to formulate targeted strategies aimed at mitigating the effects of loadshedding and enhancing energy resilience. Tackling these challenges necessitates substantial investment in infrastructure, a diversification of energy sources, and enhanced management of the electricity supply chain. 2025-12-31T00:00:00Z https://ir.unisa.ac.za/handle/10500/32250 Industrialization of plug-in electric vehicles (PEV) in South Africa Magazi, Lazola Sipelele The study intended to investigate the Industrialization of Plug-In Electric Vehicles (PEV) in South Africa. The study aimed at determining whether PEVs in South Africa (SA) could be industrialized using local resources. The study also intended to identify the possibility of industrialization, the socio-economic factors and localization. South Africa previously had a Joule Electric Vehicle that had failed to industrialize to due lack of government support, the study had learned lessons from the Joule Electric Vehicle. SA automotive industry has an important role in SA’s economy, it contributes about 4,9% to the GDP, it produces close to 1% of the global car manufacturing and exports 60% of its manufactured cars to the European market. SA is currently facing a problem of losing its major markets that is Europe Union (EU) and United Kingdom (UK). The EU and UK took a political decision that by 2030 they will only accept cars that will reduce green-house gas emissions by 55% and by the year 2035 they will only accept 100% electric vehicles produced in SA. In order for SA to retain its major market it has to shift from the production of Internal Combustion Engines to Electric Vehicles. The study aimed at determining the capacity of PEVs to drive Industrialization, to identify the socio-economic factors and to establish the impact of localization. The study was conducted through qualitative research method where the data was collected using face-face interviews and had a population size of 41 participants and a sample size of 21 participants. The study interviewed participants from Department of Transport, Automobile Industry and Department of Trade, Industry and Competition. In addition, the interviewees were Chief Executive Officers, Executive Managers, Senior Managers and Middle Managers. The study recommended future research areas that included implementation of policy, localization of PEV components and emissions caused by the manufacturing of PEVs. 2024-01-08T00:00:00Z https://ir.unisa.ac.za/handle/10500/32110 A system dynamics model to enhance on-time delivery of infrastructure projects within a power utility in South Africa Mashamba, Takalani Energy plays a fundamental role in sustainable development and poverty alleviation efforts. The energy sector is critical, looking at the economic growth perspective as it contributes about 3.2% of South Africa’s Gross Domestic Product. Due to economic growth in South Africa, the state-owned firm has witnessed increasing demand for power supply over the last 20 years. To alleviate the existing supply restriction, the electricity sector started implementing capital expansion build projects. The completion of capital expansion build projects has been delayed due to quality problems and cost overruns, which results in frequent power outages that limit overall economic activity and erode investor trust in the area. The primary motivation of this research was to develop a System Dynamics Model to enhance on-time delivery of infrastructure projects within the power utility in South Africa. The proposed study is aimed at developing a System Dynamics Model to inform policy and decision-makers in the energy sector in 2024-01-01T00:00:00Z https://ir.unisa.ac.za/handle/10500/32017 Assessment of the impact of renewable energy supply, carbon dioxide emissions, trade, and economic growth nexus on maize production from 1979-2021 Nevhutalu, Vhugala Charity The economy most susceptible to climate change is the agriculture sector. Agricultural production is negatively affected by weather patterns and temperature which ultimately impacts the sector’s economy. Food insecurity and a disturbance in the food supply chain are the aftereffects of climate change. A study by Wu et al. (2021). It is foretold that renewable energy utilization will reduce emissions responsible for climate change. The United Nations (UN) has also laid out a global mandate of “a clean and inexpensive energy for all” as part of the 17 Sustainable Development Goals (SDGs). This study specifically focuses on SDG 7 (affordable clean energy) and 13 (climate change). Many industrialized and emerging nations use maize as an energy crop; South Africa has rarely made use of this potential owing to valid food security concerns. Maize production trends in this study showed growth throughout the years despite a few declines which were mostly as a result of climate change. Trade trends also pointed out that there is minimal maize regional trade between South Africa and the rest of the African countries. At the aggregate level, maize production for human, and animal consumption and for biofuel feedstock depends on several macroeconomic factors, some of which were explored in this study. This study was backed by several macroeconomic theories namely: the Environmental Kuznets Curve (EKC), the Mercantilist Theory of Trade, the Export-Led Growth Theory, and the Endogenous Growth Theory. The main objective of this study was to assess the impact of Carbon Dioxide Emissions (CO2), Renewable Energy Supply, Trade, and Economic Growth on maize production in South Africa from 1979 to 2021. The nexus offered vital insights on initiatives that could be prioritised to advance renewable energy in the South African agriculture industry. An Auto Regressive-Distributed Lag (ARDL) model using Bounds test econometric approach was employed to estimate the short and long-run nexus between renewable energy supply, carbon dioxide emissions, trade, economic growth, and the production of maize. The existence of unit root in the time-series data was examined using the Augment Dickey-Fuller and Phillips-Perron tests; the robustness of the long-run estimate was assessed using the Fully Modified Least Squares (FMOLS) and Canonical Cointegration Regression (CCR) models. The Pair-wise Granger Causality test was used to test for causality between carbon dioxide emissions, renewable energy supply, trade, economic growth, and maize production. The short-run results indicated that Carbon Dioxide Emissions reduce maize production and renewable energy supply increases maize production both in the short-run and long-run. Granger causality results indicated a unidirectional causality between carbon dioxide emissions, economic growth, and maize production. A bidirectional causality was observed between renewable energy supply and maize production. This study contributes to economic policy regarding the energy-climate nexus in South Africa's agricultural industry. The agricultural industry is not only an energy consumer but also has the potential to contribute to renewable energy, specifically bioenergy through the supply of biomass. Considering that maize is a major global energy crop, its demand globally trickles down to maize-producing countries, and this has implications for supply and demand locally and globally. The study’s emerging insights may be used to guide the use of renewable energy biomass supply and the impact of climate change on the agricultural economy (maize production). Text in English 2024-01-01T00:00:00Z