School of Mining Engineering (ETDs)

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Start date: 2020Subject: search.filters.subject.SDG-8: Decent work and economic growth

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Now showing 1 - 5 of 5
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    An investigation into equity market timing practices by South African mining companies
    (University of the Witwatersrand, Johannesburg, 2024) Matumba, Lindelani
    This research examines the practice of equity market timing among 30 Johannesburg Stock Exchange (JSE)-listed mining companies from 2006 to 2022. Mining companies, characterised by their capital-intensive nature, rely on management for optimal capital management, which includes both the acquisition of capital through debt or equity and its optimal allocation. The concept of equity market timing, introduced by Wurgler and Baker in the 1990s, suggests that company management may engage in timing the equity market when they perceive their stock to be mispriced. This study incorporated control variables such as market-to-book value (a relative valuation metric that investors use to assess a company's market value in relation to its book value), asset tangibility, degree of leverage, and profitability. Panel regression analysis, utilising both fixed effects and random effects, revealed that market-to-book value was not statistically significant at the 5% level. The overall R-squared value was 58.8%. Given the lack of significance for market-to- book value and asset tangibility, it is recommended to consider other capital structure theories, such as the pecking order or trade-off theory. Additionally, incorporating variables like interest rates and other macroeconomic factors could help address the potential for omitted variable bias.
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    Assessing the policies for legalising artisanal and small-scale mining in south africa
    (University of the Witwatersrand, Johannesburg, 2024) Komape, Ledile Jane; Marshall, T. R.
    This research examines the regulatory framework of artisanal and small-scale mining in South Africa, discussing issues around whether the current policies are up to the challenge of managing the realities and expectations of artisanal and small-scale miners. The research was conducted through a survey of three focus groups across four areas in South Africa using structured questionnaires and interviews. Data collection involved contacting individuals at the Department of Mineral Resources and Energy, Mine Health and Safety Council, and Mining Qualifications Authority, as well as Artisanal and Small-Scale Miners and mine representatives, and conducting interviews at their offices, homes, or workplaces based on their preferences. Data collected from the three focus groups reveal a disconnect between the goals of the policies and how the artisanal and small miners’ communities experience them, emphasising the need for effective policy implementation, comprehensive education initiatives, and avoidance of unrealistic expectations. Key recommendations of the research include the adoption of digital technologies for monitoring, fostering cooperative models, and encouraging international collaboration between local and foreign operators. It underscores the importance of creating and applying inclusive, equitable and sustainable policies to improve the socio-economic and environmental conditions of artisanal and small-scale miners in South Africa.
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    Assessing the Challlenges in the Valuation of Early-Stage Secondary Diamond Deposits
    (University of the Witwatersrand, Johannesburg, 2024) Ganda, Nair da Conceição de Oliveira Gavião; Marshall, Tania R.
    Diamond mining is a fundamentally important part of the economy in many countries. Globally, some of these countries are home to early-stage alluvial diamond projects that attract significant interest from investors. Often, these investors need to understand the project’s value to make informed decisions. However, valuing early-stage alluvial projects is a complex and challenging process. This research report identifies and assesses the challenges associated with the valuation of early-stage alluvial projects through a case study of a project in Angola. For the case study, a valuation exercise was conducted using both the Cost Approach and the Market Approach. The research identified challenges specific to the Cost Approach, such as data availability and compliance with internationally recognised Resources and Reserves reporting codes. Likewise, challenges specific to the Market Approach included estimating current commodity prices and checking the performance of alluvial diamond properties on an applicable stock exchange. Additionally, it became clear that complications related to both approaches, such as experience and resource estimation methodologies, need to be addressed before a final valuation range can be determined. Although there are several difficulties, the valuation of early-stage alluvial projects is still possible. Nonetheless, these challenges impact the accuracy, consistency, and interpretation of the valuation results. Therefore, becoming familiar with these challenges and the recommendations made in the report will help valuators avoid potential pitfalls and contribute significantly to the field by guiding more informed decision-making in the valuation of early-stage alluvial diamond projects.
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    An assessment of the Angolan mineral taxation regime: considerations for possible improvements on government´s revenue
    (University of the Witwatersrand, Johannesburg, 2024) Africano, N´djamila Hilifavale Borges; Mtegha, Hudson
    Angola is host to 36 of the 51 critical minerals in the world and ranks third in mineral exports, totalling over USD 1 billion in 2020, and third in diamond production; Botswana and South Africa hold the top two slots, respectively. These untapped opportunities make the Angolan mining industry an excellent place to invest despite the mining industry contributing less than 1% to GDP and has yet to become a driver of economic diversification. In June 2022, Angola joined the EITI, bringing a welcome improvement in the transparency of the sector´s governance and reform, intended to attract new investors. The study evaluates the effectiveness of the Angolan mineral fiscal system as a tool for maximising revenue for the benefit of its citizens and securing investment (local and foreign) to promote linkages and broad-based national growth and development. Four objectives were examined in this study: (i) Conduct a situational analysis of the current fiscal regime through a comparative analysis of headline rates in regional and international countries; (ii) Qualitative evaluation of the effectiveness and efficiency of the mineral fiscal regime; (iii) Analyse the tax revenues raised by the mining industry between 2011-2021; (iv) Make possible recommendations to improve the current mining tax regime. The study employed a descriptive survey design with a qualitative and quantitative approach for data collection and analysis. The main findings include: (i); Angola's political economy setting resembles that of a hegemonic government characterised by an institutionalised one-party regime whereby the implications on the mineral fiscal regime are multifaceted, affecting investment, regulation, revenue sharing, and sustainability; (ii) Both mineral royalty and corporate income tax rates, are within regional and international norms and have consistently contributed a significant share of the government's direct tax revenues over the last eleven years; (iii) Prevailing fiscal regime can be improved through a combination of tax instruments such as resource rent-tax or profit-based royalty with a basic ad valorem tax system; (iv) However, Angola’s primary challenges point to a possible absence of enforcement and compliance mechanisms for both the mining code iii and the sector fiscal framework, as well as the need to strengthen government agency capacity to oversee and gather fiscal contributions from the sector. In light of these findings, it is recommended to (i) Improve the sector's mining code and fiscal legislative framework and enforce it; (ii) Conduct a study to analyse the effects of all government taxes (direct tax, indirect tax and non-tax instruments and tax incentives) on both the industry and the government´s treasury; and (iii) Conduct further studies on the proposed optimal mineral fiscal regime. Finally, an effective, efficient, and transparent mineral fiscal system can only exist first and foremost through intentional collaboration and alignment of objectives among the sector’s stakeholders.
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    A Data Science Framework for Mineral Resource Exploration and Estimation Using Remote Sensing and Machine Learning
    (University of the Witwatersrand, Johannesburg, 2023-08) Muhammad Ahsan, Mahboob; Celik, Turgay; Genc, Bekir
    Exploring mineral resources and transforming them into ore reserves is imperative for sustainable economic growth, particularly in low income developing economy countries. Limited exploration budgets, inaccessible areas, and long data processing times necessitate the use of advanced multidisciplinary technologies for minerals exploration and resource estimation. The conventional methods used for mineral resources exploration require expertise, understanding and knowledge of the spatial statistics, resource modelling, geology, mining engineering and clean validated data to build accurate estimations. In the past few years, data science has become increasingly important in the field of minerals exploration and estimation. This study is a step forward in this field of data science and its integration with minerals exploration and estimation. The research has been conducted to develop a state-of-the-art data science framework that can effectively use limited field data with remotely sensed satellite data for efficient mineral exploration and estimation, which was validated through case studies. Satellite remote sensing has emerged as a powerful modern technology for mineral resources exploration and estimation. This technology has been used to map and identify minerals, geological features, and lithology. Using digital image processing techniques (band ratios, spectral band combinations, spectral angle mapper and principal component analysis), the hydrothermal alteration of potential mineralization was mapped and analysed. Advanced machine learning and geostatistical models have been used to evaluate and predict the mineralization using field based geochemical samples, drillholes samples, and multispectral satellite remote sensing based hydrothermal alteration information. Several machine learning models were applied including the Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine (SVM), Support Vector Regression (SVR), Generalized Linear Model (GLM), and Decision Tree (DT). The geostatistical models used include the Inverse Distance Weighting (IDW) and Kriging with different semivariogram models. IDW was used to interpolate data points to make a prediction on mineralization, while Kriging used the spatial autocorrelation to make predictions. In order to assess the performance of machine learning and geostatistical models, a variety of predictive accuracy metrics such as confusion matrix, a receiver operating characteristic (ROC) curve, and a success-rate curve were used. In addition, Mean Absolute Error, Mean Square Error, and root mean square prediction error were also used. The results obtained based on the 10 m spatial resolution show that Zn is best predicted with RF with significant R2 values of 0.74 (p < 0.01) and 0.7 (p < 0.01) during training and testing. However, for Pb, the best prediction is made by SVR with significant R2 values of 0.72 (p < 0.01) and 0.64 (p < 0.01) for training and testing, respectively. Overall, the performance of SVR and RF outperforms the other machine learning models with the highest testing R2 values. The experimental results also showed that there is no single method that can be used independently to predict the spatial distribution of geochemical elements in streams. Instead, a combinatory approach of IDW and kriging is advised to generate more accurate predictions. For the case study of copper prediction, the results showed that the RF model exhibited the highest predictive accuracy, consistency and interpretability among the three ML models evaluated in this study. RF model also achieved the highest predictive efficiency in capturing known copper (Cu) deposits within a small prospective area. In comparison to the SVM and CNN models, the RF model outperformed them in terms of predictive accuracy and interpretability. The evaluation results have showed that the data science framework is able to deliver highly accurate results in minerals exploration and estimation. The results of the research were published through several peer reviewed journal and conference articles. The innovative aspect of the research is the use of machine learning models to both satellite remote sensing and field data, which allows for the identification of highly prospective mineral deposits. The framework developed in this study is cost-effective and time-saving and can be applied to inaccessible and/or new areas with limited ground-based knowledge to obtain reliable and up- to-date mineral information.