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In recent years, there has been increasing interest in the joint modelling of compound extreme events such as high temperatures and low rainfall. The increase in the frequency of occurrence of these events in many regions has necessitated the development of models for estimating the concurrent probabilities of such compound extreme events. The current study discusses an application of copula models in predicting the concurrent probabilities of compound low rainfall and high-temperature events using data from the Lowveld region of the Limpopo province in South Africa. The second stage discussed two indicators for monitoring compound high temperature and low rainfall events. Empirical results from the study show that elevations ranging from 100–350 m, 350–700 m and 700–1200 m exhibit varying probabilities of experiencing drought, with mild droughts having approximately 64%, 66%, and 65% chances, moderate droughts around 36%, 39%, and 38%, and severe droughts at approximately 16%, 19%, and 18%, respectively. Furthermore, the logistic regression models incorporating the southern oscillation index as a covariate yielded comparable results of copula-based models. The methodology discussed in this paper is robust and can be applied to similar datasets in any regional setting globally. These findings could be useful to disaster management decision makers, helping them formulate effective mitigation strategies and emergency response plans. [ABSTRACT FROM AUTHOR]

Cement is one of the most produced materials globally. Population growth and urbanization cause an increased demand for the cement needed for expanding infrastructures. As a result of this circumstance, the cement industry must find the optimum compromise between increasing cement production and reducing the negative environmental impact of that production. Since cement production uses a lot of energy, resources and raw materials, it is essential to assess its environmental impact and determine methods for the sector to move forward in sustainable ways. This paper uses an integrated life cycle assessment (LCA) and a system dynamics (SDs) model to predict the long-term environmental impact and future dynamics of cement production in South Africa. The first step used the LCA midpoint method to investigate the environmental impact of 1 kg of Portland cement produced in South Africa. In the cement production process, carbon dioxide (CO2), nitrogen oxides (NOx), sulphur dioxide (SO2), methane (CH4) and particulate matter (PM) were the major gases emitted. Therefore, the LCA concentrated on the impact of these pollutants on global warming potential (GWP), ozone formation, human health, fine particulate matter formation and terrestrial acidification. The system dynamics model is used to predict the dynamics of cement production in South Africa. The LCA translates its results into input variables into a system dynamics model to predict the long-term environmental impact of cement production in South Africa. From our projections, the pollutant outputs of cement production in South Africa will each approximately double by the year 2040 with the associated long-term impact of an increase in global warming. These results are an important guide for South Africa's future cement production and environmental impact because it is essential that regulations for cement production are maintained to achieve long-term environmental impact goals. The proposed LCA–SD model methodology used here enables us to predict the future dynamics of cement production and its long-term environmental impact, which is the primary research objective. Using these results, a number of policy changes are suggested for reducing emissions, such as introducing more eco-blended cement productions, carbon budgets and carbon tax. [ABSTRACT FROM AUTHOR]

The paper utilized a panel dataset to investigate the effects of the impact factors, namely, economic growth, energy intensity, energy consumption, urbanisation, passenger vehicles and transport infrastructure investments on carbon emissions (CO2-eq). The transport sector of the main buoyant economic provinces of South Africa for a consecutive period of five years was investigated using STIRPAT models. Carbon emissions coefficient method, (CECM) is made applicable to determine the quantification of the forms of fossil fuels utilized in the transportation sector. The characterized parametric panel dataset for Gauteng, KwaZulu-Natal and Western Cape were assessed from 2016 to 2020 to make objective function determinations. The results indicate that GDP and passenger vehicles contributed largely to CO2-eq emissions, however, the impact varies across the three provinces. Energy intensity and the approach to energy consumption are significant in mitigating carbon emissions, which is due to the possibilities of high fuel efficiency and pollution decrease. The outcome of the research advances climate change mitigation strategy and proffers the required attention to policy makers in South Africa and Africa as a continent. [ABSTRACT FROM AUTHOR]