The economic impact of carbon emissions in Africa is gaining traction in the extant literature. This study adopted Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to concomitantly track data on carbon emissions versus economic growth in Africa from 2018 to 2022 providing evidence from a meta-analysis. Through database searches, 591 publications were identified. A machine learning algorithm called Latent Dirichlet Allocation (LDA) was used as a visualization technique for reporting trends in the eleven papers selected for the analysis. Identifying, evaluating, and summarizing the findings of all relevant individual studies conducted in Africa on the impact of economic growth on carbon emissions contributes to the existing body of knowledge. This study fills a critical gap by surveying the studies conducted in Africa in the last five years, implying that economic growth negatively and significantly triggers CO2 emissions in Africa. The debate on the economic impact of CO2 emissions in Africa, the most vulnerable continent to climate change, is elucidated. The findings tracked sources of data for carbon emissions in Africa. The results showed that although some studies reported a positive correlation (and some a negative correlation) between economic growth and carbon emissions, most studies concur that the economic impact of carbon emissions over a timeline can be explained by the Environmental Kuznets Curve (EKC) hypothesis. Therefore, there is a dire need for African countries to strengthen economic growth without deteriorating their environment or having ecological footprint. Future research must assess whether this trend on the economic impact of carbon emissions in Africa continues. AcknowledgmentThe authors express their appreciation to the Durban University of Technology for providing the resources to conduct this study.

The last three decades have seen significant educational reform, which have influenced the transformation of assessment practices in higher education institutions (HEIs). In addition, various societal and global changes have required that higher education create adaptive graduates who acquire and develop higherorder skills which include problem-solving skills, critical thinking, creativity and autonomy. Thus, HEIs are expected to produce adaptive graduates to meet the demands of the current national and international society and workforce. Literature suggests that student-centred assessments (SCA) can promote such higherorder skills, which have also become known as 21st century skills. Since, assessment is an integral part to learning and instruction, the design of assessments have become an important aspect in HEIs. This entails HEIs adapting to the paradigm shift from traditional assessment methods to student-centred assessment methods. This study adopted a case study approach to explore staff and student’s experiences of student-centred assessments. The respondents for this study were the staff and students from a Faculty of Accounting and Informatics at a HEI in South Africa. The findings reveal that while staff and students support various SCA and activities , the implementation of these assessments are hindered by challenges faced by staff . The contribution of this paper is to emphasize how student-centred assessment (SCA) practices foster the development of adaptive graduates while addressing the challenges to their effective implementation. Furthermore, this paper is significant to policymakers, educators, and institutional leaders seeking to enhance assessment practices and prepare adaptive, future-ready graduates.

This qualitative article explores digital pedagogies methods for learning information retrieval (IR) content modules in South African Library and Information Science (LIS) higher education institutions (HEIs). The study seeks to establish the content of IR modules, and the digital pedagogies methods used for learning IR content modules in South African LIS HEIs offering LIS education. The population of this study includes the LIS students learning IR content modules. The researcher used document reviews for the IR content module descriptors and assessment plans, and focus-group discussions with the LIS students as data-collection instruments. The collected data were analysed using document analysis and thematic analyses. This study revealed that the content of IR modules comprises information literacy; information sources and resources; authority control; cataloguing; metadata; classification; subject headings; abstracting and indexing; information searching/seeking and retrieval; bibliographic control; information organisation; alerting services; designing personal websites, blogs, wikis and personal databases; scholarly communication; digital scholarship; digital curation; applications of digital technologies; principles, standards, frameworks and models for resource description and discoverability; vocabulary control; and thesaurus. This study further established that the digital pedagogies used for learning IR content modules involves the learning management system known as Moodle, and the online platforms such as Microsoft Teams and Telegram. This article recommends that the South African LIS HEIs teach and learn a standardised and digitally transformed content of the IR modules and adapt with the new teaching and learning methods of digital pedagogies.

Environmental and financial datasets often display complex distributional characteristics, including heavy tails, high skewness and the presence of extreme observations. Traditional probability models such as the exponential, gamma or log-normal distributions may not adequately capture these behaviours particularly when modelling extreme events such as rainfall, pollution levels, stock returns or loss severities. By integrating the characteristics of Pareto and exponential distributions into an exponentiated framework that can describe datasets arising from environmental and finance fields, this study presents a novel three-parameter exponentiated Pareto exponential distributions using the exponentiated Pareto family of distributions with classical exponential distribution as the baseline model. This novel model extends the classical exponential distribution with the addition of extra shape parameters which simultaneously regulate the centre and tail behaviours of the new model. The statistical and mathematical characteristics of the proposed distribution are determined and studied. The maximum likelihood estimate approach is used in a conducted simulation exercise, and the estimator’s efficiency is evaluated as seen from the results. The practical applicability of the model is illustrated with four real-life datasets utilising model adequacy and goodness-of-fit measurements such as log–likelihood, Akaike information criteria and Bayesian information criteria. The data reveal that the proposed model gives a better fit than the models chosen as comparators, making the EPE distribution useful and robust in environmental and financial fields of study.

In this work, a type II Topp-Leone-G family of distributions is parametrically transformed to create a new flexible family of continuous probability distributions called exponentiated type II Topp-Leone-G distribution through exponentiation. A variety of density shapes and hazard rate behaviors, such as increasing, decreasing, bathtub, inverted bathtub-shaped, and unimodal forms, can be captured by the proposed family, which generalizes several current lifetime models. In addition to discussing significant special cases that correspond to well-known distributions, explicit expressions for the cumulative distribution function, probability density function, survival function, hazard rate function, quantile function, actuarial measures, and linear representation of the probability density function are derived. The maximum likelihood approach is used for parameter estimation, and the simulation study provides a brief discussion of the estimator’s asymptotic characteristics. Kolmogorov–Smirnov and Cramer–Von Mises goodness-of-fit metrics and their p-values, along with information criteria like Akaike Information Criterion, Bayesian Information Criterion, Consistent Akaike Information Criterion, and Hannan–Quinn Information Criterion, are used to evaluate the appropriateness of the model. Furthermore, to visually assess model performance, graphical diagnostic techniques, such as density and distribution function overlays, quantile–quantile plots, and probability–probability plots, are used. Real-life datasets are analyzed to show the applicability of the exponentiated type II Topp-Leone-G family using Weibull distribution as the baseline, and its performance is compared with some other competing distributions. The findings demonstrate the potential utility of the proposed model in the areas of insurance and related applied domains by showing that it fits better than the competing models considered.