Energy consumption, and its impact on the environment, has become an increased focal point in Ghana, a sub-Saharan African country, following population growth and rapid urbanization. Ghana has high potential for renewable energy generation; nevertheless, low acceptance and usage have been found. An extensive study is required to understand the causes driving poor acceptability and the intentions to use renewable energy. This study aims to empirically investigate the acceptance of renewable energy using an extended technology acceptance model (TAM). A cross-sectional survey was conducted, from 1 February 2021 to 30 June 2021, using a self-administered questionnaire. The survey was carried out on Ghanaian adults of the age 18 years and above. Data was collected from 1068 respondents from Ghana’s northern and southern parts and analyzed using partial least squares structural equation modeling (PLS-SEM) with SmartPLS software. The results of the data analysis reveal that environmental awareness is the biggest predictor of the intention to use renewable energy in Ghana. Perceived affordability was also found to be the most significant predictor of attitudes towards the use of renewable energy in Ghana. The findings of this research will give policymakers, manufacturers, and the providers of renewable energy appliances a better understanding of the factors that determine the intention to use renewable energy.

This study investigates the symmetric and asymmetric linkages within environmental sustainability proxied by ecological footprint (EFP), natural resources (NRR), renewable energy consumption (REC), urbanization (URB), human capital (HC), and government effectiveness (GE) in 27 African countries divided into two subgroups (ecological deficit countries and ecological reserve countries) over the period 1990 to 2018. The study employs the auto-regressive distributed lag (ARDL) model to investigate the symmetric (linear) effect and the nonlinear auto-regressive distributed lag (NARDL) model to study the asymmetric (nonlinear) effects of the variables on EFP. Results of ARDL show that a 1% increase in REC is projected to reduce ecological footprint by 0.17 and 0.2% in ecological deficit and ecological reserve countries. A 1% increase in NRR is estimated to increase ecological footprint by 0.02% in ecological deficit countries but has no impact on the environment in countries with ecological reserves. Similarly, a 1% rise in GE is estimated to increase EFP by 0.04% in Africa but has no impact on the environment in ecological deficit countries. NARDL estimations decomposed REC into positive (negative) shocks, which show that a 1% increase (decrease) in REC is projected to decrease EFP by 0.16% (0.13%) in countries with ecological reserves. Similarly, a positive (negative) shock in NRR is expected to decrease EFP in ecological reserve countries and increase EFP in ecological deficit countries. Results of the Wald tests prove the existence of long-run asymmetry among the variables. The findings indicate that renewable energy consumption enhances environmental quality, while economic growth and natural resource rents reduce environmental quality in Africa over the sampled period.

Process mining has piqued the interest of researchers and technology manufacturers. Process mining aims to extract information from event activities and their interdependencies from events recorded by some enterprise systems. An enterprise system’s transactions are labeled based on their information content, such as an activity that causes the occurrence of another, the timestamp between events, and the resource from which the transaction originated. This paper describes a novel process of parsing event-log activities based on information content (IC). The information content of attributes, especially activity names, which are used to describe the flow processes of enterprise systems, is grouped hierarchically as hypernyms and hyponyms in a subsume tree. The least common subsume (LCS) values of these activity names are calculated, and the corresponding relatedness values between them are obtained. These values are used to create a fuzzy causal matrix (FCM) for parsing the activities, from which a process mining algorithm is designed to mine the structural and semantic relationships among activities using an enhanced gray wolf optimizer and backpropagation algorithm. The proposed approach is resistant to noisy and incomplete event logs and can be used for process mining to reflect the structure and behavior of event logs.