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.

The aim of this paper is to identify the causes of construction injuries (CIs), to summarize them into categories, to provide input in a concise form for reference and recommend possible solutions. A total of 967 peer-reviewed papers were retrieved and after identification, 42 papers were finally identified and analyzed in detail. The causes of CIs were categorized into four major factors; management commitment factors, behavioral factors, psychological factors, and demographic factors, then each reviewed separately. The main finding of the study is that management commitment and behavioral factors are the main sources of CIs. The finding regarding experience and age is the contribution of this paper. Thus, further joint research needs to be conducted using mixed methods to provide a better and in-depth understanding of the association between the empirical findings and accident causation theories to design effective safety policies. Further, the public body responsible for safety implementation should design mass media awareness advertisement to increase the general public’s awareness.

With the continued increase in international students in China, the problem of their academic adaptation has become increasingly prominent. Analysis of the factors affecting the academic adaptation of international students and corresponding management practices can suggest measures to improve their academic adaptability. Based on grounded theory, this paper first summarizes the four main factors affecting the academic adaptation of international students, then uses structural equation modeling to construct a model of academic adaptation of international students that is tested and verified by a questionnaire survey of 2540 international students in China (51% male, 49% female). The main conclusions of this paper are as follows: (1) learning communication, course learning, and self-regulation are the main factors affecting the academic adaptation of international students, of which course learning is the most important factor; (2) academic communication and course learning have significant positive effects on self-regulation, while academic communication, course learning, and self-regulation have significant positive effects on the academic adaptation of international students; and (3) there was no significant difference in academic adaptation between genders, though there were significant differences by age. Among them, the mean score for overseas students is the largest for those older than 41 years (M = 4.79; SD = 0.33), showing that these students are most adaptable to study. Accordingly, this study advances policy suggestions for strengthening international students’ academic adaptation on the part of both universities and the government to improve the academic adaptation ability of international students in China.

Coal and gas outbursts seriously threaten the mining safety of deep coal mines. The evaluation of the risk grade of these events can effectively prevent the occurrence of safety accidents in deep coal mines. Characterized as a high-dimensional, nonlinear, and small-sample problem, a risk evaluation method for deep coal and gas outbursts based on an improved quantum particle swarm optimization support vector machine (IQPSO-SVM) was constructed by leveraging the unique advantages of a support vector machine (SVM) in solving small-sample, high-dimension, and nonlinear problems. Improved quantum particle swarm optimization (IQPSO) is used to optimize the penalty and kernel function parameters of SVM, which can solve the optimal local risk and premature convergence problems of particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO) in the training process. The proposed algorithm can also balance the relationship between the global search and local search in the algorithm design to improve the parallelism, stability, robustness, global optimum, and model generalization ability of data fitting. The experimental results prove that, compared with the test results of the standard SVM, particle swarm optimization support vector machine (PSO-SVM), and quantum particle swarm optimization support vector machine (QPSO-SVM) models, IQPSO-SVM significantly improves the risk assessment accuracy of coal and gas outbursts in deep coal mines. Therefore, this study provides a new idea for the prevention of deep coal and gas outburst accidents based on risk prediction and also provides an essential reference for the scientific evaluation of other high-dimensional and nonlinear problems in other fields. This study can also provide a theoretical basis for preventing coal and gas outburst accidents in deep coal mines and help coal mining enterprises improve their safety management ability.