The environmental repercussions of extensive carbon dioxide (CO2) emissions on the environment are crucial for policymakers and scholars. The repercussions of and connection between economic growth (ECG), tourism (TOUR), and foreign direct investment (FDI) on CO2 emission mitigation have been measured and argued from empirical and theoretical perspectives by scholars. Notwithstanding, the extant body of knowledge has failed to incorporate and investigate the function of governance in decarbonizing tourism activities and FDI from CO2 emissions to attain a healthy and quality environment in Africa. Hence, this current research investigates governance’s role in the reduction processes of CO2 emissions grounded in environmental Kuznets curve (EKC) conceptual assumptions for panel data spanning 2000 through 2020 for 27 African countries. This research utilized the Westerlund panel cointegration approach for the investigation of the cointegration of the selected variables. This study applied the Driscoll–Kraay regression approach for the long-term estimation. In addition, the dynamic ordinary least squares (DOLS) and the pooled mean group (PMG) were used for robustness checks. The findings of this research indicated that the governance (GOV) indicators employed have a statistically significant effect on the CO2 emission reduction. Besides, this study found that the appreciation of the income of the nations gives credence to the formation of the EKC theory and contributes to the decline in CO2 emissions within the selected African nations. The findings revealed that tourism, FDI, ECG, and GOV are positive and significant factors leading to increased CO2 emissions in Africa. Furthermore, the results showed that effective governance and control of FDI inflows and tourism activities can support decarbonization. These findings suggest the merits of governance in ensuring effective decarbonization policies of the environment, and policy suggestions are accordingly put forward.

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The relationship between battery electric vehicles (BEV) and carbon dioxide emission (CO 2 ) has significant environmental outcomes. Notwithstanding, battery electric vehicles have not been extensively explored through econometric approach. For countries to meet their net zero targets, it is crucial to consider the role of battery electric vehicles, renewable energy consumption, and CO 2 . As a result, it is critical to scrutinize a variety of variables that contribute to a sustainable future. This study therefore examines the dynamic correlation between BEV, gross domestic product (GDP), urbanization (URB), renewable energy consumption (REC), population (POP), and CO 2 in five leading countries (the United States of America (USA), China, France, Germany, and Norway) using panel data from 2010 to 2020. The study adopted the Westerlund cointegration method to ascertain the long-term nexus among the series. The cross-sectionally augmented autoregressive distributed lag CS-ARDL technique is adopted to evaluate the variables long-run elasticity. The study applied the common correlated effect mean group (CCEMG) and augmented mean group (AMG) approach to ascertain the robustness of the long-run relationships among the variables. Dumitrescu and Hurlin’s panel causality analysis determines the extent of the significant causality linkage. The results demonstrate that increased economic growth, urbanization, and population growth accelerate carbon emissions and environmental depletion. However, BEVs were found to be more energy efficient and the adoption of renewable energy through the manufacturing and battery production process would reduce CO 2 emission especially in China and the USA. Finally, the research proposed several policy implications for policy and decision-makers in the five leading countries for combating climate change and increasing productivity in the electric vehicle market and renewable energy consumption.

The intensity and severe impact of carbon emissions on the environment has been witnessed globally. This study aims to unravel how environmental factors, personal factors, and entrepreneur outcome expectations affect entrepreneur low-carbon emission behavior (LCB) through the lens of social cognitive theory (SCT). To achieve the aims of this study, we gathered data from 1015 entrepreneurs and small, medium-sized enterprises (SMEs) in the Jiangsu province of China to analyze the predictors of entrepreneur low-carbon behavior. The proposed relationships were tested using the partial least square structural equation modelling (PLS-SEM) technique. The findings from the study indicate that environmental factors (i.e., climate change (CC), public media (PM), and corporate social responsibility (CSR) have a significant and positive relationship with entrepreneur LCB. Moreover, entrepreneur green production self-efficacy (GPS) is positively associated with entrepreneur LCB. The findings further revealed that entrepreneur personal concepts such as entrepreneur self-monitoring (SM), entrepreneur self-esteem (SE), and entrepreneur self-Preference (SP) significantly and completely affect entrepreneur GPS. Likewise, entrepreneur moral reflectiveness (EMR) substantially moderates the relationship between entrepreneur green production outcome expectation and LCB. This study concludes with recommendations for researchers interested in enhancing knowledge in this field. In summation, this study shows that a behavioral science viewpoint is critical for improving knowledge of low-carbon environment, CC drivers, mitigation strategies, and sustainable transition to our complex environment.

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Currently, the global report of COVID-19 cases is around 110 million, and more than 2.43 million related death cases as of February 18, 2021. Viruses continuously change through mutation; hence, different virus of SARS-CoV-2 has been reported globally. The United Kingdom (UK), South Africa, Brazil, and Nigeria are the countries from which these emerged variants have been notified and now spreading globally. Therefore, these countries have been selected as a research sample for the present study. The datasets analyzed in this study spanned from March 1, 2020, to January 31, 2021, and were obtained from the World Health Organization website. The study used the Autoregressive Integrated Moving Average (ARIMA) model to forecast coronavirus incidence in the UK, South Africa, Brazil, and Nigeria. ARIMA models with minimum Akaike Information Criterion Correction (AICc) and statistically significant parameters were chosen as the best models in this research. Accordingly, for the new confirmed cases, ARIMA (3,1,14), ARIMA (0,1,11), ARIMA (1,0,10), and ARIMA (1,1,14) models were chosen for the UK, South Africa, Brazil, and Nigeria, respectively. Also, the model specification for the confirmed death cases was ARIMA (3,0,4), ARIMA (0,1,4), ARIMA (1,0,7), and ARIMA (Brown); models were selected for the UK, South Africa, Brazil, and Nigeria, respectively. The results of the ARIMA model forecasting showed that if the required measures are not taken by the respective governments and health practitioners in the days to come, the magnitude of the coronavirus pandemic is expected to increase in the study’s selected countries.