Previous studies ignored the geospatial dynamics spillover effects of energy consumption on CO₂ emissions while assessing such impacts in developed and developing countries. Moreover, most studies wrongfully assess spillover effects in its aggregated format rather than decomposing by its components. This is important as not all energy sources share the same characteristics. We fill these gaps in the literature by investigating the spillover effects of various forms of energy, including fossil fuels, renewable energy, and nuclear power, on CO₂ emissions in 135 developed and developing countries from 2000 to 2019. We used the Dynamic Spatial Durbin Model (DSDM) to better understand the results. A series of indicative tests confirmed using the DSDM model and including spatial interaction of CO₂ emissions in the analysis. Our findings show evidence of indirect spillover effects of the various energy sources on CO₂ emissions. Further considering the spillover effects of the energy sources of neighbouring countries, the paper finds that the driving increase in CO₂ emissions mainly came from the energy consumption of the country itself and neighbouring countries' energy consumption. Nevertheless, the results indicate that the direct effects of energy consumption often exceed its indirect effects. The results also confirm that total and fossil energy consumption harms the environment, whereas adopting renewable and nuclear energy sources reduces CO₂ emissions. Lastly, we find nuclear energy is the most environmentally sustainable energy source. The study concludes that the Dynamic Spatial Durbin Model is paramount in estimating the environmental impact of energy consumption in our sample. The practical policy implications drawn from this study could be used to promote increased collaboration to hasten the energy transition process and address global warming and climate change.

The Kingdom of Saudi Arabia has developed rigorous strategies to control and prevent the spread of COVID-19. However, the effectiveness of these measures in containing and mitigating the epidemic has yet to be studied. This paper aims to assess the efficiency of preventive policy initiatives that Saudi Arabia has taken to reduce the spread of COVID-19, which was rapid and progressive in nature. Information on the effectiveness of measures applies to help the Saudi government adjust policy responses when considering which measures to relax once the epidemic is controlled. Data for this study were retrieved via publicly available data sources such as the Saudi Arabia Ministry of Health and the government's official news agency-Saudi Press Agency (SPA) websites. Other datasets, such as prevention measures, were gathered from the Country Policy Tracker website. Our dataset's time component extends over 590 consecutive days from 20 January 2020-31 August 2021. Moreover, a mixed-method approach combining COVID-19 data and prevention measures was adopted to assess preventative measures practice. We compiled the dataset used in this study in a Microsoft Excel database. The significance of observed differences among implementing effective strategies was determined using ANOVA and Mixed methods approach. Noticeably, the statistical analysis was performed using the open-source statistical system R version 4.2 (available at http://cran.r-project.org). Our analysis showed that only three out of the 32 (9.4%) measures significantly reduced the spread of COVID-19. Our results also show substantial variations in the spread of COVID-19 associated with preventive measures in Saudi Arabia. There was a significant positive correlation between activating and massive testing in communities and cases of COVID-19 (measure effect = 923.086 and p < 0.05). A similar result was found for complete curfew across the Kingdom and cases of COVID-19 (measure effect = 621.389 and p < 0.10). Removing slum areas interrupted the spread of Covid-19 (measure effect = 305.689 and p < 0.01). The other preventive measures did not significantly affect the COVID-19 pandemic distribution. These findings consistently concluded that activating and massive testing in communities, complete curfew across the Kingdom, and removal of slum areas were the most effective measures for reducing the impact of the COVID-19 pandemic. Only by understanding these correlations will it be possible to control and reduce the rate of COVID-19 spread and, therefore, suggest a possible exit strategy once the epidemic is controlled.

This research examines the impact of the coronavirus index on the returns and volatility of ten major cryptocurrencies during the COVID-19 pandemic. For this purpose, we applied a multivariate volatility GARCH model with an integrated dynamic conditional correlation (DCC) approach to daily cryptocurrency values observed data during the January-December, 2020 period. Moreover, we used the Granger causality test to study return-volume correlations. The findings indicate that cryptocurrency volatility declined after the World Health Organization declared on March 11, 2020, that the coronavirus was a pandemic. Unlike most of the relevant previous studies, we found that the COVID-19 crisis did not have a long-term effect on cryptocurrency returns and volatility but only presented a short-term effect. Our results have implications for investors who need to determine an optimal portfolio for a scenario other than the base.

Saudi Arabia’s Vision 2030 is closely tied to CO₂ emissions and energy consumption issues. This initiative aims to modernize the country’s economy, diversify its energy sources, and enhance sustainability. This paper examines the relationships among CO₂ emissions, Renewable Energy Consumption (REC), and Non-Renewable Energy Consumption (NREC) in Saudi Arabia, from 1990 to 2019. To assess the stationarity of the panel time-series data, the Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests were initially used. Given that the data exhibited a mixed order of integration, the Autoregressive Distributed Lag (ARDL) framework was employed. Three different lag selection criteria were applied for cointegration, using CO₂ emissions as the dependent variable. Additionally, the direction and significance of causality were analyzed within the ARDL framework. Robust tests were conducted to evaluate the generalizability of the study’s findings. We demonstrated a significant long-term relationship between climate change and both REC and NREC in Saudi Arabia. The findings indicate that in the long run, a 1% increase in REC leads to a 0.21% decrease in CO₂ emissions. Furthermore, a 1% increase in NREC corresponds to a substantial 53.4% reduction in CO₂ emissions. Finally, policy recommendations were proposed in alignment with Saudi Arabia’s Vision 2030.

This research delves into the intricate relationship between credit and liquidity risks and their far-reaching consequences on the overall profitability of banks. Leveraging a comprehensive dataset comprising 132 observations across twelve distinct financial institutions, spanning the period from 2009 to 2019, the analysis employs a sophisticated empirical framework grounded in simultaneous equations. By incorporating three meticulously chosen control variables, this approach adeptly disentangles the distinct effects of credit and liquidity risks on banks’ financial performance. The methodological trajectory unfolds in a sequential manner, commencing with isolated scrutiny of the individual impacts of credit and liquidity risks on banks’ profitability. This evaluation is gauged through robust metrics such as Returns on Assets and Economics. Subsequently, a nuanced exploration ensues, probing the intricate interplay between these two risks and their combined effect on banks’ profitability. Eminent findings emerge from this investigation, underscoring the adverse consequences of escalated credit risk on bank liquidity, accompanied by a simultaneous elevation in overall risk exposure. This disturbing trend notably casts a substantial shadow over banks’ profitability. Conclusively, this study consistently establishes the detrimental impact of the confluence of credit and liquidity risks on the financial well-being of banks. Evidently, this interaction exerts a negative influence on banks’ profitability, a perspective persistently reinforced by the authors’ analyses. The insights garnered from this study hold notable implications for the banking community and financial practitioners. By enhancing comprehension of the distinct attributes of credit and liquidity risks, this research contributes to a refined understanding of risk management dynamics. Moreover, it accentuates the urgency of fortifying the holistic management of liquidity-credit risks, a call to action that resonates deeply with both bankers and financiers seeking to navigate the intricate terrain of contemporary financial landscapes.