Explore the vast range of titles available.

More than 59 million people reside in the six member countries of the Gulf Cooperation Council (GCC) (the United Arab Emirates, Bahrain, Kuwait, Oman, Qatar, and Saudi Arabia). The rate of urbanization is more than 80% in each of these countries. To better understand the trends and knowledge structures and to aid policy design and implementation, more research is needed on the topic of urbanization in GCC countries. In order to address this knowledge gap, bibliometric analysis and scientific mapping procedures utilizing VOSviewer were employed. A total of 415 academic papers covering four decades, from 1980 to 2021, were collected from the Web of Science database and split into three time periods: 1980–2017, 2018–2019, and 2020–2021. The findings indicate that the topics related to sustainable development, environmental regulations, renewable energy, and smart cities have received the most attention. In addition, land use planning, urban growth, and energy consumption have been dominant themes over different periods. Several intertwined factors have contributed to the evolution of research over these periods. These include the widespread diffusion of the sustainability agenda, the rise of advanced ICT, and the escalating rate of urbanization. It can also be explained by the fact that the world has been grappling with numerous environmental challenges, increasingly requiring innovative solutions for sustainability. The findings of this study can be used to develop better strategies for sustainable urban development in the region.

The purpose of this paper is to empirically investigate the volatility spillover between financial stress, the Gulf Cooperation Council (GCC) stock markets, and oil and gold markets in the presence of state (regime) dynamics. We first use the Dynamic Conditional Correlation (DCC)-GARCH model in order to study the correlation between variables. Second, the authors use a continuous wavelet decomposition technique so as to examine the interactions between financial stress, oil and GCC stock, and gold markets. Finally, the authors examine the spillover effects by estimating the Baba-Engle-Kraft-Kroner (BEKK)-GARCH model. The findings indicate various patterns of spillover between GCC financial stress index and oil, gold, and GCC stock market returns in high- and low-volatility regimes, especially during the COVID-19 pandemic. The wavelet coherency results indicate a substantial co-movement between oil and GCC financial stress and between GCC financial stress and GCC stock markets in the periods of high volatility. BEKK-GARCH model outcomes confirm this relation and report the noteworthy bi-directional transmission of volatility between GCC financial stress and oil market for some countries (see Oman, Saudi Arabia, and UAE in the bearish state and Bahrain and Saudi Arabia under bullish state) and between oil market shocks and the GCC stock market returns, chiefly in the turmoil period. Thus, these results are important for investors aiming at managing and reducing portfolio risk. These empirical findings are of significant importance in the development of effective allocation strategies to mitigate financial stress (FSIs) prevalent in the Gulf Cooperation Council (GCC) region. Moreover, they hold crucial implications for portfolio diversification, risk management, and policy formulation by shedding light on the interdependencies among various asset classes and financial markets across different market conditions.

This paper examines the relationship between corporate social responsibility (CSR) and financial performance for Islamic banks in the Gulf Cooperation Council (GCC) region over the period 2000-2014 by generating CSR-related data through disclosure analysis of the annual reports of the sampled banks. The findings of this study indicate that there is a significant positive relationship between CSR disclosure and the financial performance of Islamic banks in the GCC countries. The results also show a positive relationship between CSR disclosure and the future financial performance of GCC Islamic banks, potentially indicating that current CSR activities carried out by Islamic banks in the GCC could have a long-term impact on their financial performance. Furthermore, despite demonstrating a significant positive relationship between the composite measure of the CSR disclosure index and financial performance, the findings show no statistically significant relationship between the individual dimensions of the CSR disclosure index and the current financial performance measure except for 'mission and vision' and 'products and services'. Similarly, the empirical results detect a positive significant association only between 'mission and vision' dimension and future financial performance of the examined banks.

The countries of the Gulf Cooperation Council (GCC)—Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates—hold almost 30% of the world’s total proven oil reserves and around 20% of its total proven natural gas reserves. They are also endowed with a high abundance of renewable energy resources such as solar and wind. Yet, the GCC’s primary energy consumption is still dominated by fossil fuels, and the share of renewable energy still does not exceed 1%. Drawing on secondary data, including journal articles, governmental and companies’ websites, and reports and newspaper articles, this paper assesses the reasons behind their underutilization of renewable energy resources. Whereas technical and economic feasibility issues had been identified as the main barriers to slow the uptake of renewable energy technologies in the GCC, this paper uncovered that various additional factors have remarkably influenced such delays. High hydrocarbon subsidies, low electricity tariff structure, fragmented energy policy, the absence of dedicated renewable energy regulator and regulatory framework, and a highly controlled power market are major barriers to renewable energy adoption in the GCC. The paper concludes with policy options to inform scaling up the adoption of renewable energy in the GCC.

This study investigates the impact of UAE’s Green Credit Policy on the non-performing loan. One of the main pillars in the UAE green agenda 2015–2030 is the green finance that has been growing in high acceleration in the Gulf Cooperation Council (GCC) countries and the whole world. Consequently, the main objective of this study is to investigate in the financial risks that associated with green lending and whether an increasing in green lending will decrease the non-performing loans ratio (NPLR) of UAE banks, based on the period 2015–2020 dataset of 23 UAE’s banks. To achieve this objective, we have used a regression technique that includes a two-stage least square regression analysis and random-effect regression analysis to test if the increase in green credit ratio can reduce the NPL ratio in a sample of UAE’s banks. The current study can be considered the first empirical attempt that conducted on the banking sector in UAE, to discover the variables that might have a direct impact on the NPL ratio. The results reveal that the ratio of green loans has a negative impact on the NPL ratio, as much as the return of equity, while the quality of credit, inefficiency, and the bank size have a positive impact on NPL ratio. But as was not as expected, we found that the impact of solvency ratio has a negative significant on the NPL ratio. Finally, the current study introduces a new value to the current literature about the impact of green lending policies and provides a new perspective which supports the financial sustainability in UAE.

This study investigates the impact of globalization, financial development, and energy utilization on environmental sustainability in the Gulf Cooperation Council (GCC) countries. GCC countries are currently experiencing higher demand and utilization of energy resources, high global integration, and improvements in the financial sector that poses serious environmental sustainability challenges. We have employed a relatively comprehensive proxy, i.e., ecological footprint for environmental sustainability and more advanced and robust econometric strategies (second-generation) to examine the impact of globalization, financial development, and energy utilization on environmental sustainability in the GCC countries, which have a significant departure from the extant literature. The results of this study show that globalization, financial development, and energy utilization are significantly deteriorating the environmental quality in the GCC countries. Additionally, in order to account for the national heterogeneity, we have performed country-specific analysis and interestingly, results reveal that globalization, financial development, and energy utilization negatively influence the environmental sustainability in each sample country that is consistent with the findings of overall panel. Furthermore, the findings are robust to various robustness checks that we have performed for checking the reliability of our main findings. This study also offers some useful policy implications to the stakeholder in general and specifically concerning the GCC countries for promoting their environmental sustainability.

This paper explores how AI drives GCC sector retail towards the fulfillment of the UN SDGs. Analyzing a survey conducted on 410 retail executives, using PLS-SEM, this study underlines the role of AI in promoting operational efficiency, waste reduction, and consumer engagement with greener products. Key highlights include that AI-enabled marketing strategies improve the adoption of sustainable practices among consumers; AI-powered smart distribution channels enhance supply chain efficiency, reduce carbon emissions, and optimize logistics. For a retailer, practical applications of AI include the use of AI in demand forecasting to potentially reduce waste, personalized marketing to efficiently promote sustainable products, and deploying smart systems that reduce energy consumption. While these benefits are real, data privacy and algorithmic bias remain valid concerns, thus underlining the need for ethics and transparency in the practice of AI. The following study provides actionable insights for GCC retailers on how to align AI adoption with sustainability goals, fostering competitive advantages and environmental responsibility.

This study aims to predict GCC financial stress on oil market, and GCC Stock and bond markets while considering the effect of the 2008 financial crisis, 2014 oil drop price and the 2019 novel COVID-19 outbreak. For this purpose, we use a new approach for predicting the financial stress, based on the One-Dimensional Convolutional Neural Network (1D-CNN). This article introduces a parameters optimization method, which provides the best parameters for 1D-CNN to improve the prediction performance of the financial stress indices. The results suggest that indexes of financial stress help to improve forecasting performance. It implies that the 1D-CNN model shows a better predictive performance in the out-of-sample findings.Regarding the influence of financial stress on hedging between Brent, and financial markets, the outcomes emphasize the role of oil in hedging stock market risks in positive market stress case. Another interesting result is that the out-of-sample estimates for stock–bond markets, hedging with oil have higher variability for negative (positive) financial stress. The findings highlight the predictive information captured by financial stress in accurately forecasting oil market volatility and financial markets, offering a valuable opening for investors to monitor oil market volatility using information on traded assets.

Purpose This study aims to examine the spillover effects of the mean and volatility between oil prices and stock indices of six Gulf Cooperation Council (GCC) countries (UAE, Kuwait, Saudi Arabia, Qatar, Oman and Bahrain). Design/methodology/approach Over the period 2008–2019, a bivariate VARMA-GARCH-ADCC model was combined with the maximal overlap discrete wavelet transform technique filter to shed light on a wide range of possible spillover effects in the mean and variances of level prices at various time horizons. Findings The authors find that the spillover effects between oil prices and the GCC stock markets are time-varying and spread across various time horizons. Besides, oil prices and stock market indices are directly impacted by their own shocks and variations and indirectly influenced by other price volatilities and wavelet scales. The linkages in volatility spillovers between oil prices and the GCC stock markets occur in the short-term, midterm and long-term horizons. More specifically, the results also show that the asymmetric estimates are statistically significant for the associations between oil prices and each stock market in the GCC countries. This implies that negative shocks play a more vital role than positive shocks in driving the dynamic condition correlations between oil and stock markets under study. Practical implications The significant interrelatedness between oil prices and each stock market in the GCC countries has important implications for investors, portfolio managers, and other market participants. They can use the findings of this research to create the best oil-GCC stock portfolios and predict more precisely the volatility spillover patterns in constructing their hedging strategies. Originality/value In several ways, this study differs from previous research. First, while previous empirical studies of the dynamic link between oil prices and stock markets have focused primarily on developed or emerging markets, the focus of this is on six GCC countries. Second, the linkage between oil prices and stock markets is typically studied at the original data level in the time domain in relevant literature, while frequency information is overlooked. Therefore, the current study examines this relationship from a multiscale perspective. Third, in this paper, to capture a wide range of possible spillover effects in the mean and variance of level prices at multiple wavelet scales, the authors use a VARMA-GARCH-ADCC model in conjunction with wavelet multiresolution analysis. Additionally, this article also applies wavelet hedge ratio and wavelet hedge portfolio analysis at various time horizons.