Search Results (210)

Digitization is reshaping how banks operate, yet whether it improves corporate financial performance remains unsettled. This study examines how the digital transformation that banks disclose relates to the structure of their key financial performance indicators. Using a balanced panel of 73 listed Gulf Cooperation Council (GCC) banks over 2020–2025 (438 bank–year observations), digital transformation is measured by a text-mined digital disclosure index (DDI, 0–100) constructed from annual reports and decomposed into nine themes. Bank fixed-effects regressions with Driscoll–Kraay standard errors, one-year-lagged specifications, and two-step system GMM are estimated across profitability, net interest margin, cost efficiency, credit risk and capital adequacy. Disclosed digitization more than doubled over the window, but its associations with performance are conditional rather than uniformly positive. Within banks, a higher DDI value is associated with wider net interest margins, yet also with lower profitability, higher cost-to-income ratios, modestly higher credit risk, and thinner capital buffers. This pattern is consistent with an investment or build-out phase in which the costs of digital transformation are visible before any efficiency or stability dividend and in which margins are the single offsetting benefit. The six-year window observes only this cost-bearing segment and not any later recovery, so the study documents the investment-phase drag rather than a completed cycle. The theme decomposition indicates that the margin association is closest to regulatory technology, cybersecurity, broad transformation and payments. Because the design is observational, the results are interpreted as within-bank associations rather than causal effects, and, although precisely estimated, these associations are economically modest. The study contributes a transparent theme-decomposed measure of bank digitization and evidence on its limits for corporate financial performance in an emerging-market banking region. Full article

Green finance has emerged as a critical mechanism for aligning capital markets with climate and sustainability objectives, particularly as economies face mounting pressure to transition away from carbon-intensive growth models. In hydrocarbon-dependent regions such as the Gulf Cooperation Council (GCC), this transition poses distinctive challenges that require integrated institutional, policy, and financial frameworks. The global transition toward sustainable finance has gathered significant momentum, with green capital markets emerging as a central mechanism for channeling investment toward climate and development objectives. Hydrocarbon-dependent economies face a distinctive challenge in this transition, as they must reconcile resource-based growth models with rising pressures for environmental accountability and low-carbon diversification. This study develops an integrated theoretical framework to examine how Gulf Cooperation Council (GCC) financial systems are transitioning toward green capital markets, drawing on institutional theory, environmental policy pathway analysis, and climate-finance alignment models. Using descriptive statistics from regional stock exchanges covering 2015–2024, the study maps key trends in sustainable asset growth, institutional investor preferences, and regulatory evolution across the GCC. Findings indicate progressive alignment with global ESG norms; sustainable asset valuations grew at 23.5% CAGR (UAE) and 18.7% CAGR (Saudi Arabia). A fixed-effects panel regression with panel-corrected standard errors is estimated across all six GCC economies; regulatory framework maturity emerges as the strongest predictor of green bond issuance (β = 0.47, p < 0.01). Cumulative green bond issuances reached USD 52.6 billion (2015–2024), with renewable energy accounting for 58.1% of the sectoral allocation and green transportation recording a 55.9% CAGR (2020–2024). Policy recommendations focus on GCC-wide harmonization of mandatory ESG disclosure, adoption of a unified green bond taxonomy, and expansion of concessional green financing mechanisms. Substantial cross-country heterogeneity is documented, driven by differences in energy policy commitment, financial market maturity, and institutional capacity. The proposed framework offers specific policy guidance to accelerate green financial integration in the GCC, emphasizing regulatory harmonization, institutional capacity-building, and alignment with SDG targets 7 and 13. The study contributes to the limited evidence base on green finance in hydrocarbon-dependent economies and provides a foundation for future empirical research. Given the small panel dimensions (N = 6 cross-sectional units; T = 10 years), this study is positioned as exploratory rather than confirmatory: the panel-regression estimates and the hypothesized institutional-to-policy-to-finance sequence are interpreted as associational patterns consistent with the proposed framework rather than as definitive causal tests, and the reported coefficients are offered as indicative magnitudes to be re-examined as longer GCC green-finance time series become available. Full article

The molecular epidemiology and antiretroviral (ARV) drug resistance of human immunodeficiency virus type 1 (HIV-1) remain incompletely outlined in the Middle East and North Africa (MENA). The aim of this retrospective molecular epidemiology study was to analyze MENA HIV-1 sequences for phylogenetic clustering and to delineate surveillance drug-resistance mutations (SDRMs) for nucleoside reverse-transcriptase inhibitors (NRTIs), non-nucleoside reverse-transcriptase inhibitors (NNRTIs), and protease inhibitors (PIs) across various periods, locations, and subtypes/circulating recombinant forms (CRFs). Viral sequences were retrieved from the Los Alamos HIV Sequence Database as of 15 April 2026. Analyses were done using multiple sub-gene regions (two env regions (n = 224 and n = 60) and PR (n = 2413) and RT (n = 2103) of the pol gene). Phylogeny construction was conducted using maximum-likelihood estimation, while ARV drug resistance analysis was conducted using the Stanford HIVdb algorithm. The HIV-1 MENA sequences showed a remarkable genetic diversity, with co-circulation of multiple subtypes/CRFs, including subtype B in the Maghreb, Levant, and Egypt sub-regions, subtypes A1, G, CRF01_AE, and CRF02_AG in the Gulf Cooperation Council (GCC) and Yemen sub-region, and subtypes C and D in the Horn of Africa and Sudan sub-region. The percentage of MENA HIV-1 sequences in clusters was 10.3% for env1, 8.3% for env2, 22.0% for PR and 37.2% for RT. Phylogenetic reconstruction hinted at a structured epidemic dominated by small transmission units, with most clusters comprising dyads (n = 260) or networks (n = 142) and a limited number of large clusters (n = 8) that were largely confined within national boundaries, with only occasional cross-border linkages (n = 8). Overall SDRM prevalence was 3.2% in the PR region and 14.9% in the RT region, with a higher percentage of NNRTI-associated mutations (10.0%) than NRTI-associated mutations (9.1%) and dual-class resistance observed in 4.1% of sequences. Phylogenetic clustering was not associated with the probability of harboring SDRMs; however, negative binomial models showed that non-clustered sequences had a greater burden of NRTI-associated mutations, whereas no such association was observed for NNRTI- or PI-associated mutations. The findings showed predominantly localized and fragmented MENA HIV-1 transmission dynamics. Heterogeneous ARV drug resistance dynamics indicated that resistance emergence might be shaped by broader epidemiologic and treatment-related factors rather than ongoing clustered transmission. There is a need for coordinated molecular surveillance and optimized ART strategies across the MENA countries. Full article

Date palm (Phoenix dactylifera L.) plays a vital agronomic and nutritional role in arid regions of the Gulf Cooperation Council (GCC), where sustainable crop improvement is essential. Among the many cultivars, Sukkary, Mejhoul, and Khlass stand out for their commercial importance, adaptability, and distinctive fruit characteristics. This study investigated their metabolomic profiles during fruit development, focusing on sugars, amino acids, polyphenols, and organic acids. Distinct biochemical signatures were identified among cultivars. Mejhoul exhibited the highest Brix (~79), reflecting elevated glucose and fructose, while Sukkary and Khlass (~72) showed sucrose predominance. Sugar profiles revealed cultivar-specific patterns: Sukkary accumulated more sucrose, whereas Mejhoul and Khlass had higher invert sugars, with inositol uniquely abundant in Khlass. Amino acid analysis showed Sukkary enriched in glutamine, valine, and GABA—metabolites linked to nitrogen metabolism and stress tolerance. Mejhoul contained more tyrosine and glutamine, and Khlass was rich in methionine and urea. Organic acid profiling highlighted Sukkary’s dominance in TCA intermediates (fumaric, succinic, and malic acids), Khlass in citric and citraconic acids, and lower organic acid levels in Mejhoul. Sukkary also exhibited the highest polyphenol content, consistent with elevated phenylalanine and activation of the phenylpropanoid pathway. These metabolomic distinctions reflect cultivar-specific physiological and nutritional traits: Mejhoul suits fresh consumption and fermentation, Sukkary favors functional food and storage potential, and Khlass offers balanced sugar-acid and inositol profiles. The findings provide a biochemical basis for cultivar-specific breeding, postharvest optimization, and enhanced nutritional value under climate stress. Full article

Over the past seven decades, rapid economic growth has been accompanied by a substantial increase in environmental degradation worldwide. Given the magnitude and transboundary nature of environmental externalities, country-specific policies may be insufficient, and regionally coordinated environmental strategies are likely to be more effective. For such frameworks to succeed, convergence in levels of environmental pressure among member countries is essential. In this study, convergence is examined using the ecological footprint (EFP), a comprehensive indicator of environmental pressure. The analysis covers the six member states of the Gulf Cooperation Council (GCC)—Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates—over 1971–2024 and tests for the stochastic convergence of per-capita footprints relative to the year-t regional average. Conventional ADF tests indicate convergence for Kuwait and Qatar—with Saudi Arabia at the 10% level—and Phillips–Perron results point in the same direction. Quantile ADF and Fourier quantile KSS tests with bootstrap inference show that adjustment is regime-dependent and country-specific: mean reversion is confined to below-average states in Bahrain, to above-average states in Kuwait and Saudi Arabia, and to both extremes in Qatar, while the United Arab Emirates and Oman revert to almost no quantile. The Phillips–Sul log-t procedure does not reject full-panel convergence: all six countries form a single convergence club, the result survives leave-one-country-out and cross-sectional-dependence checks, and subsample estimates date the convergence process mainly to the post-1990 period. Component-level analysis shows that aggregate convergence is carried by the carbon component (84–99% of the GCC footprint), whereas cropland, grazing, forest, and built-up components diverge. The findings support regionally coordinated energy–carbon policy combined with nationally tailored land-use policy. Full article

Background: The Chief Financial Officer (CFO) has become a central strategic actor in capital-intensive firms; however, little evidence links CFO risk-taking behaviour to firm performance outside developed markets. This study examines how CFO risk-taking affects corporate financial performance in the Industrial, Energy and Petrochemical sectors of the Gulf Cooperation Council (GCC) countries. Methods: Using 260 firm-year observations (2015–2024) from 26 listed firms, this study measures CFO risk-taking through financial leverage, capital expenditure intensity, earnings volatility, and cash flow volatility, and firm performance through Return on Assets (ROA), Return on Equity (ROE), and Earnings Per Share (EPS). Panel Fixed-Effects regression and a Vector Autoregression (VAR) model are used to estimate contemporaneous and dynamic relationships, guided by Agency Theory, Upper Echelons Theory and Prospect Theory. Results: CFO risk-taking proxies are significantly associated with ROA: leverage and cash flow volatility reduce ROA, while earnings volatility and capital expenditure raise it. The ROE model is a robust null finding, and EPS evidence is limited to earnings volatility. The VAR results indicate time-varying, exploratory, and dynamic relationships between risk-taking and performance. Conclusions: This study contributes to the literature in three ways: it shifts the analytical focus from the widely studied CEO to the increasingly influential CFO; it provides the first large-scale empirical evidence on CFO risk-taking for the under-researched GCC region; and it operationalises CFO risk-taking through a finer set of proxies than prior work. The findings imply that GCC boards and investors should treat financial leverage and cash flow stability as behavioural risk indicators and that regulators may benefit from encouraging more granular CFO-level risk disclosure. Full article

The Levelized Cost of Energy (LCOE) is the standard metric for evaluating renewable energy project economics; however, conventional formulations inadequately represent the dynamic effects of logistics performance, supply chain disruptions, geopolitical risk, and institutional constraints on project costs. This study addresses this gap by proposing the Integrated Levelized Cost of Energy (I-LCOE), a conceptual framework designed for macro-level renewable energy planning and policy analysis. An interpretivist qualitative research design was adopted, combining a systematic literature review with twelve semi-structured interviews involving renewable energy, logistics, regulatory, and academic experts from the MENA and GCC regions. Thematic analysis identified five recurring challenges: limited knowledge management, weak integration of logistics within conventional LCOE models, fragmented sustainability metrics, stakeholder coordination inefficiencies, and reliance on tacit knowledge. The findings indicate that transportation delays, customs bottlenecks, infrastructure limitations, and geopolitical disruptions generate dynamic risk premiums that are insufficiently reflected in existing macro-level cost assessment approaches. In response, the study develops the four-layer I-LCOE framework, supported by an operational proxy variable mapping framework, a comparative assessment against established uncertainty methods, and an integrated digital knowledge management platform. The proposed framework provides a structured approach for incorporating logistics-induced uncertainty into renewable energy cost assessment, supporting more informed strategic planning, investment prioritization, and policy development aligned with Sustainable Development Goal 7 and the Paris Agreement. Full article

The determinants of bank credit quality have been studied extensively, yet much of the existing evidence rests on a single regression specification, so a variable’s apparent significance may be conditioned on which controls a researcher chooses to include. We confront this problem directly for the Gulf Cooperation Council (GCC) countries, providing a robustness analysis of non-performing loans (NPL) determinants for the region’s banks. We employ a balanced panel of 45 listed commercial banks drawn from all six GCC countries over the period 2010 to 2024. We examine fifteen bank-specific and four macroeconomic potential determinants of NPLs, utilizing two variants of extreme bounds analysis (EBA), namely, the strict criterion of Leamer and the more lenient criterion of Sala-i-Martin, estimated within a panel fixed-effects framework. The findings show that of the nineteen determinants routinely cited in the literature, seventeen prove fragile once their coefficients are tested across the full range of possible model specifications. None survives Leamer’s strict criterion, whereas Sala-i-Martin’s less restricted test suggests that only two variables are robust, namely, asset quality (loan intensity), which enters positively, and capital adequacy, which enters negatively, while all four macroeconomic variables are fragile on both tests. For regulators, bank-level balance sheet indicators, especially loan intensity and capital adequacy, offer a more robust starting point for NPL early-warning and stress-testing frameworks and complement macroeconomic forecasts. Full article

Emerging contaminants (ECs), including pharmaceuticals, endocrine disruptors, pesticides, PFAS, and microplastics, are increasingly detected in aquatic environments due to their persistence, bioactivity, and limited removal by conventional treatment processes. These challenges are intensified in hyper-arid regions where desalination and wastewater reuse dominate water supply, concentrating contaminants and creating unique exposure pathways. The Arabian Gulf is one of the most environmentally stressed marine systems, characterized by hypersalinity, extreme temperatures, dense coastal development, and strong petrochemical influence. Despite its major role in global petrochemical and plastic production, the region lacks coordinated monitoring and regulatory frameworks, creating significant gaps in understanding EC occurrence, fate, and risks. Treatment systems designed for temperate climates often underperform under Gulf conditions, enabling contaminants to persist, accumulate in sediments, and enter marine food webs. This review examines the occurrence, behaviour, and removal challenges of ECs in the Gulf, where concentrations frequently exceed international benchmarks due to wastewater reuse, desalination brine discharge, maritime activities, and oil-related pollution. Regional factors such as hypersalinity, high temperatures, and petrochemical interactions further influence contaminant persistence and toxicity. Conventional treatments show reduced efficiency, while alternatives such as halophyte-based wetlands and brine valorisation show promise. However, key gaps remain in nanoplastics, PFAS speciation, and cumulative exposure, requiring coordinated monitoring and unified GCC regulations. Full article

Saudi Arabia’s Vision 2030 and global pressure for sustainability are driving organizations to adopt Green Human Resource Management (GHRM), an approach that aligns human capital practices with environmental, social, and economic goals; however, evidence on how the four GHRM dimensions of Green Recruitment and Selection (GRS), Green Training and Development (GTD), Green Performance Management (GPM), and Green Compensation and Rewards (GCR) simultaneously affect the triple bottom line of organizational sustainability remains limited, particularly in Gulf Cooperation Council (GCC) contexts. This study examines how these four GHRM practices are associated with environmental (ENV), social (SOC), and economic (ECO) sustainability in Saudi organizations. Drawing on a cross-sectional survey of 350 valid employee responses from four Saudi regions, the data were analyzed using covariance-based Structural Equation Modeling (CB-SEM) with confirmatory factor analysis to assess the measurement model and test twelve hypotheses. The measurement model demonstrated strong reliability and validity, with composite reliability ranging from 0.866 to 0.893 and average variance extracted ranging from 0.684 to 0.741. All twelve hypotheses were supported (β = 0.163–0.241, p < 0.01), and the model explained 48.7%, 41.2%, and 35.5% of the variance in environmental, social, and economic sustainability, respectively, indicating moderate to substantial explanatory power. Green Training and Development was the strongest predictor of social and economic sustainability, whereas Green Recruitment and Selection was the strongest predictor of environmental sustainability; regional differences were not statistically significant. These findings indicate that GHRM functions as a unified set of practices that support organizational sustainability across its environmental, social, and economic dimensions, underscoring the strategic importance of green training and green incentives for Saudi organizations pursuing the goals of Vision 2030. By testing all four GHRM practices against all three sustainability dimensions within a single model in an under-researched GCC setting, the study extends the cross-cultural evidence base on GHRM and offers context-specific guidance for managers and policymakers; future research should adopt longitudinal and multi-source designs to examine the causal and mediating mechanisms underlying these associations. Full article

Urban and regional planning has shifted from state-centric models toward relational, vision-based paradigms that emphasize functional spaces over fixed boundaries. In the Gulf Cooperation Council (GCC) region, cross-border planning (CBP) is gaining strategic relevance driven by national economic diversification strategies, transboundary environmental challenges, and mega-infrastructure projects. Applying a comprehensive framework across five analytical dimensions, socio-economic, social, geopolitical, spatial, and environmental, this paper examines the conceptual foundations, governance typologies, and empirical realities of regional integration. While global benchmarks like the European Union offer mature examples of institutionalized multilevel governance, GCC initiatives face unique challenges. Although spatial and socio-economic integration is advancing through projects like the GCC Railway Network, the King Fahd Causeway, and the GCC Interconnection Authority regional grid, development is frequently constrained by national sovereignty priorities, procedural friction, and implementation asymmetries. To optimize transnational corridors under Saudi Vision 2030, this study recommends delineating functional cross-border regions, establishing permanent corridor governance cells, resolving border friction through digital integration, and institutionalizing a GCC-wide planning observatory. The study’s principal original contribution is a unified five-dimensional analytical framework (socio-economic, social, geopolitical, spatial, and environmental) that is operationalized as an evaluative lens and applied consistently across global, regional, and national cases, together with an accompanying governance typology and a sequenced implementation model tailored to the sovereignty-sensitive GCC context. Full article

Infrared small target detection (ISTD) remains challenging in complex cluttered environments because targets usually occupy only a few pixels and exhibit weak thermal radiation with limited texture information. The problem becomes more severe in high-resolution infrared imaging systems, where sliding-window inference is commonly adopted under memory and computational constraints. However, the truncated field of view may lead to contextual information loss and increased false alarms in cluttered regions. To address these issues, we propose the Selective Contextual Uplift and Attention Network (SCUA-Net). The proposed network adopts a U-Net++-style densely nested encoder–decoder architecture to enhance multi-scale feature interaction and preserve fine-grained weak-target features. In addition, a Global-Context Calibration Coordinate Attention (GCC-CA) module is introduced to inject window-level contextual statistics into coordinate attention, thereby improving clutter suppression and localization robustness under sliding-window inference. During training, a joint optimization strategy combining Online Hard Example Mining (OHEM) and Dice Loss is employed to alleviate severe foreground–background imbalance. During inference, Gaussian-weighted fusion is adopted to reduce stitching artifacts between adjacent windows. Experimental results on NUDT-SIRST and IRSTD-1k validate the effectiveness of the proposed method. SCUA-Net achieves 99.15% Pd, 0.558 × 10⁻⁶ Fa, and 0.9570 IoU on NUDT-SIRST, while maintaining competitive performance on IRSTD-1k at 161.6 FPS on an NVIDIA RTX 4090 platform, demonstrating favorable accuracy, robustness, and real-time performance in complex infrared scenarios. Full article

This paper examines the long-run relationship between subjective well-being and digital transformation in the six Gulf Cooperation Council (GCC) countries—Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates—over the period 2011–2025 using a balanced country-year panel dataset. Subjective well-being is measured by the national average Cantril Ladder score from the Gallup World Poll as reported in the World Happiness Report. Explanatory variables include a binary AI Readiness Period Indicator (AI) distinguishing the pre-AI-readiness phase (2011–2018, AI = 0) from the post-AI-readiness phase (2019–2025, AI = 1), anchored by the Oxford Insights Government AI Readiness Index, Internet penetration from the International Telecommunication Union (ITU), and real GDP per capita. After accounting for cross-sectional dependence and non-stationarity, the analysis employs a panel autoregressive distributed lag (ARDL) framework estimated via the Pooled Mean Group (PMG) approach. The results indicate the existence of a stable long-run cointegrating relationship among the variables. The baseline PMG estimates suggest positive long-run associations between GDP per capita and the AI Readiness Period Indicator with subjective well-being, and a negative association between Internet penetration and well-being in a high-connectivity regional context. Short-run effects are generally weak, while the error-correction term confirms adjustment toward the long-run equilibrium. Robustness checks based on alternative estimators confirm the positive long-run effect of income, while the estimated effects of the AI Readiness Period Indicator and Internet penetration show sensitivity in sign and significance across specifications and should therefore be interpreted as indicative rather than definitive. Overall, the findings suggest that digital transformation is not a homogeneous driver of subjective well-being. Instead, the AI Readiness Period Indicator and Internet penetration operate through distinct mechanisms, with potentially different welfare implications in highly connected rentier-state economies. Full article

Devoting attention to the mechanisms of enhancing energy efficiency through the transition to clean energy sources plays a vital and active role in moving forward towards environmental sustainability in the industrial economy. Industrial CO₂ emissions across the Gulf Cooperation Council (GCC) remain persistently high despite growing regional commitments to clean energy transition and sustainability. This study examines the key determinants of industrial CO₂ emissions in all six GCC member states over the period 2004–2022, focusing on energy efficiency, electricity consumption, oil use, trade openness, and economic growth. The analysis employs advanced panel econometric techniques, including cross-sectional dependence tests, second-generation unit root tests, and panel autoregressive distributed lag estimators, to identify both short-run and long-run relationships among the variables. The results reveal that in the short run, energy intensity is the sole statistically significant driver of industrial emissions. In the long run, energy intensity continues to increase emissions, while trade openness significantly reduces them. Neither oil consumption nor industrial electricity use exerts a significant positive long-run effect on emissions, pointing to a gradual decoupling driven by improving industrial energy efficiency and cleaner electricity generation. These findings suggest an emerging decoupling between industrial activity and carbon emissions in the GCC, driven by improvements in energy efficiency. For GCC economies pursuing economic diversification and net-zero targets, reducing industrial energy intensity and expanding low-carbon energy sources remain critical pathways toward sustainable industrial development. Full article

Fishery production in the Gulf Cooperation Council (GCC) region faces growing threats from overfishing, climate change, marine pollution, and habitat degradation, which reduce stock regeneration and ecosystem stability. Inadequate management systems and limited technological adoption further constrain productivity, posing risks to food security and economic stability. This study examines the dynamic impact of climate change shocks on fishery sustainability in GCC countries, using the Panel Vector Autoregression (PVAR) framework to examine both short- and long-run interactions between climate variables and fishery production. The study observed a long-run cointegration between total fisheries production and climate variables. Results reveal strong dynamic linkages, with temperature and carbon emissions exhibiting stable long-term trends and relatively low forecast errors. In contrast, precipitation and fishery output show higher volatility and greater sensitivity to short-term shocks. Temperature shocks have a significant negative effect on fishery production, highlighting the need for climate adaptation policies that protect marine ecosystems, enhance monitoring, and promote sustainable fishing practices. The findings highlight the importance of considering climate variability and adaptive strategies to ensure sustainable fisheries in the region. The novelty of this study is applying a dynamic PVAR approach to GCC fisheries, accounting for short-run and long-run climate impacts and providing region-specific policy-relevant insights that address sustainability under climate variability. Full article