Shawkat M Hammoudeh

This study aims to provide a thorough bibliometric analysis of the existing literature on financial development, foreign direct investment (FDI), CO 2 emissions, and governance in renewable energy consumption towards achieving Sustainable Development Goal (SDG) 7. This study analyses 2,050 publications indexed in Scopus and Web of Science from 1997 to 2025. It aims to outline the evolution of the global research agenda on finance, investment, institutional quality, and CO 2 emissions, considering renewable energy consumption. Results show that the academic interest in the finance‐energy‐environment nexus is on the rise, especially after the Paris Agreement and the implementation of the 2030 Sustainable Development Agenda. A shift in focus from emissions and economic growth to the increasing importance of green finance, governance, and cross‐border investment to facilitate a clean energy transition is observed. This study highlights that, over the past years, FDI and sustainable development have become a relevant topic of research, emphasising the importance of securing finance and building institutional capacities to support a cleaner energy transition. Notably, the study uncovers significant variations in research across regions. A reliable financial and institutional base is crucial for an inclusive and sustainable energy transition, and this trend provides useful directions for policymaking based on findings from the past three decades. JFL Classifications : Q01; Q42; F21; G20; C83

The majority of European oil refining occurs in the countries of the northern region. The purpose of this study is to determine the volume of CO2 emissions at all stages of the supply chain and identify opportunities to reduce the emissions during the oil delivery. This work has developed a methodology for determining CO2 emissions from production to oil refining at northern Europe refineries.
Using this oil supply chain methodology, CO2 emissions are studied at all stages of the supply chain at oil refineries in the energy complex of the northern region of Europe. The data relates the volumes of oil supplies from 21 exporting countries to 47 refineries in the countries of the region, as well as the domestic production and refining of oil by those countries. The approach made it possible to identify opportunities for reducing emissions in elements of supply chains associated with oil transportation.

This study examines return connectedness and spillover shock effects between Islamic profit-sharing rates (PSR) and conventional deposit rates (DPR) of banking sectors across various maturities in T & uuml;rkiye and Malaysia. Overall, financing rates with various maturities in T & uuml;rkiye act as transmitters of shocks in both banking sectors. Specifically, short-term rates serve as net spillover transmitters, while long-term rates of DPR and PSR emerge as primary transmitters and receivers, respectively. Total directional connectedness analysis reveals that the degree of integration between sectors fluctuates over time, with political instability and monetary policy actions being key drivers. Although conventional deposit rates dominate their Islamic counterparts in the early part of the study period, a significant policy rate hike in 2014 shifts this balance in favour of profit-sharing rates, suggesting that the conventional banking sector has become increasingly susceptible to shocks originating from the Islamic banking sector in recent years. Moreover, T & uuml;rkiye's adoption of unconventional monetary policies has reduced integration within the banking system, thereby altering the dynamics of return spillovers, with deposit rates acting as main transmitters. We observe a relatively stable connectivity mechanism over time in Malaysia, where market risk becomes stronger as of 2020. Both short-term rates are net transmitters during most of the sample period, and medium- and longer-term tenors of the Islamic (conventional) banking sector function as net receivers (transmitters). DPR is shown to exert a substantial influence over PSR, with spillover shock effects being more pronounced for shorter tenors. While global factors strongly influence overall connectivity in T & uuml;rkiye, local factors such as exchange rates, interest rates, and credit default swaps (CDS) play a critical role in net shock transmission. DPRs are shown to be dominant factors in predicting PSRs in T & uuml;rkiye and both sectors exhibit stronger interactions at higher intervals of time.

•GCC banks exhibit significant market power with a high average Lerner Index of 0.96.•Islamic banks demonstrate higher market power than commercial and investment banks.•Market power in GCC banks is stable, with little fluctuations over the 2000–2023 period.•Increased market power generally correlates with lower bank risk, but this effect weakens at high levels.•GCC banking market structure reflects a monopolistic environment with moderate pressures from smaller banks.
This study provides a thorough analysis of the market power within the banking sector of the Gulf Cooperation Council (GCC) countries over the period from 2000 to 2023, by employing robust econometric models and indices. Utilizing a translog cost function model, the study estimates marginal costs and calculates initially the Lerner Index, thereby revealing substantial market power among the GCC banks. To address the issues of high collinearity and heteroscedasticity, the model is refined by highlighting the significant roles of the total assets and labor costs in determining the overall costs. Further, the Adjusted Lerner Index and the Boone Indicator confirm the prevalence of market power in the region’s banking sector. The Panzar-Rosse H-statistic analysis suggests that the GCC banking sector operates under an oligopolistic framework, where the competitive pressures are insufficient to diminish the substantial market power held by the GCC banks. Dynamic GMM panel data models also confirm the persistence of market power, showing that past market power strongly predicts the current levels with minimal fluctuations over time. The study also investigates the relationship between market power and bank stability, revealing that while higher market power is generally associated with lower bank risk, excessive market power may paradoxically increase risk, indicating a non-linear relationship. These findings highlight the importance of regulatory reforms aimed at enhancing competition and fostering a more resilient banking sector. By providing a comprehensive understanding of market power dynamics, this study offers valuable insights for policymakers and financial regulators in the GCC, thus, guiding the efforts to improve competition, reduce systemic risk, and strengthen financial stability.

•We use daily data and local projections to study how disasters affect sovereign risk.•Climatological and hydrological events slightly and briefly raise CDS spreads.•Imminent disaster expectations do not meaningfully move CDS spreads.•Regional and global spillovers dominate sovereign risk, showing systemic effects.•Governments need disaster plans and coordination and ESG portfolios hedge sovereigns.
Using a rich high-frequency and a cross-country panel of daily sovereign CDS spreads, we employ local projections to estimate the dynamic response of sovereign risk to the occurrence of natural disasters. We find that climatological and, to a lesser extent, hydrological events have a small and short-lived effect on the sovereign CDS spreads. We also explore whether anticipatory effects arise before a disaster unfolds, and confirm that the expectations of imminent disasters do not substantially affect CDS pricing. On the other hand, we show that the sovereign risk is dominated by regional and global financial spillovers, thus reflecting the systemic nature of the sovereign credit markets. Our results also suggest that governments may benefit from developing disaster-specific risk reduction and fiscal resilience strategies, as well as early-warning models that integrate disaster forecasting into risk monitoring frameworks. Sovereigns’ coordination and risk-pooling mechanisms may also be essential in times of regional calamities. Moreover, portfolio hedging strategies should include short-term protective positions in the vulnerable sovereigns during known disaster seasons. Disaster-integrated ESG strategies could also enhance the portfolio resilience.

The first objective is to examine the asymmetric impact of European Union carbon emission trading system (EUETS) and oil price uncertainty (OPU) on the quantiles of European equity market returns. After confirming nonlinear dynamics in the daily time-series data for EU-ETS, OPU, and European stock returns, we use the quantile-based autoregressive distributive lag (QARDL) model. The second objective is to analyze OPU's moderating impact on dynamic conditional correlations (DCCs) and asymmetric dynamic conditional correlations (ADCCs) between EU-ETS returns and European equity market returns. To extract DCCs and ADCCs between carbon and stock returns, we employ the DCC-EGARCH and ADCC-EGARCH approaches with a range of robustness diagnostics. Thirdly, we utilize the hedge ratio and optimal portfolio weight selection approaches, guided by the DCC-GARCH-t copula method, to examine the hedging effectiveness (HE) against long-term OPU shocks through short-term positioning in European financial market returns and EU-ETS returns. Overall findings reveal asymmetric spillovers in extreme conditions, negatively affecting Belgian and Spanish firms in the long term due to EU-ETS-induced price increases. Long-term investors are advised to consider reallocating investment funds to the stock markets that are favorably impacted by EU-ETS fluctuations (Finland, France, Germany, Ireland, Italy, and the Netherlands) to achieve optimal gains during bullish equity market trends. However, simultaneous short-term negative OPU effects are observed in all economies' stock markets at all quantiles. The results also underscore OPU's moderating impact on stock-carbon conditional connectedness, emphasizing the need for fund managers to acknowledge OPU as a moderating risk factor for carbon-stock hedging effectiveness.

This study investigates interconnectedness and spillover dynamics among nineteen clean energy equity sub-sectors during the COVID-19 pandemic and the Russia-Ukraine conflict. Using the Time-Varying Parameter Vector Autoregression (TVP-VAR) Joint Connectedness approach, the findings reveal intensified interconnectedness during crises, with the Total Connectedness Index (TCI) surpassing 100 % during COVID-19, while stabilizing amid the Russia-Ukraine conflict, indicating a partial resilience in clean energy markets. Sub-sectors such as Energy Management, Recycling, and Water consistently serve as risk transmitters, while Wind and Geothermal absorb risks, emphasizing heterogeneous roles within the sector. This high level of interconnectedness limits the ability to reduce risks within the clean energy sector alone during turbulent times. Policy interventions, such as subsidies and regulatory support for critical risk-transmitting sub-sectors, could stabilize the market and reduce systemic vulnerabilities. Sectors like Solar and Smart Grid adapt to market conditions, taking on different roles depending on crisis-specific factors, particularly in response to energy security and sustainability policies. Investors may enhance their portfolio stability by focusing on the risk-absorbing sub-sectors, such as Wind and Geothermal, and also adopting dynamic asset allocation strategies during crises.
•Examines spillover dynamics among 19 clean energy sub-sectors during crises.•Total connectedness peaks 100 % in COVID-19 and stabilizes during Russia-Ukraine conflict.•Energy Management, Recycling, and Water consistently transmit risk; Wind and Geothermal absorb it.•Intra-sector diversification offers limited protection during high interconnectedness periods.•Findings guide agile investment strategies and support targeted policy making.

This study explores and evaluates cash holdings patterns, including cash-driven resilience capabilities for the manufacturing and service industries, and distinguishes between business group firms and stand-alone firms. Specifically, this study uses the ANOVA Kruskal-Wallis test to examine various cash-driven resilience capabilities and the weighted least-square (WLS) to test the stated research questions. The empirical outcomes uncover that non-resilient organizations predominate over resilient ones. Moreover, the study finds that various cash-driven resilience capabilities differ significantly from a statistical viewpoint. In the process, it contributes to the literature on the impacts of COVID-19 on both manufacturing and services industries. It also uses different empirical methodologies, including Driscoll-Kraay, pooled ordinary least squares, Rogers, White, and Newey-West Fixed effects between the group estimations and the generalised method of moments (GMM) estimator to check the robustness of the findings. Based on the findings, this study recommends that the management of manufacturing and service organizations focus on increasing organizational resilience potential. This study provides a platform for managers of the business group and the standalone firms to manage the liquidity so the companies should not face any liquidity crunch during adverse economic or epidemic conditions.

We investigate the existence of the explosive behavior, long memory and causal impacts for the Islamic and conventional banking sectors in Türkiye during the 2012–2023 period. The procedure detects multiple bubble episodes in all maturities of profit-sharing rates and shorter maturities of deposit rates, mostly intensified after 2017, Islamic banking has longer-lived bubble episodes than conventional banking, and the duration decreases as the maturity increases. We find that the Hurst exponent is significantly greater than 0.50 for Islamic banking, while its magnitude decreases and goes below 0.50 for the maturity longer than 3 months in the conventional banking as the maturity increases in the full sample period. Conventional banking rates are more efficient, and the level of efficiency rises as maturity increases. We find bidirectional causalities between the 1-month maturities and report one-way short-run causal flows from the conventional banking rates for other three maturities. Multifaceted implications for investors and policymakers are discussed in detail.

This study investigates the impact of recent Artificial Intelligence (AI)-driven technological innovations on carbon prices across different quantiles, assessing the influence of AI stock prices on energy prices based on European carbon allowances while controlling for other macroeconomic factors. Using robust methods such as quantile-on-quantile regression, wavelet analysis, and transfer entropy, the research quantifies the information flow between the AI market and carbon allowances. Using daily data with four alternative AI stock prices from September 14, 2016, to December 29, 2023, the findings reveal a strong effect of AI returns on carbon prices, with significant fluctuations across price quantiles and consistent long-term average growth in market returns. The quantile-on-quantile regression analysis indicates that the short-term changes in carbon prices significantly impact the AI stock returns, with the most pronounced impact occurring below the 20th and above the 80th quantiles of carbon prices, indicating larger responses to extreme events. Additionally, large positive AI price shocks lead to substantial changes in carbon prices, particularly when the carbon prices are near their long-term average. Compared to the short term, the long-term responses are about 15 times smaller. Insights from the Rényi transfer entropy confirm these findings, while the Shannon transfer entropy estimates indicate a discernible and statistically significant information flow from the AI prices to the carbon prices. These findings offer critical insights for investors and policymakers, deepening the understanding of AI’s influence on carbon market dynamics.
•We examine the impact of Artificial Intelligence innovations on carbon prices.•We use robust methods such as the quantile-on-quantile regression, wavelet analysis, and transfer entropy.•In the short-term, changes in carbon prices significantly impact AI stock returns.•Significant positive shocks in AI prices lead to substantial changes in carbon prices.•Results from Shannon transfer entropy indicate a discernible and statistically significant information flow from AI to carbon prices.