This study examines which types of CBDCs and underlying technological architectures are most effective in enhancing financial inclusion. The results show that the launch of a CBDC significantly improves financial inclusion. A more detailed analysis reveals that retail, token-based, and non-DLT (nDLT) CBDCs are the principal drivers of financial inclusion, whereas wholesale and account-based CBDCs exhibit limited or no direct impact on household-level access to financial services. Notably, retail CBDCs are particularly effective in promoting inclusion among women, young adults, and the poorest segments of the population, underscoring their potential to reduce existing disparities in access to financial services.

Amid the growing push for sustainable and digitally enabled supply chains, the role of artificial intelligence in advancing operational efficiency and environmental performance has gained prominence. This study examines how artificial intelligence adoption and investment intensity influence supply chain efficiency among sustainability-focused firms in the European Union. Using panel data from 2022 to 2024, we analyze the impact of AI on three critical dimensions of supply chain performance: inventory turnover, gross margin return on inventory investment, and transportation costs to sales ratio. The results reveal that AI adoption significantly improves inventory responsiveness and profitability while reducing logistics costs. Moreover, higher AI investment intensity amplifies these benefits, suggesting that deeper technological integration strengthens supply chain agility and resource efficiency. These findings highlight AI's role as a strategic enabler and a sustainability lever in responsible supply chain transformation. The study contributes to the emerging discourse on the operationalization of AI within green supply chains, offering valuable implications relevant to various stakeholders at the intersection of digital innovation and environmental performance.

Purpose This paper analyses how artificial intelligence adoption and investment intensity shape financially relevant supply chain outcomes. The focus is on inventory obsolescence and deferred revenues linked to delivery performance. Design/methodology/approach The analysis relies on an unbalanced panel of publicly listed non-financial firms from the European Union. Fixed-effects panel regressions are employed to assess the effects of AI adoption and AI investment intensity. Interaction terms are introduced to capture the conditional role of logistics efficiency, proxied by inventory turnover. Firm-level financial controls and macroeconomic variables are included. Findings AI adoption is associated with lower inventory obsolescence and reduced deferred revenues. The effects strengthen with higher AI investment intensity. Results also show that AI delivers greater benefits when firms operate efficient inventory systems. Logistics efficiency amplifies the impact of AI investment indicating that digital tools are most effective when embedded in agile operational structures. Practical implications Our findings suggest that firms should move beyond symbolic AI adoption and commit resources to deep integration. Investments in AI yield stronger returns when aligned with efficient inventory management and delivery processes. For managers, AI should be treated as a structural component of supply chain strategy rather than a standalone technology. Originality/value This paper links AI engagement to financially material supply chain outcomes. It moves beyond binary adoption measures by incorporating investment intensity and operational context. The study provides new evidence on how digital transformation translates into reduced financial uncertainty within supply chains.

Investor sentiment significantly increases the vulnerability of financial markets and the fluctuations in asset prices. In this scenario, this research examines the nexus between stock prices (SP) and the fear and greed index (FGI) in the G7 countries, further exploring the hedging potential of stocks during periods of heightened fear sentiment. Utilizing the quantile correlation and quantile partial correlation methods, we find that the Japanese stock market can withstand short-term shock of fear sentiment across the full sample, as well as the pandemic and Russia-Ukraine conflict period. However, the short-term negative effects of FGI on other SPs shift in the medium and long term. Furthermore, results from the sub-sample analysis indicate that market uncertainty stemming from the pandemic and the Russia-Ukraine war heightens investors' risk aversion towards the stock market, resulting in a sharp short-term decline in SP. In the long term, investors can incorporate G7 stocks into their portfolios to mitigate investment losses arising from fear sentiment. Given the complex international environment and volatile market sentiment, governments should closely monitor investor sentiment to alleviate the adverse effects triggered by the fluctuations in SP.

The aim of the work is to solve the fractional order infectious disease model with the awareness and vaccination effects by executing reliable neural network strategies. Fractional kinds of derivatives perform higher efficiency and accuracy in comparison with the derivatives of integer kinds. The fractional order infectious disease model with the awareness and vaccination effects is separated into susceptible class, vaccinated class, infected class, quarantined class, and removed class. A construction of the proposed neural network is accomplished by a single layer construction with log-sigmoid transfer function together with 24 neurons. The model is trained using the Adam optimizer along with the Bayesian regularization, a reliable solver to perform the results of nonlinear systems. The dataset obtained between 0 and 1 with the step size of 0.01, which is divided into three states: validation 10

We investigate whether and how CEO resilience relates to green innovation. Drawing on upper echelons and imprinting theories, we argue that resilience-related experiences shape executives' strategic decisions and commitment to environmental innovation. Using a panel of Chinese listed firms from 2008 to 2021, we measure CEO resilience through two hand-collected indicators: prior experience in multiple executive roles and a military or defense background. The results show that resilient CEOs are associated with significantly higher levels of green innovation. The findings remain robust across a range of specifications, including CEO turnover analysis and instrumental-variable estimation. Additional analyses based on green patent citations indicate that CEO resilience contributes not only to the quantity but also to the quality of green innovation. We further find that resilient CEOs allocate more resources to R&D activities, exhibit stronger managerial capability, and place greater emphasis on sustainability objectives, which collectively help explain their positive association with green innovation. Moreover, the effect of CEO resilience is stronger in firms with greater resource availability and stronger external governance mechanisms. Overall, our findings highlight the importance of resilient leadership in advancing corporate environmental innovation and provide implications for boards, investors, and policymakers seeking to promote sustainable development.

ABSTRACT The supply chain is becoming less efficient because of various uncertainties. Because the majority of earlier studies have examined only individual uncertainty, determining the overall impact is challenging; earlier research did not examine how different uncertainties interact and share information. Therefore, in this paper, an analytical framework that integrates multiple dimensions of uncertainty is constructed, and the following hypothesis is proposed: Uncertainty negatively affects supply chain efficiency (SCE), and there is a synergistic inhibitory effect. Moreover, this effect is heterogeneous across different SCE quantiles. Methodologically, the joint impulse response function (jIRF), joint quantile impulse response function (jQIRF), and joint quantile forecast error variance decomposition (jQFEVD) were employed to effectively handle information overlap and capture asymmetric and time‐varying characteristics. The empirical results reveal that the individual and combined effects of these four types of uncertainties on SCE are significantly negative, and that the synergistic effect strengthens the inhibitory effect. The actual combined effect is indeed lower than the simple summation. In addition, the negative impact is significantly amplified at high quantile states, and the explanatory power of uncertainties on SCE shows a U‐shaped distribution.

The socio-energetic resilience such as alleviating energy poverty and improving resident health, as well as the eco-digital synergy between environmental regulations and digital economy, are crucial for sustainable development and social equity. This longitudinal study examines the synergistic effects of eco-digital transformations on socio-energetic resilience in China. Utilizing panel data from the 2012–2022 China Family Panel Studies (CFPS), we investigate how the convergence of environmental governance and digital economy addresses the interlinked challenges of household energy vulnerability and resident well-being. Our findings demonstrate that eco-digital synergy significantly enhances socio-energetic resilience by alleviating energy poverty and improving both physical and mental health outcomes among residents. Two primary mechanisms are identified: optimizing energy consumption structures and advancing a green digital economy. Notably, the impact varies across socioeconomic groups, with more pronounced benefits observed in disadvantaged populations. This research contributes to the literature by adopting a multidimensional approach to policy analysis, exploring the combined effects of ecological initiatives and digital transformation on socio-energetic resilience over time. It also provides insights into differential impacts across demographic segments, offering valuable guidance for targeted policy interventions. The study underscores the importance of integrated strategies for vulnerable households and highlights the potential of eco-digital synergy in fostering socio-energetic resilience and promoting energy justice within China's rapid multidimensional transformations.

The deterioration of biodiversity has become a material financial concern, influencing firm productivity and long-term value. This study examines the link between biodiversity disclosure and firm value using a sample of firms in the European Union. A composite biodiversity disclosure score is developed to capture the scope and quality of reporting across biodiversity, water, supply chain, pollution, and waste. We employ panel data from 2015 to 2023 to estimate fixed effects regressions with Tobin's Q and EV/EBITDA as valuation proxies. The results show that biodiversity disclosure is positively associated with firm valuation, particularly in biodiversity-intensive industries and among firms with higher ESG performance. As biodiversity loss and climate change are mutually reinforcing, transparent biodiversity reporting also signals firms' resilience to climate-related transition and physical risks. These findings suggest that credible biodiversity transparency enhances investor confidence, reduces information asymmetry, and reflects preparedness for both ecological and climate-related regulatory shifts. Our study contributes to the emerging literature on biodiversity and climate finance by offering a framework for measuring biodiversity disclosure and informing how investors and regulators may incorporate nature-related information into firm valuation and investment decisions.

Given the extensive environmental Kuznets curve (EKC) research, limited attention has been given to investigate technological innovation (TI), foreign direct investment (FDI), intellectual capital, and governance quality within developing economies like Malaysia. Besides, the synergistic short- and long-run environmental effects of these dimensions remain underexplored, limiting policy insights to mitigate CO2 emissions sustainably. Therefore, this study explores the impact of TI, FDI, intellectual capital, and governance quality on CO2 emissions in Malaysia, tracing in short and long-run trends by means of Pollution Haven, and Pollution Halo and EKC Hypotheses. The outcomes from the autoregressive distributed lag (ARDL) and fully modified OLS (FMOLS) exhibit that TI and intellectual capital substantially mitigate CO2 emissions, highlighting their key role in pursuing sustainable development. On the contrary, no statistical impact of FDI was found on reducing CO2 emissions. Industry growth, increased population, and GDP are also marked as dominant forces of CO2 emissions, while governance quality and government expenditure reduce CO2 emissions. Policymakers should prioritize TI and intellectual capital, while focusing on strengthening governance structures to ensure environmental compliance and improve sustainable development. Moreover, they should align FDI with green industries, promote energy efficiency, and integrate strict institutional reforms to simultaneously sustain economic growth and contribute to Malaysia's long-term CO2 emissions reduction strategy. This study extends the EKC hypothesis through the integration of TI, intellectual capital, FDI, and governance quality within a unified ARDL model. Through this integration, it further offers a multidimensional perspective on environmental sustainability, thereby contributing to the achievement of SDGs.

In this study, we examined the causal relationship between artificial intelligence (AI) and green finance (GF) in China using the rolling window causality test. Empirical results revealed a bidirectional relationship between AI and GF across different subperiods. Breakthroughs in AI and supportive policies have promoted its application in GF, but these effects are disrupted by external shocks. Conversely, GF exerts both positive and negative effects on AI development; supportive central bank policies attract social capital toward green AI, yet stagnation in GF innovation reduces its capacity to integrate AI applications, thereby imposing constraints. This study contributes by extending theoretical frameworks linking digital technology and GF by revealing time-varying causality in the context of developing green bond markets, COVID-19, and macroeconomic fluctuations. Policymakers should develop adaptive, AI-enabled policy frameworks, financial institutions need to innovate green financial instruments, and tech enterprises must advance the research and development of “Green AI”.

This paper analyzes how fossil fuel lending affects bank credit risk in the European Union. The study considers expected credit losses under IFRS 9 and the capital intensity of credit portfolios captured through credit risk weighted assets. Using regulatory and financial disclosures for 2019 to 2023, we estimate fixed effects models that relate fossil fuel exposures to both measures while controlling for bank characteristics, macroeconomic conditions, and solvency positions. Our results indicate that larger fossil fuel loan shares are associated with higher expected losses and greater risk weight intensity. Stronger capital positions weaken both relationships, which indicates that solvency shapes how transition exposed lending is reflected in accounting outcomes and prudential assessments. Our analysis also highlights the influence of sectoral concentration, operating efficiency, and past credit performance. These findings carry important policy implications. Transition exposed lending should receive greater attention in supervisory reviews and in the capital strategies that banks develop to address emerging climate risks.

This study investigates the volatility spillover dynamics associated with Central Bank Digital Currency (CBDC) uncertainty across major currencies (Chinese Yuan-CNY, U.S. dollar-USD, EURO-EUR, Japanese Yen-JPY, and British Pound-GBP) and key innovation-driven sectors, including artificial intelligence (AI), FinTech, renewable energy, and non-renewable energy. Employing a Quantile Vector Autoregression (QVAR) framework, the analysis captures timeand frequency-dependent interconnectedness across normal and extreme market regimes, with a particular focus on the COVID-19 pandemic, Russia-Ukraine war, and Israel-Hamas conflict. The results revealed that CBDC uncertainty particularly impacts CNY, followed by USD, JPY, GBP, and EUR at median quantiles. On the sectoral side, shocks in CBDCs have enormous spillover effects on renewable energy, followed by AI, FinTech, and non-renewable energy. In addition, the USD, Euro, and JPY imperatively transmit shocks to the system, but the GBP and CNY absorb shocks at different quantiles, even during the COVID-19 pandemic and the Russia-Ukraine conflict. Likewise, during the COVID-19 era, AI, renewable, and non-renewable energy transmit volatility, but FinTech absorbs shocks at different quantiles. During the Russia-Ukraine war, overall, renewable energy absorbs shocks, but AI, FinTech, and non-renewable energy strongly transmit volatility at lower and middle quantiles. However, during the Israel-Hamas conflict, all these indices transmit volatility significantly. The study offers valuable insights for investors and traders to manage risk and hedge their portfolios more effectively.

Green hydrogen holds significant promise for a sustainable energy transition, but its production, transportation, and storage (PTS) technology investments are challenged by dual uncertainties: volatile market demand and the unpredictable timing of technology breakthroughs. This paper introduces a novel multi-regime stochastic dynamic optimization framework that integrates exogenous random stopping times to optimally guide technology investment decisions in the green hydrogen sector. Utilizing a dynamic stochastic optimal control formulation solved via Hamilton-Jacobi-Bellman equations, we rigorously capture the interplay between random technological advancements and fluctuating market conditions. Our numerical simulations demonstrate that the inherent unpredictable nature of PTS technology breakthroughs significantly amplifies market volatility and leads to non-linear investment trajectories. These findings underscore the need for adaptive investment strategies, robust risk management, and dynamic policy interventions that can mitigate such uncertainties. Unique to our work is the extension of single regime-switching models to accommodate multiple sequential breakthroughs, thus providing a more realistic and comprehensive decision-making tool for stakeholders. The insights derived not only contribute to the theoretical understanding of investment under dual uncertainties but also offer practical guidance for policymakers and industry practitioners, paving the way for further research in multi-period technology breakthroughs and sustainable energy investment frameworks.

This study explores the link between energy efficiency and climate change mitigation using a broad measure of climate change, rather than relying solely on CO2 emissions. Drawing on data from 177 countries between 2000 and 2021, it employs fixed effects, quantile regression, and two-step GMM methods. The findings from the full sample indicate that higher energy efficiency correlates with higher GAIN score i.e. enhanced readiness and reduced climate vulnerability. This relationship is significant in East Asia and Pacific, Europe & Central Asia, Latin America & Caribbean, North America, south Asia and Sub-Saharan Africa, while energy efficiency does not affect GAIN in the Middle East & North Africa region. The results of quantile regression show that this relationship is more pronounced in underdeveloped countries. The results of GMM analysis show that our findings are robust. These findings suggest that policymakers may help mitigate climate change by implementing policies that improve energy efficiency.

PurposeThis study aims to investigate the impact of green technological innovation, taxes and environmental pollution on the blue economy within OECD countries.Design/methodology/approachThe Method of Moments Quantile Regression (MMQR) and Bootstrap Quantile Regression methods were used to investigate the relationship between Gross Domestic Product (GDP), Natural Resources Rents, carbon dioxide (CO2) emissions, green taxation, green innovation and blue economy. This study uses panel data covering the period from 1990 to 2022.FindingsGreen taxes demonstrate a stronger positive impact on the blue economy at lower quantiles, with this effect diminishing progressively across the distribution to higher quantiles. Green innovation exhibits a consistently robust positive relationship with the blue economy throughout the distribution, remaining statistically significant across all quantiles examined. Carbon emissions display a significant negative association with the blue economy across the entire conditional distribution, with this adverse effect intensifying at higher quantiles. The findings further reveal that both GDP and natural resources rents exert positive effects on the blue economy across all quantiles, suggesting their role as consistent drivers of blue economy development regardless of the level of blue economy performance.Originality/valueBy combining MMQR with Bootstrap Quantile Regression, this study conducts a detailed examination of how environmental and economic determinants influence blue economy performance across varying quantiles. The findings contribute to the existing body of knowledge on sustainable ocean economies and provide practical guidance for policymakers seeking to promote economic development while safeguarding environmental integrity.

To support the achievement of the United Nations' Sustainable Development Goals (SDGs), the purpose of this research is to examine the impact of population growth, poverty, and energy consumption on environmental degradation using Malthusian theory within the context of Pakistan. This research utilized the unit root test, cointegration, and the Auto-Regressive Distributed Lag (ARDL to analyze the time series data from 1972 to 2023. The findings show that population growth, poverty, and excessive energy consumption accelerate environmental degradation as proxied by COQ emissions both in the short-run and long-run. This research suggests the policymakers to develop strategies that restrict overpopulation, control poverty growth, and unsustainable overconsumption of energy to reduce the environmental degradation, ecological collapse, and ultimately contribute to the SDGs. This article provides novel contribution by examining the cumulative influence of population growth, poverty rate, and energy consumption patterns on environmental degradation proxy by CO2 emissions for the first time from the lens of Malthus theory in Pakistani context. Moreover, this study contributes to the goals of poverty alleviation, energy consumption, and addressing environmental challenges as outlined by the SDGs.

Green SMEs are vital in transitioning to a sustainable economy, but face persistent financing constraints. Banks remain cautious about lending to these firms despite policy efforts due to perceived risks and uncertain returns. This study examines how lending to green SMEs affects banking performance in the Eurozone using an exhaustive sample of banks between 2013 and 2023. We employ fixed effect panel regression and report that higher loan exposure to green SMEs enhances NIM and Z Score, indicating that sustainable lending can be profitable and stabilizing. Green lending standards improve profitability but do not directly affect solvency, suggesting that certification alone does not reduce financial risk. These findings highlight the strategic value of sustainable finance for banks while reinforcing the need for tailored risk frameworks to support green SME lending. The study provides actionable insights for entrepreneurs, banks, regulators, and policymakers, demonstrating that green SME financing can align financial returns with sustainability objectives.