This study examines the effects of mobile broadband, R&D intensity, and cash-based financial infrastructure on SDG performance in selected high-performing European countries over the period 2010-2022. In order to determine whether these relationships vary not only at the level of average effects but also across different levels of SDG performance, the analysis draws on quantile-based methods in addition to mean-based estimators. According to the findings obtained from the baseline model, long-run analysis, robustness and sensitivity checks, R&D intensity and mobile broadband use have statistically significant and positive effects on SDG performance. By contrast, ATM density, interpreted as an indicator of cash-based financial infrastructure and an earlier stage of financial digitalization, is negatively associated with SDG performance. The quantile estimates further reveal that, although the direction of these effects remains stable, their magnitude varies across the distribution of SDG performance. Overall, the findings indicate that, in advanced sustainability settings, SDG performance is shaped not simply by the expansion of infrastructure, but by the specific forms through which digital connectivity, innovation effort, and financial access are organized. The results underscore the importance of strengthening innovation capacity, expanding inclusive broadband-based digital connectivity, and promoting financial infrastructures that are more closely aligned with sustainable development.

The integration of artificial intelligence (AI) technologies into financial systems has fundamentally transformed market dynamics. To address this issue, this study aims to examine the dynamic and asymmetric effects of AI technologies on e-commerce markets by considering cross-country differences. In this context, the relationship between the Global X Artificial Intelligence Technology ETF (AIQ.Q) index and the CSI Overseas China Internet (CSI), Dow Jones Internet Commerce (DJIC), and Solactive E-commerce (SLCTV) indices is analysed between March 9, 2021, and May 30, 2025, through the Wavelet Quantile-on-Quantile Regression (WQQR) method. The findings indicate that the impact of AI investments varies significantly across countries and market structures. The findings reveal that across all quartiles and periods, an increase in the AI index is connected with an increase in the e-commerce indices, except the Chinese index. Specifically, the effect of AI on the Chinese e-commerce index (CSI) appears weak and negative in the short term, negative and insignificant in most of the quartiles in the medium term, and negative and more pronounced in the long term, while it shows strong and consistent positive effects on the U.S. (DJIC) and European (SLCTV) e-commerce indices across all time horizons. This suggests that the level of technological adaptation, investment in digital infrastructure, and the speed of AI integration play crucial roles in shaping market performance. These findings are of critical importance for both the public and private sectors in developing data driven policies that aim to enhance AI adoption and improve digital market efficiency.

This study investigates how geopolitical risks shape environmental sustainability by influencing carbon dioxide (CO2) and methane (CH4) emissions across the global economy. To provide a granular understanding of geopolitical-environmental interactions, the analysis separately examines the eight subdimensions of the Geopolitical Risk (GPR) index developed by Caldara and Iacoviello (2022), including war threats, terrorist acts, military tensions, political disputes, and nuclear risks. Using monthly data from November 2002 to March 2025, the study applies the Quantile-on-Quantile Connectedness (QQC) method to uncover the directional, asymmetric, and time-varying dependencies between geopolitical risk categories and greenhouse gas emissions. The results reveal strong nonlinear connectedness patterns, indicating that CO2 and CH4 emissions respond differently across risk regimes and emission quantiles. Military escalations, economic tensions, and disruptions in trade-related geopolitical channels intensify emissions particularly during high-volatility periods, reflecting increased pressure on energy supply chains, fossil-fuel dependency, and industrial activity. Conversely, terror-related risks and diplomatic tensions behave predominantly as shock receivers, suggesting weaker transmission toward environmental indicators. This study provides an important analytical framework for energy and environmental policymakers, economic planners, and international institutions, helping them to anticipate environmental risks arising from geopolitical fluctuations and to design more sustainable policy strategies.

As the global economy transitions towards sustainability, the interplay between established and emerging clean energy sources has emerged as a critical determinant of equity market dynamics. This study investigates the impact of clean energy and alternative clean energy on Turkey’s stock market performance within a quantile framework. The Borsa Istanbul (BIST) National 100 Index is used as a proxy for stock market performance. Clean energy indicators include Hydro, Geothermal, Wind, and Solar energy, while the combination of nuclear and other renewable resources is classified as an alternative energy source. To capture potential non-linearities and distributional effects, the study employs quantile-on-quantile regression (QQR) and quantile causality (CQ) methods. These econometric approaches are particularly well-suited for uncovering the asymmetric relationships across the distribution of variables. The empirical results indicate that both clean energy and alternative energy sources positively influence the Turkish stock market performance. Furthermore, a well-developed banking sector facilitates investment in clean energy projects. These findings provide important insights for energy and environmental investors, as well as for policymakers aiming to promote sustainable energy transitions and strengthen financial market resilience.

The inflow of refugees has already become a stable feature of the world economy, with dire fiscal, social, and macroeconomic consequences for host nations. Even though the discourse on the effect of the arrival of refugees has been largely defined in the humanitarian context, over the years, the economic consequences of the given phenomena have been contested. This study analyzes the macroeconomic impacts of refugee inflows in 17 countries that received those between 2000 and 2023, in terms of growth performance measures and stability measures. Second-generation panel econometric methods consisting of a fixed-effects model and Driscoll-Kraay standard errors (DKSE) were used to undertake the analysis to deal with heteroskedasticity, autocorrelation, and cross-sectional dependence. The robustness measure was applied using Fully Modified Ordinary Least Squares (FMOLS), Canonical Cointegrating Regression (CCR), and Fixed-Effects Ordinary Least Squares (FE-OLS). The findings show that the impact of the inflow of refugees on the growth of GDP is statistically significant, negative, and mainly due to fiscal strains, unemployment influence, and increasing inflation. On the other hand, trade openness continuously increases economic performance, whereas inflation, unemployment, and excessive government expenditure are detrimental to performance. Causality analysis reveals that, instead of having a direct effect on GDP, refugee arrivals have an indirect effect on GDP via the labor market and fiscal effects. These findings highlight that refugee inflows are structural processes with long-term economic impact. The implications of the policy are the necessity to enhance the integration of the labor market, shift fiscal resources towards productive spheres, maintain macroeconomic stability, and foster the idea of burden sharing to reconcile humanitarian demands with economic sustainability.

This study explores the largely overlooked impact of agricultural R&D investments on long-term CO2 emissions. By mapping emissions by country and year, it examines their relationship with agricultural R&D alongside economic and environmental factors. Utilizing the Dynamic System GMM approach alongside several benchmark fixed-effects and random-effects regression models, all requisite panel data diagnostic tests were conducted to ensure the robustness of the empirical results. Findings reveal that increased investment in agricultural R&D significantly reduces emissions, highlighting agriculture's untapped potential in climate change mitigation. The study underscores the crucial role of artificial intelligence, digital transformation, green mechanization, and automation in accelerating emission reduction. More than just statistics, these insights serve as a call to action for policymakers, providing concrete evidence that agriculture is a key player in global decarbonization efforts.

This paper investigates the role of human capital in fostering financial sector development in developing economies, an area that has received limited attention despite the extensive literature on the relationship between human capital and economic growth. The study empirically analyzes data from 28 developing countries over the period 1990–2019, employing FGLS, DOLS, FMOLS, and Driscoll-Kraay estimation techniques for robust results. Causality is assessed using the Dumitrescu-Hurlin bootstrap approach. The findings demonstrate that human capital significantly contributes to long-term financial sector development. Additionally, economic growth, trade openness, and remittances are positively associated with financial sector expansion. The causality analysis reveals bidirectional causality between human capital and financial sector development, as well as between other explanatory variables and financial development. The results suggest that policymakers should prioritize human capital development, alongside strategies to stimulate economic growth, trade openness, and remittance flows, to support financial sector advancement.

As nations accelerate their digital and financial transformations in pursuit of sustainability, a critical question emerges: do technological and financial advancements truly deliver environmental gains, or do they mask deeper ecological costs? This study explores the dual impact of artificial intelligence (AI) innovation and financial development (FD) on environmental sustainability in the world's 15 most innovation-driven economies over the period 2000-2023. Using the Method of Moments Quantile Regression (MMQR), the analysis captures heterogeneous effects across different levels of carbon emissions. The findings reveal a paradox: while AI innovation is often positioned as an enabler of green growth, it is associated with increased CO2 emissions in energy-intensive economies, particularly at higher quantiles. In contrast, financial development contributes positively to environmental quality, but its benefits taper off as emission levels rise. Energy consumption plays a critical role in both relationships, amplifying or dampening environmental outcomes depending on a country's emission profile. These results challenge the optimistic narrative around digital and financial solutions, urging policymakers to reconsider how innovation is governed and aligned with environmental objectives. The study offers practical insights for stakeholders seeking to bridge the gap between green promises and real-world ecological outcomes.

Recent developments in the literature reveal that nuclear energy, as well as renewable energy, can be used to combat environmental degradation. However, it is a fact that this literature, which focuses on the relationship between nuclear energy and environmental degradation, does not include green growth and green innovation in environmental pollution models. In order to fill this gap in the literature, our study analyzes the nuclear energy-ecological footprint relationship by using 1994–2021 panel data of 15 leading countries in nuclear energy consumption and adding the variables in question to the empirical model. The long-term estimates are explored using the FMOLS, DOLS, and AMG estimators. The causality analysis is carried out by the Dumitrescu-Hurlin bootstrap causality procedure. In the long run, it is found that nuclear energy consumption, green growth, and green innovation improve environmental quality by mitigating the ecological footprint. Since natural resources and economic growth encourage the ecological footprint, they have a detrimental effect on environmental quality. Causality analysis points to unidirectional causality from nuclear energy consumption, green innovation, and natural resources to the ecological footprint. Additionally, a bidirectional causal linkage is detected between green growth, economic growth, and ecological footprint. The empirical findings obtained from the analysis can give important clues to the countries in question in the fight against environmental pollution. Especially in improving environmental quality, policymakers should focus on proposals focused on nuclear energy, green growth, and green innovation.

This study examines the impact of corporate governance structures and sustainability incentives on environmental, social, and governance (ESG) performance in e-commerce companies and further analyzes the moderating role of board gender diversity in this relationship. Panel data from 193 US firms listed on NASDAQ and NYSE during the period 2019-2024 are employed, with fixed-effect estimations supported by two-step System Generalized Method of Moments (System GMM) to ensure robustness. The findings reveal that board gender diversity and independent directors significantly enhance ESG performance, whereas CEO-chairman duality undermines it, consistent with agency theory. Board size shows no significant effects, while the annual frequency of board meetings has a significant negative impact on ESG performance, underscoring the importance of governance quality over quantity. Moreover, the presence of CSR committees is found to contribute positively to ESG performance, particularly in the environmental, social, and governance dimensions. Conversely, sustainability-linked compensation incentives are found to significantly contribute to ESG performance; however, these impacts are not evident in the environmental, social, and governance dimensions. Lastly, we found that the joint effect of board gender diversification and sustainability-linked compensation incentives decreases the ESG performance; however, this effect also vanishes with robustness analysis. Overall, the results highlight critical governance mechanisms that can strengthen ESG outcomes in e-commerce companies, providing valuable implications for enhancing corporate sustainability in this rapidly evolving sector.

Artificial intelligence (AI) has become one of the main driving forces of transformation in the financial sector, while simultaneously generating significant implications for environmental sustainability and sustainable development processes. This study analyzes the effects of AI investments in the financial sector on CO2 and total greenhouse gas (GHG) emissions, as well as on Sustainable Development Goal 7 (SDG 7) performance. The analysis is conducted using data from 13 countries with sufficient data availability over the period 2014-2023, compiled from the OECD AI Policy Observatory, the World Bank's World Development Indicators (WDI), and the Sustainable Development Report. The empirical analysis employs Driscoll-Kraay standard errors and the method of moments quantile regression (MMQR) approach. In addition, the robustness of the findings against potential endogeneity is tested using the two-stage least squares (2SLS) method. The results indicate that AI investments in the financial sector have a statistically significant and negative effect on CO2 and GHG emissions, with this effect being more pronounced in countries with higher emission levels. In contrast, although the impact of AI investments on SDG 7 performance remains positive across both models, the findings provide only limited empirical support. Overall, the results suggest that AI investments in the financial sector can serve as an important tool for reducing environmental pressures. Accordingly, it is recommended that policymakers design financial digitalization processes in alignment with environmental objectives and integrate AI-based financial applications with sustainability-oriented strategies.

Sustainability has become a critical concern in finance, in particular for the insurance industry, which faces rising environmental and social risks. This paper examines the influence of environmental, social, and governance (ESG) factors on the performance of global insurance companies. Using a comprehensive dataset of 22 life and 59 non–life insurance firms from 2013 to 2022, we employ panel data analysis to explore the relationship between ESG scores and key performance metrics. Our findings reveal that higher ESG scores are significantly associated with a higher return on assets, more efficient management of expense and loss ratios, and increases in investment returns. These results show the importance of incorporating ESG factors into insurance decision-making to enhance sectoral resilience and corporate performance. The study concludes by emphasizing the need for insurers to leverage their technical expertise and strategic risk management capabilities in order to address sustainability challenges effectively.

While extensive research has explored the nexus between eco-innovations, financial development, foreign direct investment (FDI), and the ecological footprint (EFP), studies focusing specifically on Saudi Arabia—an oil-dependent economy where the Pollution Halo or Heaven effects are particularly relevant—remain limited. This study investigates the impact of eco-innovations, financial development, FDI, and clean energy on environmental quality (measured by EFP) in Saudi Arabia over the period 1995–2022, employing the autoregressive distributed lag (ARDL) model. The results indicate that eco-innovations and clean energy contribute to reducing environmental degradation, whereas financial development exacerbates it. Moreover, FDI improves environmental conditions by lowering EFP, providing evidence of the Pollution Halo effect in the Saudi context. Robustness checks, including alternative estimators, confirm the stability of these findings. Furthermore, the Granger causality analysis reveals a unidirectional causal flow from all explanatory variables to EFP, except for renewable energy. Based on these results, the study proposes several policy implications to help mitigate and manage environmental challenges in Saudi Arabia.

This study examines the dynamic interactions between selected Metaverse tokens—RENDER, FLOKI, SAND, AXS, and MANA—and key financial indicators, including Bitcoin (BTC), gold (XAU), Brent crude oil (BRT), and the U.S. dollar (USD), using the Wavelet Transform Coherence (WTC) method. By analyzing daily price data from July 25, 2024, to February 24, 2025, this research investigates how Metaverse tokens co-move with both cryptocurrency and traditional financial assets across different time and frequency domains. The results reveal statistically significant short- and medium-term coherence between Metaverse tokens and BTC, particularly in the case of FLOKI, indicating strong sensitivity to broader crypto market trends. In contrast, relationships with traditional assets such as XAU, BRT, and USD are found to be weak, intermittent, or statistically insignificant, suggesting that Metaverse tokens remain largely decoupled from conventional market dynamics. These findings highlight the potential of Metaverse tokens as diversification instruments and offer important implications for portfolio construction and risk management. The study contributes to the emerging literature on digital finance by applying a time–frequency framework to an underexplored class of tokens, offering insights into their evolving role in a hybrid financial system.

As emerging and transitional economies navigate an increasingly complex global landscape, the traditional mechanisms of risk propagation undergo profound transformations. This study investigates the risk contagion structure among Russia’s main stock index (MOEX), geopolitical risk (GPR), US-China tensions (UCT), the ruble exchange rate, and Brent oil prices using monthly data from January 1999 to February 2024. We employ the recently developed Quantile-on-Quantile Connectedness Analysis (QQCA) proposed by Gabauer and Stenfors (2024), which allows us to capture asymmetric and regime dependent spillovers across the entire joint distribution. The results reveal that connectedness intensifies sharply under extreme market conditions, with risk contagion peaking in the lower and upper tails (5th and 95th quantiles) and remaining comparatively weaker during normal periods. Geopolitical risk emerges as a dominant shock transmitter during high-risk regimes, whereas Brent oil prices and MOEX occupy structurally central positions in the system through energy and capital flow channels. In contrast, the ruble and the VIX predominantly function as shock absorbers, mitigating systemic fluctuations in most market states. These findings highlight the nonlinear and state dependent nature of financial vulnerability in energy dependent and geopolitically exposed economies. The study offers novel evidence on the systemic role of Russia within a multidimensional risk network and provides actionable implications for policymakers and investors concerned with financial stability and tail-risk management.

This manuscript investigates the nonlinear impact of Geopolitical Risk (GPR) on the returns of five leading cryptocurrencies, including Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Binance Coin (BNB), and Tron (TRX), spanning the period from 2017 to 2025. By employing the novel Quantile-on-Quantile Granger Causality (QQGC) approach and the Quantile-on-Quantile Regression (QQR) for robustness, the analysis explores how geopolitical shocks drive market dynamics across various quantiles. The empirical results reveal significant asymmetric transmissions and distributional heterogeneity, indicating that the influence of GPR varies across different market conditions and specific digital assets. These findings demonstrate that geopolitical instability acts as a critical driver of price movements in the cryptocurrency ecosystem. Consequently, the results provide essential insights for investors and policymakers regarding risk mitigation and strategic portfolio allocation. This research contributes to financial literature by clarifying the complex interplay between non-financial global shocks and digital asset pricing in an increasingly uncertain international environment.

This study investigates the safe-haven properties of different asset classes during periods of geopolitical risk in T & uuml;rkiye and examines their comovement with the Geopolitical Risk Index. The research covers the period from January 2010 to January 2023 and employs wavelet coherence analysis and quantile-on-quantile regression methods. The findings reveal that geopolitical risk has varying long-and short-term effects on financial markets and housing prices. Additionally, our results show that the housing market can anticipate geopolitical risk fluctuations in the long run, and investors tend to shift to real estate during high-risk periods. Although gold acts as a safe haven in both the long and short run when geopolitical risk rises, oil prices respond in various ways in terms of direction and timing. Furthermore, fixed-income instruments are not perceived as safe-haven assets during periods of heightened geopolitical risk; instead, they tend to increase in parallel with perceptions of rising risk. The findings offer valuable insights into how investors formulate strategies during periods of uncertainty and have significant implications for policy makers and market participants.

China’s transition from fossil fuels to clean energy to attain environmental and economic co-sustainability necessitates changes in its energy policy landscape. Such policy shifts impact the confidence of both businesses and consumers. Building on this argument, this research article investigates the response of China’s business and consumer confidence towards energy-related uncertainty for the period February 2000 to October 2022. Utilizing a novel energy-related uncertainty measure and the quantile-on-quantile regression approach, the empirical analysis underscores that China’s business confidence is hostile towards energy-related uncertainty across all quantiles. The quantiles of consumer confidence mirror the outcomes of business confidence, recording the negative impact of energy-related uncertainty, except for a few quantiles. The empirical outcomes indicate that to augment business and consumer confidence, Chinese policymakers should strive to minimize energy-related uncertainty through transparent communication with stakeholders and a gradual shift from fossil fuels to clean energy sources.

This study examines the effects of artificial intelligence (AI) investments in the agricultural sector on agricultural carbon dioxide (CO2) emissions. The study analyses nine countries that have made the most investments in AI technologies, using data from the period 2012-2023. The empirical analysis was conducted using a combination of the Panel-Corrected Standard Errors method and the Method of Moments Quantile Regression technique. The findings suggest that AI investments in agriculture may have a positive and increasing effect on carbon emissions across quantiles. This effect was observed to be more pronounced at higher emission levels. Among the control variables, per capita income (GDP) generally exhibits a weak and negative relationship, while the rural population ratio has a significant positive effect on agricultural carbon emissions. In contrast, the trade openness variable plays a role in reducing emissions. The findings are significant for policymakers, environmental economists, and agricultural technology developers, as they highlight the need to carefully assess the environmental impacts of artificial intelligence investments and develop country-specific sustainable strategies.