Data from the 2019 and 2021 waves of the China Household Finance Survey were used to examine whether digital skills and financial knowledge jointly shape financial asset diversification among Chinese households headed by adults aged 60 years or older. Digital skills were measured by smartphone use and third-party mobile-payment-account adoption, financial knowledge by correct responses to interest-rate and inflation questions, and capability complementarity by the interaction between the two standardized indices. Pooled ordinary least squares models with province and year fixed effects, temporal-prediction specifications, inverse-probability weighting, and alternative measurement and sample checks were estimated. Digital skills, financial knowledge, and their interaction were positively associated with diversification. In the preferred specification, the corresponding coefficients were 0.0134, 0.0118, and 0.0167, respectively, and all were significant at the 1% level. Capability complementarity was concentrated in broader portfolio scope: dual-enablement households were more likely to form multi-asset portfolios and less likely to hold only cash and bank deposits, with significant super additive increments. The association was stronger among households willing to accept some risk and weaker among households residing in rural areas whose heads held agricultural hukou status. The findings indicate that digital operational competence and financial judgment function as complementary capabilities and support integrating digital-skills training with financial education for older adults.
This research aims to examine the dynamic interrelationships among pivotal Jordanian macroeconomic indicators, including GDP, CPI, IPI, M2, Worker Remittances (WRMIT), and Amman Stock Exchange Index (ASEI) over the period spanning from 2012 to 2022. The study employs many statistical methodologies, including the Johansen co-integration test, ARDL approach, the Error Correction Model (ECM), and the Granger causality test. The findings of this study robustly affirm the existence of both long-term and short-term relationships between the ASE and the macroeconomic variables. Specifically, the research substantiates a sustained long-term equilibrium association between ASEI and GDP, CPI, M2, IPI, and WRMIT. Furthermore, it identifies a long-term causality running from these economic indicators to the Amman Stock Exchange, as well as a short-term causality from CPI, GDP, and WRMIT to the ASE. These results raise questions about the ASE's operational efficiency, and investors should incorporate prevailing macroeconomic variables into the investment decision process
As climate change intensifies, governments fund risk mitigation and recovery in vulnerable coastal communities. This study investigates the factors influencing grant acquisition using an original dataset of nearly 60 Massachusetts coastal towns, supplemented by 10 in-depth interviews with stakeholders in the field. Employing a mixed-methods approach, combining quantitative regression analysis and qualitative insights, we examine correlations with state-level climate grant activity. Controlling for geographic, demographic, bonding social capital, and voter turnout variables, our regression analysis reveals two significant predictors of grant acquisition: the demographic makeup of the community and bridging social capital. These findings suggest that having a greater proportion of minority population as well reduced access to external resources drive grant allocations. This research offers actionable recommendations for local communities, NGOs, and policymakers seeking to engage with residents facing the consequences of climate change.
The high-quality development of enterprises in strategic emerging industries plays a crucial role in achieving high-quality economic development. As a form of long-term strategic investment adhering to the principles of value investing, patient capital is emerging as a key driver of high-quality development in these enterprises. Based on data from 743 listed enterprises in China’s strategic emerging industries from 2014 to 2023, this study examines the differential effects of patient capital on the high-quality development of these enterprises. It further tests the mediating roles of digital-green transformation synergy, information asymmetry, and financing constraints, as well as the moderating role of artificial intelligence applications. The results indicate: First, patient capital plays a significant role in promoting the high-quality development of enterprises in strategic emerging industries; second, patient capital promotes the high-quality development of these enterprises by enhancing the level of synergy in digital and green transformation, alleviating information asymmetry, and easing financing constraints. Third, the application of artificial intelligence enhances the positive impact of patient capital on the high-quality development of enterprises in strategic emerging industries; fourth, the impact of patient capital on the high-quality development of enterprises in strategic emerging industries exhibits distinct differences across regional heterogeneity and industrial characteristics. Analysis of regional heterogeneity reveals that enterprises in the central region are more sensitive to patient capital in terms of high-quality development, while an analysis of industrial heterogeneity reveals that the effects of two distinct forms of patient capital—stable equity and relationship-based debt—are more pronounced in promoting high-quality development in the new energy vehicle industry, energy conservation and environmental protection industry, biotechnology industry, new materials industry, and next-generation information technology industry. Compared to relationship-based debt, stable equity significantly promotes high-quality development in the high-end equipment manufacturing and new energy industries. This study provides theoretical foundations and policy implications for leveraging patient capital to drive high-quality development in strategic emerging industries.
This study examines whether governance-based adaptation capacity reduces human exposure to natural disasters in 14 disaster-exposed OECD countries over the period 2000–2024. Using a two-way fixed-effects framework with Driscoll–Kraay standard errors, the analysis evaluates the joint roles of temperature anomalies, disaster frequency, and governance capacity in shaping the disaster-affected population. The results show that temperature anomalies have a nonlinear effect on human exposure, while disaster frequency increases disaster-affected population across all model specifications. By contrast, governance-based adaptation capacity reduces baseline exposure. In the preferred specification, the coefficient of governance capacity is negative (−0.983), whereas the interaction between disaster frequency and governance capacity is positive (0.071), indicating that institutional capacity lowers baseline vulnerability but does not fully offset the effect of repeated hazard occurrence. These findings suggest that governance-based adaptation plays a protective role, yet its effectiveness remains conditional on the scale and frequency of climate-related hazards. The results underline the importance of strengthening institutional capacity as part of broader disaster risk reduction and adaptation strategies.
Climate change has increased both the frequency and intensity of agricultural droughts, reinforcing the need for risk transfer instruments that rely on objective environmental indicators. This study introduces a comprehensive framework for the development of drought index insurance using openly available climatic and satellite data. The approach builds synthetic indicators that combine precipitation-based measures (SPI, SPEI) with vegetation indices (NDVI), selected through statistical learning techniques such as Lasso regression, Random Forest, and Partial Least Squares. These indicators are then embedded in parametric indemnity functions whose shape depends on their correlation with crop yields, ensuring decreasing or increasing payouts as appropriate. Calibration with historical yield data is performed to minimize basis risk, and pure premiums are derived through simulation methods including empirical resampling, bootstrap, Monte Carlo, and kernel density estimation. The framework is applied to three Moroccan regions with contrasting agro-climatic conditions and representative crops. Results indicate that the proposed design substantially lowers basis risk while preserving transparency, interpretability, and reproducibility. More broadly, the framework provides a transferable methodological contribution by linking machine learning with actuarial tools, supporting the development of weather index insurance in contexts where data availability remains limited.
The purpose of the paper, the main research process and the methods adopted, the main results and important conclusions should be expressed clearly in concise and clear language. If possible, mention as stated in Basel accord, the Creditrisk+ is a main tool for running stress testing in a credit portfolio. There are some modifications to original format of this protocol. In the current manuscript, in a sub-credit portfolio with heterogeneous obligors, with the same nonrandom probability of default, some limiting behaviors of total loss of portfolio are studied. In the case of random and correlated probabilities of default with ARTA models (autoregressive to any things model), by the Eigen analysis, some other limiting distributions are proposed. Finally, simulation results are proposed to verify some parts of the limiting results.
Bank profitability is a relevant topic in financial economics, particularly in emerging markets. This study investigates the determinants of bank profitability in Indonesia over the 2011–2024 period, focusing on the underexplored segment of Primary Dealer banks. A Panel ARDL–PMG methodology is applied to a balanced panel of 13 banks over 14 years (2011–2024) to examine the long-run and short-run effects of macroeconomic and bank-specific variables on return on assets (ROA) and return on equity (ROE). The results identify operational efficiency (BOPO) and capital adequacy (CAR) as the dominant long-run determinants of bank profitability. A key asymmetry is observed in the NPL effect: while NPL consistently erodes equity returns (ROE), its effect on asset returns (ROA) is largely insignificant in most specifications and negative only when exchange rate controls are included. Among macroeconomic variables, the policy interest rate positively affects both profitability measures in the long run, while the exchange rate exhibits asymmetric effects across ROA and ROE. In the short run, internal bank factors dominate, with macroeconomic effects emerging predominantly over the long horizon. The paper contributes to the banking literature by highlighting asymmetric profitability dynamics within a dynamic panel framework and providing policy-relevant insights for bank management and financial regulatory authorities.
In order to promote the theoretical research and practical development of tax credit rating system, this paper summarizes the research status of the impact of tax credit rating system on enterprises in China. At present, scholars’ research on the impact of China’s tax credit rating system on enterprises mainly focuses on the impact of tax credit rating system on enterprise business activities, the impact of tax credit rating system on enterprise business performance, and the impact of tax credit rating system on capital market. Overall, scholars have conducted extensive research on the impact of tax credit rating system on enterprise business activities, while research on the impact of tax credit rating system on enterprise business performance is relatively weak. In particular, the research on the impact of the tax credit rating system on the capital market is relatively weak. Future research can focus on the impact of tax credit rating system on the M&A activities of listed companies, the impact of tax credit rating system on the dividend strategy of listed companies, the synergy between tax credit rating and third-party credit rating and the internal credit rating of commercial banks.
This study aims to examine the influence of financial inclusion dimensions accessibility (ACC), availability (AVA), and usability (USE) on the Inclusive Green Growth Index (IGGI) across all provinces in Indonesia, and to assess the moderating role of Digital Infrastructure (DIF) in strengthening this relationship. The study uses panel data from all provinces in Indonesia for the period 2019-2024 and employs three analytical approaches: Fixed Effects Model (FEM), Panel Vector Error Correction Model (PVECM), and moderation analysis using DIF. The findings reveal that in the FEM, ACC and USE have a positive and significant effect on IGGI, while AVA has a negative significant effect. The dynamic analysis using PVECM indicates that in the short term, ACC negatively affects IGGI, AVA and USE positively affect IGGI, whereas in the long term, ACC remains negative, USE remains positive, and AVA is no longer significant. The moderation analysis shows that DIF does not significantly moderate the effects of ACC and USE, while AVA is negatively significant, indicating that digital infrastructure has not fully enhanced the role of financial inclusion in promoting inclusive green growth. These results imply that policy strategies should focus on expanding equitable access to financial services, enhancing financial and digital literacy, strengthening regulatory support, and tailoring digital infrastructure development to regional and demographic conditions to foster sustainable inclusive green growth. The study’s limitations include the use of quantitative DIF measures without assessing the quality of technology adoption. Future research is recommended to explore the impact of financial inclusion at the micro level, adopt more current indicators, and conduct analyses by demographic regions or cross-country comparisons to validate findings.
The growth of fintech services in emerging markets has accelerated financial inclusion while simultaneously introducing digital risks that may compromise consumer trust. This study investigates how perceived digital risks, technology dependence, and cybersecurity perceptions influence user trust in fintech platforms. Drawing from the Technology Acceptance Model (TAM) and Perceived Risk Theory, the research analyses responses from Indonesian fintech users using Structural Equation Modelling (SEM) via AMOS. The data were collected in May 2025 through an online survey. Out of 300 questionnaires distributed, 284 were returned and 250 were valid for analysis. The findings indicate that emerging risks and technology dependence significantly shape both cybersecurity perceptions and trust in digital financial platforms. Additionally, cybersecurity itself exerts a direct and statistically significant influence on consumer trust. Hypothesis testing results confirm that all proposed hypotheses (H1–H7) are supported, indicating that trust in fintech is simultaneously shaped by user perceptions of risk, technology dependence, and cybersecurity. These results suggest that trust in fintech is driven by both user perceptions of risk and the perceived strength of platform-level security systems. Practically, the study urges fintech providers to adopt transparent and user-centric security strategies, and encourages regulators to prioritize digital literacy and robust cybersecurity governance in the rapidly evolving fintech landscape.
This study examines the dynamic relationship between the adoption of artificial intelligence (AI) and carbon dioxide (CO2) emissions, focusing on the moderating roles of governance quality (GQI) and digital infrastructure (DII) across 104 countries from 2000 to 2023. Using two-step system GMM and two-stage least squares (2SLS) estimations, the findings reveal that AI, while enhancing innovation and productivity, currently contributes to higher CO2 emissions, particularly in economies with weak governance and underdeveloped digital ecosystems. Strong institutional quality and advanced digital infrastructure significantly mitigate this effect, suggesting that GQI and DII are critical for realizing AI’s potential as a sustainable technology. The results further reveal pronounced heterogeneity across energy-efficient and energy-inefficient countries as well as low-AI and high-AI stages, indicating that the environmental impact of AI is weaker in settings characterized by higher energy efficiency and early-stage AI diffusion, but stronger in energy-inefficient and AI-advanced contexts. These findings underscore the context-dependent nature of AI’s environmental outcomes and highlight the importance of governance-driven digital transformation for achieving sustainable growth.
Irregular financial activities (IFAs) pose serious challenges to regulators, especially in China where high-profile scams have highlighted gaps in oversight. This study develops a machine learning framework to identify such risks using a dataset of 540 financial cases from 2014 to 2024. Activities are classified as irregular or normal, and the performance of 18 algorithms—including traditional machine learning, ensemble methods, and deep learning models—is compared. Ensemble learning models demonstrate superior performance in detecting IFAs, balancing high accuracy with practical applicability. In particular, Bagging and LightGBM achieve the highest accuracy and robust F1-scores among all tested methods. These findings offer novel insights and technical tools for early warning of IFAs, contributing to the literature on financial risk detection and informing policy design. This comparison is among the first systematic evaluations of diverse machine learning algorithms for IFA detection in China, bridging a gap in the literature on regulatory technology and risk management. The proposed approach provides regulators with real-time, data-driven tools to identify irregularities before substantial losses occur.
This study examines the factors influencing value co-creation behavior among patients utilizing online health services, grounded in the Theory of Planned Behavior (TPB). A structured questionnaire was distributed to 304 patients who had experienced online healthcare services and regularly used social media. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings show that all hypothesized relationships were statistically significant, with Attitude, Subjective Norms, and Perceived Behavioral Control collectively explaining 78.3% of the variance in Value Co-creation Behavior. This confirms the robustness of the extended TPB model in explaining how patients participate in online healthcare platforms. Additionally, the mediation analysis indicated that Attitude, Subjective Norms, and Perceived Behavioral Control fully mediate the influence of Trust, Health Consciousness, Self-Efficacy, EWOM, and Facilitating Conditions on Value Co-creation Behavior. These results imply that cognitive, social, and technological factors affect co-creation indirectly through core TPB components. The study advances theory by incorporating additional contextual and technological factors that better reflect the complexities of digital healthcare environments. Practically, it offers valuable insights for healthcare managers and digital marketers on how to build trust, foster social engagement, and enhance technological support to promote patient involvement and value co-creation. Finally, the study recognizes its cross-sectional design as a limitation and suggests that future longitudinal and cross-cultural research could strengthen the applicability of these findings.
Transitioning from fossil fuels to renewable sources unquestionably contributes significantly to reducing carbon emissions and is the way forward to achieving sustainable development goals. This study, thus, aims to empirically investigate the effects of renewable energy consumption on environmental sustainability (carbon emissions) in the Asia-Pacific region, with a specific emphasis on governance quality as a moderator. Using the MMQR approach for 25 Asia-Pacific countries over the period 1998-2022, the analysis reveals that renewable energy consumption is significantly and negatively correlated with CO2 emissions across all quantiles, with the effect becoming more evident between the 0.20 and 0.90 quantiles. The results further highlight that effective governance significantly impacts the inverse linkage between renewable energy consumption and carbon emissions. Such a moderating effect is most pronounced in the middle and upper quantiles, where better institutions will have a higher probability of converting renewable energy policies into positive environmental results. In addition, this study employs income-segmented MMQR analysis to highlight the extent to which the drivers of carbon emissions differ across economic groups. These findings emphasize the need for a multidimensional policy approach that unites renewable energy promotion and institutional quality enhancements. Thereby, the study contributes to the existing body of literature by offering quantile-specific insights for formulating focused and effective emission-reduction policies.
The effect of sustainability reporting on the liquidity position of listed entities in Nigeria was investigated using quantitative approach and data extracted from the annual reports of 76 listed companies spanning fourteen years (2010-2023). The sustainability reporting variables included economic, environmental, social, and governance disclosures, while the performance indicator comprised liquidity. Panel regression analysis, specifically the random effects model, was conducted based on the Hausman test results, ensuring robust and reliable findings. Liquidity, as proxied by the Acid Test Ratio (ATR), was significantly influenced by sustainability reporting dimensions, with economic, environmental, social, and governance disclosures exhibiting varying effects. Economic disclosures negatively impacted liquidity, suggesting that firms emphasizing financial transparency may allocate resources to long-term investments or debt servicing rather than maintaining liquid assets. Environmental disclosures also had a significant negative effect on liquidity. Social disclosures negatively impacted liquidity, indicating that investments in community engagement, employee welfare, and other social initiatives might constrain a firm’s ability to maintain liquid assets. Governance disclosures, on the other hand, exhibited a strong positive relationship with liquidity, emphasizing their role in fostering financial discipline and resource optimization. While governance disclosures support the argument that transparency enhances stakeholder confidence and financial outcomes, the negative effects of economic, environmental, and social disclosures highlight the short-term cost implications of addressing diverse stakeholder interests. These findings suggest that firms must carefully balance the competing demands of liquidity management and sustainability reporting to achieve optimal outcomes.
This study aims to examine the impact of cryptocurrencies and blockchain technology on the global financial landscape, with a particular focus on their interaction with macroeconomic indicators and traditional financial markets. The research uses a global econometric approach based on secondary data collected for the period 2013–2024. Variables such as Bitcoin (dependent), Ethereum, S&P 500, VIX, M2, inflation, interest rates, and the Crypto Volatility Index were analyzed using the GEE (Generalized Estimating Equations) model. Data analysis was conducted with STATA, while visualizations were created using Excel. The results indicate that several macroeconomic variables such as S&P 500, VIX, inflation, interest rates, and crypto volatility have a statistically significant impact on Bitcoin prices. In contrast, Ethereum and M2 did not show a significant effect in the model. The study confirms that cryptocurrencies are increasingly influenced by traditional economic dynamics, challenging the perception of complete independence from financial systems. These findings can support financial policymakers, investors, and institutions in understanding how cryptocurrencies behave in relation to global market movements. The study also offers insights for regulatory frameworks and the future development of Central Bank Digital Currencies (CBDCs). This research contributes to the existing literature by combining blockchain, cryptocurrencies, and macroeconomic analysis through a global econometric model, offering an updated and comprehensive view of their interconnected roles in financial markets.
Rising energy crisis and overreliance on fossil fuels pose a significant threat to the environment and intensify climate change risks in emerging economies. Understanding the psychological drivers of energy-saving behavior is crucial for mitigating these risks. Thus, this study uniquely extended Value-Belief-Norm (VBN) theory and offered novel insights into the roles of environmental values, moral norms, and perceived behavioral control (PBC) in energy-saving behavior in Bangladesh. Researchers surveyed energy consumers in Dhaka and Chattogram, two major cities of Bangladesh, using the judgment sampling method preceded by a pilot study. They obtained 467 valid responses and analyzed the data using Structural Equation Modeling in SmartPLS 4. The results show that biospheric and altruistic values positively influence moral norms that, in turn, drive energy-saving behavior. However, the positive and significant impact of hedonic values on moral norms challenges earlier evidence about the motivations for energy conservation. Also, moral norms mediate the relationships between values and energy-saving behavior. Although a positive moderation impact is observed, PBC fails to significantly moderate the association between moral norms and energy-saving behavior. All these unique findings regarding consumers’ psychological drivers will enable policymakers and energy-saving advocacy groups to design messaging strategies and behavioral intervention programs in Bangladesh and similar emerging economies.
This study evaluates and ranks G20 countries' green finance utilization (GFU) using the CRITIC-TOPSIS multi-criteria decision-making method, based on fifteen indicators across financial, environmental, developmental, and innovation dimensions. Germany ranks highest due to strong performance in renewable energy, environmental sustainability, human development, and innovation. Despite leading in green bond issuance, China scores lower due to high emissions and fossil fuel dependence. Brazil ranks unexpectedly high, driven by its renewable energy mix, while France, Japan, the UK, and India also perform well. Lower rankings for Saudi Arabia, Russia, and South Africa reflect weak climate action and fossil fuel reliance. Alternative methods such as VIKOR, EDAS, MOORA, and COPRAS were applied for robustness, showing consistent country rankings. Sensitivity analysis using equal indicator weights further confirmed the stability of the results. The findings emphasize that green finance (GF) effectiveness relies on integrating financial instruments within broader sustainability strategies, offering a valuable benchmark for policymakers and guiding future research in climate finance.
This study explores the relationship between liquidity reserves (LR) and carbon emissions (CE) in emerging economies—Bangladesh, India, and China—using a balanced panel of 1,500 firm-year observations from 2015 to 2024. Drawing from agency and resource-based theories, we examine how corporate governance mechanisms and R&D investment (RDI) influence this relationship. Using a two-step system GMM estimator, we find that higher LR is positively associated with CE, suggesting potential managerial misuse of idle cash. However, specific board characteristics—such as larger board size, two-tier structures, and external consultants—significantly mitigate this effect. Notably, RDI moderates the LR–CE relationship, reducing its positive impact by 37%, highlighting its role as a strategic buffer against environmental harm. The study contributes to the literature by integrating governance and innovation as moderating mechanisms in the cash-emission nexus. These findings have significant implications for corporate strategy and environmental policy, especially in developing markets where governance structures are often weak. Encouraging RDI and governance reforms may help realign financial flexibility with sustainability goals.
