This study examines the micro-level impact of renewable energy transition on key socio-economic indicators within a resource-dependent economy. Using firm-level panel data from 42 large Saudi firms spanning 2012–2024, we employ Generalized Method of Moments (GMM) estimation and Impulse Response Function (IRF) analysis. Our purpose is to assess the effects of renewable infrastructure investment (RII) and renewable adoption on employment, energy access, and firm revenue growth (FRG). The findings indicate that RII has a short-run positive impact on employment and energy access, a trend that has been accentuated following Saudi Arabia’s Vision 2030 announcement. However, the positive effect of RII on FRG manifests only in the long run and is conditional on oil price volatility. Furthermore, the employment benefits are not distributed equally between large firms and SMEs. Practical implications suggest that the initial policy should focus on infrastructure investment and subsidies to boost short-term job creation and energy access. Subsequently, policy must pivot to foster broader structural transformation through widespread adoption of renewable energy to achieve sustainable, inclusive long-term economic growth.

This study investigates the influence of country risk on foreign direct investment (FDI) inflows in ASEAN nations over the period 1998–2022, focusing on political, economic, and financial risk components. Employing the Fixed Effects Model (FEM), this study analyzes panel data across ASEAN countries to assess how different aspects of country risk affect FDI attraction. The findings reveal that while no significant overall connection exists between country risk and FDI inflows across the entire sample, country-specific characteristics play a crucial role. Specifically, negative impacts are observed in developing nations, nations with lower FDI inflows, and those with relatively low-risk profiles. Political risks significantly discourage foreign investments, especially in developing economies, economies with difficulty attracting foreign capital, and those considered high-risk. By contrast, financial and economic risks generally exhibit no significant influence on FDI. Nonetheless, reducing economic risk emerges as an important factor for enhancing FDI in countries with low levels of FDI attraction, whereas mitigating financial risk is critical for countries that attract higher volumes of FDI. Country risk impacts FDI inflows in a nuanced, country-specific manner, with political risk being the most significant deterrent for developing and high-risk ASEAN nations. Based on this finding, we are able to provide recommendations to address distinct dimensions of country risk and foster a more favorable investment environment across ASEAN member states.

Digital Financial Inclusion (DFI) has developed rapidly worldwide and is considered an important tool to alleviate Household Financial Vulnerability (FV). However, most of the existing studies are limited to short-term effects and are based on single-time cross-sectional data, lacking a dynamic analysis of the impact of digital inclusive finance before and after. Therefore, based on the CFPS data from 2018 and 2022, employing an Ordered Probit regression model and its marginal effect analysis, combined with mediating effect tests, heterogeneity analysis, and robustness tests, the following hypotheses are verified: (1) DFI significantly reduces household financial vulnerability; (2) this effect is partly realized by increasing household income and easing credit constraints; (3) low-income households and economically underdeveloped areas rely more on DFI to alleviate financial vulnerability; (4) after the epidemic, the mitigation effect of DFI on household financial vulnerability is enhanced. The results show that digital financial inclusion plays a significant role in reducing household financial vulnerability, and digital inclusive finance can also reduce household financial vulnerability by increasing household income. At the same time, the role of digital inclusive finance is more significant in economically developed regions and low-income families. Additionally, the marginal effect of digital financial inclusion is stronger in 2022 after the pandemic compared to 2018.

The energy transition in the MENA region remains complex due to its heavy reliance on fossil fuels. Using quantile-on-quantile regression on panel data (2001–2023) for 13 MENA countries, this study examines the effect of global trade policy uncertainty on renewable energy transition, alongside income, oil rent, innovation, carbon dioxide emissions, and trade openness. The results reveal heterogeneous effects across quantiles. A 1% rise in trade policy uncertainty increases renewable energy transition by 0.26% at the 0.1 quantile but decreases it by 0.05% at the 0.9 quantile, implying that such uncertainty stimulates transition only in economies already shifting toward renewables. Income negatively affects transition (–0.82% to –0.34%), confirming a resource curse, while trade openness heightens fossil fuel dependence (–0.52% to –1.23% across quantiles). Conversely, oil rent and trade openness jointly promote renewable adoption (0.38%–0.46%), and innovation enhances transition (0.02%–0.11%). However, higher carbon dioxide emissions impede renewable growth (–0.33% to –0.53%). Overall, renewable energy progress in the MENA region is uneven, with fossil fuel-centric economies lagging. The findings highlight that while trade policy uncertainty may catalyze early-stage transitions, excessive openness and dependence on fossil resources limit renewable expansion in advanced stages. Hence, trade policies should integrate decarbonization goals, channel windfall gains into renewable projects, promote innovation spillovers, and dismantle trade barriers to accelerate sustainable energy transformation across the region.

The study investigates the effects of a single-digit (6%–9%) interest rate set by the Bangladesh Bank in 2020, involving 94 listed manufacturing companies across 10 industries from 2018 to 2023. The key objective of the research is to examine the impact of such interest rate reform on financial performance, proxied by Return on Assets (ROA), Return on Equity (ROE), and Market-to-Book (M/B) ratio. Fixed and random effects panel data analysis techniques and the system generalized method of moments (GMM) have been applied to address heteroskedasticity, autocorrelation, and endogeneity. The results show that lower interest rates improve ROA and ROE, especially for capital-intensive industries like textiles and engineering, because of reduced borrowing costs. Higher lending rates and debt leverage adversely affect the M/B ratio, indicating that investors are concerned about the increase in debt levels. Price fluctuations in the exchange rate affect firm performance, which relies on imports. Additionally, firms with larger sizes and higher GDP growth perform better in the market. The study highlights the need for a dynamic and sector-focused interest rate policy to improve the resilience of the manufacturing sector, as well as strategies like capacity building in financial management to ensure long-term sustainability. To address the limitations of this study, future research should use probability sampling together with unlisted firm data and combine primary and secondary data while expanding the study period after the post-pandemic period. Moreover, cross-industry analysis will help achieve a better understanding of long-term interest rate effects on manufacturing firm performance.

The accurate and timely prediction of financial market crises remains a persistent challenge for economists, policymakers, and investors. Traditional early warning systems (EWS) often rely on low-frequency macroeconomic indicators and static econometric models, limiting their effectiveness in dynamic market environments. This study proposes to fill this gap by developing a novel framework for crisis prediction through constructing a Composite Early Warning Index (CEWI) that integrates daily data from financial markets, macroeconomic fundamentals, and political uncertainty indicators. Principal Component Analysis (PCA) was employed to synthesize these diverse variables into a single latent factor, capturing the underlying systemic risk. Machine learning algorithms, including Logistic Regression, Random Forest, and XGBoost classifiers, were trained on historical data spanning from 2000 to 2025 to predict crisis periods, defined by sharp equity market declines and official recession declarations. The XGBoost model achieved superior performance with an ROC-AUC of 0.953. Feature importance analysis utilizing SHAP values identified market volatility (VIX), gold prices, and oil prices as the most influential predictors. The results demonstrate that combining high-frequency financial and political indicators with advanced machine learning techniques significantly enhances crisis prediction accuracy. The proposed CEWI-based framework offers a powerful tool for early risk detection and has important implications for financial regulation, investment strategy, and economic policy design.

This study constructs an analytical framework grounded in the principles of Poisson distribution and the process of discontinuous variation. Through empirical examinations conducted on all publicly listed firms in Chinese stock markets, the research uncovers an inverse relationship between the discontinuous variance of stock returns from the preceding month and the stock returns of the subsequent month. Statistically, the average monthly return of the portfolio with the lowest discontinuous variation is 1.32%, and the average monthly return of the portfolio with the highest discontinuous variation is 0.30%, indicating that an increase in discontinuous variance by 1 unit is associated with a subsequent decrease in stock returns of 1%, on average. Furthermore, portfolios engaged in long-short arbitrage, which are formulated based on sorting portfolios of different discontinuous variances, exhibit superior performance compared to the market portfolio. The effective annual return of this long-short portfolio is 1.44%, which is larger than that of the market index (1.19%). Also, the Sharpe ratio of the long-short portfolio is 0.19, which is almost double that of the market index (0.10). The results of portfolio performance suggest that trading strategies based on Poisson distribution and discontinuous variation sorting possess a significant capacity to generate abnormal returns.

This research examines the effect of non-interest income on banks' performance in Malaysia, focusing on how diversification affects banks of different sizes. The study utilized a balanced panel data set of 14 commercial banks from 2008 to 2023 and employed panel Autoregressive Distributed Lag (ARDL) methodology, using Pooled Mean Group (PMG) to identify how income diversification influences bank performance across various bank sizes in Malaysia. The findings reveal that large banks benefit from non-interest income in both the short and long term, whereas small banks still depend on traditional banking in the long term. The reliance of small banks significantly affects the overall performance of the banking sector in Malaysia. It is recommended that regulators create a supportive framework for smaller banks to diversify into non-interest income activities, especially incorporating fintech, which has driven innovation in the financial industry. The study also highlights the regulator’s role in promoting non-interest-based revenue by encouraging industry players to adopt fintech innovations. This will help improve the stability and performance of Malaysia's overall banking sector, equipping banks to face future challenges.

This study examines the impact of green product and green process innovation on the financial performance of export-oriented SMEs in Bali Province, with sustainable business strategy as a mediating variable. Grounded in the Resource-Based View (RBV) paradigm, the research emphasizes the role of distinctive resources in achieving a competitive advantage. A quantitative approach was employed, involving a survey of 452 export-oriented SME actors in Bali. Data were analyzed using Partial Least Squares-Structural Equation Modeling to explore the relationships between green innovation, sustainable business strategy, and financial performance. The results indicate that both green product innovation and green process innovation significantly and positively influence export financial performance. Furthermore, sustainable business strategy mediates the relationship between green innovation (both product and process) and financial performance. These findings highlight the importance of integrating sustainable practices to amplify the financial benefits of green innovation. The study provides actionable insights for SME actors and policymakers, suggesting that adopting sustainable business strategies can enhance competitiveness in the global market. Recommendations include fostering green innovation and embedding sustainability into core business strategies to improve financial outcomes. This research contributes to the theoretical framework by integrating green innovation and sustainable strategy concepts within the context of export-oriented SMEs.

This study investigates how institutional quality influences the linkage between economic growth and factor inputs specifically physical capital, labor, human capital, and R&D within China’s leading provinces. Drawing on annual panel data from 1996 to 2022 for Guangdong, Jiangsu, Shandong, Zhejiang, Henan, and Sichuan, a panel threshold modeling approach is employed to identify non-linear effects of institutional quality on the relationship between factor inputs and growth. A robustness check is conducted to validate the findings. The empirical results suggest that economic growth benefits from factor inputs only when institutional quality surpasses a critical threshold. This study provides empirical evidence that institutional quality mediates the effectiveness of factor inputs in driving economic growth. While physical capital, labor, and human capital remain influential across different institutional settings, R&D contributes significantly only above a certain institutional threshold. Policymakers should prioritize strengthening institutional frameworks to optimize the productivity of investments in physical capital, labor, human capital, and R&D.