Pub Date : 2026-05-01Epub Date: 2026-02-09DOI: 10.1016/j.ribaf.2026.103339
Mengtong Liu , Lidong Wu
We examine the differential impacts of deep-level and surface-level board diversity on bank risk. Using data from Chinese listed banks between 2008 and 2023, we develop a research framework to empirically analyze these relationships. Results demonstrate that deep-level diversity significantly reduces bank risk, whereas surface-level diversity increases it. Mechanism analyses reveal that decision-making prudence, risk management capability, and managerial agency behavior mediate these relationships. Furthermore, moderation analyses indicate that meeting frequency strengthens the negative relationship between deep-level board diversity and bank risk and weakens the positive relationship between surface-level board diversity and bank risk. Conversely, remote board meetings weaken the ability of deep-level board diversity to reduce bank risk and strengthen the ability of surface-level board diversity to increase bank risk.
{"title":"How do deep-level and surface-level board diversity affect bank risk?","authors":"Mengtong Liu , Lidong Wu","doi":"10.1016/j.ribaf.2026.103339","DOIUrl":"10.1016/j.ribaf.2026.103339","url":null,"abstract":"<div><div>We examine the differential impacts of deep-level and surface-level board diversity on bank risk. Using data from Chinese listed banks between 2008 and 2023, we develop a research framework to empirically analyze these relationships. Results demonstrate that deep-level diversity significantly reduces bank risk, whereas surface-level diversity increases it. Mechanism analyses reveal that decision-making prudence, risk management capability, and managerial agency behavior mediate these relationships. Furthermore, moderation analyses indicate that meeting frequency strengthens the negative relationship between deep-level board diversity and bank risk and weakens the positive relationship between surface-level board diversity and bank risk. Conversely, remote board meetings weaken the ability of deep-level board diversity to reduce bank risk and strengthen the ability of surface-level board diversity to increase bank risk.</div></div>","PeriodicalId":51430,"journal":{"name":"Research in International Business and Finance","volume":"85 ","pages":"Article 103339"},"PeriodicalIF":6.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-19DOI: 10.1016/j.ribaf.2026.103317
Mengping Liu , Wenjie Zhang , Hao Liang
As artificial intelligence drives the technological revolution, understanding its impact on corporate non-financial governance is essential. Using unbalanced panel data of China’s A-share listed firms, in this study, we employ a multi-period difference-in-differences (DID) model to examine the impact of the Artificial Intelligence Innovation and Development Pilot Zones (AIIDPZ) on corporate environmental disclosure quality, treating the policy rollout as a quasi-natural experiment. We find that the establishment of AIIDPZ significantly elevates the quality of environmental information disclosure. This finding remains robust after a series of tests, including propensity score matching-DID, placebo tests, and corrections for potential biases in two-way fixed effects models. Moreover, AIIDPZ enhances environmental information disclosure quality by improving green total factor productivity and reducing R&D costs. Heterogeneity analysis indicates that larger firms, newer firms, highly competitive industries, and firms facing higher financing constraints benefit most from the establishment of AIIDPZ. Government subsidies and organizational inertia further amplify the impact of AIIDPZ on corporate environmental information disclosure. This study contributes empirical evidence on the micro-level environmental consequences of AI industrial policies and offers policy implications for promoting green development through digital intelligence.
{"title":"Value effect of AI innovation zones: Green premium and cost reduction pathways in environmental disclosure","authors":"Mengping Liu , Wenjie Zhang , Hao Liang","doi":"10.1016/j.ribaf.2026.103317","DOIUrl":"10.1016/j.ribaf.2026.103317","url":null,"abstract":"<div><div>As artificial intelligence drives the technological revolution, understanding its impact on corporate non-financial governance is essential. Using unbalanced panel data of China’s A-share listed firms, in this study, we employ a multi-period difference-in-differences (DID) model to examine the impact of the Artificial Intelligence Innovation and Development Pilot Zones (AIIDPZ) on corporate environmental disclosure quality, treating the policy rollout as a quasi-natural experiment. We find that the establishment of AIIDPZ significantly elevates the quality of environmental information disclosure. This finding remains robust after a series of tests, including propensity score matching-DID, placebo tests, and corrections for potential biases in two-way fixed effects models. Moreover, AIIDPZ enhances environmental information disclosure quality by improving green total factor productivity and reducing R&D costs. Heterogeneity analysis indicates that larger firms, newer firms, highly competitive industries, and firms facing higher financing constraints benefit most from the establishment of AIIDPZ. Government subsidies and organizational inertia further amplify the impact of AIIDPZ on corporate environmental information disclosure. This study contributes empirical evidence on the micro-level environmental consequences of AI industrial policies and offers policy implications for promoting green development through digital intelligence.</div></div>","PeriodicalId":51430,"journal":{"name":"Research in International Business and Finance","volume":"84 ","pages":"Article 103317"},"PeriodicalIF":6.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-27DOI: 10.1016/j.ribaf.2026.103329
Manish , Rishman Jot Kaur Chahal
Aiming to bridge quantitative finance with behavioral economics, this study harnesses artificial intelligence (AI) to integrate high-quality market sentiment into portfolio optimization. It evaluates the performance of the Black–Litterman (BL) asset allocation model, incorporating investor views generated from state-of-the-art deep learning (DL) models. These models are trained on three distinct datasets—technical (TD) derived from historical US sectoral ETF prices, sentiment (SD) obtained from Refinitiv’s MarketPsych Analytics (LSEG), and their combination (TSD). The proposed framework replaces subjective expert views with data-driven forecasts to enhance accessibility for retail investors. Portfolios are constructed with daily rebalancing based on DL-forecasted prices and account for transaction costs under different market regimes and risk aversion rates. The findings reveal that BL models incorporating the integrated TSD with lower risk aversion () significantly outperform those based on TD, SD, or traditional benchmarks, underscoring the robustness of combining technical and sentiment signals for view generation and highlighting its effectiveness for growth-oriented strategies. Under normal market conditions, TD and SD-based portfolios exhibit comparable average performance on risk-adjusted evaluation metrics; however, in high-volatility regimes, TD-based portfolios consistently outperform their SD counterparts on average. This study advocates for TSD-based, DL-enhanced BL models with lower risk aversion as a robust strategy in dynamic market environments, offering practical guidance for retail investors and insights for policymakers on harnessing AI to strengthen financial decision-making.
{"title":"Bridging behavioral insights and quantitative finance: AI-powered Black–Litterman framework with technical and sentiment signals","authors":"Manish , Rishman Jot Kaur Chahal","doi":"10.1016/j.ribaf.2026.103329","DOIUrl":"10.1016/j.ribaf.2026.103329","url":null,"abstract":"<div><div>Aiming to bridge quantitative finance with behavioral economics, this study harnesses artificial intelligence (AI) to integrate high-quality market sentiment into portfolio optimization. It evaluates the performance of the Black–Litterman (BL) asset allocation model, incorporating investor views generated from state-of-the-art deep learning (DL) models. These models are trained on three distinct datasets—technical (TD) derived from historical US sectoral ETF prices, sentiment (SD) obtained from Refinitiv’s MarketPsych Analytics (LSEG), and their combination (TSD). The proposed framework replaces subjective expert views with data-driven forecasts to enhance accessibility for retail investors. Portfolios are constructed with daily rebalancing based on DL-forecasted prices and account for transaction costs under different market regimes and risk aversion rates. The findings reveal that BL models incorporating the integrated TSD with lower risk aversion (<span><math><mrow><mi>λ</mi><mo>=</mo><mn>1</mn></mrow></math></span>) significantly outperform those based on TD, SD, or traditional benchmarks, underscoring the robustness of combining technical and sentiment signals for view generation and highlighting its effectiveness for growth-oriented strategies. Under normal market conditions, TD and SD-based portfolios exhibit comparable average performance on risk-adjusted evaluation metrics; however, in high-volatility regimes, TD-based portfolios consistently outperform their SD counterparts on average. This study advocates for TSD-based, DL-enhanced BL models with lower risk aversion as a robust strategy in dynamic market environments, offering practical guidance for retail investors and insights for policymakers on harnessing AI to strengthen financial decision-making.</div></div>","PeriodicalId":51430,"journal":{"name":"Research in International Business and Finance","volume":"84 ","pages":"Article 103329"},"PeriodicalIF":6.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-22DOI: 10.1016/j.ribaf.2026.103327
Nader Naifar
This study examines the predictive power of climate risk and ESG sentiment on the performance of clean energy markets using novel non-parametric econometric techniques. To capture the dependence structures and asymmetries across the conditional distribution of returns, we employ univariate quantile-on-quantile regression (QQR), multivariate quantile-on-quantile regression (MQQR), quantile-on-quantile Granger causality (QQGC), and quantile-on-quantile connectedness (QQC) approaches using data from May 2015 to March 2025. Our findings indicate that ESG momentum is a significant and state-contingent predictor of clean energy returns, with its influence most substantial under bullish market regimes or during recovery phases following downturns. Climate policy uncertainty (CPU) exhibits a nonlinear and asymmetric impact, exerting the most significant predictive power and connectedness during both pessimistic and euphoric states. Investor attention (ATT), while more erratic, plays an amplification role under extreme sentiment conditions. The MQQR model reveals that the effects of ESG, CPU, and ATT are not isolated but interactively reinforce or offset each other, depending on the prevailing market state. The QQGC and QQC results validate the robustness of these interdependencies, confirming that dynamic, joint effects of sentiment and policy signals shape the sensitivity of clean energy markets.
{"title":"Do climate risk and ESG sentiment predict clean energy performance? Evidence from quantile-on-quantile analysis","authors":"Nader Naifar","doi":"10.1016/j.ribaf.2026.103327","DOIUrl":"10.1016/j.ribaf.2026.103327","url":null,"abstract":"<div><div>This study examines the predictive power of climate risk and ESG sentiment on the performance of clean energy markets using novel non-parametric econometric techniques. To capture the dependence structures and asymmetries across the conditional distribution of returns, we employ univariate quantile-on-quantile regression (QQR), multivariate quantile-on-quantile regression (MQQR), quantile-on-quantile Granger causality (QQGC), and quantile-on-quantile connectedness (QQC) approaches using data from May 2015 to March 2025. Our findings indicate that ESG momentum is a significant and state-contingent predictor of clean energy returns, with its influence most substantial under bullish market regimes or during recovery phases following downturns. Climate policy uncertainty (CPU) exhibits a nonlinear and asymmetric impact, exerting the most significant predictive power and connectedness during both pessimistic and euphoric states. Investor attention (ATT), while more erratic, plays an amplification role under extreme sentiment conditions. The MQQR model reveals that the effects of ESG, CPU, and ATT are not isolated but interactively reinforce or offset each other, depending on the prevailing market state. The QQGC and QQC results validate the robustness of these interdependencies, confirming that dynamic, joint effects of sentiment and policy signals shape the sensitivity of clean energy markets.</div></div>","PeriodicalId":51430,"journal":{"name":"Research in International Business and Finance","volume":"84 ","pages":"Article 103327"},"PeriodicalIF":6.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-13DOI: 10.1016/j.ribaf.2026.103299
Xinyan Xie, Junfeng Li, Kai Wu
This study investigates the influence of local optimistic expectations on corporate capital structure decisions. Leveraging a sample of Chinese A-share listed firms from 2010 to 2022, we document a robust positive association between regional optimism and corporate leverage ratios. This effect is particularly salient among firms that rely on short-term debt and operate in financially constrained regions. Instrumental variable regressions substantiate a causal interpretation of these findings. The results remain robust across a battery of sensitivity tests, including alternative variable definitions and sample restrictions. Mechanism analysis indicates that optimism shapes capital structure by driving investment expansion and amplifying the transmission of market sentiment. Collectively, our findings underscore the pivotal role of collective sentiment in shaping micro-level financial policies.
{"title":"Local optimistic expectations and corporate capital structure decisions","authors":"Xinyan Xie, Junfeng Li, Kai Wu","doi":"10.1016/j.ribaf.2026.103299","DOIUrl":"10.1016/j.ribaf.2026.103299","url":null,"abstract":"<div><div>This study investigates the influence of local optimistic expectations on corporate capital structure decisions. Leveraging a sample of Chinese A-share listed firms from 2010 to 2022, we document a robust positive association between regional optimism and corporate leverage ratios. This effect is particularly salient among firms that rely on short-term debt and operate in financially constrained regions. Instrumental variable regressions substantiate a causal interpretation of these findings. The results remain robust across a battery of sensitivity tests, including alternative variable definitions and sample restrictions. Mechanism analysis indicates that optimism shapes capital structure by driving investment expansion and amplifying the transmission of market sentiment. Collectively, our findings underscore the pivotal role of collective sentiment in shaping micro-level financial policies.</div></div>","PeriodicalId":51430,"journal":{"name":"Research in International Business and Finance","volume":"84 ","pages":"Article 103299"},"PeriodicalIF":6.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145969433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-05DOI: 10.1016/j.ribaf.2026.103335
Barbara Čeryová, Peter Árendáš, Jana Kotlebová
Artificial Intelligence (AI) has emerged as a key innovation in the global economy, with AI-related equities becoming major drivers of the recent stock market upswing. Yet, the internal dynamics and heterogeneity within the AI sector remain largely unexplored. We address this gap by analyzing the connectedness and risk transmission among four AI industries: hardware manufacturers, cloud providers, application developers, and AI-intensive BigTech firms, using self-constructed stock market indices for 2023–2025. Adopting an industry-level perspective, we explicitly account for the heterogeneous structure of the AI sector. By capturing both static dependence and dynamic patterns, we assess how intrasectoral relationships evolve over time and under varying market conditions. Results indicate strong positive relationships among the four AI industries, which intensify during periods of market stress. Cross-industry spillovers account for more than half of return variation in the AI sector, though they remain modest under normal conditions. During sharp market swings, particularly downturns, shocks propagate widely across all industries. While AI hardware manufacturers and AI BigTech typically act as net shock transmitters and AI application developers and cloud providers as net receivers, their roles shift over time. Our results emphasize the need for dynamic hedging, as static diversification offers limited protection in stressed markets, and closer monitoring of cross-industry exposures. Given the growing dependence of many sectors on AI infrastructure, disruptions in key AI segments may have wider systemic effects and should be incorporated into macroprudential oversight.
{"title":"Connectedness and risk transmission across artificial intelligence industries","authors":"Barbara Čeryová, Peter Árendáš, Jana Kotlebová","doi":"10.1016/j.ribaf.2026.103335","DOIUrl":"10.1016/j.ribaf.2026.103335","url":null,"abstract":"<div><div>Artificial Intelligence (AI) has emerged as a key innovation in the global economy, with AI-related equities becoming major drivers of the recent stock market upswing. Yet, the internal dynamics and heterogeneity within the AI sector remain largely unexplored. We address this gap by analyzing the connectedness and risk transmission among four AI industries: hardware manufacturers, cloud providers, application developers, and AI-intensive BigTech firms, using self-constructed stock market indices for 2023–2025. Adopting an industry-level perspective, we explicitly account for the heterogeneous structure of the AI sector. By capturing both static dependence and dynamic patterns, we assess how intrasectoral relationships evolve over time and under varying market conditions. Results indicate strong positive relationships among the four AI industries, which intensify during periods of market stress. Cross-industry spillovers account for more than half of return variation in the AI sector, though they remain modest under normal conditions. During sharp market swings, particularly downturns, shocks propagate widely across all industries. While AI hardware manufacturers and AI BigTech typically act as net shock transmitters and AI application developers and cloud providers as net receivers, their roles shift over time. Our results emphasize the need for dynamic hedging, as static diversification offers limited protection in stressed markets, and closer monitoring of cross-industry exposures. Given the growing dependence of many sectors on AI infrastructure, disruptions in key AI segments may have wider systemic effects and should be incorporated into macroprudential oversight.</div></div>","PeriodicalId":51430,"journal":{"name":"Research in International Business and Finance","volume":"84 ","pages":"Article 103335"},"PeriodicalIF":6.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-07DOI: 10.1016/j.ribaf.2026.103338
Li Sun
The Porter Hypothesis argues that pollution is often a sign of resource misallocation and operational inefficiency, and that environmental challenges, rather than acting purely as a cost, can drive firms to improve efficiency, innovate, and restructure. Building on this theoretical framework, we examine whether carbon emissions, as a proxy for environmental inefficiency, are associated with firms’ decisions to discontinue business operations. Using a sample of 33,323 U.S. firm-year observations from 2002 to 2023 and carbon emissions data from Trucost, we find a significant positive relation between emissions and the likelihood of discontinued operations, suggesting that firms with more emissions are more likely to discontinue business operations.
{"title":"Carbon emissions and discontinued operations","authors":"Li Sun","doi":"10.1016/j.ribaf.2026.103338","DOIUrl":"10.1016/j.ribaf.2026.103338","url":null,"abstract":"<div><div>The Porter Hypothesis argues that pollution is often a sign of resource misallocation and operational inefficiency, and that environmental challenges, rather than acting purely as a cost, can drive firms to improve efficiency, innovate, and restructure. Building on this theoretical framework, we examine whether carbon emissions, as a proxy for environmental inefficiency, are associated with firms’ decisions to discontinue business operations. Using a sample of 33,323 U.S. firm-year observations from 2002 to 2023 and carbon emissions data from Trucost, we find a significant positive relation between emissions and the likelihood of discontinued operations, suggesting that firms with more emissions are more likely to discontinue business operations.</div></div>","PeriodicalId":51430,"journal":{"name":"Research in International Business and Finance","volume":"84 ","pages":"Article 103338"},"PeriodicalIF":6.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-17DOI: 10.1016/j.ribaf.2026.103312
Saibal Ghosh
Although several aspects of liquidity creation have been addressed in the literature, one area that appears to have been largely bypassed is the impact of insolvency reforms. To address this issue, we utilise bank-level data for countries with dual banking systems from 2010 to 2020 and assess its impact. In this regard, we exploit the staggered nature of insolvency reforms across countries and use a difference-in-differences specification to assess their impact. The findings reveal that insolvency reforms reduce banks' liquidity creation by an average of 2.8 per cent, primarily on the asset side. This impact differs between conventional and Islamic banks, as well as in terms of asset and liability side drivers. We view this as an early exercise for countries with dual banking systems to assess the association between insolvency reforms and liquidity creation.
{"title":"Liquidity creation in dual banking systems: Do insolvency reforms matter?","authors":"Saibal Ghosh","doi":"10.1016/j.ribaf.2026.103312","DOIUrl":"10.1016/j.ribaf.2026.103312","url":null,"abstract":"<div><div>Although several aspects of liquidity creation have been addressed in the literature, one area that appears to have been largely bypassed is the impact of insolvency reforms. To address this issue, we utilise bank-level data for countries with dual banking systems from 2010 to 2020 and assess its impact. In this regard, we exploit the staggered nature of insolvency reforms across countries and use a difference-in-differences specification to assess their impact. The findings reveal that insolvency reforms reduce banks' liquidity creation by an average of 2.8 per cent, primarily on the asset side. This impact differs between conventional and Islamic banks, as well as in terms of asset and liability side drivers. We view this as an early exercise for countries with dual banking systems to assess the association between insolvency reforms and liquidity creation.</div></div>","PeriodicalId":51430,"journal":{"name":"Research in International Business and Finance","volume":"84 ","pages":"Article 103312"},"PeriodicalIF":6.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-03DOI: 10.1016/j.ribaf.2026.103286
Zhenyi Yang , Shuo Ding , Dafeng Ye , Xiaping Cao
How Environment, Society and Governance (ESG) affect economic welfare has been widely studied, however, how each pillar influences each other is under-explored. In this study, from a cross-country perspective, we explore how environmental policy influences the country-level social-pillar performance by focusing on the labor employment in the Association of Southeast Asian Nations (ASEAN). Based on multinational data of the ASEAN countries from 2000 to 2021, we find that the implementation of feed-in tariffs (FIT) policy, a key renewable energy promotion policy, significantly reduce unemployment rate in the ASEAN region. The two primary channels include that the renewable energy policy promotes industrial expansion and lowers the energy price. Further, we find that the FIT policy help decrease unemployment rate in countries with better fundamental infrastructure, stable political environment, lower reliance on traditional energy, and more foreign inflows. Moreover, we find that the economic linkages with China amplify the policy effect. This study shows that the environmental policy can help improve social condition in developing countries, and these countries need to upgrade domestic infrastructure and use foreign support to promote the positive effect.
{"title":"Enhancing country-level social-pilar performance through environmental policy: Evidence from ASEAN countries","authors":"Zhenyi Yang , Shuo Ding , Dafeng Ye , Xiaping Cao","doi":"10.1016/j.ribaf.2026.103286","DOIUrl":"10.1016/j.ribaf.2026.103286","url":null,"abstract":"<div><div>How Environment, Society and Governance (ESG) affect economic welfare has been widely studied, however, how each pillar influences each other is under-explored. In this study, from a cross-country perspective, we explore how environmental policy influences the country-level social-pillar performance by focusing on the labor employment in the Association of Southeast Asian Nations (ASEAN). Based on multinational data of the ASEAN countries from 2000 to 2021, we find that the implementation of feed-in tariffs (FIT) policy, a key renewable energy promotion policy, significantly reduce unemployment rate in the ASEAN region. The two primary channels include that the renewable energy policy promotes industrial expansion and lowers the energy price. Further, we find that the FIT policy help decrease unemployment rate in countries with better fundamental infrastructure, stable political environment, lower reliance on traditional energy, and more foreign inflows. Moreover, we find that the economic linkages with China amplify the policy effect. This study shows that the environmental policy can help improve social condition in developing countries, and these countries need to upgrade domestic infrastructure and use foreign support to promote the positive effect.</div></div>","PeriodicalId":51430,"journal":{"name":"Research in International Business and Finance","volume":"84 ","pages":"Article 103286"},"PeriodicalIF":6.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-16DOI: 10.1016/j.ribaf.2026.103311
Jia Jiang , Hui Zheng , Zihao Xu
Based on manually collected accounting supervision data from 2006 to 2021, this study examines the impact of the policy on short-term debt for long-term investment (SDLI). Results indicate that firms identified with accounting information quality issues experience an increase in SDLI. Mechanism analysis reveals that these firms face both reputational damage and constrained external financing. Furthermore, heterogeneity analyses indicate that the effect is more pronounced among firms in the growth stage, with less asset reversibility, and with higher risk-taking. The research enriches the literature on government regulation and SDLI, offering valuable insights for policymakers and corporate risk management.
{"title":"Short debt, long pain: How does accounting supervision amplify Chinese corporate maturity mismatch?","authors":"Jia Jiang , Hui Zheng , Zihao Xu","doi":"10.1016/j.ribaf.2026.103311","DOIUrl":"10.1016/j.ribaf.2026.103311","url":null,"abstract":"<div><div>Based on manually collected accounting supervision data from 2006 to 2021, this study examines the impact of the policy on short-term debt for long-term investment (<em>SDLI</em>). Results indicate that firms identified with accounting information quality issues experience an increase in <em>SDLI</em>. Mechanism analysis reveals that these firms face both reputational damage and constrained external financing. Furthermore, heterogeneity analyses indicate that the effect is more pronounced among firms in the growth stage, with less asset reversibility, and with higher risk-taking. The research enriches the literature on government regulation and <em>SDLI</em>, offering valuable insights for policymakers and corporate risk management.</div></div>","PeriodicalId":51430,"journal":{"name":"Research in International Business and Finance","volume":"84 ","pages":"Article 103311"},"PeriodicalIF":6.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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