Purpose New quality productive forces (NPF) represent the latest manifestation of industrial chain modernization transformation, requiring industrial chains to develop towards innovation, synergy, and greening. Green finance (GF), through capital support and green technological innovation (GTI), has become a key force driving industrial chain modernization (ICM). Design/methodology/approach Based on panel data from 156 cities within China's top ten urban agglomerations (UAs) from 2010 to 2023, this paper scientifically measures the level of green finance and the modernization of UA industrial chains using the spatiotemporal range entropy weight method. It analyzes their spatio-temporal evolution characteristics. The practical effectiveness of green finance in empowering the modernization of industrial chains within UAs is empirically tested through the benchmark regression model, mediation effect model, and spatial econometric model. Findings The findings reveal: (1) GF guides financial resources towards green industries, directly driving ICM in UAs, and indirectly empowers ICM through GTI as an intermediary transmission mechanism. (2) The development of ICM in China's UAs exhibits a distinct east-to-west spatial gradient, with an overall trend toward synergistic development. (3) Under the empowerment of GF, the ICM of sub-UAs shows significant spatial heterogeneity and agglomeration characteristics. ICM in eastern UAs tends towards centralized development, with the Yangtze River Delta UA exhibiting a pronounced polarization effect. ICM in Chengdu-Chongqing and Middle Yangtze River UAs tends towards synergistic development. Originality/value First, in research perspective, we conduct the study from the UA perspective. Exploring the centralized, synergistic, and networked characteristics of industrial chains at the UA scale provides new ideas for constructing the ICM system. Second, in data processing, we integrate multi-source data. Combining micro-enterprise data with macro-economic data, we assign indicator values using information from both temporal and spatial dimensions. GF is divided into three sub-dimensions: “credits-securities-investments”. The ICM of UAs is divided into five sub-dimensions: “foundation-green-structure-collaboration and agglomeration-innovation”. This enriches the quantitative framework of the multi-dimensional system. Third, we incorporate spatial factors into the overall analytical framework.
Purpose This study investigates transitional dual-sector financial development (DSFD), including financial markets and institutions on sustainable green growth (SGG). Meanwhile, integrated global innovation (IGI) and its strategic components are moderate variables, along with the effect of digital economy policy. Design/methodology/approach The sample consists of 64 Green Silk Road Corridor countries with balanced panel data from 2007 to 2022. We employed panel cointegration, two-step system (GMM), and 2SLS methods. OriginPro was used to show graphical trends of the variables and to generate heatmaps for the country- and income-wise analyses. Findings This study confirmed the significantly positive dynamic nature of SGG. The findings showed that the transition of DSFD significantly enhances SGG by increasing the level of green growth and facilitating the shift from a high-intensity carbon economy to a low-carbon sustainable economy. The moderating channels of IGI and interaction terms enhance a country's global SGG path and contribute to achieving the SDGs. However, the sub-indices and interaction terms on the SGG impact appear mixed. The China Silk Road digital economy policy shock contributes positively to, and boosts, the linkage between DSFD and SGG. Hence, the Silk Road innovative digital initiatives policy shock also contributes substantially to partner countries' SGG paths. Furthermore, macroeconomic conditions, regulatory governance resilience, and energy intensity contribute positively to SGG, except for the productivity rent of natural resources. Research limitations/implications The study was limited to 64 GBRI countries that participated in the 2016 green initiatives. The theoretical contribution affirms that Green Silk Road partner countries are on a sustainable trajectory, supported by positive, genuine savings proposed by economic and endogenous green growth theories. The study suggests that nations should reinvest their financial, natural, and human capital resources into reproducible forms of capital, aligning with the “Hartwick rule” principles and facilitating the transition towards a stronger, greener path. Practical implications This study emphasizes the potential of innovative technology-enabled financial systems and resilient global interconnectedness to accelerate the transition toward green development and foster integrated societies that support sustainable green growth without compromising future needs. Social implications Dual-sector financial development, regional innovation and the digital economy primarily contribute to raising and upgrading people's living standards, integrating sustainable green growth, and alleviating poverty through societal education from environmentally friendly projects. It is concluded that the underprivileged population remains large. Therefore, in the coming year, better integrated global innovation, effective implementation mechanisms and digital economy policies supporting dual-sector financial development, comprising financial markets and financial institutions, are required to boost sustainable green growth. Originality/value The study is innovative and makes valuable contributions to this emerging concept in this field, particularly within the context of Green Silk Road Corridor countries. First, this study is pioneering and innovative in addressing the critical problem of how to model sustainable green growth based on the triple bottom line, contributing through green savings (adjusted net saving index including particulate emissions), known as the Solow model, to this domain of knowledge based on the Sustainable Development Goals, including the social dimension (SDG 4) of quality education, the economic dimension (SDG 8) of decent work and economic growth and the environmental dimension (SDG13) of climate actions, which is a key objective of the COP29 on climate change. Second, this pioneering study, in the of the Green Silk Road Corridor, explores the untapped potential of the multi-dimensional integrated global innovation index and its of tech-enabling innovation sub-indexes as moderating and interaction terms to contribute to the body of knowledge on the SDGs, especially SDG 8 (based on decent economic growth through financial integration), SGD-9 (ensuring infrastructure, industrialization, and innovation), SDG 16 (helping in strong institutions, peace, justice), and SDG-17 (strengthening the implementation means for the sustainable development). Moreover, it investigates the unified dynamic influence of dual-sector financial development, including financial institution stability and market resilience, on SDG goal 8 and related theoretical and practical knowledge. This study also intends to bridge the research gap by investigating the moderating pre- and post-policy role of the spillover effects of China's Green Silk Road Corridor digital economy policy as a policy shock on sustainable green growth, thereby contributing to the body of knowledge.
Purpose This study aims to examine whether the influence of finfluencers genuinely enhances investors' financial well-being (FWB) and is associated with overconsumption of financial products. It investigates how consumers’ perceptions of finfluencer behaviour such as manipulativeness, inauthenticity and non-accountability may undermine informed decision-making through persuasive yet potentially misleading financial advice on social media. Design/methodology/approach Using stressor-strain-outcome (SSO) model and moderation analysis, the study examines the role of perceived finfluencer traits in overconsumption of financial products and FWB of the consumers. A structured survey was conducted with 457 Indian financial consumers who actively follow finfluencers. Findings The findings indicate the positive and significant association of consumers' perceived manipulativeness, inauthenticity and non-accountability of finfluencers on overconsumption of financial products. Moreover, overconsumption mediates the effect of these factors on the FWB of consumers. Additionally, social media literacy (SML) significantly moderates the relationship of perceived manipulativeness and overconsumption but fails to moderate the relationship of the other two variables of perceived inauthenticity and non-accountability on overconsumption. Practical implications This study provides insights into how perceived behavioural traits of financial influencers impact consumer decision-making in the digital finance space. It contributes to influencer marketing theory and consumer financial decision-making approach and suggests more nuanced SML interventions to address different persuasive influencer traits. Originality/value Study offers valuable perspectives into a relatively underexplored domain of marketing strategy by employing the SSO model and empirical analysis in the dynamics of finfluencer marketing, particularly its influence on consumers' financial product purchase decisions and their well-being.
Purpose This study aims to examine the interconnectedness between BRICS nations' stock markets, key commodities (oil and gold), and green assets (including ESG-based equity, green bonds, and clean energy) across different market conditions and investment periods.Design/methodology/approach This study employs a quantile frequency connectedness approach that integrates the quantile connectedness approach (Ando et al., 2022) with the frequency connectedness technique of Barun & iacute;k and K & rcaron;ehl & iacute;k (2018), as suggested by Chatziantoniou et al. (2022). This study employs three distinct portfolio strategies: the MVP (Markowitz, 1952), MCP (Christoffersen et al., 2014), and MCoP (Broadstock et al., 2022) approaches, to calculate the multivariate portfolio optimal weight across mean, bear, and bull modes, as well as the hedging effectiveness index.Findings The findings reveal that market connectedness is frequency-dependent and particularly strong during bearish and bullish periods. An asymmetric volatility spillover is observed between markets, with left-tail connectedness surpassing right-tail connectedness. Additionally, short-term connectedness is more prominent than long-term connectedness. During bear markets, the Russian stock market acts as a receiver in both short and long terms, while the ESG index serves as a transmitter across various frequencies. Bivariate portfolio analysis results are generally insignificant, except for clean energy assets, which prove to be a more effective hedge for all assets.Originality/value The study employs a new quantile time-frequency connectedness method. This methodology provides significant insight into the interconnections among equity markets, commodities, and green assets over multiple time frames and across various market states, including bear, normal, and bull scenarios.
Purpose This paper examines whether changes in the liquidity premium before and after 2000 can be explained by shifts in exposures to long-run risk.Design/methodology/approach We estimate the risk exposures of liquidity-based portfolios to long-run consumption and investment risk factors, following the framework of Hansen et al. (2008).Findings We find that illiquid stocks exhibit significantly larger exposures to both risk factors than liquid stocks, both over the full sample period from 1964 to 2023 and in the pre-2000 subsample, consistent with the positive liquidity premium observed during these intervals.Originality/value After 2000, the negative exposure of illiquid stocks to the long-run consumption risk factor accounts for the diminishing liquidity premium observed in this period.
Purpose Focusing on independent directors (IDs) with multiple directorships, this paper examines how ID attention allocation is associated with corporate ESG performance, and highlights the role of reputation-driven attention allocation as a micro-level governance mechanism. Design/methodology/approach By using ordinary least squares regression (OLS), this study mainly uses data on A-share listed companies from 2009 to 2022 on Shanghai and Shenzhen as the research sample to empirically analyse the relationship and influence mechanism between ID attention and corporate ESG performance. Findings We discover that the higher ID attention is associated with better corporate ESG performance. Additional analysis suggests that ID attention enhances corporate ESG performance by increasing green perception and green innovation (E), promoting corporate philanthropy (S), and improving information disclosure quality and internal control quality (G). Heterogeneity analysis finds that the positive correlation between ID attention and ESG performance is more significant in high-polluting companies, highly competitive product markets, and high media attention. Originality/value This paper analyses the impact of IDs holding multiple directorships under reputation incentives on the unequal attention allocation in firms, extending this effect to corporate ESG performance. It provides empirical evidence for firms' sustainability in emerging markets. Additionally, our study enriches the existing research on ID attention allocation, and provides an optimisation path for improving corporate governance mechanisms in the emerging capital market, especially in ID selection, recruitment system, and enhancing the effectiveness of performance duties.
Purpose This study aims to examine the impact of data assets on enterprise credit risk and investigate the moderating role of artificial intelligence (AI) in this relationship. Furthermore, it takes into account the heterogeneity caused by differences in ownership structure, innovation intensity, firm size, and regional marketization levels. Design/methodology/approach The authors employ regression analysis using a sample of Chinese A-share listed companies from 2007 to 2023 to assess the impact of data assets on enterprise credit risk and test the moderating effect of AI. Findings This study reveals a significant negative correlation between data assets and enterprise credit risk. Moreover, the application of AI exerts a significantly negative moderating effect on the relationship between data assets and credit risk mitigation. Practical implications This study offers valuable insights for enterprise credit management in the digital economy. First, enterprises and regulators should strengthen the standardized governance and quality certification of data assets. Second, collaborative strategies integrating AI and data assets ought to be tailored to different types of enterprises, so as to balance technological innovation and credit risk control effectively. Originality/value This study identifies an innovative pathway through which data assets mitigate enterprise credit risk. It explicitly introduces AI as a moderating variable into the analytical framework linking data assets and enterprise credit risk, and empirically verifies a significant negative moderating effect of AI in this relationship.
Purpose This study proposes a novel framework for analysing the time-varying correlations between Bitcoin and traditional financial assets, specifically the S&P 500, NASDAQ, VIX, and WTI crude oil. Design/methodology/approach The methodology employs an asymmetric Student-t distribution to model asset returns, enhanced by Generalised Autoregressive Score (GAS) dynamics to capture changing correlation patterns. Findings The empirical analysis shows that there are time varying correlations across assets. Our proposed model is effective in capturing asymmetry and heavy tails. Furthermore, the results indicate that explanatory variables, particularly gold prices and the US Treasury yields, exert a significant influence on the correlations between Bitcoin and the considered financial market indices. Minimum variance portfolios constructed using the asymmetric Student-t model outperform those based on alternative models (including DCC) across all considered pairs. Originality/value Our approach enables us to capture not only the first or second-order moments but also the broad density structure, and it further allows us to examine tail dependence, which is relevant in understanding extreme events such as market crashes or surges. By modelling extreme events jointly, one can assess how cryptocurrencies and stock indices behave under stress conditions, and understanding the complex relationship between cryptocurrencies and traditional financial assets offers insights for portfolio management and risk assessment.
Purpose - This paper investigates the relationship between household wealth, financial market participation, and asset returns in China. While classic portfolio models predict wealth neutrality in risky asset allocation, empirical evidence from Western economies challenges this view. We aim to document whether similar wealth gradients exist in China's distinct institutional context and to develop a theoretical framework capable of explaining the joint patterns of participation and return heterogeneity. The analysis also seeks to evaluate the distributional consequences of policies affecting financial access, providing insights for financial reform in emerging economies where expanding market participation remains a central policy objective. Design/methodology/approach - Using four waves of the China Household Finance Survey (CHFS, 2013-2019), we employ nonparametric portfolio analysis and panel regressions with household fixed effects to establish stylized facts about wealth, participation, and returns. We then develop a continuous-time heterogeneous-agent general equilibrium model featuring endogenous market participation. Households choose between direct finance (incurring a fixed cost) and indirect finance through intermediaries. The participation threshold is determined endogenously by value-matching and smooth-pasting conditions in a two-dimensional state space of wealth and productivity. The model is calibrated to match key moments from the data, including participation rates, wealth shares, and return spreads. Findings - We document three robust patterns: (1) wealthier households are more likely to participate in direct finance and allocate a larger share of assets to it; (2) the wealth gradient steepens at the top, indicating concentration of direct finance among the richest; and (3) wealthier households earn systematically higher returns, a relationship that survives household fixed effects. The model shows that lowering participation costs boosts stock market access but exacerbates wealth inequality, whereas reducing intermediation costs benefits poorer households through higher deposit returns. Policies promoting stock market participation may disproportionately harm bank-dependent populations, highlighting a crucial trade-off in financial reform. Originality/value - This paper advances the literature in three ways. Methodologically, it extends contingent claims methods to a two-dimensional heterogeneous-agent setting, endogenizing both participation and return heterogeneity through value-matching and smooth-pasting conditions. Theoretically, it provides a unified framework explaining why wealth-participation and wealth-return gradients emerge jointly from financial frictions. Practically, it reveals a policy paradox: reducing participation costs widens inequality, while lowering intermediation costs benefits the poor. These insights caution against one-sided financial liberalization and emphasize the redistributive role of banking systems, offering guidance for emerging economies navigating financial deepening and inclusive growth.
Purpose This paper investigates the structural drivers of corporate green bond issuance across 19 European countries from 2013 to 2024. Using a structural equation modeling framework, it analyzes how institutional governance, climate policy design, and financial market characteristics interact to influence green bond outcomes. By distinguishing between policy stringency and policy proliferation, the study clarifies how regulatory coherence, rather than policy volume alone, affects issuance. The paper highlights the enabling role of governance and the conditional impact of financial development, offering insights for policymakers seeking to align climate finance instruments with effective institutional and regulatory frameworks.Design/methodology/approach The study employs a covariance-based structural equation modeling (CB-SEM) approach to jointly estimate the relationships between institutional governance, climate policy design, financial market structure, and corporate green bond issuance. The model includes three latent constructs-Institutional Governance, Policy Stringency, and Policy Effort-derived from validated indicators using confirmatory factor analysis. Bond-level issuance data are merged with macroeconomic, institutional, and policy variables for 19 European countries from 2013 to 2024. The model captures both direct and indirect effects, allowing for interaction terms to test institutional-policy complementarity. Robustness checks include time-specific controls, macroeconomic variables, and alternative inference with clustered standard errors.Findings The results show that policy stringency significantly increases corporate green bond issuance, while a higher number of policies have a negative effect, suggesting that policy proliferation may weaken regulatory clarity. Institutional governance does not directly affect issuance but operates as a key enabler by supporting the adoption of stringent and coherent policies. Financial market access promotes issuance by lowering participation barriers, whereas financial market depth lacks a consistently positive impact. Overall, the findings highlight that credible institutions, coherent regulation, and aligned financial systems are essential to translating climate policy commitments into effective and scalable green bond market activity.Research limitations/implications The analysis is limited to corporate green bond issuance in 19 European countries from 2013 to 2024, and findings may not generalize to sovereign or emerging market contexts. The use of structural equation modeling requires cross-sectional assumptions that restrict causal interpretation. Data availability also constrains the inclusion of firm-level strategic behavior and sector-specific policy mechanisms. Despite these limitations, the study offers important policy implications: scaling green bond markets requires more than regulatory ambition-it depends on institutional credibility, regulatory coherence, and financial access. Future research could expand the framework to other regions, instruments, and multi-level governance contexts.Practical implications The findings highlight the importance of regulatory coherence, institutional quality, and financial accessibility in scaling corporate green bond markets. Policymakers should prioritize the enforcement and clarity of climate regulations over the proliferation of fragmented policies. Strengthening institutional governance enhances the credibility and impact of green policy frameworks, while improving financial market access lowers barriers for corporate issuers. EU-level initiatives such as the Green Bond Standard and Taxonomy Regulation must be complemented by consistent national implementation. Practitioners and regulators can use these insights to design more effective green finance strategies that align environmental goals with market-based instruments.Originality/value This study contributes three novel insights. First, it disentangles climate policy effects by distinguishing policy stringency from policy proliferation, showing that only stringent, well-enforced policies stimulate issuance, while excessive, poorly coordinated measures can deter market activity. Second, it reconceptualizes financial development as a conditional driver, finding that financial depth supports green finance only when aligned with robust governance and regulation. Third, it identifies institutional governance as an upstream force that shapes policy design and credibility. Together, these insights explain divergent issuance patterns across countries and challenge common assumptions in the green bond and sustainable finance literature.
Purpose This study aims to examine whether the adoption of artificial intelligence (AI) can alleviate financial mismatches and enhance capital allocation efficiency through firms' investment decisions, particularly in the context of rapid digitalization and structural financial reforms.Design/methodology/approach The study develops a "technology-efficiency-allocation" analytical framework to explain how AI promotes investment efficiency and mitigates financial mismatches. Using the Skip-gram model in Word2vec, firm-level indicators of AI application are constructed to overcome the biases of patent-based or industry-level measures. Inefficient investment is introduced as an instrumental variable, and a double machine learning (DML) framework is employed to control for high-dimensional covariates and specification errors, ensuring credible causal identification.Findings The study develops a "technology-efficiency-allocation" analytical framework to explain how AI promotes investment efficiency and mitigates financial mismatches. Using the Skip-gram model in Word2vec, firm-level indicators of AI application are constructed to overcome the biases of patent-based or industry-level measures. Inefficient investment is introduced as an instrumental variable, and a DML framework is employed to control for high-dimensional covariates and specification errors, ensuring credible causal identification.Originality/value This study provides firm-level empirical evidence clarifying AI's role in reducing financial mismatches and improving capital allocation efficiency. It extends the literature by integrating advanced textual measurement of AI application with causal machine learning methods and offers practical implications for promoting technological integration and enhancing financial efficiency in emerging economies.
Purpose The objective of this research is to develop a novel Environmental, Social, and Governance (ESG) score for the Tunisian banking sector and investigate its relationship with financial performance. In addition, the study analyzes the moderating influence of a newly constructed corporate governance quality index on this relationship across different quantiles of banking performance.Design/methodology/approachThe research constructs a new ESG score by combining quantitative and qualitative data from Tunisian banks' annual reports and social media platforms. A corporate governance quality index, based on 12 key characteristics, has also been developed. The analysis uses quantile regression to evaluate the effect of the ESG score on banking performance and to explore the moderating effect of corporate governance quality across different performance quantiles.Findings The results exhibit a significant negative relationship across ESG scores and banks' financial performance at both low and high-performance levels. However, the quality of corporate governance positively moderates this relationship. Further analysis reveals that the Environmental pillar exerts a positive influence on performance, which is enhanced by the governance quality, while the Social pillar's positive impact is mainly observed in higher-performing banks.Practical implications The newly developed ESG rating system is a valuable measure for investors, policymakers and bank managers to evaluate the sustainability efforts of Tunisian banks. The results highlight the importance of enhancing corporate governance to maximize the benefits of ESG practices.Originality/value This study introduces both the first ESG score and corporate governance index specific to the Tunisian banking sector, offering new practical evidence of the nexus between ESG, banking performance and governance in an emerging market context.
Purpose This study aims to investigate the determinants of efficiency and profitability among fintech firms in China and India, with a particular focus on macro-level information and communication technology (ICT) development as a key factor. Design/methodology/approach The study sample includes fintech firms from China and India. We apply instrumental variable techniques and the Arellano–Bond model to panel data from each country and compare the results. Findings The findings show substantial disparities between China and India. In China, a robust digital infrastructure has consistently enhanced fintech profitability and efficiency by facilitating customer acquisition and operational optimization. In contrast, India's fintech sector has yet to fully capitalize on the country's ICT growth. Practical implications These findings highlight that investing in technology alone is insufficient – it must be complemented by broader ecosystem development. In China, efforts should focus on managing costs while deepening innovation. In India, critical actions include addressing regional digital divides, improving service reliability and advancing digital inclusion to unlock the full potential of ICT for fintech performance. In both countries, mobile payment systems should be reviewed to identify opportunities for reducing costs and creating value. Originality/value This paper advances the fintech literature by empirically examining the impact of macro-level ICT development on fintech firm performance in China and India, moving beyond previous research focused predominantly on the micro level. Moreover, the study investigates the underlying mechanisms driving these associations, including scalability, customer acquisition and operational optimization. Building on previous research, we employ both static and dynamic econometric models to ensure robustness and methodological rigor, effectively capturing both cross-sectional and time-dependent effects. Furthermore, by analyzing the COVID-19 period, the study offers insights into fintech resilience and adaptability to external shocks.
Purpose- It develop a dynamic general equilibrium model, calculates fundamental house value as well as their deviations, analyzes their main factors and explores a closer housing policy in future. Design/methodology/approach- Incorporating the stylized facts, this paper initially develops a dynamic general equilibrium model to determine house price. Findings- (1) Per capita income, urban population, M2 growth rate and land finance dependence significantly and positively affect fundamental house values but minimum down payment ratio and deposit rates do not. (2) Extreme deviations stem from large swings in income, population, M2 growth, land finance, regulatory policies and housing price expectations, while down payment ratio (DPR), rational expectations and loan rates don't significantly affect deviations. (3) Excessive bubbles cluster in eastern and southern cities while severe slumps prevail in central-western and northern regions. Housing regulations have effectively curbed soaring prices but caused mild slumps. Housing policies may have partially strayed from their original goals. (4) Housing policy direction may be to align with the current stage of its development, closely monitor price trends and adjust key factors accordingly. Research limitations/implications- Firstly, interpolation methods are used to improve a few data for several years or cities for short of official statistics. Secondly, the sample city size is only limited to 35 large- and medium-sized cities instead of 70-cities, 100-cities or all prefecture cities because of workload and statistical deficiencies. Thirdly, there are some literature discussing the intra-temporal non-separability between housing and non-durable consumption in fact (for example, Khorunzhina, 2021). However, the discussion is too complex to be completed in this article. Practical implications- We provide with a theoretical evidence to explain why house price deviations are highly uneven across time and regions during the past 2 decades in China and explore the direction of their housing policies in future. Social implications- This paper helps residents and businesses to evaluate house prices reasonably, avoiding their excessive optimism or pessimism about the housing market. Originality/value- We develops a dynamic general equilibrium model with endogenized markets and six agents. Second, the stylized facts in China are introduced into the dynamic general equilibrium model (DGEM) to match house value in the real world, which consist of fueling house price, explosive currency and credit, local land leasing finance, rapid population urbanization, the housing purchase restrictions and the dominantly quantitative monetary policy. Third, we calculate the fundamental house values, the extreme deviations and their main factors. Then, we explore the direction of housing policy in the long run and in the short term.
PurposeThis study investigates whether and how management biodiversity concern (MBC) influences corporate ESG performance. Using Chinese A-share listed firms from 2010 to 2023, it examines the role of managerial cognition in translating biodiversity-related risks into sustainability outcomes. Drawing on risk governance and managerial cognition theories, the study aims to identify the internal mechanisms-particularly green governance practices-through which MBC affects ESG performance, and to assess whether external regulatory and public pressures condition this relationship.Design/methodology/approachThis study employs a firm-year panel of Chinese A-share listed companies from 2010 to 2023. Management biodiversity concern (MBC) is constructed using textual analysis of MD&A disclosures based on Word2Vec and sentiment-risk lexicons. ESG performance is measured using CNRDS ESG ratings, with alternative ESG datasets for robustness. Fixed-effects panel regressions are applied, complemented by instrumental variable estimation, propensity score matching, and double machine learning to address endogeneity. Mechanism tests examine green governance channels, while heterogeneity analyses assess the moderating roles of regulatory and public pressures.FindingsThe results show that management biodiversity concern (MBC) has a significant and positive effect on corporate ESG performance. This relationship remains robust across alternative ESG measures, model specifications, and multiple endogeneity controls, including instrumental variables, propensity score matching, and double machine learning. Mechanism analyses indicate that MBC improves ESG performance primarily through enhanced green governance, reflected in higher green investment and stronger green supply chain management. Heterogeneity analyses reveal that the effect is stronger under greater regulatory and public pressure. Dimension-level results show the strongest impact on environmental scores, with weaker effects on social and governance dimensions.Originality/valueThis study contributes to the literature by shifting the focus from biodiversity outcomes and disclosure to managerial cognition, introducing management biodiversity concern as a novel firm-level construct. It provides one of the first systematic examinations of how biodiversity-related managerial perceptions translate into ESG performance through green governance mechanisms. By integrating risk governance and managerial cognition theories and employing advanced causal identification strategies, the study offers new evidence on the economic consequences of biodiversity risk. The findings enrich ESG and sustainable finance research by highlighting biodiversity as a distinct and value-relevant dimension of corporate environmental risk beyond climate change.
Purpose This study examines the quantile-specific impact of key domestic and global macro-financial factors on the spread of sovereign credit default swaps (CDSs) in emerging economies, including Brazil, Russia, India and China (BRIC). It further investigates these dynamics under extreme uncertainty triggered by global crises, including the COVID-19 pandemic, the armed conflict between Russia and Ukraine, the 2015 Chinese stock market crash and the ongoing Israel–Palestine crisis. Design/methodology/approach Using monthly data from 2008 to 2023 obtained from Refinitiv Eikon, the study adopts a dual-method framework: the method of moments quantile regression (MMQR) and the quantile-on-quantile regression (QQR) approach. This combination enables a detailed assessment of how varying quantiles of macroeconomic indicators impact different segments of the CDS spread distribution, capturing asymmetric dependencies under diverse market conditions. Findings The findings show that sovereign CDS spreads react to explanatory variables across different quantiles during global crises, such as the COVID-19 pandemic, the Russian–Ukraine conflict, the 2015 Chinese stock market crash and the ongoing Israel–Palestine crisis. At lower quantiles during low-risk periods, variables such as the stock exchange index, gold prices, real interest rates and foreign exchange rate exhibit a weak relationship with sovereign CDS spreads. However, at upper quantiles in high-risk situations, sovereign credit risk becomes highly sensitive and significantly increases in response to the EPU index, VIX and MSCI Asia index. This heterogeneity underscores the limitations of traditional mean-based approaches. It highlights the advantages of quantile-based models in capturing the dynamic behavior of sovereign credit risk under varying market conditions. Research limitations/implications Findings are specific to BRIC economies and may not generalize to all emerging markets. Future studies could extend the scope to other regions or incorporate institutional and Environmental, Social, and Governance (ESG) factors. Practical implications The study offers policymakers actionable insights to manage sovereign risk through enhanced market stability, clearer policy, crisis preparedness and regional economic cooperation. Originality/value This study uniquely applies a QQR framework to sovereign CDS analysis, offering novel insights into how credit risk evolves across market conditions in emerging economies. By explicitly incorporating the 2015 Chinese stock market crash alongside other major crises, the study strengthens the robustness of its findings on tail-dependent risk behavior.
PurposeAs a critical pathway toward achieving agricultural modernization, high-quality agricultural development requires the efficient optimization of factor allocation. This study aims to examine green finance and assess its impact on high-quality agricultural development from the perspective of financial resource allocation.Design/methodology/approachA comprehensive evaluation index system for both green finance and high-quality agricultural development is constructed, employing the Epsilon-Based Measure and global Malmquist-Luenberger index in conjunction with the global entropy weight method for measurement. Furthermore, the system generalized method of moments (GMM) method is employed to empirically analyze the influence of green finance on high-quality agricultural development.FindingsThe conclusion that green finance can effectively drive high-quality agricultural development remains robust across multiple empirical tests. Environmental regulation plays a substantial positive role in promoting high-quality agricultural development; however, it also attenuates the positive impact of green finance on such progress.Social implicationsThis paper recommends strengthening the green financial system, diversifying green financial services and fully leveraging the potential of green finance in advancing high-quality agricultural development.Originality/valueThis research integrates green finance and high-quality agricultural development into a unified analytical framework. It theoretically explicates multiple mechanisms through which green finance influences high-quality agricultural development and empirically tests these relationships using inter-provincial panel data and the system GMM. This approach broadens the analytical scope of research on the determinants of high-quality agricultural development.
PurposeThe frequent emergence of zombie firms jeopardizes the development of the real economy, and bank credit misallocation is the key reason for this. In this regard, the paper examines whether Joint Credit Granting-a policy regulating bank credit practices-effectively mitigates corporate zombification.Design/methodology/approachThis paper analyzes a dataset of Chinese listed firms from 2013 to 2022 and uses a difference-in-differences approach. The Joint Credit Granting is regarded as a quasi-natural experiment.FindingsThe implementation of Joint Credit Granting significantly inhibits corporate zombification. This effect is realized through two primary channels: enhancing corporate information transparency and internal governance efficiency, and correcting the misallocation of bank credit.Originality/valueThis paper offers novel insights into the efficacy of Joint Credit Granting and provides valuable perspectives on the governance of zombie firms. In addition, this paper enriches the economic effects research on bank information-sharing mechanisms.
PurposeThis research aims to assess the dependence of conventional and emerging cryptocurrencies on the public perception of Donald Trump, Vladimir Putin, and Narendra Modi, as reflected in Reddit posts. We investigate the structural dependence of four cryptocurrencies, Bitcoin, Ethereum, Dogecoin, and Tether, on relevant indicators extracted through in-depth Reddit sentiment analysis.Design/methodology/approachThe relevant sentiment indicators of public opinion linked to respective world leaders are systematically extracted to ascertain their role in explaining the daily closing prices of the chosen crypto assets. Initially, the predictive nexus structure is comparatively mined using Extreme Gradient Boosting, CatBoost, and Light Gradient Boosting Machine to unveil the impact of the sentiment indicators. Subsequently, robust forecasting frameworks have been developed, combining the indicators of the chosen political leaders with technical indicators and macroeconomic variables. Google's TabNet, a transformer-based deep learning model, and Facebook Prophet, an advanced time series forecasting methodology, are used to enhance the accuracy of predictions.FindingsThe overall findings exemplify the prudent influence of public perceptions on global political leadership in the crypto market outlook. Investors and traders can effectively track relevant social media sentiment indicators in conjunction with other explanatory features to anticipate the speculative behavior of these assets.Originality/valueThe utility of Reddit Sentiment indicators in representing public opinion on prominent leaders in explaining the speculative aspects of crypto assets through a robust methodological lens underscores the novelty of the underlying work.
Purpose To investigate how the facial trustworthiness of financial analysts affects their ability to privately gather information from corporate insiders. The channels are hypothesized to be managerial features, information environment and legal enforcement. Design/methodology/approach The study employs a quantitative research design using a comprehensive sample of Chinese financial analyst reports. Machine learning techniques for facial analysis are applied to assess the trustworthiness of analysts, and the relationship between facial trustworthiness and the informativeness of the analysts' reports is analyzed. Findings We first find that more trustful-looking analysts can produce more informative reports upon their visits to the corporate insiders. The effect of analysts' facial trustworthiness is driven by the gender difference and the level of professional competency of the visited insiders. Moreover, when information disclosure of the reported firm is lower, the trust effects become more pronounced. Finally, the trust effect disappears after a legal action against an insider trading activity related to one of the analysts' visits. Originality/value We find that more trustful-looking analysts can gain more material information during their visits to the firms, particularly when interacting with managers who have lower professional competency and a greater tendency to trust others. This trust-based informational advantage is also amplified in firms with poorer information environments. Furthermore, this trust effect disappears after a legal action against an insider trading activity related to one of the analyst visits. Our results are robust after controlling for analysts' competency, facial attractiveness and industrial concentration.
