Search Results (342)

In the digital era, sustainable finance is increasingly expected not only to expand financial access, but also to strengthen ESG transparency, accountability, and the alignment between corporate disclosure and actual practice. Against this backdrop, this study examines whether digital finance enhances corporate ESG disclosure–practice consistency by mitigating corporate ESG decoupling. Using Chinese A-share listed firms from 2011 to 2024 as the sample, we further investigate the moderating roles of corporate digitalization and executives’ digital background. The results show that digital finance significantly reduces corporate ESG decoupling, and this finding remains robust after alternative variable specifications, sample adjustments, stricter fixed-effects settings, and instrumental-variable estimation. Across the environmental, social, and governance dimensions, digital finance exhibits a stronger mitigating effect on social and governance decoupling. Corporate digitalization and executives’ digital background, acting as key micro-level enabling mechanisms through which regional digital finance translates into firm-level governance improvement, both significantly strengthen the mitigating effect of digital finance on corporate ESG decoupling. Further analysis shows that this effect mainly operates through easing financing constraints and reducing information asymmetry. This study contributes to the literature on sustainable finance, digital governance, and corporate sustainability by providing new evidence on how digital finance can narrow the ESG disclosure–practice gap and improve the consistency between corporate ESG disclosure and actual performance. It also offers practical implications for advancing the high-quality development of digital finance, strengthening firms’ digital capabilities, and enhancing the digital literacy of corporate executives. Full article

With growing global concern over climate risk, high-carbon enterprises are assuming an increasingly critical role in strengthening climate resilience and fostering low-carbon development. However, how climate risk disclosure shapes their carbon performance—specifically through what mechanisms and pathways—remains a pivotal yet underexplored question. To address this gap, this study constructs a panel dataset comprising Chinese listed high-carbon companies over the period 2006–2022 and employs a two-way fixed-effects econometric model to assess how climate risk disclosure affects carbon performance while investigating the underlying mediating channel. The empirical results provide robust evidence that enhanced climate risk disclosure improves the carbon performance of high-carbon enterprises. Mechanism analysis indicates that this beneficial outcome is mainly achieved through promoting green technological innovation and easing corporate financial constraints. Heterogeneity analysis further shows that the effect is stronger among smaller companies, firms operating in less concentrated industries, and those headquartered in China’s eastern region. The policy implications derived from these findings include establishing and strengthening a mandatory climate risk disclosure framework, introducing targeted incentives for green innovation and transition finance and tailoring climate risk management strategies according to firm-specific characteristics. Overall, this study underscores climate risk disclosure as a crucial factor in supporting the shift toward low-carbon operations among high-carbon enterprises. Full article

This study examines whether firm-level digital transformation capability (DTC) is associated with stronger operational resilience and whether governance structures condition this relationship. Operational resilience is treated here as a business-sustainability dimension based on continuity and stability of operating outcomes, not as a broad measure of environmental, social, and governance (ESG), environmental, or social sustainability performance. Using an international firm-year panel that combines standardized financial data with disclosure-based measures of implemented digital practices and governance architecture, the analysis provides observational evidence on the role of DTC in strengthening firm adaptability. In the controlled fixed-effects models, DTC is positively associated with the sales resilience ratio (SRR) (β = 0.071) and the cash-flow stability index (CFSI) (β = 0.058); an interquartile increase in DTC corresponds to approximately 0.024 in SRR and 0.019 in CFSI, or roughly 16% and 10% of their sample standard deviations. The association is stronger in firms with stronger internal oversight, auditable review mechanisms, and external ecosystem monitoring. Mechanism analyses point to supply flexibility and data visibility as plausible transmission paths, while additional tests address reproducibility, disclosure-intensity bias, construct validity, alternative governance specifications, placebo timing, restricted-shock logic, and measurement boundaries. Overall, the findings provide evidence consistent with a contingent and observational association between DTC and operational resilience when digital capabilities are embedded within accountable governance frameworks. Full article

Climate-related financial risks have become a central concern for financial institutions and regulators, particularly within the European financial system. This paper examines how climate-related risks are integrated into governance, risk assessment, and regulatory practices in European financial institutions. Using a structured narrative literature review of academic and institutional sources published between 2015 and 2026, the study synthesizes evidence on physical, transition, and liability risks, as well as the frameworks and tools used to assess them, including climate stress testing, scenario analysis, and climate value-at-risk models. The findings indicate that climate considerations are increasingly embedded within governance structures and supervisory frameworks; however, implementation remains fragmented due to inconsistent data, methodological limitations, and institutional barriers. The review further highlights that existing risk models often struggle to capture the long-term and non-linear nature of climate-related uncertainty. This paper contributes to the literature by linking financial stability theory and institutional theory to explain the persistent gap between regulatory ambition and institutional practice within the European context. The study concludes by discussing implications for supervisory policy, disclosure standardization, and climate-risk integration in financial decision-making. Full article

Environmental, Social, and Governance (ESG) metrics have become a cornerstone to sustainable finance, yet their measurement and predictability remain constrained by data heterogeneity, methodological divergence, and disclosure bias. This study develops a comprehensive ESG prediction framework grounded in the Design Science Research paradigm, integrating advanced machine learning techniques with rigorous data preprocessing, feature selection, and temporal validation. Using firm-level data from Refinitiv and Bloomberg, the analysis distinguishes between ESG composite performance and disclosure-based robustness, addressing a critical gap in the literature. Ensemble learning models, including Random Forest and XGBoost, are evaluated alongside deep learning architectures using multiple sampling strategies and rolling-window validation. The results demonstrate that ESG performance is moderately forecastable, with ensemble methods consistently outperforming neural networks in structured datasets. In contrast, disclosure robustness exhibits lower predictability, reflecting its dependence on discretionary strategic reporting and institutional factors. The findings highlight the importance of data quality, model selection, and validation design in ESG analytics, while emphasizing the limitations of deep learning in tabular financial contexts. The integration of explainable artificial intelligence further enhances interpretability by identifying key predictors of ESG outcomes. Overall, the study contributes to the literature by providing a robust, interpretable, and methodologically rigorous framework for ESG prediction, with implications for investors, regulators, and corporate decision-making. Full article

Financial reporting standards aim to increase transparency; however, the expansion in disclosure volume may also create an information overload paradox for investors, an issue that remains underexplored in the context of institutional investors. Excess information beyond mandatory requirements may complicate decision environments and create cognitive burden. When information exceeds cognitive processing capacities, attention may become fragmented, making it more difficult to distinguish signal from noise and potentially leading to analysis paralysis and changes in risk perception. Drawing on bounded rationality and cognitive load theory, this study conceptualizes information overload as a behavioral constraint associated with perceived limitations in decision quality and speed and, accordingly, examines its influence on institutional investors’ decision processes through a phenomenological approach. The study employs thematic analysis based on in-depth interviews with 19 professionals in institutional investment organizations in Türkiye. The findings suggest that information overload is experienced as cognitive strain that may prolong decision processes, may be associated with analysis paralysis and perceived changes in decision quality, and may be associated with increased uncertainty and potential challenges in interpreting risk. These findings provide exploratory insight into how information density may influence risk interpretation and portfolio assessment, and how institutional investors perceive decision-making efficiency. Full article

This paper examines how the identification of climate risks relates to the declared scope of disclosures under the ESRS E1 standard, growing regulatory pressure, and potential inconsistencies between internal risk assessment and external reporting. It introduces a composite measure, the Climate Reporting Readiness Index (CRRI), which combines three elements: risk identification, declared disclosures, and the consistency between them. The study is methodological in scope and aims to propose a generalizable measurement framework. The results show a statistically significant negative association between the extent of risk identification and the scope of declared disclosures, indicating that broader internal recognition of climate risks does not necessarily translate into broader declared reporting. Differences between identified risks and disclosures are also observed, suggesting that reported information does not fully correspond to the scope of identified risks. Transition risks are identified more frequently than physical risks. Analysis of specific disclosures shows that the identification of transition risks is associated with a lower probability of declaring information on transition plans and policies, while no robust statistically significant relationship is found between physical risks and disclosures of financial effects. The findings highlight the practical need to strengthen the alignment between internal climate risk identification processes and external ESRS E1 disclosure practices, as these processes may remain partially disconnected in organizational practice. The proposed index provides a diagnostic tool for companies seeking to improve reporting processes, regulators monitoring preparedness for ESRS E1 implementation, and stakeholders assessing the credibility and maturity of climate-related disclosures. Full article

This study provides the first empirical analysis of how energy-sector corporate filing events transmit to financial markets, bridging a critical gap between corporate financial distress literature and commodity market dynamics. The analysis employs an event study methodology with Wilcoxon signed-rank tests and panel regression models to examine 51 U.S. energy firms that experienced financial distress (2015–2021) across the NYSE and NASDAQ. Post-announcement cumulative abnormal returns (CARs) show positive median values (WSR: 40.5 for NYSE in 10-day window, p < 0.10; 97.8 for NASDAQ in 10-day window, p < 0.05; 36.24 for NASDAQ in 5-day window, p < 0.10). Panel regression results show significant differences in post-announcement CARs relative to the event day for both indices (NYSE: 10-day window coefficient = 117.1, p < 0.05; NASDAQ: 10-day = 199.6, p < 0.01; 5-day = 150.8, p < 0.05), as well as in pre-announcement windows for NYSE (5-day coefficient = 93.5, p < 0.10; 10-day = 86.6, p < 0.10). The findings suggest that markets respond to energy-sector corporate distress events without broad-based disruption, likely due to early signals of financial distress, clarified expectations regarding recovery paths under Chapter 11 restructuring, and reduced information asymmetry through disclosures. Policymakers can leverage these insights to refine corporate filing frameworks for commodity-dependent sectors. Full article

The persistence of greenwashing as a strategic corporate behavior reflects a financial tradeoff between risk and return. Current literature lacks an integrative framework explaining how these risks and institutional arrangements vary across distinct contexts. This study maps the intellectual structure and contextual heterogeneity of corporate greenwashing research through a bibliometric analysis of 818 publications indexed in the Web of Science Core Collection from 2000 to 2025. The results indicate an evolutionary shift in research focus from early ethical and reputational debates toward empirical investigations of capital market consequences, ESG controversies, and the dark side of corporate sustainability. This transition is accompanied by thematic movement from voluntary disclosure and legitimacy concerns toward mandatory compliance, sustainable finance, green bond pricing, and digital detection using artificial intelligence and natural language processing. The analysis reveals substantial structural heterogeneity. Heavy-asset industries are closely associated with technological decoupling under physical and compliance constraints, whereas financial and service sectors rely heavily on information asymmetry, green label arbitrage, and greenhushing. These sectoral patterns intersect with regional governance trajectories shaped by market-driven, regulation-oriented, and state-led contexts, generating distinct incentive structures and risk conditions, while firm-level governance further moderates these behaviors. The findings position greenwashing as a context-dependent corporate strategy and provide a structured synthesis for future research and differentiated regulatory responses. Full article

Inclusive growth has increasingly emerged as a central framework for understanding how firms can align economic performance with social inclusion and environmental responsibility, particularly in emerging markets characterized by institutional volatility. In the context of geopolitical shocks and economic sanctions, such as those faced by Russia during 2022–2023, the normative meaning of inclusive growth is redefined toward prioritizing employment stability, industrial continuity, and strategic resilience at the firm level. This study aims to develop a systematic and transparent firm-level measure of inclusive growth that integrates strategic resilience with long-term business model potential. It further seeks to empirically assess cross-firm heterogeneity in inclusive growth performance within the Russian metallurgical and mining sector under geopolitical disruption conditions. This study constructs a composite Inclusive Growth Index using publicly available financial and non-financial disclosures, combining indicator normalization, variance-based weighting, and geometric aggregation. The index is applied to a panel of major Russian metallurgical and mining companies for the period 2021–2024 to evaluate their strategic resilience, business model potential, and industry-level dynamics under sanctions. The results reveal substantial heterogeneity in inclusive growth performance across firms, with higher index values being associated with stronger strategic resilience and more stable operational outcomes. The analysis further identifies a divergence between improving resilience and declining business model potential during 2022–2024, indicating a trade-off between short-term stabilization and long-term inclusive growth capabilities under the geopolitical stress. The findings suggest that inclusive growth at the firm level in a sanctioned emerging market context follows a distinct sovereignty-oriented logic in which employment stability and operational continuity take precedence over long-term innovation and governance enhancement. Overall, the proposed Inclusive Growth Index provides a robust analytical framework for assessing corporate adaptation to structural shocks and informing managerial and policy decisions in emerging market economies. Full article

This study examines corporate carbon emissions of Korean firms from an ESG perspective and develops an AI-based screening framework to improve the identification of firms likely to exceed regulatory emission thresholds. As global climate policies and carbon pricing mechanisms expand, understanding the emission profiles of listed companies has become increasingly important for regulators, investors, and policymakers. Despite growing ESG disclosure, reliable firm-level screening tools for carbon emissions remain limited. Using a pooled annual panel of KOSPI-listed non-financial firms from 2019 to 2024, the study constructs a dataset of 552 firm-year observations. Firms are classified as high-emission when annual emissions exceed the Korean Emissions Trading Scheme (K-ETS) regulatory threshold of 125,000 tCO₂e. To evaluate predictive performance, the analysis compares multiple machine learning models (RF, SVM, XGBoost, LightGBM, and CatBoost) and deep learning models (CNN, RNN, GAN, LSTM, and Transformer). In addition, a hybrid ensemble combining CatBoost, GAN, and Transformer is proposed to enhance predictive reliability. The empirical results show that ESG-augmented models consistently outperform financial-only baselines across AUC and F1 metrics. Among individual models, the ESG-enhanced Transformer achieves the strongest discriminatory power, while the proposed hybrid ensemble delivers the best overall predictive performance. The findings contribute to the literature by demonstrating the incremental value of ESG information in predicting corporate carbon emissions and by presenting a practical AI-based framework for compliance-oriented screening under carbon regulation. From a policy and investment perspective, the model provides a useful decision support tool for anticipating potential inclusion in emissions trading schemes, assessing transition exposure, and supporting data-driven decarbonization strategies. Full article

Against the policy backdrop of high-quality development and the “Dual Carbon” goals, corporate environmental responsibility and green governance have emerged as core drivers of corporate value creation and resource allocation in capital markets. However, in practice, corporate environmental disclosure has increasingly degenerated into an impression management tool. Using a sample of China’s A-share listed companies from 2011 to 2024, this paper combines text analysis of annual reports with green patent data to systematically examine the impact of performance feedback on corporate strategic environmental decoupling, drawing upon the behavioral theory of the firm and legitimacy theory. The findings are as follows: First, negative performance feedback significantly increases corporate greenwashing propensity, whereas positive performance feedback significantly strengthens corporate brownwashing behavior. Second, government regulation amplifies the costs of falsifying environmental information, significantly suppressing the positive impact of negative performance feedback on greenwashing, but exacerbating the positive impact of positive performance feedback on brownwashing. Conversely, media attention amplifies the benefits of corporate green performances, significantly strengthening the catalytic effect of negative performance feedback on greenwashing, while effectively suppressing the positive impact of positive performance feedback on brownwashing. Third, heterogeneity analysis reveals that the impact of performance feedback on corporate strategic decoupling in environmental disclosure is more pronounced among non-state-owned enterprises, firms facing high industry competitive pressure, and those in heavily polluting industries. By integrating greenwashing and brownwashing into a unified analytical framework, this study expands the research boundaries of corporate environmental disclosure and strategic behaviors. Furthermore, it deepens the application contexts of the behavioral theory of the firm within non-financial disclosure, deconstructs the myth of homogeneous governance effects under legitimacy pressure, and provides vital implications for investors, policymakers, and fund managers. Full article

While Environmental, Social, and Governance (ESG) rating divergence poses a barrier to accurate sustainability measurement and sustainable investment, how internal managerial cognition addresses this external market misalignment remains underexplored. To address the research question of how executive focus shapes market consensus on corporate sustainability, this study integrates the Attention-Based View and Signaling Theory to examine the potential mitigating role of Top Management Team (TMT) environmental attention on ESG rating divergence. Utilizing high-dimensional fixed-effects regressions and textual analysis, we analyze a sample of Chinese A-share non-financial listed firms from 2015 to 2023. Empirical results indicate that a transparent and forthcoming managerial environmental focus helps reduce rating divergence, thereby partially aligning informational baselines. This cognitive alignment can act as an information calibrator, particularly when environmental issues match the firm’s core industry materiality, and this association appears more pronounced in regions with stringent environmental regulations. Robustness checks support the notion that substantive, quantitative sustainability disclosures driven by executive attention assist in alleviating informational misalignment among external rating agencies. These findings offer socio-economic and policy insights for advancing sustainable development, suggesting that regulators could consider encouraging structured sustainability reporting to support the role of executive cognition in standardizing ESG measurements. Full article

Despite growing recognition of blockchain technology’s potential to enhance traceability, verifiability, and integrity in financial reporting, empirical evidence from regulated banking environments in developing economies remains scarce. This study investigates whether blockchain adoption is positively associated with transparency and accounting disclosure in digital financial reports among Jordanian listed banks. A structured questionnaire was distributed to managers, financial managers, and accountants across 15 banks listed on the Amman Stock Exchange, yielding 312 valid responses. Partial Least Squares Structural Equation Modeling (PLS-SEM) with 5000 bootstrap subsamples was employed for data analysis. The results show that blockchain adoption is positively and significantly associated with transparency (β = 0.361, p < 0.001) and accounting disclosure (β = 0.437, p < 0.001), explaining 13.0% and 19.1% of the variance, respectively. These findings suggest that blockchain-enabled systems are perceived by banking professionals as contributing to greater reporting credibility. By providing empirical evidence from a developing economy banking sector, this study indicates that blockchain adoption may serve as a governance-supporting mechanism associated with improved perceived transparency and disclosure quality. Full article

Recent advances in large language models, tool-using agents, and financial machine learning are shifting financial automation from isolated prediction tasks to integrated decision systems that can perceive information, reason over objectives, and generate or execute actions. The paper develops an integrative framework for analysing agentic finance: financial market environments in which autonomous or semi-autonomous AI systems participate in information processing, decision support, monitoring, and execution workflows. The analysis proceeds in three steps. First, the paper proposes a four-layer architecture of financial AI agents covering data perception, reasoning engines, strategy generation, and execution with control. Second, it introduces the Agentic Financial Market Model (AFMM), a stylised agent-based representation linking agent design parameters such as autonomy depth, heterogeneity, execution coupling, infrastructure concentration, and supervisory observability to market-level outcomes including efficiency, liquidity resilience, volatility, and systemic risk. Third, it presents an illustrative empirical application based on event studies of AI-agent capability disclosures and heterogeneous market repricing. It argues that the systemic implications of AI in finance depend less on model intelligence alone than on how agent architectures are distributed, coupled, and governed across institutions. The empirical application is intentionally exploratory: it does not validate the full AFMM but shows how one observable expectations channel can be studied using public data. In the near term, the most plausible equilibrium is bounded autonomy, in which AI agents operate as supervised co-pilots, monitoring systems, and constrained execution modules embedded within human decision processes. Full article