This study examines whether firms’ financial exposure to climate change risk influences short-term debt maturity and whether biodiversity targets condition this relationship. Using firm-year data for Asian listed firms from 2001 to 2024 obtained from the LSEG Eikon database, we measure short-term debt maturity as the share of total interest-bearing debt maturing within one, two, and three years. We focus on two dimensions of climate exposure—transition and physical climate risk—and investigate how biodiversity targets interact with these risks in shaping maturity choices. Our results show that firms exposed to either transition or physical climate risk rely less on short-term debt, consistent with a refinancing-risk channel in which climate-related uncertainty increases rollover costs and strengthens incentives to extend maturities. Biodiversity targets are additionally associated with lower short-term debt shares, indicating more long-horizon and resilient financing policies among target-setting firms. Importantly, interaction estimates suggest that biodiversity targets attenuate the marginal effect of climate-risk exposure on short-term debt maturity, implying that nature-related commitments may operate as a credibility and risk-buffer mechanism in debt contracting. Overall, the findings highlight debt maturity as a key financial adjustment margin to climate risk and underscore the role of biodiversity-oriented strategies in shaping corporate financial resilience.
This paper investigates Artificial Intelligence’s (AI) systemic impact on corporate Environmental, Social, and Governance (ESG) performance. Analyzing Chinese-listed companies from 2013 to 2022, we find that AI adoption significantly promotes ESG outcomes. Both ‘soft’ regulation (investor attention) and ‘hard’ regulation (environmental regulation) significantly strengthen this contribution. Notably, investor attention exhibits a complex threshold effect: at lower levels of attention, AI adoption negatively impacts ESG performance, suggesting that firms may prioritize efficiency over sustainability in the absence of public scrutiny. The facilitative effect only emerges once investor attention surpasses a critical threshold, highlighting AI’s ‘double-edged sword’ nature. For ‘hard’ regulation, environmental regulation positively moderates AI’s impact on environmental and governance performance but lacks a similar effect on social performance. Furthermore, AI primarily drives social and governance goals in non-state-owned enterprises (non-SOEs), while its impact in state-owned enterprises (SOEs) is concentrated in the environmental dimension. These results underscore that AI’s transformative potential is contingent upon regulatory frameworks and ownership-specific institutional logics.
This research studies the effect of the Accelerated Depreciation Policy (ADP) on the corporate sustainability of Chinese A-share firms between 2012 and 2017. We employ difference-in-differences estimation and reveal that ADP has a significant positive effect on corporate sustainability, particularly regarding employment, remuneration, and stakeholder rights. The effect is mostly attributed to increases in total factor productivity and short-term leverage, with a lesser role played by workforce skill structure upgrades. This effect is consistent and particularly prominent in firms with higher visibility and labor intensity, lower probability of obtaining long-term bank loans, and firms that are not state-controlled or politically connected. Our findings demonstrate that tax policy is vital in sustainability-related corporate decision-making.
This paper identifies the determinants of green bonds on global markets, China and Russia and evaluates the hedging effect of these instruments. We contribute to the existing literature by studying the emerging markets such as Russia and China, providing comparative analysis of the impact of identical factors on green bond yields on global, Russian and Chinese markets, and implementing the methodology for time series analyses that involve studying several subperiods. Based on the sample of 2167, 1213 and 2167 observations for global, Russian and Chinese markets in 2017–2022, respectively, we analyzed the impact of various factors on green bond returns using two empirical methods: analyses of long- and short-term linkages between variables based on the VECM model and construction of the GARCH model. The obtained results revealed that green bonds act like a hedging asset on global, Chinese and Russian markets, which proves that investors can use them to hedge their portfolios during economic crises, policy uncertainties and other market fluctuations. However, the hedging effect of green bonds differs in global, Russia and Chinese markets. In case of China, green bonds have a significant short-term hedging effect for all considered factors, while for the global green bonds the short-term hedging effect is present for all variables except the common stock market index. Russian green bonds can be used for hedging against gold and gas prices both in the short- and long-run. These results show investors how to manage their portfolios more effectively by using green bonds as a hedge asset.
This paper presents an empirical analysis of the correlation between ESG performance and cost of equity capital of listed companies in mainland China and Hong Kong from 2018 to 2022. The final empirical results of the study show that there is a negative correlation between ESG performance and cost of equity capital in China. The study also finds that, among the three dimensions of ESG, the social dimension has the most significant impact on Chinese listed companies. The geographical location and property rights of companies also have an impact on the relationship between the two variables. Compared to companies in Hong Kong, the improvement in environmental, social, and corporate governance performance of mainland Chinese companies has a more pronounced effect on reducing the cost of equity capital. Similarly, compared to state owned enterprises, the improvement in environmental, social, and corporate governance performance of non-state-owned enterprises has a more significant impact on reducing the cost of equity capital. These findings highlight the importance of incorporating ESG factors into business practices for the long-term success and sustainable growth of China’s capital markets.
The rapid increase of Environmental, Social, and Governance (ESG) investing has given rise to a complex and often contradictory body of research. This review synthesizes the corporate finance literature on ESG by applying a unifying ’Value versus Values’ framework, which distinguishes between motivations driven by financial performance and those rooted in non-pecuniary preferences. We document a consensus that the ESG information ecosystem is fractured by divergent ratings and that the primary financial benefit of ESG lies in risk mitigation rather than consistent alpha generation. Furthermore, while evidence of widespread greenwashing highlights a significant gap between rhetoric and reality, we demonstrate that stakeholder pressure and mandatory disclosure regulations are compelling tangible changes in corporate investment, financing, and operational policies. By clarifying central debates and identifying critical gaps, this paper maps the current state of knowledge and charts a course for future research focused on causality, measurement, and policy effectiveness.
This paper examines the impact of green credit policy on corporate exposure to climate risk in BRICS countries in 2000–2024. The objective of this study is to assess the extent to which access to sustainable financing can mitigate both physical and transition climate risks for companies operating in emerging economies. The methodology is based on a panel of listed companies in the BRICS countries, combining financial, climate, and regulatory data from national and international databases. We use the System GMM econometric approach, which corrects for potential endogeneity and biases related to unobserved heterogeneity. The empirical results indicate that access to green credit significantly reduces corporate exposure to climate risk, particularly in carbon-intensive sectors and in countries with strong regulatory frameworks. The effect is more pronounced for large companies and varies depending on the type of risk: transition risk is more effectively mitigated than physical risk. The study’s novelty lies in its extensive time coverage from 2000 to 2024, the integration of multidimensional data, and the rigorous application of advanced econometric techniques. This paper contributes to the literature on green finance in emerging countries and offers concrete implications for public decision-makers and financial institutions in terms of climate strategy.
This study investigates the dynamic panel threshold effect of Environmental, Social, and Governance (ESG) disclosure on firm value, while examining the critical role of corporate governance effectiveness and financial flexibility as threshold determinants in the relationship between ESG disclosure and firm value creation. The primary objective is to ascertain the extent to which ESG disclosure contributes to firm value creation, as well as to explore the variability of this impact across different levels of corporate governance and financial flexibility. The study is based on secondary data from audited annual and sustainability reports of 94 sampled companies listed on the Nigerian Exchange Group (NGX) from 2016 to 2022. The results from the differential Generalized Method of Moments (GMM) regression revealed a positive significant relationship between ESG disclosure and firm value creation, which indicates that companies that prioritize ESG disclosure can achieve superior financial performance and market valuation. The dynamic threshold model results indicate non-linear positive effects of ESG disclosure on firm value in organizations with lower governance practices, however, the effect is significantly amplified in firms demonstrating higher governance effectiveness. Also, as financial flexibility increases, the threshold effect of ESG disclosure becomes more pronounced. In firms characterized by low financial flexibility, the contribution of ESG to firm value is minimal. However, as firms enhance their financial flexibility, the positive effects of ESG disclosure become increasingly significant, suggesting that the capacity to invest in ESG initiatives is critical to realizing their value-generating potential. This study offers important insights into the complex interplay between ESG disclosure, corporate governance, and financial flexibility in the context of firm value creation. It is recommended for firms to not only engage in ESG disclosure but to also ensure the alignment of their corporate governance and financial strategy in order to maximize the resultant value.
The paper aims to develop an approach for assessing the quality of corporate governance in ESG ratings for regional companies in the Northwestern Federal District (NWFD) of Russia. A distinctive feature of these companies is that they, firstly, operate within the regional economy, and secondly, are not always publicly listed entities that come under the scrutiny of federal rating agencies. Based on a literature review and existing methodologies for measuring corporate governance quality in commercial and academic ratings, a set of indicators has been proposed for this assessment. Using the proposed set, a comparative study was conducted on the quality of corporate governance in major federal companies and NWFD companies that are not included in federal ratings. The approach outlined in the paper is implemented in two stages. In the first stage, the rating assessment of corporate governance quality is based on the degree of data disclosure on company websites. In the second stage, artificial intelligence algorithms trained on various publicly available data (company websites, news articles, and reports) are utilized. Despite a small sample size and significant discrepancies identified in some cases between the assessment results in the first and the second stage, a sufficiently high accuracy in predicting corporate governance quality ratings was achieved when training artificial intelligence algorithms. Additionally, by comparing ratings obtained from both stages, insights into the quality of corporate governance in the evaluated companies and directions for its improvement can be gained. The conclusion drawn emphasizes the necessity of employing various tools for an accurate assessment of corporate governance quality.
In the context of modern economic challenges, the effective functioning of the stock market becomes crucial for the sustainable development of the Russian economy. Attracting investments to the real sector requires creating effective tools for market information analysis, which involves processing large volumes of heterogeneous data under conditions of high volatility and geopolitical instability. The aim of the research is to develop an algorithm for building a sparse neural network. This model automatically eliminates insignificant connections between neurons for predicting stock market dynamics. The proposed approach is based on the method of solving a single-point inverse problem with a minimization of the sum of absolute parameter values, which allows reducing the model’s dimensionality. The scientific novelty of the research comprises two aspects. First, the work explores the possibility of using new factors generated by a large language model (an artificial intelligence system for text processing) for predicting stock market dynamics. Second, an original algorithm for constructing a sparse neural network has been developed. The research tested two main hypotheses. The first hypothesis aimed to verify the advantages of sparse neural networks over fully connected architectures in prediction accuracy. The second hypothesis investigated the effectiveness of using features extracted by large language models from unstructured text sources for financial forecasting. Experimental verification on three tasks of stock price and dividend forecasting confirmed both hypotheses. The sparse architecture demonstrated an advantage over fully connected models in prediction accuracy and computational efficiency. Automatic feature selection revealed the relevance of macroeconomic characteristics extracted by the large language model, confirming the promise of integrating modern natural language processing technologies into financial forecasting. The obtained results can be used to form effective strategies of stock market behavior and create intelligent decision support systems. In addition, sparse models can be used in solving other economic problems, including portfolio optimization and financial performance management.
This study examines how Environmental, Social, and Governance (ESG) performance affects financial outcomes for listed Saudi Arabian companies between 2019 and 2023. Through a study of 50 firms, we address methodological limitations in previous research by using advanced statistical methods that are correct for biases obscuring the true ESG-performance relationship. Our findings provide compelling evidence that all three ESG pillars – Environmental, Social, and Governance – significantly enhance both profitability and market valuation. Particularly noteworthy is the fact that environmental initiatives demonstrate clear financial benefits, reversing earlier findings that suggested negligible or negative impacts. The results prove robust across various analytical approaches and industry settings, including a focused banking sector analysis. Since this is the first study to establish a causal connection between ESG and financial performance in the Saudi market, these findings have important strategic implications for companies advancing sustainability under Vision 2030. The main limitation stems from the restricted sample size, a consequence of limited ESG disclosure in the Kingdom, which may limit broader applicability.
Amid intensifying climate commitments, this paper investigates whether and how carbon disclosure quality (CDQ) translates into corporate financial performance (CFP), and whether internal control (IC) strengthens this translation. Using a balanced panel of 1,218 Chinese A-share firms over 2010–2023, we build a multidimensional CDQ index – covering carbon governance, strategies and targets, footprints, and relevance/reliability – weighted via the entropy method and lagged to mitigate reverse causality. The sample comprises 15,834 firm-year observations, and models include firm, year, and industry fixed effects with clustered standard errors. Three-way fixed-effects estimations and two-step system GMM address unobserved heterogeneity and endogeneity; diagnostics (no AR (2), valid Hansen test) support instrument validity. Analyses further control for size, leverage, institutional ownership, board independence, ownership concentration, CEO duality, audit quality, heavy-polluting industry, and state ownership. The study finds that higher CDQ significantly improves CFP. Further, IC positively moderates the CDQ–CFP link: firms with stronger IC convert transparent carbon reporting into greater financial value, consistent with stakeholder theory. Results remain robust to an alternative market-based proxy (market-to-book ratio) and to exclusion of pandemic years (2020–2021), and are insensitive to multicollinearity checks. This study advances theory by demonstrating that credible climate disclosure yields financial benefits in an emerging market when coupled with effective internal governance; advances practice by highlighting that managers and regulators should pair disclosure mandates with IC reinforcement; and advances method by offering a replicable, Python-assisted scoring framework aligned with TCFD/ISSB guidance. Policy and investment implications follow: encouraging credible carbon reporting and strengthening internal control can lower information asymmetry, bolster legitimacy, and enhance firm valuation as economies transition to low carbon.
The shift toward a dovish monetary stance in Indonesia in early 2025 provides a valuable context for examining stock market efficiency under the weak form of the Efficient Market Hypothesis (EMH). This study investigates market efficiency during the transition in monetary policy by integrating firm size, liquidity, and abnormal returns as analytical dimensions. Using variance ratio (VR) tests with lags 2, 4, 6, and 8, the study evaluates 665 listed firms from October 9, 2024, to April 30, 2025. The findings show that while most portfolios exhibited significant VR values under hawkish conditions, these values became insignificant during the dovish period, indicating improved efficiency. Nevertheless, certain portfolios – particularly BLH, SIH, and SLH – continued to generate abnormal returns, reflecting anomalies within the EMH framework. Moreover, liquid small-cap portfolios with high abnormal returns displayed superior risk-adjusted performance, suggesting that monetary easing under conditions of stable inflation and strong economic growth, enhances investor sentiment and market participation. This study contributes empirical evidence to the EMH literature by demonstrating how monetary policy interacts with firm size and liquidity in shaping return dynamics. However, the focus on the Indonesian capital market over a limited timeframe constrains the generalizability of the findings to broader market contexts and longer periods.
This study applies the principal-agent framework to examine the impact of digital transformation on banks’ risk-taking behavior in Vietnam in 2012–2022. It further expands the analysis by investigating the moderating roles of bank-specific characteristics and external shocks in this relationship. The results reveal a nonlinear, U-shaped relationship: in the early stages of digital transformation, heightened information asymmetry intensifies principal-agent conflicts, thereby reducing risk-taking. As digital maturity increases, moral hazard becomes more prominent, encouraging greater risk-taking as agents respond to performance-based incentives. Furthermore, larger banks tend to exhibit more conservative behavior due to their complex organizational structures and divergent risk perceptions, while the COVID-19 pandemic coupled with rapid technological change has amplified risk aversion across the sector. These findings offer important implications for corporate financial decision-making and regulatory policy, emphasizing the need to manage agency conflicts and align digital strategies with optimal risk-taking behavior in the evolving digital finance landscape.
The terms “venture capital” and “private equity” are used in a loosely interchangeable manner when capital is invested in innovative technology-driven ideas or nascent-stage/unlisted companies. Pre-revenue idea-based/nascent-stage unlisted start-ups are financed by investors classified as venture capitalist investments, whereas post-revenue established unlisted companies are financed by private equity investors. Institutional investors fund venture capitalists and private equity investors separately since they are considered two different types of investments. The purpose of this paper is to determine whether the returns of nascent-stage start-ups (Venture Capital Index) exhibit a causal effect and transmit volatility onto the returns of the more established post-revenue-stage unlisted companies (Private Equity Index) and vice-versa. The study used vector auto regression and DCC GARCH to understand this relationship. The findings showed that the returns of nascent-stage start-up venture capital index unidirectionally trigger the growth of returns of the more established private unlisted companies’ index, and lead to volatility transmission from venture capital index to private equity index that persists over a long period of time. Also, immediate shocks transmitted from the VCI index to PEI don’t impact volatility in the short term. Institutional investors will be informed that the Venture Capital Index not only triggers the returns of the Private Equity Index, but also transmits the volatility spill over to the Private Equity Index, an effect that persists over a longer duration of time, thereby making the Private Equity Index vulnerable to the volatility of the Venture Capital Index.
This study presents a comprehensive analysis of the impact of the intensity of research and development (R&D) costs on the financial performance of Russian oil and gas companies, including in the context of external sanctions pressure. To conduct an empirical analysis, a data panel was created covering 112 companies in the industry for the period from 2017 to 2023. For the econometric assessment, an improved two-step model based on the CDM approach (Crépon – Duguet – Mairesse) [1] is used, which allows solving the problem of endogeneity. At the first stage, the key determinants of the intensity of R&D costs, including return on assets, company size, and debt burden, are determined using a fixed-effect panel regression. At the second stage, the R&D intensity values predicted at the first step are used as an independent variable in the quantile regression model. This method allows us to analyze the impact of investments in innovation on the gross margin of companies with different levels of profitability (different distribution quantiles) and with time lags from 1 to 3 years. The results obtained demonstrate that an increase in the intensity of R&D costs has a statistically significant and positive impact on the financial performance of oil and gas companies within a year after investment, especially for firms with medium and high profitability. However, this effect does not persist in the medium term (with lags of 2 and 3 years). Such a rapid but short-term financial return indicates that until recently, R&D funds have been mainly used to purchase and implement ready-made imported technological solutions, rather than to create companies’ own breakthrough technologies. In addition, it was discovered that the inclusion of a company in the list of sanctioned entities is statistically significant and has a positive effect on its financial performance in the short term in certain groups in terms of profitability. The article makes up for the lack of empirical research on the financial impact of R&D in the domestic economy and highlights the vulnerability of the current innovation model of the sector.
The purpose of our study is to identify the main scientific results and promising areas related to sanctions against companies, assessment of their effectiveness, and creation of an anti-sanctions policy. To achieve this goal, we used the text analysis methodology and expert assessment. The empirical base of the study included 724 publications about sanctions for 2014–2024, indexed in Scopus. Based on the text analysis methodology (calculating the frequency of words and phrases, correlations, conducting a thematic analysis using BERTopic), the main scientific areas were identified: a company’s internal environment, external environment – the financial and banking sector, and external environment – trade policy and foreign investment. To test model quality, we analyzed their Coherence Score and Divergence, the topics do not overlap and havesufficient internal coherence. Based on expert analysis, the main scientific ideas and authors were identified for each direction. The articles highlight Russia’s potential key partners, in particular China, and researchers attempt to predict long-term effects of sanctions or to assess the actual impact that they have already exerted. This work will be useful for researchers in the development of the proposed scientific directions, and for practitioners in formulating anti-sanction policies to mitigate the negative consequences of sanctions.
The subject of the study is an enterprise based on ESG principles, which implies its concern for the interests of not only the current but also future generations. The study was undertaken to develop a sustainable growth model that takes into account economic uncertainty and is capable of covering a period of time sufficient for ESG program implementation. Despite the widespread desire to achieve sustainable growth, there is no unanimity in regard to the methods of integrating long-term development programs with traditional commitment to current profit maximization. One of the difficulties is associated with reaching intergenerational planning horizons. In addition to their short-term nature, existing sustainable growth models do not include an indicator of the uncertainty associated with the activities of the enterprise. To solve this problem, the paper uses methods of integrating stochastic differential equations, which allows to move away from the deterministic dependencies of predecessor models. The resulting stochastic trend model takes into account the systematic long-term impact of the environment. This approach turns it into a hyper-long-term planning tool commensurate with the duration of the enterprise life cycle. According to this model, the probability density of revenue growth rate is subject to a logarithmically normal law with numerical characteristics changing under the influence of competition and inflation. The paper envisages several development scenarios characterized by different dynamics of the random component. If the enterprise follows ESG principles, then the typical growth scenario will be the most suitable one. The random component of growth of a typical enterprise degenerates over time, and its rate is determined by the risk-free interest rate. From the concept of intersecting generations, it follows that typical enterprises contribute to the dynamic efficiency of the economy.
Digital transformation and firm environmental performance have emerged as central topics in the field of corporate sustainability. However, few publications have explored how managerial characteristics influence this relationship. This paper aims to address this gap by examining the impact of digital transformation on firm environmental performance, with a particular focus on the moderating role of managerial overseas experience. Using a sample of Chinese listed companies from 2011 to 2021, we employ robust econometric models to analyse the effects and variations across firms. Our findings reveal that digital transformation significantly enhances firm environmental performance. This positive impact is more pronounced in non-high-pollution firms compared to high-pollution firms. Furthermore, we identify managerial overseas experience as a critical moderating factor, strengthening the positive relationship between digital transformation and firm environmental performance. This study provides valuable insights for firms pursuing sustainable development strategies through digital transformation, emphasizing the importance of managerial overseas experience in achieving improved environmental outcomes.
Ecosystem-based business models have received significant attention and praise in both business and research literature. Endeavours in building ecosystems sometimes prove successful, with firms transitioning to ecosystems enjoying valuation multiples significantly higher than their conventional peers. In practice, this entails firms expanding beyond their core offerings, such as a bank venturing into e-commerce. However, despite the evident interest in ecosystem-based business models, up to 85% of such ecosystems ultimately fail. Despite these notable failure rates, there has been limited discussion in research literature regarding the composition of businesses that yield reliable results within ecosystems. In this paper, we first propose a “Hook-Engage-Monetize (HEM)” framework for understanding ecosystem business composition. We apply this framework to nine case studies of successful and less successful ecosystems from nine different countries. Our analysis demonstrates the potential of HEM as a tool for selecting businesses for ecosystems and for guiding future quantitative financial research in this area.
