Search Results (70)

This study assesses the quality of financial reporting in ten Central and Eastern European countries using a methodology based on natural language processing (NLP) techniques. 570 annual reports of companies listed on the main index on the stock exchanges of 10 Central and Eastern European (CEE) countries, over the period 2019–2023, were evaluated to determine the degree of convergence of the following four measurable qualitative characteristics: relevance, exact representation, comparability and understandability. The main objective is to identify consistency in the quality of accounting information based on the application of an international financial reporting framework. The applied methodology eliminates subjective variability by implementing a standardized scoring system, aligned with the criteria developed by NiCE, using libraries such as spaCy and NLTK for term extraction, respective sentiment analysis and word frequency evaluation. The results reveal significant heterogeneity in all characteristics examined, with statistical tests confirming substantial differences between countries. The investigation of relevance revealed partial convergence, with three dimensions achieving complete uniformity, while the exact representation showed the highest variability. The assessment of comparability showed a significant difference between countries’ extreme values, and in terms of comprehensibility a formalistic approach was evident, with technical dimensions outweighing user-oriented aspects. The overall quality index varied significantly across countries, with a notable average deterioration in 2023, indicating structural vulnerabilities in financial reporting systems. These findings support initial hypotheses on the lack of homogeneity in the quality of financial reporting in the selected region, despite the implementation of international standards. Full article

In recent decades, fishery stock enhancement has been increasingly utilized as a restoration tool to mitigate population declines and enhance the resilience of marine fisheries. Nevertheless, persistent enhancement efforts risk eroding the evolutionary potential of wild populations via genetic homogenization and maladaptive gene flow. Using long-term monitoring data (2017–2023), we quantified the effects of large-scale Penaeus penicillatus stock enhancement (~10⁸ juveniles/yr) on wild population dynamics and genetic integrity in the Beibu Gulf ecosystem. Temporal genetic changes were assessed using eight highly polymorphic microsatellite loci, comparing founder (2017) and enhanced (2024) populations to quantify stocking impacts. Insignificantly lower expected heterozygosity was observed in the stocked population (He = 0.60, 2024) relative to natural populations (He = 0.62–0.66; p > 0.1), indicating genetic dilution effects from enhancement activities. No significant erosion of genetic diversity was detected post-enhancement, suggesting current stocking practices maintain short-term population genetic integrity. Despite conserved heterozygosity, pairwise Fst analysis detected significant genetic shifts between temporal cohorts (pre-enhancement—2017 vs. post-enhancement—2024; Fst = 0.25, p < 0.05), demonstrating stocking-induced population restructuring. Genetic connectivity analysis revealed that while the enhanced Beihai population (A-BH) maintained predominant self-recruitment (>90%), it experienced substantial stocking-derived gene flow (17% SW → A-BH). The post-stocking period showed both reduced genetic exchange with adjacent populations and increased asymmetric dispersal from A-BH (e.g., 5% to YJ), indicating that hatchery releases simultaneously enhanced population isolation while altering regional genetic structure. Our findings revealed the paradoxical dual effects of stock enhancement and allelic diversity while disrupting natural genetic architecture. This underscores the need for evolutionary-impact assessments in marine resource management. Full article

As participants in sustainable development, corporations face the important and controversial issue of whether they can promote corporate sustainability through environmental, social, and governance (ESG) practices. To address this issue, we examine the relationship between ESG performance and corporate sustainability, measured by green total factor productivity (GTFP). Using a panel dataset of 17,559 firm-year observations from non-financial firms listed on the Shanghai and Shenzhen stock exchanges in China between 2011 and 2019, we employ fixed-effects regression models and two-stage least squares (2SLS) with instrumental variables to empirically test the impact of ESG ratings on GTFP, identify the underlying mechanisms, and examine potential heterogeneity across firms. The results show that higher ESG ratings are significantly associated with increased GTFP. Mediation analysis further reveals that this positive relationship operates through reduced financing constraints and enhanced green innovation. Notably, the mediating role of financing constraints is more pronounced for firms with greater reliance on external capital. Heterogeneity analysis indicates that ESG ratings exert stronger effects in eastern regions, pollution-intensive sectors, and state-owned enterprises. These findings provide empirical support for the role of ESG performance as an effective mechanism to advance corporate sustainability through ethics-driven financial access and innovation capability. Full article

In the context of global climate change and the escalating energy crisis, the development of new energy vehicles (NEVs) has become a critical strategy for China to foster green transformation and achieve its carbon neutrality goals. This study focuses on A-share-listed NEV companies in China from 2015 to 2023, specifically those listed on the Shanghai or Shenzhen Stock Exchange and subject to domestic regulatory standards and disclosure requirements. These firms were selected due to the representativeness, availability, and quantifiability of their data. A super-efficient-network SBM model based on undesirable outputs and the Malmquist index were employed to assess the static and dynamic green technology innovation efficiency of 260 NEV enterprises. Additionally, the Tobit regression model was applied to analyze the influencing factors. The findings reveal that the overall green technology innovation efficiency of Chinese NEV enterprises is relatively low and has exhibited a declining trend over the years. Furthermore, the efficiency of enterprises in the western regions surpasses that of those in the eastern and central regions. Key factors, including government support, enterprise scale, and R&D investment, significantly inhibit the green technology innovation efficiency of firms. Based on these findings, this paper recommends prioritizing the innovation of core technologies, addressing regional disparities in development, and implementing tailored policies to enhance the green technology innovation efficiency and economic performance of NEV enterprises. Full article

The creative breakthroughs in policy implementation by China hold essential practical importance for promoting global sustainability. The carbon emission trading (CET) pilot policy initiated in 2011 provides a quasi-natural experimental setting to investigate the dual impacts of market-incentivized environmental regulation on corporate carbon emissions (CEs) and capacity utilization (CU) enhancement. This study employs panel data from A-share listed manufacturing companies on the Shanghai and Shenzhen stock exchanges spanning 2007–2022, constructing a corporate carbon reduction efficiency (CRE). A Generalized difference-in-differences (DID) approach is adopted to examine the policy effects. The study reveals that the execution of the CET pilot policy has shown a notable and enduring enhancement in corporate CRE, yielding the combined advantage of advancing corporate decarbonization and improving CU. These conclusions remain resilient despite thorough sensitivity analysis. Furthermore, the pilot improves CRE via three principal avenues: augmenting corporate innovation capabilities, increasing green investment intensity, and refining managerial practices. The impacts of CET pilots are most significant in state-owned firms (SOEs), capital-intensive industries (CIEs), eastern region enterprises (EEs), and sectors with little market concentration. The findings set essential empirical standards for assessing decarbonization initiatives and guiding social progress towards sustainability. Full article

Semen cryopreservation is a crucial technology in the artificial insemination of livestock and poultry. It not only contributes to the conservation of germplasm resources but also facilitates the cross-regional exchange of high-quality breeding stock. In this study, 165 Duroc boars were selected for genome-wide genotyping, and the sperm freezing/thawing motility ratio (sperm recovery rate) was used as phenotypic data for genome-wide association analysis (GWAS). Considerable individual variations in sperm recovery rates (SRRs) were detected, and the sperm structure after cyropreservation was significantly better in highly freeze-tolerant individuals compared to non-freeze-tolerant ones. The heritability of the SRR was calculated and found to be 0.199 ± 0.158, representing low heritability. Through GWAS, eight single-nucleotide polymorphism (SNP) loci and four candidate genes (SLC10A6, MYRF, GGA1, and UTRN) were identified as being significantly associated with sperm freezing tolerance. Moreover, the dominant genotypes of four SNPs were finally determined to be valuable for identifying individuals with high sperm freezing tolerance. This study reveals the heritability of the sperm recovery rate and identifies molecular markers associated with sperm freezing tolerance in Duroc boars, which is of great significance for accelerating boar genetic improvement and enhancing the economic efficiency of pig breeding industry. Full article

Green innovation, which promotes the coordinated development of the economy and ecology, serves as a critical means to achieve enterprises’ green transformation. Against the backdrop of the Internet era, retail investors, as an important supervisory group for enterprises, can generate online public opinion through interactive exchanges on social media platforms. This raises the question: Can such public opinion rooted in social media influence enterprises’ green innovation behaviors? To address this, this study uses data from Chinese A-share listed enterprises in heavily polluting industries on the Shanghai and Shenzhen Stock Exchanges from 2008–2021, comprising a total sample size of 8755, and employs ordinary least squares (OLS) regression models to empirically examine the relationship between retail investors’ social media interactions and enterprise green innovation. The findings reveal that interactive discussions by retail investors on social media significantly enhance enterprises’ green innovation levels. Mechanism tests show that social media interactions among these investors strengthen enterprises’ environmental awareness and alleviate their financing constraints, thereby promoting green innovation. Moderation effect tests indicate that the quality of social media information interaction and public opinion sentiment positively moderate the relationship between retail’s social media interactions and enterprise green innovation. Heterogeneity tests further show that the positive effect of retail’s social media interactions on enterprise green innovation is more pronounced in regions with stronger environmental information regulation and stronger investor protection. The conclusions of this study not only enrich research on the relationship between retail investors’ social media supervision and enterprises’ behavioral decision-making but also extend the literature on the influencing factors of enterprise green innovation from the perspective of public governance. These findings hold important implications for enterprises’ green transformation practices under the “double carbon” goals and provide valuable insights for corporate governance in the era of the digital economy. Full article

The Industry–Finance Collaboration Pilot (IFCP) integrates governmental green guidance with digital collaboration platforms to promote non-equity-based cooperation between industrial and financial sectors. Using a Difference-in-Differences (DID) approach and a sample of A-share listed industrial firms on the Shanghai and Shenzhen Stock Exchanges from 2011 to 2023, this study examines the IFCP’s impact on corporate green innovation (GI). Results show that the IFCP increases the number of green patent applications by 7.5% on average, indicating its effect in stimulating GI. This effect operates through two main mechanisms. First, under governmental green guidance, the IFCP encourages local green fiscal subsidies, increases green investor participation, improves environmental information disclosure, and lowers agency costs. Second, through digital finance empowerment, it mitigates information asymmetry and transaction costs in financial activities, thereby reducing credit costs and enhancing firms’ access to green credit. The effect of the IFCP on GI is more pronounced in regions with stricter environmental regulation, in pollution-intensive industries, and among firms with smaller asset sizes. Further analysis indicates that the IFCP primarily stimulates tactical, low-value GI driven by compliance or opportunistic motives, rather than promoting substantive, high-quality innovation. This study provides empirical evidence and policy insights into how governmental green guidance and digital finance empowerment can jointly promote green industrial development. Full article

Soil carbon sequestration is one of the pathways for the dairy sector to mitigate climate change. Soil carbon measures have been reviewed extensively, including estimates of their impacts on regional or national scales. Eventually, these measures are to be implemented by the farmers themselves, justifying an assessment at farm and field level. Here, we used soil and management data from 96 fields on nine dairy farms to quantify annual stock changes under current management and the effect of several carbon measures on soil carbon sequestration in relation to farm configurations. The fields were in use as permanent grassland or grass-arable rotation with forage maize or other crops. We compared the observed changes in the soil layer of 0–25 cm with the RothC simulated changes, and we also simulated the effect of carbon measures on soil carbon stocks. We found a moderate (R² = 0.30) relation between simulated and measured soil carbon changes. Factors that contribute to the uncertainties are the estimates of field-specific carbon inputs from crop residues and manures, especially for farms that temporarily exchange land with other farmers. The current standard agronomic soil sampling program is unable to reliably detect soil carbon changes at a farm or field level. The annual changes in simulated soil carbon were negatively related to the initials carbon stocks, which has important implications for the potential of additional carbon storage. Therefore, we propose an indicator that expresses the current soil carbon stock in relation to the location-specific maximal achievable carbon stock for permanent grassland that receives an equivalent of 170 kg nitrogen per ha per year from animal manure. This can be used to compare farms and indicate whether a farmer’s focus should be on additional carbon storage or the protection of existing stocks. The simulation of carbon measures showed that the proportion of grassland is key in soil carbon storage. Full article

This study investigates the impact of local government debt levels on the behavior of individual firms, which is crucial for understanding the systemic risks associated with local government debt and fostering economic vitality. Using data from publicly listed companies on the Shanghai and Shenzhen stock exchanges between 2013 and 2022, this study empirically examines the effect of local government debt on corporate innovation quality. The findings demonstrate that local government debt expansion has a significant negative impact on corporate innovation quality. The negative impact remains robust across endogeneity tests and multiple robustness checks. Channel analysis indicates that as local government debt increases, innovation subsidies and procurement funding led toward firms’ decline, while both tax and non-tax revenue demands indicated firm increases. This resource reallocation contributes to the observed decline in corporate innovation quality. Further heterogeneity analysis reveals that regions with lower levels of government intervention and fiscal pressure exhibit a smaller negative effect of local government debt on innovation quality. Finally, examining the economic outcomes reveals that the decline in innovation quality, resulting from current local debt expansion, significantly reduces total factor productivity and firm value in the subsequent year, posing challenges for sustainable corporate development. Full article

In the context of increasing carbon emissions and strengthening regulatory measures, an increasing number of stakeholders are paying more attention to corporate carbon information. To further explore the relationship between the quality of carbon information disclosure and enterprise value, this study uses a sample of companies listed on the Shanghai and Shenzhen stock exchanges from 2013 to 2021. The aim is to investigate the link between the quality of carbon information disclosure and enterprise value, while also analyzing the role of green innovation in this relationship. The empirical results show that the quality of carbon information disclosure can significantly enhance enterprise value, with green innovation playing a mediating role in this effect. After robustness checks, including replacing the measurement variables and addressing endogeneity issues, the conclusions remain valid. Further analysis reveals that the effect of carbon information disclosure quality on enhancing enterprise value is more pronounced in non-high-pollution industries, non-state-owned enterprises, and firms located in eastern regions. This study provides valuable insights for future policy optimization related to carbon information disclosure and the promotion of low-carbon development in enterprises. Full article

This study explores the resilience of the Indian stock market in the face of global shocks in the post-pandemic era, focusing on its volatility dynamics and interconnections with international indices. Through a combination of Vector Autoregression (VAR), DCC-GARCH, and wavelet analysis, we analysed the time-varying relationships between the National Stock Exchange (NSE) of India and major global indices, including those from the U.S., Europe, Asia-Pacific, Hong Kong and Japan. Time series data of the selected indices have been collected for the period 1 January 2021 to 30 September 2024. Results reveal that while the NSE demonstrates resilience through rapid adjustments following shocks, it remains vulnerable to substantial spillover effects from markets such as the S&P 500 and European indices. Wavelet coherence analysis identifies periods of high correlation, particularly during major economic events, indicating that regional and global factors can periodically compromise market stability. Moreover, the DCC-GARCH results show a persistent but fluctuating correlation with specific markets, reflecting a connected and adaptive nature of the Indian market that is influenced by regional dynamics. This study emphasises the importance of strategic risk management. It highlights critical periods and indices that policymakers and investors should monitor closely to understand the economic resilience of the Indian financial market better. Further research could explore sector-specific impacts and the role of macroeconomic factors in shaping market responses. Full article

Carbon emissions trading pilots are an essential environmental regulation tool for incentivizing companies to reduce carbon emissions and a critical initiative for achieving “dual carbon” targets. This study, based on 2366 observations of 169 high-carbon listed companies on the Shanghai and Shenzhen stock exchanges from 2009 to 2022, uses double machine learning analysis to examine the impact and mechanisms of pilot policy on the low-carbon competitiveness of high-carbon industry-listed companies. The empirical results show that, first, pilot policy significantly enhances the low-carbon competitiveness of high-carbon industry-listed companies, and this conclusion holds after considering a series of robustness checks. Second, mechanism analysis indicates that alleviating green financing constraints and enhancing total factor productivity are pathways through which pilot policy influences low-carbon competitiveness. Heterogeneity analysis shows that the policy effects are stronger for state-owned enterprises, small- and medium-sized enterprises, and companies in eastern regions. Further analysis reveals that pilot policy enhances low-carbon competitiveness and increase enterprise value. Based on the study’s conclusions, the government should ensure the incentivizing effect of pilot policy, promote expansion of the carbon emissions trading market, assist enterprises in overcoming green financing constraints, improve total factor productivity, and formulate tailored policies according to the development levels and resource endowments of regions and companies. Full article

As China’s financial markets become increasingly integrated and the carbon market undergoes financialization, the impact of carbon emission price fluctuations on financial markets has emerged as a key area of systemic risk research. This study employs the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model and the optimal Copula function to investigate the dynamic correlation between carbon prices and China’s financial markets. Building on this, the Monte Carlo simulation and Copula CoVaR models are used to explore the spillover effects of carbon price volatility on China’s financial markets. The findings reveal the following: (1) Carbon price fluctuations generate spillover effects on all financial markets, but the intensity varies across different markets. The foreign exchange market experiences the strongest spillover effect, followed by the bond market, while the stock and money markets are relatively less affected. (2) The optimal Copula functions differ between the carbon market and China’s financial markets, indicating heterogeneous characteristics across regional markets. (3) There is a degree of interdependence between the carbon market and various sub-markets in China’s financial system. The carbon market has the strongest positive correlation with the commodity market and a relatively high negative correlation with the real estate market. These findings underscore the importance of integrating carbon price volatility into financial risk management frameworks. For policymakers, it highlights the need to consider market stability measures when crafting carbon emission regulations. Market managers can leverage these insights to develop strategies that mitigate risk spillover effects, while investors can use this analysis to inform their portfolio diversification and risk assessment processes. Full article

The realization of intelligent and green manufacturing represents two core challenges currently faced by manufacturing enterprises. The “Intelligent Manufacturing Pilot Demonstration List” issued by the Ministry of Industry and Information Technology (MIIT) of the People’s Republic of China provides a sample of firms that have undergone stringent selection processes, demonstrating secure and controllable technological capabilities, with no intellectual property disputes. Using data from manufacturing firms listed on the Shanghai and Shenzhen stock exchanges from 2011 to 2020, we identify those that implemented intelligent manufacturing based on the aforementioned pilot list from 2015 to 2019 as the treatment group and the remaining firms as the control group to investigate whether intelligent manufacturing increases the number of green patent applications. The findings indicate that the implementation of intelligent manufacturing significantly increases green patent applications by 3.676% through three main pathways: reducing the level of financing constraints, improving resource utilization efficiency, and increasing R&D investment. Heterogeneity analysis reveals that state-owned enterprises exhibit a significantly stronger promotion effect on green innovation post-implementation of intelligent manufacturing compared to non-state-owned enterprises, with enterprises in the western region demonstrating the most pronounced enhancement in green innovation. Based on these findings, we propose corresponding recommendations from the perspectives of policy support and enterprise strategy. Full article