Search Results (1,070)

Understanding the systemic synergy of peer effects on digital transformation is essential for overcoming development bottlenecks and stimulating digital vitality across industrial and regional ecosystems. Utilizing data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2010 to 2024, this study empirically investigates the impact of peer effects on corporate digital transformation and its underlying influencing factors from a systems perspective. The findings reveal significant industry and regional peer effects in corporate digital transformation, indicating that firms’ decision-making is interdependent within broader ecosystems. A greater distance between a focal firm’s prior digital transformation level and that of its peers is associated with a higher level of enthusiasm for such transformation. Similarly, the more a focal firm’s prior performance falls below that of its peers, the stronger its impetus for digital transformation becomes. Furthermore, the influence of the transformation distance on digital transformation enthusiasm exhibits a non-linear threshold effect, which varies with the performance gap. Finally, further analysis indicates that peer effects exert a multiplier effect and that industry-level peer effects in digital transformation significantly enhance firm performance. These conclusions contribute to a deeper understanding of the systemic mechanisms and pathways of corporate digital transformation and offer both theoretical and empirical support for fostering resilient digital economic ecosystems across industries and regions. Full article

This study analyzes how environmental, social and governance (ESG) factors affect the valuation of listed companies in Central Europe. It therefore validates the financial incentives for corporates in this region to embark on or continue with sustainable business models. It discusses the theoretical foundations of the impact of ESG factors on company value, examining both firm- and investor-centered approaches. The empirical section analyzes the main market valuation indicators based on earnings per share, book value, enterprise value and EBITDA for all companies listed on stock exchanges in the region for which Sustainalytics ESG risk ratings were calculated. The econometric modeling uses the generalized least squares method. The research evidences that companies with strong ESG risk ratings, reflecting sustainable business models, trade at a premium vs. their ESG-weaker peers. This suggests that investors place significant value on sustainability and effective ESG risk management practices. Additionally, this study reveals a non-linear relationship between ESG ratings and market valuations. While investors may show less differentiation among companies with low ESG risk, they impose substantial penalties on those with poor ESG management. From a practical perspective, the findings support investing in ESG risk management and corporate governance as effective strategies to raise company valuation and generate financial benefits for shareholders. The study also indicates that ESG ratings can be applied in forecasting company valuations, which is an important consideration for investors. This study makes an original contribution by providing insights focused on Central European markets, where empirical research on sustainability standards remains in the early stages of development. Full article

Steroidal pharmaceutical wastewater, such as stock liquid and cell lysate, is conventionally treated at a high cost due to its complex composition and high organic content. To treat steroidal pharmaceutical wastewater, make it harmless, and utilize it as a resource, engineering exploration of large-scale biogas engineering was carried out based on its anaerobic digestion characteristics, and the microbial population in the digestion process was analyzed. The results showed that, at a medium temperature of 35 °C and a total solid percentage of 6.5% ± 0.5%, both stock liquid and cell lysate wastewater could be anaerobically fermented normally, with the potential for anaerobic digestion treatment. The cumulative biogas production of lysate gas from the supernatant could reach 758 mL/gVS, which was significantly better than that of traditional raw materials such as straw and feces. The methane content reached 78.9%, and the total VFAs reached 10,204 mg/L on the ninth day. Moreover, we found that co-digestion of steroidal pharmaceutical wastewater with corn straw (CS) significantly enhanced system stability and biogas production efficiency, with synergistic improvement reaching up to 42%. This approach effectively shortened the lag phase observed in the mono-digestion of steroidal pharmaceutical wastewater. Actual treatment in a large-scale biogas project revealed that, after the addition of two kinds of wastewater, the main and auxiliary reactors presented serious acidification problems. Of these, the total volatile fatty acids in the main reactor reached up to 21,000 mg/L, and the methane content in the biogas production decreased to 25%. Additionally, 16S rRNA high-throughput sequencing analysis showed that, after the addition of steroidal pharmaceutical wastewater, the archaea community in the anaerobic reactor changed significantly due to the stress of changes in the fermentation environment. Euryarchaeota became the absolute dominant bacteria, and the methanogenic pathway also changed to the hydrogen trophic methanogenic pathway with Methanothermobacter as the absolute dominant bacterium. This is the first successful industrial-scale application of biogas engineering for treating steroid wastewater, demonstrating its technical feasibility and energy recovery potential. These research outcomes provide critical engineering parameters and practical experience for large-scale resource recovery from similar wastewater streams, offering important reference values for advancing pharmaceutical wastewater treatment from compliance discharge to energy utilization. Full article

Environmental, Social, and Governance (ESG) performance has become a pivotal driver of firm valuation, investment flows, and capital market stability and a critical dimension of corporate sustainability and investor decision-making. Yet, emerging markets face structural barriers to standardized ESG measurement due to limited data availability and inconsistent disclosures. This study addresses this gap by developing a simplified, transparent and indicator-based ESG assessment model tailored to the Moroccan capital market using publicly available data from 20 companies listed in the MASI ESG Index on the Casablanca Stock Exchange. The framework evaluates 12 equally weighted indicators across environmental, social, and governance pillars, and employs the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a Multi-Criteria Decision-Making (MCDM) method, to generate firm-level ESG scores and rankings. In addition to equal-weighted rankings, the model was stress-tested using entropy-based and expert-informed weights. Results reveal a wide disparity in ESG maturity: while environmental reporting is relatively advanced, social and governance disclosures lag behind. Top-ranking firms align closely with international frameworks such as GRI, whereas others lack fundamental transparency. By offering a replicable, low-data ESG scoring method applicable to other emerging markets, this research provides actionable insights for investors, regulators, and corporate leaders. The findings contribute to the financial literature on ESG integration, support the design of sustainable investment strategies, and advance policy efforts to strengthen capital market resilience across the MENA region. Full article

Precise stock price forecasting is essential for informed decision-making in financial markets. This study examines the combination of large language models (LLMs) with data augmentation approaches, utilizing improvements in cognitive computing to enhance stock price prediction. Traditional methods rely on structured data and basic time-series analysis. However, new research shows that deep learning and transformer-based architectures can effectively process unstructured financial data, such as news articles and social media sentiment. This study employs models, such as RNN, mBERT, RoBERTa, and GPT-4 based architectures, to illustrate the efficacy of our suggested method in forecasting stock movements. The research employs data augmentation techniques, including synthetic data creation using Generative Pre-trained Transformers, to rectify imbalances in training datasets. We assess metrics like accuracy, F1-score, recall, and precision to verify the models’ performance. We also investigate the influence of preprocessing methods like text normalization and feature engineering. Extensive tests show that transformer models are much better at predicting how stock prices will move than traditional methods. For example, the GPT-4 based model got an F1 score of 0.92 and an accuracy of 0.919, which shows that LLMs have a lot of potential in financial applications. Full article

This work develops an inverse data envelopment analysis (Inverse DEA) framework for portfolio optimization, treating return as a desirable output and volatility as an undesirable output. Using 20 industry-level portfolios from the Taiwan Stock Exchange (1365 stocks; FY-2020), we first evaluate efficiency with a directional-distance DEA model and identify 7 inefficient industries. We then formulate an Inverse DEA model that holds inputs and desirable outputs fixed and estimates the maximum feasible reduction in volatility. Estimated reductions range from 0.000827 to 0.007610, and substituting these targets into the base model drives each portfolio’s inefficiency score to zero $(\varphi =0)$, thereby making them efficient. To test robustness, we extend the analysis to a calm pre-crisis year (2019) and a recovery year (2021), which confirm that inefficiency and volatility-reduction targets behave logically across regimes, smaller cuts in stable markets, larger cuts in stressed conditions, and intermediate adjustments during recovery. We interpret these targets as theoretical envelopes that inform risk-reduction priorities rather than investable guarantees. The approach adds a forward-planning layer to DEA-based performance evaluation and provides portfolio managers with quantitative, regime-sensitive volatility-reduction targets at the industry level. Full article

Corporate ownership structure has been extensively studied as a complementary indicator of corporate performance; however, there is a lack of research on specific sectors. This research aims to understand the effects of ownership structure on debt levels and the efficiency of companies in the electricity sector. The efficiency of electricity companies can help reduce dependence on fossil fuels. The analysis covers 7777 observations of 38 companies from 1996 to 2023, including all publicly traded electricity companies on the Brazilian stock exchange (B3). The method is based on a panel data regression model with statistical tests to support the results. The results were robust and significant. Regarding efficiency, ownership structure revealed a negative relationship (−0.107); however, as company capital becomes more dispersed, it ceases to influence company efficiency. In terms of debt levels, the relationship was positive (0.095) and, despite the dispersion of capital among the five largest common shareholders (0.103), continues to exert influence. This research contributes to the scientific literature by confirming relationships and providing evidence between new and previously unexplored variables specific to the electricity sector. It is expected to create a benchmark for future analyses, highlighting the importance of ownership structure in the performance of electricity sector companies, which can contribute to improving the management of these companies, making them more competitive and sustainable. Full article

On Wednesday, 25 June 2025, the Federal Reserve voted to make changes to the Tier 1 Capital requirements for eight global systemically important banks. The changes include a cut to the enhanced supplementary leverage ratio (eSLR). The purpose of this study is to examine the immediate impact of this announcement on the stock prices of these eight systemically important banks. Using event study analysis and controlling for interest rate movements and general market conditions, we find that most of these banks generated superior returns during the event period. When compared to the KBW Index, however, results are mixed. Some banks do have superior returns even on pre-event day. The heterogeneous effects among banks emphasize that the benefits of capital regulation changes depend on a bank’s size, structure, and scope of operations. Full article

The paper contributes to the debate on the holistic sustainability assessment of real estate projects, integrating economic, financial, environmental, and social aspects. A methodological study is presented to support decision-making processes involving the preferability ranking of alternative investment scenarios: new building production vs. retrofitting the existing stock, in the context of urban transformation interventions. The study integrates life cycle approaches by introducing the social components besides the economic and environmental ones. Firstly, a composite unidimensional (monetary) indicator calculation is illustrated. The sustainability components are internalized in the NPV calculation through a Discounted Cash-Flow Analysis (DCFA). Life Cycle Costing (LCC) and Life Cycle Assessment (LCA) are suggested to assess the economic and environmental impacts, and the Social Return on Investment (SROI) to assess the intervention’s extra-financial value. Secondly, a methodology based on multicriteria techniques is proposed. The Hierarchical Analytical Process (AHP) model is suggested to harmonize various performance indicators. Focus is placed on the criticalities emerging in both the methodological approaches, while highlighting the relevance of multidimensional approaches in decision-making processes and for supporting urban policies and urban resilience. Full article

Investment decision-making is often characterized by uncertainty and the subjective weighting of criteria. This study aims to develop a more robust decision support framework by integrating the Generalized Interval-Valued Hesitant Intuitionistic Fuzzy Soft Set (GIVHIFSS) with the Analytic Hierarchy Process (AHP) to objectively weight criteria and handle multi-evaluator hesitancy. In the proposed GIVHIFSS-AHP model, the AHP is employed to derive mathematically consistent criterion weights, which are subsequently embedded into the GIVHIFSS structure to accommodate interval-valued and hesitant evaluations from multiple decision-makers. The model is applied to a numerical case study evaluating five investment alternatives. Its performance is assessed through a comparative analysis with standard GIVHIFSS and GIFSS models, as well as a sensitivity analysis. The results indicate that the model produces financially rational rankings, identifying blue-chip technology stocks as the optimal choice (score: +2.4). The comparative analysis confirms its superiority over existing models, which yielded less-stable rankings. Moreover, the sensitivity analysis demonstrates the robustness of the results against minor perturbations in criterion weights. This research introduces a novel and synergistic integration of the AHP and GIVHIFSS. The key advantage of this approach lies in its ability to address the long-standing issue of arbitrary criterion weighting in Fuzzy Soft Set models by embedding the AHP as a foundational mechanism for ensuring validation and objectivity. This integration results in mathematically derived, consistent weights, thereby yielding empirically validated, more reliable, and defensible decision outcomes compared with existing models. Full article

Given that the stock of coal gangue is increasing annually, and especially considering the problem of resource utilization after the spontaneous combustion of coal gangue accumulations with large thickness, the post-spontaneous combustion of coal gangue (SC coal gangue) from Yangquan, Shanxi, was selected as a research object. After crushing and screening, SC coal gangue was used as a coarse and fine aggregate, and through concrete mix design and a trial mix of concrete and mix ratio adjustment, concrete of strength grade C20 was obtained. Through experiments, the strength, elastic modulus, frost resistance, carbonation depth and other performance indicators of the concrete were measured. Using the SC coal gangue concrete, a 20 mm thick SC coal gangue panel was designed and manufactured. Through experimental tests, the bearing capacity, hanging force, impact resistance, impermeability and other properties of the board met the requirements of the relevant standards for building wallboard. For the SC coal gangue panel composite rock wool, its heat transfer coefficient decreased by 34.0%, air sound insulation was $\ge 45 \mathrm{d}\mathrm{B}$, and the self-weight of the external wallboard was reduced by 37.5%, so the related performance was better than the requirements of the current standard. The research results have been successfully applied to an office building project in Shanxi, China. Using SC coal gangue to make the external wallboard of the building, the reduction and recycling of solid waste are realized. In addition, the production of wall panels has been industrialized, thereby improving the construction efficiency. Full article

Financial time series prediction remains a challenging task due to the inherent non-stationarity, noise, and complex temporal dependencies present in market data. Traditional forecasting methods often fail to capture the multifaceted nature of financial markets, where temporal proximity, trend dynamics, and volatility patterns simultaneously influence price movements. To address these limitations, this paper proposes the Enhanced Multi-Aspect Transformer (EMAT), a novel deep learning architecture specifically designed for stock market prediction. EMAT incorporates a Multi-Aspect Attention Mechanism that simultaneously captures temporal decay patterns, trend dynamics, and volatility regimes through specialized attention components. The model employs an encoder–decoder architecture with enhanced feed-forward networks utilizing SwiGLU activation, enabling superior modeling of complex non-linear relationships. Furthermore, we introduce a comprehensive multi-objective loss function that balances point-wise prediction accuracy with volatility consistency. Extensive experiments on multiple stock market datasets demonstrate that EMAT consistently outperforms a wide range of state-of-the-art baseline models, including various recurrent, hybrid, and Transformer architectures. Our ablation studies further validate the design, confirming that each component of the Multi-Aspect Attention Mechanism makes a critical and quantifiable contribution to the model’s predictive power. The proposed architecture’s ability to simultaneously model these distinct financial characteristics makes it a particularly effective and robust tool for financial forecasting, offering significant improvements in accuracy compared to existing approaches. Full article

Emotion detection in Brazilian Portuguese is less studied than in English. We benchmarked a large language model (Mistral 24B), a language-specific transformer model (BERTimbau), and the lexicon-based EmoAtlas for classifying emotions in Brazilian Portuguese text, with a focus on eight emotions derived from Plutchik’s model. Evaluation covered four corpora: 4000 stock-market tweets, 1000 news headlines, 5000 GoEmotions Reddit comments translated by LLMs, and 2000 DeepSeek-generated headlines. While BERTimbau achieved the highest average scores (accuracy 0.876, precision 0.529, and recall 0.423), an overlap with Mistral (accuracy 0.831, precision 0.522, and recall 0.539) and notable performance variability suggest there is no single top performer; however, both transformer-based models outperformed the lexicon-based EmoAtlas (accuracy 0.797) but required up to 40 times more computational resources. We also introduce a novel “emotional fingerprinting” methodology using a synthetically generated dataset to probe emotional alignment, which revealed an imperfect overlap in the emotional representations of the models. While LLMs deliver higher overall scores, EmoAtlas offers superior interpretability and efficiency, making it a cost-effective alternative. This work delivers the first quantitative benchmark for interpretable emotion detection in Brazilian Portuguese, with open datasets and code to foster research in multilingual natural language processing. Full article

Pastoralist socio-ecological systems across Africa, Asia, and Latin America are transforming under climate stress, with adaptation patterns shaped by gendered power. I systematically reviewed 35 empirical studies (2013–2025) using PRISMA 2020 and the SWiM protocol. Searches in Web of Science and Scopus applied pre-registered inclusion criteria (empirical, pastoralist/agro-pastoralist focus, gender analysis); screening used a single reviewer with a 25% independent audit. The objective of the research was to examine power as an organising principle across four interconnected domains: labour redistribution, resource control, decision-making authority, and knowledge recognition. Most studies (≈70–80%), report increased women’s workloads alongside male control of land, water, and high-value stock, decision-making that is mitigated by committee presence without agenda/budget authority, and women’s knowledge being recorded as informal rather than actionable. Exceptions arise where inheritance or titling and decision procedures change. The paper’s innovation is a relational agency framework that links roles, rights, and records to specify tractable, auditable levers that convert participation into consequential authority. The goal is to guide context-sensitive reforms that redistribute power and improve adaptation in pastoralist systems. Full article

Historic buildings make up a significant proportion of the existing building stock. Most are characterised by poor technical condition and high energy demand. In Poland, many historic buildings are still in use today, but it is also common to find these buildings subjected to adaptive reuse. Adaptive reuse, often combined with modernisation, is problematic, especially in terms of finding a use that is optimal in the light of use-specific decision criteria. In previous studies, the authors used and developed the potential for the modelling and structural analysis of decision-making problems for the selection of new uses for historic buildings. In this paper, we present a test of this methodology on a Polish historic building. To further the application of our approach in sustainability-focused contexts, we performed the analysis using criteria focused on environmental and energy performance, in addition to other established criteria. In our study, the highest ranking use was a kindergarten, which scored 18% higher than the second-ranked alternative and over 90% higher than the lowest-ranked alternative. Full article