This article analyses the evolution, structure, and dynamics of the academic literature on green finance in the
context of climate change, using a bibliometric approach applied to publications indexed in the Web of Science – Core
Collection for the period 2001–2025. The methodology is based on descriptive and relational bibliometric indicators,
including the analysis of scientific production, sources, authors’ impact, co-authorship networks, and keyword co
occurrence, complemented by thematic maps and temporal analyses of emerging themes, conducted using the
Bibliometrix package within the R environment. The results highlight an accelerated growth of academic interest after
2016, with a concentration of publications in economics and finance journals such as Energy Economics, Finance
Research Letters, and International Review of Financial Analysis, as well as a polycentric structure of international
collaborations dominated by East Asia and Europe. The conceptual analysis reveals three major thematic clusters: the
performance and impact of green investments, energy transition and sustainable economic growth, and systemic risks
and financial stability. The emergence of themes such as financial digitalisation, fintech, and artificial intelligence
indicates recent directions of research diversification. The article contributes by providing a systematic mapping of a
rapidly maturing field and by identifying epistemic gaps, highlighting the need to expand comparative studies,
interdisciplinary approaches, and analyses of green finance in emerging and transition economies.
Commercial geography. Economic geography, Economics as a science
This study explores the relationship between green banking disclosure, firm performance, and firm value, with firm size and age as moderating variables. The study analyzed 578 observations from 43 banking companies in Indonesia. The findings reveal that green banking disclosure significantly negatively impacts firm performance and value, suggesting that green banking efforts may not always yield positive shortterm financial outcomes. However, firm size and age were found to moderate these relationships. Based on these findings, the study highlights the importance of carefully designed green banking strategies and a deeper understanding of their financial impacts by banking management. It also emphasizes the need for sustained commitment to learning and adapting to integrate sustainability into banking operations.
The financial industry's growing demand for advanced natural language processing (NLP) capabilities has highlighted the limitations of generalist large language models (LLMs) in handling domain-specific financial tasks. To address this gap, we introduce the LLM Pro Finance Suite, a collection of five instruction-tuned LLMs (ranging from 8B to 70B parameters) specifically designed for financial applications. Our approach focuses on enhancing generalist instruction-tuned models, leveraging their existing strengths in instruction following, reasoning, and toxicity control, while fine-tuning them on a curated, high-quality financial corpus comprising over 50% finance-related data in English, French, and German. We evaluate the LLM Pro Finance Suite on a comprehensive financial benchmark suite, demonstrating consistent improvement over state-of-the-art baselines in finance-oriented tasks and financial translation. Notably, our models maintain the strong general-domain capabilities of their base models, ensuring reliable performance across non-specialized tasks. This dual proficiency, enhanced financial expertise without compromise on general abilities, makes the LLM Pro Finance Suite an ideal drop-in replacement for existing LLMs in financial workflows, offering improved domain-specific performance while preserving overall versatility. We publicly release two 8B-parameters models to foster future research and development in financial NLP applications: https://huggingface.co/collections/DragonLLM/llm-open-finance.
Financial sentiment has become a crucial yet complex concept in finance, increasingly used in market forecasting and investment strategies. Despite its growing importance, there remains a need to define and understand what financial sentiment truly represents and how it can be effectively measured. We explore the nature of financial sentiment and investigate how large language models (LLMs) contribute to its estimation. We trace the evolution of sentiment measurement in finance, from market-based and lexicon-based methods to advanced natural language processing techniques. The emergence of LLMs has significantly enhanced sentiment analysis, providing deeper contextual understanding and greater accuracy in extracting sentiment from financial text. We examine how BERT-based models, such as RoBERTa and FinBERT, are optimized for structured sentiment classification, while GPT-based models, including GPT-4, OPT, and LLaMA, excel in financial text generation and real-time sentiment interpretation. A comparative analysis of bidirectional and autoregressive transformer architectures highlights their respective roles in investor sentiment analysis, algorithmic trading, and financial decision-making. By exploring what financial sentiment is and how it is estimated within LLMs, we provide insights into the growing role of AI-driven sentiment analysis in finance.
Countries worldwide are increasingly focused on addressing the imbalance between the supply and demand for EV charging infrastructure, with the community-shared charging post (CSCP) co-construction project emerging as a promising solution. The broad participation and investment support of the residents are the keys to the success of the CSCP co-construction project. This study, grounded in the theory of planned behavior (TPB) from social psychology, incorporated factors such as community identity, perceived green value, economic benefit, uncivil behaviors, and perceived risk to construct a structural model explaining community residents’ intention to invest in the CSCP co-construction project. This research confirmed that (1) 85.73% of respondents expressed strong recognition of the CSCP co-construction project, with a mean recognition score of 5.56 out of a possible 7; (2) an individual’s social-related perceptions, including the subjective norms and community identity are the strongest determinant of the intention to invest in the CSCP co-construction project; (3) the willingness to invest in CSCP co-construction project differs significantly between the EV group and the non-EV group. Economic benefit was significant only for the non-EV group, while uncivil behaviors were significant only for the EV group. These results provide valuable guidelines for governments and corporations that are promoting or pursuing sharing community for the residents.
We study the effectiveness of textual information in predicting the returns of crude oil futures and understanding the behavior of market participants. Using a machine learning method to extract oil market sentiment from news articles, we find that the computed sentiment is significantly effective in explaining the crude oil futures returns, while existing textual analyses based on pre-defined dictionaries may mislead the contexts in the oil market. Consistent with previous findings that returns help explain the change in traders’ positions, the sentiment scores based on the machine learning method are also useful in explaining the behavior of different types of traders. Our empirical findings underscore the fact that accurately identifying textual information can increase the accuracy of oil price predictions and explain divergent behaviors of oil traders.
Abstract Political economists continue to imagine the twentieth century in terms of three interlocking transformations: the neoclassical revolution in economics, the political triumph of neoliberalism, and the financialisation of the world economy. In his new book, The Sexual Economy of Capitalism, Noam Yuran tells a completely different story, identifying an obscene financial kernel already present at the dawn of modern capitalism and tracing the effects of its later blooming across a wide range of contemporary settings. In this essay, I develop an exaggerated version of Yuran’s narrative, drawing particular attention to the theoretical and philosophical implications of an obscene perspective on financial life today.
Mengming Michael Dong, Theophanis C. Stratopoulos, Victor Xiaoqi Wang
This paper provides a review of recent publications and working papers on ChatGPT and related Large Language Models (LLMs) in accounting and finance. The aim is to understand the current state of research in these two areas and identify potential research opportunities for future inquiry. We identify three common themes from these earlier studies. The first theme focuses on applications of ChatGPT and LLMs in various fields of accounting and finance. The second theme utilizes ChatGPT and LLMs as a new research tool by leveraging their capabilities such as classification, summarization, and text generation. The third theme investigates implications of LLM adoption for accounting and finance professionals, as well as for various organizations and sectors. While these earlier studies provide valuable insights, they leave many important questions unanswered or partially addressed. We propose venues for further exploration and provide technical guidance for researchers seeking to employ ChatGPT and related LLMs as a tool for their research.
Abstract Crowdfunding has become important in increasing financial support for the development of green technologies. Self-disclosed information significantly affects supporters’ decisions and is important for the success of green project funding. However, current studies still lack investigations into the impact of information disclosure on green crowdfunding performance. This research aims to fill this knowledge gap by exploring eight information disclosure-relevant factors in green crowdfunding performance. Applying machine learning techniques (e.g., Natural Language Processing and Computer Vision) and logistic regression, this study investigates 720 green crowdfunding campaigns on GoFundMe and empirically finds that the duration, length of campaign introductions, and length of the title influence fundraising outcomes. However, no evidence supports the impact of goal size, emotion of campaign introduction, or image content on funding success. This study clarifies the information disclosure-related data that green crowdfunding campaigns should consider and provides founders with a constructive guide to smoothly raise money for a green crowdfunding campaign. This study also contributes to data processing methods by providing future studies with an approach for transferring unstructured data to structured data.
Public finances are one of the fundamental mechanisms of economic governance that refer to the financial activities and decisions made by government entities to fund public services, projects, and operations through assets. In today's globalized landscape, even subtle shifts in one nation's public debt landscape can have significant impacts on that of international finances, necessitating a nuanced understanding of the correlations between international and national markets to help investors make informed investment decisions. Therefore, by leveraging the capabilities of artificial intelligence, this study utilizes neural networks to depict the correlations between US and International Public Finances and predict the changes in international public finances based on the changes in US public finances. With the neural network model achieving a commendable Mean Squared Error (MSE) value of 2.79, it is able to affirm a discernible correlation and also plot the effect of US market volatility on international markets. To further test the accuracy and significance of the model, an economic analysis was conducted that aimed to correlate the changes seen by the results of the model with historical stock market changes. This model demonstrates significant potential for investors to predict changes in international public finances based on signals from US markets, marking a significant stride in comprehending the intricacies of global public finances and the role of artificial intelligence in decoding its multifaceted patterns for practical forecasting.
Decentralized finance, powered by blockchain technology, is growing day by day. This field, which emerged a few years ago, today manages $70 billion in assets. In this study, the concept of decentralized finance is discussed and explained the differences from traditional finance. Then, compliance with the legal regulations and the requirements to ensure compliance are mentioned. An evaluation has been made about the financial services offered by the decentralized finance field and the stock market and stablecoins that it uses as a tool while providing these services. Its economic effects, security and, privacy dimensions are examined. In the study, the differences between centralized and decentralized finance, which generally covers legal, economic, security, privacy, and market manipulation, are systematically analyzed. A structured methodology is presented to distinguish between centralized and decentralized financial services. Keywords: decentralized finance, FinTech, financial regulation, blockchain, distributed ledger technology.
This paper investigates the causes of the FTX digital currency exchange's failure in November 2022. We identify the collapse of the Terra-Luna ecosystem as the pivotal event that triggered a significant decrease in the exchange's liquidity. Analysing on-chain data, we report that FTX heavily relied on leveraging and misusing its native token, FTT, and we show how this behaviour exacerbated the company's fragile financial situation. To gain further insights into the downfall, we study evolutionary dependency structures of 199 cryptocurrencies on an hourly basis, and we investigate public trades at the time of the events. Results suggest that the collapse was actively accelerated by Binance tweets causing a systemic reaction in the cryptocurrency market. Finally, identifying the actors who mostly benefited from the FTX's collapse and highlighting a generalised trend toward centralisation in the crypto space, we emphasise the importance of genuinely decentralised finance for a transparent, future digital economy.
For the past 100 years, Turkishwomen have had the opportunity to obtain an education and enter the labor force due to the democratic regime established in 1923. Despite some economic and social barriers, they have taken advantage of these opportunities and advanced in their careers to some extent. However, as with all women around the world, they face barriers in their career paths. This study aims to analyze the scientific research studies on the glass ceiling syndrome conducted in Türkiye to gain detailed insights into people’s perception of the glass ceiling and capture their perspectives on the factors that contribute to it. This is a meta-synthesis study aimed at conducting a systematic review of selected qualitative studies and integrating their findings. A systematic search was conducted across local academic databases, namely, Dergipark and Tubitak Ulakbim-Equal. MAXQDA2022 software was used to code and analyze the articles. The factors forming glass ceiling defined by the studies were renamed as 18 subthemes in total and classified under three themes: (1) personal factors, (2) sociocultural factors and (3) organizational factors. Although the role of motherhood and work life balance was found to be the most frequently referred factor, some current research revealed that not only women but also men experience this syndrome in Türkiye, despite the patriarchal culture of the country. The study’s limitations are noted, and the implications and future research directions are discussed.
This paper captures advances in prudential regulation and supervision for challenger banks and fintech in the UK. It presents a critical analysis of the prudential supervisory approaches towards fintech. The focus is placed on fast-growing firms (FGFs), building on the review performed by the Prudential Regulation Authority (PRA) of the Bank of England (BoE) in 2019. Specifically, it comprises a critical examination of the underlying regulatory framework in relation to the robustness of stress testing practices, as part of the review of FGF risk management practices and the weakness identified in the Internal Capital Adequacy Assessment Process (ICAAP). The economic analysis of law comprises the underlying methodology, using economic theory to analyse regulation and its effectiveness regarding fintech regulation and supervision. Recommendations for enhancements towards supervisory practices about the prudential governance and management of FGFs and fintech are included, with advances to the underlying regulatory framework in the UK. Overall, this critical legal research examines the supervisory practices of FGFs and fintech in the UK, under the lens of prudential regulation and risk management approaches, focusing on the design, development and implementation of the stress testing tool and scenario practices.
Omnichannel is not just a marketing, e-commerce, or customer support buzzword. This future customer engagement platform helps businesses communicate with customers through centralized channels on a smart interface. It is difficult to achieve customer loyalty when the risk in online transactions, which creates anxiety, exists in all transaction processes in an omnichannel system. Hence, the purpose of this research was to analyze the influence of anxiety on relationships when clients purchase from an omnichannel platform using the stimulus–organism–response (SOR) paradigm. To fulfill study aims, qualitative and quantitative research approaches were used. In-depth interviews and focus group discussions were used to acquire qualitative data, while survey responses from 485 participants were used to collect quantitative data. This study’s results revealed relationships between consumer psychology factors such as perceived mental benefits, hedonic value, and anxiety. Moreover, customer anxiety in omnichannel can be measured as a novel and exact concept in marketing science and have a moderating role in the effect of perceived mental benefits on electronic loyalty and perceived mental benefits on hedonic value in omnichannel systems. As a result, enterprises were also offered various managerial implications to develop their omnichannel system.
Small mining towns are often single-industry towns that turn to ghost towns or face negative socio-economic impacts upon mine closure. This study qualitatively explores the roles that mining companies and other key stakeholders (should) play in the development of local economies of the small mining communities to bring about economic sustainability, employing a constant comparative analysis. A small mining town in South Africa is the case study. Legislative and policy frameworks were scrutinized for their effectiveness in promoting economic sustainability. In-depth interviews with key stakeholders were carried out. Key factors limiting the effective implementation of developmental strategies were also explored. The study finds that weak community involvement, lack of trust, poor collaboration, poor municipal capacity, and legislation and policy flaws impact economic sustainability. Sustainable local economic development efforts are reported though. However, such efforts are insufficient because of the legislation and policy frameworks that are promoting short-term growth. Also, the town’s overdependence on the mining company, local government not optimally fulfilling their roles and responsibilities, and minimal community members’ participation on local economic development are other hindrances. However, the fact that the mining company and local municipality do acknowledge the shortcomings in their efforts towards promoting economic sustainability is a promise in minimizing the socio-economic effects of mine closures.
History of scholarship and learning. The humanities, Social Sciences
Pengpeng Yue, Aslihan Gizem Korkmaz, Zhichao Yin
et al.
This study focuses on the impact of digital finance on households. While digital finance has brought financial inclusion, it has also increased the risk of households falling into a debt trap. We provide evidence that supports this notion and explain the channel through which digital finance increases the likelihood of financial distress. Our results show that the widespread use of digital finance increases credit market participation. The broadened access to credit markets increases household consumption by changing the marginal propensity to consume. However, the easier access to credit markets also increases the risk of households falling into a debt trap.
The concept of conditional expectation is important in applications of probability and statistics in many areas such as reliability engineering, economy, finance, and actuarial sciences due to its property of being the best predictor of a random variable as a function of another random variable. This concept also is essential in the martingale theory and theory of Markov processes. Even though, there has been studied and published many interesting properties of conditional expectations with respect to a sigma-algebra generated by a random variable it remains an attractive subject having interesting applications in many fields. In this paper, we present some new properties of the conditional expectation of a random variable given another random variable and describe useful applications in problems of per-share-price of stock markets. The copula and dependence properties of conditional expectations as random variables are also studied. We present also some new equalities having interesting applications and results in martingale theory and Markov processes. Keywords: Conditional expectation, sigma algebra, per-share price, order statistics, prediction Conflicts of interest statement: We declare that have no conflicts of interest.
يهدف البحث إلى التعرف على دور الاستشراف الاستراتيجي في تعزيز عملية التغيير التنظيمي، استخدام استمارة الاستبيان بوصفها أداة رئيسة لجمع البيانات والمعلومات التي تخص البحث والمتعلقة بالجانب الميداني، ولقد تم اختيار دائرة صحة صلاح الدين كميدان للبحث، وتم توزيع (144) استمارة على أفراد العينة من قيادات الادارية، تم استرجاع بالكامل (4) استمارة منها غير صالحة وبنسبة الاستجابة (2.97%) من مجموع الاستمارات الموزعة، وتم التحليل بواسطة البرنامج الاحصائي (SPSS-V.23) وتوصل البحث إلى مجموعة من النتائج أهمها وجود علاقة ارتباط ذات دلالة معنوية بين الاستشراف الاستراتيجي وبين التغيير التنظيمي في وكانت العلاقة إيجابية قوية جدا بين المتغيرين فضلا عن ذلك أظهرت نتائج البحث وجود أثر ذو دلالة معنويـة للاستشراف الاستراتيجي وبين التغيير التنظيمي، وأهم التوصيات التي توصل البحث هي ضرورة اهتمام القيادات الإدارية بممارسة أساليب الاستشراف الاستراتيجي وكذلك اهتمام القيادات الإدارية بنمط الاتصال مع العاملين من خلال إعطاء بعض التفويضات في الصلاحيات من أجل توفير المرونة الكافية في الهيكل التنظيمي لاستيعاب التغييرات المستقبلية التي تقوم بدورها في زيادة فرص الابداع والتطوير في مجال العمل الصحي.
Modelling in finance is a challenging task: the data often has complex statistical properties and its inner workings are largely unknown. Deep learning algorithms are making progress in the field of data-driven modelling, but the lack of sufficient data to train these models is currently holding back several new applications. Generative Adversarial Networks (GANs) are a neural network architecture family that has achieved good results in image generation and is being successfully applied to generate time series and other types of financial data. The purpose of this study is to present an overview of how these GANs work, their capabilities and limitations in the current state of research with financial data, and present some practical applications in the industry. As a proof of concept, three known GAN architectures were tested on financial time series, and the generated data was evaluated on its statistical properties, yielding solid results. Finally, it was shown that GANs have made considerable progress in their finance applications and can be a solid additional tool for data scientists in this field.