Hasil untuk "Finance"

S. Myers

C. Mayer

Katharina Pistor, M. Raiser, S. Gelfer

D. Hulme, P. Mosley

Roy W. Bahl

Zunrong Zhou, Xiang Li

This study examines how green finance influences high-quality economic development, with a particular focus on its spatial spillover mechanisms. Specifically, we investigate the competing roles of technology spillover and the pollution haven effect. Using provincial panel data from China (2010–2021) and applying a Spatial Durbin Model (SDM), we deconstruct the total effect of green finance into three distinct components: the local technological progress effect, the positive technology spillover effect, and the negative pollution haven effect. While acknowledging limitations related to the macro-level data granularity and the indirect nature of the mechanism tests, our analysis yields three main findings. First, green finance development shows significant regional disparities. It has progressed most rapidly in the eastern region, remained relatively stable in the central region, and declined in the western region. Second, green finance exerts a strong positive direct effect on local high-quality economic development. This promoting effect becomes even stronger in more developed regions. Third, green finance generates significant negative spatial spillovers on neighboring regions. These are primarily driven by the pollution haven effect, which involves the cross-regional relocation of polluting industries. However, local technological progress partially mitigates these adverse externalities. Overall, our findings reveal the dual nature of the spatial externalities associated with green finance. They also highlight the urgency of coordinated regional environmental governance to prevent “green leakage” and to promote balanced, high-quality economic development.

Xiaohui Yan, Shiqing Liu, Yongjian Su et al.

Free radicals are a class of reactive substances produced during the operation of proton exchange membrane fuel cells (PEMFCs), which have a great impact on the durability of PEMFCs. Previous research on the fuel cell degradation mechanism mainly focused on the degradation of the membrane electrode assembly (MEA) in high Pt loading PEMFCs, especially the chemical degradation of proton exchange membrane (PEM). However, there are significant differences in the characteristics and performance of PEMFCs with low and high Pt loading especially under the high current density, which is mainly due to the oxygen transport process in cathode catalyst layers (CCLs). Currently, few relevant research has explored the impact of chemical degradation on oxygen transport in the cathode of low-Pt PEMFCs. Therefore, this work investigates the effects of free radical attack on the structure of ionomer films, the local oxygen transport process and the evolution of the ionomer coated Pt/C structure in CCLs through physicochemical characterizations, electrochemical measurements and molecular dynamic simulations. Our research found that free radical attacks decreased the electrochemical active area of CCLs, but it also temporarily improved the cell performance at high current densities. Furthermore, molecular dynamics simulations demonstrated that the ionomer exhibited higher oxygen self-diffusion and a more relaxed structure after degradation.

M. Lounsbury

W. Carlin, C. Mayer

Mengxiang Zhu, Riccardo Rastelli

As a core policy tool for China in addressing climate risks, green finance plays a strategically important role in shaping carbon mitigation outcomes. This study investigates the nonlinear and interaction effects of green finance on carbon emission intensity (CEI) using Chinese provincial panel data from 2000 to 2022. The Climate Physical Risk Index (CPRI) is incorporated into the analytical framework to assess its potential role in shaping carbon outcomes. We employ Bayesian Additive Regression Trees (BART) to capture complex nonlinear relationships and interaction pathways, and use SHapley Additive exPlanations values to enhance model interpretability. Results show that the Green Finance Index (GFI) has a statistically significant inverted U-shaped effect on CEI, with notable regional heterogeneity. Contrary to expectations, CPRI does not show a significant impact on carbon emissions. Further analysis reveals that in high energy consumption scenarios, stronger green finance development contributes to lower CEI. These findings highlight the potential of green finance as an effective instrument for carbon intensity reduction, especially in energy-intensive contexts, and underscore the importance of accounting for nonlinear effects and regional disparities when designing and implementing green financial policies.

Haonan Xu, Alessio Brini

This paper applies deep reinforcement learning (DRL) to optimize liquidity provisioning in Uniswap v3, a decentralized finance (DeFi) protocol implementing an automated market maker (AMM) model with concentrated liquidity. We model the liquidity provision task as a Markov Decision Process (MDP) and train an active liquidity provider (LP) agent using the Proximal Policy Optimization (PPO) algorithm. The agent dynamically adjusts liquidity positions by using information about price dynamics to balance fee maximization and impermanent loss mitigation. We use a rolling window approach for training and testing, reflecting realistic market conditions and regime shifts. This study compares the data-driven performance of the DRL-based strategy against common heuristics adopted by small retail LP actors that do not systematically modify their liquidity positions. By promoting more efficient liquidity management, this work aims to make DeFi markets more accessible and inclusive for a broader range of participants. Through a data-driven approach to liquidity management, this work seeks to contribute to the ongoing development of more efficient and user-friendly DeFi markets.

Haoyu Dong, Pengkun Zhang, Yan Gao et al.

We introduce FinWorkBench (a.k.a. Finch), a benchmark for evaluating agents on real-world, enterprise-grade finance and accounting workflows that interleave data entry, structuring, formatting, web search, cross-file retrieval, calculation, modeling, validation, translation, visualization, and reporting. Finch is built from authentic enterprise workspaces from Enron (15,000 files and 500,000 emails) and other financial institutions spanning 2000 to 2025, preserving the in-the-wild messiness of multimodal artifacts such as tables and charts across diverse domains including budgeting, trading, and asset management. We propose a workflow construction process that combines LLM-assisted mining of workflows from authentic enterprise environments with expert annotation. Specifically, we use LLM-assisted, expert-verified derivation of workflows from real-world email threads and spreadsheet version histories, followed by meticulous workflow annotation requiring more than 700 hours of expert effort. This process yields 172 composite workflows with 384 tasks, involving 1,710 spreadsheets with 27 million cells, along with PDFs and other artifacts, capturing the intrinsically messy, long-horizon, knowledge-intensive, and collaborative nature of enterprise work. We conduct both human and automated evaluations of frontier AI systems, including GPT 5.1, Claude Sonnet/Opus 4.5, Gemini 3 Pro, Grok 4, and Qwen 3 Max. GPT 5.1 Pro spends an average of 16.8 minutes per workflow yet passes only 38.4% of workflows. Comprehensive case studies further highlight the challenges that real-world enterprise workflows pose for AI agents.

Rafik Mankour, Yassine Chafai, Hamada Saleh et al.

Climate Finance Bench introduces an open benchmark that targets question-answering over corporate climate disclosures using Large Language Models. We curate 33 recent sustainability reports in English drawn from companies across all 11 GICS sectors and annotate 330 expert-validated question-answer pairs that span pure extraction, numerical reasoning, and logical reasoning. Building on this dataset, we propose a comparison of RAG (retrieval-augmented generation) approaches. We show that the retriever's ability to locate passages that actually contain the answer is the chief performance bottleneck. We further argue for transparent carbon reporting in AI-for-climate applications, highlighting advantages of techniques such as Weight Quantization.

Kevin McNamara, Rhea Pritham Marpu

The global financial system stands at an inflection point. Stablecoins represent the most significant evolution in banking since the abandonment of the gold standard, positioned to enable "Banking 2.0" by seamlessly integrating cryptocurrency innovation with traditional finance infrastructure. This transformation rivals artificial intelligence as the next major disruptor in the financial sector. Modern fiat currencies derive value entirely from institutional trust rather than physical backing, creating vulnerabilities that stablecoins address through enhanced stability, reduced fraud risk, and unified global transactions that transcend national boundaries. Recent developments demonstrate accelerating institutional adoption: landmark U.S. legislation including the GENIUS Act of 2025, strategic industry pivots from major players like JPMorgan's crypto-backed loan initiatives, and PayPal's comprehensive "Pay with Crypto" service. Widespread stablecoin implementation addresses critical macroeconomic imbalances, particularly the inflation-productivity gap plaguing modern monetary systems, through more robust and diversified backing mechanisms. Furthermore, stablecoins facilitate deregulation and efficiency gains, paving the way for a more interconnected international financial system. This whitepaper comprehensively explores how stablecoins are poised to reshape banking, supported by real-world examples, current market data, and analysis of their transformative potential.

Conghao Zhu, Tingting Wang

Against the backdrop of China's growing emphasis on corporate ESG performance and the prevalence of involutional competition within industries, this study examines the impact of new firm entry on the ESG performance of incumbent firms, using panel data from Chinese A-share listed companies spanning 2008 to 2022. The results reveal a significant inverted U-shaped relationship: moderate levels of new entry enhance ESG performance, whereas excessive entry suppresses it due to intensified resource constraints. Mechanism analysis identifies innovation capacity, profitability, and disclosure quality as key transmission channels. Further moderation analysis shows that supply chain resilience and ownership concentration amplify the effects of competitive pressure, while firm age mitigates them. Moreover, new firm entry primarily affects the social dimension of ESG, especially employee compensation. This study enriches the literature on ESG behavior from a competition-based perspective, uncovers the nonlinear dynamics between market competition and sustainable strategy, and offers both theoretical and empirical insights for corporate governance and policy formulation in emerging markets.

Younes Nobakht

Objective In addition to its impact on public health, the COVID-19 pandemic posed significant challenges to global economies, particularly stock markets. Consequently, the pandemic's effects on stock markets opened diverse avenues for research. Scholars worldwide have sought to explore this topic from various perspectives. This article aims to conduct a bibliometric analysis of studies on COVID-19 and the stock market in Iran by mapping and analyzing scientific research trends in this domain.   Methods This applied study employs a bibliometric approach. The statistical population comprises all articles published on the subject of COVID-19 and the stock market in Iran between February 2020 and January 2023. Data analysis was conducted using Excel, Ravar PreMap, and VOSviewer software.   Results Considering the process of publishing articles in the field of COVID-19 research and the stock market in Iran, the results show that the trend of publishing articles in this field is upward, with the highest number of published articles in 2022, accounting for 26 titles and 50% of all articles. Regarding the intellectual structure of research in the field of COVID-19 and the stock market in the country, the evaluation of the intellectual structure in bibliometric analysis is usually done by examining the most frequent keywords and their co-occurrence. The most frequent keywords help identify the main topics that make up the intellectual structure of the research and word co-occurrence is used to identify clusters, track time trends, and analyze the density of keywords. These methods help introduce dominant, developed, saturated, extinct, or emerging topics. In this research, to investigate the most frequent keywords, the main words used in the title, abstract, and the keywords selected by the authors in each article were analyzed. In total, researchers in the field of COVID-19 and the stock market in Iran have used 312 main keywords in their articles. The keywords "COVID-19" (47 occurrences) and "stock market" (42 occurrences) are the most frequent in this field, followed by "financial markets," "pandemic," "crisis," "stock market index," "stock market return," and "stock return." These keywords form the main core of the topics that make up the intellectual structure of research in this field in Iran. To investigate word co-occurrence, social network mapping was used. Analysis of the social network map of word co-occurrence was conducted using three methods: 1) Analysis of clusters of the most frequent keywords, 2) Investigation of the time process of the formation of clusters of the most frequent keywords, and 3) Density analysis of the most frequent keywords. However, due to the newness of the field and the small number of articles in this area in Iran, analyzing the articles by examining the time trend and the density of the most frequent keywords would not be meaningful. Therefore, word co-occurrence was analyzed only through the examination of clusters of the most frequent keywords. Drawing the word co-occurrence map in the field of COVID-19 research and the stock market in Iran identified only one thematic cluster. This cluster includes eight main keywords: COVID-19, stock market, financial markets, epidemic, crisis, stock market index, stock market return, and stock return, all connected by red lines. Thus, it can be said that the keywords in this cluster indicate the direction of studies in the field of COVID-19 and the stock market in Iran, which has developed in recent years. Regarding cooperation networks among universities in the field of COVID-19 research and the stock market in Iran, the results show that although there has been good cooperation between universities in this field, more cooperation is expected, especially with universities in other countries.   Conclusion Facilitating the process of publishing articles in this field in Iran, it is expected that the results and suggestions of this research will serve as a roadmap for researchers to explore potential research opportunities in this field.

Kai Li, N. Prabhala

Thorsten Beck, Thorsten Beck, A. Demirguc-Kunt et al.

Habibullah Safi, Ali Imran Jehangiri, Zulfiqar Ahmad et al.

The Internet of Things (IoT) is a growing network of interconnected devices used in transportation, finance, public services, healthcare, smart cities, surveillance, and agriculture. IoT devices are increasingly integrated into mobile assets like trains, cars, and airplanes. Among the IoT components, wearable sensors are expected to reach three billion by 2050, becoming more common in smart environments like buildings, campuses, and healthcare facilities. A notable IoT application is the smart campus for educational purposes. Timely notifications are essential in critical scenarios. IoT devices gather and relay important information in real time to individuals with special needs via mobile applications and connected devices, aiding health-monitoring and decision-making. Ensuring IoT connectivity with end users requires long-range communication, low power consumption, and cost-effectiveness. The LPWAN is a promising technology for meeting these needs, offering a low cost, long range, and minimal power use. Despite their potential, mobile IoT and LPWANs in healthcare, especially for emergency response systems, have not received adequate research attention. Our study evaluated an LPWAN-based emergency response system for visually impaired individuals on the Hazara University campus in Mansehra, Pakistan. Experiments showed that the LPWAN technology is reliable, with 98% reliability, and suitable for implementing emergency response systems in smart campus environments.

Felix K. Rioja, N. Valev