Hasil untuk "Public finance"

L. Meulbroek

E. Ostrom, Larry D. Schroeder, S. Wynne

U. Sumaila, M. Walsh, K. Hoareau et al.

The ocean, which regulates climate and supports vital ecosystem services, is crucial to our Earth system and livelihoods. Yet, it is threatened by anthropogenic pressures and climate change. A healthy ocean that supports a sustainable ocean economy requires adequate financing vehicles that generate, invest, align, and account for financial capital to achieve sustained ocean health and governance. However, the current finance gap is large; we identify key barriers to financing a sustainable ocean economy and suggest how to mitigate them, to incentivize the kind of public and private investments needed for topnotch science and management in support of a sustainable ocean economy. The ocean supports many livelihoods, but this is currently not sustainable with pressures on the climate and ecosystems. Here, in this perspective, the authors outline the barriers and solutions for financing a sustainable ocean economy.

Helen S. Toxopeus, Friedemann Polzin

Obtaining public and/or private finance for upscaling urban nature-based solutions (NBS) is a key barrier for reaching urban sustainability goals, including climate mitigation and adaptation. We carry out a systematic review of the academic literature to understand the key barriers and corresponding strategies for financing urban NBS. First, we report on specific financing challenges and strategies found for NBS uptake in four urban ecological domains: buildings, facades and roofs; urban green space (parks, trees); allotment gardens (including urban agriculture); and green-blue infrastructure. Across domains, we identify two overarching barriers of NBS finance: (1) coordination between private and public financiers and (2) integration of NBS benefits into valuation and accounting methods. We discuss strategies found in the literature that address these barriers; here, two things stand out. One, there is a large variety of valuation strategies that does not yet allow for an integrated accounting and valuation framework for NBS. Two, strategies aimed at coordinating public/private finance generally look for ways to encourage specific actors (real estate developers, residents) that benefit privately from an NBS to provide co-financing. We visualize our findings into a framework for enabling (public and/or private) finance for upscaling urban NBS.

John T. Tlhakanelo, John T. Tlhakanelo, John Ele-Ojo Ataguba et al.

IntroductionDespite global market complexities, Botswana acquired about 2.6 million COVID-19 vaccine doses between March 2021 and March 2022, 76% of which were purchased while 24% were donations. Thus, the study was envisaged to aggregate evidence on the case of Botswana's COVID-19 vaccine access patterns, hesitancy, and uptake.Materials and methodsWe conducted a scoping reviewof Botswana-based articles using a predetermined search strategy to search databases including Medline, CINAHL, Web of Science, PubMed, Scopus, and Google Scholar. The review included all the English-language written peer-reviewed and grey literature reporting on vaccination in Botswana, to broaden coverage in recognition of limited publications on COVID-19 vaccinartion in Botswana. Non-English articles were excluded due to limited translation resources. Due to the heterogeneity of studies, a narrative synthesis approach was used to collect, synthesize, and map the literature.ResultsAs of 31 December 2021, 80.6% of the Botswana national target of 1,390,856 people over 18 years had received at least one dose of a COVID-19 vaccine, while 71.9% were fully vaccinated. Various vaccine distribution channels were utilized, including public facilities and outreaches, to improve access and uptake of vaccines. COVID-19 vaccine acceptance was considered generally high (73.4% amongst adults), and found positively associated with the male gender, those with comorbidities, those with non-restrictive religious beliefs, and those aged 55–64 years who thought the vaccine was safe for use. COVID-19 vaccine delivery relied on existing Expanded Program on Immunization (EPI) structures and therefore experienced to existing EPI challenges including, lack of transport, shortage of human resources, and vaccine stock-outs.ConclusionsUnder-performance of immunization programs at the district level, characterized by declining immunization coverage and inadequate outreach services, exacerbates disparities in vaccine access. Efforts to strengthen healthcare infrastructure and expand outreach services are essential for reaching populations with limited access to healthcare facilities, particularly in rural and hard-to-reach areas. Collaboration with other government entities and the private sector improved vaccine access.

Kateryna Hrytsiv, Jekaterina Kartašova

This paper explores the multifaceted nature of risk in financial decision-making by integrating traditional finance models with insights from behavioural finance. It assesses the application of models such as the Capital Asset Pricing Model (CAPM), Weighted Average Cost of Capital (WACC), and Risk-Adjusted Discount Rates in real-world scenarios, examining how their effectiveness is influenced by psychological biases such as overconfidence, loss aversion, and herd behaviour. The study illustrates the impact of psychological and emotional factors on individual investor actions and corporate long-term capital investment decisions through a practical application example. The findings advocate for a comprehensive approach that combines computational tools with behaviorally informed human judgment, aiming to enhance risk analysis and improve financial returns for investors and managers.

Burak Öz, Christof Ferreira Torres, Christoph Schlegel et al.

Decentralized finance (DeFi) markets spread across Layer-1 (L1) and Layer-2 (L2) blockchains rely on arbitrage to keep prices aligned. Today most price gaps are closed against centralized exchanges (CEXes), whose deep liquidity and fast execution make them the primary venue for price discovery. As trading volume migrates on-chain, cross-chain arbitrage between decentralized exchanges (DEXes) will become the canonical mechanism for price alignment. Yet, despite its importance to DeFi-and the on-chain transparency making real activity tractable in a way CEX-to-DEX arbitrage is not-existing research remains confined to conceptual overviews and hypothetical opportunity analyses. We study cross-chain arbitrage with a profit-cost model and a year-long measurement. The model shows that opportunity frequency, bridging time, and token depreciation determine whether inventory- or bridge-based execution is more profitable. Empirically, we analyze one year of transactions (September 2023 - August 2024) across nine blockchains and identify 242,535 executed arbitrages totaling 868.64 million USD volume. Activity clusters on Ethereum-centric L1-L2 pairs, grows 5.5x over the study period, and surges-higher volume, more trades, lower fees-after the Dencun upgrade (March 13, 2024). Most trades use pre-positioned inventory (66.96%) and settle in 9s, whereas bridge-based arbitrages take 242s, underscoring the latency cost of today's bridges. Market concentration is high: the five largest addresses execute more than half of all trades, and one alone captures almost 40% of daily volume post-Dencun. We conclude that cross-chain arbitrage fosters vertical integration, centralizing sequencing infrastructure and economic power and thereby exacerbating censorship, liveness, and finality risks; decentralizing block building and lowering entry barriers are critical to countering these threats.

Dhanashekar Kandaswamy, Ashutosh Sahoo, Akshay SP et al.

As decentralized finance (DeFi) evolves, distinguishing between user behaviors - liquidity provision versus active trading - has become vital for risk modeling and on-chain reputation. We propose a behavioral scoring framework for Uniswap that assigns two complementary scores: a Liquidity Provision Score that assesses strategic liquidity contributions, and a Swap Behavior Score that reflects trading intent, volatility exposure, and discipline. The scores are constructed using rule-based blueprints that decompose behavior into volume, frequency, holding time, and withdrawal patterns. To handle edge cases and learn feature interactions, we introduce a deep residual neural network with densely connected skip blocks inspired by the U-Net architecture. We also incorporate pool-level context such as total value locked (TVL), fee tiers, and pool size, allowing the system to differentiate similar user behaviors across pools with varying characteristics. Our framework enables context-aware and scalable DeFi user scoring, supporting improved risk assessment and incentive design. Experiments on Uniswap v3 data show its usefulness for user segmentation and protocol-aligned reputation systems. Although we refer to our metric as zScore, it is independently developed and methodologically different from the cross-protocol system proposed by Udupi et al. Our focus is on role-specific behavioral modeling within Uniswap using blueprint logic and supervised learning.

Lingfei Qian, Weipeng Zhou, Yan Wang et al.

As the fundamental capability behind decision-making in finance, financial reasoning poses distinct challenges for LLMs. Although reinforcement learning (RL) have boosted generic reasoning, the progress in finance is hindered by the absence of empirical study of building effective financial chain-of-thought (CoT) corpus, a systematic comparison of different RL methods, and comprehensive benchmarks. To address these gaps, we introduce FinCoT, the first open high-fidelity CoT corpus for finance, distilled from seven QA datasets by a novel three-stage pipeline that incorporates domain supervision, iterative LLM refinement, and difficulty-aware filtering. Based on FinCoT, we develop Fin-o1, the first open financial reasoning models trained via supervised fine-tuning and GRPO-based RL. Our models outperform existing financial reasoning models and SOTA general models such as GPT-o1, DeepSeek-R1, and GPT-4.5. We also investigate the effectiveness of three different RL methods in improving domain-specific reasoning, offering the first such empirical study. We finally propose FinReason, the first financial reasoning benchmark covering multi-table analysis, long-context reasoning, and equation-based tasks, and evaluate 29 LLMs. Our extensive experiments reveal general reasoning models excel on standard benchmarks yet exhibit obvious performance degradation in financial contexts; even finance-tuned models like Dianjin-R1 and FinR1 degrade on lengthy documents. In contrast, our Fin-o1 models consistently outperform their backbones and larger GPT-o1 and DeepSeek-R1, confirming the effectiveness of our data building and model training strategy. Our study further shows that GRPO yields reliable gains whereas PPO and DPO do not, highlighting the need for targeted data and optimisation rather than scale alone.

Ali Kakhbod, Peiyao Li

We present NoLBERT, a lightweight, timestamped foundational language model for empirical research -- particularly for forecasting in economics, finance, and the social sciences. By pretraining exclusively on text from 1976 to 1995, NoLBERT avoids both lookback and lookahead biases (information leakage) that can undermine econometric inference. It exceeds domain-specific baselines on NLP benchmarks while maintaining temporal consistency. Applied to patent texts, NoLBERT enables the construction of firm-level innovation networks and shows that gains in innovation centrality predict higher long-run profit growth.

Georgios Pavlidis

This chapter explores the convergence of two major developments: the rise of environmental, social, and governance (ESG) investing and the exponential growth of artificial intelligence (AI) technology. The increased demand for diverse ESG instruments, such as green and ESG-linked loans, will be aligned with the rapid growth of the global AI market, which is expected to be worth $1,394.30 billion by 2029. AI can assist in identifying and pricing climate risks, setting more ambitious ESG goals, and advancing sustainable finance decisions. However, delegating sustainable finance decisions to AI poses serious risks, and new principles and rules for AI and ESG investing are necessary to mitigate these risks. This chapter highlights the challenges associated with norm-setting initiatives and stresses the need for the fine-tuning of the principles of legitimacy, oversight and verification, transparency, and explainability. Finally, the chapter contends that integrating AI into ESG non-financial reporting necessitates a heightened sense of responsibility and the establishment of fundamental guiding principles within the spheres of AI and ESG investing.

A. Isah, Michael O. Dioha, Ramit Debnath et al.

Background Achieving climate targets will require a rapid transition to clean energy. However, renewable energy (RE) firms face financial, policy, and economic barriers to mobilizing sufficient investment in low-carbon technologies, especially in low- and middle-income countries. Here, we analyze the challenges and successes of financing the energy transition in Nigeria and Brazil using three empirically grounded levers: financing environments, channels, and instruments. Results While Brazil has leveraged innovative policy instruments to mobilize large-scale investment in RE, policy uncertainty and weak financing mechanisms have hindered RE investments in Nigeria. Specifically, Brazil’s energy transition has been driven by catalytic finance from the Brazilian Development Bank (BNDES). In contrast, bilateral agencies and multilateral development banks (MDBs) have been the largest financiers of renewables in Nigeria. Policy instruments and public–private partnerships need to be redesigned to attract finance and scale market opportunities for RE project developers in Nigeria. Conclusions We conclude that robust policy frameworks, a dynamic public bank, strategic deployment of blended finance, and diversification of financing instruments would be essential to accelerate RE investment in Nigeria. Considering the crucial role of donors and MDBs in Nigeria, we propose a multi-stakeholder model to consolidate climate finance and facilitate the country’s energy transition.

R. Cornes, T. Sandler

Noha Alessa

International capital providers such as the World Bank suggest that inefficient resource allocation in developing countries remains a major challenge for borrowing countries. Therefore, the purpose of this study is to examine whether the adoption of International Public Sector Accounting Standards (IPSAS) improves the resource allocation efficiency of developing countries. A robust econometric modeling including fixed effect and Two-Step Generalized Method of Moments is employed on a sample of 64 developing countries between 2005 and 2021. The results are not sensitive to potential endogeneity issues. The findings indicate that the IPSAS coefficient is significantly and positively correlated at a 5% level or higher. This suggests a strong and significant relationship between IPSAS adoption and resource allocation, indicating that using IPSAS improves efficient resource allocation. Additionally, the resource allocation coefficient is positive and highly significant at a 5% level or higher. These results are particularly notable in countries with low bureaucratic quality, suggesting that IPSAS adoption strengthens policies and regulations in the public sector’s financial structure, ultimately leading to more efficient resource allocation. Therefore, these findings imply that adopting IPSAS is crucial for developing countries to ensure efficient resource allocation and attract international capital providers.

Michael A. McCarthy

In this essay, I argue that Christophers’ description of asset-manager society is best characterized by a logic of ‘acquire and extract’. I build on his insights to delve into the less-explored world of emancipatory alternatives. I argue for radical transformations – what I term ‘democratic ruptures’ – that shift the investment logic of asset managers toward one of ‘build and nourish’. With insights from the failure to establish economic democracy over large pools of finance by unions in the postwar period, I argue that the crucial missing ingredient in the social and ecological disaster of asset-manager society today is democracy. I conclude with a radical reimagining of financial democracy for the twenty-first century.

عبير منصور عبد الحميد

ملخص:هدفت هذه الدراسة إلى توضيح الأثار الاقتصادية الناتجة عن تعرض القطاع السياحى المصرى للأزمات والصدمات المختلفة سواء كانت أزمات سياسية أو أمنية أو اقتصادية أو حتى صحية محلية ودولية خلال الفترة الزمنية (1995م وحتى عام 2022م) ، وتم الاعتماد على المنهج التحليلى لتوضيح أكثر الأزمات تأثيراً على هذا القطاع ووضع مجموعة من الحلول والمقترحات لتقليل حدة المخاطر التى يتعرض لها القطاع السياحى جراء حدوث تلك الأزمات ، وتوصلت الدراسة إلى أن القطاع السياحى المصرى شديد الحساسية للأزمات والصدمات التى يتعرض لها ولكنه سرعاناً ما يتعافى ويعود مرة أخرى إلى معدلات نموه ، وأن أكثر الأزمات التى أثرت سلباً وبقوة على هذا القطاع هى الأزمات السياسية والأمنية والإرهابية ، وخير مثال على ذلك هو أثر أحداث ثورة 25 يناير 2011م وأحداث الطائرة الروسية فى عام 2015م على هذا القطاع كانت قوية وحادة وطويلة الأمد حيث استمرت منذ 2011م وحتى 2017م.وعلى الرغم من أن أزمة جائحة كورونا (كوفيد-19) كانت من أشد الأزمات التى تعرض لها العالم حيث عانت حكومات كافة الدول من توقف أنشطتها الاقتصادية والاجتماعية والثقافية والرياضية والدينية والسياحية والتعليمية ، إلا أن تأثيرها على القطاع السياحى المصرى على الرغم من شدته وقوته خلال فترة الأزمة إلا إنها سرعان ما تعافى هذا القطاع من أثار تلك الأزمة واستطاع استيعاد قوته مرة أخرى بعد الانفتاح.

Meni Brief, Oded Ovadia, Gil Shenderovitz et al.

The application of large language models (LLMs) in domain-specific contexts, including finance, has expanded rapidly. Domain-specific LLMs are typically evaluated based on their performance in various downstream tasks relevant to the domain. In this work, we present a detailed analysis of fine-tuning LLMs for such tasks. Somewhat counterintuitively, we find that in domain-specific cases, fine-tuning exclusively on the target task is not always the most effective strategy. Instead, multi-task finetuning - where models are trained on a cocktail of related tasks - can significantly enhance performance. We demonstrate how this approach enables a small model, such as Phi-3-Mini, to achieve state-of-the-art results, even surpassing the much larger GPT-4-o model on financial benchmarks. Our study involves a large-scale experiment, conducting over 200 training experiments using several widely adopted LLMs as baselines, and empirically confirms the benefits of multi-task fine-tuning. Additionally, we explore the use of general instruction data as a form of regularization, suggesting that it helps minimize performance degradation. We also investigate the inclusion of mathematical data, finding improvements in numerical reasoning that transfer effectively to financial tasks. Finally, we note that while fine-tuning for downstream tasks leads to targeted improvements in task performance, it does not necessarily result in broader gains in domain knowledge or complex domain reasoning abilities.

M. Mitchell, J. Mulherin

Kuang-Hua Hu, Fu-Hsiang Chen, Ming-Fu Hsu et al.

Abstract A broad range of companies around the world has welcomed artificial intelligence (AI) technology in daily practices because it provides decision-makers with comprehensive and intuitive messages about their operations and assists them in formulating appropriate strategies without any hysteresis. This research identifies the essential components of AI applications under an internal audit framework and provides an appropriate direction of strategies, which relate to setting up a priority on alternatives with multiple dimensions/criteria involvement that need to further consider the interconnected and intertwined relationships among them so as to reach a suitable judgment. To obtain this goal and inspired by a model ensemble, we introduce an innovative fuzzy multiple rule-based decision making framework that integrates soft computing, fuzzy set theory, and a multi-attribute decision making algorithm. The results display that the order of priority in improvement—(A) AI application strategy, (B) AI governance, (D) the human factor, and (C) data infrastructure and data quality—is based on the magnitude of their impact. This dynamically enhances the implementation of an AI-driven internal audit framework as well as responds to the strong rise of the big data environment.