The COVID-19 pandemic disrupted local economic activity and exposed vulnerabilities in subnational public finance. However, limited empirical evidence explains how the structure of the local tax base influences fiscal resilience to mobility shocks in developing countries. Drawing on tax handle theory, this study examines the impact of the COVID-19 pandemic on local tax revenue in Indonesia, focusing on heterogeneity across tax types and the moderating role of pre-pandemic fiscal vulnerability, defined as dependence on mobility-based taxes. Using a fixed-effects model on panel data covering 3,136 observations from 448 regencies and municipalities over 2017–2023, the results show that the aggregate decline in local tax revenue was primarily driven by contractions in mobility-sensitive taxes, particularly restaurant and entertainment taxes. This relationship is confirmed by the main moderation results, which show that regions with high pre-pandemic fiscal vulnerability exhibit a significantly stronger negative relationship. Although weakened, the effect persisted into the recovery period (2022–2023). This study provides empirical evidence that the local tax base structure determines fiscal resilience to mobility shocks more than the general level of fiscal autonomy, highlighting the need for local governments to diversify their revenue sources toward those that are relatively more resilient to external shocks.
Yuriy Bilan, Halyna Yurchyk, Natalia Samoliuk
et al.
The increasing number of internally displaced persons (IDPs) in wartime Ukraine leads to growing problems in social protection funding. Under these circumstances, the evaluation of the effectiveness of public finance use is of increasing importance. The study aims to evaluate the effectiveness of public finance for internally displaced persons’ social protection, adapting the KPI methodology for analysis on the national level. The effectiveness is considered following the OECD approach as the extent to which the intervention achieved its objectives and results. At macrolevel of research, the integral indicator was developed based on indicators of input (financing of social protection programs), output (involvement of IDPs in social programs), activity (funding per recipient and multiplicative effect in GDP growth), mechanism (administrative costs for achieving results), and control (effectiveness of IDPs’ social protection compared to other demographic groups). Thirty indicators in total were used (e.g., budgetary funding allocated for housing assistance; budget expenditures on staff salaries of the authorities responsible for certain programs; coverage rate of unemployed IDPs receiving vocational training). The essential distance from the maximum level of expected results (1.0) allows concluding the low effectiveness in this area of public finance use: from 0.330 in 2020 to 0.668 in 2023. Gaps are evident in each direction, especially in input performance (the highest value did not exceed 0.370). The best results were achieved in housing funding and employment governance. The proposed approach is useful for analyzing gaps and identifying opportunities to improve the management of other social programs.
This issue of Public Finance Journal highlights how fiscal systems are shaped by governance, politics, and social norms, featuring studies on AI-assisted revenue forecasting, city-centric sales tax models, structural inequities in municipal bond ratings, and citizen engagement in budgeting, alongside reviews of works on government accountability, critical tax theory, and pension management—together underscoring that public finance is both technical and deeply tied to questions of equity, justice, and institutional design.
Quantum computing is transforming the world profoundly, affecting businesses, organisations, technologies, and human beings' information systems, and will have a profound impact on accounting and finance, particularly in the realm of cybersecurity. It presents both opportunities and risks in ensuring confidentiality and protecting financial data. The purpose of this article is to show the application of quantum technologies in accounting cybersecurity, utilising quantum algorithms and QKD to overcome the limitations of classical computing. The literature review reveals the vulnerabilities of the current accounting cybersecurity to quantum attacks and the need for quantum-resistant cryptographic mechanisms. It elaborates on the risks associated with conventional encryption in the context of quantum capabilities. This study contributes to the understanding of how quantum computing can transform accounting cybersecurity by enhancing quantum-resistant algorithms and using QKD in accounting. The study employs PSALSAR systematic review methodology to ensure rigour and depth. The analysis shows that quantum computing enhances encryption techniques to superior possibilities than classical ones. Using quantum technologies in accounting minimises data breaches and unauthorised access. The study concludes that quantum-resistant algorithms and quantum key distribution (QKD) are necessary for securing the accounting and finance systems of the future. Keywords Quantum Computing, Cybersecurity, Accounting, Machine Learning, Artificial Intelligence, Quantum Key Distribution, Operations Management
Zheng Hui, Yijiang River Dong, Ehsan Shareghi
et al.
As large language models (LLMs) are increasingly deployed in high-risk domains such as law, finance, and medicine, systematically evaluating their domain-specific safety and compliance becomes critical. While prior work has largely focused on improving LLM performance in these domains, it has often neglected the evaluation of domain-specific safety risks. To bridge this gap, we first define domain-specific safety principles for LLMs based on the AMA Principles of Medical Ethics, the ABA Model Rules of Professional Conduct, and the CFA Institute Code of Ethics. Building on this foundation, we introduce Trident-Bench, a benchmark specifically targeting LLM safety in the legal, financial, and medical domains. We evaluated 19 general-purpose and domain-specialized models on Trident-Bench and show that it effectively reveals key safety gaps -- strong generalist models (e.g., GPT, Gemini) can meet basic expectations, whereas domain-specialized models often struggle with subtle ethical nuances. This highlights an urgent need for finer-grained domain-specific safety improvements. By introducing Trident-Bench, our work provides one of the first systematic resources for studying LLM safety in law and finance, and lays the groundwork for future research aimed at reducing the safety risks of deploying LLMs in professionally regulated fields. Code and benchmark will be released at: https://github.com/zackhuiiiii/TRIDENT
Amid China's dual-carbon transition, the synergistic alignment of green finance with green-technology innovation is pivotal for co-controlling pollution and CO2 emissions. Using panel data for 266 Chinese prefecture-level cities over 2007-2023, We construct the coupling coordination index system of green finance and green technology innovation via a coupling-coordination model and systematically analyzes influencing mechanism of synergistic effect of pollution and carbon reduction. Four findings emerge.(1) The coupled-coordination significantly enhances the synergy, and energy efficiency plays a partial intermediary role in the relationship between the two.(2) The effect is heterogeneous: pronounced in the eastern and western regions, negligible in the central region, and stronger in non-resource-based and non-Yangtze River Basin cities.(3) A double-threshold model reveals a non-linear strengthening pattern as green-finance depth increases.(4) Spatial Durbin estimates show positive spillovers: the coupling of green finance and green technology innovation not only improves the level of local coordination, but also drives the improvement of environmental performance in adjacent areas. These results provide quantitative guidance for allocating green-finance resources, elevating green-innovation efficiency, and designing regionally coordinated mitigation policies.
This paper analyzes the strategic interactions between a profit-maximizing monopolist and a free, capacity-constrained public option. By restricting its own supply, the monopolist intentionally congests the public option and induces rationing, which increases consumers' willingness to pay for guaranteed access. Counterintuitively, expanding the public option's capacity may raise the monopoly price and lower consumer welfare. However, I derive conditions under which all buyer types benefit from a capacity expansion, and extend these results to a setting where an oligopoly competes with a public option. These findings have implications for mixed public-private markets, such as housing, education, and healthcare.
Differentially private (DP) machine learning often relies on the availability of public data for tasks like privacy-utility trade-off estimation, hyperparameter tuning, and pretraining. While public data assumptions may be reasonable in text and image domains, they are less likely to hold for tabular data due to tabular data heterogeneity across domains. We propose leveraging powerful priors to address this limitation; specifically, we synthesize realistic tabular data directly from schema-level specifications - such as variable names, types, and permissible ranges - without ever accessing sensitive records. To that end, this work introduces the notion of "surrogate" public data - datasets generated independently of sensitive data, which consume no privacy loss budget and are constructed solely from publicly available schema or metadata. Surrogate public data are intended to encode plausible statistical assumptions (informed by publicly available information) into a dataset with many downstream uses in private mechanisms. We automate the process of generating surrogate public data with large language models (LLMs); in particular, we propose two methods: direct record generation as CSV files, and automated structural causal model (SCM) construction for sampling records. Through extensive experiments, we demonstrate that surrogate public tabular data can effectively replace traditional public data when pretraining differentially private tabular classifiers. To a lesser extent, surrogate public data are also useful for hyperparameter tuning of DP synthetic data generators, and for estimating the privacy-utility tradeoff.
Embedding models play a crucial role in representing and retrieving information across various NLP applications. Recent advances in large language models (LLMs) have further enhanced the performance of embedding models. While these models are often benchmarked on general-purpose datasets, real-world applications demand domain-specific evaluation. In this work, we introduce the Finance Massive Text Embedding Benchmark (FinMTEB), a specialized counterpart to MTEB designed for the financial domain. FinMTEB comprises 64 financial domain-specific embedding datasets across 7 tasks that cover diverse textual types in both Chinese and English, such as financial news articles, corporate annual reports, ESG reports, regulatory filings, and earnings call transcripts. We also develop a finance-adapted model, Fin-E5, using a persona-based data synthetic method to cover diverse financial embedding tasks for training. Through extensive evaluation of 15 embedding models, including Fin-E5, we show three key findings: (1) performance on general-purpose benchmarks shows limited correlation with financial domain tasks; (2) domain-adapted models consistently outperform their general-purpose counterparts; and (3) surprisingly, a simple Bag-of-Words (BoW) approach outperforms sophisticated dense embeddings in financial Semantic Textual Similarity (STS) tasks, underscoring current limitations in dense embedding techniques. Our work establishes a robust evaluation framework for financial NLP applications and provides crucial insights for developing domain-specific embedding models.
This paper provides an in-depth review of the evolving role of quantum computing in the financial sector, emphasizing both its computational potential and cybersecurity implications. Distinguishing itself from existing surveys, this work integrates classical quantum computing applications - such as portfolio optimization, risk analysis, derivative pricing, and Monte Carlo simulations with a thorough examination of blockchain technologies and post-quantum cryptography (PQC), which are crucial for maintaining secure financial operations in the emerging quantum era. We propose a structured four-step framework to assess the feasibility and expected benefits of implementing quantum solutions in finance, considering factors such as computational scalability, error tolerance, data complexity, and practical implementability. This framework is applied to a series of representative financial scenarios to identify domains where quantum approaches can surpass classical techniques. Furthermore, the paper explores the vulnerabilities quantum computing introduces to digital finance-related applications and blockchain security, including risks to digital signatures, hash functions, and randomness generation, and discusses mitigation strategies through PQC and quantum-resilient alternatives of classical digital finance tools and blockchain architectures. By addressing both quantum blockchain, quantum key distribution (QKD) as well as quantum communication networks, his review presents a more holistic perspective than prior studies, offering actionable insights for researchers, financial practitioners, and policymakers navigating the intersection of quantum computing, blockchain, and secure financial systems.
The accelerated pace of global urbanization has exacerbated the tension between anthropogenic activities and land resource allocation, rendering systematic evaluation of urban expansion’s spatial impacts on regional land-use patterns imperative for informing sustainable land-use planning strategies and facilitating coordinated socioeconomic development. Nanchang was selected as the study area to investigate urban land-use dynamics. Multitemporal Landsat imagery spanning 2005 to 2020 was subjected to spatial pattern analysis, followed by CA-Markov modeling projections of land-use evolution. This integrated methodology enabled systematic evaluation of land resource allocation efficacy in Nanchang, providing empirical evidence for optimizing spatial planning decisions. The results show that: From 2005 to 2020, woodland areas exhibited a continuous decline, with a cumulative reduction of 300.27 km 2 over the 15-year period. Concurrently, unused land underwent conversion to construction and agricultural land uses, resulting in a proportional decrease. While both grassland and construction land demonstrated proportional increases, the expansion rate of construction land showed a marked acceleration during the study period. Agricultural land, woodland, grassland and water areas are greatly affected by elevation slope. Construction land is greatly affected by the convenience of transportation, and old construction land, as the initial boundary of expansion, has driven the transformation of surrounding land use. In 2035, agricultural land will remain largely stable; woodland will slightly decrease; both grassland and water areas will show a decreasing trend; and construction land will increase by 306.31 km 2 . A comprehensive understanding of land use dynamics in Nanchang provides critical insights for sustainable urban planning. The integration of developmental initiatives with ecological conservation constitutes a fundamental requirement for achieving sustainable land resource management and regional sustainable development.
History of scholarship and learning. The humanities, Social Sciences
Abdelmohsen A. Nassani, Muhammad Imran, Shiraz Khan
et al.
Abstract Financial integration plays an important role in fostering global economic growth. Energy demand, technology transfer, sustainable production, and climate change have emerged as key drivers of sustainable development. This study explores the influence of financial integration, bolstered by renewable energy-induced trade, industry-driven technology, and environmental concerns, on regional economic growth. This study analyzes a panel of 39 high- and upper-middle-income European and Central Asian countries in 2017–2021. Using a panel generalized method of moments estimator, we reveal an inverted U-shaped relationship between regional economic growth and carbon emissions. Moreover, renewable energy-induced trade contributes positively to regional growth while trade openness and technology transfer further enhance this growth. Industry-driven technology negatively impacts regional growth owing to inadequate financial integration. The absence of sustainable energy infrastructure and industrialization also negatively impacts regional growth. Our study underscores the importance of increasing financial integration to promote sustainable energy-driven trade openness and technology transfer in line with the United Nations’ sustainable development agenda.
As Artificial Intelligence (AI) increasingly influences various aspects of society, there is growing public interest in its potential benefits and risks. In this paper we present results of public perception of AI from a survey conducted with 10,000 respondents spanning ten countries in four continents around the world. The results show that currently an equal percentage of respondents who believe AI will change the world as we know it, also believe AI needs to be heavily regulated. However, our findings also indicate that despite the general sentiment among the global public that AI will replace workers, if a company were to use AI to innovate to improve lives, the public would be more likely to think highly of the company, purchase from them and even be interested in a job in that company. Our results further reveal that the global public largely views AI as a tool for problem solving. These nuanced results underscore the importance of AI directed towards challenges that the public would like science and technology-based innovations to address. We draw on a multi-year 3M study of public perception of science to provide further context on what the public perceives as important problems to be solved.
When individuals interact in groups, the evolution of cooperation is traditionally modeled using the framework of public goods games. These models often assume that the return of the public good depends linearly on the fraction of contributors. In contrast, in real life public goods interactions, the return can depend on the size of the investor pool as well. Here, we consider a model in which the multiplication factor (marginal per capita return) for the public good depends linearly on how many contribute, which results in a nonlinear model of public goods. This simple model breaks the curse of dominant defection found in linear public goods interactions and gives rise to richer dynamical outcomes in evolutionary settings. We provide an in-depth analysis of the more varied decisions by the classical rational player in nonlinear public goods interactions as well as a mechanistic, microscopic derivation of the evolutionary outcomes for the stochastic dynamics in finite populations and in the deterministic limit of infinite populations. This kind of nonlinearity provides a natural way to model public goods with diminishing returns as well as economies of scale.
In this paper, I conduct a policy exercise about how much the introduction of a cash transfer program as large as a Norwegian-sized lottery sector to the United States would affect startups. The key results are that public cash transfer programs (like lottery) do not increase much the number of new startups, but increase the size of startups, and only modestly increase aggregate productivity and output. The most important factor for entrepreneurs to start new businesses is their ability.
Umme Rubab, Muhammad Rahies Khan, Muhammad Mutasim Billah Tufai
Achieving sustainable performance is a challenging but useful tool for firms in this competitive environment. The literature highlights several avenues to address sustainable performance, but there is alack of emphasis on common practices and strategies. This study examines the role of green initiatives, specifically eco-design, green purchasing and reverse logistics, in addressing environmental performance. Practice-based view theory is used to evaluate the influence of these common green practices on a firm's environmental performance. A total of 214 participants were approached to participate in this study and data analysis was conducted using AMOS. The findings reveal a significant and positive impact of eco-design, green purchasing and reverse logistics on environmental performance. This study provides implications for practitioners, policymakers and academics regarding environmentally oriented business operations that could better serve manufacturing firms. Additionally, firms are encouraged to focus on easily imitable, easy-to-transfer and easy-to-understand practices to address sustainable performance.
Organizational behaviour, change and effectiveness. Corporate culture, Marketing. Distribution of products
Arturo Leccadito, Alessandro Staino, Pietro Toscano
Abstract This study introduces the dynamic Gerber model (DGC) and evaluates its performance in the prediction of Value at Risk (VaR) and Expected Shortfall (ES) compared to alternative parametric, non-parametric and semi-parametric methods for estimating the covariance matrix of returns. Based on ES backtests, the DGC method produces, overall, accurate ES forecasts. Furthermore, we use the Model Confidence Set procedure to identify the superior set of models (SSM). For all the portfolios and VaR/ES confidence levels we consider, the DGC is found to belong to the SSM.
كوثر محمد عبدالحافظ محمد, محمود إبراهيم محمد عبدالموجود, أسامه أحمد جمال هلالي
يهدف هذا الــبحث إلــى توضيح مدى إمكانية الاستفادة من الآليات التي تستند إليها تـكنولوجيــا سلاسل الكتــل في دعم (التخفيف من) التأثيرات الموجبة (السالبة) للتقديرات الـمحاسبيــة في الخصائص النوعية للمعلومات المفيدة كمقياس لجودة المعلومات الـمحاسبيــة، وذلك في ضوء سعي الدولة المصرية إلى زيادة شفافية التقارير المالية من خــلال التوسع في تطبيق تـقـنيات الأعمال الحديثة، مع الأخذ في الحسبان منافع ومحددات التطبيق في بيئة الأعمال المصرية.وتم الاعتماد على أسلوب الاستبيان كأداة بحث لاستطلاع آراء مجتمع الدراسة والمتمثل في الشركات المدرجة بمؤشر EGX 30 خلال عامي 2022/2023م، وقد اشتملت عينة الدراسة على ثلاث فئات: شركات القطاع المالي؛ وشركات القطاع غير المالي، ومراجعي الحسابات. وتم توزيع الاستبانات إلكترونياً على عينة الدراسة وتجميع الردود من عدد 114 مفردة تنتمي للثلاث فئات السابقة.النتائج: وجود تأثير جوهري لتـكنولوجيــا سلاسل الـكتــل في مهنة المحاسبة والمراجعة سواء على مستوى الفرص أو التحديات، حيث يمكن استخدام تلك التـكنولوجيــا في تخـفـيف الـتأثيرات السالبــة لـلتقديرات الـمحاسبيــة فــي خاصية الــتمثيل الصادق بأبعادها الثلاث – الاكتمال والحيادية والخلو من الأخطاء؛ وكذلك تخفيف التأثير السالب للتقديرات الـمحاسبيــة في خصائص القابلية للفهم والمقارنة والتحقق، بالإضافة إلى دعم التأثيرات الموجبة للتقديرات الـمحاسبيــة في خاصيتي الملاءمة والوقتية، وذلك من خلال التأثير الإيجابي المباشر لتلك التـكنولوجيــا في كافة الخصائص النوعية للمعلومات المفيدة.القيمة المضافة: التأثير المتداخل لتـكنولوجيــا سلاسل الـكتــل في العلاقة بين التقديرات المحاسبية والخصائص النوعية للمعلومات المفيدة لـم يسبـق أن تـنـاولتـه أياً مـن الـبحوث التـي تـمـت في الـبيئة المصريــة، كما يمكن الاستفادة من النتائج المتوصل إليها في سرعة تبني وتطبيق تلك التـكنولوجيــا فـي العمــل الـمحاسبي بـالـبيئة المصريــة لما تحققه من زيادة جودة عمليتي القيــاس والـتوصيل المحاسبي.
ABSTRACT This paper examines the drivers of private investment in renewable energy by source of financing for 13 economies over the period 2008–2018, with a focus on a sub-panel of Asian economies. Sources of financing – asset finance, corporate research and development (R&D), public market, and venture capital and private equity – vary not only across years and renewable energy sources, but also across countries. Using a fixed effects panel model, this paper provides a first quantitative estimate of the effect of government renewable energy policies on private investment across different sources of financing, with four main findings. First, while government expenditure on R&D positively affects private investment from asset finance and corporate R&D, it is not the most important driver in terms of the magnitude of the elasticity. Second, feed-in tariffs have a particularly strong effect on stimulating renewable energy investment financed through public markets, with the findings particularly strong for the Asian sub-sample. Third, tax incentives have a mixed impact across sources of financing. Fourth, technology costs and energy prices have considerable effects on driving renewable energy investment from asset finance, with the impact notably more pronounced for the Asian sub-sample. Key policy insights To maximize the impact of government R&D, policies should aim to facilitate a smoother investment environment for the private sector in the areas of asset finance and corporate R&D. This could include targeted subsidies and tax relief measures. Enhanced FIT mechanisms should be developed, particularly in Asia, to leverage greater investment financed via publicly quoted markets. This could also include more favourable initial fiscal incentives and terms of agreement. Tax incentives should be used with caution. While tax incentives have a positive effect on investment in renewable energy overall, they may negatively affect investment financed by corporate R&D and venture capital and private equity, i.e. private financing sources that are crucial for technology R&D and manufacturing scale-up. Countries with lower regulatory quality may need to offer higher FIT rates for policies to be effective in attracting private investment. GRAPHICAL ABSTRACT