Climate finance is the study of local and global financing of public and private investment that seeks to support mitigation of and adaptation to climate change. In 2017, the Review of Financial Studies launched a competition among scholars to develop research proposals on the topic with the goal of publishing this special volume. We describe the competition, how the nine projects featured in this volume came to be published, and frame their findings within what we view as a broader climate finance research program.
آیدین ابوطالبی, کامبیز پیکارجو, ابراهیم رضایی
et al.
رمزارزها، بهویژه بیتکوین، بهعنوان داراییهای دیجیتال مبتنی بر فناوری بلاکچین، به دلیل ویژگیهایی همچون غیرمتمرکز بودن، شفافیت تراکنشها و سرعت انتقال، توجه روزافزون سرمایهگذاران و پژوهشگران مالی را به خود جلب کردهاند. با این حال، این بازار ذاتاً با نوسانات شدید همراه است و تحتتأثیر عوامل اقتصادی، سیاسی و فناوری قرار دارد. این موضوع در شرایط بحران اقتصادی ایران، ناشی از تحریمهای بینالمللی، نوسانات نرخ ارز و تورم بالا، اهمیت توسعه مدلهای پیشبینی دقیقتر را دوچندان میسازد. هدف این پژوهش امکان سنجی و ارزیابی عملکرد الگوریتمهای فراابتکاری در بهینهسازی شبکههای عصبی مصنوعی جهت پیشبینی روند قیمتی بیتکوین است. در این راستا، الگوریتم ترکیبی شاهین آتشین- ساخت اهرام جیزه (FHO–GPC) با الگوریتم بهینهساز گاومیش مشکی (MO) مورد استفاده قرار گرفت و داده ها به دو بخش آموزش (۸۰ ) و آزمون (۲۰ ) تقسیم شدند. پارامترهای شبکه عصبی پرسپترون چندلایه (MLP) توسط این الگوریتمها بهینهسازی شدند؛ بهگونهای که FHO جستجوی سراسری را انجام داده و GPC بهینهسازی موضعی را بر عهده داشت. یافتهها نشان داد که الگوریتم MO با کاهش معنادار شاخصهای خطا (RMSE) و (MAE) و افزایش ضریب تعیین (R²)، عملکرد دقیقتری نسبت به مدل ترکیبی ارائه کرده است. این برتری بهویژه در شرایط پرنوسان و بحرانی اقتصاد ایران برجستهتر بود. نتایج این پژوهش بیانگر آن است که الگوریتم MO میتواند بهعنوان رویکردی کارآمد برای بهبود دقت پیشبینی و کاهش ریسک سرمایهگذاری در بازارهای مالی نوظهور و پرریسک مورد استفاده قرار گیرد.
Reliable financial reasoning requires knowing not only how to answer, but also when an answer cannot be justified. In real financial practice, problems often rely on implicit assumptions that are taken for granted rather than stated explicitly, causing problems to appear solvable while lacking enough information for a definite answer. We introduce REALFIN, a bilingual benchmark that evaluates financial reasoning by systematically removing essential premises from exam-style questions while keeping them linguistically plausible. Based on this, we evaluate models under three formulations that test answering, recognizing missing information, and rejecting unjustified options, and find consistent performance drops when key conditions are absent. General-purpose models tend to over-commit and guess, while most finance-specialized models fail to clearly identify missing premises. These results highlight a critical gap in current evaluations and show that reliable financial models must know when a question should not be answered.
Innovation and entrepreneurship are core drivers of high-quality economic development; however, their inherent characteristics, high risk, heavy investment, and slow return, significantly constrain efficiency in the spatial allocation of financial capital. Difficult and expensive financing has long been a key bottleneck restricting the high-quality development of innovation and entrepreneurship in China. As the primary financing channel for enterprises, bank credit efficiency is strongly influenced by geographical distance: Bank-enterprise proximity can alleviate information asymmetry, reduce transaction costs, and ultimately strengthen financial support for innovation and entrepreneurship. However, against the backdrop of rapid digital finance development weakening spatial constraints and continuous government Research and Development (R&D) investment shaping the innovation ecosystem, the impact of bank-enterprise proximity on the quality of urban innovation and entrepreneurship is not a simple linear relationship. Its effectiveness may exhibit heterogeneity with changes in institutional environments and resource conditions. Existing studies have neither fully clarified the internal logic of this nonlinear relationship nor sufficiently addressed the boundary-setting role of government R&D investment. Based on this, from the perspective of financial geography, this study used panel data of 213 prefecture-level and above cities in China from 2003 to 2020. Various empirical methods, including baseline regression, threshold effect modeling, heterogeneity analysis, and robustness tests, were comprehensively applied to systematically explore the impact of bank-enterprise geographical proximity on the quality of urban innovation and entrepreneurship, verify the internal mechanisms of financial support, and identify the threshold effect of government R&D investment. Bank-enterprise proximity significantly promoted the quality of urban innovation and entrepreneurship, and this finding remained robust after controlling for endogeneity. Government R&D investment exerted a significant single-threshold moderating effect on this relationship, with a threshold value of 23.3% (measured as the sum of scientific and educational expenditure as a share of total general public budget expenditure). Only when R&D investment exceeded this threshold did the enabling effect of bank-enterprise proximity became significantly amplified; when investment was insufficient, the effect was insignificant. The level of digitalization also presented a single-threshold characteristic: Below the threshold, traditional bank-enterprise geographical proximity played a dominant role, whereas above the threshold, digital finance supplemented geographical proximity by improving information transmission efficiency and replacing its core position. In terms of heterogeneity, in the context of high R&D investment, the promoting effect of bank-enterprise proximity was the most prominent in eastern regions and in super-large or mega cities, followed by central regions and large cities, while it was relatively weak in western regions and medium and small cities owing to weak economic foundations and insufficient resource agglomeration. Further mechanism tests confirmed that alleviating corporate financing constraints was the key channel through which bank-enterprise proximity operated. The academic value of this study is reflected in three dimensions: First, focusing on the new characteristics of the financial geography structure in the digital era, this study verifies the continued importance of bank-enterprise proximity against the background of weakened spatial constraints, enriching the interdisciplinary research in financial geography and innovation economics. Second, this study is the first to identify the single-threshold moderating effect of government R&D investment, clarifying the boundary conditions of the nonlinear relationship between bank-enterprise proximity and innovation and entrepreneurship quality, and providing new empirical support for reconciling divergent conclusions in existing studies. Third, this study constructs a multi-dimensional integrated analytical framework of "government factor (R&D investment)-technological factor (digitalization)," deepening the systematic understanding of the driving mechanism of urban innovation and entrepreneurship quality. At the practical level, this study provides clear implications for local governments to formulate relevant policies. Governments should shorten bank-enterprise distance by optimizing the spatial layout of bank branches, increase R&D investment to exceed the critical threshold of 23.3%, promote the deep integration of digital finance and traditional banking, and strengthen policy support for central and western regions and for medium or small cities. These measures can jointly enhance the synergistic effectiveness of financial support and government intervention in boosting the high-quality development of innovation and entrepreneurship.
Dear Authors and Readers,The closing issue of “Zeszyty Teoretyczne Rachunkowości” (ZTR, “The Theoretical Journal of Accounting”) for 2025, vol. 49, number 4, once again provides an engaging and multidimensional review of contemporary research trends in accounting. This Special Issue, titled Contemporary challenges, conditions and directions of development of accounting, gathers 13 studies that explore the ongoing transformation of the accounting discipline driven by technological advancements, sustainability demands, and evolving expectations from professionals and educators. The featured articles reflect a diverse range of approaches, from theoretical modelling and comparative analysis to bibliometric synthesis and empirical evaluation, offering a comprehensive perspective on the accounting field as it advances into a new digital and regulatory era.At the intersection of behavioural finance and accounting communication, Adeel Ali Qureshi and Mateusz Lemańczyk present a comprehensive literature review in their paper Attention metrics and stock market reactions to accounting events: A literature review. By combining bibliometric analysis with the TCCM frame- work, they investigate how investor attention, measured by media coverage, online search activity, and textual complexity, influences market reactions to accounting disclosures. Their findings highlight the increasing significance of behavioural insights and data analytics in understanding how financial information is perceived, processed, and priced.The paper by Mateja Brozović, Sanja Sever Mališ, and Dominik Piršić, titled Financial accounting analysis of leverage and profitability: Evidence from Croatian SMEs, expands the discussion to corporate financial performance. Using key financial ratios from small and medium-sized enterprises in Croatia, the authors analyse the relationship between leverage and profitability, providing empirical evidence that enhances understanding of the financial resilience and risk structures of SMEs, a vital yet often overlooked segment of the European economy.Renáta Hornická and Renáta Pakšiová examine the development of non-financial disclosure in their paper Scope of sustainability reporting in the largest companies in Slovakia in 2017 and 2022. By analysing textual data from the annual and sustainability reports of major Slovak firms, they document a noticeable growth in the scope and depth of ESG reporting following the introduction of the Non-Financial Reporting Directive. Their findings offer timely insight into how regulatory pressure drives increased corporate accountability and the institutionalisation of sustainability reporting in Central and Eastern Europe.A broader institutional and regulatory perspective on sustainability assurance is examined by Tanja Laković, Daniel Zdolšek, and Milica Vukčević in their paper Development of the regulatory framework for sustainability assurance: A comparative analysis of the transition from NFRD to CSRD in Slovenia and Montenegro. This comparative study highlights the challenges and opportunities of implementing the new EU Corporate Sustainability Reporting Directive in Montenegro, a non-EU member state. It highlights differences in readiness and institutional adaptation between EU member and candidate countries.From a theoretical perspective, Serhii Lehenchuk and Viktoriia Makarovych offer an innovative conceptual discussion in Theoretical foundations of accounting for intellectual investment property: Towards standard setting. Their paper develops a framework for recognising and measuring intellectual investment property, bridging gaps between traditional accounting and emerging forms of intangible capital. By proposing theoretical principles for potential standardisation, the study adds a significant perspective to debates on accounting for knowledge-based assets in the digital economy.The linguistic and communicative aspects of accountability are examined in Raili Lilo, Elina Paemurru, and Ülle Pärl’s paper, Accountability through linguistic features: A holistic theoretical framework for sustainability reports. Through a meta- -analysis of previous empirical studies, the authors incorporate insights from legitimacy, stakeholder, signalling, and institutional theories to illustrate how language can both promote and conceal accountability in sustainability reporting. Their comprehensive framework offers a valuable basis for analysing how textual choices such as tone, clarity, and structure can influence stakeholders’ perceptions of corporate responsibility and transparency.The public sector perspective is presented by Diana Papradanova and Ventsislav Vechev in their paper An evaluation of the accounting model for reporting public sector entities’ revenues in Bulgaria in the context of the International Public Sector Accounting Standards. The authors carry out a detailed comparative analysis of Bulgarian regulations and IPSAS provisions, highlighting conceptual differences and gaps that impede transparency and comparability. Their findings offer practical recommendations for aligning public-sector accounting practices with international standards and fiscal accountability principles.The human factor and digital transformation in accounting are central themes in Katarzyna Prędkiewicz and Krzysztof Biegun’s article, Factors that influence accountants’ acceptance of Artificial Intelligence: An extended Technology Acceptance Model, which incorporates technology anxiety and experience. The authors empirically expand the Technology Acceptance Model by including variables related to technological anxiety and professional experience, offering fresh insights into how accountants view, accept, and adopt AI tools in their work. Their findings emphasise both the opportunities and psychological barriers in the move towards automation and intelligent systems in accounting practice.The contribution by Ana Rep Romić, Marzena Remlein, and Sanja Sever Mališ, titled Information technology in accounting education: A bibliometric-systematic literature review (2006–2025), focuses on the intersection of pedagogy and digitalisation. Drawing on a bibliometric and systematic literature review spanning two decades of research, the authors map global trends in the integration of IT into accounting education. Their study identifies emerging competencies, evolving educational technologies, and the changing role of educators in developing digitally literate accounting professionals capable of responding to sustainability and AI-driven challenges.Kristina Rudžionienė, Aušrinė Tamulevičiūtė, and Aurelija Kustienė’s study, The relationship between CSR and earnings management in Lithuanian listed companies, explores how sustainability efforts relate to financial behaviour in a small, transitional economy. Contrary to prior expectations, their results indicate a positive link between corporate social responsibility and both accrual- and real-activity earnings management. This surprising outcome suggests that, in some cases, CSR initiatives might be strategically used to hide opportunistic actions. The study offers new empirical insights into ethical authenticity and transparency in financial reporting across Central and Eastern Europe.The intersection of family business and accounting research is explored in Amin Soheili’s paper Family business and accounting research: A structured literature review. Through a systematic review of seventy peer-reviewed papers published between 2000 and 2024, the author maps the theoretical and methodological development of accounting research within family business contexts. Using a SWOT framework, the study highlights the underrepresentation of socioemotional and qualitative dimensions. The review advocates a broader investigation into private and emerging-market family firms, emphasising the need for interdisciplinary approaches that account for the behavioural and relational dynamics of family-owned enterprises.Gintarė Špogienė, Daiva Tamulevičienė, and Kristina Rudžionienė analyse five leading Lithuanian retail chains in their paper Integrating corporate social responsibility into internal decision-making in leading retail chains in Lithuania: A responsibility accounting perspectiveThey highlight a gap between publicly disclosed CSR and the information that genuinely influences managerial decisions. To reduce “informational noise” and enhance accountability, they suggest adapting responsibility accounting and reporting (RAR) to incorporate stakeholder-impact assessment and to categorise decisions as financial, philanthropic, or socially responsible, aligning internal controls with public CSR commitments and fostering more transparent, ethics-based governance.Finally, considering preparedness for the EU’s sustainability regime, Aleksandra Sulik-Górecka, Marzena Strojek-Filus, and Daniel Iskra, in their article Assessment of Polish companies’ preparedness for ESG reporting in the context of its determinants as evaluated by report preparers, explore Polish companies’ readiness through a nationwide survey and non-parametric inference. Most respondents rated themselves as only moderately prepared, with preparedness significantly linked to firm size (but not industry), about 70% viewing ESG reporting as complex, and they highlight a need for investment in personnel and reporting technologies. The study places these findings in the context of the roll-out of CSRD/ESRS and presents them as a baseline for more in-depth quality analysis.Taken together, the articles in this Special Issue reflect the complexity of modern accounting as a discipline that is simultaneously technological, behavioural, regulatory, and ethical. The contributions show how accounting continues to broaden beyond its traditional financial scope, including data analytics, artificial intelligence, linguistic transparency, and sustainability assurance. Each paper not only advances academic discussion but also provides valuable insights for practitioners, educators, and policymakers, enhancing the quality, relevance, and integrity of accounting information.The Editorial Team extends its gratitude to all authors and reviewers for their valuable contributions and diligent work in preparing this issue. We also thank our readers for their continued interest and engagement with the journal. We hope that the studies presented here will inspire further discussion, research, and innovation in the ever-evolving field of accounting.Marzena Remlein* Ana Rep Romić**The Editorial Team of ZTR is pleased to announce that in ZTR’s 49th year of publication, its four quarterly issues contained 39 articles: 25 in English and 14 in Polish. Their authors come from eleven countries (Bulgaria, Estonia, Croatia, Montenegro, Lithuania, Poland, the Czech Republic, Slovakia, Slovenia, Sweden, and Ukraine). We thank all the authors for their cooperation with the Editorial Team and the reviewers of their articles. The manuscripts submitted to ZTR were reviewed in 2025 by 73 reviewers, including 52 from Poland and 21 from abroad. The Editorial Team would like to thank all specialists who provided anonymous reviews and insightful feedback. The list of Polish and foreign reviewers is included in this issue of ZTR and on our journal’s website at https://ztr.skwp.pl/ cms/reviewers. We encourage authors and readers to visit ZTR’s website at https://ztr.skwp.pl/, which contains extensive information about ZTR, including its presence in databases (including Scopus, Web of Science, BazEkon, EBSCO Business Source Ulti-mate, Erich Plus, CEEOL, Cejsh, CROSSREF, DOAJ, and ICI Journals Master List), as well as an invitation to a thematic issue of ZTR in 2026 titled Accounting’s Expanded Horizon: Redefining Internal Practices for Organizational Flourishing (for more, see Call for papers published in ZTR, Vol. 49, No. 2 and at https://ztr.skwp.pl/cms/CMS:647). On behalf of the entire ZTR Editorial Team, I wish all authors, reviewers, members of the Editorial Board, and readers of ZTR a lot of health, happi-ness, and peace, as well as numerous professional successes in 2026. Yours sincerely,Anna Szychta
حامد باشکوه اجیرلو, حمید مرتضی نیا, محمد سلگی
et al.
رتبهبندی اعتباری برای مقایسه شرکتها بهلحاظ ریسک اعتباری با یکدیگر، تصمیمگیری مشارکتکنندگان بازار سرمایه را تسهیل میکند. این پژوهش با هدف شناسایی، وزندهی و طراحی الگوی رتبهبندی اعتباری شرکتهای پذیرفته شده در بازار سرمایه برای 19 صنعت انجام شد.برای شناسایی شاخصهای تعیینکننده و طراحی الگوی رتبهبندی اعتباری از رویکرد (کیفی-کمی) بهرهگرفته شد. در بخش کیفی شاخصهای نهایی از مطالعه ادبیات نظری و سپس اعتبارسنجی با خبرگان بهدست آمد. در بخش کمی نیز از معادلات ساختاری برای تعیین روابط بین شاخصها و مولفهها شد.با مطالعه ادبیات نظری و اعتبارسنجی با خبرگان 46 شاخص نهایی به عنوان عوامل تاثیرگذار روی رتبه اعتباری شرکتهای پذیرفته شده در بازار سرمایه شناسایی شد. با تعیین روابط بین شاخصها و مولفهها با استفاده از معادلات ساختاری، 6 شاخص حذف و مشخض شد که مولفههای نقدینگی، سودآوری و رشد، مسئولیت اجتماعی و زیستمحیطی، کیفیت حسابداری و کیفیت دارایی و اندازه شرکت به صورت مستقیم و مولفههای صنعت، نسبتهای ارزش بازار، نسبتهای اهرمی، نسبتهای کارایی، کیفیت حسابرسی و کیفیت مدیریت به صورت غیرمستقیم و از طریق مولفههای دیگر روی رتبه اعتباری تاثیرگذار هستند. مولفه نسبتهای نقدینگی و سودآوری و رشد به ترتیب با ضریب رگرسیونی 0.574 و 0.352 تاثیرگذارترین عوامل روی رتبه اعتباری شرکتها بودند.تاثیرگذاری 12 مولفه روی رتبه اعتباری نشان میدهد که شرکتهای پذیرفته شده در بازار سرمایه برای بهبود وضعیت اعتباری خود، میبایست به همه عوامل از جمله وضعیت نقدینگی ،ساختار سرمایه شرکت و سودآوری و رشر توجهات لازم را داشته باشند تا بتوانند بهسهولت به منابع مالی مورد نیاز دسترسی داشته باشند.
The extraordinary rise in public debt in many nations during the recent global recession has reignited interest in the mechanisms of debt buildup. This circumstance has been especially disturbing in the Eurozone, where markets have questioned the viability of debt for those nations facing higher borrowing costs because of rising bond rates. Governments and supranational organisations implemented concerted fiscal consolidation measures in response to the perceived threats of contagion, with the goal of gaining control and solvency over stretched public budgets. Fiscal retrenchment is designed for governments to manage their public finances in times of economic/financial crises. This study examines the potency of fiscal retrenchment as an instrument of public debt management in Nigeria. The theory of Expansionary Fiscal Contraction (EFC) was adopted for the study. A documentary research design was used in the study. Findings of the study revealed that government borrowing to fund deficit-budgets has not corrected Nigeria’s fiscal problems but rather led to a vicious cycle of debts, that have spillover effects on present and future revenue prospects. The study recommends a reduction in government spending and an increase in taxes as appropriate fiscal measures to resolve the public debt crises in Nigeria.
Farzaneh Ahmadian-Yazdi, Amin Sokhanvar, Soheil Roudari
et al.
Abstract This study utilizes two complementary models, the Time-Varying Parameter Vector Autoregressive Diebold–Yilmaz (TVP-VAR-DY) and the Time-Varying Parameter Vector Autoregressive Baruník–Křehlík (TVP-VAR-BK), to investigate the dynamic volatility transmission between exchange rates and stock returns in major commodity-exporting and -importing countries. The analysis focuses on periods of quantitative easing (QE) and quantitative tightening (QT) from March 15, 2020 to December 30, 2022. The countries examined are Canada and Australia (major commodity exporters) and the UK and Germany (major commodity importers). An essential contribution of this paper is new empirical insights into the dynamics of stock market returns and the transmission of volatility between these markets and exchange rates during the QE and QT periods. The results reveal that causality primarily flows from stock markets to exchange rates, especially during the QT period across all investment horizons. The Toronto Stock Exchange (TSX) emerges as the principal net driver among the markets under study. Furthermore, the Canadian exchange rate (USDCAD) and the Australian Stock Exchange (ASX) are the most significantly affected indices within the network across various investment horizons (excluding the long-term). These findings underscore the importance for investors and policymakers to consider the interplay between exchange rates and stock market returns, particularly in the context of the QE and QT periods, as well as other economic, political, and health-related events. Our findings are relevant to various stakeholders, including governments, traders, portfolio managers, and multinationals.
This study provides the first comprehensive assessment of consistency and reproducibility in Large Language Model (LLM) outputs in finance and accounting research. We evaluate how consistently LLMs produce outputs given identical inputs through extensive experimentation with 50 independent runs across five common tasks: classification, sentiment analysis, summarization, text generation, and prediction. Using three OpenAI models (GPT-3.5-turbo, GPT-4o-mini, and GPT-4o), we generate over 3.4 million outputs from diverse financial source texts and data, covering MD&As, FOMC statements, finance news articles, earnings call transcripts, and financial statements. Our findings reveal substantial but task-dependent consistency, with binary classification and sentiment analysis achieving near-perfect reproducibility, while complex tasks show greater variability. More advanced models do not consistently demonstrate better consistency and reproducibility, with task-specific patterns emerging. LLMs significantly outperform expert human annotators in consistency and maintain high agreement even where human experts significantly disagree. We further find that simple aggregation strategies across 3-5 runs dramatically improve consistency. We also find that aggregation may come with an additional benefit of improved accuracy for sentiment analysis when using newer models. Simulation analysis reveals that despite measurable inconsistency in LLM outputs, downstream statistical inferences remain remarkably robust. These findings address concerns about what we term "G-hacking," the selective reporting of favorable outcomes from multiple generative AI runs, by demonstrating that such risks are relatively low for finance and accounting tasks.
Traditional stochastic control methods in finance struggle in real world markets due to their reliance on simplifying assumptions and stylized frameworks. Such methods typically perform well in specific, well defined environments but yield suboptimal results in changed, non stationary ones. We introduce FinFlowRL, a novel framework for financial optimal stochastic control. The framework pretrains an adaptive meta policy learning from multiple expert strategies, then finetunes through reinforcement learning in the noise space to optimize the generative process. By employing action chunking generating action sequences rather than single decisions, it addresses the non Markovian nature of markets. FinFlowRL consistently outperforms individually optimized experts across diverse market conditions.
Blockchain is a technological innovation that has the potential to radically change our financial markets by providing an alternative management approach to the "promise market", which is the foundation of our financial systems. Its disruptive potential also extends to corporate finance, where blockchain is beginning to influence valuation methods and capital allocation strategies, offering new perspectives on how companies are assessed and financed. However, for a new financial architecture based on blockchain and advancements in technology -- what is commonly referred to as Fintech -- to replace, in whole or in part, traditional finance, it will need to overcome significant challenges such as regulation, environmental sustainability, its association with illegal activities, and achieving greater efficiency in cryptocurrency markets. For this reason, the future of Fintech is likely to be more conventional -- yet also more transparent, efficient, and regulated -- ultimately evolving to resemble the traditional finance we know.
Justin B. Bullock, Janet V. T. Pauketat, Hsini Huang
et al.
Governance institutions must respond to societal risks, including those posed by generative AI. This study empirically examines how public trust in institutions and AI technologies, along with perceived risks, shape preferences for AI regulation. Using the nationally representative 2023 Artificial Intelligence, Morality, and Sentience (AIMS) survey, we assess trust in government, AI companies, and AI technologies, as well as public support for regulatory measures such as slowing AI development or outright bans on advanced AI. Our findings reveal broad public support for AI regulation, with risk perception playing a significant role in shaping policy preferences. Individuals with higher trust in government favor regulation, while those with greater trust in AI companies and AI technologies are less inclined to support restrictions. Trust in government and perceived risks significantly predict preferences for both soft (e.g., slowing development) and strong (e.g., banning AI systems) regulatory interventions. These results highlight the importance of public opinion in AI governance. As AI capabilities advance, effective regulation will require balancing public concerns about risks with trust in institutions. This study provides a foundational empirical baseline for policymakers navigating AI governance and underscores the need for further research into public trust, risk perception, and regulatory strategies in the evolving AI landscape.
This study investigates how information about municipal credit ratings influences voters' evaluations of incumbent mayors. Through an original survey experiment, we assess the impact of credit rating downgrades and crime rate increases on citizens' perceptions of mayoral performance. Our findings reveal that information on both issues significantly affects voters' evaluations, with negative news about public finance and crime rates leading to decreased support for incumbents. Notably, the effects of credit downgrades are nearly as substantial as those of crime increases despite public finance being a more complex and less salient issue. Additionally, we observe that voters with varying political knowledge respond similarly to changes in municipal credit ratings, suggesting that such information serves as a useful heuristic in local elections. Our study underscores the importance of accessible financial information in promoting accountability in local governance.
Urban expansion is commonly attributed to market-oriented forces, while public finance factors connected to government forces, especially land finance, in China’s institutional environment, can provide a more interesting explanation. Using a fixed-effect threshold model, the relationships of land finance and differing local fiscal pressures to urban expansion in China were explored mathematically and empirically. It was found that land finance positively and significantly influences urban expansion. It was also found that the impact of land finance on urban spatial size varies with fiscal pressure. Results suggested that capital flows in the process of urban expansion should be carefully regulated by local governments.
Research objectives and hypothesis/research questions
The aim is to critically analyze the challenges and inequalities in the management of the financing of the tasks of local government units (LGUs) in Poland, with particular emphasis on the impact of legislative, political, and financial factors on the effectiveness of their tasks.
Research questions:
1. Does the presence of councilors employed in units subordinate to local government units lead to a conflict of interest, which negatively impacts the transparency and independence of financial decisions made?
2. Does the amount of subsidies and subsidies awarded depend solely on the economic situation of municipalities, or is it also influenced by political links between local authorities and the ruling party at the central level?
3. As a result of underestimating the educational subsidy, are local government units forced to redirect their funds to finance educational tasks at the expense of other public activity areas?
4. Do the currently used algorithms for the distribution of subsidies reflect the real needs of local government units, and, as a result, there is an optimal allocation of public funds?
5. Is there equal access for local government units to European and national funds?
Research methods
1. Analysis of empirical data: Examination of data from local government units (LGUs) between 2019 and 2023.
2. Comparative analysis: Evaluation of financial indicators for LGUs based on their size, own revenues, and political affiliations.
3. Statistical analysis: Investigation of differences in the allocation of financial resources to identify disparities.
4. Analysis of source documents: Review legal documents, Supreme Audit Office (NIK) reports, and local budget data from LGUs.
5. Case study: Analysis of municipalities in the Radomsko focusing on underestimating educational subsidies and conflicts of interest.
6. Critical literature review: Examination of domestic and international literature to provide context and identify relevant issues.
Main results
1. The amount of subsidies and grants awarded often depends on the political affiliations of local authorities with the ruling party.
2. Educational subsidies fall short of covering actual educational costs, straining resources for other public responsibilities.
3. Councilors employed by subordinate LGU units cause conflicts of interest, harming transparency and financial independence.
4. Under governmental support programs, grant allocation processes lacked transparency and clear criteria, enabling abuses and discretionary fund distribution.
5. Financial support was unevenly distributed, worsening inequalities between wealthier and poorer regions.
Implications for theory and practice
For theory: the research brought a new perspective to the analysis of decentralization and self-government, showing the impact of political, legislative, and financial factors on the functioning of local governments. In particular, the results confirm the importance of political distribution theory, pointing to the practice of favoring individuals associated with the ruling party, reflecting the phenomenon of political allocation of resources. The problems of unequal allocation of resources and underestimation of education subsidies bring new elements to the theory of distributive justice, highlighting the imbalance in access to public resources between regions.
For practice: research indicates an urgent need for legislative reforms aimed at simplifying and stabilizing the regulations governing the activities of local government units. Recommendation for the introduction of more transparent mechanisms for allocating public funds. Emphasize the importance of support for less developed local government units, which would reduce regional inequalities and make more efficient use of available funds.
Management. Industrial management, Management information systems
Kioko, S., & Marlowe, J. (2023). Financial strategy for public managers (4th ed.). University of Washington, 254pp., $0.00 (ebook), ISBN: 978-1-927-47259-0. https://uw.pressbooks.pub/financialstrategy/