The 15-minute city is a powerful planning concept to counter car-dependence by promoting active mobility to amenities and fostering inclusive urban environments. However, this policy has challenges in amenity-poor urban peripheries. Public transport remains underexplored in this discourse despite its role in distant access. Here, we propose a framework that incorporates public transport into the 15-minute city model using openly available data. By comparing Helsinki, Madrid, and Budapest, we demonstrate that multimodal mobility substantially increases access to amenities and enhances socio-spatial integration within a 15-minute reach. Although urban periphery benefit significantly from radial or high-speed public transport lines in their social mixing potential, such lines alone do not improve their access to amenities. These findings underscore the need to optimize polycentric public transport networks that can improve inclusive urban accessibility and complement active mobility in polycentric cities.
Abstract Open finance initiatives are emerging worldwide, yet stakeholders are still lagging in achieving full adoption. Within the financial sector, data democratization is heralded as a novel paradigm facilitating data valorization initiatives that drive innovation and competition. However, there is a scarcity of managerial exploration of these concepts, and limited studies intersect them to uncover the necessities of an open finance ecosystem. This study, based on a systematic literature review of 97 documents from 2000 to 2023 and a qualitative survey of 207 decision-makers from financial companies, identifies the core principles of open finance, data democratization, and strategic data democratization within an open finance ecosystem. The findings produce a data democratization framework, emphasizing practical implications for various data stakeholders (regulators, traditional financial institutions, fintech startups, techfin companies, customers, technology developers, and nonfinancial third parties) and the ecosystem performance. Senior managers are provided with data democratization initiatives (processes, culture, capabilities, and governance) and collaborative strategies to enhance financial and nonfinancial performance in the short and long term within an open finance ecosystem. Policymakers could establish guidelines for data democratization to further stimulate innovation and competition. The study's novelty lies in its strategic approach to data democratization, enabling data stakeholders to develop synergies and coevolve within an innovative and competitive open finance ecosystem.
This study proposes and implements the first LLM agents based agentic pipeline for multi task public opinion analysis. Unlike traditional methods, it offers an end-to-end, fully automated analytical workflow without requiring domain specific training data, manual annotation, or local deployment. The pipeline integrates advanced LLM capabilities into a low-cost, user-friendly framework suitable for resource constrained environments. It enables timely, integrated public opinion analysis through a single natural language query, making it accessible to non-expert users. To validate its effectiveness, the pipeline was applied to a real world case study of the 2025 U.S. China tariff dispute, where it analyzed 1,572 Weibo posts and generated a structured, multi part analytical report. The results demonstrate some relationships between public opinion and governmental decision-making. These contributions represent a novel advancement in applying generative AI to public governance, bridging the gap between technical sophistication and practical usability in public opinion monitoring.
Stefanie Schwaar, Franziska Diez, Michael Trebing
et al.
In German public administration, there are 45 different offices to which incoming messages need to be distributed. Since these messages are often unstructured, the system has to be based at least partly on message content. For public service no data are given so far and no pretrained model is available. The data we used are conducted by Governikus KG and are of highly different length. To handle those data with standard methods different approaches are known, like normalization or segmentation. However, text classification is highly dependent on the data structure, a study for public administration data is missing at the moment. We conducted such a study analyzing different techniques of classification based on segments, normalization and feature selection. Thereby, we used different methods, this means neural nets, random forest, logistic regression, SVM classifier and SVAE. The comparison shows for the given public service data a classification accuracy of above 80\% can be reached based on cross validation. We further show that normalization is preferable, while the difference to the segmentation approach depends mainly on the choice of algorithm.
Pavlák, Miroslav, Mentlík, Roman, Halouzka, Miroslav
et al.
Background: The post-conflict reconstruction of Ukraine, especially in the housing and construction sector, has become a strategic priority for the European Union and its member states. Despite declared support, the real participation of Central European businesses, including those from the Czech Republic, remains limited due to multiple legal, security and financial barriers.
Aim: The paper aims to identify the conditions, instruments and risks influencing the potential engagement of Czech enterprises in the reconstruction of Ukraine's housing and construction sector after 2022, with emphasis on investment frameworks, scenario modelling, and institutional capacities.
Methods: The study combines a qualitative case study (cooperation between the University of Finance and Administration and V. N. Karazin University in Kharkiv), stakeholder analysis based on coded interviews with Czech entrepreneurs, and quantitative investment scenario modelling (2024–2033). Data triangulation was applied to ensure internal validity.
Results: Findings confirm that while institutional and financial instruments (e.g., Ukraine Facility) are in place, their uptake is limited by high perceived risk and a lack of implementation facilitators. Investment scenarios range from 65 to 95 billion USD depending on security and absorption conditions. Czech SMEs face specific constraints such as insufficient legal safeguards and capacity limits yet remain strategically positioned to benefit from targeted support schemes.
Recommendations: Policy actors should prioritise the development of national coordination platforms, risk insurance schemes (e.g., via EGAP), and pilot cooperation models with Ukrainian institutions. Stronger links between academia, public sector and private firms are essential to de-risk market entry and build long-term resilience.
Practical relevance/social implications: The research provides applicable insights for government agencies, export organisations and business associations aiming to support Czech firms in entering high-risk post-conflict markets. Moreover, it demonstrates the role of academic institutions as platforms for international capacity building and post-war recovery.
Originality/value: This is the first study focusing on the Czech context of post-war investment in Ukraine, combining scenario modelling with a grounded case study. The integration of qualitative and quantitative methods provides a comprehensive Framework for further research and policy development.
Abstract With the rapid development of artificial intelligence, there is an increasing utilization of intelligent devices by older adults. The relationship between the utilization of intelligent devices and household medical expenditure has garnered widespread attention in academic circles. This paper employs data from the 2020 China Longitudinal Aging Social Survey (CLASS) to investigate the impact of intelligent device utilization by 9,718 older adults on household medical consumption. The research findings indicate that the utilization of intelligent devices significantly reduces household medical expenditure, and this conclusion remains valid after placebo tests and endogeneity treatments. From the perspective of heterogeneity, the impact of intelligent device utilization on household medical expenditure is more pronounced among higher age, those living with family, residents of eastern and western regions, and areas with high digital coverage. Quantile regression results reveal a “inverted U-shaped” trend in the impact of intelligent device utilization on household medical expenditure, with an initial increase followed by a decrease. Mechanism analysis suggests that intelligent device utilization reduces household medical expenditure by improving the health behaviors and decreasing the demand for medical services. Based on these findings, this paper argues that enterprises and research institutions should continue to develop intelligent devices tailored to the characteristics of older adults. The government should provide financial subsidies to purchase intelligent devices for older adults. By fully enhancing the utilization of intelligent devices in elderly health management, we can together provide a reference for controlling the excessive growth of medical expenses.
Philippe G. LeFloch, Jean-Marc Mercier, Shohruh Miryusupov
For three applications of central interest in finance, we demonstrate the relevance of numerical algorithms based on reproducing kernel Hilbert space (RKHS) techniques. Three use cases are investigated. First, we show that extrapolating from few pricer examples leads to sufficiently accurate and computationally efficient results so that our algorithm can serve as a pricing framework. The second use case concerns reverse stress testing, which is formulated as an inversion function problem and is treated here via an optimal transport technique in combination with the notions of kernel-based encoders, decoders, and generators. Third, we show that standard techniques for time series analysis can be enhanced by using the proposed generative algorithms. Namely, we use our algorithm in order to extend the validity of any given quantitative model. Our approach allows for conditional analysis as well as for escaping the `Gaussian world'. This latter property is illustrated here with a portfolio investment strategy.
Renan Lima Baima, Iván Abellán Álvarez, Ivan Pavić
et al.
In response to the European Commission's aim of cutting carbon emissions by 2050, there is a growing need for cutting-edge solutions to promote low-carbon energy consumption in public infrastructures. This paper introduces a Proof of Concept (PoC) that integrates the transparency and immutability of blockchain and the Internet of Things (IoT) to enhance energy efficiency in tangible government-held public assets, focusing on curbing carbon emissions. Our system design utilizes a forecasting and optimization framework, inscribing the scheduled operations of heat pumps on a public sector blockchain. Registering usage metrics on the blockchain facilitates the verification of energy conservation, allows transparency in public energy consumption, and augments public awareness of energy usage patterns. The system fine-tunes the operations of electric heat pumps, prioritizing their use during low-carbon emission periods in power systems occurring during high renewable energy generations. Adaptive temperature configuration and schedules enable energy management in public venues, but blockchains' processing power and latency may represent bottlenecks setting scalability limits. However, the proof-of-concept weakness and other barriers are surpassed by the public sector blockchain advantages, leading to future research and tech innovations to fully exploit the synergies of blockchain and IoT in harnessing sustainable, low-carbon energy in the public domain.
Human-robot interaction requires to be studied in the wild. In the summers of 2022 and 2023, we deployed two trash barrel service robots through the wizard-of-oz protocol in public spaces to study human-robot interactions in urban settings. We deployed the robots at two different public plazas in downtown Manhattan and Brooklyn for a collective of 20 hours of field time. To date, relatively few long-term human-robot interaction studies have been conducted in shared public spaces. To support researchers aiming to fill this gap, we would like to share some of our insights and learned lessons that would benefit both researchers and practitioners on how to deploy robots in public spaces. We share best practices and lessons learned with the HRI research community to encourage more in-the-wild research of robots in public spaces and call for the community to share their lessons learned to a GitHub repository.
Beth Goldberg, Diana Acosta-Navas, Michiel Bakker
et al.
Two substantial technological advances have reshaped the public square in recent decades: first with the advent of the internet and second with the recent introduction of large language models (LLMs). LLMs offer opportunities for a paradigm shift towards more decentralized, participatory online spaces that can be used to facilitate deliberative dialogues at scale, but also create risks of exacerbating societal schisms. Here, we explore four applications of LLMs to improve digital public squares: collective dialogue systems, bridging systems, community moderation, and proof-of-humanity systems. Building on the input from over 70 civil society experts and technologists, we argue that LLMs both afford promising opportunities to shift the paradigm for conversations at scale and pose distinct risks for digital public squares. We lay out an agenda for future research and investments in AI that will strengthen digital public squares and safeguard against potential misuses of AI.
A pressing issue for both academia and industry is determining how to improve the quality of corporate environmental information disclosure. This study investigates the impact of digital finance on the quality of such disclosure, focusing on non-financial listed companies in China’s Shanghai and Shenzhen A-share markets from 2011 to 2022. The findings are as follows: First, digital finance and its sub-dimensions exert a significant positive influence on disclosure quality, a conclusion validated through a series of robustness checks. Second, a channel mechanism analysis reveals that digital finance enhances disclosure quality primarily by alleviating financing constraints and reducing agency costs, reflecting its effects on resource acquisition and corporate governance. Third, the positive influence of digital finance is more pronounced under weaker environmental regulation and lower public pressure for environmental accountability, suggesting that digital finance functions as an effective complement to existing environmental information supervision. This study contributes to the literature by elucidating the consequences of digital finance for corporate environmental information disclosure and extending the theoretical framework of digital finance in the context of green development.
This study is the first to investigate whether pawnshops, financial institutions for low-income populations, have contributed to the decline in mortality in the early twentieth century. Using ward-level panel data from Tokyo City, this study revealed that the popularity of public pawnshops was associated with a 4% and 5% decrease in infant mortality and fetal death rates, respectively, during 1927-1935. The historical context implies that the potential channels of the relationships were improving nutrition and hygiene and covering childbirth costs. Moreover, a cost-effectiveness calculation highlighted that the establishment of public pawnshops was a cost-effective public investment for better public health. Contrarily, for-profit private pawnshops showed no significant association with health improvements.
John Kwaku Amoh, Kenneth Ofori-Boateng, Randolph Nsor-Ambala
et al.
AbstractAs a result of the failure to meet tax collection targets, policymakers, economists, and financiers have focused their attention in recent years on how a country’s tax effort has been employed to combat tax evasion and maximise tax collections for economic growth. The study looked at the nexus between tax efforts, tax evasion, and economic development, as well as the effect of institutional quality on moderating the nexus in Ghana. The maximum likelihood (ML) estimation and structural equation modelling (SEM) techniques were used in the study to analyse a sample of quartered data from 1996 to 2020. Testing the hypotheses reveals that both tax efforts and tax evasion have negative effects on the economic freedom of the world index (EFWI) but positive effects on urbanisation. A test of the third hypothesis shows that institutional quality moderates tax evasion in Ghana in order to influence economic development. The findings imply that the idea that tax evasion is bad for an economy or that tax efforts drive domestic revenue mobilisation is based mainly on prima facie evidence. Tax efforts such as tax amnesty may appear to compliant taxpayers as an incentive for tax evaders, which could affect their compliance. The adoption of the tax efforts index measure to examine its econometric impact on economic development is one of the pioneering attempts in the field. The study recommends well-thought-out strategies to ensure that tax efforts achieve their intended goals.
Water pollution is closely related to the development of water pollution-intensive industries, but there is a lack of relevant research, and few studies to verify the existence of “pollution heaven.” This paper aims to study the layout and the spatio-temporal evolution of water-polluting enterprises. Taking Zhejiang Province, China as an example, this study visualized the spatial distribution of water pollution enterprises under the “Five Water Treatment” regulations during 2018–2022. At the same time, based on the Mann-Whitney U test, this paper verifies the hypothesis of pollution paradise in Zhejiang Province. The results show that the distribution of water pollution enterprises in Zhejiang is clustered, and water pollution control has been realized to a certain extent. However, water pollution enterprises still tend to be located in areas with lower environmental standards and weak environmental regulations. In view of this, the government should optimize the industry structure, strengthen the supervision of suburban water pollution enterprises.
We show that public firm profit rates fell by half since 1980. Inferred as the residual from the rise of US corporate profit rates in aggregate data, private firm profit rates doubled since 1980. Public firm financial returns matched their fall in profit rates, while public firm representativeness increased from 30% to 60% of the US capital stock. These results imply that time-varying selection biases in extrapolating public firms to the aggregate economy can be severe.
Gael M. Martin, David T. Frazier, Worapree Maneesoonthorn
et al.
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem -- model, parameters, latent states -- is able to be quantified explicitly, and factored into the forecast distribution via the process of integration or averaging. Allied with the elegance of the method, Bayesian forecasting is now underpinned by the burgeoning field of Bayesian computation, which enables Bayesian forecasts to be produced for virtually any problem, no matter how large, or complex. The current state of play in Bayesian forecasting in economics and finance is the subject of this review. The aim is to provide the reader with an overview of modern approaches to the field, set in some historical context; and with sufficient computational detail given to assist the reader with implementation.
Association of entrepreneurial orientation with performance, viewed as both a unidimensional and multidimensional concept, has been widely researched, especially in the small and medium enterprises context. However, there is a gap in the literature related to how the components of entrepreneurial orientation are inter-related and how their intricacies drive small firm performance. Rather than looking into configurations between entrepreneurial orientation and various external factors, this article investigates the different configurations within the entrepreneurial orientation components and how they affect performance. This article builds on the work by Putni?š, T.J. and Sauka, A. "Why does entrepreneurial orientation affect company performance?" who used financial economics theory to explore the direct relationship between risk-taking and performance. They used innovativeness as a moderator and proactiveness as mediators of the relationship between risk-taking and performance. This article uses a configurational approach to investigate the effect of individual roles of each of entrepreneurial orientation's dimensions and their interactions on small firm performance. Using survey data from 202 Croatian small and medium-sized firms, results reveal that entrepreneurial orientation and all of its three components are positively associated with small firm performance. Relationships between innovativeness and proactiveness with small firm performance are significant when controlling for risk-taking; therefore, they do not obtain this relationship through their association with risk-taking. Proactiveness does not have an indirect, positive relationship with small firm performance via risk-taking as a mediator. Innovativeness is a moderator that further strengthens the positive relationship between risk-taking and performance.
Abstract Governments worldwide are implementing mass vaccination programs in an effort to end the novel coronavirus (COVID-19) pandemic. Here, we evaluated the effectiveness of the COVID-19 vaccination program in its early stage and predicted the path to herd immunity in the U.S. By early March 2021, we estimated that vaccination reduced the total number of new cases by 4.4 million (from 33.0 to 28.6 million), prevented approximately 0.12 million hospitalizations (from 0.89 to 0.78 million), and decreased the population infection rate by 1.34 percentage points (from 10.10 to 8.76%). We built a Susceptible-Infected-Recovered (SIR) model with vaccination to predict herd immunity, following the trends from the early-stage vaccination program. Herd immunity could be achieved earlier with a faster vaccination pace, lower vaccine hesitancy, and higher vaccine effectiveness. The Delta variant has substantially postponed the predicted herd immunity date, through a combination of reduced vaccine effectiveness, lowered recovery rate, and increased infection and death rates. These findings improve our understanding of the COVID-19 vaccination and can inform future public health policies.