This paper explores the Decentralized Finance (DeFi) ecosystem. We examine how DeFi is emerging on top of the public Ethereum smart contract platform, compare it to the centralized architecture of traditional financial markets and highlight opportunities and potential risks of this ecosystem. We propose a multi-layered framework to analyze the implicit architecture and the various DeFi building blocks, including token standards, decentralized exchanges, decentralized debt markets, blockchain derivatives and on-chain asset management protocols. We conclude that DeFi still is a niche market with certain risks, but also has interesting properties in terms of efficiency, transparency, accessibility and interoperability. As such, it may potentially contribute to a more robust and transparent financial infrastructure.
Abstract We survey 861 finance academics, professionals, and public sector regulators and policy economists about climate finance topics. They identify regulatory risk as the top climate risk to businesses and investors over the next five years, but they view physical risk as the top risk over the next 30 years. By an overwhelming margin, respondents believe that asset prices underestimate climate risks rather than overestimate them. We also tabulate opinions about the expected correlation between growth and climate change, social discount rates appropriate for projects that mitigate the effects of climate change, most influential forces for reducing climate risks, and most important research topics.
The financing sector drives the Future of Environmental Funds to achieve climate financing. In this study, we have employed panel regression analysis and the generalized two-step moment method (GMM) for the 25 EU countries from 2000 to 2021 to explore the relationship between green financing and the portfolio structure of green climate funds. According to the findings of this research, green financing significantly impacts quality economic growth. The GCFs enhance the capacity to channel public and private funding while contributing to de-risking more conventional forms of funding, increasing climate financing, and boosting the GCFs. In addition, the study concluded that Global Climate Support might fund nonbankable components of more significant “almost bankable projects” by analyzing the portfolio’s policies and methods.
Extreme values and the tail behavior of probability distributions are essential for quantifying and mitigating risk in complex systems of all kinds. In multivariate settings, accounting for correlations is crucial. Although extreme value analysis for infinite correlated systems remains an open challenge, we propose a practical framework for handling a large but finite number of correlated time series. We develop our approach for finance as a concrete example but emphasize its generality. We study the extremal behavior of high-frequency stock returns after rotating them into the eigenbasis of the correlation matrix. This separates and extracts various collective effects, including information on the correlated market as a whole and on correlated sectoral behavior from idiosyncratic features, while allowing us to use univariate tools of extreme value analysis. This holds even for high-frequency data where discretization effects normally complicate analysis. We employ a peaks-over-threshold approach and thereby fully avoid the analysis of block maxima. We estimate the tail shape of the rotated returns while explicitly accounting for nonstationarity, a key feature in finance and many other complex systems. Our framework facilitates tail risk estimation relative to larger trends and intraday seasonalities at both market and sectoral levels.
In recent years, behavioral economics has revolutionized various fields, including finance, marketing, and public policy. Its application in people management, however, remains an emerging area of exploration. By integrating psychological insights into economic decision-making, behavioral economics offers a nuanced understanding of human behavior, essential for designing effective HR practices. While many of the concepts are not new in organizational behavior research and related fields, thanks to the incorporation of formalized models of choice, behavioral economics brings analytical clarity to domains traditionally studied through descriptive or qualitative methods in the behavioral sciences. This review article delves into how behavioral economics can shed light on key aspects of people management, focusing on five domains: incentives, decision-making, leadership, personalization, and organizational change. We offer a critical overview integrating some of the most well-known findings with applicability in these areas as well as promising avenues for future research. One of the main conclusions is that behavioral economics offers a powerful lens to approach people management, but also that behavioral principles need to be understood in depth (beyond average effects, for example) as generalization is often flawed, claiming for personalized solutions and interventions grounded on comprehensive perspectives.
Enhancing the subjective well-being of poor households is crucial for the world’s sustainable development. Using a comprehensive household-level dataset from the China Household Finance Survey (CHFS) spanning 2011 to 2019, this study employed a multi-period difference-in-differences (DID) approach to systematically identify the causal effect and underlying mechanisms of the Targeted Poverty Alleviation (TPA) program on the subjective well-being of poverty-stricken households. Then it explored the heterogeneous effects of different assistance measures on their subjective well-being. We found that the TPA program significantly improves the subjective well-being of rural poor households after a series of robustness checks. The analysis indicated that the TPA program improves the happiness of poor households by reducing their relative poverty and promoting their labor participation to eliminate poverty. We found that providing basic public services, means of agricultural production, and communication infrastructure all enhance the positive impact of TPA on happiness, while the housing relocation program, transportation infrastructure investment, and agricultural technical support do not. The conclusions of this study have important policy implications for ensuring equitable access to basic public services, consolidating the effective link between poverty alleviation achievements and rural revitalization in the post-poverty era, thereby promoting the common prosperity of rural households.
Throughout the world, freedom of information laws have been put into place to ensure citizens the opportunity to hold their government accountable. In practice, previous literature often finds that private gain or self-serving interests account for most national information requests, crowding out the original accountability-focused intentions of these laws. While there has been some national research around the demand for these information requests, research from a local level has been lacking. Here, data from public records requests to the City of Bellevue, Washington’s Finance and Asset Management department from 2019 to 2023 are analyzed to determine what the public really wants to know about public finance. Local data mirrors the national research, with 71% of the total requests being for private gain; specifically, 47% of all public records requests are procurement-related commercial inquiries.
The rise of decentralized finance (DeFi) has created a growing demand for accurate yield and performance forecasting to guide liquidity allocation strategies. In this study, we benchmark six models, XGBoost, Random Forest, LSTM, Transformer, quantum neural networks (QNN), and quantum support vector machines with quantum feature maps (QSVM-QNN), on one year of historical data from 28 Curve Finance pools. We evaluate model performance on test MAE, RMSE, and directional accuracy. Our results show that classical ensemble models, particularly XGBoost and Random Forest, consistently outperform both deep learning and quantum models. XGBoost achieves the highest directional accuracy (71.57%) with a test MAE of 1.80, while Random Forest attains the lowest test MAE of 1.77 and 71.36% accuracy. In contrast, quantum models underperform with directional accuracy below 50% and higher errors, highlighting current limitations in applying quantum machine learning to real-world DeFi time series data. This work offers a reproducible benchmark and practical insights into model suitability for DeFi applications, emphasizing the robustness of classical methods over emerging quantum approaches in this domain.
Personalized financial advice requires consideration of user goals, constraints, risk tolerance, and jurisdiction. Prior LLM work has focused on support systems for investors and financial planners. Simultaneously, numerous recent studies examine broader personal finance tasks, including budgeting, debt management, retirement, and estate planning, through agentic pipelines that incur high maintenance costs, yielding less than 25% of their expected financial returns. In this study, we introduce a novel and reproducible framework that integrates relevant financial context with behavioral finance studies to construct supervision data for end-to-end advisors. Using this framework, we create a 19k sample reasoning dataset and conduct a comprehensive fine-tuning of the Qwen-3-8B model on the dataset. Through a held-out test split and a blind LLM-jury study, we demonstrate that through careful data curation and behavioral integration, our 8B model achieves performance comparable to significantly larger baselines (14-32B parameters) across factual accuracy, fluency, and personalization metrics while incurring 80% lower costs than the larger counterparts.
The global acceleration of population urbanization has transformed cities into primary spatial hubs of human activity. As urban populations continue to expand, identifying the socioeconomic drivers of urbanization and elucidating their underlying mechanisms are essential for achieving Sustainable Development Goal 11, established by the United Nations. This study leverages machine learning and big data to investigate the determinants of population urbanization in China over the period 1991–2023. Utilizing the XGBoost algorithm combined with SHAP (Shapley Additive Explanations), the analysis reveals a tripartite structure of key drivers encompassing industrial support, employment orientation, and infrastructure accessibility. Regional assessments indicate distinct urbanization patterns: Eastern coastal areas are predominantly driven by finance and service industries; central inland regions follow an investment-led trajectory anchored in infrastructure development and real estate expansion, while the western interior relies mainly on employment-centered strategies. Partial Dependence Plots (PDPs) highlighted spatial variations in the effects of sensitive factors, with interaction analyses revealing synergistic effects between tertiary sector shares and the working-age share in eastern coastlands, structural amplification by real estate investment with appropriate working-age population shares in the central inlands, and balancing interactions between GDP growth rates and tertiary sector shares in the western interior. These findings contribute to a more nuanced understanding of the socioeconomic forces shaping urbanization and offer evidence-based recommendations for policymakers in other developing countries seeking to foster sustainable urban growth.
Authority is a fundamental tool of social integration. By selecting the most laudable and exemplary patterns, behaviours, actions or events – whether real or merely imagined – from the entire range of potentialities, it creates a community capable of communal action, by transforming an undifferentiated mass of independent individuals into a somewhat cohesive social group able to find common ground on matters vital for their shared existence, thus turning mere quantity into quality. It is thus evident that the existence of a certain degree of authority is also the basis for the viability of public administration, public policy and public management – to name but a few – in any given society. This paper will examine the conditions, manifestations and correlations of authority in the various domains of its social context, in order to provide a comprehensive account of its existence, its inevitability, but also the dangers inherent in its weakening.
The system of scientific innovation can be characterized as a complex, multi-layered network of actors, their products and knowledge elements. Despite the progress that has been made, a more comprehensive understanding of the interactions and dynamics of this multi-layered network remains a significant challenge. This paper constructs a multilayer longitudinal network to abstract institutions, products and ideas of the scientific system, then identifies patterns and elucidates the mechanism through which actor collaboration and their knowledge transmission influence the innovation performance and network dynamics. Aside from fostering a collaborative network of institutions via co-authorship, fine-grained knowledge elements are extracted using KeyBERT from academic papers to build knowledge network layer. Empirical studies demonstrate that actor collaboration and their unique and diverse ideas have a positive impact on the performance of the research products. This paper also presents empirical evidence that the embeddedness of the actors, their ideas and features of their research products influence the network dynamics. This study gains a deeper understanding of the driving factors that impact the interactions and dynamics of the multi-layered scientific networks.
Abstract As a crucial digital general-purposed technology (DGPT) in the current digital economy era, the rapid development of mobile Internet technology not only promotes the digital upgrading of the economy, but has a profound impact on firm labor demands. Based on microdata from China’s listed companies and macrodata from prefecture-level cities, the paper evaluates the effect of mobile Internet development on employment at the firm level. Research shows that there is a roust causal relationship between mobile Internet development and the growth of firm labor demands. Influencing path tests found that mobile Internet can change firm labor demands by affecting its productivity, digital process, production scale and business scope. In terms of changes in employment structure, the paper found that mobile Internet is conducive to promoting the flow of labor to the advanced industrial structure; mobile Internet development significantly increased firm’s demands for high-skilled labor and low-level labor, so the technical polarization effect of mobile Internet applications on employment has not been observed. mobile Internet development improved firm labor demands for positions that are highly related to digital applications, but has reduced the labor demands for positions of routine tasks, which shows the bias of mobile Internet technology from the side. The above research provides theoretical and practical reference for us to promote mobile Internet technology innovation and innovative applications to achieve fuller employment and optimize employment structure in the era of digital economy.
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/
Blockchain technology and decentralized finance (DeFi) are reshaping global financial systems. Despite their impact, the spatial distribution of public sentiment and its economic and geopolitical determinants are often overlooked. This study analyzes over 150 million geo-tagged, DeFi-related tweets from 2012 to 2022, sourced from a larger dataset of 7.4 billion tweets. Using sentiment scores from a BERT-based multilingual classification model, we integrated these tweets with economic and geopolitical data to create a multimodal dataset. Employing techniques like sentiment analysis, spatial econometrics, clustering, and topic modeling, we uncovered significant global variations in DeFi engagement and sentiment. Our findings indicate that economic development significantly influences DeFi engagement, particularly after 2015. Geographically weighted regression analysis revealed GDP per capita as a key predictor of DeFi tweet proportions, with its impact growing following major increases in cryptocurrency values such as bitcoin. While wealthier nations are more actively engaged in DeFi discourse, the lowest-income countries often discuss DeFi in terms of financial security and sudden wealth. Conversely, middle-income countries relate DeFi to social and religious themes, whereas high-income countries view it mainly as a speculative instrument or entertainment. This research advances interdisciplinary studies in computational social science and finance and supports open science by making our dataset and code available on GitHub, and providing a non-code workflow on the KNIME platform. These contributions enable a broad range of scholars to explore DeFi adoption and sentiment, aiding policymakers, regulators, and developers in promoting financial inclusion and responsible DeFi engagement globally.
This study adopted the double difference method to study the effect of open government data (OGD) on city green land-use efficiency (CGLUE). It was found that opening government data had a significant promotional effect on CGLUE, and a number of robustness tests were the foundation for this finding. A mechanism analysis demonstrated that two key avenues via which government data openness can promote CGLUE are raising public awareness of environmental issues and strengthening urban green innovation potential. A heterogeneity analysis found that the effect of government data openness on CGLUE was more obvious in eastern cities, cities with higher levels of digital finance, and non-resource-based cities. In addition, open government data also reduced urban carbon emissions while improving CGLUE, contributing to China’s “double carbon” goal.