With the acceleration of urbanization and the promotion of the “dual carbon” goal, the road transport system is facing the triple challenges of efficiency bottlenecks, excessive carbon emissions, and data security risks. In view of the shortcomings of the existing research in dynamic response, multi-objective collaboration and privacy protection, this paper proposes a three-in-one intelligent management framework: (1) construct a real-time dynamic path optimization model based on Deep Reinforcement Learning (DRL), and realize the precise regulation of traffic flow through multi-source data fusion and adaptive reward mechanism; (2) Design a multi-objective optimization model integrating carbon trading mechanism to quantify the synergistic relationship between transportation efficiency, carbon emissions and economic costs; (3) Develop a distributed data management framework based on blockchain, and use zero-knowledge proof and smart contract technology to protect user privacy. The peak simulation experiment based on the fifth ring road section of Beijing shows that the proposed method reduces the average traffic time by 18.7%, the carbon emission by 23.5%, and the risk of data leakage by 76% compared with the traditional algorithm. This study provides theoretical and technical support for the construction of a safe, efficient and low-carbon intelligent transportation system.
, a novel, multi-layered framework designed to overcome these critical limitations in the Medical IoT domain. Med-Q Ledger integrates a permissioned Hyperledger Fabric for transactional integrity with a scalable Holochain Distributed Hash Table for high-volume telemetry, achieving horizontal scalability and sub-second commit times. To fortify long-term data security, the framework incorporates post-quantum cryptography (PQC), specifically CRYSTALS-Di lithium signatures and Kyber Key Encapsulation Mechanisms. Real-time, privacy-preserving intelligence is delivered through an edge-based federated learning (FL) model, utilizing lightweight autoencoders for anomaly detection on encrypted gradients. We validate Med-Q Ledger's efficacy through a critical application: the prediction of intestinal complications like necrotizing enterocolitis (NEC) in preterm infants, a condition frequently necessitating emergency colostomy. By processing physiological data from maternal wearable sensors and infant intestinal images, our integrated Random Forest model demonstrates superior performance in predicting colostomy necessity. Experimental evaluations reveal a throughput of approximately 3400 transactions per second (TPS) with ~180 ms end-to-end latency, a >95% anomaly detection rate with <2% false positives, and an 11% computational overhead for PQC on resource-constrained devices. Furthermore, our results show a 0.90 F1-score for colostomy prediction, a 25% reduction in emergency surgeries, and 31% lower energy consumption compared to MQTT baselines. Med-Q Ledger sets a new benchmark for secure, high-performance, and privacy-preserving IoMT analytics, offering a robust blueprint for next-generation healthcare deployments.
This paper introduces Lyquor, a decentralized platform that reimagines blockchain infrastructure through a service-centric model where nodes selectively host smart contracts (called Lyquids) while preserving global composability. We present three key innovations: (1) Fate-Constrained Ordering (FCO), which decouples consensus from execution to enable selective hosting without sacrificing Layer-1 grade composability; (2) Direct Memory Architecture (DMA), which eliminates state access bottlenecks by providing each contract with persistent, byte-addressable virtual memory; and (3) Universal Procedure Call (UPC), which enables fault-tolerant, programmable coordination across distributed off-chain computation. Together, these components are powered by a Rust-macroed unified programming model where on-chain and off-chain logic coexist seamlessly, supporting both traditional smart contract patterns and novel distributed applications. Lyquor addresses critical limitations in existing systems while maintaining compatibility with Ethereum APIs, offering a path toward truly scalable decentralized computation.
Access to safe and sustainable drinking water services remains a significant challenge, particularly in rapidly urbanizing and climate-impacted urban and peri-urban areas. Traditional utilities, often operating under public-private partnerships, are frequently constrained by underinvestment, fragmented governance, and weak accountability, resulting in service gaps. Decentralized Drinking Water Services (DWS) offer adaptable, user-centered alternatives through locally tailored technologies and alternative water sources. However, their adoption is constrained by financial barriers, regulatory uncertainty, and limited community engagement. While previous studies have emphasized the technical and operational aspects of DWS, this study addresses a gap by examining how business model innovation can enable long-term adoption and scalability. Using a mixed-methods approach, combining a systematic literature review with interviews of private-sector providers, this research identifies Product-Service Systems, Social Enterprises, and Community-Based Management as key business models. Findings reveal that hybrid approaches, which bridge formal/informal, centralized/decentralized, and public/private boundaries, are central to navigating heterogeneous infrastructure configurations in diverse contexts. Providers must balance financial viability with equitable access, adapt to varying institutional capacities, and respond to evolving policy landscapes. The analysis shows that regions with advanced water infrastructure industries often shape service models in markets with more constrained resources, underscoring the need to adapt these transfers to local socio-political and cultural contexts. Scalable DWS delivery depends not only on technological innovation but also on coherent regulatory frameworks, inclusive governance, and financing strategies aligned with community affordability. Finally, the study calls for future policy efforts that prioritize coherent regulatory frameworks, including clear quality standards, permitting procedures for alternative water sources, mechanisms for public oversight, and community engagement to ensure long-term functionality and local ownership. • Identifies business models enabling Decentralized Drinking Water Services in urban and peri-urban areas. • Combines a systematic literature review with private-sector interviews. • Shows hybrid models bridging formal/informal and centralized/decentralized systems. • Highlights policy and regulations as key to scaling decentralized solutions. • Finds financial viability and equitable access as central trade-offs for providers.
Ethereum’s introduction of smart contracts has significantly expanded blockchain use cases, enabling decentralized applications. Since all transactions are publicly available, the system can be modeled as a complex network, allowing us to uncover emergent user behavior and explore the underlying dynamics of the ecosystem. In this study, we focus on analyzing the structural differences within the Ethereum system across three distinct market regimes: bull, bear, and sideways. To achieve this, we apply a Hidden Markov Model to the log-return time series to uncover the underlying states, revealing three differentiated states, each corresponding to a specific market regime. Next, we investigate the network structural differences across these regimes, finding meaningful variations. During the bear regime, the out-degree distribution is more heterogeneous, with the largest hub exhibiting more extreme out-degree values. Additionally, during the bull and sideways regimes, we observe higher levels of reciprocity, clustering, and modularity compared to the bear regime. These findings suggest that during bull and sideways markets, the interaction patterns are more complex, and the community structure is more cohesive. Overall, our work underscores how market conditions shape trading patterns and the structural properties of the Ethereum transaction network, providing new insights into the interplay between market regimes, network topology, and user behavior in decentralized ecosystems.
Yousef Tahboub, Anthony Revilla, Jaydon Lynch, Greg Floyd
Casting a ballot from a phone or laptop sounds appealing, but only if voters can be confident their choice remains secret and results cannot be altered in the dark. This paper proposes a hybrid blockchain-based voting model that stores encrypted votes on a private blockchain maintained by election organizers and neutral observers, while periodically anchoring hashes of these votes onto a public blockchain as a tamper-evident seal. The system issues voters one-time blind-signed tokens to protect anonymity, and provides receipts so they can confirm their vote was counted. We implemented a live prototype using common web technologies (Next.js, React, Firebase) to demonstrate end-to-end functionality, accessibility, and cost efficiency. Our contributions include developing a working demo, a complete election workflow, a hybrid blockchain design, and a user-friendly interface that balances privacy, security, transparency, and practicality. This research highlights the feasibility of secure, verifiable, and scalable online voting for organizations ranging from small groups to larger institutions.
Andrés Fábrega, Amy Zhao, Jay Yu, James Austgen · 8 authors
Decentralized Autonomous Organizations (DAOs) use smart contracts to foster communities working toward common goals. Existing definitions of decentralization, however -- the 'D' in DAO -- fall short of capturing the key properties characteristic of diverse and equitable participation. This work proposes a new framework for measuring DAO decentralization called Voting-Bloc Entropy (VBE, pronounced ''vibe''). VBE is based on the idea that voters with closely aligned interests act as a centralizing force and should be modeled as such. VBE formalizes this notion by measuring the similarity of participants' utility functions across a set of voting rounds. Unlike prior, ad hoc definitions of decentralization, VBE derives from first principles: We introduce a simple (yet powerful) reinforcement learning-based conceptual model for voting, that in turn implies VBE. We first show VBE's utility as a theoretical tool. We prove a number of results about the (de)centralizing effects of vote delegation, proposal bundling, bribery, etc. that are overlooked in previous notions of DAO decentralization. Our results lead to practical suggestions for enhancing DAO decentralization. We also show how VBE can be used empirically by presenting measurement studies and VBE-based governance experiments. We make the tools we developed for these results available to the community in the form of open-source artifacts in order to facilitate future study of DAO decentralization.
Given a universe of N assets, investors often form equally weighted portfolios (EWPs) by selecting subsets of assets. EWPs are simple, robust, and competitive out-of-sample, yet the uncertainty about which subset truly performs best is largely ignored. Traditional approaches typically rely on a single selected portfolio, but this fails to consider alternative investment strategies that may perform just as well when accounting for statistical uncertainty. To address this selection uncertainty, we introduce the Selection Confidence Set (SCS) for EWPs: the set of all portfolios that, under a given loss function and at a specified confidence level, contains the unknown set of optimal portfolios under repeated sampling. The SCS quantifies selection uncertainty by identifying a range of plausible portfolios, challenging the idea of a uniquely optimal choice. Like a confidence set, its size reflects uncertainty -- growing with noisy or limited data, and shrinking as the sample size increases. Theoretically, we establish that the SCS covers the unknown optimal selection with high probability and characterize how its size grows with underlying uncertainty, corroborating these results through Monte Carlo experiments. Applications to the French 17-Industry Portfolios and Layer-1 cryptocurrencies underscore the importance of accounting for selection uncertainty when comparing equally weighted strategies.
Ketidakpastian ekonomi dan inflasi mendorong investor untuk mencari aset lindung nilai. Emas telah lama dianggap sebagai safe haven, sementara Bitcoin mulai dipandang sebagai alternatif digital. Penelitian ini bertujuan untuk menganalisis pengaruh harga emas terhadap pergerakan harga Bitcoin dengan indeks Dow Jones sebagai variabel kontrol. Metode yang digunakan adalah regresi linear dengan pendekatan Ordinary Least Squares (OLS), menggunakan data sekunder bulanan dari tahun 2019 hingga Oktober 2024. hasil penelitian menunjukkan bahwa emas memiliki pengaruh positif dan signifikan terhadap harga Bitcoin, sedangkan indeks Dow Jones tidak menunjukkan pengaruh yang signifikan. Temuan ini mendukung teori penyimpan nilai (store of value) dan semakin memperkuat peran Bitcoin sebagai aset alternatif dalam menghadapi ketidakpastian ekonomi. Hasil ini dapat memberikan implikasi praktis bagi investor dan pengambil keputusan dalam menggunakan pergerakan harga emas sebagai indikator untuk mengantisipasi pergerakan harga Bitcoin dan strategi manajemen risiko portofolio di tengah transisi global menuju digitalisasi ekonomi.
This dissertation investigates the crime of money laundering in the context of cryptoasset acquisition, from a functional-reductive perspective of criminal law. The central objective is to establish precise and coherent criteria for the incidence of criminal norms, aiming to limit the irrationality of punitive power and promote a counter-selective application, without compromising the accountability for complex criminal typologies. The study is pursued through a bibliographic review, analyzing national and international doctrine, legislation, and jurisprudence, complemented by interdisciplinary sources from economics, technology, and sociology. The first chapter establishes the theoretical premises of Zaffaroni's negative/agnostic theory of punishment and the reductive penal system. Criminal law, from this perspective, functions to limit punitive power. The second chapter delves into the crime of money laundering, analyzing its evolution, phases, the affected legal interest (socioeconomic order, with a focus on free competition and initiative), and its typical elements, based on the established theoretical framework. The third chapter comprehends the phenomenon of cryptoassets, detailing the functioning of Bitcoin and blockchain, the universe of decentralized finance (DeFi), forms of acquisition, and the Brazilian regulatory framework. The fourth chapter applies the dogmatic framework to the analysis of money laundering's typicality in different forms of cryptoasset acquisition and discusses the aggravating factor for the use of virtual assets. The research achieves its general objective by demonstrating a path for criminal dogmatics to establish precise criteria and limits for the incidence of money laundering, legitimizing judicial decisions by curbing punitive arbitrariness. Criteria are established to identify typical and atypical cases of money laundering involving cryptoasset acquisition, and requirements are set for the application of the special aggravating factor, conditioning it on the concrete demonstration that the use of cryptoassets intensified the harm to the legal interest. The interdisciplinary approach and consideration of the Brazilian reality are crucial to reject the trivialization of the institute and fulfill the counter-selective function of the penal system, preventing the automatic imputation of this serious crime without a proper technological understanding and knowledge of money laundering's limits
The deployment of distributed digital twin systems in sectors such as healthcare, manufacturing, and critical infrastructure has significantly heightened the importance of data privacy. These systems interact with numerous devices and users, increasing the risk of data leakage or unauthorized access to sensitive information. Traditional centralized identity management and access control mechanisms no longer meet the scalability, autonomy, and privacy requirements of modern distributed architectures. This article explores how smart contracts operating in blockchain environments can provide decentralized access management for digital twin systems. Smart contracts enable transparent and reliable enforcement of access policies without relying on centralized authorities. The study examines the integration of modern cryptographic technologies into smart contract workflows, including zero-knowledge proofs, decentralized identifiers (DIDs), and confidential computing. These technologies make it possible to verify access rights and perform secure operations without revealing sensitive data. The article also analyzes the limitations of existing solutions, such as the high transaction costs of public blockchains, the limited performance of traditional smart contracts, and the challenges of integrating confidential computing into resource-constrained devices. The authors outline future research directions, including optimizing Layer 2 architectures to improve performance, developing secure auditing mechanisms, and ensuring compatibility with self-sovereign identity systems. The conclusions emphasize that privacy should be treated as a fundamental property of digital twin systems. In these environments, smart contracts must serve not only as governance logic but also as trusted agents that guarantee compliance with access policies and regulatory requirements in decentralized ecosystems.
Sustainable trade requires verifiable, granular, and trustworthy data across multi-jurisdictional supply chains. This paper argues that blockchain’s binding constraints are institutional, not technical, and proposes the Green Trade Blockchain Governance Trilemma: no design can simultaneously maximize (i) transactional efficiency, (ii) regulatory verifiability, and (iii) decentralized governance with commercial privacy. Comparative cases—TradeLens, IBM Food Trust, Everledger, and Power Ledger—show divergent institutional choices and outcomes: TradeLens faltered under perceived hegemonic control; Food Trust succeeded via a buyer mandate; Everledger thrived through symbiosis with trusted authorities; Power Ledger scaled within a regulatory sandbox. We further analyze the Oracle Problem as the key limit to verifiability and assess privacy-enhancing technologies, especially zero-knowledge proofs, as partial mitigations that protect sensitive data while enabling compliance checks. We conclude that success hinges on context-specific institutional design—certified oracles plus verifiable computation—rather than a one-size-fits-all stack, offering actionable guidance for policymakers, consortia, and firms building credible green-trade infrastructure.
Lokman Kantar, Tarana Azimova, Murat Akkaya, Yildirim Hasan-Huseyin
This study investigates the volatility dynamics and time-varying correlations between Bitcoin (BTC) and major financial and commodity markets, including gold, oil, NASDAQ, NIKKEI, FTSE, DAX, and the U.S. Dollar Index (USDINX). Using daily data and GARCH-family models, we quantify persistence, asymmetry, and mediumterm memory in BTC volatility. Model selection using loglikelihood, SIC, and AIC criteria identifies EGARCH asthe best model for capturing conditional variance behavior. We then employ a DCC-MGARCH framework to estimate evolving cross-market correlations. Results indicate that BTC volatility is highly persistent, exhibits stronger reactions to negative shocks, and shows moderate mean reversion. Gold displays the lowest persistence, confirming its role as a stable diversifier. DCC-MGARCH estimates reveal weak positive BTC-Gold correlations, negative BTC-USDINX correlations, and no significant BTC-Oil or BTC-DAX linkages, implying substantial diversification potential. Notably, BTC-NIKKEI correlations strengthened during the COVID-19 period, while BTC-Gold correlations modestly increased. These findings underscore the importance of dynamic portfolio strategies, as optimal weights shift in response to evolving conditional covariances, rendering static allocations suboptimal. For policymakers, volatility persistence and correlation thresholds can inform leverage and exposure limits, particularly when the linkages between BTC and traditional assets intensify.
Idi Amin Germán Silva-Jug, Klender-Aimer Cortez-Alejandro, Adrián Wong-Boren, Roberto Chiquet- Jiménez
This study aims to analyze the regulations applicableto Bitcoin in force in seven Latin American countries to assess the need to strengthen the regulatory framework and its implementation in the region. For this purpose, a qualitative study was designed with an exploratory scope and a non-experimental cross-sectional approach. Information is collected from secondary sources throughthe web and a survey of academics from institutions affiliated with the ALAFEC. The results show the regulatory advances regarding cryptocurrencies. Current regulations are described for Argentina, Brazil, Chile, Colombia, El Salvador, Mexico, and Venezuela. All the countries analyzed have mechanisms for the prevention of money laundering, terrorist financing, and the use of resources of illicit origin. Nonetheless, it is necessary to strengthen the current regulations by including mechanisms for the protection of all participants. The results show the normative advances regarding the regulation of cryptocurrencies. Current regulations are described for Argentina, Brazil, Chile, Colombia, El Salvador, Mexico, and Venezuela. All the countries analyzed have mechanisms for the prevention of money laundering, the financing of terrorism, and the use of resources of illicit origin. Nonetheless, it is necessary to strengthen the current regulations by including mechanisms for the protection of all participants.
Cryptocurrencies have emerged miraculously all over the globe due to their legitimacy, transparency, immutability, and the traceability that blockchain technology provides. However, the benefits it provides are dwarfed by how unpredictable and extremely price-volatile the cryptocurrencies are. That makes it really tough for investors to find their profitable opportunities in such volatile markets. Social media sources, like Twitter and Reddit, have evolved as crucial tools of sentiment estimation above the explosively volatile price movements of decentralized currencies. Here we introduce an attention-based hybrid CNN-LSTM model optimized for social media sentiment analysis to use them towards investment decisions in a broad portfolio of cryptocurrencies. The existing Convolutional Neural Network (CNN) effectively extracts the essential features, and Long Short-Term Memory (LSTM) has the potential to capture the long dependencies between phrases. Although these models can process massive textual data, they limit treating all the features equally important. Therefore, the proposed model induces the attention mechanism into hybrid CNN-LSTM for emphasizing more or fewer weights on different words according to their contributions and optimizes the parameters of employed neural networks using grid search. In our pipeline, the attention-augmented CNN-LSTM first transforms each tweet/review into a 512-dimensional task-specific embedding; a calibrated radial-basis SVM then serves as the final decision layer, refining the margin for classes that the neural network alone tends to blur. This sequential ('deep-features-plus-SVM') architecture boosts F1 by 3.2 pp over a pure Softmax head while adding only 0.4 ms of inference time. Extensive experiments conducted on cryptocurrency-related tweets and Reddit reviews reveal the outperformance of the proposed model over existing Deep Neural Networks (DNNs) and state-of-the-art models. Trained on 9.9 k crypto-tweets and 33 k Reddit comments, AEH attains 98.7% accuracy, 0.987 F1, and κ = 0.94, outperforming strong baselines (pure LSTM + 8.3 pp; pure CNN + 19.3 pp) and the widely-used VADER toolkit (+ 11.8 pp). On the forecasting side, a complementary GRU regressor trained on eight-year price series yielded MAE = 0.0315, MAPE = 5.95%, and MSE = 0.0022 for Bitcoin, beating an ARIMA benchmark at p < 0.001. The primary objective of the proposed hybrid model attributed to processing huge social sentiments with an attention mechanism to break the dilemma of cryptocurrency investors.
The article addresses the issue of ensuringconfidential exchange of personal data in inter-organizationalinformation systems under conditions of increasing digitalinteraction between public and private sector entities. It is notedthat centralized models for processing and exchanging personaldata fail to provide an adequate level of protection againstunauthorized access, transaction tampering, and do not ensuresufficient transparency of data operations. These limitationshinder full compliance with regulatory requirements, particularlythe provisions of the General Data Protection Regulation(GDPR), ISO/IEC 27001 and 27701 standards, as well asnational legislation on information protection. The study substantiates the feasibility of using a permissioned blockchain as the architectural basis forimplementing a secure, decentralized exchange of personal datawith guaranteed access control, transaction audit, and dataimmutability. A conceptual model of the information system isproposed, involving smart contracts for managing data subjectconsent, access control, and the integration of the InterPlanetaryFile System (IPFS) for robust off-chain data storage. The modelalso includes the use of Zero-Knowledge Proof (ZKP) cryptographic mechanisms and behavioral verification criteriafor transactions. Particular attention is given to risk analysis associated withpersonal data processing in inter-organizational environments, and to the application of supplementary protection tools—suchas masking, pseudonymization, and data perturbation—tomitigate potential losses in the event of data leakage. A set oftechnical and organizational compliance criteria withinternational and national information security standards isoutlined. The aim of this research is to design an architectural modelfor inter-organizational personal data exchange based onpermissioned blockchain that ensures confidentiality, integrity, controlled access, and regulatory compliance in the field ofinformation protection.
Securities trading systems have settlement efficiency, audit transparency, and fraud prevention concerns due to centralized intermediaries and aging infrastructure. Existing research models risk counterparty trading due to delayed settlements, opaque record keeping, and human compliance checks. The study aims to design and evaluate a blockchain-based equities trading platform for transaction security and traceability. Provable Atomic Consensus for Trading (PACT), a blockchain-based architecture for regulated financial institutions' trading environments, combines hybrid consensus with a privacy-preserving cryptographic approach. A hybridized consensus process for efficient transaction finality, zero-knowledge proof enabled atomic settlements for instant delivery vs. payment while protecting commercial secrecy, and regulator-accessible smart contracts for real-time compliance checks are used in the PACT algorithm PACT found a 20% reduction in consensus finality time, 53% reduction in proof verification time, 56% improvement in smart contract vulnerability, and 42% improvement in auditability index on a permissioned blockchain with hardware-accelerated smart contracts. The study indicated 35.6% lower throughput and 41.7% lower Tx volume over 10 validators. Latency over 10 validators is 24% lower and Tx volume is 23.2% lower than existing research models. Blockchain improves securities infrastructure speed, reliability, and transparency without affecting compliance, according to studies.
This study provides a structured literature review of consumer behaviour in the metaverse, exploring motivations for metaverse use, adoption, avatar engagement, virtual goods purchases, non-fungible tokens (NFTs), and the impact of brand experiences on real-world product purchase intentions. For this purpose, a systematic review of peer-reviewed articles published over the past two decades was conducted. From an initial pool of 209 articles retrieved from electronic databases, 36 met the inclusion criteria and were thematically analysed. Key trends, knowledge gaps, and future research directions were identified. A novel adaptation of the engagement framework was proposed, categorizing consumer behaviour in the metaverse into three stages: pre-engagement, engagement, and post-engagement. The review reveals that Second Life is the most studied platform, with surveys being the predominant research method. Research has primarily focused on retail, fashion, and tourism, particularly virtual product purchases. Despite providing valuable insights, existing studies reveal substantial research gaps and limited theoretical development. The proposed framework organizes recurring themes and provides a foundation for future research, highlighting the need for empirical evidence to further advance the field. This study is one of the first to systematically review consumer behaviour research in the metaverse and propose a stage-based framework, contributing to theoretical understanding and offering structured directions for future empirical research. JEL Classification: M31; O32; Q55 Article History: Received: June 30, 2025; Reviewed: September 5, 2025; Accepted: September 22, 2025; Available online: September 29, 2025.
The rise of Web3 and Decentralized Finance (DeFi) has enabled borderless access to financial services empowered by smart contracts and blockchain technology. However, the ecosystem's trustless, permissionless, and borderless nature presents substantial regulatory challenges. The absence of centralized oversight and the technical complexity create fertile ground for financial crimes. Among these, money laundering is particularly concerning, as in the event of successful scams, code exploits, and market manipulations, it facilitates covert movement of illicit gains. Beyond this, there is a growing concern that cryptocurrencies can be leveraged to launder proceeds from drug trafficking, or to transfer funds linked to terrorism financing. This survey aims to outline a taxonomy of high-level strategies and underlying mechanisms exploited to facilitate money laundering in Web3. We examine how criminals leverage the pseudonymous nature of Web3, alongside weak regulatory frameworks, to obscure illicit financial activities. Our study seeks to bridge existing knowledge gaps on laundering schemes, identify open challenges in the detection and prevention of such activities, and propose future research directions to foster a more transparent Web3 financial ecosystem -- offering valuable insights for researchers, policymakers, and industry practitioners.
Essossinam Pali, Coffi Cyprien Aholou, François Paul Yatta
After several hesitant attempts, Togo has made renewed progress in implementing sustainable decentralization. Municipal and regional elections held in 2019 and 2024 marked a significant institutional step forward. However, this implementation phase remains marked by both achievements and structural challenges. This article explores how local elected officials perceive the decentralization policy and its financing in their municipalities. It formulates the general hypothesis that decentralization fosters the implementation of local public policies when supported by appropriate institutional mechanisms. Based on a quantitative survey conducted in early 2024 among 487 local actors including 477 municipal councilors and 10 prefects the results highlight a range of perceptions. While some elected officials acknowledge improvements in service delivery and institutional support (through tools such as FACT and ANFCT), others stress the persistence of constraints related to financial autonomy, administrative capacities, and citizen participation. The findings suggest that decentralization in Togo is progressing, albeit unevenly, and requires further efforts to consolidate its institutional and operational foundations.
Moritz Grundei, Vipindev Adat Vasudevan, Kishori Konwar, Muriel Medard
The data availability problem is a central challenge in blockchain systems and lies at the core of the accessibility and scalability issues faced by platforms such as Ethereum. Modern solutions employ several approaches, with data availability sampling (DAS) being the most self-sufficient and minimalistic in its security assumptions. Existing DAS methods typically form cryptographic commitments on codewords of fixed-rate erasure codes, which restrict light nodes to sampling from a predetermined set of coded symbols. In this paper, we introduce a new approach to DAS that modularizes the coding and commitment process by committing to the uncoded data while performing sampling through on-the-fly coding. The resulting samples are significantly more expressive, enabling light nodes to obtain, in concrete implementations, up to multiple orders of magnitude stronger assurances of data availability than from sampling pre-committed symbols from a fixed-rate redundancy code as done in established DAS schemes using Reed Solomon or low density parity check codes. We present a concrete protocol that realizes this paradigm using random linear network coding (RLNC).
Decentralization has an important geographic dimension that conventional metrics, such as stake distribution, often overlook. Where validators operate affects resilience to regional shocks (e.g., outages, natural disasters, or government intervention) as well as fairness in reward access. Yet major blockchain protocols do not encode geographical location in their rules; instead, validator locations emerge from a combination of economic incentives, regulatory constraints, infrastructure availability, and validator deployment choices. When certain locations offer systematic advantages, validators may strategically co-locate to maximize expected rewards, as observed in Ethereum, where validators cluster along the Atlantic corridor, which exhibits favorable latency. In this paper, we propose a formal model of validators' geographical positioning incentives under Ethereum's protocol design, capturing the interaction between its two block-building paradigms, local and external block building, and the geographical distribution of validators and information sources. We analytically characterize the model under a mean-field approximation and complement this analysis with an agent-based simulation calibrated with real-world latency data to quantify how these incentives translate into geographical concentration under heterogeneous geographic and infrastructural conditions. Our results show that Ethereum's block-building architecture is not geographically neutral. Both paradigms generate location-dependent payoffs and incentives to relocate closer to payoff-relevant parties in order to reduce propagation delays, although through different underlying mechanisms. Asymmetric access to information sources further amplifies geographical centralization. We also demonstrate that consensus parameters, such as attestation thresholds and slot times, modulate latency sensitivity and can amplify these effects, acting as protocol-level levers. Finally, we discuss the implications of our findings for protocol design and outline potential mitigation directions informed by our analysis.
Over the past decade alternatives to traditional insurance and banking have grown in popularity. The desire to encourage local participation has lead products such as peer-to-peer insurance, reciprocal contracts, and decentralized finance platforms to increasingly rely on network structures to redistribute risk among participants. In this paper, we develop a comprehensive framework for linear risk sharing (LRS), where random losses are reallocated through nonnegative linear operators which can accommodate a wide range of networks. Building on the theory of stochastic and doubly stochastic matrices, we establish conditions under which constraints such as budget balance, fairness, and diversification are guaranteed. The convex order framework allows us to compare different allocations rigorously, highlighting variance reduction and majorization as natural consequences of doubly stochastic mixing. We then extend the analysis to network-based sharing, showing how their topology shapes risk outcomes in complete, star, ring, random, and scale-free graphs. A second layer of randomness, where the sharing matrix itself is random, is introduced via Erdős--Rényi and preferential-attachment networks, connecting risk-sharing properties to degree distributions. Finally, we study convex combinations of identity and network-induced operators, capturing the trade-off between self-retention and diversification. Our results provide design principles for fair and efficient peer-to-peer insurance and network-based risk pooling, combining mathematical soundness with economic interpretability.
In Qing Dynasty China, the inter-government fiscal arrangement was characterized by a “dual-track fiscal system” in which a formal system with a central budget of revenues and spending coexisted with a decentralized and fragmented informal local fiscal system financed by miscellaneous extra-legal taxes. Why did the dual-track fiscal system endure despite its obvious flaws? Why was the Qing state unable to establish a stable and rationalized fiscal federalism? This chapter investigates the causes and consequences of the dual-track fiscal system. Through historical institutional analysis and several empirical studies, we propose a novel explanation based on the opportunistic behavior and credible commitment by the central government. Due to the central government’s inability to credibly commit to not encroaching on the formal fiscal powers of lower-level governments, the decentralized, off-budget, and informal local finances represent an “institutional equilibrium”.