The energy sector underwent a significant transformation with increasing demand for efficiency, transparency, and sustainability. The traditional or conventional system often faces several challenges, such as inefficient energy trading, a lack of transparency in renewable energy generation verification, and complex regulatory guidelines that affect its widespread adoption. Thus, blockchain technology has emerged as a potential solution to overcome these challenges, as it is known for its transparent, secure, and decentralized nature. However, despite the promising application of blockchain, its integration into the energy supply chain (ESC) is underexplored. The purpose of this research is to analyze the potential applications of blockchain technology in ESC in order to enhance efficiency, transparency, and sustainability in energy systems. The aim is to investigate the integration of blockchain with emerging technologies (such as IoTs, smart contracts, and P2P energy trading) in order to optimize energy production, distribution, and consumption. Furthermore, by comparing different blockchain platforms (like Ethereum, Solana, Hedera, and Hyperledger Fabric), this study discusses the security and scalability challenges of using blockchain in energy systems. It also examines the practical use cases of blockchain for the tokenization of RECs, dynamic energy pricing, and P2P energy trading by providing the Energy Web Foundation and Power Ledger as real-world examples. The article concludes that blockchain technology has the potential to transform ESC by enabling decentralized energy trading, which subsequently enhances transparency in energy transactions and the verification of renewable energy generation. It also identifies smart contracts and tokenization of energy assets as key parameters for dynamic pricing models and efficient trading mechanisms. However, regulatory and scalability challenges remain significant obstacles to its widespread adoption. Finally, this study provides the basis for future advancement in the adoption of blockchain technology in ESC, which offers a valuable resource for industry professionals, regulating authorities, and researchers.
M Vishwas Bharadwaj, M Vishwas Bharadwaj, P Shreyas Gowda, Sanjana Desh · 5 authors
The rapid rise of cryptocurrencies has impacted the global socio-economic landscape, encouraging investors to seek income through crypto trading. Due to the market’s volatility and complex interdependencies, researchers have built various prediction models using machine learning, deep-learning, and sentiment-based hybrid algorithms. Notably, the DLCFS (Deep Learning Cryptocurrency Forecasting considering Sentiment) framework incorporates market features, trading volume, and sentiment from Reddit to improve price predictions for Bitcoin, Ethereum, and Litecoin—achieving high accuracy when compared to traditional machine learning models. Alongside forecasting, sentiment analysis plays an important role in understanding market trends and investor behavior. With growing user-generated content across different platforms like social media and news sites, extracting public sentiment through NLP has become essential. Recent works explore advanced models and datasets tailored to the unique linguistic features of crypto-related content, highlighting the need for robust and adaptive sentiment analysis techniques in this dynamic domain.
Verifiable Delay Function (VDF) is a cryptographic concept that ensures a minimum delay before output through sequential processing, which is resistant to parallel computing. One of the significant VDF protocols academically reviewed is the VDF protocol proposed by Pietrzak. However, for the blockchain environment, the Pietrzak VDF has drawbacks including long proof size and recursive protocol computation. In this paper, we present an implementation study of Pietrzak VDF verification on Ethereum Virtual Machine (EVM). We found that the discussion in the Pietrzak's original paper can help a clear optimization in EVM where the costs of computation are predefined as the specific amounts of gas. In our results, the cost of VDF verification can be reduced from 4M to 2M gas, and the proof length can be generated under 8 KB with the 2048-bit RSA key length, which is much smaller than the previous expectation.
In blockchain networks, so-called "full nodes" serve data to and relay transactions from clients through an RPC interface. This serving layer enables integration of "Web3" data, stored on blockchains, with "Web2" mobile or web applications that cannot directly participate as peers in a blockchain network. In practice, the serving layer is dominated by a small number of centralized services ("node providers") that offer permissioned access to RPC endpoints. Clients register with these providers because they offer reliable and convenient access to blockchain data: operating a full node themselves requires significant computational and storage resources, and public (permissionless) RPC nodes lack financial incentives to serve large numbers of clients with consistent performance. Permissioned access to an otherwise permissionless blockchain network raises concerns regarding the privacy, integrity, and availability of data access. To address this, we propose a Permissionless Accountable RPC Protocol (PARP). It enables clients and full nodes to interact pseudonymously while keeping both parties accountable. PARP leverages "light client" schemes for essential data integrity checks, combined with fraud proofs, to keep full nodes honest and accountable. It integrates payment channels to facilitate micro-payments, holding clients accountable for the resources they consume and providing an economic incentive for full nodes to serve. Our prototype implementation for Ethereum demonstrates the feasibility of PARP, and we quantify its overhead compared to the base RPC protocol.
Adrian Victor VEVERA, Andreea Cătălina CRĂCIUN, Mihail Dumitrache, Ionut SANDU · 6 authors
The Domain Name System (DNS) remains a foundational component of the Internet infrastructure, which is frequently exploited by cybercriminals through increasingly diverse and sophisticated attack vectors.This paper explores the evolving cybersecurity challenges faced by domain name systems (DNSs) and their decentralized counterparts in the Web3 ecosystem, particularly the Ethereum Name Service (ENS), as such, it surveys both the established and novel attack patterns.Furthermore, it explores the implications of decentralized naming systems like the ENS, which introduced novel cybersecurity challenges within the Web3 environments and it highlights the future risks and possible research directions related to the transition to decentralized web services.This study provides a comparative analysis of the cyberattacks targeting the DNS and the ENS, highlighting the evolving threat landscape for each of the two ecosystems.By examining the architectural differences between the DNS and ENS, their common attack vectors, and their security mechanisms, it underscores both the distinct vulnerabilities inherent to each ecosystem and the overlapping risks they share.
Traditional portfolio optimization techniques predominantly rely on the classical mean–variance framework introduced by Markowitz, which focuses on balancing expected returns against risk, typically measured by variance. However, in volatile and structur-ally unstable markets such as cryptocurrencies, this approach often fails to capture the full spectrum of uncertainty and diversification potential. This paper introduces an al-ternative methodology grounded in entropy, a fundamental concept in information theory that quantifies uncertainty and disorder. By incorporating entropy into the portfolio optimization process, we offer a more generalizable, distribution-free approach that enhances diversification and resilience.We develop and analyze three distinct en-tropy-based models: the maximum Shannon entropy model, the second-order entropy (Tsallis) model, and the maximum weighted Shannon entropy model. These formula-tions extend the traditional mean–variance approach by integrating nonlinear uncer-tainty measures, enabling a richer representation of investor preferences and asset in-terdependencies. Analytical solutions to the proposed models are derived using the method of Lagrange multipliers, ensuring mathematical rigor and interpretability.The proposed models are empirically validated using a portfolio composed of four leading cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)—with market data from January to March 2025. The case studies demonstrate how entropy-based optimization leads to well-diversified portfolios, robust under market turbulence and heavy-tailed return distributions. Notably, the models facilitate dynamic adjustments in asset allocation in response to shifts in return–risk characteristics and entropy levels. This study contributes to the ongoing generalization of portfolio theory by positioning entropy as both a diversification enhancer and a structural risk measure. It provides theoretical insight, practical tools for asset allocation in high-volatility environments, and paves the way for future research in entropy-driven financial optimization frameworks.
Sami Mejri, Francisco Jareño, Nasir Khan, Arturo Leccadito
This study examines the impact of geopolitical risk (GPR) on black and green cryptocurrencies during crisis times, focusing on their potential as hedging instruments and safe havens. Using daily data on nine cryptocurrencies (Bitcoin, Ethereum, Binance, Litecoin, Ripple, EOS, IOTA, Stellar and Tezos) and the Geopolitical Risk Index from January 3rd, 2019, to January 20th, 2025, the research employs a Regime-Switching Global Vector Autoregressive (RSGVARX) model and a quantile-on-quantile (QQ) approach to capture heterogeneous responses across market states and quantiles. In addition, the Dynamic Conditional Correlation (DCC) GARCH copula and Dynamic Gerber Correlation (DGC) models assess the hedging effectiveness and optimal portfolio weights of various cryptocurrency pairs. The study uniquely combines the RSGVARX and QQ methods to provide a comprehensive understanding of the dynamic interactions between GPR and cryptocurrency returns and introduces robust portfolio optimisation analysis using advanced econometric models. The results show that the impact of GPR on black cryptocurrencies is generally negative and statistically insignificant in Regime 1, with mixed effects in Regime 2, while green cryptocurrencies show similar heterogeneous responses. Several cryptocurrencies show resilience to GPR shocks in certain scenarios, highlighting their potential as reliable assets in times of geopolitical instability. The portfolio optimisation analysis identifies Bitcoin paired with Ethereum, Binance and Litecoin as the most effective combination for hedging throughout the sample period and during the stressful Russia-Ukraine war and Israeli-Palestinian conflict. These results suggest that investors should consider market states and transition probabilities when developing portfolio strategies involving cryptocurrencies, providing valuable insights for managing risk and ensuring financial stability during geopolitical crises.
Krzysztof Gogol, Manvir Schneider, Claudio Tessone
We study the economics of transaction reverts on Ethereum rollups and show that they are not accidental failures but equilibrium outcomes of MEV strategies. Using execution traces from major L2s, we find that over 80% of reverted transactions are swaps, with half targeting USDC-WETH pools on Uniswap v3, v4. Clustering reveals distinct bot archetypes, including split-trade arbitrageurs, atomic duplicators, and end-of-block spammers, demonstrating that reverts follow systematic patterns rather than random noise. Empirically, we show that priority fee auctions on rollups do not allocate blockspace efficiently: transaction placement is mis-ordered, round-number bidding dominates, and duplication spam inflates base fees. As a result, reverted transactions contribute disproportionately more to sequencer fee revenues than to gas consumption, shifting welfare from users to sequencers. To explain these dynamics, we develop a model proving that trade-splitting and duplication strictly dominate one-shot execution under convex adversarial loss. Our findings establish reverts as a structural feature of rollup MEV microstructure and highlight the need for protocol-level reforms to sequencing, fee markets, and revert protection.
Deep learning and hybrid deep learning models are widely regarded as some of the most effective predictive modeling techniques to date. Their hierarchical architecture enables them to capture complex, non-linear relationships among features and uncover hidden patterns within data, making them particularly powerful for tasks involving high-dimensional and unstructured inputs. But, these models are computationally intensive and require substantial processing time. Moreover, their predictive efficiency is highly dependent on the availability of large-scale datasets. In this study, meta learning model is employed for the prediction of two financial markets: equity market and crypto market. NASDAQ and S&P 500 index has been taken for equity market prediction. On the other hand, Bitcoin & Ethereum are considered for crypto market. Three deep learning models: LSTM, GRU and CNN are trained for the prediction of these four indices and a hybrid deep learning model of GRU and CNN is also developed. Based on RMSE, MAE and R 2 values, it is observed that meta learning yields best results among all trained models with minimum time and using scarce computation resources based on small dataset.
Automated Market Makers (AMMs) with a conservative function, such as Uniswap, Balancer, Curve and others, are an integral part of decentralised finance. This article examines the effect of the exchange fees on the divergence losses of the automated market-making systems in public blockchain networks. The study consists of several parts: theoretical background, detailed description of the exchange mechanics, the derivation of explicit formulas, the results of modelling using the hyperparameters of pools from the Ethereum network and the analysis of the proposed approach using historical data. For the first time, the obtained closed formulas (Uniswap, Balancer) and modelling results (Uniswap, Balancer, Curve) indicate the presence of the impermanent gain for liquidity providers in the case of non-zero fees when the trading volume does not exceed a certain amount. The results indicate that the proposed methodology significantly affects the definition of ‘impermanent loss of a liquidity provider’ widely used in the blockchain community since there can always be a profitable range of values. As a practical part of the study, statistics on the share of trades with the effect of impermanent gain in Ethereum pools are provided, and the approach for managing the fee rate is considered during this observation. Explicit relationships for mostly used AMMs with non-zero trading fees are derived. The article may be useful for both practitioners and researchers in the field of decentralised finance seeking a deeper understanding of the dynamics of automated market-making in an ever-changing DeFi environment.
Ethereum’s transaction pool (mempool) dynamics and fee market efficiency critically affect transaction inclusion, validator workload, and overall network performance. This research empirically analyzes gas price variations, mempool clearance rates, and block finalization times in Ethereum’s proof-of-stake ecosystem using real-time data from Geth and Prysm nodes. We observe that high-fee transactions are consistently prioritized, while low-fee transactions face delays or exclusion—despite EIP-1559’s intended improvements. Mempool congestion remains a key factor in validator efficiency and proposal latency. We provide empirical evidence of persistent fee-based disparities and show that extremely high fees do not always guarantee faster confirmation, revealing inefficiencies in the current fee market. To address these issues, we propose congestion-aware fee adjustments, reserved block slots for low-fee transactions, and improved handling of out-of-gas vulnerabilities. By mitigating prioritization bias and execution inefficiencies, our findings support more equitable transaction inclusion, enhance validator performance, and promote scalability. This work contributes to Ethereum’s long-term decentralization by reducing dependence on high transaction fees for network participation.
Modern logistics monitoring solutions increasingly depend on the integration of IoT devices for real-time data collection, shipment tracking, goods and vehicle monitoring, and informed decision- making. However, current IoT-based logistics systems face significant challenges, including complex data management, limited interoperability among stakeholders, and inefficiencies resulting from centralized control mechanisms. Blockchain technology has emerged as a promising solution to address these critical issues within logistics and supply chain management. This paper presents a comparative analysis of traditional centralized logistics systems and blockchain-based decentralized solutions, emphasizing the evaluation of blockchain’s strengths such as transparency, immutability, and automated transaction execution via smart contracts and its weaknesses, particularly scalability limitations and implementation complexity. The research specifically examines how smart contracts can effectively manipulate IoT-generated data to automate logistical transactions and ensure secure, transparent data management. Through a structured analysis, this article identifies specific scenarios in logistics where blockchain technology adds significant value and discusses key practical considerations for its effective adoption. Additionally, this research critically evaluates Ethereum Virtual Machine (EVM)-based smart contracts and proposes AWS Hyperledger Fabric smart contract (chaincode) as a more scalable and cost- effective alternative for enterprise logistics applications. The study provides valuable insights and guidelines for logistics practitioners, facilitating informed decision-making about integrating blockchain solutions to enhance operational efficiency, trust, and interoperability within complex supply chain environments.
Cryptocurrency is an attribute derived from blockchain that serves the sole purpose of money exchange, similar to Bitcoin. Cryptocurrency can have major benefits but it could push the limits if it would be used for money laundering and terrorist finances since there is an absence of intermediate checks to ensure the transaction integrity and no one comes to know the identity of people who’ve made the transaction. This paper touches upon the meaning and definition of blockchain and cryptocurrency, a comparison of bitcoin and Ethereum, smart contracts, mining, advantages and drawbacks of cryptocurrency, Properties of blockchain, A study on (The bulk of bitcoins handled by the USA, and how google stops showcasing advertisements of bitcoin and cryptocurrency), Proposed suggestion and Scope for future study.
The article examines the functional characteristics of centralized (CEX) and decentralized (DEX) cryptocurrency exchanges, which play a key role in the operation of the digital economy. The architectural, organizational, and technological aspects of various types of exchanges are analyzed, with their advantages and limitations identified in terms of usability, security, liquidity, regulatory compliance, and availability of trading instruments. It is noted that centralized exchanges offer high order execution speed, extensive functionality, integration with payment infrastructure, and user support. At the same time, they require identity verification, store assets in internal accounts, and are therefore subject to certain risks of centralized control. In contrast, decentralized exchanges operate on the basis of smart contracts, do not store user assets, and do not require KYC procedures, thus ensuring a high level of anonymity. However, DEX platforms are characterized by lower liquidity, a limited range of order types, and a higher requirement for users’ technical competence. The study presents a comparative analysis of trading volumes for key cryptocurrencies on Binance, Bybit, and Coinbase Exchange, as well as on decentralized platforms such as Uniswap, PancakeSwap, and Curve. The results reveal a significant lag in trading volume on DEX compared to centralized platforms. Among the analyzed cryptocurrencies, Ethereum demonstrates the highest daily trading volume on both centralized and decentralized exchanges. The study also focuses on user and asset security on cryptocurrency exchanges. It provides a detailed analysis of the use of two-factor authentication (2FA) mechanisms, customer identification procedures (KYC), and anti-money laundering and counter-terrorist financing (AML/CFT) policies. These tools are an integral part of the infrastructure of centralised exchanges that seek to comply with financial regulations and increase user trust. Based on the conducted analysis, the article outlines the prospects for the development of hybrid exchange models that combine the advantages of centralization and decentralization, and defines directions for further research aimed at enhancing the efficiency, security, and accessibility of digital trading platforms. Keywords: cryptocurrency exchange, centralised exchange, decentralised trading platform, digital security, KYC, DEX, CEX.
Megha Dabas, Thakur Mohit Singh, G. Sai Gautham, Pradeepthi kaniki · 5 authors
The Know Your consumer (KYC) process, which ensures the security and legality of consumer identity, is a requirement for financial institutions to comply with regulatory standards.With its immutability, security, and transparency, blockchain technology presents a ground-breaking approach to enhancing the KYC process.By using decentralized platforms like Ethereum, blockchain technology enables more efficient and cost-effective customer data management.This significantly reduces the amount of time and money required for compliance.Blockchain technology can help banks overcome the challenges they have when conducting KYC and customer onboarding.To ensure that large payments are accurately recorded and authenticated, it also puts in place a system that demands KYC identity for clients who make significant transactions that exceed a predefined threshold.A central regulatory body oversees the thorough registration of financial firms and rigorously enforces KYC regulations in the proposed architecture.In addition to improving security, reducing fraud, and ensuring compliance, this solution provides a quick and efficient process for safely handling both routine and complex transactions.
Muhammad Iqbal, Kunal Raj, K.V. Narasimha Reddy, Mohd. Mudaseer Mazharuddin
In today's digital age, student academic data is still largely controlled by educational institutions, which creates major risks and limitations.Centralized systems are vulnerable to data loss due to natural disasters, political instability, or system failures.They also make it difficult for students to access or share their records when participating in exchange programs or pursuing lifelong learning across different platforms.To solve these issues, this paper introduces a decentralized approach where students have full control over their educational data.Using blockchain technology-specifically the Ethereum public network-and Web3 tools, we present DecentralEduChain, a framework that allows students to securely store and manage their academic records through smart contracts.Educational institutions can interact with these contracts via integrated Learning Management Systems (LMS), enabling both the reading and updating of student records without relying on centralized databases.This system not only enhances security and transparency but also empowers students with ownership of their data, making it easier to share academic credentials across institutions.The paper also outlines the practical steps for implementing the system, including smart contract creation and integration with LMS platforms, making it a promising solution for the future of educational data management.
Krzysztof Gogol, Szczepan Gurgul, Faizan Nehal Siddiqui, David Branes · 5 authors
Ethereum's scalability limitations pose significant challenges for the adoption of decentralized applications (dApps). Zero-Knowledge Rollups (ZK Rollups) present a promising solution, bundling transactions off-chain and submitting validity proofs on-chain to enhance throughput and efficiency. In this work, we examine the technical underpinnings of ZK Rollups and stress test their performance in real-world applications in decentralized finance (DeFi). We set up a proof-of-concept (PoC) consisting of ZK rollup and decentralized exchange, and implement load balancer generating token swaps. Our results show that the rollup can process up to 71 swap transactions per second, compared to 12 general transaction by Ethereum. We further analyze transaction finality trade-offs with related security concerns, and discuss the future directions for integrating ZK Rollups into Ethereum's broader ecosystem.
Federated learning (FL) is increasingly recognised for addressing security and privacy concerns in traditional cloud-centric machine learning (ML), particularly within personalised health monitoring such as wearable devices. By enabling global model training through localised policies, FL allows resource-constrained wearables to operate independently. However, conventional first-order FL approaches face several challenges in personalised model training due to the heterogeneous non-independent and identically distributed (non-iid) data by each individual's unique physiology and usage patterns. Recently, second-order FL approaches maintain the stability and consistency of non-iid datasets while improving personalised model training. This study proposes and develops a verifiable and auditable optimised second-order FL framework BFEL (blockchain enhanced federated edge learning) based on optimised FedCurv for personalised healthcare systems. FedCurv incorporates information about the importance of each parameter to each client's task (through fisher information matrix) which helps to preserve client-specific knowledge and reduce model drift during aggregation. Moreover, it minimizes communication rounds required to achieve a target precision convergence for each client device while effectively managing personalised training on non-iid and heterogeneous data. The incorporation of ethereum-based model aggregation ensures trust, verifiability, and auditability while public key encryption enhances privacy and security. Experimental results of federated CNNs and MLPs utilizing mnist, cifar-10, and PathMnist demonstrate framework's high efficiency, scalability, suitability for edge deployment on wearables, and significant reduction in communication cost.
Abstract: Ensuring free and fair elections is the foundation of democratic nations, but conventional voting systems are still susceptible to manipulation, fraud, and inefficiencies. With the advancement of digital infrastructure, electronic voting (evoting) has become a reality, but usually at the expense of transparency and security because of centralized control. Blockchain technology, and specifically Ethereum with its smart contract feature, provides a chance to transform voting systems through decentralization, immutability, and end-to-end verifiability. This suggests a next-generation e-voting system on the Ethereum blockchain with secure voter authentication, transparent vote casting, and smart contract-based automated result counting. Experimental results confirm the system's fraud resistance, scalability for medium-sized elections, and capability to present realtime, tamper-proof election results.
Актуальность исследования обусловлена стремительным развитием криптовалютного рынка и растущим интересом инвесторов к альтернативным способам получения дохода. Одним из таких инструментов становится стейкинг криптовалют, позволяющий получать вознаграждение за участие в поддержании блокчейн-сетей. Проблематика развития данного инструмента заключается в недостаточной осведомленности инвесторов о механизмах стейкинга, его разновидностях, рисках и потенциальной доходности, особенно на фоне высокой волатильности крипторынка. Цель статьи – проанализировать стейкинг как форму альтернативного инвестирования, определить его преимущества и уязвимости, а также оценить перспективность для различных категорий инвесторов. В рамках исследования авторами применены методы сравнительного анализа, теоретического обзора источников, а также произведена оценка рисков и доходности на примерах конкретных криптовалют и платформ. В статье рассматриваются основные виды стейкинга: фиксированный, гибкий, ликвидный и делегированный. Приведены примеры популярных криптовалют, таких как Tezos, Cardano, Algorand, Polkadot и Ethereum, использующих алгоритм консенсуса Proof-of-Stake (PoS - доказательство доли владения), и сопоставлены их потенциальные доходности. Результаты исследования показывают, что при грамотном подходе стейкинг может быть эффективным инструментом создания пассивного дохода. Выявлены ключевые риски: волатильность, ограниченная ликвидность, угроза потери активов и технические сложности. Научным результатом являются предложенные стратегии минимизации рисков, включающие диверсификацию, выбор ликвидных активов и использование проверенных платформ. Таким образом, стейкинг представляет собой перспективный и гибкий инвестиционный механизм в рамках цифровой экономики. The relevance of the study is due to the rapid development of the cryptocurrency market and the growing interest of investors in alternative ways of generating income. One of these tools is cryptocurrency staking, which allows you to receive rewards for participating in the maintenance of blockchain networks. The problem with the development of this tool lies in the lack of investor awareness about staking mechanisms, its varieties, risks and potential returns, especially against the background of high volatility of the crypto market. The purpose of the article is to analyze staking as a form of alternative investment, identify its advantages and vulnerabilities, and assess its prospects for various categories of investors. As part of the study, the authors applied methods of comparative analysis, a theoretical review of sources, and an assessment of risks and profitability based on examples of specific cryptocurrencies and platforms. The article discusses the main types of staking: fixed, flexible, liquid and delegated. Examples of popular cryptocurrencies such as Tezos, Cardano, Algorand, Polkadot, and Ethereum using the Proof-of-Stake (PoS proof of ownership) consensus algorithm are given, and their potential returns are compared. The results of the study show that, with the right approach, staking can be an effective tool for creating passive income. Key risks have been identified: volatility, limited liquidity, the threat of asset loss and technical difficulties. The scientific result is the proposed risk minimization strategies, including diversification, the choice of liquid assets and the use of proven platforms. Thus, staking is a promising and flexible investment mechanism within the digital economy.
Abstract: The real estate sector faces persistent challenges,includingintermediary dependence, information asymmetry, fraud vulnerability, and limited liquidity,despite technological advancements in other industries. This paper presents the design, implementation, and evaluation of a blockchain-based solution addressing these challenges. Your platform, developed using Ethereum smart contracts, comprises a distributed property ledger, automated transaction processing, and property tokenization capabilities. Performance evaluation demonstrates substantial improvements over traditional methods: transaction times decreased by 90%, costs reduced by 80-90%, and security enhanced through immutable record-keeping. Property transfers that traditionally require weeks were executed in minutes, with smart contracts automating escrow management and document verification. The empirical results provide concrete evidence of blockchain's efficacy in real estate transactions and establish a framework adaptable across different property markets and regulatory environments
Blockchains support a rapidly growing digital economy. Through decentralization, they enable the ownership and transfer of digital assets without centralized intermediaries while resisting attacks, faults, and collusion. Yet decentralization is not guaranteed and must be sustained against external shocks. What design choices help blockchains maintain decentralization when disrupted? We find that resource flexibility, how easily consensus resources can be moved and redeployed, is key to sustained decentralization. Using three real-world shocks—China’s 2021 crypto mining ban, Hetzner’s 2022 shutdown of Solana validators, and Ethereum’s transition from proof-of-work to proof-of-stake—we show that blockchains whose consensus resources are more flexible recover decentralization faster. These findings offer actionable guidance. Blockchain designers should consider resource flexibility as a first-order design parameter. Operators should diversify infrastructure across jurisdictions to reduce vulnerability to localized shocks. Policymakers should be aware that regulations targeting blockchains can have unintended effects on decentralization.