This study presents SmartMix Web3, a framework combining ensemble machine learning and blockchain technology to optimize low-carbon concrete design. It addresses two key challenges: (1) the limitations of conventional models in predicting concrete performance, and (2) ensuring data reliability and overcoming collaboration issues in AI-driven sustainable construction. Validated with 61 real-world experiments in Cameroon and 752 mix designs, the framework shows major improvements in predictive accuracy and decentralized trust. To address the first research question, a stacked ensemble model comprising Extreme Gradient Boosting (XGBoost)–Random Forest and a Convolutional Neural Network (CNN) was developed, achieving a 22% reduction in Root Mean Square Error (RMSE) for compressive strength prediction and embodied carbon estimation compared to traditional methods. The 29% reduction in Mean Absolute Error (MAE) results confirms the superiority of Extreme Learning Machine (EML) in low-carbon concrete performance prediction. For the second research question, SmartMix Web3 employs blockchain to ensure tamper-proof traceability and promote collaboration. Deployed on Ethereum, it automates verification of tokenized Environmental Product Declarations via smart contracts, reducing disputes and preserving data integrity. Federated learning supports decentralized training across nine batching plants, with Secure Hash Algorithm (SHA)-256 checks ensuring privacy. Field implementation in Cameroon yielded annual cost savings of FCFA 24.3 million and a 99.87 kgCO2/m3 reduction per mix design. By uniting EML precision with blockchain transparency, SmartMix Web3 offers practical and scalable benefits for sustainable construction in developing economies.
The rapid digitization of the healthcare sector has led to the generation of massive volumes of Electronic Health Records (EHRs), necessitating a robust, secure, and scalable system capable of efficiently managing and accessing this ever-growing data. Ensuring privacy, security, and scalability in managing voluminous and sensitive healthcare data, particularly when stored across various geographical locations, poses critical challenges that require innovative solutions. To address these issues, MeDiStore, a decentralized framework built on the Ethereum blockchain, is proposed. By integrating the InterPlanetary File System (IPFS), MeDiStore ensures scalable and secure storage while mitigating centralization risks and providing improved accessibility for EHRs. The framework leverages Elliptic Curve Cryptography (ECC) to encrypt and secure patient records, ensuring data ownership remains with the patient. To further enhance scalability, security, and reliability, of the blockchain network, the MeDiStore Trust Protocol, introduced a modified Proof of Stake (PoS) consensus mechanism that evaluates validators based on their network stake and reputation score, derived from their historical performance. Additionally, a Data Translation Layer is incorporated to ensure interoperability by converting EHRs into Fast Healthcare Interoperability Resources (FHIR) or Health Level 7 (HL7) systems without compromising security. Performance evaluation across 200 consensus rounds highlights metrics such as smart contract execution time, average IPFS file upload time, and reputation score behavior of validators. A comprehensive security analysis simulates Sybil attack scenarios, demonstrating the system's resilience through reputation-based validator selection. By integrating these factors, MeDiStore offers a scalable, secure, privacy-preserving, and interoperable solution tailored for efficient EHR management in the healthcare domain.
Mansi Sharma, Enrico Sartor, Marc Cavazza, Helmut Prendinger
Forecasting stock and cryptocurrency prices is challenging due to high volatility and non-stationarity, influenced by factors like economic changes and market sentiment. Previous research shows that Echo State Networks (ESNs) can effectively model short-term stock market movements, capturing nonlinear patterns in dynamic data. To the best of our knowledge, this work is among the first to explore ESNs for cryptocurrency forecasting, especially during extreme volatility. We also conduct chaos analysis through the Lyapunov exponent in chaotic periods and show that our approach outperforms existing machine learning methods by a significant margin. Our findings are consistent with the Lyapunov exponent analysis, showing that ESNs are robust during chaotic periods and excel under high chaos compared to Boosting and Naïve methods.
Academic credential fraud threatens educational integrity, especially in developing countries like Bangladesh, where verification methods are primarily manual and inefficient. To address this challenge, we present ShikkhaChain, a blockchain-powered certificate management platform designed to securely issue, verify, and revoke academic credentials in a decentralized and tamper-proof manner. Built on Ethereum smart contracts and utilizing IPFS for off-chain storage, the platform offers a transparent, scalable solution accessible through a React-based DApp with MetaMask integration. ShikkhaChain enables role-based access for governments, regulators, institutions, and public verifiers, allowing QR-based validation and on-chain revocation tracking. Our prototype demonstrates enhanced trust, reduced verification time, and improved international credibility for Bangladeshi degrees, promoting a more reliable academic and employment ecosystem.
The exponential growth in digital healthcare infrastructure has resulted in an overwhelming increase in sensitive medical data generation. However, traditional centralized Electronic Medical Records (EMR) systems continue to face critical security and privacy challenges. These include single points of failure, limited interoperability, data tampering, and unauthorized access. This paper introduces a robust and scalable blockchain-based framework for secure EMR management. Leveraging Ethereum blockchain, IPFS decentralized storage, and smart contracts, the framework ensures tamper-proof data logging and fine-grained access control. The system stores encrypted patient health records on IPFS and logs the corresponding content identifier (CID) on the Ethereum blockchain, eliminating the risk of data exposure. The architecture is designed for future compatibility with Mobile Edge Computing (MEC), allowing for faster data processing closer to the point of care. By offering immutable audit trails, decentralized access governance, and high availability, the proposed framework ensures transparency, security, and data ownership for all healthcare stakeholders.
The rise in illicit financial activities across the South Africa–Zimbabwe corridor, with an estimated annual loss of $3.1 billion demands advanced AI solutions to augment traditional detection methods. This study introduces FALCON, a groundbreaking hybrid transformer–GNN model that integrates temporal transaction analysis (TimeGAN) and graph-based entity mapping (GraphSAGE) to detect illicit financial flows with unprecedented precision. By leveraging data from South Africa’s FIC, Zimbabwe’s RBZ, and SWIFT, FALCON achieved 98.7%, surpassing Random Forest (72.1%) and human auditors (64.5%), while reducing false positives to 1.2% (AUC-ROC: 0.992). Tested on 1.8 million transactions, including falsified CTRs, STRs, and Ethereum blockchain data, FALCON uncovered $450 million laundered by 23 shell companies with a cross-border detection precision of 94%, directly mitigating illicit financial flows in Southern Africa. For regulators, FALCON met FAFT standards, yielding 92% court admissibility, and its GDPR-compliant design (ε = 1.2 differential privacy) met stringent legal standards. Deployed on AWS Graviton3, FALCON processed 2 million transactions/second at $0.002 per 1000 transactions, demonstrating real-time scalability, making it cost-effective for financial institutions in emerging markets. As the first AI framework tailored for Southern Africa’s financial ecosystems, FALCON sets a new benchmark for ethical AML solutions in emerging economies with immediate applicability to CBDC supervision. The transparent validation of publicly available data underscores its potential to transform global financial crime detection.
The interest in blockchain for Industry 4.0 applications and the integration of the Industrial Internet of Things (IIoT) has grown considerably. This is due to the unique characteristics of blockchain, including the immutability of the distributed ledger, transparency, traceability of transactions, and security based on cryptographic techniques that certify the integrity of the data. So, the integration of blockchain can enhance data integrity, transparency, and security while eliminating trusted third parties. However, these characteristics make blockchains difficult to scale and typically involve high costs for a large number of transactions. It makes it difficult to manage large amounts of data which are generally produced in IIoT environments. Some recent results have demonstrated how the use of private blockchains can improve the issues of scalability and transaction costs. This paper addresses the following research question: Can blockchain technology, in its current state, be suitable for implementing industrial monitoring applications that use IIoT devices? Our objective is to propose a blockchain-based application integrated with IIoT devices in order to control the air and water pollution produced by industrial activities. The application we propose complies with current European regulations. Finally, we perform an empirical evaluation of the solution's performance to understand its applicability in real-world scenarios.
We investigate the high-frequency dynamics of Bitcoin and Ethereum perpetual futures traded on Binance from January 2020 to December 2024. After a thorough discussion of the stylized facts and particularities of Bitcoin perpetual futures, based on previous research in futures markets, we evaluate the fit of two competing models of market microstructure: the Mixture of Distributions Hypothesis (MDH) and the Intraday Trading Invariance Hypothesis (ITIH). Using intraday data at different levels of aggregation, we investigate the relationship between return volatility per transaction and trade size. We find evidence favoring the MDH in the crypto futures market.
Global horticultural supply chains face escalating vulnerabilities from pathogenic outbreaks, climate disruptions, and regulatory demands. This systematic mini-review examines the Edge-Cloud-Blockchain-Terminal (ECBT) framework—an integrated architecture positioning blockchain as the trust backbone connecting distributed computing, edge intelligence, and user terminals—for comprehensive traceability. Following PRISMA guidelines, we analyzed 40 high-quality studies selected from 156 peer-reviewed articles retrieved from Web of Science, Scopus, and IEEE Xplore databases (2022–2025) using combined technology (“IoT” OR “blockchain” OR “AI” OR “edge computing”) and application (“traceability” OR “supply chain”) search terms. Technology coverage analysis revealed fragmented adoption: IoT dominates (45%, n = 18), followed by blockchain (32%, n = 13) and AI/ML (23%, n = 9), with only 3% achieving full ECBT integration despite demonstrated benefits. Blockchain implementations achieve 94.2% storage optimization through selective anchoring while maintaining cryptographic verification, with latency reduced by 73% through the CRPBFT consensus mechanism. While edge computing achieves a 65% reduction in latency, its integration with blockchain’s global state management presents persistent architectural challenges. Critical barriers persist: technical interoperability (23% metadata loss in cross-chain transitions), economic exclusion (42% of smallholder annual income for deployment), and scalability constraints (processing 47 million daily data points). The review identifies blockchain’s triple role as trust orchestrator, semantic preservator, and incentive aligner as key to overcoming the integration paradox. Future research should focus on agricultural-specific consensus, semantic interoperability, and inclusive deployment models to resolve the integration paradox.
Hakam Dzakwan Diash, Vannesa Nathania, Mohammad Idhom, Trimono Trimono
The volatile and dynamic Ethereum (ETH) market demands an accurate predictive model to support investment decision making. The complexity of ETH time series data and the influence of various external factors make price prediction a challenge in itself. This study aims to develop an ETH price prediction model using a combined architecture of Convolutional Neural Network (CNN) and also Bidirectional Long Short-Term Memory (BiLSTM). CNN is used to extract local features from historical ETH closing price data, while BiLSTM models bidirectional temporal patterns. The dataset used includes ETH daily price from January 2020 to January 2025, which are obtained from Yahoo Finance and have gone through a normalization process and transformation into sequential form. The model is trained for 100 epochs with an early stopping mechanism to prevent overfitting and evaluated using the MAPE and coefficient of determination (R²) metrics. The evaluation results show that the CNN-BiLSTM model is able to predict ETH prices with a MAPE value of 2.8546% and an R² of 0.9415, indicating high performance in capturing actual data trends. This study shows that the hybrid CNN-BiLSTM approach is effective for Ethereum price prediction.
Type of the article: Research Article AbstractDecentralization and renewable energy have gained significant global attention due to their potential to enhance energy security, promote sustainability, and democratize energy access. This study aims to provide a comprehensive bibliometric analysis of research trends, key contributors, and thematic developments in the field of the decentralization of energy sources and their renewability. The research methodology involves a bibliometric analysis based on data extracted from the Scopus database, covering publications from 1973 to 2025. The analysis reveals exponential growth in research output, particularly after 2014, with over 3,700 publications recorded in 2023 alone. Citation trends indicate that foundational studies on decentralized microgrids and distributed energy systems remain highly influential, while recent works on blockchain-based energy trading and AI-driven energy management are gaining prominence. The study identifies China (11.7% of total publications), the United States (6.5%), and India (5.7%) as the leading contributors, with significant research activity also observed in European countries. Additionally, journals such as Applied Energy, Renewable Energy, and Energies serve as the primary publication platforms in this domain. Thematic analysis highlights a shift from bioenergy and land-use studies toward smart grids, energy storage, artificial intelligence, and decentralized finance for energy markets. Furthermore, co-authorship and international collaboration have increased significantly, with 25% of papers involving multi-country research efforts. Keyword analysis indicates growing research interest in emerging topics such as hydrogen energy, demand-side management, and digitalization in decentralized energy systems. These findings underscore the increasing interdisciplinary nature of decentralized energy research, integrating technological, economic, and policy dimensions. AcknowledgmentThis study was prepared as part of the project IZURZ1_224119/1 (Swiss National Science Foundation).
Gossip algorithms are pivotal in the dissemination of information within decentralized systems. Consequently, numerous gossip libraries have been developed and widely utilized especially in blockchain protocols for the propagation of blocks and transactions. A well-established library is libp2p, which provides two gossip algorithms: floodsub and gossipsub. These algorithms enable the delivery of published messages to a set of peers. In this work we aim to enhance the performance and reliability of libp2p by introducing OPTIMUMP2P, a novel gossip algorithm that leverages the capabilities of Random Linear Network Coding (RLNC) to expedite the dissemination of information in a peer-to-peer (P2P) network while ensuring reliable delivery, even in the presence of malicious actors capable of corrupting the transmitted data. Preliminary research from the Ethereum Foundation has demonstrated the use of RLNC in the significant improvement in the block propagation time [14]. Here we present extensive evaluation results both in simulation and real-world environments that demonstrate the performance gains of OPTIMUMP2P over the Gossipsub protocol.
Cross-chain asset exchange is crucial for blockchain interoperability. Existing solutions rely on trusted third parties and risk asset loss, or use decentralized alternatives like atomic swaps, which suffer from grief attacks. Griefing occurs when a party prematurely exits, locking the counterparty's assets until a timelock expires. Hedged Atomic Swaps mitigate griefing by introducing a penalty premium; however, they increase the number of transactions from four (as in Tier Nolan's swap) to six, which in turn introduces new griefing risks. Grief-Free (GF) Swap reduces this to five transactions by consolidating assets and premiums on a single chain. However, no existing protocol achieves grief-free asset exchange in just four transactions. This paper presents 4-Swap, the first cross-chain atomic swap protocol that is both grief-free and bribery-safe, while completing asset exchange in just four transactions. By combining the griefing premium and principal into a single transaction per chain, 4-Swap reduces on-chain transactions, leading to faster execution compared to previous grief-free solutions. It is fully compatible with Bitcoin and operates without the need for any new opcodes. A game-theoretic analysis shows that rational participants have no incentive to deviate from the protocol, ensuring robust compliance and security.
Digital ecological art represents an emergent frontier where biological media converge with virtual environments. This study examines the paradigm shift from anthropocentric to plant-centered artistic narratives within the metaverse, contextualizing how digital platforms transform ecological expression. However, current frameworks fail to systematically guide artists in leveraging plant agency for digital symbiosis that transcends human-centered creation. We propose the Biocentric-Creation Transformation Ideology (BCTI) framework and validate it through multimodal case studies spanning bio-art, NFTs, and VR ecosystems (2013-2023). Our analysis reveals: (1) Metaverse ecosystems enable unprecedented plant-algorithm co-creation, with biological artworks increasing by 133% in premier archives (2020 vs 2013); (2) Digital symbiosis manifests through blockchain DAOs where plants govern human-plant collaborations; (3) Algorithmic photosynthesis in VR environments reshapes ecological aesthetics through real-time biodata translation. The BCTI framework advances ecological art theory by systematizing the transition from representation to plant-centered agency, offering artists a blueprint for post-anthropocene creation. This redefines environmental consciousness in virtual realms while establishing new protocols for cross-species digital collaboration.
Hou-Wan Long, Yujun Pan, Xiongfei Zhao, Yain-Whar Si
As blockchain technology rapidly evolves, researchers face a significant challenge due to diverse and non-standardized simulation parameters, which hinder the replicability and comparability of research methodologies. This paper introduces a Generic Framework for Optimization in Blockchain Simulators (GFOBS), a comprehensive and adaptable solution designed to standardize and optimize blockchain simulations. GFOBS provides a flexible platform that supports various optimization algorithms, variables, and objectives, thereby catering to a wide range of blockchain research needs. The paper's key contributions are threefold: the development of GFOBS as a versatile tool for blockchain simulation optimization; the introduction of an innovative optimization method using warm starting technique; and the proposition of a novel concurrent multiprocessing technique for simultaneous simulation processes. These advancements collectively enhance the efficiency, replicability, and standardization of blockchain simulation experiments.
Ilias Chrysovergis, Alexandros-Apostolos A. Boulogeorgos, Theodoros A. Tsiftsis, Dusit Niyato
This article introduces a comprehensive metaverse framework, which is designed for the simulation, emulation, and interaction with wireless systems. The proposed framework integrates core metaverse technologies such as extended reality (XR), digital twins (DTs), artificial intelligence (AI), internet of things (IoT), blockchain, and advanced 6G networking solutions to create a dynamic, immersive platform for both system development and management. By leveraging XR, users can visualize and engage with complex systems, while DTs enable real-time monitoring and optimization. AI generates the three-dimensional (3D) content, enhances decision-making and system performance, whereas IoT devices provide real-time sensor data for boosting the simulation accuracy. Additionally, blockchain ensures secure, decentralized interactions, and 5G/6G networks offer the necessary infrastructure for seamless, low-latency communication. This framework serves as a robust tool for exploring, developing, and optimizing wireless systems, aiming to provide valuable insights into the future of networked environments.
This study explores the sustainability-enhancin g financial effects of blockchain on carbon-linked digital markets. Drawing on a panel dataset of daily transactions from leading tokenized carbon platforms between 2020 and 2023, the study applies a fixed-effects Difference-in-Differences (DiD) framework to assess how the introduction of blockchain-based infrastructure influences carbon asset prices and trading volumes. Our findings reveal that higher transaction costs, often viewed negatively, may actually signal trusted infrastructure in illiquid sustainability markets, boosting investor confidence. The results confirm that blockchain adoption improves pricing efficiency under specific liquidity conditions, while exhibiting limited short-term effects on volume. It offers new evidence on how blockchain can strengthen carbon markets; reduce transactional inefficiencies, and advance climate action and sustainable development goals (SDGs). These insights inform policymakers, regulators, and investors aiming to design resilient, efficient, and scalable digital carbon markets.
Sure Mamatha, Laxmiprasanna Ambati, P. Vishala, Mamatha Gadde
Blockfund leverages blockchain technology to make philanthropy more accountable and transparent in a world where people’s faith in it is called into question. Trust, integrity, and data security are the three main concerns for this generation of service providers. We see blockchain technology being used to secure gifts and inventions in the future. Prior to the introduction of blockchain, the financial system faced numerous difficulties. There are concerns over their impact because they are sometimes imperceptible and unseen. Security issues have also been brought up because cryptocurrencies alter numerous financial institutions, and data transfer techniques in blockchain deployments are subject to fraud and abuse. For safe financial transactions, it makes use of an interface and a cryptocurrency wallet similar to MetaMask. All transactions become straightforward, safe, and transparent as a result. Through astute communication, transparency is increased by automating the distribution of money according to predetermined standards. Donors will be able to trace their contributions and observe the results of their kindness thanks to BlockFund’s comprehensive donation reporting. The establishment of this Intelligent Alliance is an example of global philanthropy for successful change and societal advancement. Major Findings: BlockFund transforms crowdfunding through blockchain, ensuring transparency and security via Ethereum smart contracts that automate payments and remove intermediaries. By integrating MetaMask and leveraging AI/IoT, it enables global, tamper-proof donations while reducing costs and enhancing donor trust through real-time tracking and decentralized governance.
As a type of cryptocurrency designed to maintain a stable value, stablecoins have been attracting more and more attention over the last decade, particularly due to their low cost and high efficiency for cross-border payments and remittances. In recent months, stablecoins have made frequent headlines in global media as governments and regulatory bodies in several major economies, especially in the United States, advance efforts to establish clear legal frameworks for their use. This paper introduces the evolution, classification, mechanisms, and applications of stablecoins, including fiat-collateralized, commodity-backed, crypto-collateralized, and algorithmic stablecoins. The benefits of stablecoins used in cross-border transactions, decentralized finance (DeFi), and emerging markets, as well as the risks associated with depegging, regulatory uncertainty, lack of transparency for certain stablecoins, and systemic vulnerabilities, are discussed from the economic perspectives. This paper also clarifies common misconceptions and assesses the future outlook of stablecoins in the global financial landscape.
Cryptocurrencies have become prominent alternative investments. Unlike traditional financial assets, their intrinsic value is a subject of ongoing debate since they do not have a tangible backing asset. As a result, investor sentiment heavily influences price volatility and serves as a key indicator of perceived value based on collective investor beliefs. However, major events such as the FTX scandal can severely weaken investor confidence. Social media drives market discussions, making sentiment analysis vital for understanding behavior and predicting price movements. This study examined sentiment analysis techniques to construct an investor sentiment index and investigate its relationship with cryptocurrency returns during the FTX collapse. We employed DistilBERT and the AFINN lexicon method to develop sentiment index, finding that DistilBERT achieves an F1-score of 76.49%, significantly outperforming AFINN's 30.65%. Furthermore, our results indicate a positive correlation between investor sentiment and cryptocurrency returns during the FTX collapse. Our findings indicate that deep learning models can be more effective than lexicon-based approaches for sentiment analysis in financial markets
This research aims to investigate financing decisions of capital-constrained small and medium-sized enterprise (SME) manufacturers and distributors under a Green Supply Chain (GSC) framework. By evaluating the impact of Supply Chain Finance (SCF) instruments, this study utilizes Stackelberg game model to explore a decentralized decision-making system. To our knowledge, this investigation represents the first exploration of game models that uniquely compares financing through trade credit, where the manufacturer offers zero-interest credit without discounts with reverse factoring, while also considering distributor’s efforts on sustainable marketing under the impact of supportive government policies. Our study suggests that manufacturers should adopt reverse factoring for optimal profits and actively participate in distributors’ financing decisions to address inefficiencies in decentralized systems. Furthermore, the distributor’s demand quantity, profits and sustainable marketing efforts show significant increase under reverse factoring, aided by favorable policies. Finally, the results are validated through Python 3.8.8 simulations in the Anaconda distribution, offering meaningful insights for policymakers and supply chain managers.
Bárbara A. Rebelo, M. Rita Ventura, Rita Abranches
Abstract Astaxanthin and canthaxanthin are high-value carotenoids with growing demand due to their antioxidant properties and applications in food, cosmetic, and pharmaceutical sectors. However, natural sources are limited and current production methods are often costly or unsustainable. In this study, we developed a plant-based platform for ketocarotenoid biosynthesis using metabolically engineered Nicotiana tabacum BY-2 cell suspension cultures. Specifically, we expressed a marine bacterial crtW gene ( β-carotene ketolase ) alone or in combination with overexpressed plant psy ( phytoene synthase ) and crtI ( phytoene desaturase ) genes. The resulting cell lines displayed visually distinct pigmentation and accumulated different ketocarotenoid profiles based on their genetic modifications. Single-gene transformants expressing crtW produced up to 50 µg g⁻¹ DW of canthaxanthin and 127 µg g⁻¹ DW of astaxanthin. Co-expression of all three genes significantly increased canthaxanthin accumulation to 788 µg g⁻¹ DW. Our results establish suspended undifferentiated plant cells as a scalable and sustainable system for ketocarotenoid production, offering a biological alternative to natural producers and chemical synthesis.
Francisco von Hafe, Yash Wagle, Federico Guede-Fernández, Ana Paula Giordano · 6 authors
Introduction The decentralised nature of blockchain technology challenges traditional legal frameworks, creating regulatory gaps in asset classification, taxation, and consumer protection. In Europe, divergent approaches, from specialised blockchain laws to adaptations of general financial legislation, hinder cross-border deployment and limit blockchain’s potential. These disparities make compliance difficult for firms and increase the risks for consumers. This study compares blockchain regulations across six European geographies: Switzerland, Liechtenstein, and Malta (blockchain-specialised regulators) versus the European Union (EU), Estonia, and Portugal (generalist regulators) to map key divergences in legal maturity, asset classification, taxation, anti-money laundering/know-your-customer enforcement, and supervisory structures. A secondary objective is to evaluate how these differences impact the scalability of innovation. Methods This study compares blockchain regulations across six European jurisdictions through a three-phase analysis. The scoping phase identified five regulatory themes and selected geographies based on maturity, innovation, and economic specialisation. Primary legal texts and policy data (2020–2025) were analysed to map convergences and divergences between blockchain-specialised and generalist regulators. Results The comparison reveals differences: blockchain-specialised geographies have dedicated Distributed Ledger Technology laws, centralised oversight, and crypto-friendly tax regimes; for example, Switzerland exempts private capital gains, and Malta offers Value Added Tax exemptions. In contrast, generalist regulators, such as the EU’s Markets in Crypto-Assets Regulation (MiCA), which theoretically harmonise rules, face inconsistent enforcement across member states. Meanwhile, Portugal’s tax exemptions and Estonia’s rigid capital requirements create opposing market incentives. Only Liechtenstein’s Blockchain Act comprehensively regulates Decentralised Finance, whereas other geographies either adapt existing financial regulations or do not regulate it. NFTs face fragmented treatment, are excluded under MiCA, classified as securities in Estonia, and left to case-by-case analysis in Switzerland, which contributes to market uncertainty. Discussion This study reveals a tension in blockchain governance: specialised geographies demonstrate that comprehensive, tailored frameworks foster mature ecosystems. Conversely, generalist approaches struggle with fragmentation, as seen in MiCA’s uneven enforcement and Estonia’s restrictive licensing. Yet, regulatory ambiguity carries paradoxical benefits; Portugal’s minimal rules and the EU’s transitional gaps have also fueled competitive innovation. For policymakers, these results underscore the importance of striking a balance between oversight and flexibility to foster and scale up innovation.