Non-fungible tokens (NFTs) serve as a representative form of digital asset ownership and have attracted numerous investors, creators, and tech enthusiasts in recent years. However, related fraud activities, especially phishing scams, have caused significant property losses. There are many graph analysis methods to detect malicious scam incidents, but no research on the transaction patterns of the NFT scams. Therefore, to fill this gap, we are the first to systematically explore NFT phishing frauds through graph analysis, aiming to comprehensively investigate the characteristics and patterns of NFT phishing frauds on the transaction graph. During the research process, we collect transaction records, log data, and security reports related to NFT phishing incidents published on multiple platforms. After collecting, sanitizing, and unifying the data, we construct a transaction graph and analyze the distribution, transaction features, and interaction patterns of NFT phishing scams. We find that normal transactions on the blockchain accounted for 96.71% of all transactions. Although phishing-related accounts accounted for only 0.94% of the total accounts, they appeared in 8.36% of the transaction scenarios, and their interaction probability with normal accounts is significantly higher in large-scale transaction networks. Moreover, NFT phishing scammers often carry out fraud in a collective manner, targeting specific accounts, tend to interact with victims through multiple token standards, have shorter transaction cycles than normal transactions, and involve more multi-party transactions. This study reveals the core behavioral features of NFT phishing scams, providing important references for the detection and prevention of NFT phishing scams in the future.
Shaista Ashraf Farooqi, Aedah Abd Rahman Rahman, Amna Saad
The growing integration of the Internet of Medical Things (IoMT) into healthcare has amplified the need for secure and privacy-preserving artificial intelligence. Federated Learning (FL) has emerged as a pivotal paradigm for decentralized medical data processing; however, it still faces challenges concerning data confidentiality, trust management, and scalability. This review presents an extended theoretical comparison of two prominent privacy-preserving frameworks—Federated Learning with Differential Privacy (FL-DP) and Federated Learning with Blockchain (FL-BC)—to assess their suitability for ensuring data security, transparency, and regulatory compliance in IoMT environments. The FL-DP framework safeguards patient data through noise injection during model updates, offering mathematically proven privacy guarantees. Conversely, the FL-BC framework reinforces trust and integrity via immutable ledgers and consensus mechanisms such as Proof of Stake (PoS) and Byzantine Fault Tolerance (BFT). Reviewing literature published between 2021 and 2025, this study examines trade-offs in privacy, scalability, latency, and energy efficiency, while highlighting emerging hybrid architectures that integrate both approaches. The findings reveal that FL-DP provides stronger privacy control, whereas FL-BC ensures verifiable trust and traceability—together forming the foundation for next-generation secure and trustworthy federated learning systems in IoMT-driven healthcare.
Transaction fees play a crucial role in determining the efficiency and scalability of blockchain networks, particularly in Ethereum, where gas fees fluctuate significantly due to network congestion and competitive bidding. This study analyzes transaction fee patterns in the Ethereum blockchain and their impact on network efficiency by examining key blockchain metrics such as block density, transaction size, and transaction fee variability. The findings indicate that the mean transaction fee is 0.0342 ETH, with a median of 0.0008 ETH, demonstrating significant fee variability. The study also finds a strong positive correlation (r ≈ 0.75, p < 0.01) between transaction fees and block density, as well as a moderate correlation with transaction size (r ≈ 0.58, p < 0.01), highlighting the direct impact of network congestion on fee structures. Time series forecasting with Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models reveals cyclical trends in transaction fees, often influenced by major network activities such as NFT releases, DeFi protocol surges, and high-frequency trading. The LSTM model achieves a lower RMSE (0.09) compared to ARIMA (0.15), demonstrating its superior predictive capability for fee trends. Additionally, anomaly detection techniques identify outlier transactions with fees exceeding 2.5 ETH, often associated with front-running strategies, priority gas auctions (PGA), and inefficient smart contract executions. Despite improvements introduced by EIP-1559, the findings indicate that Ethereum’s transaction fee market remains highly volatile, with block density fluctuating between 512.0% and 3896.0%, causing extreme fee spikes during congestion periods. The presence of large transactions (maximum size: 250 bytes) further amplifies fee inefficiencies, reinforcing the need for improved scalability solutions. This study underscores the necessity of Layer-2 rollups, dynamic block size adjustments, and more adaptive fee mechanisms to enhance blockchain efficiency. Future research should explore comparative studies across blockchain networks, advanced predictive modeling techniques, and the role of miner extractable value (MEV) in transaction ordering fairness. The study’s insights provide valuable guidance for developers, users, and policymakers aiming to optimize Ethereum’s transaction fee structure and enhance overall blockchain performance.
In today’s technologically advancing world, many fields from finance to healthcare and education are shifting toward a digital and decentralized format. A significant transformation is underway with the currency of the masses. Blockchain-based cryptocurrencies like Bitcoin and Ethereum allow users to generate fungible tokens anonymously through smart contracts. However, these features also facilitate illicit transactions and cybercrimes like fraud, phishing, and money laundering. The proposed work explores the identification of suspicious transactions on the Ethereum blockchain by leveraging advanced machine-learning techniques. An Extreme Gradient Boosting (XGBoost) classifier is optimized for spotting unauthorized or malicious transactions, exploring features like transaction patterns and value anomalies. Feature scaling and log transformations normalize skewed distributions, while rigorous model training and hyperparameter tuning enhance the system's precision, recall, and overall accuracy. Other aids, such as feature importance rankings, precision-recall curves, and diagnostic statistics, provide useful information on fraud patterns. Evaluation of the model shows that integrating cost-sensitive learning significantly reduces false positives, from 51 to 44, representing a 13.7% decrease, which enhances practical usability by minimizing false alerts and manual verification efforts. Although there was a slight increase in false negatives (from 14 to 15), the overall classification accuracy improved. The model demonstrated strong performance in managing class imbalance which is common in fraud detection contexts.
Neide Judith Faria de Oliveira, Francisco Carneiro da Silva Filho
A pesquisa discute o uso da tecnologia NFT (Non-Fungible Tokens) no mercado de arte, considerando a exclusividade dessas obras. As NFTs permitem exibição em galerias virtuais no Metaverso, associadas ao blockchain, tanto como ativos físicos quanto intangíveis (propriedade intelectual). A tecnologia reforça noções de herança e propriedade, mas enfrenta desafios como altos custos, falta de regulamentação, consumo de energia e direitos autorais. A pesquisa explora as potencialidades dessa tecnologia e a necessidade de procedimentos claros para garantir transações justas e transparentes no mercado artístico.
Ethereum, as a leading blockchain platform, experiences high variability in transaction fees due to network congestion, gas bidding, and computational complexity. This study analyzes 10,000 Ethereum transactions to identify key factors influencing transaction fees, block density, and staking mechanisms. The results show that transaction fees vary significantly, with an average of 0.1826 ETH and a standard deviation of 0.2381 ETH, indicating substantial fluctuations. A strong positive correlation (r = 0.72) between transaction size and transaction fee confirms that larger transactions incur higher costs due to increased computational demand. Time-series analysis reveals periodic spikes in gas fees, aligning with network congestion patterns. Block density averages 1718.8% (std = 501.01%), showing that some blocks are highly congested while others are underutilized. An Isolation Forest anomaly detection model identifies 3.4% of transactions as outliers, exhibiting unusually high gas fees, which may be caused by priority-based bidding, inefficient smart contract execution, or potential fee manipulation. Further analysis demonstrates that Coin Age and Stake Reward significantly influence transaction success rates. Transactions with older coins show a 7.8% higher success rate, indicating that validators may prioritize transactions with greater historical weight. Additionally, Stake Reward positively affects the Block Generation Rate (p < 0.05), confirming its role in securing the network and optimizing transaction processing. These findings provide valuable insights for Ethereum users, developers, and validators to optimize gas fees, transaction timing, and staking incentives. While this study offers critical observations, future research should focus on real-time gas fee monitoring, deep learning-based congestion forecasting, and the impact of Layer-2 scaling solutions. Understanding Ethereum’s Proof-of-Stake (PoS) dynamics will be essential for ensuring fair transaction processing, reducing gas fees, and improving blockchain efficiency.
This study examines the relationship between gas prices and transaction values on the Ethereum blockchain, providing a detailed analysis of transaction dynamics and the factors influencing gas price determination. The correlation coefficient between gas prices and transaction values is -0.0273, indicating a very weak and negative relationship. Instead, gas prices are driven by factors such as computational intensity, network congestion, and user prioritization. Functions with higher computational demands, such as mint, recorded the highest mean gas price of 120.45 Gwei, with a standard deviation of 15.30 Gwei, while functions like approve and transfer exhibited mean gas prices of 98.30 Gwei and 110.80 Gwei, respectively. Recipient address analysis reveals a strong concentration of transaction values, with the top recipient address receiving 49.95 ETH consistently, indicating high-value operations directed toward specific accounts. High-gas transactions, defined as those above the 90th percentile, displayed a mean gas price of 191.96 Gwei with minimal variability, while their corresponding transaction values varied widely, with a mean of 23.91 ETH and a standard deviation of 13.66 ETH. These findings provide critical insights into Ethereum transaction behavior, emphasizing the role of function type and user prioritization in shaping gas price decisions. Future research should investigate the impact of network upgrades such as EIP-1559, the adoption of Layer-2 scaling solutions, and temporal trends in transaction behavior to enhance network scalability and cost efficiency as Ethereum continues to evolve.
Aleksei Olkhovikov, Yash Madhwal, Arsen Andrian, Hamza Imran · 8 authors
• Prototype system with Raspberry Pi and dual ultrasonic sensors for data acquisition. • Real-time data signing and blockchain submission using web3.py and EVM chain. • Smart contract for secure data logging, access control, and gas-efficient events. • Frontend with Streamlit MVP and Vue3 dashboard supporting secure user login. • Experiments on 15M-record dataset to evaluate gas cost, batching, and scalability. Integrity and traceability of sensor data in oilfield operations are essential for safe, efficient, and compliant resource extraction. This paper presents a blockchain-enabled proof-of-concept (PoC) IoT framework that facilitates decentralized, tamper-evident monitoring of oil extraction infrastructure. The system integrates field-deployed sensors with a Raspberry Pi-based edge controller to capture, buffer, and cryptographically sign telemetry data, which is then submitted to an EVM-compatible blockchain using smart contracts. The PoC demonstrates historical and real-time data visualization through a web-based dashboard that authenticates and displays blockchain event streams. A real-world drilling data set comprising more than 15 million records is used for the experimental evaluation of the prototype. Gas consumption metrics are analyzed under varying payload sizes and batching strategies, revealing linear scalability with respect to parameter volume and significant efficiency gains through transaction batching. These results demonstrate measurable improvements in resource utilization and operational cost, confirming the framework’s efficiency and robustness for large-scale industrial telemetry. The architecture supports secure access control, structured metadata annotation, and transparent logging without reliance on centralized intermediaries. By addressing key challenges in data authenticity and operational visibility, the proposed solution establishes a scalable foundation for secure telemetry in oil and gas operations, with potential applicability to other critical infrastructure domains such as energy grids, mining, and water resource management. Unlike prior blockchain-IoT frameworks focusing primarily on architectural design or off-chain coordination, the proposed system demonstrates an end-to-end implementation directly linking field-level sensors to on-chain storage and visualization. Through large-scale validation on a 15 M-record drilling dataset, this work provides one of the first empirical analyses of gas-efficient, real-time telemetry submission in industrial settings.
Michele Pasqua, Sofia Mari, Ferdinando Santoro, Mariano Ceccato
With Ethereum’s rise as the leading platform for decentralized applications, securing Ethereum smart contracts, very often having a financial impact, becomes paramount. Existing research lacks a comprehensive overview of Ethereum defects (and the terminology is often inconsistent), making it difficult for researchers, developers, and industry professionals to navigate this nowadays critical topic. This necessitates a unified source of information detailing defects affecting Ethereum and its smart contracts, along with their root causes, impact, and mitigation strategies. In this paper, we propose a knowledge base of defects , encompassing security vulnerabilities and code flaws found in the Ethereum blockchain and its smart contracts. We started by performing a systematic literature review to identify the currently known defects and then created a hierarchical tag system to classify them. This system was then used to build an ontology allowing users to easily search and learn about Ethereum defects. We also implemented EDOV, a tool to graphically navigate and explore the ontology, perform search queries, and visualize defect details, such as examples of defective/fixed code. As new defects may appear in the future, the ontology and the tool are built with extensibility in mind. We believe this research is a valuable contribution to helping developers and practitioners avoid common mistakes, improving the overall security and reliability of the Ethereum ecosystem.
Zero-knowledge proofs (ZKPs) enable a prover to convince a verifier of knowledge of a secret without revealing it. The ZKP for the square-root problem has many applications in network and cloud security, such as user authentication and privacy-preserving cloud storage auditing. Classical protocols for the quadratic residuosity (square-root) relation require multiple iterations to reach negligible soundness error, incurring latency and communication costs that are critical in cloud settings. This paper proposes a new single-round zero-knowledge proof (SR-ZKP) for the square-root problem that achieves the same soundness as iterative schemes by increasing the challenge length. The protocol requires only one execution of a 4-message protocol (request, commit, challenge, response) and can be transformed into a one-message non-interactive ZKP via the Fiat–Shamir heuristic. The completeness, soundness, and zero-knowledge properties of the proposed scheme are formally proven. The results of this study show that the proposed protocol can achieve approximately \(97\%\) reduction in communication overhead and latency, when compared to an 80-round iterative ZKPs with RSA modulus n of size 2048 bits. This provides a substantial advantage for cloud applications.
Virtual Power Plant (VPP) trading mechanisms confront unprecedented challenges from behavioral complexities and technological uncertainties that conventional rational choice models inadequately address. This research develops an integrated framework combining prospect theory-driven decision modeling with evolutionary smart contracts and multi-stage negotiation protocols to enhance trading effectiveness in cross-regional energy markets. We establish mathematical foundations incorporating loss aversion, probability distortion, and reference-dependent preferences into VPP decision-making, while developing adaptive contracts capable of autonomous evolution responding to market changes. Through composite game-theoretic analysis examining nested interactions between contract evolution and negotiation dynamics, we validate the framework across three comprehensive scenarios: emergency dispatch under extreme weather, renewable energy integration, and cross-regional collaboration. Simulation results demonstrate 15–25% negotiation efficiency improvements compared to traditional mechanisms, with behavioral models capturing significant heterogeneity in loss aversion coefficients (2.1–3.4) across VPP configurations. The evolutionary contracts successfully adapt within 72-hour windows to policy changes and technological developments, while maintaining system stability. Cross-regional analysis reveals how cultural distance and information asymmetries influence trading outcomes, with the framework achieving superior market integration despite these barriers. These findings establish new paradigms for behaviorally-informed energy market design, offering transformative implications for renewable integration and decentralized electricity systems.
Abstract Blockchain technology is emerging as one of the most profound and cutting-edge innovations of the twenty-first century, providing a decentralized, immutable system for recording transactions. It has enabled the tokenization of distinctive digital assets, including art, music and real estate, through non-fungible tokens (NFTs). NFTs enable asset transfers by operating on pseudonymous blockchain networks, thereby preventing the disclosure of the owner’s real-world identity. While it enhances user privacy and innovation, it also creates significant anti-money laundering and counter-terrorism financing challenges. Fraudsters and other bad-faith actors can use these assets to obfuscate dirty money and illicit financial transactions, given lax or non-existent regulations on NFTs and extremely lax Know-Your-Customer compliance. In light of the above, the authors explore the nexus between NFTs and financial crime (with a particular focus on the legal frameworks of the Sultanate of Oman, the United Arab Emirates and the United Kingdom) in this article. The paper aims to evaluate how each jurisdiction’s response to NFT-related abuse has evolved and been effective in practice. This will be done through a review of existing laws, enforcement, regulations and regulatory gaps. The article ends with specific policy recommendations to enhance regulatory certainty, enforcement effectiveness and international cooperation, supporting an innovation-first approach to the NFT space tempered by necessary measures to prevent criminal abuse.
Jianlong Xu, C. F. Xu, Rongtao Zhang, Feixiang Diao · 5 authors
With the wide application of blockchain technology in finance, IoT, healthcare, and other fields, phishing scams have emerged as a growing security threat. Existing detection methods often lack in-depth modeling of the directional properties of transaction flows and struggle to effectively capture diverse transaction behaviors, directional relationships, and key neighbor dependencies. To address these limitations, we propose TGAT-MPGCN, a direction-aware phishing detection model that constructs three complementary first-order subgraphs, a sending graph, a receiving graph, and a bidirectional graph to explicitly capture transaction directionality. By integrating a graph-attention mechanism with weighted neighbor aggregation, the model enhances feature learning. Experimental evaluations on an Ethereum transaction dataset demonstrate the superior performance of our approach, achieving an accuracy of 97.21%, an AUC of 0.9721, an F1-score of 0.9719, a recall of 0.9629, and a precision rate of 98.11%, significantly outperforming traditional detection methods. This study offers a practical and scalable solution for accurate phishing detection in blockchain transaction networks.
The development of blockchain technology has given rise to new innovations in the form of smart contracts, which are widely used in digital asset transactions, including Non-Fungible Tokens (NFTs). One case that highlights this phenomenon is Ghozali Everyday, where smart contracts play a crucial role in regulating the buying and selling of NFTs. However, the implementation of smart contracts in Indonesia faces legal challenges, particularly regarding the use of cryptocurrency as a payment instrument, which is still prohibited by Bank Indonesia regulations, even though electronic contracts are recognized as valid by the ITE Law. This study uses a normative juridical method with a legislative and conceptual approach. The analysis is conducted using Hans Kelsen's Hierarchy of Norms theory and Gustav Radbruch's Legal Validity theory to assess legal certainty, fairness, and utility in regulating smart contracts and cryptocurrencies in Indonesia. The results show a conflict of norms that creates legal uncertainty and limited legal protection for digital asset transaction actors. Therefore, regulatory updates are needed that are adaptive, consistent with the hierarchy of laws and regulations, and provide more comprehensive protection for consumers and businesses.
Decentralized Autonomous Organizations (DAOs) represent a transformative shift in organizational structures, leveraging blockchain technology to enable decentralized governance, transparency, and automation through smart contracts
Web3, referring to the next-generation decentralized web, has gained extensive attention from industry and academia. Its popularity drives considerable demand for continuous development and delivery of web3 applications and services. Blockchain is an essential technology that enables web3 but comes with considerable operational cost. Blockchain-as-a-service (BaaS) is considered as a promising solution for supporting web3 applications; however, existing BaaS platforms are conceptual, built for specific applications, or not optimized for emerging web3 applications. This work analyzes the primary features and fundamental requirements of decentralized web3 applications and articulates the critical role of blockchain for web3. To fill the gap, we introduce DAPPaaS, the first customizable BaaS platform specializing in web3 applications, with carefully designed goals and principles to meet the goals. Our key technical contributions lie on the approaches enabling component modularization, communication efficiency, automated scaling and performance monitoring, distributed deployment, and resource optimization. We implement a real-world application based on DAPPaaS and conduct extensive performance evaluations to validate its utility and efficiency.
This deposit formalizes a decentralized framework for certifying and archiving AI models, scientific content, and intellectual creations using NFTs and blockchain metadata. It includes a ready-to-use NFT smart contract (ERC-721), structured metadata, digital certificate with SHA256 hash, and a minting interface. The DOI reference is embedded for scientific traceability. All files are timestamped, signed (Anne Povie), and packed for archival or minting purposes. This submission is intended to support researchers, developers, and creators in producing immutable, verifiable scientific artifacts using Web3 infrastructur e.
Bitcoin operates as a macroeconomic paradox: it combines a strictly predetermined, inelastic monetary issuance schedule with a stochastic, highly elastic demand for scarce block space. This paper empirically validates the Endogenous Constraint Hypothesis, positing that protocol-level throughput limits generate a non-linear negative feedback loop between network friction and base-layer monetary velocity. Using a verified Transaction Cost Index (TCI) derived from Blockchain.com on-chain data and Hansen's (2000) threshold regression, we identify a definitive structural break at the 90th percentile of friction (TCI ~ 1.63). The analysis reveals a bifurcation in network utility: while the network exhibits robust velocity growth of +15.44% during normal regimes, this collapses to +6.06% during shock regimes, yielding a statistically significant Net Utility Contraction of -9.39% (p = 0.012). Crucially, Instrumental Variable (IV) tests utilizing Hashrate Variation as a supply-side instrument fail to detect a significant relationship in a linear specification (p=0.196), confirming that the velocity constraint is strictly a regime-switching phenomenon rather than a continuous linear function. Furthermore, we document a "Crypto Multiplier" inversion: high friction correlates with a +8.03% increase in capital concentration per entity, suggesting that congestion forces a substitution from active velocity to speculative hoarding.
Medical imaging is essential for clinical diagnosis, yet real-world data frequently suffers from corruption, noise, and potential tampering, challenging the reliability of AI-assisted interpretation. Conventional reconstruction techniques prioritize pixel-level recovery and may produce visually plausible outputs while compromising anatomical fidelity, an issue that can directly impact clinical outcomes. We propose a semantic-aware medical image reconstruction framework that integrates high-level latent embeddings with a hybrid U-Net architecture to preserve clinically relevant structures during restoration. To ensure trust and accountability, we incorporate a lightweight blockchain-based provenance layer using scale-free graph design, enabling verifiable recording of each reconstruction event without imposing significant overhead. Extensive evaluation across multiple datasets and corruption types demonstrates improved structural consistency, restoration accuracy, and provenance integrity compared with existing approaches. By uniting semantic-guided reconstruction with secure traceability, our solution advances dependable AI for medical imaging, enhancing both diagnostic confidence and regulatory compliance in healthcare environments.
Kundan Mukhia, Buddha Nath Sharma, Salam Rabindrajit Luwang, Md. Nurujjaman · 7 authors
We study how the 2024 U.S. presidential election, viewed as a major political risk event, affected cryptocurrency markets by distinguishing human-driven peer-to-peer stablecoin transactions from automated algorithmic activity. Using structural break analysis, we find that human-driven Ethereum Request for Comment 20 (ERC-20) transactions shifted on November 3, two days before the election, while exchange trading volumes reacted only on Election Day. Automated smart-contract activity adjusted much later, with structural breaks appearing in January 2025. We validate these shifts using surrogate-based robustness tests. Complementary energy-spectrum analysis of Bitcoin and Ethereum identifies pronounced post-election turbulence, and a structural vector autoregression confirms a regime shift in stablecoin dynamics. Overall, human-driven stablecoin flows act as early-warning indicators of political stress, preceding both exchange behavior and algorithmic responses.
This study proposes a gamification-based educational model that integrates blockchain concepts and reinforcement learning (RL) principles for elementary students.While blockchain education is often abstract and unsuitable for younger learners, the proposed card game-based approach allows students to experience hash functions, consensus algorithms, and distributed ledgers through interactive activities.Instructional design followed the Dick and Carey model, and the MDA framework was applied to align game mechanics with cognitive, emotional, and social objectives.RL mechanisms such as exploration-exploitation balance and reward shaping were embedded to sustain engagement and motivation.The model's effectiveness was evaluated through expert review involving four technology specialists and nine elementary school teachers.Results showed consistently positive ratings across five metrics (Innovation, Effectiveness, Applicability, Motivation, Efficiency), with averages above 3.8 on a 5-point scale.Particularly high scores were recorded for Innovation (M=4.14) and Efficiency (M=4.09).These findings indicate that the model is both novel and practical, offering a promising approach to making abstract technical concepts accessible at the elementary level.
This study examines the emerging convergence of triple-entry accounting, blockchain technology, and machine learning as a transformative framework for enhancing financial transparency. Using a bibliometric analysis of Scopus-indexed publications from 2000 to 2025, the research identifies key intellectual structures, thematic clusters, and temporal trends that shape this field. The results show that blockchain serves as the foundational infrastructure enabling immutable, verifiable accounting records, while machine learning functions as an analytical layer that strengthens anomaly detection, continuous auditing, and fraud prevention. Triple-entry accounting is found to be evolving from a conceptual innovation into a practical accounting architecture supported by cryptographic verification and distributed ledger systems. The study highlights significant implications for auditors, regulators, and organizations seeking to modernize financial reporting through automation and secure digital ecosystems. Although promising, the research also notes limitations related to data scope, conceptual depth, and the need for empirical validation. Overall, the findings underscore the potential of technologically integrated accounting systems to redefine trust, accountability, and transparency in modern financial environments.
Abstract Taxation justice and fiscal federalism are foundational pillars for building an inclusive, democratic, and sustainable nation. As countries diversify economically and socially, the role of equitable taxation becomes central to financing public goods, reducing inequality, and strengthening socio-political cohesion. Fiscal federalism, which concerns the distribution of financial powers and resources across central, state, and local governments, further reinforces the principles of subsidiarity, autonomy, and accountability required in a modern democratic state. This research paper analyses how taxation justice and fiscal federalism contribute to nation-building, examines structural gaps in existing fiscal arrangements, and highlights the need for transparent resource allocation, participatory governance, and decentralized fiscal empowerment. Using qualitative secondary data and descriptive analysis, the study demonstrates that taxation systems that are equitable, efficient, and progressive combined with a well-designed fiscal federalism framework help strengthen democratic participation, reduce regional disparities, support sustainable development, and stabilize public finance. The paper concludes by offering policy recommendations to enhance the equity and efficiency of taxation systems, thereby improving fiscal governance and promoting inclusive nation-building.
Margaret Simangunsong, Zamzami Zamzami, Parmadi Parmadi
This study aims to: 1) identify and analyze the degree of fiscal decentralization and the level of financial independence of regencies and cities in North Sumatra Province during the period 2015–2024; and 2) analyze the variation in regional revenue realization based on its components, as well as the variation in regional financial capacity across regencies/cities during the same period. The methods used in this research include the Fiscal Decentralization Degree Ratio (DDF), the Regional Financial Independence Ratio (RKKD), and a Two-Way ANOVA test, supported by SPSS 20 software. The findings show that the degree of fiscal decentralization remains relatively low from year to year, indicating a strong dependence on central government transfers. Similarly, the regional financial independence ratio is also categorized as low, with an instructive pattern of relationship, meaning that regional governments still have limited ability to finance development needs independently. The Two-Way ANOVA test results reveal significant differences in regional revenue realization both across regencies/cities and across years within the study period. The largest variations are attributed to the differing characteristics of each regency/city, including economic potential, effectiveness in managing locally generated revenue, and variations in regional fiscal structures. These findings highlight the importance of enhancing fiscal capacity and optimizing local revenue sources throughout North Sumatra.