The advent of blockchain technology has created a paradigm shift in the way digital data can be securely, transparently and decentralized managed, a paradigm that could potentially replace the longstanding centralized digital information systems. This is a full journal-grade review of blockchain as a tool to ensure trustless, immutability and decentralized data governance in a variety of important application fields. The study, which is based on a systematic review of 20 peer-reviewed publications from 2023 to 2025, explores the essential structural elements of blockchain systems: Distributed ledger structures, cryptographic hash functions, Merkle tree integrity verification, consensus mechanisms, and smart contracts, and how they all contribute to removing single points of failure and institutional trust dependencies. There is a comparative study of the various public, private and consortium blockchain types, as well as the evaluation of the various consensus algorithms, such as Proof of Work (PoW), Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT). The results show that data management systems based on blockchain technology always have superior data integrity, access auditability, censorship resistance, and user data sovereignty properties compared to centralized systems, and come with trade-offs in scalability, energy efficiency, and compliance with regulations. Evidence collected for the application has come from health care organizations' record management, supply chain traceability, decentralized identity systems, Internet of Things (IoT) data integrity, an energy company data management system, and cybersecurity threat intelligence, among other contexts. Key challenges and emerging technologies, such as quantum computing systems, post quantum cryptographic standards, layer-two rollups, sharding and zero-knowledge proofs, are explored in tandem with the blockchain trilemma, GDPR compliance issues and cross-chain interoperability. The study finds that blockchain-based data management is moving from the experimental stage to becoming a core component to the digital economy's infrastructure.
Proof of Work (PoW) and Proof of Stake (PoS) remain the dominant consensus mechanisms for public blockchains, yet both suffer from well-documented limitations: PoW incurs prohibitive energy costs and scales poorly with network size, while PoS tends to concentrate validator influence among large stake holders, reintroducing a form of economic centralization.This paper proposes Proof of Effort (PoE), a novel consensus mechanism in which validator eligibility is determined exclusively by verifiable, continuous protocol participation rather than by computational expenditure or financial stake.PoE integrates four lightweight security primitives -node-identity binding via asymmetric key pairs, sequence-number-based replay prevention, digital-signature message authentication, and threshold-based malicious-node detection -chosen specifically to impose negligible per-node overhead while maintaining Byzantine resilience.We formalize the effort accumulation model mathematically, provide a detailed pseudocode specification of the protocol, and evaluate the mechanism through an agent-based simulation implemented in NetLogo 6.4 with 500 heterogeneous agents (350 honest, 150 adversarial).Simulation results, presented with full graphical and tabular support, demonstrate that PoE achieves an average throughput of 900 tx/s (vs. 25 tx/s for PoS and 7 tx/s for PoW), reduces average consensus latency to 56 ms (vs.400 ms and 1,200 ms), and correctly identifies 92-97% of malicious agents while accepting fewer than 0.2% of forged or replayed messages.The effort gap between honest and adversarial nodes remained below 0.21%, confirming protocol fairness under adversarial load, and the validator-selection Gini coefficient of 0.18 represents a 71% improvement in participation equality over PoS.These results position PoE as a compelling candidate for permissioned and consortium blockchain deployments where energy efficiency, participation fairness, and lightweight security are primary requirements.
Ethereum, one of the largest blockchain platforms, manages millions of dollars in digital assets daily and depends on consistent community standards and cohesive implementations to maintain network consensus. This study introduces a novel methodology for identifying and measuring temporal inconsistencies in opcode mappings across Ethereum clients and the Solidity compiler. Using this methodology, we uncover significant and previously undocumented discrepancies both among different components of Ethereum’s implementation and between theoretical specifications and practical execution behavior. We further demonstrate the potential consequences of these inconsistencies by illustrating their impact on the detection of vulnerabilities in Solidity smart contracts. Such discrepancies can substantially undermine the accuracy and reliability of vulnerability analysis, highlighting the critical need for strict consistency in Ethereum’s operational semantics. Ensuring this consistency is essential for preserving transaction security and the overall robustness of the platform.
Decentralised Autonomous Organisations (DAOs) raise fundamental questions for private law. While scholarly and regulatory attention has primarily focused on the legal status and governance of DAOs, comparatively little consideration has been given to how disputes involving these organisations should be resolved. This article examines the suitability of dispute resolution mechanisms for DAO-related disputes. Analysing the principal categories of disputes that have emerged in practice, the article critically evaluates blockchain-based dispute resolution mechanisms alongside traditional arbitration, in light of due process, enforceability, party autonomy, and the unique features of decentralised governance. It argues that neither purely code-based dispute resolution nor conventional litigation provides a satisfactory response to the complexity of DAO disputes. It concludes that arbitration, appropriately adapted to the technological and organisational realities of DAOs, offers the most promising framework for balancing decentralisation, legal certainty, and procedural fairness within the evolving Web3 ecosystem.
Vabuk Pahari, B. Chandrasekaran, Johnnatan Messias, Krishna P. Gummadi · 5 authors
A decentralized autonomous organization (DAO) is a governance entity that allows its stakeholders to manage blockchain-based protocols through smart contracts. The DAO explicitly specifies how stakeholders make and enforce decisions concerning a protocol's operation in a smart contract, aptly referred to as its governance contract. The design of this governance contract, therefore, has far-reaching implications for the security (trust) and privacy (transparency) of the smart contracts managed by the DAO and its stakeholders. In this work, we (i) explicate the trust and transparency trade-offs of the design choices in implementing a DAO and (ii) highlight how poor choices introduce critical vulnerabilities, using real-world examples as case studies. To this end, we analyze $48$ public, actively used Ethereum-based DAOs that control a vast capital. We classify the design choices into a handful of key dimensions that succinctly capture how a DAO's stakeholders initiate a protocol change, vote on it, and, based on the voting outcome, execute that change. Our analyses crucially uncover a new class of attacks, which we call governance attacks, that directly exploit the fundamental design of a DAO's governance mechanisms, even if we assume bug-free implementations.
Jemima Owusu-Tweneboah, Amani Altarawneh, Deepti Gupta, Maria Luisa Figueroa
Cybersecurity and privacy frameworks such as NIST SP~800--53, ISO/IEC~27001, GDPR, and HIPAA are widely used to guide organizational security posture and regulatory compliance. In practice, however, framework adoption is often assessed through point-in-time audits, self-attestations, and fragmented evidence reviews, providing limited assurance that controls are consistently implemented, independently validated, and sustained over time, particularly in environments that rely on third-party vendors. These limitations are amplified in multi-vendor ecosystems, such as healthcare remote patient monitoring (RPM), where compliance obligations span organizational boundaries and assessments are conducted by multiple independent assessors. This paper investigates how permissioned blockchain systems can support framework implementation maturity measurement rather than static compliance verification. We propose a blockchain-based Third-Party Risk Assessment (TPRA) framework that operationalizes assessment workflows, enforces multi-party governance, and preserves longitudinal assessment state using programmable smart contracts. Building on this framework, we introduce a set of evaluation metrics and a qualitative maturity model designed to assess whether compliance controls are verifiably implemented, governed, and sustained across repeated assessment cycles.
Blockchains rely on transparency and immutability to ensure trust, but these guarantees come at the cost of an ever-growing ledger that increasingly threatens decentralization by making it more expensive to store and maintain the full transaction history. In this work, we introduce state-invariant transactions, defined as transactions whose inclusion or removal does not affect the resulting blockchain state beyond transaction fees. We argue that these transactions constitute a form of on-chain spam because they consume execution, bandwidth, storage, and blockspace without contributing to the final ledger state. We present the first large-scale measurement of state-invariant transactions across Ethereum, Optimism, and Base, identifying nearly 1.4 billion such transactions. While only 2.6% of Ethereum transactions are state-invariant, they account for 24% of transactions on Optimism and 37% on Base, representing a significant source of unnecessary resource consumption on Layer-2 blockchains. We show that speculative Maximal Extractable Value (MEV) is the dominant source of state-invariant transactions on Optimism and Base, accounting for 57% and 68%, respectively, but is not the only source as previously assumed. Moreover, despite its popularity, speculative MEV is not the most profitable strategy once the costs of state-invariant transactions are considered. Beyond MEV, we identify substantial malicious activity, with address poisoning campaigns accounting for 53% of non-reverted state-invariant transactions on Ethereum. Our findings suggest that mitigating state-invariant transactions could substantially reduce blockchain resource consumption and transaction costs while limiting phishing campaigns and other forms of blockchain abuse.
The long and distinguished history of the Wikimedia ecosystem has provided valuable insights and lessons learned regarding the dispute over control of knowledge on the web. This activity aims to contribute to the point: “Pilot experiments in decentralized decision-making” of the Wikimedia Foundation's Annual Plan, bringing to the public community discussion table practical and real use cases in which, beyond the purely speculative and financial aspects of the blockchain ecosystem, Web3 can offer highly efficient tools to promote and guarantee knowledge equity. In this space, the functioning of some Web3 tools for decentralized governance will be presented in a practical way, establishing parallels with existing mechanisms in the Wikimedia ecosystem, such as MetaWiki SecurePoll, RFCs, Community Voting, and the Board of Trustees.
The Financial Technology (FinTech) ecosystem has become a disruptive one which changed the face of the financial services industry with digital technology, innovative business models and new regulatory framework. The development, distribution and use of financial products and services have been revolutionized by technologies like artificial intelligence, blockchain, cloud, big data analytics, Internet of Things and open banking. This review paper aims to integrate and consolidate the available literature to gain an overview of the development of FinTech from Finance 1.0 to Finance 4.0, and their technological innovations as the backbone of the modern financial systems. It also explores some of the key FinTech business models like digital payments, digital lending, WealthTech, InsurTech and embedded finance, and the essential role of relevant government policies, digital public infrastructure and governance for responsible FinTech innovation. The paper also identifies relevant challenges in the fields of cybersecurity, data privacy, ethics in artificial intelligence, regulatory complexity and digital inclusion that remain to significantly impact the sustainable development of the FinTech ecosystem. Last but not least, new research opportunities are identified in the field of generative artificial intelligence, decentralized finance, green FinTech, and digital financial governance to be pursued by academia in the future. The multidisciplinary perspective employed in this review gives a comprehensive picture of the current developments in FinTech and can help researchers, practitioners and policymakers to understand the opportunities and risks associated with digital financial transformation.
The global healthcare supply chain is experiencing increasing challenges with respect to maintaining the security of medications, adequate storage of medications, and identifying potential issues with medications that may pose a risk to patient safety. This chapter introduces a prototype called the 'Smarter, Safe Healthcare Supply Chain.' The system brings together fast telemetry (simulating 4G and 5G), edge computing, blockchain smart contracts, and explainable anomaly detection to help stop compromised medications from reaching patients. Included in the prototype's design are an IoT temperature and location data sensing simulator; an edge service that checks the signature of a device and executes "explainable" checks; and two smart contracts associated with device tracking and alerting. Tools like latency simulation, on-chain device tracking, device allowance (i.e., allowing only those devices that have been verified via smart contracts to access the network), and audit logs allow for the prototype to demonstrate how new technologies can enhance, accelerate, and streamline operations and build trust throughout the supply chain. Some of the key results of the prototype include faster-than-anticipated response times under 5G simulated conditions, successful verification of devices (both at the edge and enterprise-level) via smart contract(s), and accurate alerting developed based on a predefined set of rules. The framework upon which the prototype is built follows the principles of Zero Trust (e.g., NIST SP 800-207A, GSMA 5G IoT Guidelines, and use case-specific Healthcare Compliance Controls). Limitations exist within the prototype (such as being a single-node blockchain and the use of rule-based alerting versus leveraging full machine learning capabilities); however, it presents a viable operational model for practical application within a regulated supply chain (e.g., pharmaceuticals). Future work will include multi-party blockchain networks, evolving AI algorithms, and demonstrating full integration of the prototype into existing regulatory workflow(s).
This paper investigates the evolving link between cryptocurrency and equity markets in the context of the recent wave of corporate Bitcoin (BTC) treasury strategies. We assemble a dataset of 39 publicly listed firms holding BTC, from their first acquisition through April 2025. Using daily logarithmic returns, we first document significant positive co-movements via Pearson correlations and single factor model regressions, discovering an average BTC beta of 0.62, and isolating 12 companies, including Strategy (formerly MicroStrategy, MSTR), exhibiting a beta exceeding 1. We then classify firms into three groups reflecting their exposure to BTC, liquidity, and return co-movements. We use transfer entropy (TE) to capture the direction of information flow over time. Transfer entropy analysis consistently identifies BTC as the dominant information driver, with brief, announcement-driven feedback from stocks to BTC during major financial events. Our results highlight the critical need for dynamic hedging ratios that adapt to shifting information flows. These findings provide important insights for investors and managers regarding risk management and portfolio diversification in a period of growing integration of digital assets into corporate treasuries.
Abstract A secure platform for exchanging and storing agricultural data is provided via a blockchain-powered framework. By integrating edge computing, blockchain technology, and the Internet of Things (IoT) the production of crops can be boosted while using fewer natural resources. In the sector of agriculture, sensors and equipment gather various data about the landscape, which can subsequently be delivered to a server in a cloud environment. Due to its extreme fragility, these data must be securely stored and guarded from unwanted access. The core aim of this work is to propose a hybrid Reconditioned Random value-based Wombat Optimization with Adaptive Multi-scale Vision Transformer-based EfficientNet (RRWO-AMViT-ENet) model integrated with Ethereum smart contracts for secure pest detection and smart irrigation in IoT environments. The gathered agricultural images are initially stored and managed using the Ethereum blockchain. Then, node authentication is performed using the Smart Contract-based Adaptive Deep Support Vector Machine (SC-ADSVM). A Reconditioned Random value-based Wombat Optimization (RRWO) is utilized to optimize the variables of the developed SC-ADSVM. In order to perform pest detection and smart irrigation, the Adaptive Multi-scale Vision Transformer-based EfficientNet (AMViT-ENet) is used. The proposed model is implemented on the IP102-Dataset, where it obtained an accuracy of 96.39% in the pest detection operation. Thus, the proposed model provides effective results for pest detection and the smart irrigation process. From the attained results, it is concluded that the recommended strategy can provide intelligent service to the farmer.
Chibueze Ubochi Nwamouh, Okereke Aru-Eze, Wilson C. Ahiara, Isaac Adewale Samuel
One potential remedy for the problems with online voting methods is blockchain technology. Online voting has several benefits, such lower costs and higher voter turnout, but its broad adoption has been hampered by security and manipulation issues. With features like non-repudiation and end-to-end verification, blockchain&s;s decentralized and secure architecture provides an alternative to electronic voting. In addition to examining the present status of research and development in this area, this article gives an overview of blockchain-based electronic voting systems. The objective is to pinpoint current issues and predict upcoming developments in blockchain-based voting. Paper clarifies the core features of blockchain technology that are pertinent to voting and highlights the conceptual framework of blockchain-based electronic voting systems. According to the report, blockchain technologies might help with some of the problems that conventional election systems have. Transaction speed and privacy protection, however, are two significant obstacles that stand out. Blockchain-based electronic voting systems must be implemented sustainably, which requires resolving scalability issues with transaction speed and guaranteeing the security of distant participation. Current frameworks must be improved in order to properly utilize blockchain technology in voting systems. This entails strengthening privacy safeguards and figuring out ways to speed up transactions. Blockchain-based electronic voting systems have the potential to be a safe and effective alternative for next elections if these issues are resolved.
Blockchain technology provides a decentralized and secure data-management system. However, PoW consensus remains vulnerable to the risk of 51% attacks, where a single miner or pool controls over half of the network and can manipulate blocks and execute double-spending. This paper proposes a defense mechanism called Consensus-Level Restriction (CLR) for Ethereum Classic. It limits the number of sequential blocks from a single miner to reduce the feasibility of 51% attacks. We evaluated the effectiveness of CLR by using BlockSim simulator. Results show that CLR reduces the success rate of 51% attacks while maintaining a decentralized structure. This work strengthens blockchain security without compromising PoW principles.
When is honest Bitcoin mining rational? This question is central to the incentive design of proof-of-work blockchains. Sapirshtein et al. computationally derived near-tight lower and upper bounds on the incentive-compatibility threshold using a Markov Decision Process. Kiayias et al.'s Blockchain Mining Games instead derived theoretical lower and upper bounds. However, this theoretical approach has two limitations: its model restricts miners to a narrow action space and assumes idealized tie behavior, and its lower and upper bounds are far from tight. We resolve both limitations. We develop a more realistic model with a broader miner action space and asymmetric tie-breaking parameters $γ^-$ and $γ^+$. We then propose an algorithm that computes lower and upper bounds on the incentive-compatibility threshold with a maximum error of $9.98006\times10^{-4}$.
We study the impact that two miners equipped with quantum computers purpose-built for quantum Bitcoin mining will have on the 51% attack threshold of the Bitcoin network, given that the miners are playing a competitive game against each other to be the first to mine a block. We extend an existing game-theoretic framework for Bitcoin mining and compute the resultant payoff matrices. From these payoff matrices, we determine optimal quantum mining strategies for two non-colluding and aggressive quantum miners with multiple opportunities at finding a valid block in an otherwise classical Bitcoin network. We show that these optimal quantum mining strategies have a negligible effect on the 51% attack threshold. The novelty of our work is the inclusion of the Aggressive Quantum Mining Strategy and the realistic approach of allowing the quantum miners to restart their search if their measurements do not yield a valid block when determining the optimal quantum mining strategies. Our result is important for evaluating quantum-mining threats on cryptocurrencies based on Proof-of-Work, e.g. Bitcoin
The widespread problem of cyberbullying in today’s digital environment is made worse by the quick spread of IoT devices and the difficulties in organizing and protecting digital evidence. Traditional forensic methods are often inadequate due to their inability to provide a tamper-proof chain-of-custody and their limited scalability under high data loads. To overcome these constraints, our work makes use of innovative technologies such as a permissioned blockchain (Hyperledger Fabric), powerful encryption methods (AES-256 and RSA), and decentralized off-chain storage (IPFS). We propose an integrated, blockchain-driven forensic evidence collection framework that ensures secure, real-time evidence acquisition from diverse IoT devices, automated validation via smart contracts, and efficient, Role-Based Access Control for evidence retrieval. A hybrid consensus mechanism, combining elements of PBFT and Proof-of-Stake, enhances the system’s security and scalability while reducing processing latency and energy consumption. Experimental results demonstrate that our approach achieves high throughput and low latency, making it a strong and reliable solution for forensic investigations in cyberbullying cases. Within our experimental scope, the framework strengthens the integrity and authenticity of digital evidence and addresses key regulatory considerations, though real-world validation remains future work.
The governance of new technology projects in remote and developing economies is frequently undermined by severe information asymmetry and fragmented telecommunications infrastructure. While theoretical public policy advocates for the deployment of distributed ledger technologies to enhance institutional design and regulatory transparency, evaluating the economic impact of these systems relies heavily on static, retrospective datasets. This paper proposes a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time, serverless pipeline for telecommunications governance and blockchain integration. By deploying asynchronous Python middleware integrated with simulated smart contracts, the proposed system programmatically ingests high-frequency network telemetry and cross-border telecommunications data. The system translates these inputs into a dynamic Institutional Transparency Index, instantly identifying regulatory bottlenecks and pricing friction across public-private partnerships. Preliminary architectural evaluations demonstrate that decoupling the governance tracking from legacy, centralized state databases significantly reduces information asymmetry, providing policy researchers with a deterministic, highly scalable tool for modeling the economics of distributed ledgers in developing contexts.
Diese Masterarbeit untersucht, ob Posts von Elon Musk auf Twitter (jetzt: X) die Bitcoin-Volatilität beeinflussen können. Einige meinen, dass Musk in der Lage sei, den Bitcoin-Kurs mit einem einzigen Tweet zu beeinflussen. Deshalb untersuche ich diese Frage, indem ich die Volatilität von Bitcoin modelliere und prognostiziere. Dafür verwende ich ein heterogenes autoregressives Modell der realisierten Volatilität (HARRV) basierend auf Hochfrequenz-Daten von Bitcoin-Preisen. Das Modell erweitere ich nicht nur durch Variablen, die für die Tweets von Musk stehen, sondern auch durch andere. Beispielsweise eine Variable, die zwischen Wochentagen und Wochenenden unterscheidet und eine Variable, die die Häufigkeit der Google-Suchen nach dem Wort Bitcoin widerspiegelt. In der Masterarbeit zeige ich, dass Tweets von Elon Musk, die Interaktionen über dem Durchschnitt aufweisen, einen starken signifikanten Effekt auf die realisierte Volatilität haben. Außerdem zeigt sich, dass das Hinzufügen der Tweets-Variablen zum HAR-RV-Modell dazu beiträgt, die Modellierung und Vorhersage der Volatilität von Bitcoin zu verbessern.
In the Bitcoin system, transactions arrive continuously at miners' mempools and await inclusion in future blocks. Every non-coinbase transaction must spend one or more unspent outputs created by previous transactions, inducing dependency constraints among transactions in the mempool. At the same time, miners are economically incentivized to prioritize transactions with higher fee rates, measured as transaction fee per unit size. This paper formulates the mempool linearization problem: given a set of transactions with associated fees, sizes, and dependency relationships, compute a dependency-respecting transaction ordering that maximizes fee-rate efficiency while supporting efficient updates as the mempool evolves dynamically. The problem is characterized through a partition of transactions into disjoint dependency-respecting subsets ordered by decreasing aggregate fee rate, together with an equivalent LP formulation. Motivated by structural properties of basic feasible solutions in the simplex method, a new algorithm called spanning forest linearization (SFL) is developed. Operating directly on the transaction dependency graph, SFL iteratively merges and splits chunks of transactions to refine a global ordering, and is guaranteed to terminate at an optimal solution. Evaluation on both synthetic and real-world Bitcoin mempool data shows that SFL consistently computes optimal linearizations with substantially lower runtime than competing approaches, including a method based on the parametric preflow algorithm of Gallo, Grigoriadis, and Tarjan. These results indicate that SFL provides a practical and scalable framework for transaction prioritization by decentralized miners in large and rapidly evolving mempools. SFL has also been incorporated into the Bitcoin Core codebase for transaction cluster linearization.
Arman Kolozyan, Tom Sorger, Alexander Hicks, Stefanos Chaliasos
Zero-knowledge proofs (ZKPs) have become a core technology for privacy and verifiable computing. They are used to secure blockchains that handle billions of dollars and identity applications dealing with sensitive personal data. However, ZKP systems are complex, and subtle implementation errors can completely break their guarantees, letting attackers forge money or false proofs of identity. Researchers and practitioners have therefore developed a growing set of bug detection and formal verification methods to secure these systems. Yet their real-world effectiveness and adoption remain unclear. In this paper, we aim to shed light on the state of ZKP security tooling. We first systematize the landscape of these tools and observe that most target Circom, leaving newer DSLs and zkVMs with limited support. We then evaluate six tools across 70 real-world vulnerabilities and find that while the tools detect 45.7% of bugs on isolated targets, their effectiveness drops to 19.6% on full codebases, with important vulnerability classes left unaddressed. We also present the first systematic analysis of formal verification efforts, revealing that current work focuses primarily on constraint correctness and identifying key gaps and risks. Finally, we survey 48 practitioners, showing that development and security remain human-led, LLMs are widely used, and practitioners prioritize tools with clearer guarantees and lower integration effort. Overall, our results highlight the need for better integration of security tooling with the development and auditing process, and we provide actionable insights for researchers and practitioners.
The aim of this study is to analyze the price dynamics of blockchain-based carbon credit tokens, namely Base Carbon Tonne (BCT), Moss Carbon Credit (MCO2), and KlimaDAO (KLIMA) as well as mainstream crypto assets such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Solana (SOL) and the speculative asset Carboncoin (CARBON). In addition, the Fear & Greed Index, which represents investor sentiment, has been incorporated into the model in line with the role of sentiment-driven effects in price formation processes in cryptocurrency markets, as highlighted in the literature. The study utilized daily closing prices from the period October 21, 2021, to November 1, 2025; correlation analyses were performed on raw daily price series using the Pearson correlation method, which was chosen to examine the direction and strength of the linear relationship between variables. Prior to modeling, the dataset was cleaned, Min-Max normalization was applied, and it was split into a 70% training set and a 30% test set while preserving chronological integrity. While the assumption of stationarity in time series is important from the perspective of classical econometric approaches, this study focuses on deep learning-based methods within the scope of nonlinear modeling frameworks. The data used in the study were obtained from Yahoo Finance and the AI Key API. The findings indicate that there are strong internal linkages among carbon credit tokens. In particular, while a strong positive relationship was observed between BCT and MCO2, it was determined that these tokens exhibit a weak negative correlation with Bitcoin. This suggests that carbon credit tokens are only marginally linked to the broader crypto market but form a more cohesive structure within their own ecosystem. Additionally, it was observed that the CARBON asset exhibits relationships ranging from weak to moderate with major crypto assets. The Fear & Greed Index, meanwhile, showed moderate relationships with BTC, ETH, and SOL, and weaker relationships with carbon credit tokens. During the modeling process, LSTM, GRU, Transfer-LSTM, and Transfer-GRU architectures were used; the data was split into 70% training, 30% validation, and 30% test sets while maintaining chronological integrity; the models were evaluated using MSE, RMSE, MAE, MAPE, and R² metrics. The results show that the GRU architecture generally offers the highest prediction accuracy, while transfer learning models perform relatively better in predictions for the KLIMA and Fear & Greed (F&G) Index. Overall, the study demonstrates that deep learning and transfer learning approaches are effective in modeling price behavior in tokenized carbon credit markets. Here, it is assessed that transfer learning does not automatically provide an advantage in every scenario, but offers strategic contributions for specific asset groups. In conclusion, the study demonstrates that AI-based models can be used as a decision-support mechanism in the pricing of sustainable financial instruments in the digital economy.