Abstract - In the fast-growing digital age, systems for communicating through cyber means allow people, organisations and governments to share information in real-time. Unfortunately, the majority of the current communications networks use a centralised architecture, which are susceptible to cyber threats like data breaches, identity theft, man in the middle attack, and distributed denial of service attacks. These vulnerabilities degrade the confidentiality, integrity, availability and trust in the digital communication systems. This research proposes a secure cyber communication framework using blockchain technology to bypass the limitations of the current systems. The framework uses decentralised distributed ledger technology, public/private key cryptography, digital signatures, cryptographic hash functions and smart contracts to facilitate the secure transmission of messages, authenticate user identity and maintain a tamper-proof record of messages. All messages will be encrypted, digitally signed for authenticity and validated using consensus before being permanently recorded onto the blockchain. This architecture will reduce reliance on third-party intermediaries while providing greater resilience and transparency against cyber-attacks, as well as eliminating single points of failure. Performance, scalability and energy consumption will also be discussed in this study. According to the results, use of blockchain for cyber-communication is associated with high levels of security; therefore, the use of blockchain in future decentralised communication networks should result in more secure, trustworthy, and reliable systems within all key industries. Key Words: Blockchain Technology, Cyber Communication, Secure Messaging, Distributed Ledger, Cryptography, Digital Signatures, Smart Contracts, Decentralization, Data Integrity, Network Security
Blockchain technology stands at the forefront of transforming digital communication, addressing entrenched issues like data breaches, privacy erosion, and centralized control. This systematic literature review synthesizes insights from over 50 peer-reviewed articles, industry reports, and case studies published between 2018 and 2025, focusing on blockchain's core principles and their application to secure messaging, decentralized social networks, IoT ecosystems, and telecommunications. Drawing on databases such as Google Scholar, IEEE Xplore, and Scopus, we identify key benefitsâdecentralization for resilience, immutability for integrity, and cryptography for confidentialityâwhile critically examining barriers like scalability trilemma, regulatory conflicts, and user adoption hurdles. Emerging trends, including zero-knowledge proofs and modular architectures, signal a path toward scalable Web3 paradigms. The review concludes with societal implications for trust-building and data sovereignty, proposing research directions for hybrid models that balance innovation with compliance. This work underscores blockchain's potential to foster a user-empowered, equitable communication landscape.
Climate change, driven by global warming and associated greenhouse gas (GHG) emissions, poses a significant global challenge. International organizations and governments are actively pursuing emission reduction strategies, yet these efforts are often constrained by the direct relationship between emissions and national economic activity. This paper proposes a blockchain-based carbon footprint (CF) management system named P u r e C a r b o P r i n t , that leverages data from IoT devices, and security is ensured by zero-knowledge proofs (ZKP) to track and reduce CF at the individual level. Individual data is collected and converted into carbon coin, a hybrid (online-offline) crypto coin operating on the Pure Chain network. A smart contract, deployed on the Pure Chain network using the Pure Chain coin, governs the proposed system. Additionally, zk-SNARK is applied to implement ZKP for the validity and integrity of the information verification without revealing private information. Based on theoretical models and previous studies on behavior-based carbon reductions, it is expected that the system can achieve up to a 30% reduction in CF per user within the first year.
The rapid expansion of digital payment ecosystems has transformed global financial transactions through the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT). Smart point-of-sale terminals, wearable payment devices, biometric authentication systems, and cloud-integrated banking platforms generate massive volumes of real-time transactional data, demanding intelligent, scalable, and secure processing frameworks. Conventional security architectures struggle to address evolving cyber-financial threats, including adaptive fraud schemes, adversarial attacks, identity compromise, and decentralized finance exploits. An integrated AIâIoT security paradigm offers a resilient solution by enabling real-time anomaly detection, adaptive risk scoring, device-level authentication, and continuous behavioral monitoring across distributed financial infrastructures. This book chapter presents a comprehensive exploration of AI-driven analytics, reinforcement learningâbased adaptive decision models, federated learning for privacy-preserving intelligence, and lightweight deployment strategies tailored for resource-constrained IoT financial devices. A unified Zero-Trust architecture combined with blockchain-assisted auditability strengthens transaction integrity while ensuring regulatory compliance and data governance alignment. Emphasis is placed on explainable AI mechanisms to enhance transparency in automated financial decision-making and to support accountability within high-stakes payment environments. Emerging research challenges, including adversarial robustness, energy-efficient model optimization, and cross-platform interoperability, are critically examined to establish a forward-looking framework for secure digital finance. The proposed perspective advances a scalable and privacy-aware AIâIoT integrated security architecture designed to mitigate financial risk, reduce false positives, and enhance trust in decentralized and intelligent payment systems. This contribution aims to support researchers, financial technologists, and policy architects in developing next-generation digital payment infrastructures capable of sustaining security, efficiency, and transparency in an increasingly connected global economy.
Smart contracts, serving as self-executing programs on blockchain platforms, have emerged as a key innovation for enhancing data security. Despite advancements in both blockchain technology and smart contracts (SCs), Ethereum-based SCs remain vulnerable to security breaches. Exploitation of these vulnerabilities can result in substantial financial losses for both service providers and users. Consequently, the detection and mitigation of security vulnerabilities in smart contracts are critical to ensuring the security and reliability of blockchain platforms. Machine learning approaches are emerging as effective alternatives to traditional vulnerability detection methods, though many rely heavily on expert knowledge and primarily target familiar vulnerabilities. This chapter explores the creation of an AI-driven framework for detecting vulnerabilities in smart contracts, aimed at reducing risks and improving the reliability of blockchain systems. By incorporating advanced Machine Learning (ML) and Deep Learning (DL) techniques, the framework seeks to improve the accuracy and efficiency of vulnerability detection, addressing the shortcomings of traditional static and dynamic analysis methods. The proposed approach not only strengthens the security of smart contracts but also contributes to the broader goal of building more resilient and reliable blockchain ecosystems. Through an in-depth analysis of methodologies and case studies, this chapter highlights the essential role of AI in advancing the secure development and deployment of smart contracts.
The rapid evolution of digital finance has transformed the way payments are processed globally, leading to a growing comparison between cryptocurrency-based payment systems and traditional payment systems. Traditional payment systems, such as banks, credit cards, and online payment gateways, have long been the backbone of financial transactions, offering reliability and regulatory oversight. However, these systems often face challenges related to transaction speed, high processing costs, and centralized security risks. In contrast, cryptocurrency payment systems leverage blockchain technology to enable decentralized, peer-to-peer transactions that promise faster settlement, reduced transaction fees, and enhanced transparency. This study compares crypto-based payment systems and traditional payment systems across three critical dimensions: speed, cost, and security.
Open access
Blockchain Technology Applications and Security
Innovations and Analysis in Business and Education
Blockchain technology has evolved from its initial application in cryptocurrencies such as Bitcoin to a versatile decentralized infrastructure supporting decentralized finance (DeFi), digital identity systems, smart contracts, and Web3 ecosystems. Despite its transformative potential, the rapid expansion of blockchain platforms has significantly increased the security attack surface, exposing networks to threats such as double-spending, Sybil attacks, smart contract vulnerabilities, transaction laundering, and large-scale financial fraud. At the same time, the emergence of quantum computing introduces a fundamental challenge to classical cryptographic mechanisms particularly Elliptic Curve Digital Signature Algorithm (ECDSA) and RSA that form the backbone of blockchain authentication and transaction verification. This paper presents a comprehensive study of Machine Learning (ML) techniques and Post-Quantum Cryptographic (PQC) frameworks for strengthening blockchain security and threat detection. The study reviews supervised, unsupervised, and deep learning models used for fraud detection, anomaly identification, smart contract vulnerability analysis, and blockchain transaction monitoring. In parallel, it examines quantum-resistant cryptographic algorithms emerging from the NIST post-quantum standardization process, including lattice-based, hash-based, and code-based schemes, and evaluates their suitability for blockchain environments. Furthermore, the paper analyzes the limitations of ML-based security mechanisms and the practical challenges of integrating PQC into decentralized infrastructures, including scalability, key size overhead, and performance trade-offs. A comparative analysis highlights that ML enhances adaptive behavioral threat detection, while PQC ensures long-term cryptographic resilience against quantum attacks. Therefore, the study emphasizes the importance of a hybrid MLâPQC security model that combines intelligent anomaly detection with quantum-resistant cryptographic protection. Finally, the paper identifies key research challenges and outlines future directions toward building scalable, adaptive, and quantum-secure blockchain ecosystems capable of supporting next-generation decentralized applications.
Ameer Majeed Dahdouh Al-Alili, Zaid Salam Abdullah
This research aims to clarify the actual works of criminal liability for crimes committed using cryptocurrency and to highlight the flaws of Iraqi legislation about this modern type of crime. Accordingly, an attempt has been made to analyse the elements, kinds, and difficulties of evidence, leading up to determining the legal system in which to protect from and suppress this type of crime, of which cyberspace is a part. This research is descriptive-analytical in nature, where legislation has been examined. The research indicates that the wide scope of risks involving cryptocurrency crimes makes it difficult to subject them to existing laws on movable property, especially since the legislator has omitted the criminalization of certain attacks like wallet hacking, while the sophisticated nature of making inquiries and collecting evidence complicates establishing a definitive link between the perpetrator and the transaction. All in all, this study finishes off with the need to develop or amend legislation to extend the definition of digital assets and criminalise attacks against them, strengthen investigative capacity in electronic tracking, establish units for cryptocurrency crimes, and regulate digital seizures and confiscation mechanisms. This further highlights the importance of modernising legislation in light of the criminal threatâs cryptocurrencies pose to upholding economic and legal security.
The rapid integration of Cloud computing and the Internet of Things (IoT) has enabled large-scale data storage, real-time analytics, and automation across a wide range of applications. Nonetheless, centralized architectures in Cloud-IoT integrations are subject to serious issues related to data security, trust, transparency, and automated access control. This paper introduces a smart contract framework based on blockchain to distribute data and implement a computerized system in Cloud-IoT securely. The proposed solution can be achieved through blockchain, which provides a decentralized, tamper-resistant architecture, thereby guaranteeing data integrity, non-repudiation, and a clear transaction log between heterogeneous IoT devices and Cloud services. Smart contracts enforce policies of data sharing, authentication, authorization, and service-level agreements without using trusted third parties. The framework enables access control at a fine-grain scale and dynamic policy enforcement, which allows a safe and effective process of data exchange in multi-stakeholder ecosystems. Moreover, the off-chain Cloud storage with on-chain verification will resolve the issues of blockchain scaling and storage capacity and ensure security assurances. A qualitative relationship results in the proposed architecture taking a long step in enhancing trust, minimizing operational overhead, and limiting the common security threats, including data manipulation, unauthorized access, and single points of failure. The proposed solution provides a strong foundation for next-generation Cloud-IoT systems in applications such as smart cities, healthcare, industrial automation, and intelligent transportation systems.
Block chain technology has gained significant attention across multiple domains such as finance, healthcare, education, and real estate. It serves as the foundational technology behind cryptocurrencies, enabling secure and decentralized digital transactions. Transactions are carried out using digital wallets on computing devices and are permanently recorded as blocks linked together in a distributed ledger known as the block chain. This paper presents a comprehensive study of block chain technology, its operational principles, consensus mechanisms, and real-world applications. It also explores the integration of artificial intelligence techniques to enhance security, scalability, and trust in block chain-based cryptocurrency systems.
The most important problems of sustainable vegetable farming are pests, inefficient data-based decision making, and poor irrigation. Another threat to productivity and the environment is that the conventional methods will cause excess use of pesticides, over-irrigation and unreliable harvest. In this research, we suggest a Blockchain Ethereum-Backed IoT Platform to ensure that these problems are resolved and provide pest detection in real-time and smart irrigation control. IoT sensors are used to measure soil moisture, temperature, and humidity and edge devices with lightweight deep learning models can detect pest infestations with a high accuracy. The resulting data is encrypted and checked in the Ethereum blockchain smart contracts are used to run irrigation programs and send alerts to control pests. It uses Layer-2 solutions of Ethereum to reduce latency and transaction cost to achieve scalability and efficiency. It is scientifically proven that the proposed platform can detect pests with an accuracy of 93.8 %, use 35 % less water, and produce 20% more crops than traditional solutions. Moreover, surveys of farmers show that the level of trust and readiness to implement solutions based on blockchain has risen considerably. The contribution of this work is a secure and transparent and resource-efficient digital agriculture framework enabling the development of precision-based farming and supporting sustainable food production.
This research presents a decentralized medical data management system integrating a Flask-based backend, an SQLite relational database, and an Ethereum-compatible blockchain to enhance the security, integrity, and transparency of healthcare data. The system adopts a modular architecture using Flask Blueprints to manage authentication, hospital data retrieval, OTP verification, and prescription handling. Smart contracts developed with the Truffle framework ensure immutable and auditable storage of critical medical proofs, particularly prescription records, while Web3 enables secure interaction between the backend and the blockchain. Future improvements include replacing SQLite with cloud-native databases such as PostgreSQL or MongoDB for scalability, implementing advanced encryption with dynamic key rotation, and adopting decentralized identity (DID) for patient-centric access control. Additionally, integrating real-time analytics, AI-based anomaly detection, automated compliance auditing, and Layer-2 blockchain solutions can further enhance system performance, security, and efficiency.
The increasing incidents of forged academic certificates and the inefficiencies of traditional verification systems highlight the urgent need for a secure, transparent, and reliable credential management mechanism. Conventional systems are largely centralised, time-consuming, and prone to manipulation, resulting in high administrative overhead and verification delays. We prepared an AI-Based Decentralized Academic Credential Verification System that leverages blockchain technology, smart contracts, and artificial intelligence to provide a tamper-proof platform for issuing, storing, and validating academic records. Artificial Intelligence is integrated to perform anomaly detection during certificate issuance and AI-based facial authentication for students, enhancing security and preventing fraudulent entries before blockchain storage. Students gain permanent, secure access to their verified credentials, while verifiers, such as employers, can instantly authenticate certificates using blockchain records or QR code scanning, eliminating the need for intermediaries. By integrating Ethereum, Solidity, Web3.js, IPFS, React.js, and AI models, the proposed system delivers a decentralized, scalable, and cost-effective solution that enhances trust, reduces verification time, and effectively combats academic credential fraud.
Dr. A. Radhika, D. Avinash, D. Sowjanya, K. Karthik ¡ 5 authors
The increasing use of digital communication has made it essential to maintain the confidentiality, integrity, and authenticity of sensitive information. Conventional image steganographic methods offer data hiding in digital images, but they fail to offer effective tamper proofing and secure ownership verification. To overcome these issues, this paper presents a Blockchain-Integrated Secure Image Steganography system using IPFS and Ethereum. In the proposed system, secret data is hidden within digital images using a Least Significant Bit (LSB) image steganographic method developed in Python. The stego images are then stored in the Inter Planetary File System (IPFS) for efficient and decentralized data storage. To ensure data integrity and secure access, the cryptographic hash values of the stego images and their corresponding IPFS Content Identifiers (CIDs) are securely stored on the Ethereum blockchain using smart contracts. The use of blockchain technology provides immutability, transparency, and tamper resistance, and IPFS provides decentralized storage without depending on centralized storage servers. The proposed system is validated to offer high image quality with negligible distortion and robust data security and traceability. This system is applicable for secure data sharing in confidential communication, digital forensics, and secure document transfer.
Open access
Advanced Steganography and Watermarking Techniques
Abstract The rapid growth of digital financeâincluding FinTech platforms, online payment gateways, and e-commerce marketplacesâhas revolutionized global financial systems while significantly expanding the cyber-attack surface. Sophisticated attacks such as AI-generated deepfakes, automated malware, ransomware, and synthetic identity fraud now threaten financial transactions. In response, cybersecurity strategies are evolving toward decentralized models, AI-enabled detection systems, Zero Trust architectures, and quantum-safe cryptography. This paper synthesizes recent academic research and industry developments (2025â2026), covering threat taxonomies, defensive strategies, emerging attack vectors, and regulatory enhancements in payment authentication. The integration of these trends underscores the necessity of robust, AI-driven, and compliance-aware security architectures for securing modern financial ecosystems.
Chukwuebuka Francis Ikenga-Metuh, Abel Yeboah-Ofori
Background: Blockchain technology has emerged as a transformative communication solution for securing distributed systems. However, several vulnerabilities exist during transactions, including latency and network congestion issues during mempool processing, topology weaknesses, cross-chain bridge exploits, and cryptographic weaknesses. These vulnerabilities have led to attacks that have threatened system integrity, including Block Extractable Value (BEV) attacks, Maximal Extractable Value (MEV) attacks, sandwich attacks, liquidation, and Decentralized Finance (DeFi) reordering attacks, among others. Thus, implementing a robust security framework based on the Confidentiality, Integrity, and Availability (CIA) triad remains critical for addressing modern blockchain technology threats. Objective: This paper examines blockchain technology, its various vulnerabilities, and attacks to determine how criminals exploit the system during transactions. Further, it evaluates its impact on users. Then, implement a blockchain attack in a âMasterChainâ virtual environment to demonstrate how vulnerable spots can be practically exploited and discuss the application of the CIA security triad through modern cryptographic primitives. Methods: The approach considers Hevnerâs design science framework, which emphasizes creating innovative artifacts that address identified problems while contributing to the knowledge base through rigorous evaluation. Furthermore, we developed a MasterChain tool using Python with Flask for distributed node communication, utilizing the Elliptic Curve Digital Signature Algorithm (ECDSA) with the Standards for Efficient Cryptography Prime 256-bit Koblitz curve 1 (secp256k1) for digital signatures and Secure Hash Algorithm 3 (SHA-3) (Keccak-256) hashing for block integrity. Results: show how the CIA has been implemented to provide secure communication through ECDSA-based transactions, SHA-3 chain integrity verification, and a multi-node distributed architecture, respectively. The performance analysis shows that ECDSA provides 256-bit security with 64-byte signatures compared to 2048-bit RivestâShamirâAdleman (RSA)âs 256-byte signatures, achieving a 75% reduction in bandwidth overhead. SHA-3 provides immunity to length extension attacks while maintaining equivalent collision resistance to SHA-256. Conclusions: The MasterChain framework provides a practical foundation for implementing blockchain security that addresses both classical and emerging vulnerabilities. The adoption of ECDSA and SHA-3 (Keccak-256) positions the system favourably for modern blockchain applications, while providing insights into the cryptographic trade-offs between performance, security, and compatibility.
Abstract This study explores transformation of business and IT through the lens of five emerging technology fields: artificial intelligence, Machine Learning, Data Analytics, Data Science and Blockchain. The contemporary business landscape is undergoing a profound transformation driven by the convergence AI, ML, DS, DA, and Blockchain technology. Individually, these technologies offer significant advancements: AI and ML provide sophisticated decision- making and automation capabilities, while data analytics and data science extract actionable insights and non-obvious patterns from vast datasets. Blockchain technology, a decentralized and immutable ledger, establishes a foundation of trust, transparency, and security in data management and transactions. By facilitating automation, data-driven decision-making and Data security all the above technologies transforming number of industries. The synergistic integration of these technologies creates novel business models and powerful operational enhancements in smart contract, Data sharing, Decentralized AI Marketplaces, cybersecurity. Important methods to use with these technology are covered including supervised learning, unsupervised learning, deep learning, descriptive analytics, predictive analytics, prescriptive analytics and distributed ledger technology. The challenges are also discussed, such as data privacy and quality, high cost, skill gap and interoperability. This study highlights opportunities and challenges in current trends available in AI, ML, DA, DS and Blockchain on business and IT sector. Though challenges related to scalability, regulatory compliance, and implementation complexity exist, ongoing technological advancements are actively addressing these barriers. It will be overcome by doing a thorough assessment of recent studies and identifying the potential benefits, impacts, and future directions of all the five technologies.
The global logistics sector is confronted with crucial data reliability challenges wherein traditional centralized systems have a 15-20% manual error rate and are highly susceptible to counterfeiting. In this regard, the current research proposes Sentinel, a decentralized supply chain tracking framework utilizing the Polygon Proof-of-Stake blockchain coupled with smart contracts in Solidity for granting immutability to data governance. It follows a hybrid architecture wherein on-chain cryptographic verification is coupled with MongoDB for high-speed off-chain data retrieval. Extensive performance testing was performed on a simulated supply chain network with 10,000 transaction cycles of creation, transfer, and delivery. It shows that Sentinel has been able to achieve 100% in data integrity, thus rejecting all 500 unauthorized ledger modifications attempted during security stress testing. In terms of efficiency, the proposed framework minimized data retrieval latency to less than 180 ms, which was an improvement of 92% compared to traditional decentralized architectures. Additionally, it minimized the transaction cost to roughly âš0.45/unit, thus offering a cost reduction of about 99.9% compared to traditional Ethereum Layer-1 implementations.
Abstract The rapid evolution of computer technology is changing digital ecosystems, business processes, governmental operations, and how humans use computers to perform tasks. This paper is a comprehensive analysis of modern computer technology trends, including advancements in artificial intelligence; cloud computing; edge computing; the internet of things (IoT); 5G networks; blockchain; cybersecurity; quantum computing; emerging technologies such as immersive technologies and robots; big data; and sustainable computing. In this extensive review of how these advances work together to drive digital transformation, this paper synthesizes current research from academic literature with real-world applications of computer technologies from industry. The paper includes discussions regarding the emergence of generative AI and multimodal ML methods, explainable AI, and intelligent automation as new methods to generate better decision-making results and innovations within the business sector. It includes descriptions of multi-cloud/hybrid architectures, serverless computing, edge AI, and fog computing as ways to achieve low-latency scalable infrastructure; and ultimately describes use cases for using IoT with AI-enabled analytic platforms for smart cities; IIoT; and real-time data ecosystems. Cybersecurity subjects discussed in this paper include innovations such as Zero Trust Architecture, AI-based threat detection, and quantum-resistant cryptography. Emerging technology paradigms like blockchain-powered decentralized apps (DApps), Web3 environments, quantum algorithms, AR/VR/MR technologies, and smart robots are examined for potential to change organisations and challenges encountered during their adoption. 'Green computing' strategies are discussed in terms of developing low carbon power systems, creating carbon aware IT systems, and developing sustainable IT practices that reduce environmental impact. This study also explores advances in the fields of human computer interaction, accessibility technology, and ethical governance frameworks, with a focus on society's responsibility to develop inclusive and responsible technological products. The research has revealed multiple challenges that prevent sustainable technology development from progressing, including: scalability; interoperability; regulatory compliance; security threats; digital equity; and adapting to the workforce's new skill sets caused by this shift to sustainable technology. Therefore, developing sustainable technology will require multi-disciplinary co-operation; ethical guidance/path; strategic governance; and continuous innovation in technology development. By combining a technical assessment of IT technology along with a social perspective; an umbrella of knowledge will form to forecast how IT technologies will advance during the period referred to as the era of Intelligent Connected Systems.
Modern precision agriculture depends on safe and effective fertilizer management. However, existing systems lack real-time decision-making capabilities, rarely incorporate secure traceability methods, and mainly concentrate on nutrient prediction without determining the type of soil fertilizer utilized for a specific crop. To classify fertilizer types (organic vs. inorganic) in real-time based on soil nutrient parameters (temperature, pH, EC, N, P, and K), this investigation suggests an innovative, lightweight self-attention transformer neural network (TNN) based Fertilizer class contract network (FCCN) model. The proposed research is one of the first to combine secure blockchain recording, fertigation, and fertilizer-type detection into a single edge-based pipeline that operates in real time. The process integrates blockchain-based transaction logging and IoT-edge computing for recording transparent and secure agricultural activity. Whenever deficits emerge, the suggested method uses Venturi irrigation to automatically activate fertigation after processing real-time sensor data at the edge to determine the types of fertilizer utilized and the nutritional status. This work uses a decentralized and scalable architecture compared to cloud-dependent or AI-based-only models. Fertilizer classification and fertigation actions based on the real-time nutrient level recommendation are recorded as immutable transactions on an Ethereum blockchain using a Proof-of-Stake (PoS) consensus. Before the final on-chain recording, validator logic confirms the accuracy of field data, fertigation events, and real-time soil nutrient levels. Real-time blockchain measurements reveal transaction completion speeds of less than 0.03 seconds, gas consumption of less than 62,000 units, and throughput of 15-35. Experimental findings show that FCCN categorization accuracy surpasses 98.85%.
Building structural designs, utilizing materials such as steel, concrete, and cross-laminated timber, contribute significantly to embodied carbon emissions in construction projects. However, traditional carbon accounting methods employed to quantify and record these emissions are often characterized by a lack of traceability, transparency, and immutability. This limitation undermines the reliability of emissions data, making it challenging for stakeholders to establish credible emissions records and implement regulatory strategies, such as carbon credits, taxes, subsidies, and green certifications, for buildingâs structural designs and materials. This paper addresses these challenges by proposing a transformational emissions accounting system that integrates Building Information Modeling (BIM) for automatic extraction of emissions-relevant data, alongside blockchain-enabled smart contracts to ensure traceability and immutability of emissions records. The proposed system enables data-driven decision-making for low-carbon structural designs and materials, while also facilitating the application of emissions regulations to support their implementation based on trustworthy emissions accounting.