Against the backdrop of increasingly severe global climate change and environmental challenges, green finance, as a bridge connecting capital and sustainable development, is facing problems such as information asymmetry, transaction frictions and regulatory challenges. This study systematically explores the innovative applications of blockchain technology in the field of green finance and its economic effects. Through literature research and case analysis, we found that blockchain technology, with its characteristics of decentralization, immutability and smart contracts, has shown significant advantages in improving transparency, reducing transaction costs and alleviating the phenomenon of "greenwashing". Research shows that the application of blockchain in areas such as green bonds, carbon trading, renewable energy certification and green supply chain finance has produced economic benefits such as reduced financing costs, improved market efficiency and enhanced information transparency. This paper constructs a comprehensive assessment framework to provide guidance for policymakers and market participants to promote the innovative development of green finance.
Mingyue Xie, Jun Liu, Shuyu Chen, Ming‐Wei Lin · 7 authors
Blockchain-based IoT applications in agriculture have drawn extensive attention in recent years, allowing the implementation of smart agriculture solutions. By transmitting collected relevant data to a control center through the blockchain, corresponding regulation can be realized in agricultural production management systems. However, existing efforts for directly adopting the technique to data transmission are obstructed by several issues. The traceability of agricultural data stored in the blockchain leads to the exposure of the identity of the data collecting devices. And the tracing difficulty of completely invisible data for identity protection also exists in the smart agricultural system. To tackle these limitations, we propose a novel blockchain-based smart agricultural IoT system framework for regulating the agricultural production environment through trusted data. First, the elliptic curve integrated encryption scheme (ECIES) and the group signature scheme are integrated to guarantee the traceability and identity protection of the data and equipment, respectively. Then, to enhance the security of session key transmission in the ECIES scheme, we further design an on-chain-off-chain key agreement protocol (ECIES-OOKA). In addition, we propose a novel group manager selection method based on probabilistic linguistic term sets (PLTSs) for the group signature implementation. Finally, a practical example is provided to demonstrate the group manager selection process and verify the feasibility of the proposed method. The security and performance analysis for the system framework are also presented.
In recent years, the integration of smart contracts into supply chain management has garnered significant attention due to their potential to enhance efficiency, transparency, and reliability. This study explores the application of smart contracts within supply chains, focusing on their impact on inventory management, process optimization, and overall operational effectiveness. Through a comprehensive analysis of existing literature and case studies, we identify key benefits and challenges associated with implementing smart contracts in supply chain contexts. Our findings suggest that while smart contracts offer substantial improvements in automating processes and reducing errors, considerations regarding technological infrastructure and stakeholder readiness remain critical for successful adoption.
Aiming at the severe challenges of Intelligent Connected Vehicle (ICV) in the field of data security, this paper designs a four-layer ICV data security framework, which covers the whole process from data collection, processing, storage and sharing to privacy protection. In the security framework, three technical methods of blockchain, Zero-Knowledge Proof (ZKP) and Post-Quantum Cryptography (PQC) are integrated. Blockchain is used to provide distributed trust mechanism and tamper-resistant data storage. ZKP is used to protect data privacy and realize data verification without leaking sensitive information. PQC is used to enhance data encryption and authentication mechanism to resist quantum computing attacks. At the same time, the data sharing security strategy is formulated, including initialization and key generation, data transmission preparation, zero-knowledge proof generation and verification, data reception and verification, blockchain recording and consensus, access control and data usage, etc., to ensure the security and privacy protection of data during the sharing process. The simulation experiment data of delay time, throughput and privacy protection intensity in the simulation environment show that the scheme not only enhances data privacy protection and data integrity verification but also improves the system's ability to resist future quantum computing threats and provides a solid guarantee for the data security of intelligent connected vehicles.
This paper proposes a supply chain data security optimization system based on blockchain, which uses data deduplication and verification algorithms to solve the redundancy and consistency problems in multi-party data interaction. The system ensures real-time sharing of data in all links of the supply chain through the distributed ledger technology of blockchain, and improves the security and reliability of data transmission. To verify the performance of the system, this paper constructs an algorithm model and performs data analysis in a simulation environment. The results show that the system has significant effects in reducing data redundancy and improving data accuracy. In terms of economic benefits, simulation data show that by reducing data errors and information delays, the system can significantly reduce supply chain operating costs and improve resource utilization. The research results show that the designed blockchain optimization scheme not only enhances the data security of the supply chain, but also brings substantial economic benefits to enterprises. This study provides new technical ideas for supply chain management based on blockchain and has good application prospects.
The research examines the increased popularity of electronic currencies as well as the radical change towards a digital economy.In the last decade, the use of cryptocurrencies like Bitcoin, Ethereum, and newly formed central bank digital currencies (CBDCs) has gained momentum, capturing extraordinary shifts in financial frameworks as well as in global economic models.The study examines the factors driving the growing adoption of digital currencies, noting their advantages including decentralization, improved security, lower transaction costs, and the capacity to enable cross-border payments.Aside from analysing the economic and technological drivers of digital currency adoption, the research looks at the general implications of digital finance on the existing banking systems, monetary policy, and regulatory regimes.The research also discusses the potential role of digital currencies in promoting financial inclusion, especially in areas with limited access to mainstream banking services.In addition, the research examines how the growth of the digital economy, typified by the convergence of digital currencies, blockchain technology, and decentralized finance (DeFi), is changing business models and consumer behaviours in various industries.The research, conducted through a mixture of surveys, case studies, and interviews with experts, cites major challenges including volatility, regulatory ambiguity, and security risks that may affect future stability and development of digital currencies. REVIEW OF LITERATUREDevlin (2019) -An Analysis of main and subsidiary credit card holding and spending.This research aims to investigate why the majority of multiple credit card holders hold a "main" card (i.e., one that is more frequently used than the others) and "subsidiary" cards (i.e., ones used less frequently or in an emergency situation) and the spending behaviour on main and subsidiary cards.
The urgency of this research is to increase efficiency, transparency and sustainability in increasingly complex and challenging agribusiness supply chains. The aim of this research is to develop an integrated system that combines IoT capabilities in collecting agricultural data in real-time, AI to analyze data and provide recommendations for action, as well as security and transparency guaranteed by blockchain technology. The method used is a mixed methods approach, this approach combines qualitative and quantitative elements to obtain a deeper understanding. A qualitative approach is used to gain a contextual perspective, while a quantitative approach is used to measure performance empirically. This research uses a case study design on a sensor-based agricultural monitoring system because of its ability to provide in-depth and holistic insights. The research population consists of users and stakeholders in sensor-based agricultural monitoring systems. The sample was selected purposively to cover various aspects of the supply chain. Data was collected through in-depth interviews, direct observation, surveys of system users and collection of sensor and transaction data from agricultural monitoring systems. The research results show that the integration of IoT, AI, and blockchain significantly improves operational efficiency in agribusiness supply chains. Implementation of this integrated system resulted in an increase in productivity of up to 22%, a reduction in pesticide use by 35%, an increase in water use efficiency by 30%, and a reduction in operational costs by 18%. Statistical analysis confirmed a strong positive correlation between the use of integrated technology and increased operational efficiency (R=0.85, p<0.01).
Blockchain technology, characterized by its immutability, decentralization, transparency, security, and traceability, has shown vast potential for applications in secure IoT communications and data protection through its deployed smart contracts. While machine learning-based code generation systems aim to automate high-quality programming solutions, they face significant challenges when addressing blockchain-related issues. This paper analyzes the limitations of machine learning in identifying vulnerabilities within blockchain smart contracts and proposes robust solutions. To achieve this, this paper suggests organizing multiple security experts for labeling, developing efficient labeling tools, employing semi-supervised learning to reduce dependency on labeled data, and establishing a continuous update mechanism for labeled datasets to adapt to evolving threat landscapes. To address the scarcity of training samples in the blockchain domain, this paper introduces a method for generating additional Solidity smart contract training samples using data augmentation techniques. Given that traditional data augmentation methods are not suitable for Solidity, the approach involves converting Solidity contracts into Python code for processing, then reverting them back to Solidity post-augmentation, with provided code examples. Furthermore, leveraging established non-blockchain code to train blockchain-related models enhances model performance and generalization capabilities. These strategies effectively tackle the issue of insufficient training samples in the blockchain field and offer new perspectives on the conversion between Solidity and Python.
Data sharing among robots will be an important issue in a future robotic society including many kinds of networked robots. This study proposes a model-sharing system for autonomous mobile robot networks. The model in this study means an action model trained by deep reinforcement learning for mobile robot navigation. The proposed system assumes that the mobile robots in the network can share useful and efficient models among robots. The proposed system uses the Ethereum blockchain as a platform to consider the value and ownership of action models. The owners of the models can receive payments from robots that use the shared models for autonomous navigation. The proposed system finally aims to build a future robotic economic system that includes data generation and accumulation, data sharing, and currency transactions among model owners and user robots. This paper introduces the autonomous navigation method of switching action models according to the navigation environment, the data-sharing system using the Ethereum blockchain, and the flow of autonomous navigation using the proposed system. Simulation results shows that the proposed system can achieve navigation of multiple mobile robots through model and currency transactions among Ethereum network participants.
At present, the existing databases in medical institutions are characterized by singularity and centralization in storage methods. This makes it difficult to guarantee the security, integrity, and traceability of electronic medical data, thus posing a threat to patients' medical privacy. Although some studies have proposed data security storage solutions based on cloud storage and the like, such solutions rely on a completely trustworthy third-party to ensure the reliability of interactions. To address the above-mentioned problems, this paper proposes a decentralized health management data storage mechanism by integrating blockchain technology based on the improved PBFT consensus algorithm. This mechanism uses the improved PBFT consensus algorithm and the optimized Hash encryption algorithm to securely and effectively store health management data in a distributed database, ensuring the integrity and traceability of the data. At the same time, a new data interaction system is designed to prevent direct interaction between the third-party and the database, preventing untrustworthy third-parties from maliciously damaging the data and ensuring data security. In addition, through access control and the Lucene retrieval mechanism, patient privacy is protected and rapid retrieval of health management data is achieved. Experimental analysis shows that compared with algorithms such as Proof of Work (POW) and Delegated Proof of Stake (DPOS), the improved PBFT consensus algorithm endows the medical blockchain system with better stability and throughput. Compared with the ordinary database interaction method, the designed data interaction system effectively prevents direct operations on the database and has good security and anti-tampering properties. Experimental data indicates that the decentralized health management data storage system constructed by integrating blockchain technology based on the improved PBFT consensus algorithm realizes the secure storage, traceability, and anti-tampering of health management data, overcomes the problems of centralized storage, non-traceability, and vulnerability to attacks of health management data, and lays a foundation for the in-depth application and development of blockchain technology in the medical information field.
This paper proposes a supply chain transparency improvement and smart contract optimization method based on blockchain, artificial intelligence and communication network. First, combining the decentralized characteristics of blockchain, a transparent and tamper-proof data management platform is designed to automatically execute supply chain transactions through smart contracts, and at the same time, the efficient data transmission characteristics of the communication network are used to achieve rapid synchronization of information between multiple nodes. Secondly, machine learning and deep learning algorithms are used to optimize demand forecasting, resource scheduling and smart contract terms in the supply chain, and real-time data collection and transmission are achieved through the communication network, further improving the overall operation efficiency of the supply chain. Experimental simulation results show that the supply chain system based on this method has significant advantages in improving transparency, the execution efficiency of smart contracts is improved by 30%, and the overall response time is shortened by 20%.
Smart contracts, as one of the important applications of blockchain technology, provide the possibility for automated execution of payment systems. This article explores an automated execution algorithm for smart contracts in payment systems based on blockchain and big data technology to address the issues of low accuracy and efficiency in contract execution. This algorithm utilizes the immutability and decentralization features of blockchain to ensure the security and transparency of payment transactions. At the same time, combined with the powerful analytical capabilities of big data, deep mining of transaction data is carried out to achieve accurate prediction and risk management of transactions. The automated execution function of smart contracts further simplifies the transaction process, reduces human intervention, and improves the efficiency and reliability of payment systems. The experimental results show that through the proposed algorithm, the payment system can provide more secure, efficient, and personalized services, promoting the digital transformation and development of the financial transaction field.
With the rapid globalization of e-commerce, enterprises are increasingly seeking to expand into international markets, necessitating efficient cross-border payment solutions. This paper applies blockchain technology, leveraging the Ripple consensus algorithm, to create a smart and automated cross-border payment system in e-commerce. The proposed system aims to enhance transparency, automation, and efficiency in managing international transactions. Using a distributed ledger, cryptographic algorithms, and consensus mechanisms, the Ripple-based technology ensures transaction security, traceability, and real-time processing. Unlike traditional cross-border payments, this smart payment system minimizes intermediaries, thereby reducing transaction costs and time while preventing data tampering and fraud. The case analysis of Ripple’s cross-border payment platform highlights numerous advantages for e-commerce, including real-time settlement, reduced costs, and enhanced operational efficiency. Moreover, the system addresses challenges like exchange rate fluctuations and payment delays, making cross-border payments more reliable, automated, and user-friendly. Despite its benefits, risks such as cyber attacks and privacy concerns remain; thus, implementing robust cybersecurity and compliance frameworks is essential. In summary, the Ripple consensus algorithm provides a forward-looking approach for intelligent cross-border payments in e-commerce, advancing global payment systems toward smarter, more resilient, and efficient solutions with significant practical impact and potential for broad adoption.
This study explores the optimization path of the e-commerce supply chain logistics management model using blockchain technology, breaks the bottlenecks of information barriers, low operational efficiency and high costs in the traditional operation model, adopts design strategies to build a blockchain technology application architecture, integrates distributed ledger technology, smart contract functions, Internet of Things technology and decentralized identity authentication methods, and implements refined improvements in each stage of the e-commerce supply chain. Data collection is conducted on the order execution process, logistics distribution links and cost components of the three e-commerce platforms A, B and C, and an in-depth comparative analysis is conducted on the differences between the traditional model and the blockchain optimization model in terms of efficiency, economic cost and customer satisfaction. The research results show that the optimization measures using blockchain technology have significantly improved the efficiency of order processing and distribution, effectively reduced logistics cost expenditures, and significantly increased customer satisfaction levels.
随着全球数字贸易的深化发展，跨境电商作为国际贸易新业态正面临信任机制重构的重要转折点。本文聚焦区块链技术在跨境电商信任机制中的创新应用及其现实挑战，旨在探索技术赋能与制度创新的协同路径。通过系统分析区块链的分布式账本、智能合约等核心技术特征，揭示其在商品溯源、支付结算、供应链管理等关键场景中的信任构建机理，提出信息透明化、流程自动化等突破性解决方案，研究进一步剖析区块链技术落地的多维瓶颈，包括跨境法律冲突、隐私保护悖论、行业标准缺失等复合型挑战，并针对性提出跨链技术优化、智能合约法律确权、零知识证明加密等创新性改进策略。研究不仅为区块链驱动的信任机制优化提供理论框架，更从技术适配与制度调适的双重视角，为跨境电商企业构建合规高效的区块链应用体系提供实践指引，推动跨境贸易生态向可信化、智能化方向演进。As global digital trade continues to evolve, cross-border e-commerce is at a crucial juncture regarding the establishment of trust mechanisms. This paper examines the innovative use of blockchain technology in enhancing trust within cross-border e-commerce and the practical challenges it faces. The goal is to investigate how technology and institutional innovation can work together. By analyzing key features of blockchain, such as distributed ledgers and smart contracts, the paper highlights how these elements can foster trust in critical areas like product traceability, payment processing, and supply chain management. It suggests solutions like increased information transparency and automated processes while also addressing various implementation challenges, including international legal issues, privacy concerns, and the absence of industry standards. The paper proposes strategies for improvement, such as optimizing cross-chain technology, confirming legal rights for smart contracts, and employing zero-knowledge proof encryption. This research not only offers a theoretical framework for enhancing blockchain-based trust mechanisms but also provides practical advice for cross-border e-commerce businesses to develop compliant and efficient blockchain systems, ultimately steering the evolution of cross-border trade towards a more trustworthy and intelligent future.
The cryptocurrency market, with its 24/7 trading and high volatility, challenges traditional quantitative strategies in path dependency, high-frequency optimization, and risk control. A "prediction-decision" framework is proposed, integrating Gradient Boosting Regression Trees (GBRT) for short-term forecasting and deep reinforcement learning techniques including Rainbow Deep Q-Network (Rainbow DQN) and Soft Actor-Critic (SAC) algorithms for dynamic optimization. The framework combines the complementary strengths of GBRT's pattern recognition capabilities and deep reinforcement learning's adaptive decision-making mechanisms. A spatiotemporal experience replay mechanism tailored to cryptocurrency fat-tailed distributions boosts BTC/USDT annual returns by 37.2% and enhances TD3's drawdown control by 63% during the LUNA crisis. Empirical results show SAC achieves 152% excess returns (Sharpe 2.81) in ETH/USDT trading, while Rainbow DQN yields 287% returns in trend markets. A dynamic reward function reduces maximum drawdown from 42.7% to 19.3%, and the hybrid architecture curtails losses by 23.8% during the 2020 "March 12" crash. Curriculum learning accelerates TD3 convergence by 59% with 37% GPU memory reduction. This study establishes a verifiable algorithmic framework and advances high-frequency trading through synergistic ML/RL integration, offering a dynamic decision-making paradigm for evolving financial markets.
Contemporarily, blockchain techniques have been developed rapidly. This study describes the development of a blockchain-based website designed to display cryptocurrency information securely and transparently. The site utilizes Ethereum and smart contracts to ensure data immutability and transaction transparency. The front end is developed using the React framework to enhance the user experience, and the back end uses Web3.js for real-time blockchain interaction. A detailed case study of the cryptocurrency demo evaluated the performance of the website and the practical application of blockchain technology. The findings demonstrate the effectiveness of blockchain in securely managing and displaying sensitive financial data. In addition, the study highlights blockchain's potential to revolutionize fintech infrastructure by improving security and transparency, which are essential for the dissemination of financial information. These results makes a significant contribution to the use of blockchain in web development, drawing a blueprint for future research directions with a focus on optimizing transaction processes and improving user accessibility. Overall, the study demonstrates the transformative potential of blockchain technology in digital transactions and sets the stage for future innovation in this rapidly evolving field.
As an important product of the Internet era, the essence of green sharing economy is to rely on Internet technology. In particular, big data technology will re-use idle social resources and create new market value. However, with the increasing application of sharing economy in the field of green industry, the problems of data security, circulation, sharing and privacy protection gradually appear. Big data technology has become the biggest bottleneck for the further development of sharing economy. Blockchain technology is a new Internet architecture composed of various technologies and communication protocols. Through its encryption sharing, distributed ledger and other characteristics, it provides new methods and ideas for data circulation and sharing, which complement big data technology. Therefore, the integration of blockchain technology and big data technology can subvert the traditional business model of sharing economy. This can innovate the mode and development of traditional green industry, and provide new opportunities for the development of green sharing economy.
T A Mohanaprakash, Ranganayaki V.C, M. S. Minu, D. A. · 5 authors
Many have long aimed to create a safe electronic voting system that maintains the confidentiality and integrity of traditional voting methods while using the convenience and openness of modern technology. Ballot paper or electronic voting machines are the current voting schemes in every nation, and democratic voting is a significant event in every country. Problems with these procedures abound, including lack of openness, poor voter turnout, vote manipulation, mistrust of the election body, forgery of unique identification (voter ID card), delays in disseminating results, and, most importantly, security breaches. Prioritizing the security of digital voting is of utmost importance when contemplating implementing a digital voting system. The article assesses the objective of building a blockchain-based e-voting system [BC-E-VOT] that uses digital voting technology. Electronic voting methods that leverage the distributed ledger attract much attention because they can make digital voting more transparent, secure, and honest. As shown in this research, a successful strategy for electronic voting may be achieved by using Blockchain's cryptographic underpinnings and transparency. Due to its complete transparency, the suggested approach satisfies the essential criteria for electronic voting systems. Since Blockchain employs a decentralized mechanism for data storage rather than storing all of the data in one central place, it becomes challenging to tamper with the data when utilizing this technology to build a decentralized application. By creating a decentralized system using the WSN platform, a third party is no longer needed to oversee the election's access control. This article provides a system for electronic voting that guarantees privacy, trustworthiness, and security. The suggested approach is practical and secure, according to the findings.
The research on international trade settlement and tracking system based on blockchain technology aims to solve the problems of low efficiency, lack of transparency and data security in the traditional cross-border trade settlement process. By introducing distributed ledger technology, the system can realize real-time tracking of all links of cross-border trade, ensure the immutability of data, and thus improving data transparency and traceability. The application of zero-knowledge proof technology in this system further guarantees the privacy of transaction data, that is, the legality and authenticity of the transaction can be verified without revealing specific sensitive information. This system can not only significantly reduce the trust cost of relying on intermediaries, but also improve settlement efficiency through automated processes. Through simulation analysis, the test results show that the system shows high stability and reliability when processing high-concurrency transactions, especially in protecting data privacy, reducing trust costs and improving settlement efficiency. The precise data analysis of the simulation results further verifies the feasibility and effectiveness of the system in the actual international trade environment, laying a solid foundation for future promotion and application.