Advances in Blockchain Challenges

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 4452

Special Issue Editors


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Guest Editor
Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei City 10610, Taiwan
Interests: data science; FinTech; AI agent design; blockchain

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Guest Editor
Wells Fargo Bank, New York, NY 10017, USA
Interests: quantum AI; privacy preserving machine learning; distributed computing

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue on "Advances in Blockchain Challenges", inviting researchers, practitioners, and experts to contribute cutting-edge research and innovative solutions that address the evolving challenges in blockchain technology. With blockchain’s rapid expansion into various industries, such as finance, healthcare, supply chain management, and carbon credit markets, overcoming critical obstacles—scalability, security, privacy, and fraud detection—has never been more pressing.

While blockchain offers decentralized and immutable record-keeping systems, several challenges hinder its full potential. Issues such as limited scalability, high energy consumption, privacy concerns, regulatory hurdles, and transparency in fraud prevention pose significant barriers to widespread adoption. As industries increasingly integrate blockchain to improve transparency, security, and sustainability, addressing these challenges through advanced research and innovative approaches is crucial.

This Special Issue highlights recent advancements in blockchain technology that tackle these obstacles. Topics of interest include but are not limited to the following:

  • Scalability Solutions: Novel approaches to enhance transaction throughput, reduce latency, and improve consensus mechanisms in large-scale blockchain systems.
  • Privacy-Preserving Techniques: Advanced cryptographic methods, such as zero-knowledge proofs, homomorphic encryption, and privacy-preserving quantum AI, to ensure user privacy and data security on blockchain platforms.
  • Energy Efficiency: Research on reducing the environmental impact of blockchain systems, including energy-efficient consensus algorithms and the integration of green technologies.
  • Quantum-Resistant Blockchain: Exploring quantum computing’s impact on blockchain security, including developing quantum-resistant cryptographic protocols and quantum machine learning algorithms that enhance blockchain resilience.
  • Blockchain for Carbon Credit Trading: Papers that explore the role of blockchain and smart contracts in carbon credit markets, emphasizing transparency, fraud detection, and compliance with mechanisms such as the Carbon Border Adjustment Mechanism (CBAM) and the Clean Competition Act (CCA). These papers should highlight how blockchain can improve carbon trading efficiency and support sustainability goals.

Dr. Yun-Cheng Tsai
Dr. Samuel Yen-Chi Chen
Guest Editors

Manuscript Submission Information

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Keywords

  • blockchain scalability
  • privacy-preserving blockchain
  • quantum-resistant blockchain
  • carbon credit trading
  • blockchain fraud detection

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Published Papers (1 paper)

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Research

15 pages, 5155 KiB  
Article
Enhancing Transparency and Fraud Detection in Carbon Credit Markets Through Blockchain-Based Visualization Techniques
by Yun-Cheng Tsai
Electronics 2025, 14(1), 157; https://doi.org/10.3390/electronics14010157 - 2 Jan 2025
Viewed by 4122
Abstract
Net-zero emission targets require transparent and efficient carbon credit trading systems. This paper introduces a blockchain-based data visualization framework to enhance decision-making in the production and logistics sectors by simplifying blockchain transaction records and identifying potential arbitrage activities. The framework integrates real-time decision [...] Read more.
Net-zero emission targets require transparent and efficient carbon credit trading systems. This paper introduces a blockchain-based data visualization framework to enhance decision-making in the production and logistics sectors by simplifying blockchain transaction records and identifying potential arbitrage activities. The framework integrates real-time decision support tools, enabling production system managers to monitor carbon offset activities, detect fraudulent behaviors, and streamline operations. This research provides actionable insights into supply chain emissions management and operational risk reduction by leveraging advanced visualization techniques. The proposed approach offers innovative solutions to address the complexities of blockchain-based carbon trading, emphasizing transparency and sustainability. Our analysis demonstrates the effectiveness of these techniques in mitigating fraud and supporting compliance with international carbon trading standards. The findings contribute to integrating advanced technologies into sustainable production systems, offering practical implications for achieving global climate change mitigation goals and fostering a more efficient and secure carbon credit market. Full article
(This article belongs to the Special Issue Advances in Blockchain Challenges)
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