Enhancing Transparency and Fraud Detection in Carbon Credit Markets Through Blockchain-Based Visualization Techniques

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This study proposes a visualization method that combines blockchain technology and the carbon credit market to increase transaction transparency and support fraud detection. The focus on detecting circular transactions and analyzing arbitrage is a concrete methodology for improving the transparency of the carbon market. In particular, the “method of visualizing the size of nodes and the thickness of edges in terms of transaction volume and frequency” is a practical approach for intuitively representing the complexity of data.

1. Technology related to regulatory structure

A description of how the proposed framework aligns with existing regulatory structures needs to be provided.

Please add a description of the alignment with existing regulatory structures to the appropriate section.

 

2. Description of algorithm verification

The author has published code for collecting and processing data to verify the paper's algorithms on GitHub.

The details of the preprocessing of the data (e.g. removal of duplicate transactions, three-tier tracking of wallets) are explained in “README.md”, but the specific code and parameters (e.g. filtering conditions, cluster threshold) are not described. Please add reference values for “MAX_DEPTH:” and “TX_COUNT_THRESHOLD:” so third parties can easily verify them. Also, please add a description in the paper that the reference values for “MAX_DEPTH:” and “TX_COUNT_THRESHOLD:” are listed in the “README.md” file.

 

 

3. Revision of format

Review of section numbers

L.246 .2. Using Examples from the Dataset to Detect Arbitrage

 

The section number for this line is inappropriate. Please fix it.

Please also look at the section numbers for subsequent sections in conjunction with this revision.

Author Response

Thank you very much for your valuable feedback and constructive suggestions. We have carefully addressed all your comments and incorporated the revisions into the manuscript. We have summarized the detailed responses and highlighted the changes in the attached PDF document to facilitate your review.

Please refer to the uploaded PDF file for a point-by-point response to your comments, including specific revisions and their corresponding locations in the manuscript.

Should you have any further suggestions or clarifications, we will be more than happy to address them.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

1. While the paper claims that blockchain-based visualization techniques improve transparency in carbon credit trading, it lacks quantitative comparisons to demonstrate how much more effective these techniques are compared to existing methods. There is no experimental validation of how useful and intuitive the visualization techniques are for decision-makers.

2. The claim that the framework supports real-time decision-making is not backed by specific case studies or performance evaluations.

3. Although the framework identifies behaviors indicative of arbitrage, the paper does not provide clear evidence that these behaviors were indeed fraudulent. The superiority of the proposed framework compared to existing fraud detection methods is not explained.

4.  It does not include user studies or feedback to demonstrate how intuitive and practical the visualization tools are for stakeholders.

5. The paper does not sufficiently analyze existing studies on blockchain-based carbon credit trading, nor does it clearly explain the distinct contributions of the proposed framework.

6. The paper does not consider key performance metrics such as transactions per second (TPS), latency, or gas fees of the blockchain network (Ethereum) used. There is no discussion on scalability solutions (e.g., Layer 2, sharding) to handle high transaction volumes.

7. The paper does not evaluate whether blockchain transaction processing can meet real-time data processing requirements. Potential delays or bottlenecks in the data collection and processing stages are not addressed.

 

Author Response

Thank you very much for your valuable feedback and constructive suggestions. We have carefully addressed all your comments and incorporated the revisions into the manuscript. We have summarized the detailed responses and highlighted the changes in the attached PDF document to facilitate your review.

Please refer to the uploaded PDF file for a point-by-point response to your comments, including specific revisions and their corresponding locations in the manuscript.

Should you have any further suggestions or clarifications, we will be more than happy to address them.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The authors present a framework that uses advanced visualization techniques to address the inherent complexities of blockchain transactions, to enhance transparency and fraud detection within carbon credit trading systems.

The proposed framework can be used to identify arbitrage opportunities, enabling stakeholders to detect and capitalize on discrepancies within the carbon credit trading market.

Research findings suggest that integrating blockchain with real-time decision support tools can substantially improve the traceability and reliability of carbon credit markets.

The proposed approach significantly contributes to creating a more transparent and secure system for trading carbon credits, promoting sustainability and trust in this emerging field.

The authors provide the data set and the code used on github.

Weaknesses:

1. A graphic description of the architecture of the proposed system would be useful

2. A more detailed explanation of the elements in the presented figures and the meaning of the colors is necessary

3. it is not clear what are the outputs that the proposed framework generates

4. The discussion of the obtained results needs to be clearer

Author Response

Thank you very much for your valuable feedback and constructive suggestions. We have carefully addressed all your comments and incorporated the revisions into the manuscript. We have summarized the detailed responses and highlighted the changes in the attached PDF document to facilitate your review.

Please refer to the uploaded PDF file for a point-by-point response to your comments, including specific revisions and their corresponding locations in the manuscript.

Should you have any further suggestions or clarifications, we will be more than happy to address them.

Author Response File: Author Response.pdf