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

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 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.

1. Introduction

The escalating urgency to address climate change has led to global initiatives focused on achieving net-zero emission targets, particularly in carbon-intensive industries such as manufacturing and logistics. Among these initiatives, carbon credit trading has emerged as a critical mechanism to incentivize organizations to reduce greenhouse gas emissions. This market-based approach allows companies to trade emission allowances, creating financial motivations to minimize their carbon footprint. However, the complexity and opacity of carbon trading systems, especially those utilizing blockchain technology, present significant challenges in ensuring transparency and preventing fraudulent activities, such as arbitrage [1,2,3].

Blockchain technology offers unique advantages in carbon credit trading by providing a decentralized and immutable ledger that ensures traceability and security. These features make it an ideal solution for enhancing transparency in carbon credit transactions [4,5]. However, the intricate nature of blockchain transaction records, characterized by hash codes and complex wallet addresses, often hinders clear interpretation and decision-making. This limitation is particularly problematic for production managers, supply chain operators, and policymakers, as it can obscure critical information and enable exploitative practices like arbitrage, undermining the integrity of carbon markets [6,7].

To address these challenges, advanced data-driven tools are needed to enhance the readability of blockchain transaction records and provide actionable insights for decision-makers. When integrated with real-time decision support frameworks, visualization techniques hold significant potential to simplify the interpretation of blockchain data, identify suspicious activities, and improve market efficiency. By enabling production and logistics managers to monitor carbon trading pathways and detect anomalies, these tools can contribute to maintaining the integrity of carbon markets while fostering sustainability in industrial operations [8,9,10].

This paper proposes an innovative visualization framework that addresses the complexities of blockchain-based carbon credit trading. The framework aims to enhance transparency, streamline operations, and mitigate fraudulent activities by offering real-time decision support for production and logistics systems practitioners. By analyzing the application of these techniques in real-world scenarios, this research provides valuable insights into the intersection of blockchain technology and sustainable production systems, supporting global efforts to combat climate change [11,12,13].

2. Literature Review

2.1. Introduction to Carbon Credit Trading and Blockchain

Carbon credit trading is a crucial market-based mechanism for reducing greenhouse gas emissions. It enables companies to trade credits that allow them to emit a specified amount of carbon dioxide. This incentivizes organizations to minimize emissions by saving money or generating profits from surplus credits. With its decentralized and immutable ledger, blockchain technology has emerged as a promising solution to enhance transparency and traceability in carbon credit trading [1,2].

Prior research has validated the effectiveness of blockchain-based visualization techniques. For example, Peters and Panayi (2016) demonstrated how visualization techniques enhance the traceability and comprehension of complex financial transactions, improving transparency in distributed ledgers [14]. Similarly, Tovanich et al. (2019) systematically reviewed visualization tools and highlighted their role in simplifying blockchain transaction data for decision-makers [15]. These studies support the claims presented in this framework, particularly regarding transparency improvement and usability for stakeholders.

Tapscott and Tapscott (2016) highlight that blockchain ensures each carbon credit is unique and accounted for, promoting trust among market participants [3]. Crosby et al. (2016) emphasize the elimination of intermediaries, leading to reduced transaction costs and increased efficiency [4]. However, despite these advantages, the inherent complexity of blockchain transaction records can hinder their accessibility and practical use [5,6].

2.2. Blockchain Technology in Carbon Credit Trading

Blockchain’s role in carbon credit trading has been increasingly explored due to its transparency, security, and decentralized architecture. Tapscott and Tapscott (2016) describe blockchain’s ability to track carbon credits transparently while ensuring immutability and accountability [3]. Additionally, Crosby et al. (2016) stress the cost-efficiency benefits of blockchain, which eliminates reliance on intermediaries, further streamlining trading processes [4].

Blockchain’s ability to provide instantaneous immutable transaction updates supports real-time decision-making. As noted by Tapscott and Tapscott (2016), blockchain enables a real-time data-sharing environment where transactions are updated as soon as they occur [3]. Zhang and Wen (2017) further emphasize blockchain’s role in IoT-based real-time data analysis, which can be integrated with visualization frameworks to facilitate real-time monitoring and anomaly detection [16,17]. This reinforces the framework’s capability to support immediate decision-making without reliance on intermediaries. Studies by Anderson (2020) and Mukherjee (2018) extend these findings, showing blockchain’s potential to enhance operational efficiency across industries, including environmental sustainability [7,8]. These advantages make blockchain an attractive option for carbon credit markets striving for transparency and efficiency.

2.3. Advantages of Blockchain Technology

Blockchain offers significant advantages in carbon credit trading, particularly in achieving net-zero emissions goals.

• Transparency: Blockchain records all transactions on an immutable ledger, providing verifiable proof of carbon offset activities [1,5];
• Security: Its decentralized nature ensures data integrity and protection against malicious tampering [4,9]. While the current framework focuses on identifying behaviors indicative of arbitrage, it does not claim to classify these behaviors as fraudulent definitively. Instead, it serves as a decision-support tool highlighting anomalies and suspicious trading patterns for further investigation. Compared to traditional fraud detection approaches, such as machine learning-based anomaly detection [16,17], this visualization-based framework prioritizes interpretability and real-time anomaly identification. The visual outputs enable decision-makers to quickly pinpoint potential irregularities without requiring specialized technical expertise. Future work could integrate machine learning techniques to automate anomaly classification and provide quantitative assessments of fraud detection accuracy, further enhancing the framework’s utility and effectiveness;
• Traceability: Blockchain enables end-to-end tracking of carbon credits, ensuring their authenticity and uniqueness [3,10];
• Global Participation: Blockchain reduces global stakeholders’ barriers to engaging in carbon offset activities, promoting broader environmental protection efforts [11,12].

These benefits collectively enhance the credibility and functionality of carbon credit markets.

2.4. Introduction to Moss Carbon Credit (MCO2) Tokens

Moss Carbon Credit (MCO2) tokens represent an innovative digital asset aimed at offsetting carbon emissions. Each token corresponds to a verified reduction in greenhouse gas emissions, supporting projects like reforestation and renewable energy [13].

• Purpose: MCO2 tokens enable individuals and organizations to offset their carbon footprint while supporting environmental projects;
• Issuer: Moss, a climate-tech company, rigorously verifies and certifies these credits [13];
• Blockchain Integration: MCO2 tokens utilize the blockchain to ensure transparency, traceability, and security;
• Market Dynamics: These tokens are traded on cryptocurrency exchanges, fostering liquidity and price discovery;
• Challenges: Market volatility and regulatory inconsistencies remain barriers to widespread adoption [18].

Integrating blockchain with carbon credits demonstrates its transformative potential in addressing climate change.

2.5. Challenges in Reading Blockchain Transaction Records

Despite its benefits, blockchain presents challenges in interpreting transaction data. Records are encoded in complex hash codes and wallet addresses, making them difficult for ordinary users to comprehend [5,6]. Catalini and Gans (2016) note that this complexity can obscure transparency, potentially allowing fraudulent activities like arbitrage and fraud to go unnoticed [19].

2.6. Importance of Visualization in Blockchain Transactions

Visualization tools significantly enhance the usability of blockchain data. Peters and Panayi (2016) argue that visualization techniques improve comprehension by making transaction pathways and wallet relationships more accessible [10]. Tapscott and Tapscott (2016) further emphasize that these tools can detect abnormal patterns, enabling users to identify potentially fraudulent activities [3]. This functionality is crucial for ensuring compliance and maintaining the integrity of carbon credit markets.

2.7. Compliance and Fraud Detection

Visualization tools also play a critical role in regulatory compliance and fraud detection. By mapping transaction pathways, these tools can identify activities that deviate from established guidelines, enabling timely intervention [4]. Studies demonstrate their effectiveness in preventing fraud and enhancing the security of blockchain-based systems [9,20,21].

2.8. Proposed Visualization Method for Carbon Credit Trading

This study proposes a visualization framework to address challenges related to the readability and interpretability of blockchain transaction records. The framework aims to improve transaction tracking and ensure compliance with trading guidelines, enhancing the security and transparency of carbon credit markets [9,10,22].

2.9. Identifying and Mitigating Arbitrage Activities

Arbitrage, or the exploitation of price differences across markets, poses a significant risk to market integrity. Visualization techniques can help identify and mitigate these activities by highlighting unusual transaction patterns and cycles. Zhang and Wen (2017) suggest that data-driven visualization can effectively analyze trading data, improve market transparency, and reduce arbitrage risks [17].

3. Materials and Methods

3.1. Data Collection

The dataset for this study consists of blockchain transaction records related to Moss Carbon Credit (MCO2) tokens. Historical transaction data were retrieved from the Ethereum blockchain using the Etherscan API. Wallet addresses of interest were selected to trace carbon credit trading activities across three layers of transaction depth. The primary dataset includes transaction timestamps, block numbers, hash values, wallet addresses, token names, token symbols, and transaction values. To ensure data accuracy and relevance, transactions with zero value or those involving unverified contracts were excluded. The processed data were stored in CSV format, with fields including the following:

• Layer: Recursive layer of transaction tracking;
• BlockNumber: The block number associated with each transaction;
• TimeStamp: Human-readable transaction time;
• Hash: Unique identifier for each transaction;
• From and To: Wallet addresses involved in the transaction;
• Value: Token amount in standardized units;
• TokenName and TokenSymbol: Information about the token involved.

3.2. Data Processing

To handle the volume and complexity of the blockchain data, the following techniques were employed:

• Recursive Data Retrieval: Transactions were tracked layer-by-layer up to a depth of three, starting with a predefined set of wallet addresses. This method ensured a comprehensive capture of transaction pathways;
• Asynchronous Processing: Python’s asyncio and aiohttp libraries optimized data retrieval, enabling concurrent processing of multiple requests to the Etherscan API;
• Data Cleaning: Irrelevant or duplicate transactions were filtered out, and data were formatted into a human-readable structure.

To ensure reproducibility, key preprocessing parameters include MAX_DEPTH = 3 (the maximum depth for transaction tracking) and TX_COUNT_THRESHOLD = 10 (the minimum transaction frequency used to identify high-frequency nodes). These values and detailed instructions are documented in the project’s accompanying GitHub repository [https://github.com/peculab/TrackingCarbon, accessed on 27 November 2024]. The data collection and processing code is publicly accessible for third-party verification and further analysis.

3.3. Visualization Framework

A visualization framework was developed to analyze and display transaction patterns:

• Network Graph Construction: Nodes represented wallet addresses, and edges depicted transactions. Node size indicated transaction frequency, while edge thickness reflected transaction value;
• Clustering and Temporal Analysis: Clusters of wallet addresses with high transaction activity were identified, and temporal patterns were analyzed using time-sliding tools;
• Visualization Tools: We utilized Python 3.8+ libraries for data visualization, incorporating Pandas 1.5.x for static plotting and Plotly 5.x for interactive network analysis.

3.4. Arbitrage Detection

To identify potential arbitrage activities, the following steps were conducted:

• Cyclic Transaction Analysis: Cyclic patterns among wallets were identified to trace repetitive trading behaviors;
• Node Degree Analysis: High-frequency nodes (with numerous incoming and outgoing transactions) were flagged for further investigation;
• Cross-Market Analysis: Transaction times were cross-referenced with price charts from various exchanges to identify price discrepancies;
• Machine Learning: Statistical anomaly detection models were applied to flag irregular transaction patterns.

4. Results

4.1. Transaction Network Analysis

The visualization framework successfully constructed transaction networks for Moss Carbon Credit (MCO2) tokens, highlighting critical patterns in the trading ecosystem.

• Node and Edge Characteristics: A network graph illustrating the structure of all transactions, with node size indicating transaction frequency, node color representing total transaction value, and edge thickness reflecting transaction volume (see Figure 1.)

  ০

  A total of 1245 unique wallet addresses and 3628 transactions were identified within the three-layer network;

  ০

  High-transaction-volume nodes, such as “0xb8ba36e591facee901ffd3d5d82df491551ad7ef”, emerged as central hubs with significant activity. They conducted 1188 transactions with a cumulative value of 702,271 MCO2 tokens. These nodes often interacted with smaller nodes, creating dense clusters indicative of coordinated trading behavior.

• Cluster Analysis:

  ০

  Dense Clusters: Nodes with high transaction frequencies were observed to cluster around centralized exchanges such as “Mercado Bitcoin 1”, which facilitated 180 transactions totaling 159,204.74 MCO2 tokens. These clusters demonstrated streamlined trading routes between a small number of key participants. The dense cluster, which lasted from 29 April 2018 to 17 April 2024, features a prominent large red node with intense transaction activity, as shown in Figure 2. It involved 117 wallet addresses, 180 transactions, and a total value of 159,204.741094. Further investigation identifies this central node as “Mercado Bitcoin 1”, a centralized cryptocurrency exchange platform established in 2013 in Brazil that offers 228 currencies and 281 trading pairs;

  ০

  Sparse Clusters: Transaction cycles were identified in sparse clusters, with recurring patterns emerging between April 2020 and April 2021. These cycles often involved multiple wallets with coordinated trading behaviors (see Figure 3).

4.2. Cyclic Patterns and Arbitrage Detection

The cyclic transaction analysis identified key behaviors consistent with arbitrage strategies. It shows the Router 2 contract and its transactional links with other wallets, emphasizing repetitive patterns (see Figure 4).

• A prominent node, identified as the Router 2 contract (see Figure 5) in the Uniswap V2 protocol, facilitated 22 cyclic transactions involving MCO2 and Wrapped Ether (WETH) tokens. Each transaction cycle exhibited balanced input and output, indicating potential arbitrage activities;
• These cycles were conducted within tight timeframes, with transaction intervals as short as 30 s, optimizing opportunities to exploit price discrepancies across liquidity pools.

In Figure 4, the overlapping elements remain to preserve the original design, and the key address is displayed at the bottom. Although part of the figure appears truncated, this does not impede understanding, as the focus is the color change and the corresponding transaction flows.

4.3. Using Examples from the Dataset to Detect Arbitrafige

Our analytical methods have identified significant arbitrage opportunities within the carbon credit trading ecosystem, particularly involving Moss Carbon Credit (MCO2) tokens. We have uncovered key patterns and behaviors indicative of arbitrage activities by tracing MCO2’s transaction history across three layers from 29 April 2018 to 17 April 2024.

4.3.1. Case 1—High-Frequency Trading Addresses

Select the Suspected Nodes

• Observation: Notably, the node 0xb8ba36e591facee901ffd3d5d82df491551ad7ef tops the list with 1188 transactions, as shown in Figure 6 and Figure 7. This node shows significant transaction activity, primarily concentrated in the first layer, with a total value of 702,271 and 55 degrees of connection. The second layer has a total value of 145,920 and 125 degrees of connection. This activity concentration suggests that the node plays a central role in potential arbitrage patterns;
• Potential Issue: The high frequency of transactions may indicate potential money laundering activities;
• Description: Focus on nodes with high transaction volumes. Use analytical tools to zoom in and examine the transaction details between these nodes. By analyzing the transaction patterns and values associated with these high-frequency addresses, we can identify whether the activities are part of regular trading or indicative of arbitrage.

By integrating these steps, we can systematically detect and analyze high-frequency trading addresses, ensuring a comprehensive understanding and appropriate response to potential arbitrage activities within transaction networks. The example demonstrates how focusing on transaction values and patterns can reveal repetitive high-value transactions characteristic of arbitrage, leading to identifying suspicious activities.

In Figure 7, these yellow nodes represent first-tier recipients of transactions originating from the green node. Meanwhile, on the left, red nodes generally indicate other high-activity addresses, purple nodes act as bridging or intermediary addresses, and blue nodes have minimal or no transactions. The gray arrows depict the direction of transactions flowing from a source to a target address. Although the figure is partially cropped, this does not impede understanding of the overall pattern. The key takeaway is the central role of the green node and how it disperses transactions to its immediate neighbors—suggesting potential arbitrage or bridging activities in the network.

4.3.2. Case 2—Anomalous Large Transactions

Transaction Values and Patterns

• List Out Top 20 Suspicious Cycles: Identify and list the top 20 suspicious cycles. Observing these cycles, you may notice many overlaps with addresses exhibiting high transaction frequencies. This correlation suggests a pattern of behavior that warrants further investigation. Both top suspicious cycles and top frequency addresses often show duplications, indicating potential coordinated activities;
• Observation: For instance, the address 0xa9d1e08c7793af67e9d92fe308d5697fb81d3e43 conducted multiple high-frequency transactions on December 26, 2023, totaling 2830.05 units shown in Figure 8 and Figure 9;
• Potential Issue: These transactions’ large volume and frequency suggest possible arbitrage activities;
• Description: Analyze the values transferred in each transaction and their consistency over time. Arbitrage might manifest as repetitive high-value transactions conducted in a predictable pattern. Such analysis helps understand whether the transactions are part of regular trading activities or indicative of arbitrage;
• Check Frequency of Cycles: Recognize that the presence of a cycle does not guarantee fraudulent or laundering activities. The swap transactions might happen over a broad period. Therefore, it is crucial to check the frequency of these cycles. A higher frequency of suspicious cycles in a short period increases the likelihood of identifying fraudulent activities. Visualization tools with features like a time slider can help visualize these cycles, providing insights into their temporal patterns.

By integrating these steps, you can thoroughly analyze detected arbitrage cycles, ensuring a comprehensive understanding and appropriate response to potential arbitrage activities within transaction networks. The example demonstrates how focusing on transaction values and patterns can reveal repetitive high-value transactions characteristic of arbitrage, leading to identifying suspicious activities.

In Figure 8, the yellow nodes attached to the green node represent its immediate neighbors—i.e., addresses that send or receive transactions from this central node. On the left side of the figure, red nodes generally denote other high-value addresses. In contrast, purple nodes serve as bridging or intermediary addresses, and blue nodes have minimal or no transactions. The gray arrows indicate the direction of transaction flow, pointing from a source address to a target address. Although the figure shows only partial clusters, the main takeaway is how the highlighted green node is linked to other nodes, suggesting a key role in potential arbitrage or high-value transaction chains within the MCO2 network.

In Figure 9, each row shows the sender (From), recipient (To), transferred amount (Value), network connectivity (Degree), and transaction date/time (Time). Frequent high-value transfers and a relatively high degree indicate that this address may actively engage in opportunistic trading or other forms of arbitrage within the MCO2 network.

4.3.3. Case 3—Collaborative Trading Clusters

Cross-Platform Analysis

• Observation: Our analysis has uncovered interesting nodes that exhibit patterns of single-way dumping tokens to large nodes or gathering substantial funds from several leaf nodes. These patterns indicate potential arbitrage activities that need further investigation. Multiple addresses within a cluster are frequently traded with each other. This is evident in the transaction network visualization shown in Figure 10 and Figure 11. For instance, the node 0x3f5ce5fbfe3e9af3971dd833d26ba9b5c936f0be has a value of 254818.31252 and a degree of 13, interacting frequently with multiple addresses,
• Potential Issue: This frequent trading among clustered addresses may indicate collaborative arbitrage activities. Such activities could exploit price discrepancies across different platforms or chains, suggesting a coordinated effort to maximize profits through arbitrage;
• Description: If the data permits, verify whether the transactions are cross-chain or involve different platforms where price discrepancies might be more likely. This cross-platform analysis is crucial for confirming the presence of arbitrage activities. For instance, transactions involving the same addresses across multiple blockchain networks or exchanges can highlight arbitrage opportunities being exploited.

By integrating cross-platform analysis, you can systematically verify the presence of arbitrage activities. This involves examining whether transactions span different platforms or chains and identifying clusters of addresses that frequently trade with each other. The example demonstrates how collaborative cluster trading can indicate potential arbitrage activities, reinforcing the need for thorough cross-platform verification.

In Figure 10, surrounding yellow nodes are its immediate neighbors (first-tier addresses to or from which it transacts). The gray arrows represent transaction directions, flowing from senders to receivers. On the right side, the large red node emerges as a hub with numerous inbound and outbound transactions, indicating potential collaborative trading or arbitrage patterns. Despite partial cropping, the figure highlights key clusters and illustrates how specific addresses, particularly the green and red nodes, may coordinate transactions to exploit market opportunities.

5. Discussion

The findings of this study reveal significant arbitrage activities in the carbon credit trading ecosystem. While these activities may highlight inefficiencies and opportunities within the market, ensuring that all identified strategies align with legal and ethical standards is critical. Blockchain-based carbon trading, by design, aims to foster transparency and trust. Exploiting arbitrage opportunities without regulatory compliance or ethical consideration can undermine the credibility of these markets and impede their adoption for sustainability goals.

The visualization framework introduced in this study provides policymakers and market regulators with a valuable tool to monitor trading behaviors, detect anomalies, and ensure compliance with relevant laws. The proposed framework works well with existing rules for carbon credit trading because it has three essential features: real-time monitoring, better traceability, and anomaly detection. These features help ensure that carbon trading follows international agreements like the Kyoto Protocol and the Paris Agreement.

• Real-time Monitoring: The framework can monitor carbon credit transactions immediately. It tracks key details such as each trade’s time, price, amount, and participants. By watching the market in real-time, regulators can quickly spot unusual activities or potential fraud. This helps keep the market fair and in line with international rules;
• Improved Traceability: Using blockchain technology, the framework ensures that every carbon credit transaction is recorded and cannot be changed. This makes it easy to trace the history of each credit, including where it came from and how it was traded. This transparency prevents problems like double counting (using the same credit twice) or fake transactions. It also meets the requirement for precise and accurate reporting set by the Paris Agreement;
• Anomaly Detection: The framework uses intelligent tools to detect unusual activities, such as sudden price changes, fake trades, or unexpectedly high trading volumes. This helps regulators identify and stop dishonest behaviors early. By finding these problems quickly, the system helps keep the carbon market reliable and trustworthy.

While arbitrage presents profit-making opportunities in volatile cryptocurrency markets, it is inherently accompanied by risks. High transaction volumes and rapid price fluctuations can result in significant losses if trades are not executed efficiently. Moreover, cross-market arbitrage faces operational challenges, including transaction delays, unexpected fees, and sudden price corrections. Addressing these risks requires ongoing collaboration between technical and legal professionals to develop comprehensive guidelines and solutions that tackle emerging challenges in blockchain-based trading systems.

The proposed visualization framework supports risk management by identifying potential arbitrage opportunities in real-time, allowing traders to make informed decisions. Integrating features like stop-loss mechanisms, portfolio diversification analytics, and market trend monitoring can further enhance risk mitigation for practical implementation. Future iterations of the framework could incorporate predictive analytics and machine learning to anticipate market shifts better and protect users from adverse outcomes.

The results of this study contribute to the growing body of research on blockchain’s role in fostering sustainability. Previous studies have highlighted blockchain’s potential to enhance transparency and security in carbon credit trading [1,2], but few have addressed its limitations in interpreting complex transaction data and mitigating fraud. This research bridges that gap by introducing a data-driven visualization framework, demonstrating how advanced visualization techniques can address the inherent complexities of blockchain-based systems.

From an operational perspective, this framework provides production managers, policymakers, and researchers with actionable insights into market dynamics, enabling better decision-making and fostering a more sustainable trading ecosystem. Moreover, the findings suggest that integrating blockchain with real-time decision support tools can substantially improve the traceability and reliability of carbon credit markets.

Future research should explore the integration of artificial intelligence with the visualization framework to enhance anomaly detection and prediction accuracy. Additionally, examining the applicability of this framework in other blockchain-based markets, such as renewable energy credits or supply chain sustainability tracking, could further extend its utility. Studies that address the socio-economic impacts of arbitrage activities in carbon markets and propose mechanisms to balance profit motives with environmental goals are also needed.

6. Conclusions

This study underscores the critical role of blockchain-based visualization techniques in enhancing transparency and fraud detection within carbon credit trading systems. By addressing the complexities of blockchain transactions, the proposed framework offers innovative solutions for identifying arbitrage activities and supporting compliance with legal and ethical standards.

The findings highlight the importance of integrating advanced technologies into carbon credit markets to foster sustainability and operational efficiency. Future research should continue exploring the intersection of blockchain, data visualization, and AI to support global efforts to achieve net-zero emissions and ensure market integrity.

In short, the framework’s real-time monitoring, precise record-keeping, and problem-detection features make it a potent tool for regulators. It helps ensure that carbon credit trading is fair, transparent, and compliant with international standards like the Kyoto Protocol and the Paris Agreement.