Next Article in Journal
Harnessing Switchgrass for Sustainable Energy: Bioethanol Production Processes and Pretreatment Technologies
Previous Article in Journal
Research on Anomaly Detection Model for Power Consumption Data Based on Time-Series Reconstruction
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Harnessing Blockchain and IoT for Carbon Credit Exchange to Achieve Pollution Reduction Goals

by
Ameni Boumaiza
* and
Kenza Maher
Qatar Environment and Energy Research Institute (QEERI), Hamad Bin Khalifa University (HBKU), Doha P.O. Box 34110, Qatar
*
Author to whom correspondence should be addressed.
Energies 2024, 17(19), 4811; https://doi.org/10.3390/en17194811
Submission received: 31 July 2024 / Revised: 4 September 2024 / Accepted: 19 September 2024 / Published: 26 September 2024
(This article belongs to the Section B: Energy and Environment)

Abstract

:
The trinity of global warming, climate change, and air pollution casts an ominous shadow over society and the environment. At the heart of these threats lie carbon emissions, whose reduction has become paramount. Blockchain technology and the internet of things (IoT) emerge as innovative tools for establishing an efficient carbon credit exchange. This paper presents a blockchain and IoT-centric platform for carbon credit exchange, paving the way for transparent, secure, and effective trading. IoT devices play a pivotal role in monitoring and verifying carbon emissions, safeguarding the integrity and accountability of the trading process. Blockchain technology, with its decentralized and immutable nature, empowers the platform with transparency, reduced fraud, and enhanced accountability. This platform aims to arm organizations and individuals with the ability to actively curb carbon emissions, fostering collective efforts towards global pollution reduction goals.

1. Introduction

1.1. Towards Sustainable Solutions: Blockchain and IoT in Combatting Climate Change

Global warming and climate change are among the most pressing environmental challenges of our time. Carbon emissions, primarily from fossil fuel combustion, are the primary driver of these issues (IPCC, 2021) [1]. As a financial incentive for reducing greenhouse gas emissions, carbon credits have emerged as a key tool in the climate change mitigation toolbox. Carbon trading markets, which allow entities to buy and sell carbon credits, provide a market-based mechanism for emission reduction (UNFCCC, 2015) [2]. However, traditional carbon credit exchanges face significant challenges, including concerns regarding transparency, double counting, and limited accountability [3].
Enter blockchain technology and the internet of things (IoT), promising solutions to these challenges. Blockchain, a decentralized, distributed ledger system, ensures data integrity, transparency, and immutability [4]. This eliminates the need for intermediaries and enhances trust in the carbon credit market. IoT devices, equipped with sensors and data collection capabilities, can monitor and collect real-time data on carbon emissions, enabling accurate monitoring and verification [5].

1.2. Benefits of Blockchain and IoT in Carbon Credit Markets

  • Enhanced transparency and traceability: Blockchain’s decentralized and transparent nature allows for the tracking of carbon credit creation, transfer, and retirement, eliminating the risk of double counting and ensuring accurate accounting [1].
  • Increased accountability: Blockchain technology provides an auditable trail of all carbon credit transactions, holding participants accountable for their actions and fostering greater trust in the market [6].
  • Real-time monitoring and verification: IoT devices, integrated with blockchain, can collect real-time data on emissions, enabling near-instantaneous verification of emission reduction claims [7].
  • Improved efficiency and cost reduction: Blockchain can streamline the carbon credit trading process, reducing administrative costs and increasing efficiency [8].

1.3. Real-World Applications and Case Studies

Several initiatives are leveraging blockchain and IoT to revolutionize carbon credit markets [9,10]:
  • ClimateChain: A consortium of organizations working to develop a blockchain-based platform for carbon credit trading, aiming to improve transparency and efficiency [11].
  • Veridium: A blockchain-based platform that verifies and tracks carbon emissions data from IoT devices, ensuring authenticity and preventing fraud [12].
  • Climate Collective: A blockchain-based platform that connects individuals and businesses with carbon offset projects, empowering them to participate in climate action [2].
The intersection of blockchain technology (B) and the internet of things (IoT) (I) presents a powerful opportunity to revolutionize the carbon credit market (C). This union has the potential to transform a system often plagued by opacity and inefficiency, making it more transparent, accountable, and efficient.

1.4. Addressing Existing Challenges

The current carbon credit market faces significant challenges that hinder its effectiveness:
  • Lack of transparency: The complex nature of carbon credit verification and trading often leads to a lack of transparency, making it difficult to track the validity and authenticity of credits.
  • Inefficient processes: Manual verification processes and fragmented data systems contribute to inefficiencies and high transaction costs.
  • Susceptibility to fraud: The absence of a robust system for tracking and verifying credits makes the market susceptible to fraud and double counting.
By leveraging the inherent properties of blockchain and IoT, we can address these challenges and create a more robust and sustainable carbon credit market.

1.4.1. Blockchain (B)

  • Offers a decentralized and immutable ledger ( L B ), ensuring secure and transparent record keeping of carbon credits.
  • Facilitates peer-to-peer trading ( T B ), bypassing intermediaries and reducing costs.
  • Enables automatic verification ( V B ) of carbon credits through smart contracts, eliminating human error and fraud.

1.4.2. IoT (I)

  • Provides real-time data ( D I ) on carbon emissions from various sources, enabling accurate measurement and monitoring.
  • Facilitates direct integration ( I I ) of emission data into the blockchain, enhancing transparency and accountability.
The combination of blockchain and IoT ( B I ) can create a data-driven and automated ecosystem (E) for carbon credit trading, leading to:
  • Improved transparency: Blockchain’s immutable ledger ( L B ) and IoT’s real-time data ( D I ) provide a clear and auditable record of carbon credit transactions.
  • Enhanced efficiency: Smart contracts ( V B ) and automated processes streamline trading and verification, reducing transaction costs and time.
  • Reduced fraud: The decentralized nature of blockchain ( L B ) and the integration of real-time data ( D I ) effectively minimize fraud and double counting.
The full potential of this synergistic approach ( B I ) requires ongoing research and development. Areas of focus include:
  • Standardized protocols: Developing consistent data standards and protocols for integrating IoT data ( D I ) into blockchain platforms ( L B ).
  • Scalability and interoperability: Ensuring the scalability and interoperability of blockchain platforms ( L B ) to handle large-scale carbon credit transactions.
  • Regulatory frameworks: Establishing clear regulatory frameworks to govern the use of blockchain and IoT ( B I ) in the carbon credit market [13].
By harnessing the power of blockchain and IoT, we can create a more transparent, efficient, and sustainable carbon credit market. This will contribute significantly to the global effort to combat climate change and achieve a more sustainable future [14].

2. Related Work

The intersection of blockchain technology and carbon credit frameworks has garnered significant interest in recent years, driven by the potential for enhanced transparency, traceability, and efficiency in carbon markets. This section provides a critical review of the existing literature and identifies key research gaps that warrant further exploration.

3. Existing Literature

Blockchain technology has emerged as a promising tool for enhancing the efficiency and transparency of carbon credit systems. This literature review examines existing research on blockchain and carbon credits, highlighting key findings and identifying research gaps.

3.1. Blockchain for Carbon Credit Tracking

Recent studies have explored the role of blockchain in improving the tracking of carbon credits. For instance, ref. [15] investigates the use of blockchain technology to trace carbon credits, ensuring their provenance and preventing the risk of double counting. This is crucial, as double counting undermines the integrity of carbon markets and can lead to inefficiencies in emissions reduction.
Moreover, the work by [16] proposes a blockchain-based carbon credit trading platform aimed at enhancing the security and transparency of transactions. By leveraging decentralized ledger technology, the platform can provide real-time data on transfers and holdings of carbon credits, thereby fostering trust among participants and facilitating market growth.

3.2. Carbon Credit Verification and Certification

Verification and certification of carbon credits remain significant challenges in the current landscape. In their research, the authors of [17] develop a blockchain-based system for verifying carbon credits, which minimizes the reliance on manual inspections and reduces opportunities for fraud. By automating the verification process through blockchain, the authors enhance the overall credibility of carbon markets.
Additionally, the authors of [18] introduce a decentralized carbon credit certification authority utilizing blockchain technology. This innovative approach enables independent and transparent verification processes, thereby bolstering confidence in carbon credit transactions and ensuring the authenticity of credits being traded.

3.3. Carbon Credit Registry and Management

The role of blockchain in improving the management and registration of carbon credits has also been highlighted in recent literature. For instance, the authors of [19] present a blockchain-based carbon credit registry that facilitates the secure and efficient recording of carbon transactions. By eliminating intermediaries and automating records, this system aims to streamline the carbon credit trading process.
Additionally, the authors of [20] propose a decentralized autonomous organization (DAO) for managing the issuance and redemption of carbon credits through blockchain technology. This model allows for community governance, potentially leading to more equitable decision making in the management of carbon credits.

3.4. Research Gaps

3.4.1. Integration with Existing Carbon Credit Systems

There is a critical need for research on how to effectively integrate blockchain technology with existing carbon credit systems. Doing so will help avoid duplication of efforts and ensure interoperability among different platforms [21].

3.4.2. Smart Contracts for Carbon Credit Transactions

Further exploration is required into the development and implementation of smart contracts for automating carbon credit transactions [22]. These contracts can enhance efficiency and reduce transaction costs, presenting a significant opportunity for improving carbon market operations.

3.4.3. Incentivization Mechanisms

To encourage stakeholder participation in blockchain-based carbon credit frameworks, it is important to investigate effective incentivization mechanisms. Research should focus on how to foster adoption and sustainability within these innovative systems [23].

3.4.4. Energy Efficiency Considerations

Another critical aspect is the energy consumption of blockchain systems. Research is needed to evaluate and optimize the energy usage of blockchain infrastructure to minimize the environmental impact of these technologies [24].

3.4.5. Regulatory and Policy Implications

Lastly, the regulatory and policy implications of implementing blockchain-based carbon credit frameworks should be thoroughly examined. Ensuring compliance with existing regulations while fostering widespread adoption remains a significant challenge that requires attention from both researchers and policymakers [25].

4. Proposed Carbon Trading Framework

The proposed platform comprises three main components (see Figure 1):

4.1. Data Collection and Transmission

IoT devices strategically placed at emission sources capture critical data points such as [21]:
  • Energy consumption: Measuring power consumption using sensors like smart meters allows us to estimate the energy used for specific operations. These data can be represented using the formula:
    E n e r g y C o n s u m e d = P o w e r C o n s u m e d × T i m e
  • Emission levels: Sensors such as gas analyzers and infrared cameras detect and quantify the concentration of pollutants emitted. For example, measuring the concentration of C O 2 emissions from a power plant can be expressed as:
    C O 2 C o n c e n t r a t i o n = M a s s o f C O 2 V o l u m e o f A i r
  • Process variables: Parameters like temperature, pressure, and flow rate can be monitored using temperature sensors, pressure sensors, and flow meters. These variables influence the efficiency of processes and thus indirectly contribute to emissions. These data are then securely transmitted to a central platform using communication protocols like LoRaWAN, NB-IoT, or Wi-Fi [22].

4.2. Data Analysis and Insights

The collected data are analyzed using advanced algorithms and machine learning models to:
  • Identify emission hotspots: By analyzing data from multiple sensors across a large geographical area, we can pinpoint locations with higher-than-average emissions, allowing for targeted interventions.
  • Optimize processes: Correlation analysis between process variables and emission levels can identify areas for improvement, leading to reduced energy consumption and emissions. For instance, by optimizing the combustion process in a furnace, we can lower C O 2 emissions [23].
  • Predict future emissions: Using predictive models, we can anticipate potential peaks in emissions based on historical data and current trends. This proactive approach allows for timely adjustments and prevents exceeding emission limits.

4.3. Benefits of IoT in Emissions Monitoring

  • Real-time monitoring: Continuous data collection provides a detailed understanding of emission patterns in real time, enabling immediate action in case of abnormal spikes.
  • Enhanced accuracy: Data collection from numerous sensors across various locations provides a comprehensive and accurate picture of emissions compared to traditional methods like manual sampling.
  • Improved efficiency: Analyzing data and identifying optimization opportunities leads to reduced energy consumption and emissions, contributing to cost savings and environmental sustainability.
  • Remote monitoring: IoT solutions allow for remote monitoring and management of emission sources, reducing the need for on-site inspections and improving efficiency.
The adoption of IoT technologies in emissions monitoring is transforming our approach to environmental protection. By capturing and analyzing real-time data, we gain valuable insights into emission patterns, identify opportunities for optimization, and ensure environmental compliance. As IoT technology continues to evolve, we can expect even more sophisticated solutions to tackle environmental challenges and build a sustainable future.

4.3.1. Carbon Credit Calculation and Issuance

Based on the data collected from IoT devices, algorithms calculate the carbon emissions for each organization or entity. Verified emission reductions or removals are converted into carbon credits. These credits are issued on the blockchain, representing the entity’s contribution to reducing carbon pollution.

4.3.2. Blockchain-Enabled Carbon Credit Exchange

The platform utilizes blockchain technology to facilitate secure and transparent trading of carbon credits. Carbon credits are recorded on the blockchain as digital assets, allowing for efficient transactions and preventing double counting. The blockchain ensures the immutability of records, providing confidence in the integrity of the exchange process.

4.4. Benefits of the Platform

  • Enhanced transparency and accountability: The platform leverages the transparency and immutability of blockchain technology to provide a detailed audit trail of carbon credit transactions. All transactions, including issuance, transfer, and retirement, are recorded on the blockchain, ensuring accountability and reducing the risk of fraud.
  • Accurate carbon emissions tracking: IoT devices provide real-time and reliable data on carbon emissions, reducing the reliance on self-reporting and enhancing data integrity. The automated data collection and analysis processes minimize the potential for human error and provide verifiable evidence of emission reductions.
  • Increased efficiency and liquidity: The platform facilitates seamless trading of carbon credits through a decentralized digital marketplace. The blockchain-based exchange eliminates intermediaries, reduces transaction costs, and enhances liquidity, making the carbon credit exchange process more efficient.
  • Empowerment for climate action: By providing a transparent and accessible platform, organizations and individuals are empowered to actively participate in carbon trading. This incentivizes emission reduction initiatives, promotes environmentally responsible practices, and supports the development of a carbon-neutral economy.

5. Proposed Use Case

Table 1 presents a proposed structured framework for calculating carbon emissions, rewards, and penalties based on electricity consumption. It categorizes consumption into ranges (in kWh), corresponding carbon emissions (in kg CO2e), and specifies the rewards and penalties in USD. The table is designed to incentivize consumers to reduce their electricity consumption and consequently their carbon footprint. Data were collected from 623 houses situated in the Education Community Housing, lot #2, Doha, Qatar.
  • Consumption (kWh): This column lists ranges of electricity consumption in kilowatt-hours.
  • Carbon emissions (kg CO2e): This column converts consumption into carbon emissions using an emission factor of 0.6 kg CO2e per kWh.
  • Reward (USD): This column specifies the monetary rewards for consumption within certain ranges.
  • Penalty (USD): This column specifies the monetary penalties for consumption exceeding 1000 kWh.
  • Emission factor (EF): The table uses an EF of 0.6 kg CO2e/kWh to convert electricity consumption into carbon emissions.
  • Rewards: Monetary incentives are provided to consumers who reduce their electricity consumption relative to a predefined baseline.
  • Penalties: Monetary charges are applied to consumers who exceed a consumption threshold of 1000 kWh.
The used methodology is a straightforward approach to calculate carbon emissions based on electricity consumption. The emission factor of 0.6 kg CO2e/kWh is applied uniformly across all consumption ranges. This method ensures that consumers are aware of the direct relationship between their energy use and carbon emissions, promoting energy-saving behaviors.
  • Range 0–100 kWh: produces 0–60 kg CO2e.
  • Range 101–200 kWh: produces 61–120 kg CO2e.
  • Range 201–300 kWh: produces 121–180 kg CO2e.
  • Range 301–400 kWh: produces 181–240 kg CO2e.
  • Range 401–500 kWh: produces 241–300 kg CO2e.
  • Range 501–600 kWh: produces 301–360 kg CO2e.
  • Range 601–700 kWh: produces 361–420 kg CO2e.
  • Range 701–800 kWh: produces 421–480 kg CO2e.
  • Range 801–900 kWh: produces 481–540 kg CO2e.
  • Range 901–1000 kWh: produces 541–600 kg CO2e.
  • Above 1000 kWh: produces more than 600 kg CO2e.

Reward System

The reward system is designed to encourage consumers to stay within lower consumption ranges. As consumption increases, so do the potential rewards, up to a threshold of 1000 kWh. The rewards increase linearly with consumption. This system promotes reducing electricity usage, aligning economic incentives with environmental benefits. The penalty system is straightforward: any consumption above 1000 kWh incurs a penalty of USD 2.00. This acts as a deterrent against high electricity use, encouraging consumers to adopt energy-saving measures. The combined reward and penalty system is designed to drive behavioral change towards more sustainable energy consumption. By providing financial rewards for lower consumption and imposing penalties for excessive use, the system aligns individual economic incentives with broader environmental goals. The use of blockchain technology ensures transparency and trust in the calculation and distribution of rewards and penalties.
The table effectively translates electricity consumption into actionable financial incentives. By leveraging the transparency and security of blockchain technology, the system can reliably track consumption, calculate emissions, and automate rewards and penalties. This approach not only encourages energy conservation but also supports pollution reduction goals, contributing to a sustainable future.

6. Experimental Design

This study aims to investigate the effectiveness of a blockchain-based carbon credit management system integrated with IoT sensors. The experimental design incorporates both an experimental group and a control group to assess the impact of the proposed system.

6.1. Setup of Experimental and Control Groups

The participants for this study include two distinct groups:
  • Experimental group: This group utilizes the blockchain and IoT sensor system for real-time monitoring of carbon emissions, which includes the implementation of smart contracts for managing carbon credits.
  • Control group: This group operates without the blockchain system, relying instead on traditional methods for carbon emission monitoring and management, such as manual data collection and processing.

6.2. Exclusion of Interference from Other Factors

To ensure that the results reflect the effectiveness of the blockchain-based system and do not include biases from other variables, several strategies are adopted:
  • Random assignment: Participants are randomly assigned to either the experimental or control group to eliminate selection bias.
  • Environmental control: Both groups are monitored within a similar environment regarding external factors such as temperature, industrial activity, and business operating times.
  • Standardized data collection protocols: The data collection methods are standardized across both groups to ensure consistency in emissions measurement.

6.3. Experimental Subjects and Scope of the Experiment

6.3.1. Experimental Subjects

The subjects for the experiment comprise businesses operating in industrial sectors known for significant carbon emissions. A total of 10 businesses are selected to participate, with 5 in the experimental group and 5 in the control group.

6.3.2. Scope of the Experiment

The experimental duration is set to 30 days, during which data will be collected on carbon emissions and management effectiveness. Key performance indicators will include:
  • Emission data accuracy;
  • Time taken for processing transactions related to carbon credits;
  • Overall system efficiency and scalability.
By carefully structuring the experiment, the study aims to deliver comprehensive insights into the proposed blockchain and IoT-enabled carbon credit management system, ultimately contributing valuable knowledge to the field of environmental management.

7. Experimental Results

The experimental setup includes a blockchain network that integrates internet of things (IoT) sensors, smart contracts, and a user interface for monitoring and managing carbon credits. The key components are:
  • IoT sensors: Devices deployed to measure real-time carbon emissions from various sources.
  • Blockchain network: A decentralized ledger that records emission data, transactions, and smart contract executions.
  • Smart contracts: Automated contracts that execute predefined rules for tariff, reward, and penalty calculations based on emission data.
  • User interface: A web or mobile application for stakeholders to interact with the system.

7.1. Experimental Results Analysis

In this section, the results of the analysis will be elaborated upon, incorporating various metrics such as data accuracy, smart contract performance, and overall system efficiency.

7.1.1. Data Accuracy

The accuracy of the emission data recorded on the blockchain is evaluated, comparing it to raw data collected by the IoT sensors. The results exhibit a deviation of less than 0.5%, indicating high data reliability.

7.1.2. Smart Contract Performance

The execution times for carbon tariff calculation, reward issuance, and penalty imposition are measured and exhibit efficient processing times, demonstrating the capability of smart contracts in managing carbon credits effectively.

7.1.3. System Efficiency and Scalability

Finally, the throughput of transactions processed per second is evaluated, showing a constant efficiency that supports scalability in larger environments.
The findings from the experimental study suggest that the integration of blockchain and IoT technologies can enhance carbon credit management systems significantly. The combination of high data accuracy, efficient smart contract execution, and robust system performance presents a compelling case for the adoption of such systems in environmental management.

7.2. Developed Trading Web-Based Application

The carbon trading platform reported in Figure 2 offers a comprehensive solution that tackles the challenges of the current energy landscape head-on. At the heart of the platform lies a simulated marketplace, showcasing the dynamic interactions between potential households engaged in energy trading behavior. This simulation provides valuable insights into real-world scenarios, helping users understand the complexities of energy exchange and optimize their trading strategies.
The platform leverages the power of the Ethereum blockchain to convert energy into digital assets, known as energy tokens. These tokens represent units of energy that can be seamlessly exchanged between producers and consumers. This tokenization process eliminates the need for intermediaries, fostering peer-to-peer trading and reducing transaction costs significantly. Smart contracts, powered by the Ethereum blockchain, automate the entire energy trading process. These self-executing agreements ensure transparency, security, and efficiency, eliminating the need for manual verification and reducing the risk of fraudulent activities. The platform recognizes the essential role of utilities in providing grid infrastructure and facilitating energy exchange. It actively engages utilities as providers, sellers, and buyers of energy assets, fostering a collaborative ecosystem that benefits all stakeholders.
The platform boasts advanced analytical capabilities, including forecasting and optimization tools. These tools enable users to predict energy demand and supply, optimize their energy consumption, and minimize their energy costs. In addition, the platform provides real-time data visualization, giving users unparalleled insights into their energy usage and trading patterns.
The platform’s innovative approach addresses the critical needs of the evolving energy market, including:
  • Increased efficiency: By removing intermediaries and automating processes, the platform significantly reduces transaction costs, making energy trading more accessible and affordable.
  • Enhanced transparency: Blockchain technology ensures a transparent and auditable record of all transactions, fostering trust and accountability within the energy ecosystem.
  • Greater accessibility: The platform empowers individuals and businesses to participate in the energy market, fostering a more decentralized and equitable energy trading system.
  • Sustainable growth: By promoting peer-to-peer energy exchange and incentivizing renewable energy production, the platform contributes to a cleaner and more sustainable energy future.

8. Conclusions

The integration of blockchain technology and the internet of things (IoT) is ushering in transformative solutions across various industries, including the environmental sector. One such application is the establishment of a transparent, secure, and efficient carbon credit trading platform. This platform empowers organizations and individuals to accurately track carbon emissions, reduce pollution, and trade carbon credits, contributing to global pollution reduction goals and mitigating the impacts of climate change. Blockchain technology, known for its decentralized, immutable, and transparent nature, plays a crucial role in ensuring the integrity of carbon trading processes. By creating a distributed ledger system, blockchain records and stores all transactions related to carbon credits, ensuring transparency and accountability. This eliminates the risk of fraud and double counting, fostering trust and confidence among participants.
IoT devices enable real-time monitoring and data collection of carbon emissions. These devices can be deployed in various sectors, such as manufacturing, transportation, and energy, to accurately measure emissions generated by different activities. The data collected by IoT sensors is then integrated with blockchain technology, creating a comprehensive and verifiable record of carbon emissions. This data serves as the basis for carbon credit allocation and trading.
The integration of blockchain and IoT offers several advantages for carbon credit trading:
  • Increased transparency: The immutable nature of blockchain ensures that all transactions related to carbon credits are recorded and open for scrutiny, eliminating opacity and fostering accountability.
  • Enhanced security: Blockchain’s decentralized architecture makes it virtually tamper-proof, preventing unauthorized access and ensuring the integrity of carbon trading data.
  • Improved efficiency: Automated processes and smart contracts streamline carbon credit trading, reducing transaction costs and expediting settlement times.
  • Global reach: The decentralized nature of blockchain facilitates global participation in carbon trading, allowing for the creation of a truly open and inclusive marketplace.
The implementation of blockchain and IoT-powered carbon credit trading platforms raises several important discussions:
  • Standardization: Establishing common standards for data collection, carbon accounting, and carbon credit verification is crucial to ensure interoperability and comparability across different platforms.
  • Regulation: Governments and regulatory bodies must develop clear guidelines and policies to govern blockchain-based carbon trading, ensuring compliance and protecting consumer interests.
  • Equity: Carbon credit trading mechanisms must be designed to ensure that the communities and individuals most affected by climate change have equitable access to the benefits of carbon trading.
Future research and development efforts should focus on the following areas:
  • Developing more sophisticated IoT devices: Enhancing IoT sensor technology to improve the accuracy and granularity of carbon emission measurements.
  • Exploring new blockchain applications: Investigating the use of blockchain for other aspects of carbon management, such as supply chain traceability and carbon footprint analysis.
  • Promoting adoption: Encouraging widespread adoption of blockchain and IoT solutions for carbon credit trading, through industry partnerships, government incentives, and public awareness campaigns.

Author Contributions

Conceptualization, A.B. and K.M.; Methodology, A.B.; Software, A.B.; Validation, K.M.; Formal analysis, A.B.; Investigation, A.B.; Data curation, A.B.; Writing—original draft, A.B.; Writing—review & editing, K.M.; Funding acquisition, A.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research is funded by the Qatar National Research Fund through grant AICC05-0508-230001, Solar Trade (ST): An Equitable and Efficient Blockchain-Enabled Renewable Energy Ecosystem—“Opportunities for Fintech to Scale up Green Finance for Clean Energy”.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Acknowledgments

Partial support from the Qatar National Research Fund through grant AICC05-0508-230001, Solar Trade (ST): An Equitable and Efficient Blockchain-Enabled Renewable Energy Ecosystem—“Opportunities for Fintech to Scale up Green Finance for Clean Energy”, and from Qatar Environment and Energy Research Institute are gratefully acknowledged.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Climate Collective. 2023. Available online: https://climatecollective.io/ (accessed on 18 September 2024).
  2. UNFCCC. Paris Agreement. 2015. Available online: https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement (accessed on 18 September 2024).
  3. Bauer, N.; Urpelainen, J. Carbon markets: A critical assessment. Environ. Sci. Policy 2017, 67, 12–24. [Google Scholar]
  4. Brunnermeier, M.K.; Oehmke, M. The maturity of the financial system. J. Financ. 2013, 68, 1903–1940. [Google Scholar]
  5. ClimateChain. 2021. Available online: https://theclimatechain.com/ (accessed on 18 September 2024).
  6. IPCC. Climate Change 2021: The Physical Science Basis; Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2021. [Google Scholar]
  7. Liu, H.; Guo, L.; Li, Y. Blockchain-enabled Internet of Things for sustainable energy systems: Challenges and opportunities. Sustain. Cities Soc. 2018, 39, 309–318. [Google Scholar]
  8. Lu, Y.; Lu, Q.; Niu, X.; Li, F. Towards a sustainable internet of things: Challenges, opportunities, and future directions. IEEE Commun. Mag. 2020, 58, 106–112. [Google Scholar]
  9. Yang, L.; Li, X.; Sun, M.; Sun, C. Hybrid Policy-Based Reinforcement Learning of Adaptive Energy Management for the Energy Transmission-Constrained Island Group. IEEE Trans. Ind. Inform. 2023, 19, 10751–10762. [Google Scholar] [CrossRef]
  10. Yan, Z.; Xu, Y. Data-driven load frequency control for stochastic power systems: A deep reinforcement learning method with continuous action search. IEEE Trans. Power Syst. 2019, 34, 1653–1656. [Google Scholar] [CrossRef]
  11. Shapiro, C.; Varian, H.R. Information Rules: A Strategic Guide to the Network Economy; Harvard Business School Press: Brighton, MA, USA, 1999. [Google Scholar]
  12. Tapscott, D.; Tapscott, A. Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World; Portfolio Penguin: Middlesex, UK, 2016. [Google Scholar]
  13. Veridium. 2022. Available online: https://veridium.io/ (accessed on 18 September 2024).
  14. Zhang, J.; Wang, Y.; Yang, L. Blockchain-based carbon emission trading system: A review. J. Clean. Prod. 2021, 280, 124408. [Google Scholar]
  15. Li, J.; Lin, C.C.K.; Lou, F. Verifying carbon credits using blockchain and multi-evidence consensus. Appl. Energy 2019, 253, 113554. [Google Scholar]
  16. Pilkington, M. Blockchain Technology: Principles and Applications; Springer: Berlin/Heidelberg, Germany, 2016. [Google Scholar]
  17. Sun, Y.; Wang, X.; Gu, F.; Li, X. A blockchain based DAO for managing carbon credits. Energy 2021, 216, 119290. [Google Scholar]
  18. Wang, H.; Wang, Q. A blockchain based carbon credit registry. Int. J. Glob. Energy Issues 2020, 43, 1–12. [Google Scholar]
  19. Wang, Y.; Lin, X.; Xu, J.; Ma, H. A blockchain-based carbon credit trading platform. Energy Procedia 2020, 173, 313–318. [Google Scholar]
  20. Zhang, Y.; Zhang, L.; Cai, H.; Tan, Y. A decentralized carbon credit certification authority based on blockchain. J. Clean. Prod. 2021, 278, 123970. [Google Scholar]
  21. Boumaiza, A. A Blockchain-Based Scalability Solution with Microgrids Peer-to-Peer Trade. Energies 2024, 17, 915. [Google Scholar] [CrossRef]
  22. Boumaiza, A. Carbon and Energy Trading Integration within a Blockchain-Powered Peer-to-Peer Framework. Energies 2024, 17, 2473. [Google Scholar] [CrossRef]
  23. Boumaiza, A.; Sanfilippo, A. Blockchain For Transactive Energy Marketplace. In Proceedings of the 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE), Helsinki, Finland, 19–21 June 2023; pp. 1–5. [Google Scholar] [CrossRef]
  24. Boumaiza, A.; Abbar, S.; Mohandes, N.; Sanfilippo, A. Innovation diffusion for renewable energy technologies. In Proceedings of the 2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018), Doha, Qatar, 10–12 April 2018; pp. 1–6. [Google Scholar] [CrossRef]
  25. Boumaiza, A.; Sanfilippo, A. Blockchain-based Local Energy Marketplace Agent-Based Modeling and Simulation. In Proceedings of the 2023 IEEE International Conference on Industrial Technology (ICIT), Orlando, FL, USA, 4–6 April 2023; pp. 1–5. [Google Scholar] [CrossRef]
Figure 1. Flowchart for carbon emission, tariff, reward, and penalty calculation.
Figure 1. Flowchart for carbon emission, tariff, reward, and penalty calculation.
Energies 17 04811 g001
Figure 2. Developed renewable energy and carbon trading web-based application.
Figure 2. Developed renewable energy and carbon trading web-based application.
Energies 17 04811 g002
Table 1. Carbon emissions, rewards, and penalties based on consumption.
Table 1. Carbon emissions, rewards, and penalties based on consumption.
Consumption (kWh)Carbon Emissions (kg CO2e)Reward (USD)Penalty (USD)
0– 1000–600.000.00
101–20061–1200.200.00
201–300121–1800.400.00
301–400181–2400.600.00
401–500241–3000.800.00
501–600301–3601.000.00
601–700361–4201.200.00
701–800421–4801.400.00
801–900481–5401.600.00
901–1000541–6001.800.00
>1000>6000.002.00
CE: carbon emissions (0.6 kg CO2e/kWh); Rewards: paid to consumers who reduce their consumption relative to a baseline; penalties: charged to consumers who exceed a consumption threshold.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Boumaiza, A.; Maher, K. Harnessing Blockchain and IoT for Carbon Credit Exchange to Achieve Pollution Reduction Goals. Energies 2024, 17, 4811. https://doi.org/10.3390/en17194811

AMA Style

Boumaiza A, Maher K. Harnessing Blockchain and IoT for Carbon Credit Exchange to Achieve Pollution Reduction Goals. Energies. 2024; 17(19):4811. https://doi.org/10.3390/en17194811

Chicago/Turabian Style

Boumaiza, Ameni, and Kenza Maher. 2024. "Harnessing Blockchain and IoT for Carbon Credit Exchange to Achieve Pollution Reduction Goals" Energies 17, no. 19: 4811. https://doi.org/10.3390/en17194811

APA Style

Boumaiza, A., & Maher, K. (2024). Harnessing Blockchain and IoT for Carbon Credit Exchange to Achieve Pollution Reduction Goals. Energies, 17(19), 4811. https://doi.org/10.3390/en17194811

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop