Blockchain-Based Carbon Accounting for Green Grid Infrastructure: A Decision-Making Approach Integrating Life Cycle Assessment and Multi-Agent Simulation

Authors

  • Yinlu Zhang Development Planning Department (Marketing and Listing Office), State Grid Yingda International Holdings Co., LTD., Dongcheng District, Beijing, China, 100005.
  • Hui Yang Development Planning Department (Marketing and Listing Office), State Grid Yingda International Holdings Co., LTD., Dongcheng District, Beijing, China, 100005.
  • Tong Zhang Development Planning Department (Marketing and Listing Office), State Grid Yingda International Holdings Co., LTD., Dongcheng District, Beijing, China, 100005.
  • Yuanjin Zhang Development Planning Department (Marketing and Listing Office), State Grid Yingda International Holdings Co., LTD., Dongcheng District, Beijing, China, 100005.
  • Chuang Zhu Chairman, State Grid Yingda Carbon Asset Management (Shanghai) Co., LTD., Pudong New Area, Shanghai, China, 200126.

Keywords:

Carbon Emission Accounting, Blockchain Technology, Multi-Energy Flow, Monte Carlo Simulation (MCS), Life Cycle Assessment (LCA)

Abstract

To support the achievement of carbon neutrality, a transparent mechanism for monitoring carbon emissions throughout the entire lifecycle of energy infrastructure, including UHV power grids and electricity transmission networks, is required. This study introduces a blockchain-enabled framework that integrates electricity, heating, gas, and cooling demands to facilitate comprehensive carbon emission tracing. The proposed framework enables carbon emission records to be stored securely in an immutable and transparent manner through blockchain technology. Moreover, it expands carbon accounting to encompass every stage of the energy lifecycle, beginning with energy production and continuing through system decommissioning. To capture uncertainties associated with energy consumption and blockchain operations, Monte Carlo Simulation (MCS) is applied. Critical system variables, including energy utilisation, emission indicators, and transaction duration, are modelled under varying operational conditions to examine the framework's reliability. Furthermore, the framework incorporates Life Cycle Assessment (LCA) alongside multi-agent simulation to investigate the long-term environmental impacts of energy infrastructure. A case study undertaken in Hengqin, China, indicates that carbon emissions increased to 480 tonnes in June owing to greater cooling demand, while emissions reached 490 tonnes in January because of heating requirements. The capability of the power transmission network to accommodate these seasonal fluctuations contributed to lower emission levels during off-peak periods. The findings confirm that blockchain provides a secure, transparent, and decentralised solution for carbon accounting in integrated energy systems by removing intermediary involvement and lowering operational expenditure. Additionally, the framework offers stakeholders greater authority over energy pricing and distribution processes. Overall, the proposed approach establishes a comprehensive carbon accounting framework for green grid infrastructure, enhancing security, operational efficiency, and system flexibility across the entire energy lifecycle.

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References

[1] Anita, S., Rodrigues, P., Nagabhooshanam, N., Londhe, G. V., Salunkhe, S. S., Kumar, P. D., L, N., & Bhima Raju, P. S. D. (2024). Energy trading and optimum scheduling for microgrids using multiple agents based DL approach. Electric Power Components and Systems, 1-19. https://doi.org/10.1080/15325008.2023.2300329

[2] Shu, L., Mo, Y., & Zhao, D. (2024). Energy retrofits for smart and connected communities: Scopes and technologies. Renewable and Sustainable Energy Reviews, 199, 114510. https://doi.org/10.1016/j.rser.2024.114510

[3] Raza, A., Jingzhao, L., Adnan, M., & Ahmad, I. (2024). Optimal load forecasting and scheduling strategies for smart homes peer-to-peer energy networks: A comprehensive survey with critical simulation analysis. Results in Engineering, 22, 102188. https://doi.org/10.1016/j.rineng.2024.102188

[4] Rathnayake, Bimali, Lahiru Gunathilake, Ruwini Edirisinghe, and Srinath Perera. "EcoConstruct: a blockchain-based system for carbon trading in construction projects." Construction Innovation 25, no. 7 (2025): 213-234. https://doi.org/10.1108/CI-08-2024-0224

[5] Chandra, I., Singh, N. K., & Samuel, P. (2024). A comprehensive review on coordinated charging of electric vehicles in distribution networks. Journal of Energy Storage, 89, 111659. https://doi.org/10.1016/j.est.2024.111659

[6] Raza, A., Liaqat, M., Adnan, M., Iqbal, M. S., Jingzhao, L., & Ahmad, I. (2024). SAARC super smart grid: Navigating the future - unleashing the power of an energy-efficient integration of renewable energy resources in the saarc region. Computers and Electrical Engineering, 118, 109405. https://doi.org/10.1016/j.compeleceng.2024.109405

[7] Meydani, A., Shahinzadeh, H., Ramezani, A., Moazzami, M., Nafisi, H., & Askarian-Abyaneh, H. (2024). State-of-the-art analysis of blockchain-based industrial iot (IIoT) for smart grids 2024 9th International Conference on Technology and Energy Management (ICTEM), Tehran, Iran. https://doi.org/10.1109/ICTEM60690.2024.10631931

[8] Hosseinian, H., Shahinzadeh, H., Gharehpetian, G. B., Azani, Z., & Shaneh, M. (2020). Blockchain outlook for deployment of IoT in distribution networks and smart homes. International Journal of Electrical and Computer Engineering, 10(3), 2787-2796. https://doi.org/10.11591/ijece.v10i3.pp2787-2796

[9] Arévalo, P., & Jurado, F. (2024). Impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids. Energies, 17(17), 4501. https://doi.org/10.3390/en17174501

[10] Kumar, P., Kumar, K., Adhikary, N., & Tesfaye, E. L. (2025). Analysis of control and computational strategies for green energy integration for sociotechnical ecological power infrastructure in Indian and African markets. Scientific Reports, 15(1), 13953. https://doi.org/10.1038/s41598-025-96773-2

[11] Liu, F., Tang, C., Wei, S., & Yang, X. (2025). Research on gain scheduling optimization of CHMCS based on multi-agent Nash equilibrium in the large scale integration scenario of wind and Photovoltaic Power. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5229774

[12] El Maghraoui, A., El Hadraoui, H., Ledmaoui, Y., El Bazi, N., Guennouni, N., & Chebak, A. (2024). Revolutionizing smart grid-ready management systems: A holistic framework for optimal grid reliability. Sustainable Energy, Grids and Networks, 39, 101452. https://doi.org/10.1016/j.segan.2024.101452

[13] Kilthau, M., Asman, M., Karmann, A., Suriyamoorthy, G., Beck, J. P., Regener, V., Derksen, C., Loose, N., Volkmann, M., Tripathi, S., Gehlhoff, F., Korotkiewicz, K., Steinbusch, P., Skwarek, V., Zdrallek, M., & Fay, A. (2023). Integrating peer-to-peer energy trading and flexibility market with self-sovereign identity for decentralized energy dispatch and congestion management. IEEE Access, 11, 145395-145420. https://doi.org/10.1109/ACCESS.2023.3344855

[14] Khalid, M. (2024). Energy 4.0: AI-enabled digital transformation for sustainable power networks. Computers and Industrial Engineering, 193, 110253. https://doi.org/10.1016/j.cie.2024.110253

[15] Cárdenas, R., Arroba, P., Risco-Martín, J. L., & Moya, J. M. (2023). Modeling and simulation of smart grid-aware edge computing federations. Cluster Computing, 26(1), 719-743. https://doi.org/10.1007/s10586-022-03797-8

[16] Mohanty, S., Panda, S., Parida, S. M., Rout, P. K., Sahu, B. K., Bajaj, M., Zawbaa, H. M., Kumar, N. M., & Kamel, S. (2022). Demand side management of electric vehicles in smart grids: A survey on strategies, challenges, modelling, modeling, and optimization. Energy Reports, 8, 12466-12490. https://doi.org/10.1016/j.egyr.2022.09.023

[17] Khan, A. A., & Abonyi, J. (2022). Simulation of sustainable manufacturing solutions: Tools for enabling circular economy. Sustainability, 14(15), 9796. https://doi.org/10.3390/su14159796

[18] Naraindath, N. R., Bansal, R. C., & Naidoo, R. M. (2023). The uprising of blockchain technology in the energy market industry. In Recent Developments in Electrical and Electronics Engineering: Select Proceedings of ICRDEEE 2022 (Vol. 979, pp. 497-509). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-7993-4_41

[19] Li, J., Xing, Y., & Zhang, D. (2022). Planning method and principles of the cloud energy storage applied in the power grid based on charging and discharging load model for distributed energy storage devices. Processes, 10(2), 194. https://doi.org/10.3390/pr10020194

[20] Huang, Y., Zhang, J., Ren, Z., Xiang, W., Sifat, I., Zhang, W., Zhu, J., & Li, B. (2023). Next generation decentralized water systems: A water-energy-infrastructure-human nexus (WEIHN) approach. Environmental Science: Water Research and Technology, 9(10), 2446-2471. https://doi.org/10.1039/d3ew00506b

[21] Couraud, B., Andoni, M., Robu, V., Norbu, S., Chen, S., & Flynn, D. (2023). Responsive FLEXibility: A smart local energy system. Renewable and Sustainable Energy Reviews, 182, 113343. https://doi.org/10.1016/j.rser.2023.113343

[22] Di, Huiya. "Blockchain-based carbon footprint tracking and circular network construction for the sharing economy." International Journal of Information and Communication Technology 27, no. 18 (2026): 60-78. https://doi.org/10.1504/IJICT.2026.152222.

[23] Bhattacharjee, Partha Pratim, Chaitali Koley, and Saibal Chatterjee. "Blockchain-Based Clean and Green Energy (Pico Hydel Power) Monitoring System." In International Conference on Communication, Devices and Networking, pp. 253-269. Singapore: Springer Nature Singapore, 2022. https://doi.org/10.1007/978-981-99-1983-3_26

[24] Monroe, J. G., Hansen, P., Sorell, M., & Berglund, E. Z. (2020). Agent-based model of a blockchain enabled peer-to-peer energy market: Application for a neighborhood trial in Perth, Australia. Smart Cities, 3(3), 1072-1099. https://doi.org/10.3390/smartcities3030053

[25] Elbashbishy, T. S., Ali, G. G., & El-adaway, I. H. (2022). Blockchain technology in the construction industry: Mapping current research trends using social network analysis and clustering. Construction Management and Economics, 40(5), 406-427. https://doi.org/10.1080/01446193.2022.2056216

[26] Kumar, N. M., Chand, A. A., Malvoni, M., Prasad, K. A., Mamun, K. A., Islam, F. R., & Chopra, S. S. (2020). Distributed energy resources and the application of AI, IoT, and blockchain in smart grids. Energies, 13(21), 5739. https://doi.org/10.3390/en13215739

[27] Tkachuk, R. V., Ilie, D., Robert, R., Kebande, V., & Tutschku, K. (2023). Towards efficient privacy and trust in decentralized blockchain-based peer-to-peer renewable energy marketplace. Sustainable Energy, Grids and Networks, 35, 101146. https://doi.org/10.1016/j.segan.2023.101146

[28] Aziz, S., Ahmed, I., Khan, K., & Khalid, M. (2024). Emerging trends and approaches for designing net-zero low-carbon integrated energy networks: A review of current practices. Arabian Journal for Science and Engineering, 49(5), 6163-6185. https://doi.org/10.1007/s13369-023-08336-0

[29] Akkaoui, R., Stefanov, A., Palensky, P., & Epema, D. H. J. (2022). A Taxonomy and lessons learned from blockchain adoption within the internet of energy paradigm. IEEE Access, 10, 106708-106739. https://doi.org/10.1109/ACCESS.2022.3212148

[30] Binyamin, S. S., & Ben Slama, S. (2022). Distributed peer-to-peer energy trading in virtual microgrids: A blockchain survey for future smart grid. In Authorea Preprints (Ed.). https://doi.org/10.22541/au.166370069.93846480/v1

[31] Muqeet, H. A., Javed, H., Akhter, M. N., Shahzad, M., Munir, H. M., Nadeem, M. U., Bukhari, S. S. H., & Huba, M. (2022). Sustainable solutions for advanced energy management system of campus microgrids: Model opportunities and future challenges. Sensors, 22(6), 2345. https://doi.org/10.3390/s22062345

[32] Liu, X., Liu, X., Jiang, Y., Zhang, T., & Hao, B. (2023). Photovoltaics and energy storage integrated flexible direct current distribution systems of buildings: Definition, technology review, and application. CSEE Journal of Power and Energy Systems, 9(3), 829-845. https://doi.org/10.17775/CSEEJPES.2022.04850

[33] Kim, Hyeonggyun, Sohyun Kim, Jaewook Lee, Kwangbok Jeong, Taehoon Hong, and Jongbaek An. "Development of a blockchain-based platform for real-time carbon emission assessment of building material supply chain." Building and Environment (2025): 113907. https://doi.org/10.1016/j.buildenv.2025.113907

[34] Yu, J., Liu, J., Sun, J., & Shi, M. (2023). Evolutionary game of digital-driven photovoltaic–storage–use value chain collaboration: A value intelligence creation perspective. Sustainability, 15(4), 3287. https://doi.org/10.3390/su15043287

[35] Liang, Y., Wang, Z., & Abdallah, A. B. (2024). Robust vehicle-to-grid energy trading method based on smart forecast and multi-blockchain network. IEEE Access, 12, 8135-8153. https://doi.org/10.1109/ACCESS.2024.3352631

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Published

2026-07-27

How to Cite

Yinlu Zhang, Hui Yang, Tong Zhang, Yuanjin Zhang, & Chuang Zhu. (2026). Blockchain-Based Carbon Accounting for Green Grid Infrastructure: A Decision-Making Approach Integrating Life Cycle Assessment and Multi-Agent Simulation. Decision Making: Applications in Management and Engineering, 9(1), 50–67. Retrieved from https://www.dmame-journal.org/index.php/dmame/article/view/1750

Issue

Vol. 9 No. 1 (2026): Decision Making: Applications in Management and Engineering

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Regular articles

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