10.57647/ijeee.2025.1601.01

Research on Carbon Finance Risk Prediction of the Optimal Integration Index of New Energy Resources

PDF
  • Jiantao Jiang*1,
  1. Department of Finance, School of Applied Economics, Guangdong Baiyun University, Guangzhou, China

Received: 2025-01-15

Revised: 2025-03-17

Accepted: 2025-03-25

Published in Issue 2025-03-31

Copyright (c) 2025 Jiantao Jiang (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Jiang, J. (2025). Research on Carbon Finance Risk Prediction of the Optimal Integration Index of New Energy Resources. International Journal of Energy and Environmental Engineering, 16(01). https://doi.org/10.57647/ijeee.2025.1601.01
Crossref
Scopus
Google Scholar
Europe PMC

PDF views: 68

Abstract

As a research hotspot in the field of energy, the development degree of new energy is related to the development of carbon finance market. In order to quickly promote the realization of the dual carbon goals and improve the sustainability of new energy development, this paper proposes a carbon finance analysis method, combined with ant colony algorithm to analyze new energy data such as wind, photovoltaic and hydropower, and judge the carbon finance market. Then, risk prediction is carried out based on the energy value created by wind and photovoltaic power, as well as the depth of energy development. Finally, the utilization effect of new energy sources such as wind power and photovoltaic power is output, the stability of energy output, and the development depth of wind power and photovoltaic energy. The results show that there is a fluctuating relationship between the energy output of wind and photovoltaic power generation and their risk level, and the increase of electricity will inhibit the financial risk, and the energy output value is greater than the threshold of 15%. The risk reduction rate was 36.28% and 34.50%, and the integration of new energy and carbon finance was -0.2896, indicating that new energy has an impact on carbon finance, and the score of carbon emission was relatively low, which was 0.0178. The index integrates energy to reduce the risk rate of carbon finance, and the reduction rate is about 25%. Therefore, the research on carbon finance risk prediction can provide support for the development of wind power and photovoltaic power generation, expand the application scope of new energy, and realize the application depth of wind energy and photovoltaic power.

Keywords

  • New energy,
  • Photovoltaic,
  • Wind power,
  • Resource,
  • Fusion index,
  • Carbon finance risk,
  • Prediction
PDF

References

  1. Gao, J.-T., Tang, Y.: Renewable energy utilization and stability through dynamic grid connection strategy and AI-driven solution approach. J. Energy Storage 95 (2024). https://doi.org/10.1016/j.est.2024.112546
  2. Huang, J.-C., Cheng, H.-C., Shu, M.-H., Huang, H.-C.: Market-based optimization of integrated energy systems: Modeling and analysis of multi-carrier energy networks. Electr. Power Syst. Res. 239 (2025). https://doi.org/10.1016/j.epsr.2024.111245
  3. Li, J., Li, K., Ai, M., Zhong, Y., Ge, X.: Carbon emission optimization in renewable powered cellular networks based on stochastic geometric distribution. Sci. Sin. Inf. 54(4), 788–802 (2024)
  4. Nassar, Y. F., El-Khozondar, H. J., Salem, M. A., Alatrash, A. A., Elnaggar, M., Salah, W. A., & Bashir, M. J. (2025). Estimation of CO2 Emission within Libya's Electricity Generation Sector. Next Research, 100567.
  5. Nassar, Y., Irhouma, M., Salem, M., El-Khozondar, H., Suliman, S., Elmnifi, M., ... & Rekik, S. (2025). Towards green economy:: case of electricity generation sector in Libya. Solar Energy and Sustainable Development Journal, 14(1), 334-360.
  6. Ma, X., Dong, X., Li, Y., Liang, C., Xu, R.: Research on the two-layer optimal strategy of active power and reactive power coordination considering integration of new energy. Power Syst. Clean Energy 40(1), 137–142, 149 (2024)
  7. Pan, D., Wang, X., Yang, X., Shi, T., Shi, D., Chen, Z.: Collaborative planning of energy storage system considering peak regulation demand and renewable energy integration. J. Phys.: Conf. Ser. 2774(1), 012069 (2024). https://doi.org/10.1088/1742-6596/2774/1/012069
  8. Son, Y.G., Kim, S.Y.: Optimal planning and operation of integrated energy systems in South Korea: Introducing a novel ambiguity set based distributionally robust optimization. Energy 307 (2024). https://doi.org/10.1016/j.energy.2024.132503
  9. Tang, Q., Hu, M., Bian, Y., Wang, Y., Lei, Z., Peng, X., Li, K.: Optimal energy efficiency control framework for distributed drive mining truck power system with hybrid energy storage: A vehicle-cloud integration approach. Appl. Energy 374 (2024). https://doi.org/10.1016/j.apenergy.2024.123989
  10. Tian, C., Liu, M., Zhang, D., Liu, F., Gao, Z.: Energy storage optimal configuration in new energy stations considering battery life cycle. Electr. Eng. 106(6), 7867–7878 (2024). https://doi.org/10.1007/s00202-024-02487-5
  11. Abodwair, A., Guneser, M., Khaleel, M., Nassar, Y., El-Khozondar, H., & Elbaz, A. (2024). Feasibility assessment of hybrid renewable energy based ev charging station in Libya. Solar Energy and Sustainable Development Journal, 13(2), 311-349.
  12. Nassar, Y. Y., Salem, M., & El-Khozondar, H. (2025). Estimation of CO2 emissions from the electric power industry sector in Libya. Solar Energy and Sustainable Development Journal, 14(1), 42-55.
  13. Zhang, S., Lu, H., Zhao, F.: Research on the power loss control method of distribution networks under new energy access. Power Syst. Clean Energy 40(10), 115–121, 131 (2024a)
  14. Zhang, X., Su, J., Jiao, F., Zeng, R., Pan, J., He, X., Deng, Q., Li, H.: Performance investigation and operation optimization of an innovative hybrid renewable energy integration system for commercial building complex and hydrogen vehicles. Energy 301 (2024b). https://doi.org/10.1016/j.energy.2024.131640
  15. Bai, Y., Zhang, Q. & Zheng, B. News-driven carbon trading market: machine learning-based carbon price prediction. Environ Dev Sustain (2025). https://doi.org/10.1007/s10668-025-06709-z
  16. Nassar, Y. F., El-Khozondar, H. J., El-Osta, W., Mohammed, S., Elnaggar, M., Khaleel, M., ... & Alsharif, A. (2024). Carbon footprint and energy life cycle assessment of wind energy industry in Libya. Energy conversion and management, 300, 117846.
  17. Jin, X. Extreme Risk Connectedness and its Determinants Between Carbon, Green Finance and Energy Markets. Asia-Pac Financ Markets (2025). https://doi.org/10.1007/s10690-025-09536-4
  18. Kumar, P. M., Kamruzzaman, M. M., Alfurhood, B. S., Hossain, B., Nagarajan, H., & Sitaraman, S. R. (2024). Balanced performance merit on wind and solar energy contact with clean environment enrichment. IEEE Journal of the Electron Devices Society, 12, 808-823.