Empirical Study and Prediction on the Influencing Factors of Carbon Emission Rights Trading Prices-Taking the National Carbon Emission Rights Trading Market in China as an Example

Mingyu Liang; Yiling Zuo

doi:10.54097/ppbeq220

Authors

Mingyu Liang College of Economic, Shenzhen University, Shenzhen, 518055, China
Yiling Zuo School of Economics, Wuhan University of Technology, Wuhan,430070, China

DOI:

https://doi.org/10.54097/ppbeq220

Keywords:

Carbon Emission Rights Market, Carbon Trading, Carbon Price Regulation and Prediction.

Abstract

Under the backdrop of global climate change, market-based emission reduction has become a consensus. This paper takes the national carbon market from 2021 to 2025 as the object of study, selects variables such as policies, EUA prices of the European Union, Gross Domestic Product (GDP), HS300, coal prices, and international oil prices, and unifies daily and quarterly data into monthly data for OLS regression analysis. The research findings indicate that policies and energy prices are the core influencing factors of carbon prices: lenient policies (such as a slight increase in quotas and the restart of CCER) and a decline in energy prices will lower carbon prices, while an increase in crude oil prices will push carbon prices up. The EUA price in the European Union has an overflow effect. At the same time, the research has successfully solved the problem of integrating multi-frequency data, verified the rationality of energy price indicators, and the constructed model can predict the trend of carbon prices in the next month. This research fills the empirical gap in the national carbon market, provides quantitative basis for market stability and mechanism improvement, and helps to achieve the "dual carbon" goals.

Downloads

Download data is not yet available.

References

[1] Chen, J., Peng, D., Liu, Z., Wu, L., & Jiang, M. (2024). A sustainable model for forecasting carbon emission trading prices. Sustainability, 16, 8324.

[2] Chen, T., Ye, J., Zhou, Y., Yu, Q., Wang, S., & Li, G. (2025). Prediction of carbon emission rights trading prices based on the CNN–LSTM model in the context of carbon peak: Taking Guangdong Province as an example. Concurrency and Computation: Practice and Experience, 37(12-14), e70121.

[3] Chen, X., & Wang, Z. (2012). Empirical study on factors affecting carbon emission trading prices: Taking the EU emission trading system (EUETS) as an example. Systems Engineering, 30(2), 53–60.

[4] Chen, Z., Gao, X., Chen, N., Zhao, Y., & Guo, S. (2025). Economy or climate? Impact of policy uncertainty on price volatility of China’s carbon emission trading markets. Energies, 18(10), 1996–1073.

[5] Liao, M., Long, F., Tian, X., Bi, F., Tian, W., Li, X., & Ge, C. (2025). A study on factors influencing the national carbon emission trading price in China. PLoS One, 20(10), e0333788.

[6] Liu, T., Guan, X., Wei, Y., Xue, S., & Xu, L. (2023). Impact of economic policy uncertainty on the volatility of China’s emission trading scheme pilots. Energy Economics, 121, 106626.

[7] Ma, B., Guo, R., Wang, X., & Zhu, F. (2024). Research on the influencing factors of carbon emission trading prices. Journal of Advanced Business Management, 2024, 061102.

[8] Wang, J., Zhao, X., & Wang, L. (2025). China carbon price prediction based on genetic algorithm - particle swarm optimization - backpropagation neural network model. Sustainable Development, 17, 59.

[9] Wang, Y., Pan, H., Cao, R., & Xu, B. (2025). Price dynamics and forecasting of China's tradable green certificates: An analysis of linkages with the carbon emissions trading market. Energy Policy, 206, 114767.

[10] Yin, H., Yin, Y., Li, H., Zhu, J., Xian, Z., Tang, Y., Xiao, L., Rong, J., Li, C., Zhang, H., Xie, Z., & Meng, A. (2025). Carbon emissions trading price forecasting based on temporal-spatial multidimensional collaborative attention network and segment imbalance regression. Applied Energy, 377(Part A), 124357.

[11] Yu, Q., AbdulRahman, R., & Wu, Y. (2024). Carbon price prediction in China based on ensemble empirical mode decomposition and machine learning algorithms. Environmental Science and Pollution Research. Advance online publication.

[12] Yu, Z., Wang, J., Cong, W., Hu, K., Liang, Z., & Tan, X. (2025). Research on factors affecting carbon price. Energy, (1), 74–81.

[13] Zhang, T., & Deng, M. (2025). A study on the differentiation of carbon prices in China: Insights from eight carbon emission trading pilots. Journal of Cleaner Production, 501, 145279.