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Integrated prediction of carbon price in China based on heterogeneous structural information and wall-value constraints

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  • Sun, Qingqing
  • Chen, Hong
  • Long, Ruyin
  • Chen, Jiawei

Abstract

Carbon price is not a “free-flowing” product of market transactions, but an economic symbol with political attributes that has a bearing on the endogenous security of China's national economic development. This study takes into account the key factors affecting carbon price, such as international carbon quota trading price, changes in macroeconomic environment, basic energy price and public concern about climate change. Based on the high-frequency, low-frequency, trend term decomposition and integrated noise reduction processing of carbon price data by DWT, CEEMDAN and SSA, the Transformers prediction model is constructed. Further, based on the combination of scenarios and the current status of power enterprises' revenues, a real-world carbon market “tolerance wall value” and a cut-off point are determined. The results show that (1) the predictions of the models using combined modal data are all better than those using unimodal and raw data. (2) The “wall value” of carbon price is 117.6 yuan/ton, 142.3 yuan/ton, and 182.7 yuan/ton under the low carbon follow (LCF) basic as usual (BAU), and economic follow (EF) scenarios, respectively, and all of them are reached by 2029. (3) Measured with realistic data, China's electric power enterprises have paid more than the value of profits (4 % of profits) with a low tolerance level to take part in carbon trading in the market. And under the EF and BAU scenarios, with high tolerance as a parameter, the trading price in China's carbon market will reach the wall value level (119.5yuan/ton) in June 2025 and October 2026, respectively. This research contributes to a more thorough understanding and forecasting of the function and future trajectory of China's carbon market and carbon pricing system.

Suggested Citation

  • Sun, Qingqing & Chen, Hong & Long, Ruyin & Chen, Jiawei, 2024. "Integrated prediction of carbon price in China based on heterogeneous structural information and wall-value constraints," Energy, Elsevier, vol. 306(C).
    • Handle: RePEc:eee:energy:v:306:y:2024:i:c:s0360544224022576
    DOI: 10.1016/j.energy.2024.132483
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    as
    1. Qi, Shaozhou & Cheng, Shihan & Tan, Xiujie & Feng, Shenghao & Zhou, Qi, 2022. "Predicting China's carbon price based on a multi-scale integrated model," Applied Energy, Elsevier, vol. 324(C).
    2. Eugenia Sanin, María & Violante, Francesco & Mansanet-Bataller, María, 2015. "Understanding volatility dynamics in the EU-ETS market," Energy Policy, Elsevier, vol. 82(C), pages 321-331.
    3. Han, Meng & Ding, Lili & Zhao, Xin & Kang, Wanglin, 2019. "Forecasting carbon prices in the Shenzhen market, China: The role of mixed-frequency factors," Energy, Elsevier, vol. 171(C), pages 69-76.
    4. Woo, C.K. & Chen, Y. & Zarnikau, J. & Olson, A. & Moore, J. & Ho, T., 2018. "Carbon trading’s impact on California’s real-time electricity market prices," Energy, Elsevier, vol. 159(C), pages 579-587.
    5. Hammoudeh, Shawkat & Nguyen, Duc Khuong & Sousa, Ricardo M., 2014. "Energy prices and CO2 emission allowance prices: A quantile regression approach," Energy Policy, Elsevier, vol. 70(C), pages 201-206.
    6. Fang, Guochang & Tian, Lixin & Fu, Min & Sun, Mei, 2014. "Government control or low carbon lifestyle? – Analysis and application of a novel selective-constrained energy-saving and emission-reduction dynamic evolution system," Energy Policy, Elsevier, vol. 68(C), pages 498-507.
    7. Liu, Yu & Lu, Yingying, 2015. "The Economic impact of different carbon tax revenue recycling schemes in China: A model-based scenario analysis," Applied Energy, Elsevier, vol. 141(C), pages 96-105.
    8. Wen, Fenghua & Zhao, Haocen & Zhao, Lili & Yin, Hua, 2022. "What drive carbon price dynamics in China?," International Review of Financial Analysis, Elsevier, vol. 79(C).
    9. Buso, Marco & Stenger, Anne, 2018. "Public-private partnerships as a policy response to climate change," Energy Policy, Elsevier, vol. 119(C), pages 487-494.
    10. Zhang, Wei & Shen, Dehua & Zhang, Yongjie & Xiong, Xiong, 2013. "Open source information, investor attention, and asset pricing," Economic Modelling, Elsevier, vol. 33(C), pages 613-619.
    11. Byun, Suk Joon & Cho, Hangjun, 2013. "Forecasting carbon futures volatility using GARCH models with energy volatilities," Energy Economics, Elsevier, vol. 40(C), pages 207-221.
    12. Tan, Xue-Ping & Wang, Xin-Yu, 2017. "Dependence changes between the carbon price and its fundamentals: A quantile regression approach," Applied Energy, Elsevier, vol. 190(C), pages 306-325.
    13. Tang, Ling & Wu, Jiaqian & Yu, Lean & Bao, Qin, 2015. "Carbon emissions trading scheme exploration in China: A multi-agent-based model," Energy Policy, Elsevier, vol. 81(C), pages 152-169.
    14. Song, Xiang & Wang, Dingyu & Zhang, Xuantao & He, Yuan & Wang, Yong, 2022. "A comparison of the operation of China's carbon trading market and energy market and their spillover effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    15. Lin, Boqiang & Xu, Bin, 2021. "A non-parametric analysis of the driving factors of China's carbon prices," Energy Economics, Elsevier, vol. 104(C).
    16. Wang, Bangyan & Wang, Xiuli & Wei, Fengting & Shao, Chengcheng & Zhou, Jiahao & Lin, Jintian, 2023. "Multi-stage stochastic planning for a long-term low-carbon transition of island power system considering carbon price uncertainty and offshore wind power," Energy, Elsevier, vol. 282(C).
    17. Qin, Chaoyong & Qin, Dongling & Jiang, Qiuxian & Zhu, Bangzhu, 2024. "Forecasting carbon price with attention mechanism and bidirectional long short-term memory network," Energy, Elsevier, vol. 299(C).
    18. Anne Stenger-Letheux & Marco Buso, 2018. "Public-private partnerships as a policy response to climate change," Post-Print hal-01952098, HAL.
    19. Chang-Jing Ji & Yu-Jie Hu & Bao-Jun Tang, 2018. "Research on carbon market price mechanism and influencing factors: a literature review," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(2), pages 761-782, June.
    20. Gao, Feng & Shao, Xueyan, 2022. "A novel interval decomposition ensemble model for interval carbon price forecasting," Energy, Elsevier, vol. 243(C).
    21. Fan, John Hua & Todorova, Neda, 2017. "Dynamics of China’s carbon prices in the pilot trading phase," Applied Energy, Elsevier, vol. 208(C), pages 1452-1467.
    22. Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
    23. Song, Yan & Zhang, Ming, 2019. "Research on the gravity movement and mitigation potential of Asia's carbon dioxide emissions," Energy, Elsevier, vol. 170(C), pages 31-39.
    24. Li, Mingquan & Gao, Huiwen & Abdulla, Ahmed & Shan, Rui & Gao, Shuo, 2022. "Combined effects of carbon pricing and power market reform on CO2 emissions reduction in China's electricity sector," Energy, Elsevier, vol. 257(C).
    25. Sun, Wei & Zhang, Chongchong, 2018. "Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm," Applied Energy, Elsevier, vol. 231(C), pages 1354-1371.
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