IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v324y2022ics0306261922010637.html
   My bibliography  Save this article

Predicting China's carbon price based on a multi-scale integrated model

Author

Listed:
  • Qi, Shaozhou
  • Cheng, Shihan
  • Tan, Xiujie
  • Feng, Shenghao
  • Zhou, Qi

Abstract

Carbon price is one of the core indicators of the carbon market. Making accurate predictions of carbon prices based on insight into the volatility characteristics of the carbon market will not only provide a scientific basis for investors and regulators to make decisions and greatly promote emission reduction, but also effectively promote the healthy development of the carbon financial market. Based on the historical carbon price data of the national carbon market, this paper constructs the EEMD-BP-ELM model to predict the high and low frequency components of China's future carbon price. On this basis, according to China's goal of peaking carbon emissions before 2030 and achieving carbon neutrality before 2060, the CHINAGEM-E model is used to predict the trend component of the carbon price under the baseline and the 2060 carbon neutral scenarios. Finally, the trend component is combined with the high and low frequency components to obtain the price range from 2022 to 2060. We find that under the baseline scenario, China's carbon price range in 2060 is [343, 785] CNY/tCO2, while under the 2060 carbon neutral scenario, this price range is [1543, 3531] CNY/tCO2.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:324:y:2022:i:c:s0306261922010637
    DOI: 10.1016/j.apenergy.2022.119784
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922010637
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.119784?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhu, Bangzhu & Wei, Yiming, 2013. "Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology," Omega, Elsevier, vol. 41(3), pages 517-524.
    2. 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.
    3. Allan, Grant & Lecca, Patrizio & McGregor, Peter & Swales, Kim, 2014. "The economic and environmental impact of a carbon tax for Scotland: A computable general equilibrium analysis," Ecological Economics, Elsevier, vol. 100(C), pages 40-50.
    4. 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.
    5. Otto, Vincent M. & Loschel, Andreas & Dellink, Rob, 2007. "Energy biased technical change: A CGE analysis," Resource and Energy Economics, Elsevier, vol. 29(2), pages 137-158, May.
    6. Babatunde, Kazeem Alasinrin & Begum, Rawshan Ara & Said, Fathin Faizah, 2017. "Application of computable general equilibrium (CGE) to climate change mitigation policy: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 61-71.
    7. Leroutier, Marion, 2022. "Carbon pricing and power sector decarbonization: Evidence from the UK," Journal of Environmental Economics and Management, Elsevier, vol. 111(C).
    8. Xu, Jia & Tan, Xiujie & He, Gang & Liu, Yu, 2019. "Disentangling the drivers of carbon prices in China's ETS pilots — An EEMD approach," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 1-9.
    9. Xu, Hua & Wang, Minggang & Jiang, Shumin & Yang, Weiguo, 2020. "Carbon price forecasting with complex network and extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    10. Fujimori, Shinichiro & Masui, Toshihiko & Matsuoka, Yuzuru, 2014. "Development of a global computable general equilibrium model coupled with detailed energy end-use technology," Applied Energy, Elsevier, vol. 128(C), pages 296-306.
    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. Zhao, Xin & Han, Meng & Ding, Lili & Kang, Wanglin, 2018. "Usefulness of economic and energy data at different frequencies for carbon price forecasting in the EU ETS," Applied Energy, Elsevier, vol. 216(C), pages 132-141.
    14. Feng, Shenghao & Howes, Stephen & Liu, Yu & Zhang, Keyu & Yang, Jun, 2018. "Towards a national ETS in China: Cap-setting and model mechanisms," Energy Economics, Elsevier, vol. 73(C), pages 43-52.
    15. Yinhua Mai & Peter B. Dixon & Maureen Rimmer, 2010. "CHINAGEM: A Monash-Styled Dynamic CGE Model of China," Centre of Policy Studies/IMPACT Centre Working Papers g-201, Victoria University, Centre of Policy Studies/IMPACT Centre.
    16. Quande Qin & Huangda He & Li Li & Ling-Yun He, 2020. "A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1249-1273, April.
    17. Shenghao Feng & Xiujian Peng & Philip Adams, 2021. "Energy and Economic Implications of Carbon Neutrality in China -- A Dynamic General Equilibrium Analysis," Centre of Policy Studies/IMPACT Centre Working Papers g-318, Victoria University, Centre of Policy Studies/IMPACT Centre.
    18. Zhu, Bangzhu & Ye, Shunxin & Wang, Ping & He, Kaijian & Zhang, Tao & Wei, Yi-Ming, 2018. "A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting," Energy Economics, Elsevier, vol. 70(C), pages 143-157.
    19. 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.
    20. Zhu, Bangzhu & Han, Dong & Wang, Ping & Wu, Zhanchi & Zhang, Tao & Wei, Yi-Ming, 2017. "Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression," Applied Energy, Elsevier, vol. 191(C), pages 521-530.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dong, Shihao & Yu, Yuelong & Wang, Hao & Yao, Yang & Ni, Long, 2023. "An economic-energetic-environmental evaluation algorithm for hybrid mid-depth geothermal heating system," Energy, Elsevier, vol. 282(C).
    2. Zhang, Chongchong & Lin, Boqiang, 2024. "Impact of introducing Chinese certified emission reduction scheme to the carbon market: Promoting renewable energy," Renewable Energy, Elsevier, vol. 222(C).
    3. Zhou, Zhou & Cai, Guotian & Huang, Yuping & Bai, Ruxue & Nie, Shuai & Chen, Xiaoyu, 2024. "Spatial and temporal evolution of cost-competitive offshore hydrogen in China: A techno-economic analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).
    4. Zhang, Fan & Li, Mo & Zhang, Shouhong & Liu, Jiakai & Ren, Yufei & Cao, Yini & Li, Feilong, 2024. "China's National Reserve Forest Project contribution to carbon neutrality and path to profitability," Forest Policy and Economics, Elsevier, vol. 160(C).
    5. Bai, Yun & Deng, Shuyun & Pu, Ziqiang & Li, Chuan, 2024. "Carbon price forecasting using leaky integrator echo state networks with the framework of decomposition-reconstruction-integration," Energy, Elsevier, vol. 305(C).
    6. Liu, Ying Lin & Zhang, Jing Jie & Fang, Yan, 2023. "The driving factors of China's carbon prices: Evidence from using ICEEMDAN-HC method and quantile regression," Finance Research Letters, Elsevier, vol. 54(C).
    7. Li, Chengzhe & Zhang, Libo & Wang, Qunwei & Zhou, Dequn, 2024. "Towards low-carbon steel: System dynamics simulation of policies impact on green hydrogen steelmaking in China and the European Union," Energy Policy, Elsevier, vol. 188(C).
    8. 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).
    9. Wang, Longze & Zhang, Yan & Li, Zhehan & Huang, Qiyu & Xiao, Yuxin & Yi, Xinxing & Ma, Yiyi & Li, Meicheng, 2023. "P2P trading mode for real-time coupled electricity and carbon markets based on a new indicator green energy," Energy, Elsevier, vol. 285(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huang, Yumeng & Dai, Xingyu & Wang, Qunwei & Zhou, Dequn, 2021. "A hybrid model for carbon price forecastingusing GARCH and long short-term memory network," Applied Energy, Elsevier, vol. 285(C).
    2. Chen, Linfei & Zhao, Xuefeng, 2024. "A multiscale and multivariable differentiated learning for carbon price forecasting," Energy Economics, Elsevier, vol. 131(C).
    3. Wen Zhang & Zhibin Wu, 2022. "Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 615-632, April.
    4. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2022. "Forecasting European carbon returns using dimension reduction techniques: Commodity versus financial fundamentals," International Journal of Forecasting, Elsevier, vol. 38(3), pages 944-969.
    5. Shenghao Feng & Xiujian Peng & Philip Adams, 2021. "Energy and Economic Implications of Carbon Neutrality in China -- A Dynamic General Equilibrium Analysis," Centre of Policy Studies/IMPACT Centre Working Papers g-318, Victoria University, Centre of Policy Studies/IMPACT Centre.
    6. Peng Ye & Yong Li & Abu Bakkar Siddik, 2023. "Forecasting the Return of Carbon Price in the Chinese Market Based on an Improved Stacking Ensemble Algorithm," Energies, MDPI, vol. 16(11), pages 1-39, June.
    7. Jesús Molina‐Muñoz & Andrés Mora‐Valencia & Javier Perote, 2024. "Predicting carbon and oil price returns using hybrid models based on machine and deep learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
    8. Lei, Heng & Xue, Minggao & Liu, Huiling, 2022. "Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors," Energy Economics, Elsevier, vol. 113(C).
    9. Jianguo Zhou & Dongfeng Chen, 2021. "Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm," Sustainability, MDPI, vol. 13(9), pages 1-20, April.
    10. Gao, Feng & Shao, Xueyan, 2022. "A novel interval decomposition ensemble model for interval carbon price forecasting," Energy, Elsevier, vol. 243(C).
    11. Katarzyna Rudnik & Anna Hnydiuk-Stefan & Aneta Kucińska-Landwójtowicz & Łukasz Mach, 2022. "Forecasting Day-Ahead Carbon Price by Modelling Its Determinants Using the PCA-Based Approach," Energies, MDPI, vol. 15(21), pages 1-23, October.
    12. Liao, Haolan & Wu, Di & Wang, Yuhan & Lyu, Zeyu & Sun, Hongmei & Nie, Yongyou & He, He, 2022. "Impacts of carbon trading mechanism on closed-loop supply chain: A case study of stringer pallet remanufacturing," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    13. Wang, Jujie & Zhuang, Zhenzhen & Gao, Dongming, 2023. "An enhanced hybrid model based on multiple influencing factors and divide-conquer strategy for carbon price prediction," Omega, Elsevier, vol. 120(C).
    14. Wang, Minggang & Zhu, Mengrui & Tian, Lixin, 2022. "A novel framework for carbon price forecasting with uncertainties," Energy Economics, Elsevier, vol. 112(C).
    15. Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
    16. 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).
    17. Jianguo Zhou & Shiguo Wang, 2021. "A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors," Energies, MDPI, vol. 14(5), pages 1-20, March.
    18. Li, Jingmiao & Liu, Dehong, 2023. "Carbon price forecasting based on secondary decomposition and feature screening," Energy, Elsevier, vol. 278(PA).
    19. Jujie Wang & Zhenzhen Zhuang, 2023. "A novel cluster based multi-index nonlinear ensemble framework for carbon price forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6225-6247, July.
    20. Zhang, Wen & Wu, Zhibin & Zeng, Xiaojun & Zhu, Changhui, 2023. "An ensemble dynamic self-learning model for multiscale carbon price forecasting," Energy, Elsevier, vol. 263(PC).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:324:y:2022:i:c:s0306261922010637. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.