IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v383y2025ics0306261925001023.html
   My bibliography  Save this article

Implementing a provincial-level universal daily industrial carbon emissions prediction by fine-tuning the large language model

Author

Listed:
  • Feng, Zhengyuan
  • Sun, Yuheng
  • Ning, Jun
  • Tang, Shoujuan
  • Liu, Guangxin
  • Liu, Fangtao
  • Li, Yang
  • Shi, Lei

Abstract

Accurate daily predictions of industrial carbon emissions can improve our understanding of industrial activities. In this paper, we propose a novel approach to predict daily industrial carbon emissions by fine-tuning pre-trained language models. This method constructs a general carbon emissions model applicable to all provinces, providing accurate predictions in natural language format and outperforming traditional carbon emissions models. It achieves Mean Absolute Error (MAE) of 0.005757, Root Mean Squared Error (RMSE) of 0.008982, and Mean Absolute Percentage Error (MAPE) of 5.485714 %. The results suggest that fine-tuned T5 with prompt text significantly improves the predictive accuracy for daily industrial carbon emissions compared with typical multi-layer perception and long short-term memory numerical models. A single model can be adapted to the prediction of industrial carbon emissions in 31 provinces of China.

Suggested Citation

  • Feng, Zhengyuan & Sun, Yuheng & Ning, Jun & Tang, Shoujuan & Liu, Guangxin & Liu, Fangtao & Li, Yang & Shi, Lei, 2025. "Implementing a provincial-level universal daily industrial carbon emissions prediction by fine-tuning the large language model," Applied Energy, Elsevier, vol. 383(C).
    • Handle: RePEc:eee:appene:v:383:y:2025:i:c:s0306261925001023
    DOI: 10.1016/j.apenergy.2025.125372
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925001023
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2025.125372?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. Zhenggang Huo & Xiaoting Zha & Mengyao Lu & Tianqi Ma & Zhichao Lu, 2023. "Prediction of Carbon Emission of the Transportation Sector in Jiangsu Province-Regression Prediction Model Based on GA-SVM," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
    2. Xinli Yu & Zheng Chen & Yuan Ling & Shujing Dong & Zongyi Liu & Yanbin Lu, 2023. "Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting," Papers 2306.11025, arXiv.org.
    3. Kaifeng Bi & Lingxi Xie & Hengheng Zhang & Xin Chen & Xiaotao Gu & Qi Tian, 2023. "Accurate medium-range global weather forecasting with 3D neural networks," Nature, Nature, vol. 619(7970), pages 533-538, July.
    4. Xu, Ning & Ding, Song & Gong, Yande & Bai, Ju, 2019. "Forecasting Chinese greenhouse gas emissions from energy consumption using a novel grey rolling model," Energy, Elsevier, vol. 175(C), pages 218-227.
    5. Zhu Liu & Philippe Ciais & Zhu Deng & Steven J. Davis & Bo Zheng & Yilong Wang & Duo Cui & Biqing Zhu & Xinyu Dou & Piyu Ke & Taochun Sun & Rui Guo & Olivier Boucher & Francois-Marie Breon & Chenxi Lu, 2020. "Carbon Monitor: a near-real-time daily dataset of global CO2 emission from fossil fuel and cement production," Papers 2006.07690, arXiv.org.
    6. Sen, Parag & Roy, Mousumi & Pal, Parimal, 2016. "Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization," Energy, Elsevier, vol. 116(P1), pages 1031-1038.
    7. Lavender Yao Jiang & Xujin Chris Liu & Nima Pour Nejatian & Mustafa Nasir-Moin & Duo Wang & Anas Abidin & Kevin Eaton & Howard Antony Riina & Ilya Laufer & Paawan Punjabi & Madeline Miceli & Nora C. K, 2023. "Health system-scale language models are all-purpose prediction engines," Nature, Nature, vol. 619(7969), pages 357-362, July.
    8. 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.
    9. Song, Chao & Wang, Tao & Chen, Xiaohong & Shao, Quanxi & Zhang, Xianqi, 2023. "Ensemble framework for daily carbon dioxide emissions forecasting based on the signal decomposition–reconstruction model," Applied Energy, Elsevier, vol. 345(C).
    10. Kaifeng Bi & Lingxi Xie & Hengheng Zhang & Xin Chen & Xiaotao Gu & Qi Tian, 2023. "Author Correction: Accurate medium-range global weather forecasting with 3D neural networks," Nature, Nature, vol. 621(7980), pages 45-45, September.
    Full references (including those not matched with items on IDEAS)

    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. Fabian Dvorak & Regina Stumpf & Sebastian Fehrler & Urs Fischbacher, 2024. "Generative AI Triggers Welfare-Reducing Decisions in Humans," Papers 2401.12773, arXiv.org.
    2. Song Chen & Jiaxu Liu & Pengkai Wang & Chao Xu & Shengze Cai & Jian Chu, 2024. "Accelerated optimization in deep learning with a proportional-integral-derivative controller," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    3. Yuchen Cai & Jia Yang & Yutang Hou & Feng Wang & Lei Yin & Shuhui Li & Yanrong Wang & Tao Yan & Shan Yan & Xueying Zhan & Jun He & Zhenxing Wang, 2025. "8-bit states in 2D floating-gate memories using gate-injection mode for large-scale convolutional neural networks," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    4. Huaisheng Tu & Haotian Liu & Tuqiang Pan & Wuping Xie & Zihao Ma & Fan Zhang & Pengbai Xu & Leiming Wu & Ou Xu & Yi Xu & Yuwen Qin, 2025. "Deep empirical neural network for optical phase retrieval over a scattering medium," Nature Communications, Nature, vol. 16(1), pages 1-9, December.
    5. Lei Chen & Xiaohui Zhong & Hao Li & Jie Wu & Bo Lu & Deliang Chen & Shang-Ping Xie & Libo Wu & Qingchen Chao & Chensen Lin & Zixin Hu & Yuan Qi, 2024. "A machine learning model that outperforms conventional global subseasonal forecast models," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    6. Hu, Rong & Zhou, Kaile & Lu, Xinhui, 2025. "Integrated loads forecasting with absence of crucial factors," Energy, Elsevier, vol. 322(C).
    7. Yingzhe Cui & Ruohan Wu & Xiang Zhang & Ziqi Zhu & Bo Liu & Jun Shi & Junshi Chen & Hailong Liu & Shenghui Zhou & Liang Su & Zhao Jing & Hong An & Lixin Wu, 2025. "Forecasting the eddying ocean with a deep neural network," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    8. Khan, Taimoor & Choi, Chang, 2025. "Attention enhanced dual stream network with advanced feature selection for power forecasting," Applied Energy, Elsevier, vol. 377(PC).
    9. Zhou, Zhen & Gu, Ziyuan & Qu, Xiaobo & Liu, Pan & Liu, Zhiyuan & Yu, Wenwu, 2024. "Urban mobility foundation model: A literature review and hierarchical perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 192(C).
    10. Frank Brückerhoff-Plückelmann & Hendrik Borras & Bernhard Klein & Akhil Varri & Marlon Becker & Jelle Dijkstra & Martin Brückerhoff & C. David Wright & Martin Salinga & Harish Bhaskaran & Benjamin Ris, 2024. "Probabilistic photonic computing with chaotic light," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    11. Mattia Cavaiola & Federico Cassola & Davide Sacchetti & Francesco Ferrari & Andrea Mazzino, 2024. "Hybrid AI-enhanced lightning flash prediction in the medium-range forecast horizon," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    12. Florian Achermann & Thomas Stastny & Bogdan Danciu & Andrey Kolobov & Jen Jen Chung & Roland Siegwart & Nicholas Lawrance, 2024. "WindSeer: real-time volumetric wind prediction over complex terrain aboard a small uncrewed aerial vehicle," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    13. Zhenjia Chen & Zhenyuan Lin & Ji Yang & Cong Chen & Di Liu & Liuting Shan & Yuanyuan Hu & Tailiang Guo & Huipeng Chen, 2024. "Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    14. Wang, Yaqi & Zhao, Xiaomeng & Li, Zheng & Zhu, Wenbo & Gui, Renzhou, 2024. "A novel hybrid model for multi-step-ahead forecasting of wind speed based on univariate data feature enhancement," Energy, Elsevier, vol. 312(C).
    15. Francesco Carlucci & Francesco Fiorito, 2024. "Simulation of Responsive Envelopes in Current and Future Climate Scenarios: A New Interactive Computational Platform for Energy Analyses," Energies, MDPI, vol. 17(21), pages 1-26, October.
    16. Wang, Tao & Zhou, Hanxu & Fang, Qing & Han, Yanan & Guo, Xingxing & Zhang, Yahui & Qian, Chao & Chen, Hongsheng & Barland, Stéphane & Xiang, Shuiying & Lippi, Gian Luca, 2024. "Reservoir computing-based advance warning of extreme events," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    17. Bai, Huimin & Gong, Zhiqiang & Li, Li & Ma, Junjie & Dogar, Muhammad Mubashar, 2025. "Vegetation coverage variability and its driving factors in the semi-arid to semi-humid transition zone of North China," Chaos, Solitons & Fractals, Elsevier, vol. 191(C).
    18. Li Hu Wang & Xue Mei Liu & Yang Liu & Hai Rui Li & Jia QI Liu & Li Bo Yang, 2023. "Emergency entity relationship extraction for water diversion project based on pre-trained model and multi-featured graph convolutional network," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-18, October.
    19. He, Jinhua & Hu, Zechun & Wang, Songpo & Mujeeb, Asad & Yang, Pengwei, 2024. "Windformer: A novel 4D high-resolution system for multi-step wind speed vector forecasting based on temporal shifted window multi-head self-attention," Energy, Elsevier, vol. 310(C).
    20. Ding, Song & Zhang, Huahan, 2023. "Forecasting Chinese provincial CO2 emissions: A universal and robust new-information-based grey model," Energy Economics, Elsevier, vol. 121(C).

    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:383:y:2025:i:c:s0306261925001023. 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.