IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i2p587-d478901.html
   My bibliography  Save this article

An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting

Author

Listed:
  • Yi-Chung Hu

    (College of Management & College of Tourism, Fujian Agriculture and Forestry University, Fuzhou 350002, China
    Department of Business Administration, Chung Yuan Christian University, Taoyuan 32023, Taiwan)

  • Peng Jiang

    (School of Business, Shandong University, Weihai 264209, China)

  • Jung-Fa Tsai

    (Department of Business Management, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Ching-Ying Yu

    (College of Management, Yuan Ze University, Taoyuan 32003, Taiwan)

Abstract

Because grey prediction does not demand that the collected data have to be in line with any statistical distribution, it is pertinent to set up grey prediction models for real-world problems. GM(1,1) has been a widely used grey prediction model, but relevant parameters, including the control variable and developing coefficient, rely on background values that are not easily determined. Furthermore, one-order accumulation is usually incorporated into grey prediction models, which assigns equal weights to each sample, to recognize regularities embedded in data sequences. Therefore, to optimize grey prediction models, this study employed a genetic algorithm to determine the relevant parameters and assigned appropriate weights to the sample data using fractional-order accumulation. Experimental results on the carbon dioxide emission data reported by the International Energy Agency demonstrated that the proposed grey prediction model was significantly superior to the other considered prediction models.

Suggested Citation

  • Yi-Chung Hu & Peng Jiang & Jung-Fa Tsai & Ching-Ying Yu, 2021. "An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting," IJERPH, MDPI, vol. 18(2), pages 1-12, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:2:p:587-:d:478901
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/2/587/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/2/587/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lin, Chiun-Sin & Liou, Fen-May & Huang, Chih-Pin, 2011. "Grey forecasting model for CO2 emissions: A Taiwan study," Applied Energy, Elsevier, vol. 88(11), pages 3816-3820.
    2. Yi-Chung Hu, 2017. "Electricity consumption prediction using a neural-network-based grey forecasting approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1259-1264, October.
    3. Mason, Karl & Duggan, Jim & Howley, Enda, 2018. "Forecasting energy demand, wind generation and carbon dioxide emissions in Ireland using evolutionary neural networks," Energy, Elsevier, vol. 155(C), pages 705-720.
    4. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
    5. 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.
    6. Liu, Liwei & Zong, Haijing & Zhao, Erdong & Chen, Chuxiang & Wang, Jianzhou, 2014. "Can China realize its carbon emission reduction goal in 2020: From the perspective of thermal power development," Applied Energy, Elsevier, vol. 124(C), pages 199-212.
    7. Wu, Wenqing & Ma, Xin & Zeng, Bo & Wang, Yong & Cai, Wei, 2019. "Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model," Renewable Energy, Elsevier, vol. 140(C), pages 70-87.
    8. Ma, Xin & Mei, Xie & Wu, Wenqing & Wu, Xinxing & Zeng, Bo, 2019. "A novel fractional time delayed grey model with Grey Wolf Optimizer and its applications in forecasting the natural gas and coal consumption in Chongqing China," Energy, Elsevier, vol. 178(C), pages 487-507.
    9. Pao, Hsiao-Tien & Tsai, Chung-Ming, 2011. "Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil," Energy, Elsevier, vol. 36(5), pages 2450-2458.
    10. Piecyk, Maja I. & McKinnon, Alan C., 2010. "Forecasting the carbon footprint of road freight transport in 2020," International Journal of Production Economics, Elsevier, vol. 128(1), pages 31-42, November.
    11. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
    12. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, vol. 9(4), pages 527-529, December.
    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. Pingping Xiong & Xiaojie Wu & Jing Ye, 2023. "Building a novel multivariate nonlinear MGM(1,m,N|γ) model to forecast carbon emissions," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 9647-9671, September.
    2. Huiping Wang & Zhun Zhang, 2022. "Forecasting CO 2 Emissions Using A Novel Grey Bernoulli Model: A Case of Shaanxi Province in China," IJERPH, MDPI, vol. 19(9), pages 1-22, April.

    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. Zeng, Qingshun & Shi, Changfeng & Zhu, Wenjun & Zhi, Jiaqi & Na, Xiaohong, 2023. "Sequential data-driven carbon peaking path simulation research of the Yangtze River Delta urban agglomeration based on semantic mining and heuristic algorithm optimization," Energy, Elsevier, vol. 285(C).
    2. Yi-Chung Hu, 2021. "Forecasting tourism demand using fractional grey prediction models with Fourier series," Annals of Operations Research, Springer, vol. 300(2), pages 467-491, May.
    3. Ma, Xuejiao & Jiang, Ping & Jiang, Qichuan, 2020. "Research and application of association rule algorithm and an optimized grey model in carbon emissions forecasting," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    4. Ma, Xuejiao & Wang, Yong & Wang, Chen, 2017. "Low-carbon development of China's thermal power industry based on an international comparison: Review, analysis and forecast," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 942-970.
    5. Ofosu-Adarkwa, Jeffrey & Xie, Naiming & Javed, Saad Ahmed, 2020. "Forecasting CO2 emissions of China's cement industry using a hybrid Verhulst-GM(1,N) model and emissions' technical conversion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    6. Gao, Mingyun & Yang, Honglin & Xiao, Qinzi & Goh, Mark, 2022. "A novel method for carbon emission forecasting based on Gompertz's law and fractional grey model: Evidence from American industrial sector," Renewable Energy, Elsevier, vol. 181(C), pages 803-819.
    7. Peng Zhang & Xin Ma & Kun She, 2019. "Forecasting Japan’s Solar Energy Consumption Using a Novel Incomplete Gamma Grey Model," Sustainability, MDPI, vol. 11(21), pages 1-23, October.
    8. Wang, Zheng-Xin & Wang, Zhi-Wei & Li, Qin, 2020. "Forecasting the industrial solar energy consumption using a novel seasonal GM(1,1) model with dynamic seasonal adjustment factors," Energy, Elsevier, vol. 200(C).
    9. 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).
    10. Qian, Wuyong & Wang, Jue, 2020. "An improved seasonal GM(1,1) model based on the HP filter for forecasting wind power generation in China," Energy, Elsevier, vol. 209(C).
    11. Aysha Malik & Ejaz Hussain & Sofia Baig & Muhammad Fahim Khokhar, 2020. "Forecasting CO2 emissions from energy consumption in Pakistan under different scenarios: The China–Pakistan Economic Corridor," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 10(2), pages 380-389, April.
    12. Wu, Lifeng & Liu, Sifeng & Liu, Dinglin & Fang, Zhigeng & Xu, Haiyan, 2015. "Modelling and forecasting CO2 emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model," Energy, Elsevier, vol. 79(C), pages 489-495.
    13. Liu, Chong & Wu, Wen-Ze & Xie, Wanli & Zhang, Jun, 2020. "Application of a novel fractional grey prediction model with time power term to predict the electricity consumption of India and China," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    14. Peng Zhang & Xin Ma & Kun She, 2019. "A Novel Power-Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China," Complexity, Hindawi, vol. 2019, pages 1-22, November.
    15. Huiru Zhao & Guo Huang & Ning Yan, 2018. "Forecasting Energy-Related CO 2 Emissions Employing a Novel SSA-LSSVM Model: Considering Structural Factors in China," Energies, MDPI, vol. 11(4), pages 1-21, March.
    16. Hakan Cetintas & I. Murat Bicil & Kumru Turkoz, 2017. "Turkiye'de Enerji Uretiminde Fosil Yakit Kullanimi ve CO2 Emisyonu Iliskisi: Bir Senaryo Analizi," EconWorld Working Papers 17002, WERI-World Economic Research Institute, revised Mar 2017.
    17. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    18. Jia, Zong-qian & Zhou, Zhi-fang & Zhang, Hong-jie & Li, Bo & Zhang, You-xian, 2020. "Forecast of coal consumption in Gansu Province based on Grey-Markov chain model," Energy, Elsevier, vol. 199(C).
    19. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    20. Wen-Ze Wu & Chong Liu & Wanli Xie & Mark Goh & Tao Zhang, 2023. "Predictive analysis of the industrial water-waste-energy system using an optimised grey approach: A case study in China," Energy & Environment, , vol. 34(5), pages 1639-1656, August.

    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:gam:jijerp:v:18:y:2021:i:2:p:587-:d:478901. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.