IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i9p2475-d170423.html
   My bibliography  Save this article

Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm

Author

Listed:
  • Menglu Li

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Wei Wang

    (School of Humanities and Social Sciences, North China Electric Power University, Beijing 102206, China)

  • Gejirifu De

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Xionghua Ji

    (School of Economics and Management, Yan’an University, Yan’an 716000, China)

  • Zhongfu Tan

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China
    School of Economics and Management, Yan’an University, Yan’an 716000, China)

Abstract

Carbon emissions and environmental protection issues have brought pressure from the international community during Chinese economic development. Recently, Chinese Government announced that carbon emissions per unit of GDP would fall by 60–65% compared with 2005 and non-fossil fuel energy would account for 20% of primary energy consumption by 2030. The Beijing-Tianjin-Hebei region is an important regional energy consumption center in China, and its energy structure is typically coal-based which is similar to the whole country. Therefore, forecasting energy consumption related carbon emissions is of great significance to emissions reduction and upgrading of energy supply in the Beijing-Tianjin-Hebei region. Thus, this study thoroughly analyzed the main energy sources of carbon emissions including coal, petrol, natural gas, and coal power in this region. Secondly, the kernel function of the support vector machine was applied to the extreme learning machine algorithm to optimize the connection weight matrix between the original hidden layer and the output layer. Thirdly, the grey prediction theory was used to predict major energy consumption in the region from 2017 to 2030. Then, the energy consumption and carbon emissions data for 2000–2016 were used as the training and test sets for the SVM-ELM (Support Vector Machine-Extreme Learning Machine) model. The result of SVM-ELM model was compared with the forecasting results of SVM (Support Vector Machine Algorithm) and ELM (Extreme Learning Machine) algorithm. The accuracy of SVM-ELM was shown to be higher. Finally, we used forecasting output of GM (Grey Prediction Theory) (1, 1) as the input of the SVM-ELM model to predict carbon emissions in the region from 2017 to 2030. The results showed that the proportion of energy consumption seriously affects the amount of carbon emissions. We found that the energy consumption of electricity and natural gas will reach 45% by 2030 and carbon emissions in the region can be controlled below 96.9 million tons. Therefore, accelerating the upgradation of industrial structure will be the key task for the government in controlling the amount of carbon emissions in the next step.

Suggested Citation

  • Menglu Li & Wei Wang & Gejirifu De & Xionghua Ji & Zhongfu Tan, 2018. "Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm," Energies, MDPI, vol. 11(9), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2475-:d:170423
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/9/2475/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/9/2475/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liu, Hui & Mi, Xiwei & Li, Yanfei, 2018. "An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm," Renewable Energy, Elsevier, vol. 123(C), pages 694-705.
    2. Ji, Ling & Zhang, Bei-Bei & Huang, Guo-He & Xie, Yu-Lei & Niu, Dong-Xiao, 2018. "GHG-mitigation oriented and coal-consumption constrained inexact robust model for regional energy structure adjustment – A case study for Jiangsu Province, China," Renewable Energy, Elsevier, vol. 123(C), pages 549-562.
    3. 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.
    4. Shiqing Zhang & Jianwei Wang & Wenlong Zheng, 2018. "Decomposition Analysis of Energy-Related CO 2 Emissions and Decoupling Status in China’s Logistics Industry," Sustainability, MDPI, vol. 10(5), pages 1-21, April.
    5. Safdarnejad, Seyed Mostafa & Hedengren, John D. & Baxter, Larry L., 2015. "Plant-level dynamic optimization of Cryogenic Carbon Capture with conventional and renewable power sources," Applied Energy, Elsevier, vol. 149(C), pages 354-366.
    6. Hong Chang & Wei Sun & Xingsheng Gu, 2013. "Forecasting Energy CO 2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model," Energies, MDPI, vol. 6(3), pages 1-22, March.
    7. Inglesi-Lotz, Roula & Dogan, Eyup, 2018. "The role of renewable versus non-renewable energy to the level of CO2 emissions a panel analysis of sub- Saharan Africa’s Βig 10 electricity generators," Renewable Energy, Elsevier, vol. 123(C), pages 36-43.
    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. Atif Maqbool Khan & Magdalena Osińska, 2021. "How to Predict Energy Consumption in BRICS Countries?," Energies, MDPI, vol. 14(10), pages 1-21, May.
    2. Haibin Cao & Peng Jiang & Ming Zeng, 2021. "A Novel Comprehensive Benefit Evaluation of IEGES Based on the TOPSIS Optimized by MEE Method," Energies, MDPI, vol. 14(3), pages 1-19, February.
    3. Georgiana Moiceanu & Mirela Nicoleta Dinca, 2021. "Climate Change-Greenhouse Gas Emissions Analysis and Forecast in Romania," Sustainability, MDPI, vol. 13(21), pages 1-21, November.
    4. Pruethsan Sutthichaimethee & Boonton Dockthaisong, 2018. "A Relationship of Causal Factors in the Economic, Social, and Environmental Aspects Affecting the Implementation of Sustainability Policy in Thailand: Enriching the Path Analysis Based on a GMM Model," Resources, MDPI, vol. 7(4), pages 1-26, December.
    5. Peng Jiang & Jun Dong & Hui Huang, 2019. "Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm," Energies, MDPI, vol. 12(7), pages 1-24, April.
    6. Wenting Zhao & Juanjuan Zhao & Xilong Yao & Zhixin Jin & Pan Wang, 2019. "A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand," Energies, MDPI, vol. 12(7), pages 1-28, April.
    7. Qifan Guan, 2023. "Decomposing and Decoupling the Energy-Related Carbon Emissions in the Beijing–Tianjin–Hebei Region Using the Extended LMDI and Tapio Index Model," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    8. Huang, Jiaqing & Wang, Linlin & Siddik, Abu Bakkar & Abdul-Samad, Zulkiflee & Bhardwaj, Arpit & Singh, Bharat, 2023. "Forecasting GHG emissions for environmental protection with energy consumption reduction from renewable sources: A sustainable environmental system," Ecological Modelling, Elsevier, vol. 475(C).
    9. Shuxia Yang & Xianguo Zhu & Shengjiang Peng, 2020. "Prospect Prediction of Terminal Clean Power Consumption in China via LSSVM Algorithm Based on Improved Evolutionary Game Theory," Energies, MDPI, vol. 13(8), pages 1-17, April.
    10. Wei Sun & Yufei Hou & Lanjiang Guo, 2018. "Analyzing and Forecasting Energy Consumption in China’s Manufacturing Industry and Its Subindustries," Sustainability, MDPI, vol. 11(1), pages 1-26, December.

    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. Ostadzad, Ali Hossein, 2022. "Innovation and carbon emissions: Fixed-effects panel threshold model estimation for renewable energy," Renewable Energy, Elsevier, vol. 198(C), pages 602-617.
    2. Adekoya, Oluwasegun B. & Olabode, Joshua K. & Rafi, Syed K., 2021. "Renewable energy consumption, carbon emissions and human development: Empirical comparison of the trajectories of world regions," Renewable Energy, Elsevier, vol. 179(C), pages 1836-1848.
    3. Xu, Deyi & Sheraz, Muhammad & Hassan, Arshad & Sinha, Avik & Ullah, Saif, 2022. "Financial development, renewable energy and CO2 emission in G7 countries: New evidence from non-linear and asymmetric analysis," Energy Economics, Elsevier, vol. 109(C).
    4. Mojtaba Qolipour & Ali Mostafaeipour & Mohammad Saidi-Mehrabad & Hamid R Arabnia, 2019. "Prediction of wind speed using a new Grey-extreme learning machine hybrid algorithm: A case study," Energy & Environment, , vol. 30(1), pages 44-62, February.
    5. Waseem Yousaf & Muhammad Sajjad Hussain & Anam Aziz, 2021. "The Role of Green Energy on Reducing the Carbon Emission in ASEAN Countries," iRASD Journal of Energy and Environment, International Research Association for Sustainable Development (iRASD), vol. 2(1), pages 34-39, June.
    6. Lu, Changxiang & Ye, Yong & Fang, Yongjun & Fang, Jiaqi, 2023. "An optimal control theory approach for freight structure path evolution post-COVID-19 pandemic," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    7. Bi-Huei Tsai & Yao-Min Huang, 2023. "Comparing the Substitution of Nuclear Energy or Renewable Energy for Fossil Fuels between the United States and Africa," Sustainability, MDPI, vol. 15(13), pages 1-16, June.
    8. Moataz Elshimy & Khadiga M. El-Aasar, 2020. "Carbon footprint, renewable energy, non-renewable energy, and livestock: testing the environmental Kuznets curve hypothesis for the Arab world," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(7), pages 6985-7012, October.
    9. Arminen, Heli & Menegaki, Angeliki N., 2019. "Corruption, climate and the energy-environment-growth nexus," Energy Economics, Elsevier, vol. 80(C), pages 621-634.
    10. Kang, Sang Hoon & Islam, Faridul & Kumar Tiwari, Aviral, 2019. "The dynamic relationships among CO2 emissions, renewable and non-renewable energy sources, and economic growth in India: Evidence from time-varying Bayesian VAR model," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 90-101.
    11. Avom, Désiré & Nkengfack, Hilaire & Fotio, Hervé Kaffo & Totouom, Armand, 2020. "ICT and environmental quality in Sub-Saharan Africa: Effects and transmission channels," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    12. Karaaslan, Abdulkerim & Çamkaya, Serhat, 2022. "The relationship between CO2 emissions, economic growth, health expenditure, and renewable and non-renewable energy consumption: Empirical evidence from Turkey," Renewable Energy, Elsevier, vol. 190(C), pages 457-466.
    13. Wei Sun & Ming Duan, 2019. "Analysis and Forecasting of the Carbon Price in China’s Regional Carbon Markets Based on Fast Ensemble Empirical Mode Decomposition, Phase Space Reconstruction, and an Improved Extreme Learning Machin," Energies, MDPI, vol. 12(2), pages 1-27, January.
    14. Ehigiamusoe, Kizito Uyi & Dogan, Eyup, 2022. "The role of interaction effect between renewable energy consumption and real income in carbon emissions: Evidence from low-income countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    15. Zhicong Zhang & Hao Xie & Jubing Zhang & Xinye Wang & Jiayu Wei & Xibin Quan, 2022. "Prediction and Trend Analysis of Regional Industrial Carbon Emission in China: A Study of Nanjing City," IJERPH, MDPI, vol. 19(12), pages 1-23, June.
    16. Shuyu Dai & Dongxiao Niu & Yaru Han, 2018. "Forecasting of Energy-Related CO 2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability," Sustainability, MDPI, vol. 10(4), pages 1-17, March.
    17. Yunzhao, Lu, 2022. "Modelling the role of eco innovation, renewable energy, and environmental taxes in carbon emissions reduction in E−7 economies: Evidence from advance panel estimations," Renewable Energy, Elsevier, vol. 190(C), pages 309-318.
    18. Hao Wang & Chen Peng & Bolin Liao & Xinwei Cao & Shuai Li, 2023. "Wind Power Forecasting Based on WaveNet and Multitask Learning," Sustainability, MDPI, vol. 15(14), pages 1-22, July.
    19. Wang, Zhaohua & Pham, Thi Le Hoa & Sun, Kaining & Wang, Bo & Bui, Quocviet & Hashemizadeh, Ali, 2022. "The moderating role of financial development in the renewable energy consumption - CO2 emissions linkage: The case study of Next-11 countries," Energy, Elsevier, vol. 254(PB).
    20. Qureshi, Muhammad Imran & Qayyum, Shazia & Nassani, Abdelmohsen A. & Aldakhil, Abdullah Mohammed & Qazi Abro, Muhammad Moinuddin & Zaman, Khalid, 2019. "Management of various socio-economic factors under the United Nations sustainable development agenda," Resources Policy, Elsevier, vol. 64(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:gam:jeners:v:11:y:2018:i:9:p:2475-:d:170423. 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.