IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v252y2022ics036054422201009x.html
   My bibliography  Save this article

Scenario simulations of China's natural gas consumption under the dual-carbon target

Author

Listed:
  • Fan, Jingjing
  • Wang, Jianliang
  • Liu, Mingming
  • Sun, Wangmin
  • Lan, Zhixuan

Abstract

A dual-carbon target (i.e., carbon peak and carbon neutrality) has been a critical external factor for the usage of China's fossil energy in the future. As the lowest-carbon fossil energy source, the uncertainty of China's natural gas usage is much higher than that of coal and oil. Thus, a detailed analysis of China's future natural gas consumption (NGC) under the dual-carbon target has attracted great attention from academia. This paper constructs a system dynamics-based forecasting model for China's NGC, and predicts the trends of China's primary energy demand, natural gas demand, and carbon emissions under four scenarios of high-, medium-, low-, and business-as-usual transitions. The low transition scenario, which is in line with the actual situation of China, shows that: (1) primary energy consumption peaks around 2040 at approximately 7 billion tons of standard coal equivalent. (2) Carbon dioxide emissions peak in 2030 at approximately 10.6 billion tons of carbon dioxide, and “net zero” carbon emissions will be achieved in 2057 and 2059 under high and low carbon sinks, respectively. (3) NGC is expected to peak in 2038 at approximately 630 billion cubic meters. (4) Industrial gas is expected to peak around 2035 at approximately 450 billion cubic meters.

Suggested Citation

  • Fan, Jingjing & Wang, Jianliang & Liu, Mingming & Sun, Wangmin & Lan, Zhixuan, 2022. "Scenario simulations of China's natural gas consumption under the dual-carbon target," Energy, Elsevier, vol. 252(C).
    • Handle: RePEc:eee:energy:v:252:y:2022:i:c:s036054422201009x
    DOI: 10.1016/j.energy.2022.124106
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422201009X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124106?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. Ardakani, F.J. & Ardehali, M.M., 2014. "Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types," Energy, Elsevier, vol. 65(C), pages 452-461.
    2. Pao, H.T., 2009. "Forecasting energy consumption in Taiwan using hybrid nonlinear models," Energy, Elsevier, vol. 34(10), pages 1438-1446.
    3. Li, Junchen & Dong, Xiucheng & Shangguan, Jianxin & Hook, Mikael, 2011. "Forecasting the growth of China’s natural gas consumption," Energy, Elsevier, vol. 36(3), pages 1380-1385.
    4. Li, Nu & Wang, Jianliang & Wu, Lifeng & Bentley, Yongmei, 2021. "Predicting monthly natural gas production in China using a novel grey seasonal model with particle swarm optimization," Energy, Elsevier, vol. 215(PA).
    5. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    6. Li, Lanlan & Ming, Huayang & Fu, Weizhong & Shi, Quan & Yu, Shiwei, 2021. "Exploring household natural gas consumption patterns and their influencing factors: An integrated clustering and econometric method," Energy, Elsevier, vol. 224(C).
    7. Yoo, Seung-Hoon & Lim, Hea-Jin & Kwak, Seung-Jun, 2009. "Estimating the residential demand function for natural gas in Seoul with correction for sample selection bias," Applied Energy, Elsevier, vol. 86(4), pages 460-465, April.
    8. Liu, Guixian & Dong, Xiucheng & Jiang, Qingzhe & Dong, Cong & Li, Jiaman, 2018. "Natural gas consumption of urban households in China and corresponding influencing factors," Energy Policy, Elsevier, vol. 122(C), pages 17-26.
    9. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    10. Saab, Samer & Badr, Elie & Nasr, George, 2001. "Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon," Energy, Elsevier, vol. 26(1), pages 1-14.
    11. Karadede, Yusuf & Ozdemir, Gultekin & Aydemir, Erdal, 2017. "Breeder hybrid algorithm approach for natural gas demand forecasting model," Energy, Elsevier, vol. 141(C), pages 1269-1284.
    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. Zhang, Bo & Guo, Yaning & Li, Nian & He, Peng & Guo, Xiangji, 2023. "Experimental study of gas–liquid behavior in three-flow vortex tube with sintered metal porous material as the drain part," Energy, Elsevier, vol. 263(PA).
    2. Cao, Jiaojiao & Wu, Jiansong & Zhao, Yimeng & Cai, Jitao & Bai, Yiping & Pang, Lei, 2023. "Suppression effects of energy-absorbing materials on natural gas explosion in utility tunnels," Energy, Elsevier, vol. 281(C).
    3. Yang, Tingru & Li, Hui & Zhang, Lingyue & Chen, Tianqi, 2022. "The impact of city gas on income inequality in China: A regional heterogeneity analysis," Energy Policy, Elsevier, vol. 169(C).
    4. Sun, Bo & Li, Mingzhe & Wang, Fan & Xie, Jingdong, 2023. "An incentive mechanism to promote residential renewable energy consumption in China's electricity retail market: A two-level Stackelberg game approach," Energy, Elsevier, vol. 269(C).
    5. Wen, Kai & Qiao, Dan & Nie, Chaofei & Lu, Yangfan & Wen, Feng & Zhang, Jing & Miao, Qing & Gong, Jing & Li, Cuicui & Hong, Bingyuan, 2023. "Multi-period supply and demand balance of large-scale and complex natural gas pipeline network: Economy and environment," Energy, Elsevier, vol. 264(C).
    6. Zhang, Ruihang & Wang, Zexin & Wei, Xiaoming & Peng, Xiaowan & Chen, Wan & Deng, Chun & Liu, Bei & Sun, Changyu & Chen, Guangjin, 2023. "Modelling and optimization of ethane recovery process from natural gas via ZIF-8/water-glycol slurry with low energy consumption," Energy, Elsevier, vol. 263(PA).
    7. Rongbin Wang & Weifeng Zhang & Wenlong Deng & Ruihao Zhang & Xiaohui Zhang, 2022. "Study on Prediction of Energy Conservation and Carbon Reduction in Universities Based on Exponential Smoothing," Sustainability, MDPI, vol. 14(19), pages 1-11, September.
    8. Yu Hu & Yuanying Chi & Wenbing Zhou & Zhengzao Wang & Yongke Yuan & Ruoyang Li, 2022. "Research on Energy Structure Optimization and Carbon Emission Reduction Path in Beijing under the Dual Carbon Target," Energies, MDPI, vol. 15(16), pages 1-17, August.
    9. Pengcheng Xue & Jiaxin Liu & Binbin Liu & Chuang Zhu, 2023. "Impact of Urbanisation on the Spatial and Temporal Evolution of Carbon Emissions and the Potential for Emission Reduction in a Dual-Carbon Reduction Context," Sustainability, MDPI, vol. 15(6), pages 1-14, March.
    10. Wang, Shiwei & Wang, Chao & Ding, Hongbing & Zhang, Yu & Dong, Yuanyuan & Wen, Chuang, 2023. "Joule-Thomson effect and flow behavior for energy-efficient dehydration of high-pressure natural gas in supersonic separator," Energy, Elsevier, vol. 279(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. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    2. Sun-Youn Shin & Han-Gyun Woo, 2022. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms," Energies, MDPI, vol. 15(13), pages 1-20, July.
    3. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    4. Suat Ozturk & Feride Ozturk, 2018. "Forecasting Energy Consumption of Turkey by Arima Model," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 8(2), pages 52-60, February.
    5. Lee, Yi-Shian & Tong, Lee-Ing, 2012. "Forecasting nonlinear time series of energy consumption using a hybrid dynamic model," Applied Energy, Elsevier, vol. 94(C), pages 251-256.
    6. Zhang, Wen Yu & Hong, Wei-Chiang & Dong, Yucheng & Tsai, Gary & Sung, Jing-Tian & Fan, Guo-feng, 2012. "Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting," Energy, Elsevier, vol. 45(1), pages 850-858.
    7. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
    8. Xi Zhang & Zheng Li & Linwei Ma & Chinhao Chong & Weidou Ni, 2019. "Forecasting the Energy Embodied in Construction Services Based on a Combination of Static and Dynamic Hybrid Input-Output Models," Energies, MDPI, vol. 12(2), pages 1-26, January.
    9. Akdi, Yılmaz & Gölveren, Elif & Okkaoğlu, Yasin, 2020. "Daily electrical energy consumption: Periodicity, harmonic regression method and forecasting," Energy, Elsevier, vol. 191(C).
    10. Zhang, Yunxin & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2023. "A novel grey Lotka–Volterra model driven by the mechanism of competition and cooperation for energy consumption forecasting," Energy, Elsevier, vol. 264(C).
    11. Nguyen, Hang T. & Nabney, Ian T., 2010. "Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models," Energy, Elsevier, vol. 35(9), pages 3674-3685.
    12. Yongwei, Cheng & Dong, Mu & Huanyu, Ren & Tijun, Fan & Jianbang, Du, 2020. "Using a temporal input-output approach to analyze the ripple effect of China’s energy consumption," Energy, Elsevier, vol. 211(C).
    13. Sen, Doruk & Günay, M. Erdem & Tunç, K.M. Murat, 2019. "Forecasting annual natural gas consumption using socio-economic indicators for making future policies," Energy, Elsevier, vol. 173(C), pages 1106-1118.
    14. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    15. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    16. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    17. Movagharnejad, Kamyar & Mehdizadeh, Bahman & Banihashemi, Morteza & Kordkheili, Masoud Sheikhi, 2011. "Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network," Energy, Elsevier, vol. 36(7), pages 3979-3984.
    18. Syed Aziz Ur Rehman & Yanpeng Cai & Rizwan Fazal & Gordhan Das Walasai & Nayyar Hussain Mirjat, 2017. "An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan," Energies, MDPI, vol. 10(11), pages 1-23, November.
    19. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
    20. Pruethsan Sutthichaimethee & Kuskana Kubaha, 2018. "The Efficiency of Long-Term Forecasting Model on Final Energy Consumption in Thailand’s Petroleum Industries Sector: Enriching the LT-ARIMAXS Model under a Sustainability Policy," Energies, MDPI, vol. 11(8), pages 1-18, 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:eee:energy:v:252:y:2022:i:c:s036054422201009x. 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.journals.elsevier.com/energy .

    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.