IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v25y2023i9d10.1007_s10668-022-02494-1.html
   My bibliography  Save this article

Forecasting energy demand, structure, and CO2 emission: a case study of Beijing, China

Author

Listed:
  • Zhixiong Weng

    (Beijing University of Technology)

  • Yuqi Song

    (The University of Chicago)

  • Hao Ma

    (BGRIMM Technology Group)

  • Zhong Ma

    (Renmin University of China)

  • Tingting Liu

    (Beijing University of Technology)

Abstract

The primary energy use in cities accounts for a large share of global energy consumption, leading to significant carbon emissions. Exploring the city’s current characteristics and future trends in energy demand has policy implications for carbon reduction policy-making. We establish a city-level energy forecast demand model to predict Beijing’s future energy demand. Results show that economic growth has strong positive correlations with energy consumption. Beijing’s energy demand is predicted to stay at a pretty high level by the end of 2020, between 98.72 and 121.60 Mtce, or between 4.12 and 5.08 tce per capita under three distinctive GDP growth (High, Medium and Low) scenarios. Comparing with the start of historical time series 1980, this is a huge increase of 563.50% (High), 489.90% (Medium) and 423.91% (Low) in terms of total demand, while is increases of 147.98%, 120.47% and 95.81% per capita. Based on an energy structure model estimated by a Markov-chain method, we find that our predictions would follow the current trend of energy structure, making Beijing’s coal demand decline towards zero share of energy consumption, natural gas rises approaching 50%, oil stays almost constant around 32.3%, electricity decreases below 20%, and other fuels keep in a small amount in 2035. We would expect to see almost 8000 kg per capita carbon emissions in Beijing if the GDP growth rate is high, and still over 6000 kg per capita if GDP growth is low. In terms of total carbon emissions, we show that though coal consumption will keep decreasing towards zero in the next years, total carbon emission still rises by 36.5–72.8% comparing with 2010. These findings can provide references for policymakers to implement some incentive policies to promote renewable and clean energy.

Suggested Citation

  • Zhixiong Weng & Yuqi Song & Hao Ma & Zhong Ma & Tingting Liu, 2023. "Forecasting energy demand, structure, and CO2 emission: a case study of Beijing, China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 10369-10391, September.
  • Handle: RePEc:spr:endesu:v:25:y:2023:i:9:d:10.1007_s10668-022-02494-1
    DOI: 10.1007/s10668-022-02494-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-022-02494-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10668-022-02494-1?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. Ozturk, Ilhan & Aslan, Alper & Kalyoncu, Huseyin, 2010. "Energy consumption and economic growth relationship: Evidence from panel data for low and middle income countries," Energy Policy, Elsevier, vol. 38(8), pages 4422-4428, August.
    2. Wang, Yuanyuan & Wang, Jianzhou & Zhao, Ge & Dong, Yao, 2012. "Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China," Energy Policy, Elsevier, vol. 48(C), pages 284-294.
    3. V. Masson & Colette Marchadier & Luc Adolphe & Rahim Aguejdad & P. Avner & Marc Bonhomme & Geneviève Bretagne & X. Briottet & B. Bueno & Cécile de Munck & O. Doukari & Stéphane Hallegatte & Julia Hida, 2014. "Adapting cities to climate change: A systemic modelling approach," Post-Print hal-01136215, HAL.
    4. Belke, Ansgar & Dobnik, Frauke & Dreger, Christian, 2011. "Energy consumption and economic growth: New insights into the cointegration relationship," Energy Economics, Elsevier, vol. 33(5), pages 782-789, September.
    5. Farhani, Sahbi & Solarin, Sakiru Adebola, 2017. "Financial development and energy demand in the United States: New evidence from combined cointegration and asymmetric causality tests," Energy, Elsevier, vol. 134(C), pages 1029-1037.
    6. Yuan, Xiao-Chen & Sun, Xun & Zhao, Weigang & Mi, Zhifu & Wang, Bing & Wei, Yi-Ming, 2017. "Forecasting China’s regional energy demand by 2030: A Bayesian approach," Resources, Conservation & Recycling, Elsevier, vol. 127(C), pages 85-95.
    7. Dong, Kangyin & Jiang, Qingzhe & Shahbaz, Muhammad & Zhao, Jun, 2021. "Does low-carbon energy transition mitigate energy poverty? The case of natural gas for China," Energy Economics, Elsevier, vol. 99(C).
    8. Zhou, Sheng & Tong, Qing & Pan, Xunzhang & Cao, Min & Wang, Hailin & Gao, Ji & Ou, Xunmin, 2021. "Research on low-carbon energy transformation of China necessary to achieve the Paris agreement goals: A global perspective," Energy Economics, Elsevier, vol. 95(C).
    9. Huang, Bwo-Nung & Hwang, M.J. & Yang, C.W., 2008. "Causal relationship between energy consumption and GDP growth revisited: A dynamic panel data approach," Ecological Economics, Elsevier, vol. 67(1), pages 41-54, August.
    10. Feng, Y.Y. & Chen, S.Q. & Zhang, L.X., 2013. "System dynamics modeling for urban energy consumption and CO2 emissions: A case study of Beijing, China," Ecological Modelling, Elsevier, vol. 252(C), pages 44-52.
    11. Trotta, Gianluca, 2018. "Factors affecting energy-saving behaviours and energy efficiency investments in British households," Energy Policy, Elsevier, vol. 114(C), pages 529-539.
    12. Hoff, Jens V. & Rasmussen, Martin M.B. & Sørensen, Peter Birch, 2021. "Barriers and opportunities in developing and implementing a Green GDP," Ecological Economics, Elsevier, vol. 181(C).
    13. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques," Energy, Elsevier, vol. 161(C), pages 821-831.
    14. Zheng, Huanyu & Song, Malin & Shen, Zhiyang, 2021. "The evolution of renewable energy and its impact on carbon reduction in China," Energy, Elsevier, vol. 237(C).
    15. Lee, Chien-Chiang, 2005. "Energy consumption and GDP in developing countries: A cointegrated panel analysis," Energy Economics, Elsevier, vol. 27(3), pages 415-427, May.
    16. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    17. Xiao, Jin & Li, Yuxi & Xie, Ling & Liu, Dunhu & Huang, Jing, 2018. "A hybrid model based on selective ensemble for energy consumption forecasting in China," Energy, Elsevier, vol. 159(C), pages 534-546.
    18. Masih, Abul M. M. & Masih, Rumi, 1996. "Energy consumption, real income and temporal causality: results from a multi-country study based on cointegration and error-correction modelling techniques," Energy Economics, Elsevier, vol. 18(3), pages 165-183, July.
    19. Li, Ke & Lin, Boqiang, 2015. "The improvement gap in energy intensity: Analysis of China's thirty provincial regions using the improved DEA (data envelopment analysis) model," Energy, Elsevier, vol. 84(C), pages 589-599.
    20. Arnulf Grubler & Charlie Wilson & Nuno Bento & Benigna Boza-Kiss & Volker Krey & David L. McCollum & Narasimha D. Rao & Keywan Riahi & Joeri Rogelj & Simon Stercke & Jonathan Cullen & Stefan Frank & O, 2018. "A low energy demand scenario for meeting the 1.5 °C target and sustainable development goals without negative emission technologies," Nature Energy, Nature, vol. 3(6), pages 515-527, June.
    21. Stern, David I., 1993. "Energy and economic growth in the USA : A multivariate approach," Energy Economics, Elsevier, vol. 15(2), pages 137-150, April.
    22. Khalifa, Ahmed & Caporin, Massimiliano & Di Fonzo, Tommaso, 2019. "Scenario-based forecast for the electricity demand in Qatar and the role of energy efficiency improvements," Energy Policy, Elsevier, vol. 127(C), pages 155-164.
    23. Akinlo, A.E., 2008. "Energy consumption and economic growth: Evidence from 11 Sub-Sahara African countries," Energy Economics, Elsevier, vol. 30(5), pages 2391-2400, September.
    24. 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.
    25. Wang, Qiao & Yi, Hongtao, 2021. "New energy demonstration program and China's urban green economic growth: Do regional characteristics make a difference?," Energy Policy, Elsevier, vol. 151(C).
    26. Wang, Meng & Wang, Wei & Wu, Lifeng, 2022. "Application of a new grey multivariate forecasting model in the forecasting of energy consumption in 7 regions of China," Energy, Elsevier, vol. 243(C).
    27. Dhakal, Shobhakar, 2009. "Urban energy use and carbon emissions from cities in China and policy implications," Energy Policy, Elsevier, vol. 37(11), pages 4208-4219, November.
    28. Liu, Zhu & Liang, Sai & Geng, Yong & Xue, Bing & Xi, Fengming & Pan, Ying & Zhang, Tianzhu & Fujita, Tsuyoshi, 2012. "Features, trajectories and driving forces for energy-related GHG emissions from Chinese mega cites: The case of Beijing, Tianjin, Shanghai and Chongqing," Energy, Elsevier, vol. 37(1), pages 245-254.
    29. Oikonomou, V. & Becchis, F. & Steg, L. & Russolillo, D., 2009. "Energy saving and energy efficiency concepts for policy making," Energy Policy, Elsevier, vol. 37(11), pages 4787-4796, November.
    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. Tiba, Sofien & Omri, Anis, 2017. "Literature survey on the relationships between energy, environment and economic growth," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1129-1146.
    2. Sofien, Tiba & Omri, Anis, 2016. "Literature survey on the relationships between energy variables, environment and economic growth," MPRA Paper 82555, University Library of Munich, Germany, revised 14 Sep 2016.
    3. Farzana Sharmin & Mohammed Robayet Khan & Mohammed Robayet Khan, 2016. "A Causal Relationship between Energy Consumption, Energy Prices and Economic Growth in Africa," International Journal of Energy Economics and Policy, Econjournals, vol. 6(3), pages 477-494.
    4. Farhani, Sahbi & Shahbaz, Muhammad & Sbia, Rashid & Chaibi, Anissa, 2014. "What does MENA region initially need: Grow output or mitigate CO2 emissions?," Economic Modelling, Elsevier, vol. 38(C), pages 270-281.
    5. Sahbi FARHANI & Jaleleddine BEN REJEB, 2015. "Link between Economic Growth and Energy Consumption in Over 90 Countries," Working Papers 2015-614, Department of Research, Ipag Business School.
    6. Farhani, Sahbi & Shahbaz, Muhammad & Sbia, Rashid, 2013. "What is MENA Region Initially Needed: Grow Output or Mitigate CO2 Emissions?," MPRA Paper 48859, University Library of Munich, Germany, revised 05 Aug 2013.
    7. repec:ipg:wpaper:2014-529 is not listed on IDEAS
    8. Apergis, Nicholas & Payne, James E., 2009. "Energy consumption and economic growth: Evidence from the Commonwealth of Independent States," Energy Economics, Elsevier, vol. 31(5), pages 641-647, September.
    9. Pradhan, Rudra P. & Arvin, Mak B. & Nair, Mahendhiran & Bennett, Sara E. & Hall, John H., 2018. "The dynamics between energy consumption patterns, financial sector development and economic growth in Financial Action Task Force (FATF) countries," Energy, Elsevier, vol. 159(C), pages 42-53.
    10. Wang, Shaojian & Li, Guangdong & Fang, Chuanglin, 2018. "Urbanization, economic growth, energy consumption, and CO2 emissions: Empirical evidence from countries with different income levels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2144-2159.
    11. Caraiani, Chirața & Lungu, Camelia I. & Dascălu, Cornelia, 2015. "Energy consumption and GDP causality: A three-step analysis for emerging European countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 198-210.
    12. Apergis, Nicholas & Tang, Chor Foon, 2013. "Is the energy-led growth hypothesis valid? New evidence from a sample of 85 countries," Energy Economics, Elsevier, vol. 38(C), pages 24-31.
    13. Islam, Faridul & Shahbaz, Muhammad & Rahman, Mohammad Mafizur, 2013. "Trade Openness, Financial Development Energy Use and Economic Growth in Australia:Evidence on Long Run Relation with Structural Breaks," MPRA Paper 52546, University Library of Munich, Germany, revised 28 Dec 2013.
    14. Eggoh, Jude C. & Bangake, Chrysost & Rault, Christophe, 2011. "Energy consumption and economic growth revisited in African countries," Energy Policy, Elsevier, vol. 39(11), pages 7408-7421.
    15. Hasanov, Fakhri & Bulut, Cihan & Suleymanov, Elchin, 2017. "Review of energy-growth nexus: A panel analysis for ten Eurasian oil exporting countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 369-386.
    16. Nermin Ya ar, 2017. "The Relationship between Energy Consumption and Economic Growth: Evidence from Different Income Country Groups," International Journal of Energy Economics and Policy, Econjournals, vol. 7(2), pages 86-97.
    17. Menegaki, Angeliki N., 2014. "On energy consumption and GDP studies; A meta-analysis of the last two decades," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 31-36.
    18. Salahuddin, Mohammad & Gow, Jeff, 2014. "Economic growth, energy consumption and CO2 emissions in Gulf Cooperation Council countries," Energy, Elsevier, vol. 73(C), pages 44-58.
    19. Yildirim, Ertugrul & Aslan, Alper, 2012. "Energy consumption and economic growth nexus for 17 highly developed OECD countries: Further evidence based on bootstrap-corrected causality tests," Energy Policy, Elsevier, vol. 51(C), pages 985-993.
    20. Narayan, Seema, 2016. "Predictability within the energy consumption–economic growth nexus: Some evidence from income and regional groups," Economic Modelling, Elsevier, vol. 54(C), pages 515-521.
    21. Ahmed, Mumtaz & Azam, Muhammad, 2016. "Causal nexus between energy consumption and economic growth for high, middle and low income countries using frequency domain analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 653-678.

    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:spr:endesu:v:25:y:2023:i:9:d:10.1007_s10668-022-02494-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.