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Exploring the production of natural gas through the lenses of the ACEGES model

Author

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  • Voudouris, Vlasios
  • Matsumoto, Ken'ichi
  • Sedgwick, John
  • Rigby, Robert
  • Stasinopoulos, Dimitrios
  • Jefferson, Michael

Abstract

Due to the increasing importance of natural gas for modern economic activity, and gas's non-renewable nature, it is extremely important to try to estimate possible trajectories of future natural gas production while considering uncertainties in resource estimates, demand growth, production growth and other factors that might limit production. In this study, we develop future scenarios for natural gas supply using the ACEGES computational laboratory. Conditionally on the currently estimated ultimate recoverable resources, the ‘Collective View’ and ‘Golden Age’ Scenarios suggest that the supply of natural gas is likely to meet the increasing demand for natural gas until at least 2035. The ‘Golden Age’ Scenario suggests significant ‘jumps’ of natural gas production – important for testing the resilience of long-term strategies.

Suggested Citation

  • Voudouris, Vlasios & Matsumoto, Ken'ichi & Sedgwick, John & Rigby, Robert & Stasinopoulos, Dimitrios & Jefferson, Michael, 2014. "Exploring the production of natural gas through the lenses of the ACEGES model," Energy Policy, Elsevier, vol. 64(C), pages 124-133.
    • Handle: RePEc:eee:enepol:v:64:y:2014:i:c:p:124-133
    DOI: 10.1016/j.enpol.2013.08.053
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    References listed on IDEAS

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    Cited by:

    1. Jean Gaston Tamba & Salom Ndjakomo Essiane & Emmanuel Flavian Sapnken & Francis Djanna Koffi & Jean Luc Nsouand l & Bozidar Soldo & Donatien Njomo, 2018. "Forecasting Natural Gas: A Literature Survey," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 216-249.
    2. Ediger, Volkan Ş. & Berk, Istemi, 2023. "Future availability of natural gas: Can it support sustainable energy transition?," Resources Policy, Elsevier, vol. 85(PA).
    3. 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.
    4. Sen, Doruk & Hamurcuoglu, K. Irem & Ersoy, Melisa Z. & Tunç, K.M. Murat & Günay, M. Erdem, 2023. "Forecasting long-term world annual natural gas production by machine learning," Resources Policy, Elsevier, vol. 80(C).
    5. Najm, Sarah & Matsumoto, Ken'ichi, 2020. "Does renewable energy substitute LNG international trade in the energy transition?," Energy Economics, Elsevier, vol. 92(C).

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