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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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    1. Russell Davidson, 2012. "Statistical inference in the presence of heavy tails," Econometrics Journal, Royal Economic Society, vol. 15(1), pages 31-53, February.
    2. De Rossi, Giuliano & Harvey, Andrew, 2009. "Quantiles, expectiles and splines," Journal of Econometrics, Elsevier, vol. 152(2), pages 179-185, October.
    3. Benes, Jaromir & Chauvet, Marcelle & Kamenik, Ondra & Kumhof, Michael & Laxton, Douglas & Mursula, Susanna & Selody, Jack, 2015. "The future of oil: Geology versus technology," International Journal of Forecasting, Elsevier, vol. 31(1), pages 207-221.
    4. Hallock, John L. & Tharakan, Pradeep J. & Hall, Charles A.S. & Jefferson, Michael & Wu, Wei, 2004. "Forecasting the limits to the availability and diversity of global conventional oil supply," Energy, Elsevier, vol. 29(11), pages 1673-1696.
    5. Tesfatsion, Leigh, 2001. "Introduction to the special issue on agent-based computational economics," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 281-293, March.
    6. Mohr, S.H. & Evans, G.M., 2011. "Long term forecasting of natural gas production," Energy Policy, Elsevier, vol. 39(9), pages 5550-5560, September.
    7. DuMoulin, Hans & Eyre, John, 1979. "Energy scenarios a learning process," Energy Economics, Elsevier, vol. 1(2), pages 76-86, April.
    8. Schnabel, Sabine K. & Eilers, Paul H.C., 2009. "Optimal expectile smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4168-4177, October.
    9. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
    10. Newey, Whitney K & Powell, James L, 1987. "Asymmetric Least Squares Estimation and Testing," Econometrica, Econometric Society, vol. 55(4), pages 819-847, July.
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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. 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.
    3. Ediger, Volkan Ş. & Berk, Istemi, 2023. "Future availability of natural gas: Can it support sustainable energy transition?," Resources Policy, Elsevier, vol. 85(PA).
    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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