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The ACEGES laboratory for energy policy: Exploring the production of crude oil

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  • Voudouris, Vlasios
  • Stasinopoulos, Dimitrios
  • Rigby, Robert
  • Di Maio, Carlo

Abstract

An agent-based computational laboratory for exploratory energy policy by means of controlled computational experiments is proposed. It is termed the ACEGES (agent-based computational economics of the global energy system). In particular, it is shown how agent-based modelling and simulation can be applied to understand better the challenging outlook for oil production by accounting for uncertainties in resource estimates, demand growth, production growth and peak/decline point. The approach emphasises the idea that the oil system is better modelled not as black-box abode of 'the invisible hand' but as a complex system whose macroscopic explananda emerges from the interactions of its constituent components. Given the estimated volumes of oil originally present before any extraction, simulations show that on average the world peak of crude oil production may happen in the broad vicinity of the time region between 2008 and 2027. Using the proposed petroleum market diversity, the market diversity weakness rapidly towards the peak year.

Suggested Citation

  • Voudouris, Vlasios & Stasinopoulos, Dimitrios & Rigby, Robert & Di Maio, Carlo, 2011. "The ACEGES laboratory for energy policy: Exploring the production of crude oil," Energy Policy, Elsevier, vol. 39(9), pages 5480-5489, September.
    • Handle: RePEc:eee:enepol:v:39:y:2011:i:9:p:5480-5489
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    References listed on IDEAS

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    4. Arthur Pewsey, 2018. "Parametric bootstrap edf-based goodness-of-fit testing for sinh–arcsinh distributions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 147-172, March.
    5. John Sherwood & Michael Carbajales-Dale & Becky Roselius Haney, 2020. "Putting the Biophysical (Back) in Economics: A Taxonomic Review of Modeling the Earth-Bound Economy," Biophysical Economics and Resource Quality, Springer, vol. 5(1), pages 1-20, March.
    6. Adam, Timo & Mayr, Andreas & Kneib, Thomas, 2022. "Gradient boosting in Markov-switching generalized additive models for location, scale, and shape," Econometrics and Statistics, Elsevier, vol. 22(C), pages 3-16.
    7. Anna Klabunde, 2014. "Computational Economic Modeling of Migration," Ruhr Economic Papers 0471, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    8. Klabunde, Anna, 2014. "Computational Economic Modeling of Migration," Ruhr Economic Papers 471, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    9. Bentley, Roger & Bentley, Yongmei, 2015. "Explaining the price of oil 1971–2014 : The need to use reliable data on oil discovery and to account for ‘mid-point’ peak," Energy Policy, Elsevier, vol. 86(C), pages 880-890.
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    12. Hallock, John L. & Wu, Wei & Hall, Charles A.S. & Jefferson, Michael, 2014. "Forecasting the limits to the availability and diversity of global conventional oil supply: Validation," Energy, Elsevier, vol. 64(C), pages 130-153.
    13. Fernanda De Bastiani & Robert A. Rigby & Dimitrios M. Stasinopoulous & Audrey H.M.A. Cysneiros & Miguel A. Uribe-Opazo, 2018. "Gaussian Markov random field spatial models in GAMLSS," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 168-186, January.
    14. Jakobsson, Kristofer & Söderbergh, Bengt & Snowden, Simon & Aleklett, Kjell, 2014. "Bottom-up modeling of oil production: A review of approaches," Energy Policy, Elsevier, vol. 64(C), pages 113-123.
    15. Matsumoto, Ken'ichi & Voudouris, Vlasios & Stasinopoulos, Dimitrios & Rigby, Robert & Di Maio, Carlo, 2012. "Exploring crude oil production and export capacity of the OPEC Middle East countries," Energy Policy, Elsevier, vol. 48(C), pages 820-828.
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