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Homogeneous and heterogeneous diffusion models: Algerian natural gas production

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  • Guseo, Renato
  • Mortarino, Cinzia
  • Darda, Md Abud

Abstract

Similar to other non-renewable resources, different options for natural gas production in wide geographic areas may be described through diffusion of innovation models. They provide an indirect estimate of an ultimately recoverable resource, URR, capture the quantitative effects of observed strategic interventions, and allow ex ante assessments of future scenarios over time. The present study offers a framework for systematizing the historical production of conventional Algerian natural gas contrasting homogeneity versus latent heterogeneity hypotheses of the agents involved in extraction dynamics. New models are proposed, and their comparative performances are discussed. In particular, diffusion models with latent heterogeneity of the agents perform better. Our results show a decreasing trend in conventional Algerian natural gas production, which is in agreement with recent published results. Some differences refer to lower URR estimates.

Suggested Citation

  • Guseo, Renato & Mortarino, Cinzia & Darda, Md Abud, 2015. "Homogeneous and heterogeneous diffusion models: Algerian natural gas production," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 366-378.
  • Handle: RePEc:eee:tefoso:v:90:y:2015:i:pb:p:366-378
    DOI: 10.1016/j.techfore.2014.05.011
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    as
    1. Guseo, Renato, 2011. "Worldwide cheap and heavy oil productions: A long-term energy model," Energy Policy, Elsevier, vol. 39(9), pages 5572-5577, September.
    2. Bernard Pras & Gilles Laurent & Gary L. Lilien, 1994. "Research Traditions in Marketing," Post-Print halshs-00150675, HAL.
    3. Rabik Ar Chatterjee & Jehoshua Eliashberg, 1990. "The Innovation Diffusion Process in a Heterogeneous Population: A Micromodeling Approach," Management Science, INFORMS, vol. 36(9), pages 1057-1079, September.
    4. Aguilera, Roberto F. & Aguilera, Roberto, 2012. "World natural gas endowment as a bridge towards zero carbon emissions," Technological Forecasting and Social Change, Elsevier, vol. 79(3), pages 579-586.
    5. Chi, K.C. & Reiner, D.M. & Nuttall, W.J., 2009. "Dynamics of the UK Natural Gas Industry: System Dynamics Modelling and Long-Term Energy Policy Analysis," Cambridge Working Papers in Economics 0922, Faculty of Economics, University of Cambridge.
    6. V. Srinivasan & Charlotte H. Mason, 1986. "Technical Note—Nonlinear Least Squares Estimation of New Product Diffusion Models," Marketing Science, INFORMS, vol. 5(2), pages 169-178.
    7. Guseo, Renato & Mortarino, Cinzia, 2012. "Sequential market entries and competition modelling in multi-innovation diffusions," European Journal of Operational Research, Elsevier, vol. 216(3), pages 658-667.
    8. Nancy L. Rose & Paul L. Joskow, 1990. "The Diffusion of New Technologies: Evidence from the Electric Utility Industry," RAND Journal of Economics, The RAND Corporation, vol. 21(3), pages 354-373, Autumn.
    9. Brandt, Adam R., 2010. "Review of mathematical models of future oil supply: Historical overview and synthesizing critique," Energy, Elsevier, vol. 35(9), pages 3958-3974.
    10. Brandt, Adam R., 2007. "Testing Hubbert," Energy Policy, Elsevier, vol. 35(5), pages 3074-3088, May.
    11. Guseo, Renato & Guidolin, Mariangela, 2010. "Cellular Automata with network incubation in information technology diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(12), pages 2422-2433.
    12. Robertson, Alastair & Soopramanien, Didier & Fildes, Robert, 2007. "Segmental new-product diffusion of residential broadband services," Telecommunications Policy, Elsevier, vol. 31(5), pages 265-275, June.
    13. Connelly, Michael C. & Sekhar, J.A., 2012. "U. S. energy production activity and innovation," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 30-46.
    14. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    15. Islam, Towhidul & Meade, Nigel, 2012. "The impact of competition, and economic globalization on the multinational diffusion of 3G mobile phones," Technological Forecasting and Social Change, Elsevier, vol. 79(5), pages 843-850.
    16. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    17. Renato Guseo & Alessandra Valle, 2005. "Oil and gas depletion: Diffusion models and forecasting under strategic intervention," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 14(3), pages 375-387, December.
    18. Christophe Van den Bulte & Gary L. Lilien, 1997. "Bias and Systematic Change in the Parameter Estimates of Macro-Level Diffusion Models," Marketing Science, INFORMS, vol. 16(4), pages 338-353.
    19. Sergei Savin & Christian Terwiesch, 2005. "Optimal Product Launch Times in a Duopoly: Balancing Life-Cycle Revenues with Product Cost," Operations Research, INFORMS, vol. 53(1), pages 26-47, February.
    20. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
    21. Wang, Jianliang & Feng, Lianyong & Zhao, Lin & Snowden, Simon, 2013. "China's natural gas: Resources, production and its impacts," Energy Policy, Elsevier, vol. 55(C), pages 690-698.
    22. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    23. Frank M. Bass, 2004. "Comments on "A New Product Growth for Model Consumer Durables The Bass Model"," Management Science, INFORMS, vol. 50(12_supple), pages 1833-1840, December.
    24. Albert C. Bemmaor & Janghyuk Lee, 2002. "The Impact of Heterogeneity and Ill-Conditioning on Diffusion Model Parameter Estimates," Marketing Science, INFORMS, vol. 21(2), pages 209-220, November.
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    3. Berk, Istemi & Ediger, Volkan Ş., 2016. "Forecasting the coal production: Hubbert curve application on Turkey's lignite fields," Resources Policy, Elsevier, vol. 50(C), pages 193-203.
    4. Bey, M. & Hamidat, A. & Benyoucef, B. & Nacer, T., 2016. "Viability study of the use of grid connected photovoltaic system in agriculture: Case of Algerian dairy farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 333-345.
    5. Darda, Md Abud & Guseo, Renato & Mortarino, Cinzia, 2015. "Nonlinear production path and an alternative reserves estimate for South Asian natural gas," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 654-664.
    6. Ediger, Volkan Ş. & Berk, Istemi, 2023. "Future availability of natural gas: Can it support sustainable energy transition?," Resources Policy, Elsevier, vol. 85(PA).

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