Environmental Modeling & Assessment

, Volume 17, Issue 1–2, pp 77–90 | Cite as

Statistical Simulation to Estimate Uncertain Behavioral Parameters of Hybrid Energy-Economy Models

  • Dale BeuginEmail author
  • Mark JaccardEmail author


In energy-economy modeling, new hybrid models attempt to combine the technological explicitness of bottom-up models with the macroeconomic feedbacks and statistically estimated behavioral parameters of top-down models. However, statistical estimation of behavioral parameters (portraying firm and household technology choices) with such models is challenged by the number of uncertain variables and the lack of historical data on technologies in terms of capital costs, operating costs, and market shares. Multiple combinations of parameter values might equally explain past technology choices. This paper reports on the application of a Bayesian statistical simulation approach for estimating the most likely values for these key behavioral parameters in order to best explain past technology choices and then simulate policies to influence future technology choices. The method included (1) data collection of key technology market shares, capital costs, and operating costs over the past; (2) backcasting a hybrid energy-economy model over a historical time period; and (3) the application of Markov chain Monte Carlo statistical simulation using the Metropolis–Hastings algorithm as a tool for estimating distributions for key parameters in the model. The results provide a means of indicating the uncertainty bounds around key behavioral parameters when generating forecasts of the effect of certain policies. However, the results also indicate that this approach may have limited applicability, given that future available technologies may differ substantially from past technologies and that it is difficult to separate the effects of parameter uncertainty from model structure uncertainty.


Hybrid energy-economy models Bayesian simulation Markov chain Monte Carlo 


  1. 1.
    Axsen, J., Mountain, D., & Jaccard, M. (2009). Combining stated and revealed choice research to simulate preference dynamics: The case of hybrid-electric vehicles. Resource and Energy Economics, 31(3), 221–238.CrossRefGoogle Scholar
  2. 2.
    Balakrishnan, S., Roy, A., Ierapetritou, M., Flach, G., & Georgopoulos, P. (2003). Uncertainty reduction and characterization of complex environmental fate and transport models: An empirical Bayesian framework incorporating the stochastic response surface method. Water Resources Research, 39, 12.CrossRefGoogle Scholar
  3. 3.
    Bataille, C., Jaccard, M., Nyboer, J., Rivers, N. (2006). Towards general equilibrium in a technology-rich model with empirically estimated behavioral parameters. The Energy Journal, Special Issue on Hybrid Energy-Economy Modeling, pp. 93–112Google Scholar
  4. 4.
    Bosetti, V., & Tavoni, M. (2009). Uncertain R&D, backstop technology and GHGs stabilization. Energy Economics, 31, S18–S26.CrossRefGoogle Scholar
  5. 5.
    Canada. (2005). Energy consumption of major household appliances shipped in Canada: Trends for 1990–2001. Ottawa: Natural Resources Canada, Office of Energy Efficiency.Google Scholar
  6. 6.
    Consumer Reports. (1995). The new refrigerators: How much energy do they save? Consumer Reports, 60(5), 310.Google Scholar
  7. 7.
    Denison, D. G. T., Holmes, C. C., Mallick, B. K., & Smith, A. F. M. (2002). Bayesian methods for nonlinear classification and regression. Chichester: Wiley.Google Scholar
  8. 8.
    Dowlatabadi, H., & Oravetz, M. A. (2006). US long term energy intensity: Backcast and projection. Energy Policy, 34, 3245–3256.CrossRefGoogle Scholar
  9. 9.
    Gelman, A., Carlin, J. B., Stern, H. A., & Rubin, D. B. (2004). Bayesian data analysis (2nd ed.). Boca Raton: Chapman & Hall.Google Scholar
  10. 10.
    Gerlagh, R., & van der Zwaan, B. C. C. (2004). A sensitivity analysis of timing and costs of greenhouse gas emission reductions under learning effects and niche markets. Climatic Change, 65, 39–71.CrossRefGoogle Scholar
  11. 11.
    Horne, M., Jaccard, M., & Tiedemann, K. (2005). Improving behavioral realism in hybrid energy-economy models using discrete choice studies of personal transportation decisions. Energy Economics, 27, 59–77.CrossRefGoogle Scholar
  12. 12.
    Hourcade, J-C., Jaccard, M., Bataille, C. & Ghersi, F. (2006). Hybrid modeling: New answers to old challenges. The Energy Journal, Introduction to the Special Issue on Hybrid Energy-Economy Modeling, pp. 1–12.Google Scholar
  13. 13.
    Jaccard, M. (2005). Hybrid energy-economy models and endogenous technological change. In R. Loulou, J. Waaub, & G. Zaccour (Eds.), Energy and environment (pp. 1–29). New York: Springer.Google Scholar
  14. 14.
    Jaccard, M., Nyboer, J., Bataille, C., & Sadownik, B. (2003). Modeling the cost of climate policy: Distinguishing between alternative cost definitions and long-run cost dynamics. The Energy Journal, 24(1), 49–73.CrossRefGoogle Scholar
  15. 15.
    Jaccard, M., & Rivers, N. (2007). Estimating the effect of the Canadian Government’s 2006–2007 greenhouse gas policies. Toronto: CD Howe Institute.Google Scholar
  16. 16.
    Louviere, J., Hensher, D., & Swait, J. (2000). Stated choice methods: Analysis and applications. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  17. 17.
    Mau, P., Eyzaguirre, J., Jaccard, M., Collins-Dodd, C., & Tiedemann, K. (2008). The neighbor effect: Simulating dynamics in consumer preferences for new vehicle technologies. Ecological Economics, 68, 504–516.CrossRefGoogle Scholar
  18. 18.
    Morgan, M. G., & Henrion, M. (1990). Uncertainty: A guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge: Cambridge University Press.Google Scholar
  19. 19.
    Rivers, N., & Jaccard, M. (2005). Combining top-down and bottom-up approaches to energy-economy modeling using discrete choice methods. The Energy Journal, 26(1), 83–106.CrossRefGoogle Scholar
  20. 20.
    Smith, L. A. (2003). Predictability past, predictability present. Proceedings of ECMWF Seminar on Predictability, Reading, UK: ECMWF, pp. 291–242.Google Scholar
  21. 21.
    Tanner, M. (1996). Tools for statistical inference: Methods for the exploration of posterior distributions and likelihood functions (3rd ed.). New York: Springer.Google Scholar
  22. 22.
    Tarantola, A. (2005). Inverse problem theory and methods for model parameter estimation. Philadelphia: SIAM.CrossRefGoogle Scholar
  23. 23.
    Train, K. (2002). Discrete choice methods with simulation. Cambridge: Cambridge University Press.Google Scholar
  24. 24.
    Train, K. (1985). Disount rates in consumers’ energy-related discussions: A review of the literature. Energy, 10(12), 1243–1253.CrossRefGoogle Scholar
  25. 25.
    Tschang, F. T., & Dowlatabadi, H. (1995). A Bayesian technique for refining the uncertainty in global energy model forecasts. International Journal of Forecasting, 11, 43–61.CrossRefGoogle Scholar
  26. 26.
    Urban, G., Weinberg, B., & Hauser, J. (1996). Premarket forecasting of really-new products. Journal of Marketing, 60, 47–60.CrossRefGoogle Scholar
  27. 27.
    Walters, C., & Ludwig, D. (1994). Calculation of Bayes’ posterior probability distributions for key population parameters. Canadian Journal of Aquatic Sciences, 51, 713–722.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.School of Resource and Environmental ManagementSimon Fraser UniversityVancouverCanada
  2. 2.Sky Curve ConsultingOttawaCanada

Personalised recommendations