Statistical Simulation to Estimate Uncertain Behavioral Parameters of Hybrid Energy-Economy Models
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- 作者：Dale Beugin (2) dale.beugin@gmail.com
Mark Jaccard (1) jaccard@sfu.ca - 关键词：Hybrid energy ; economy models – ; Bayesian simulation – ; Markov chain Monte Carlo
- 刊名：Environmental Modeling and Assessment
- 出版时间：March 2012
- 出版年：2012
- 期刊代码：6_14202026
- 类别：phi
- 卷：17
- 期：1-2
- 页码：77-90
- 数据来源：sp
摘要
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.