A new robustness analysis for climate policy evaluations: a CGE application for the EU 2020 targets
This paper introduces a new method for stochastic sensitivity analysis for computable general equilibrium (CGE) model based on Gauss Quadrature and applies it to check the robustness of a large-scale climate policy evaluation. The revised version of the Gauss-quadrature approach to sensitivity analy...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
April 2013
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| In: |
Energy policy
Year: 2013, Volume: 55, Pages: 27-35 |
| ISSN: | 1873-6777 |
| DOI: | 10.1016/j.enpol.2012.08.007 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.enpol.2012.08.007 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S0301421512006647 
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| Author Notes: | Claudia Hermeling, Andreas Löschel, Tim Mennel |
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| 520 | |a This paper introduces a new method for stochastic sensitivity analysis for computable general equilibrium (CGE) model based on Gauss Quadrature and applies it to check the robustness of a large-scale climate policy evaluation. The revised version of the Gauss-quadrature approach to sensitivity analysis reduces computations considerably vis-à-vis the commonly applied Monte-Carlo methods; this allows for a stochastic sensitivity analysis also for large scale models and multi-dimensional changes of parameters. In the application, an impact assessment of EU2020 climate policy, we focus on sectoral elasticities that are part of the basic parameters of the model and have been recently determined by econometric estimation, alongside with standard errors. The impact assessment is based on the large scale CGE model PACE. We show the applicability of the Gauss-quadrature approach and confirm the robustness of the impact assessment with the PACE model. The variance of the central model outcomes is smaller than their mean by order four to eight, depending on the aggregation level (i.e. aggregate variables such as GDP show a smaller variance than sectoral output). | ||
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