The role of uncertainty in future costs of key CO2 abatement technologies: a sensitivity analysis with a global computable general equilibrium model
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- 作者：Matthias Weitzel
- 关键词：Carbon capture and storage ; Gauss quadrature ; Renewable energy ; Climate policy ; Systematic sensitivity analysis ; Uncertainty
- 刊名：Mitigation and Adaptation Strategies for Global Change
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：22
- 期：1
- 页码：153-173
- 全文大小：
- 刊物类别：Earth and Environmental Science
- 刊物主题：Atmospheric Sciences; Climate Change Management and Policy; Environmental Management;
- 出版者：Springer Netherlands
- ISSN：1573-1596
- 卷排序：22
文摘
Deep emission cuts rely on the use of low carbon technologies like renewable energy or carbon capture and storage. There is considerable uncertainty about their future costs. We carry out a sensitivity analysis based on Gauss Quadrature for cost parameters describing these technologies in order to evaluate the effect of the uncertainty on total and marginal mitigation costs as well as composition changes in the energy system. Globally, effects in total cost often average out, but different regions are affected quite differently from the underlying uncertainty in costs for key abatement technologies. Regions can be either affected because they are well suited to deploy a technology for geophysical reasons or because of repercussions through international energy markets. The absolute impact of uncertainty on consumption increases over the time horizon and with the ambition of emission reductions. Uncertainty in abatement costs relative to expected abatement costs are however larger under a moderate ambition climate policy scenario because in this case the marginal abatement occurs in the electricity sector where the cost uncertainty is implemented. Under more ambitious climate policy in line with the two degree target, the electricity sector is always decarbonized by 2050, hence uncertainty has less effect on the electricity mix. The findings illustrate the need for regional results as global averages can hide distributional consequences on technological uncertainty.