Sensitivity to energy technology costs: A multi-model comparison analysis

Abstract

In the present paper we use the output of multiple expert elicitation surveys on the future cost of key low-carbon technologies and use it as input of three Integrated Assessment models, GCAM, MARKAL_US and WITCH. By means of a large set of simulations we aim to assess the implications of these subjective distributions of technological costs over key model outputs. We are able to detect what sources of technology uncertainty are more influential, how this differs across models, and whether and how results are affected by the time horizon, the metric considered or the stringency of the climate policy. In unconstrained emission scenarios, within the range of future technology performances considered in the present analysis, the cost of nuclear energy is shown to dominate all others in affecting future emissions. Climate-constrained scenarios, stress the relevance, in addition to that of nuclear energy, of biofuels, as they represent the main source of decarbonization of the transportation sector and bioenergy, since the latter can be coupled with Carbon Capture and Storage (CCS) to produce negative emissions.

Large scale scenario analysis of future low carbon energy options

“…Thus, another avenue for future work is to test the degree to which the specific relationships found in this study (such as the complementarity of CCS and Bio-electricity) will hold when analyzed by a range of models. See Bosetti et al (2015) for a study comparing the sensitivity of several IAM's outputs to different energy input cost assumptions.…”

Interactions between social learning and technological learning in electric vehicle futures

“…IAMs show that technology plays a crucial role in reducing greenhouse gas emissions across regions and sectors , Kriegler et al 2014 and in determining the cost and feasibility of meeting specified climate targets (Bosetti et al 2015). Important aspects of technology transitions such as heterogeneity in consumer preferences and social learning are often omitted from IAM analysis.…”

Introduction to the RoSE special issue on the impact of economic growth and fossil fuel availability on climate protection

“…More concretely, RoSE conducted a model comparison on harmonized variations in economic growth, population and fossil resource assumptions across four integrated assessment models: GCAM (Edmonds and Reilly 1985;Edmonds et al 1997; http://wiki.umd.edu/gcam), IPAC (Jiang et al 2000), REMIND Bauer et al 2012;Leimbach et al 2010; http://pikpotsdam.de/research/sustainable-solutions/models/remind), and WITCH (Bosetti et al 2006;Bosetti et al 2009; http://doc.witchmodel.org/). This fills an important gap in the literature, since previous sensitivity studies mostly focused on technology assumptions (Clarke et al 2008;McJeon et al 2011;Bosetti et al 2015).…”