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This study gives a synthesis of a model comparison assessing the technological feasibility and economic consequences of achieving greenhouse gas concentration targets that are sufficiently low to keep the increase in global mean temperature below 2 degrees Celsius above pre-industrial levels. All five global energy-environment-economy models show that achieving low greenhouse gas concentration targets is technically feasible and economically viable. The ranking of the importance of individual technology options is robust across models. For the lowest stabilization target (400 ppm CO 2 eq), the use of bio-energy in combination with CCS plays a crucial role, and biomass potential dominates the cost of reaching this target. Without CCS or the considerable extension of renewables the 400 ppm CO 2 eq target is not achievable. Across the models, estimated aggregate costs up to 2100 are below 0.8% global GDP for 550 ppm CO 2 eq stabilization and below 2.5% for the 400 ppm CO 2 eq pathway.

International audienceIntegrated assessments of how climate policy interacts with energy-economy systems can be performed by a variety of models with different functional structures. In order to provide insights into why results differ between models, this article proposes a diagnostic scheme that can be applied to a wide range of models. Diagnostics can uncover patterns of model behavior and indicate how results differ between model types. Such insights are informative since model behavior can have a significant impact on projections of climate change mitigation costs and other policy-relevant information. The authors propose diagnostic indicators to characterize model responses to carbon price signals and test these in a diagnostic study of 11 global models. Indicators describe the magnitude of emission abatement and the associated costs relative to a harmonized baseline, the relative changes in carbon intensity and energy intensity, and the extent of transformation in the energy system. This study shows a correlation among indicators suggesting that models can be classified into groups based on common patterns of behavior in response to carbon pricing. Such a classification can help to explain variations among policy-relevant model results

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