Critical analysis of a publication by the european climate foundation, roadmap 2050: a practical guide to a low-carbon europe

Project managers for the CMA: Nadia MAÏZI et Sandrine SELOSSE

In "Roadmap 2050: a Practical Guide to a Prosperous, Low-Carbon Europe", the European Climate Foundation has carried out a technical and economic study on the feasibility of reducing greenhouse gas (GHG) emissions in Europe by 80% in 2050 in relation to the level in 1990, all while keeping the level of electricity supply as reliable as it is today.

This study was mainly interested in the electricity industry for which this objective will have a considerable impact and has depicted three scenarios for the evolution of the electricity mix.

A critical analysis of this study was devised by IER Stuttgart, representing Germany in the International Energy Agency (IEA) consortium that deployed the TIMES Germany model and the CMA, representing France in the IEA consortium that developed the TIMES France model in the context of the Energy Technology System Analysis program.

Besides their specific national expertise, these two laboratories worked together to develop and deploy a European model in the context of the European NEEDS project.

The study took place in three parts: the first consisted in an identification and critical analysis of the hypotheses used in the report, the second aimed at choosing the most plausible scenario thanks to prospective modelling tools like TIAM-FR. Finally the third part consisted in assessing the impact of this scenario on the different business activities on a European scale and on the electricity mix of each country.

This analysis showed that a certain number of the report’s hypotheses are contradictory or insufficiently justified. For example, while the electricity industry remains central to reduce GHG emissions, there are no explanations in the Roadmap analysis to explain why it would be necessary to curb this industry’s emissions by 95%. Furthermore, we can note that while the scenario advocating 40% of renewable energies is the closest to an “optimum” solution as assessed with the TIMES model, the associated energy mix is a lot more sensitive to the deployment of the capacities specific to each region than what is analyzed.

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