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Assessing climate change adaptation strategies—the case of drought and heat wave in the French nuclear sector


Nuclear energy is a very important component of overall power supply in France. If the effects of future extreme weather events or climate shifts are not addressed, energy systems will be highly vulnerable to extreme weather events or shifts in weather patterns, such as changes in precipitation. Because of the deep uncertainties involved in climate projections and response strategies, any strategy implementation should perform adequately regardless of which scenario actually materialises. In this paper, we analyse the effects of drought and heat wave in the French nuclear energy sector using the Strategy Robustness Visualisation Method. The key feature of the method is the modelling of uncertainty of the quantitative indicators by (min, max) values plotted on radar plots such that each strategy option’s performance can be visually inspected for robustness. The method can be utilised as a “module” of its own in different uncertainty management approaches. Based on the case study, the presented adaptation strategies “Maintaining industrial production and final demand” and “Smart grid infrastructure” were more robust than the “No planned or automatic adaptation”.

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The research presented in this paper was funded by the EU Framework 7 project Tool-support policy-development for regional adaptation (ToPDAd) (

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Corresponding author

Correspondence to Jyri Hanski.

Additional information

Editor:Xiangzheng Deng.


Appendix 1

The SRVM method can be formalised as follows (Hanski and Rosqvist 2016):

Sc i :

scenario i

St j :

strategy j

\( {\overline{\mathbf{v}}}_{\mathrm{ij}}^{\wedge } \) :

optimistic performance valuations (multiple criteria or indicators) of strategy j under scenario i.

\( {\overline{\mathbf{v}}}_{\mathrm{ij}}^{\vee } \) :

pessimistic performance valuations (multiple criteria or indicators) of strategy j under scenario i.

\( \left({\mathrm{Sc}}_{\mathrm{i}},{St}_{\mathrm{j}}\right)\to {\overline{\mathbf{v}}}_{\mathrm{i}\mathrm{j}} \) :

is a mapping of a scenario and strategy—combination to multiple performance levels or valuations which are uncertain (a random vector which get values from model runs or experts’ opinions).

A set of robust strategy is such that the following conditions are met: \( \left\{\mathrm{j}:{\overline{\mathbf{v}}}_{\mathrm{ij}}^{\wedge }>\kern0.62em \overline{\mathbf{0}}\ \mathrm{and}\ {\overline{\mathbf{v}}}_{\mathrm{ij}}^{\vee }>\overline{\mathbf{0}}\kern1em \forall i\right\} \), meaning that a robust strategy outperforms the current strategy given in any scenario and related optimistic and pessimistic valuations across the decision criteria.

Appendix 2

Table 1 Scales for assessing the performance of the decision criteria

Appendix 3

Table 2 Performance of the adaptation strategies based on ARIO modelling

Appendix 4

Table 3 Scaled modelling results

Appendix 5

Table 4 Performance of the adaptation strategies based on expert opinion

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Hanski, J., Rosqvist, T. & Crawford-Brown, D. Assessing climate change adaptation strategies—the case of drought and heat wave in the French nuclear sector. Reg Environ Change 18, 1801–1813 (2018).

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  • Climate change
  • Adaptation
  • Strategy assessment
  • French
  • Nuclear energy