## 2 Data and Methods
### Methods
#### 2.3.4 Assessment of Future Wave Energy Resource  
The assessment of the future wave energy resource was similar to the one used for the future wind resource, since both wind and wave resources share the same characteristics and constraints. Previous studies assessed the wave energy resource [38; 62; 63], taking into account several parameters which evaluated the energy production (linked to a specific WEC), extreme events, availability of the optimal wave height, accessibility, and monthly variability of the resource. Future wave energy resource was classified following a method similar to previous studies [38; 49], based on a Delphi technique. In particular, experts were selected from the steering committee of, and participants in, the COST Action WECANet: A pan-European Network for Marine Renewable Energy ([[https://www.wecanet.eu/](https://www.wecanet.eu/)]([https://www.wecanet.eu/](https://www.wecanet.eu/))). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Richness**} & \multicolumn{2}{c}{**Variability**} & \multicolumn{2}{c}{**Risk**} & \multicolumn{2}{c}{**Cost**} \\ \hline \(\mathbf{W_{ann}}\) & **RLO** & **DWNT** & \(\mathbf{C_{v}}\) & \(\mathbf{M_{v}}\) & **EWS** & **WD** & **DC** \\ \hline
0.22 \(\pm\) 0.02 & 0.10 \(\pm\) 0.01 & 0.22 \(\pm\) 0.02 & 0.10 \(\pm\) 0.01 & 0.05 \(\pm\) 0.01 & 0.14 \(\pm\) 0.01 & 0.07 \(\pm\) 0.01 & 0.10 \(\pm\) 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Weight coefficient in the wind energy classification. The standard deviation was calculated as \(\sigma=\sqrt{\sum_{i=1}^{N}\left|A_{i}-\overline{A}\right|^{2}/N}\), where A is the weight coefficient, \(\overline{A}\) is the mean value of \(A\) and N is the number of experts (10) consulted in [37]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Normalized.** & **RLO (\%)** & **DWNT (\%)** & \(\mathbf{C_{v}}\) & \(\mathbf{M_{v}}\) & **EWS (ms\({}^{-1}\))** \\ \hline
0/9 & \(<\)10 & \(<\)10 & \(>\)1.9 & \(>\)2.5 & \(>\)27 \\
1/9 & 10–20 & 10–20 & 1.7–1.9 & 2.25–2.5 & 25.5–27.0 \\
2/9 & 20–30 & 20–30 & 1.5–1.7 & 2.0–2.25 & 24.0–25.5 \\
3/9 & 30–40 & 30–40 & 1.3–1.5 & 1.75–2.0 & 22.5–24.0 \\
4/9 & 40–50 & 40–50 & 1.1–1.3 & 1.5–1.75 & 21.0–22.5 \\
5/9 & 50–60 & 50–60 & 0.9–1.1 & 1.25–1.5 & 19.5–21.0 \\
6/9 & 60–70 & 60–70 & 0.7–0.9 & 1.0–1.25 & 18.0–19.5 \\
7/9 & 70–80 & 70–80 & 0.5–0.7 & 0.75–1.0 & 16.5–18.0 \\
8/9 & 80–90 & 80–90 & 0.3–0.5 & 0.5–0.75 & 15.0–16.5 \\
9/9 & \(>\)90 & \(>\)90 & \(<\)0.3 & \(<\)0.5 & \(<\)15.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Normalization criterion used for rich level occurrence (RLO), downtime (DWNT), coefficient of variation index (\(C_{v}\)), monthly variability index (\(M_{v}\)), and extreme wind speeds (EWS) indices related to richness factors, variability factors, and environmental risk factors. \begin{table}
\begin{tabular}{c c} \hline \hline
**WD (m)** & **DC (deg)** \\ \hline \(>\)500 & \(>\)4 \\
100–500 & 3–4 \\
50–100 & 2–3 \\
25–50 & 0.5–2 \\
0–25 & \(<\)0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Normalization criterion used for water depth (WD) and distance to coast (DC) indices related to cost factors. As a first step, experts were questioned about the factors that they considered to be influential for wave energy converters. Experts were consulted only about general factors that did not depend on the particular features of a particular type of device. These factors, which include drivers that depend on the resource (mainly the amount of wave energy and its variability), on the environmental risk, and on cost constraints, were proposed and agreed by the experts. In a second step, a normalization method was adopted to compare different factors. Finally, as the last step, experts were consulted again to determine the weight of each factor following the protocol described latter.