previously, no remedial measures or protective coverings on the structural components of concrete have been considered applied within the examined building stock.

Secondly, $C_{T,m}^2$ is used to compute the total normalized repair cost per category, $C_{T,cat}$. This second part of the methodology is given in section 3.3. To evaluate the surface of the building exposed to corrosion, the two groups of buildings, ‘old’ and ‘recent’, were further divided into the so-called **categories of buildings** that address characteristics such as number of stories, load-resisting systems, and type of settlement where buildings are located. $C_{T,cat}$ can be aggregated at regional, national and EU levels to estimate global costs. More details on the used parameters and data to define building categories are given in section 3.3 and Chapter 5.

## 3.2 Total normalized repair cost per square meter

### 3.2.1 Overview

Figure 2 presents the flowchart of the proposed methodology to estimate the total normalized repair costs per square meter. The inputs of the method, blue components of Figure 2 are: (i) carbonation depth, $x_c$, (ii) building characteristics (concrete cover thickness, $c_t$, and construction year), and (iii) the repair costs. The components in green are the main outputs.

The considerations for defining these site specific input variables are detailed in section 3.2.2. The carbonation depth and the building characteristics are used to estimate the probability of corrosion initiation by considering, in a simplified way, the uncertainty related with the concrete cover thickness (section 3.2.2). This probability is after used to estimate the times to repair and related costs since the construction time, or another reference time such as the current year, until 2100. Based on the surrounding environmental conditions, concrete cover and construction year, buildings could be repaired more than once during its lifetime. The number of repairs over time is identified by the index *i* in the middle green box shown in Figure 2. A total normalized repair cost per square meter is finally obtained for a group of structures built during a given period and exposed to particular environmental conditions including climate change (section 3.2.4).

<figure><img src="image_7.png" /><figcaption>**Figure 2.** Flowchart to assess the total normalized repair costs per square meter.</figcaption></figure>

### 3.2.2 Input data

#### Carbonation depth

Carbonation depths are provided by carbonation models that account for environmental conditions including climate change effects. Figure 3 presents two examples of carbonation depths used to illustrate the proposed methodology. Figure 3 was obtained using equation (1) where the carbonation coefficient, $K_c$, which is a constant parameter driving the carbonation process, is fixed at 0.5 and 0.4545 for the scenarios with and without climate change, respectively. For these values, it is observed that the carbonation depth is larger for a given time when climate change is considered. Or, for a cover depth of 3 cm, the time to carbonation initiation $t_{ini}$ is anticipated when climate change is considered, referred as $t_{ini,1}$ in Figure 3, relatively to a situation neglecting climate change ($t_{ini,2}$ in Figure 3), i.e., $t_{ini,1} < t_{ini,2}$. Different findings could be expected when more realistic models and data are considered, such as climate projections for a given location in Europe as it is discussed in Chapter 4.
<figure><img src="image_7.png" /><figcaption>**Figure 3.** Example of carbonation depths.</figcaption></figure>

## Building characteristics for existing structures

Determining the building characteristics for existing structures at national or European levels is a challenging task. For the considered methodology to evaluate the normalized repair cost the required parameters will be the year of construction and the concrete cover.

### Year of construction

This study considers reinforced concrete buildings built since 1965. As mentioned in section 3.1, to simplify the analysis, the cost assessment is be carried out for two groups of structures: ‘old’, built before 2000 and ‘recent’, built after 2000. The low boundary of each period is used for estimating the evolution of the carbonation depth as well as the times to repair in each case. The durability design recommendations of national standards at given decades is included in the study when the information is available, see more details in Chapter 5.

### Concrete cover

Construction standards provide simplified design recommendations to account for uncertainties related to material properties, models, loading, geometry, etc. These standards consider different mechanical (bending, shear, etc.) or durability (chloride ingress, carbonation, etc.) properties for specific conditions (i.e., exposure classes, seismic zones, wind actions, etc.).

Concerning concrete carbonation, design standards provide recommendations to reduce corrosion initiation risks as well as corrosion propagation consequences. These recommendations include the use of minimum concrete cover, the concrete composition (e.g., cement type and content, admixtures, etc.), and the use of other additional protection measures (coatings, stainless steel, cathodic protection etc.). However, EN 1992-1-1:2004 (Eurocode 2: Design of concrete structures - Part 1-1: General rules and rules for buildings) and the European Standard EN 206-1:2000 and its amendments (Concrete - Part 1: Specification, performance, production and conformity) include mainly provisions concerning maximum w/c ratio, and minimum cement content, concrete cover and compressive strength. The National Annexes to EN 206-1, provide additional recommendations about use of admixtures with supplementary cementitious materials (fly ash, silica fume, etc.). The values adopted in this study for the maximum w/c ratio, the minimum cement content, and the concrete cover (c_t) are presented in detail in section 5.2.

As the understanding of material behaviour and physical phenomena improves and we gain more practical experience, construction standards also evolve. Table 1 shows the evolution in time of the minimum concrete cover recommended by reinforced concrete design standards in France, as an example of evolution of standards. These standards were published as official circulars in France. The first circular on 20 October 1906, recommended a minimum concrete cover varying between 15 and 20 mm for the main reinforcement without distinction of the kind of exposure. In 1934, a new circular recommended 35 mm cover for structures close to the sea and 20 mm for structures located inland. Concrete cover was increased to 40 mm and 50 mm in 1964 and 1992, respectively for the cases when reinforced concrete structures are subjected to chloride ingress. It can be observed that there is no specific mention to carbonation induced corrosion until 2004 when EN 1992-1-1:2004 included it among its exposure classes (chlorides, carbonation, freeze/thaw attack, etc.).

This study aims at assessing climate change related costs for existing reinforced concrete (RC) buildings subjected to carbonation. It particularly focuses on reinforced concrete components under the most aggressive external conditions corresponding to an exposure class XC4 (Table 1). Based on the review of standard recommendations in France, Table 1 provides the considered concrete covers for existing structures. It is noted
<figure><img src="image_8.png" /><figcaption>**Figure 3.** Example of carbonation depths.</figcaption></figure>

## Building characteristics for existing structures

Determining the building characteristics for existing structures at national or European levels is a challenging task. For the considered methodology to evaluate the normalized repair cost the required parameters will be the year of construction and the concrete cover.

### Year of construction

This study considers reinforced concrete buildings built since 1965. As mentioned in section 3.1, to simplify the analysis, the cost assessment is be carried out for two groups of structures: ‘old’, built before 2000 and ‘recent’, built after 2000. The low boundary of each period is used for estimating the evolution of the carbonation depth as well as the times to repair in each case. The durability design recommendations of national standards at given decades is included in the study when the information is available, see more details in Chapter 5.

### Concrete cover

Construction standards provide simplified design recommendations to account for uncertainties related to material properties, models, loading, geometry, etc. These standards consider different mechanical (bending, shear, etc.) or durability (chloride ingress, carbonation, etc.) properties for specific conditions (i.e., exposure classes, seismic zones, wind actions, etc.).

Concerning concrete carbonation, design standards provide recommendations to reduce corrosion initiation risks as well as corrosion propagation consequences. These recommendations include the use of minimum concrete cover, the concrete composition (e.g., cement type and content, admixtures, etc.), and the use of other additional protection measures (coatings, stainless steel, cathodic protection etc.). However, EN 1992-1-1:2004 (Eurocode 2: Design of concrete structures - Part 1-1: General rules and rules for buildings) and the European Standard EN 206-1:2000 and its amendments (Concrete - Part 1: Specification, performance, production and conformity) include mainly provisions concerning maximum w/c ratio, and minimum cement content, concrete cover and compressive strength. The National Annexes to EN 206-1, provide additional recommendations about use of admixtures with supplementary cementitious materials (fly ash, silica fume, etc.). The values adopted in this study for the maximum w/c ratio, the minimum cement content, and the concrete cover (c_t) are presented in detail in section 5.2.

As the understanding of material behaviour and physical phenomena improves and we gain more practical experience, construction standards also evolve. Table 1 shows the evolution in time of the minimum concrete cover recommended by reinforced concrete design standards in France, as an example of evolution of standards. These standards were published as official circulars in France. The first circular on 20 October 1906, recommended a minimum concrete cover varying between 15 and 20 mm for the main reinforcement without distinction of the kind of exposure. In 1934, a new circular recommended 35 mm cover for structures close to the sea and 20 mm for structures located inland. Concrete cover was increased to 40 mm and 50 mm in 1964 and 1992, respectively for the cases when reinforced concrete structures are subjected to chloride ingress. It can be observed that there is no specific mention to carbonation induced corrosion until 2004 when EN 1992-1-1:2004 included it among its exposure classes (chlorides, carbonation, freeze/thaw attack, etc.).

This study aims at assessing climate change related costs for existing reinforced concrete (RC) buildings subjected to carbonation. It particularly focuses on reinforced concrete components under the most aggressive external conditions corresponding to an exposure class XC4 (Table 1). Based on the review of standard recommendations in France, Table 1 provides the considered concrete covers for existing structures. It is noted
that in France the current standard has doubled the first recommended value of 1906. Similar analysis was carried out for other countries and additional assumptions were done for countries where the information was missing (see section 5.2).

<table><caption>**Table 1.** Summary of standard recommendations concerning durability of RC structures in France.</caption><thead><tr><th>Standard</th><th>Exposure</th><th>Recommended concrete cover (mm)</th></tr></thead><tbody><tr><td>Circular of 20 October 1906</td><td>Without distinction</td><td>15 to 20</td></tr><tr><td rowspan="2">Circular of 16 July 1934</td><td>Chlorides</td><td>35</td></tr><tr><td>Other exposures</td><td>20</td></tr><tr><td rowspan="3">Circular 70 of 14 November 1964</td><td>Chlorides</td><td>40</td></tr><tr><td>Other exposures</td><td>20</td></tr><tr><td>Concrete inside closed structures without condensation</td><td>10</td></tr><tr><td rowspan="4">Circular 80-70 of 23 May 1980<sup>a</sup></td><td>Chlorides</td><td>40</td></tr><tr><td>Unformed concrete subjected to aggressive conditions</td><td>30</td></tr><tr><td>Concrete exposed to external weather, condensation or in contact with liquids</td><td>20</td></tr><tr><td>Concrete inside closed structures without condensation</td><td>10</td></tr><tr><td rowspan="3">Circular 92-75 of 1 December 1992 (BAEL91)</td><td>Chlorides</td><td>50</td></tr><tr><td>Formed or unformed concrete subjected to aggressive conditions, external weather, condensation or in contact with liquids</td><td>30<sup>b</sup></td></tr><tr><td>Surfaces inside closed structures without condensation</td><td>10</td></tr><tr><td rowspan="4">EN 1992 1 1:2004 and NF EN 206-1:2004<sup>c</sup></td><td>XC1 dry or permanently wet: concrete inside buildings with low air humidity or permanently submerged in water</td><td>25</td></tr><tr><td>XC2 wet, rarely dry: concrete surfaces subject to long-term water contact. Many foundations</td><td>35</td></tr><tr><td>XC3 moderate humidity: concrete inside buildings with moderate or high air humidity. External concrete sheltered from rain</td><td>35</td></tr><tr><td>XC4 cyclic wet and dry: concrete surfaces subject to water contact, not within exposure class XC2</td><td>40</td></tr></tbody></table>

Source: Developed by authors.

<footnote><sup>a)</sup> First recommendation on chloride content since this standard (350 to 400 cement kilograms per cube meter of concrete)</footnote>

<footnote><sup>b)</sup> This value could be reduced to 20 mm if the concrete strength f'c > 40MPa</footnote>

<footnote><sup>c)</sup> Recommendations only for carbonation exposure under the following considerations: structural class S2 (buildings), rebar diameter ≤ 16 mm, aggregate size ≤ 32mm and execution tolerance = 10 mm.</footnote>
that in France the current standard has doubled the first recommended value of 1906. Similar analysis was carried out for other countries and additional assumptions were done for countries where the information was missing (see section 5.2).

<table><caption>**Table 1.** Summary of standard recommendations concerning durability of RC structures in France.</caption><thead><tr><th>Standard</th><th>Exposure</th><th>Recommended concrete cover (mm)</th></tr></thead><tbody><tr><td>Circular of 20 October 1906</td><td>Without distinction</td><td>15 to 20</td></tr><tr><td rowspan="2">Circular of 16 July 1934</td><td>Chlorides</td><td>35</td></tr><tr><td>Other exposures</td><td>20</td></tr><tr><td rowspan="3">Circular 70 of 14 November 1964</td><td>Chlorides</td><td>40</td></tr><tr><td>Other exposures</td><td>20</td></tr><tr><td>Concrete inside closed structures without condensation</td><td>10</td></tr><tr><td rowspan="4">Circular 80-70 of 23 May 1980<sup>a</sup></td><td>Chlorides</td><td>40</td></tr><tr><td>Unformed concrete subjected to aggressive conditions</td><td>30</td></tr><tr><td>Concrete exposed to external weather, condensation or in contact with liquids</td><td>20</td></tr><tr><td>Concrete inside closed structures without condensation</td><td>10</td></tr><tr><td rowspan="3">Circular 92-75 of 1 December 1992 (BAEL91)</td><td>Chlorides</td><td>50</td></tr><tr><td>Formed or unformed concrete subjected to aggressive conditions, external weather, condensation or in contact with liquids</td><td>30<sup>b</sup></td></tr><tr><td>Surfaces inside closed structures without condensation</td><td>10</td></tr><tr><td rowspan="4">EN 1992 1 1:2004 and NF EN 206-1:2004<sup>c</sup></td><td>XC1 dry or permanently wet: concrete inside buildings with low air humidity or permanently submerged in water</td><td>25</td></tr><tr><td>XC2 wet, rarely dry: concrete surfaces subject to long-term water contact. Many foundations</td><td>35</td></tr><tr><td>XC3 moderate humidity: concrete inside buildings with moderate or high air humidity. External concrete sheltered from rain</td><td>35</td></tr><tr><td>XC4 cyclic wet and dry: concrete surfaces subject to water contact, not within exposure class XC2</td><td>40</td></tr></tbody></table>

Source: Developed by authors.

<footnote>a) First recommendation on chloride content since this standard (350 to 400 cement kilograms per cube meter of concrete)</footnote>

<footnote>b) This value could be reduced to 20 mm if the concrete strength f'c > 40MPa</footnote>

<footnote>c) Recommendations only for carbonation exposure under the following considerations: structural class S2 (buildings), rebar diameter ≤ 16 mm, aggregate size ≤ 32mm and execution tolerance = 10 mm.</footnote>
<table><caption>**Table 2.** Design cover for existing structures subjected to carbonation-induced corrosion in France.</caption><thead><tr><td>Construction year</td><td>Design cover (mm)</td></tr></thead><tbody><tr><td>1906-1992</td><td>20</td></tr><tr><td>1992-2004</td><td>30</td></tr><tr><td>2004-present</td><td>40</td></tr></tbody></table>

Source: Developed by authors.

## Repair cost

Total repair costs refer to the sum of all costs associated with repairing corroding reinforced concrete structures. This cost includes the cost of labour, materials, and any other direct or indirect expenses incurred during the repair process. The total repair cost depends on the damage extent, the repair strategy, and the indirect costs. This section focuses on establishing unitary direct repair costs for generalised carbonated concrete replacement on the basis of a literature review.

The direct and indirect costs of repair or replacement for corroded reinforced concrete structures are considerable. Val and Stewart, 2003 assumed that the cost of RC bridge deck replacement doubles the construction cost; nevertheless, this assumption is probably over-estimating repair costs. Yunovivh et al., 2001 estimated that concrete patch repair using ordinary Portland cement costs about 286€/m² for bridges in US. This cost has been later validated and used to assess repair costs in UK and Australia (BRE, 2003; Mullard and Stewart, 2012). Bastidas-Arteaga and Stewart, 2013, updated this cost to 325€/m² to account for minor user disruptions. Considering indirect costs at a European scale is a very challenging task because these costs are highly site and structure specific. For reinforced concrete buildings, these costs could be reduced since the repaired elements are external structural members such as walls, columns, or facade panels. Taking into account the above mentioned considerations, this study will consider only direct costs C<sub>rep</sub>. Table 3 summarizes the costs of damage of RC slabs and beams of the Agri-foodstuffs terminal of the Nantes Saint-Nazaire Port (Srifi, 2012). The values include costs of damage reports, preparation of the building site, repair (removal and reconstruction of the cover) and operating losses. These costs were computed considering maintenance operations for three repair alternatives:

* Repair 1 (preventive): the structure is repaired before corrosion initiation. Polluted concrete cover is repaired by removing and rebuilding of surface material to the depth of 4 cm. Reinforcement bars are not replaced.

* Repair 2 (corrective): repair takes place after corrosion initiation, but the loss of cross-sectional area of rebars is not significant. Polluted concrete cover is repaired by replacing about 6 cm deep surface material. Corroded bars are cleaned but not replaced.

* Repair 3 (corrective): repair is done after severe concrete corrosion damage where the loss of cross-sectional area of rebars is significant. Polluted concrete cover is repaired by replacing about 6 cm deep surface material as well as the corroded rebars are replaced.

<table><caption>**Table 3.** Direct repair costs estimated based on the repair of the Agri-foodstuffs terminal of the Nantes Saint-Nazaire Port (Srifi 2012; Bastidas-Arteaga and Stewart 2016).</caption><thead><tr><td>Item</td><td>Repair 1 (€/m²)</td><td>Repair 2 (€/m²)</td><td>Repair 3 (€/m²)</td></tr></thead><tbody><tr><td>Damage reports, site installation, scaffolding</td><td>65.0</td><td>76.1</td><td>76.3</td></tr><tr><td>Concrete removal</td><td>62.7</td><td>71.8</td><td>71.8</td></tr><tr><td>Repair<sup>a</sup></td><td>111.6</td><td>134.3</td><td>147.7</td></tr><tr><td>Operating losses</td><td>11.4</td><td>11.4</td><td>11.4</td></tr><tr><td>Total</td><td>250.7</td><td>293.6</td><td>307.2</td></tr></tbody></table>

Source: Developed by authors.

<footnote><sup>a</sup> Repair includes only direct costs associated to concrete cover, rebuilding and replacement of corroded bars for the repair alternative 3</footnote>
<table><caption>**Table 2.** Design cover for existing structures subjected to carbonation-induced corrosion in France.</caption><thead><tr><td>Construction year</td><td>Design cover (mm)</td></tr></thead><tbody><tr><td>1906-1992</td><td>20</td></tr><tr><td>1992-2004</td><td>30</td></tr><tr><td>2004-present</td><td>40</td></tr></tbody></table>

Source: Developed by authors.

## Repair cost

Total repair costs refer to the sum of all costs associated with repairing corroding reinforced concrete structures. This cost includes the cost of labour, materials, and any other direct or indirect expenses incurred during the repair process. The total repair cost depends on the damage extent, the repair strategy, and the indirect costs. This section focuses on establishing unitary direct repair costs for generalised carbonated concrete replacement on the basis of a literature review.

The direct and indirect costs of repair or replacement for corroded reinforced concrete structures are considerable. Val and Stewart, 2003 assumed that the cost of RC bridge deck replacement doubles the construction cost; nevertheless, this assumption is probably over-estimating repair costs. Yunovivh et al., 2001 estimated that concrete patch repair using ordinary Portland cement costs about 286€/m² for bridges in US. This cost has been later validated and used to assess repair costs in UK and Australia (BRE, 2003; Mullard and Stewart, 2012). Bastidas-Arteaga and Stewart, 2013, updated this cost to 325€/m² to account for minor user disruptions. Considering indirect costs at a European scale is a very challenging task because these costs are highly site and structure specific. For reinforced concrete buildings, these costs could be reduced since the repaired elements are external structural members such as walls, columns, or facade panels. Taking into account the above mentioned considerations, this study will consider only direct costs C<sub>rep</sub>. Table 3 summarizes the costs of damage of RC slabs and beams of the Agri-foodstuffs terminal of the Nantes Saint-Nazaire Port (Srifi, 2012). The values include costs of damage reports, preparation of the building site, repair (removal and reconstruction of the cover) and operating losses. These costs were computed considering maintenance operations for three repair alternatives:

* Repair 1 (preventive): the structure is repaired before corrosion initiation. Polluted concrete cover is repaired by removing and rebuilding of surface material to the depth of 4 cm. Reinforcement bars are not replaced.

* Repair 2 (corrective): repair takes place after corrosion initiation, but the loss of cross-sectional area of rebars is not significant. Polluted concrete cover is repaired by replacing about 6 cm deep surface material. Corroded bars are cleaned but not replaced.

* Repair 3 (corrective): repair is done after severe concrete corrosion damage where the loss of cross-sectional area of rebars is significant. Polluted concrete cover is repaired by replacing about 6 cm deep surface material as well as the corroded rebars are replaced.

<table><caption>**Table 3.** Direct repair costs estimated based on the repair of the Agri-foodstuffs terminal of the Nantes Saint-Nazaire Port (Srifi 2012; Bastidas-Arteaga and Stewart 2016).</caption><thead><tr><td>Item</td><td>Repair 1 (€/m²)</td><td>Repair 2 (€/m²)</td><td>Repair 3 (€/m²)</td></tr></thead><tbody><tr><td>Damage reports, site installation, scaffolding</td><td>65.0</td><td>76.1</td><td>76.3</td></tr><tr><td>Concrete removal</td><td>62.7</td><td>71.8</td><td>71.8</td></tr><tr><td>Repair<sup>a</sup></td><td>111.6</td><td>134.3</td><td>147.7</td></tr><tr><td>Operating losses</td><td>11.4</td><td>11.4</td><td>11.4</td></tr><tr><td>Total</td><td>250.7</td><td>293.6</td><td>307.2</td></tr></tbody></table>

Source: Developed by authors.

<footnote><sup>a</sup> Repair includes only direct costs associated to concrete cover, rebuilding and replacement of corroded bars for the repair alternative 3</footnote>
It is noted from Table 3 that most of the costs are related to concrete replacement (removal and rebuilding) operations, followed by the costs of damage reporting and preparation of the building site and scaffolding. The values in Table 3 were obtained for reinforced concrete structures subjected to chloride induced-corrosion. However, as the repair technique is the same for carbonated reinforced concrete structures, these values will be adopted in the present study. Furthermore, the costs mentioned above only account for the repair of the damaged reinforced concrete (RC) structural element, excluding any additional renovation or refurbishment works that could potentially enhance or alter the protective barrier of said structural element. Examples of such works could include energy refurbishment aimed at improving the building's thermo-hygrometric performance or the replacement of building envelope materials in the course of a renovation.

In this study, the assessment of the cost of repair of the damage due to carbonation is performed under the assumption that the repair takes place before the loss of cross-sectional area of rebars becomes significant, i.e. the considered option is Repair 2 (corrective).

### 3.2.3 Probability of corrosion initiation

The probability of corrosion initiation is defined as the probability that the carbonation depth ($x_c$) reaches the concrete cover ($c_t$) and is estimated as:

$$P_{ini}(t)=P[g(X,t)\le0] \qquad (9)$$

where X is the vector of random variables to be considered and g(X,t) is the limit state function defined as:

$$g(X,t)=c_t(X)-x_c(t) \qquad (10)$$

There are several sources of uncertainty to be included in X for the assessment of additional repair costs due to the exposure to new climate change conditions. Climate change will affect the durability performance for repaired and not repaired structures. These uncertainties are related to the material properties, weather predictions, error models, concrete cover, etc. and should be integrated to provide an accurate cost assessment. However, only the uncertainty related to the concrete cover is considered in this methodology to be able to carry out the study at an EU scale.

The assessment of the corrosion initiation time is quite sensitive to the depth of concrete cover that is in turn a parameter with significant uncertainty related. In this problem the main sources of uncertainty for $c_t$ are (i) the intrinsic uncertainty related to the variation of concrete cover during the construction of the building and (ii) the uncertainty linked to the lack of knowledge about concrete cover recommendations for a country during a given period. To deal with these uncertainties, it is supposed that this parameter follows a uniform distribution defined in the interval [$c_{t,min}, c_{t,max}$]. With this assumption, and for a given carbonation depth, the probability of corrosion initiation could be estimated as:

$$P_{ini}(x_c) = \begin{cases} 0 & \text{If } x_c < c_{t,min} \\ \frac{x_c - c_{t,min}}{c_{t,max} - c_{t,min}} & \text{If } c_{t,min} \le x_c < c_{t,max} \\ 1 & \text{If } x_c \ge c_{t,max} \end{cases} \qquad (11)$$

Figure 4 plots this probability for $c_{t,min} = 2$ cm and $c_{t,max} = 4$ cm. As expected, $P_{ini}(t)$ varies linearly between these boundaries. The carbonation depth, $x_c(t)$, provided by simulating carbonation evolution in different locations in Europe under several climate change scenarios could be introduced in equation (11) to estimate the $P_{ini}(t)$ at a given location. For instance, in Figure 5 are provided the probabilities of corrosion initiation for the two carbonation depths depicted in Figure 4. For this example, for the same time, the largest probabilities of corrosion initiation correspond to the scenario with climate change where the carbonation depths are larger. These curves will be used to estimate the times left to repair for a given probability threshold that will be in turn considered to assess additional costs related to climate change.
It is noted from Table 3 that most of the costs are related to concrete replacement (removal and rebuilding) operations, followed by the costs of damage reporting and preparation of the building site and scaffolding. The values in Table 3 were obtained for reinforced concrete structures subjected to chloride induced-corrosion. However, as the repair technique is the same for carbonated reinforced concrete structures, these values will be adopted in the present study. Furthermore, the costs mentioned above only account for the repair of the damaged reinforced concrete (RC) structural element, excluding any additional renovation or refurbishment works that could potentially enhance or alter the protective barrier of said structural element. Examples of such works could include energy refurbishment aimed at improving the building's thermo-hygrometric performance or the replacement of building envelope materials in the course of a renovation.

In this study, the assessment of the cost of repair of the damage due to carbonation is performed under the assumption that the repair takes place before the loss of cross-sectional area of rebars becomes significant, i.e. the considered option is Repair 2 (corrective).

### 3.2.3 Probability of corrosion initiation

The probability of corrosion initiation is defined as the probability that the carbonation depth ($x_c$) reaches the concrete cover ($c_t$) and is estimated as:

$$P_{ini}(t)=P[g(X,t)\le0] \qquad (9)$$

where X is the vector of random variables to be considered and g(X,t) is the limit state function defined as:

$$g(X,t)=c_t(X)-x_c(t) \qquad (10)$$

There are several sources of uncertainty to be included in X for the assessment of additional repair costs due to the exposure to new climate change conditions. Climate change will affect the durability performance for repaired and not repaired structures. These uncertainties are related to the material properties, weather predictions, error models, concrete cover, etc. and should be integrated to provide an accurate cost assessment. However, only the uncertainty related to the concrete cover is considered in this methodology to be able to carry out the study at an EU scale.

The assessment of the corrosion initiation time is quite sensitive to the depth of concrete cover that is in turn a parameter with significant uncertainty related. In this problem the main sources of uncertainty for $c_t$ are (i) the intrinsic uncertainty related to the variation of concrete cover during the construction of the building and (ii) the uncertainty linked to the lack of knowledge about concrete cover recommendations for a country during a given period. To deal with these uncertainties, it is supposed that this parameter follows a uniform distribution defined in the interval [$c_{t,min}$, $c_{t,max}$]. With this assumption, and for a given carbonation depth, the probability of corrosion initiation could be estimated as:

$$P_{ini}(x_c) = \begin{cases} 0 & \text{If } x_c < c_{t,min} \\ \frac{x_c - c_{t,min}}{c_{t,max} - c_{t,min}} & \text{If } c_{t,min} \le x_c < c_{t,max} \\ 1 & \text{If } x_c \ge c_{t,max} \end{cases} \qquad (11)$$

Figure 4 plots this probability for $c_{t,min}$ = 2 cm and $c_{t,max}$ = 4 cm. As expected, $P_{ini}(t)$ varies linearly between these boundaries. The carbonation depth, $x_c(t)$, provided by simulating carbonation evolution in different locations in Europe under several climate change scenarios could be introduced in equation (11) to estimate the $P_{ini}(t)$ at a given location. For instance, in Figure 5 are provided the probabilities of corrosion initiation for the two carbonation depths depicted in Figure 4. For this example, for the same time, the largest probabilities of corrosion initiation correspond to the scenario with climate change where the carbonation depths are larger. These curves will be used to estimate the times left to repair for a given probability threshold that will be in turn considered to assess additional costs related to climate change.
<figure><img src="image_8.png" /><figcaption>**Figure 5.** Probability of corrosion initiation over the time for two scenarios: with and without climate change.</figcaption></figure>

<figure><img src="image_9.png" /></figure>

Considering the historical data, the maximum and minimum concrete cover values are given in Table 4 for old and recent structures in France, as an example. The range of variation $c_{t,max}-c_{t,min}$ is defined considering the allowance in design for deviation, $\Delta c_{dev.}$, whose recommended value is 10mm according to EN 1992-1-1, clause 4.4.1.3(1). Consultation on standards and exchanges with experts from several countries led to the adoption of the maximum and minimum concrete cover values for all EU countries as described in section 5.2.

<table><caption>**Table 4.** Adopted concrete cover values for existing buildings in France.</caption><thead><tr><td>Year of construction</td><td>Concrete cover c<sub>t</sub> (mm)</td><td>c<sub>t,min</sub> (mm)</td><td>c<sub>t,max</sub> (mm)</td></tr></thead><tbody><tr><td>Before 2000 (old)</td><td>20</td><td>15</td><td>25</td></tr><tr><td>After 2000 (recent)</td><td>40</td><td>35</td><td>45</td></tr></tbody></table>

Source: Developed by authors.
<figure><img src="image_9.png" /><figcaption>**Figure 5.** Probability of corrosion initiation over the time for two scenarios: with and without climate change.</figcaption></figure>

<figure><img src="image_10.png" /></figure>

Considering the historical data, the maximum and minimum concrete cover values are given in Table 4 for old and recent structures in France, as an example. The range of variation $c_{t,max}-c_{t,min}$ is defined considering the allowance in design for deviation, $\Delta c_{dev.}$, whose recommended value is 10mm according to EN 1992-1-1, clause 4.4.1.3(1). Consultation on standards and exchanges with experts from several countries led to the adoption of the maximum and minimum concrete cover values for all EU countries as described in section 5.2.

<table><caption>**Table 4.** Adopted concrete cover values for existing buildings in France.</caption><thead><tr><td>Year of construction</td><td>Concrete cover c<sub>t</sub> (mm)</td><td>c<sub>t,min</sub> (mm)</td><td>c<sub>t,max</sub> (mm)</td></tr></thead><tbody><tr><td>Before 2000 (old)</td><td>20</td><td>15</td><td>25</td></tr><tr><td>After 2000 (recent)</td><td>40</td><td>35</td><td>45</td></tr></tbody></table>

Source: Developed by authors.
### 3.2.4 Assessment of times to repair and associated costs

A traditional lifecycle cost analysis would be a challenging task when assessing costs at a European level by considering several building typologies designed under different codes, built using different construction practices and exposed to various environmental conditions. Therefore, it is proposed to estimate a total normalized repair cost that considers repairs carried out from a reference time, for instance the year of construction or the current year, until 2100, $C_{T,m}^2$ in €/(year m²):

$$C_{T,m}^2 = \sum_{i=1}^{n} C_{r,i} \qquad (12)$$

where n is the total number of repairs from the reference year until 2100 and $C_{r,i}$ is the i-th normalized repair cost for the time to repair $t_{rep_i}$ estimated as:

$$C_{r,i} = \frac{C_{rep}}{t_{rep_i}} \qquad (13)$$

where $C_{rep}$ is the repair cost described in section 3.2.2 (Table 3). The time to repair is determined as the time from construction or i-1 repair until the i-th repair. The normalization of repair costs by the time to repair aims at considering that climate change could shorten the times to repair depending on the climate change scenario.

Figure 6 illustrates the assessment of the times to repair for a building built in 1965 by considering and neglecting climate change. The indexes W and $S_1$ indicate without climate change and with climate change, respectively. It is observed that by the end of the baseline period in 2010, when climate change anomalies start to be studied (see Chapter 4), the probability of corrosion initiation is close for both cases (with and without climate change).

Afterwards, the building will be subjected to different environmental conditions related to climate change. Then, supposing that larger atmospheric $CO_2$ concentrations and temperatures related to climate change increase the carbonation rates, the probabilities of corrosion initiation are larger when climate change is considered. Consequently, for a threshold probability of corrosion initiation of 0.5, Figure 6 shows that the time to repair is larger when climate change is neglected ($t_{rep_1,W} > t_{rep_1,S_1}$). Therefore, according to equation (13), the total normalized repair cost for the climate change scenario, $C_{T,S_1}$, will be higher than the value obtained without considering climate change, $C_{T,W}$.

<figure><img src="image_10.png" /><figcaption>Figure 6. Illustration of assessment of times to repair.</figcaption></figure>

It is also noted in Figure 6 that costs and times to repair will take place at different times and discounting could be used to estimate a present value. However, the costs are not discounted to avoid introducing additional assumptions about the definition of the discount factor for each country.
### 3.2.4 Assessment of times to repair and associated costs

A traditional lifecycle cost analysis would be a challenging task when assessing costs at a European level by considering several building typologies designed under different codes, built using different construction practices and exposed to various environmental conditions. Therefore, it is proposed to estimate a total normalized repair cost that considers repairs carried out from a reference time, for instance the year of construction or the current year, until 2100, $C_{T,m}^2$ in €/(year m²):

$$C_{T,m}^2 = \sum_{i=1}^{n} C_{r,i} \qquad (12)$$

where n is the total number of repairs from the reference year until 2100 and $C_{r,i}$ is the i-th normalized repair cost for the time to repair $t_{rep_i}$ estimated as:

$$C_{r,i} = \frac{C_{rep}}{t_{rep_i}} \qquad (13)$$

where $C_{rep}$ is the repair cost described in section 3.2.2 (Table 3). The time to repair is determined as the time from construction or i-1 repair until the i-th repair. The normalization of repair costs by the time to repair aims at considering that climate change could shorten the times to repair depending on the climate change scenario.

Figure 6 illustrates the assessment of the times to repair for a building built in 1965 by considering and neglecting climate change. The indexes W and $S_1$ indicate without climate change and with climate change, respectively. It is observed that by the end of the baseline period in 2010, when climate change anomalies start to be studied (see Chapter 4), the probability of corrosion initiation is close for both cases (with and without climate change).

Afterwards, the building will be subjected to different environmental conditions related to climate change. Then, supposing that larger atmospheric $CO_2$ concentrations and temperatures related to climate change increase the carbonation rates, the probabilities of corrosion initiation are larger when climate change is considered. Consequently, for a threshold probability of corrosion initiation of 0.5, Figure 6 shows that the time to repair is larger when climate change is neglected ($t_{rep_1,W} > t_{rep_1,S_1}$). Therefore, according to equation (13), the total normalized repair cost for the climate change scenario, $C_{T,S_1}$, will be higher than the value obtained without considering climate change, $C_{T,W}$.

<figure><img src="image_11.png" /><figcaption>Figure 6. Illustration of assessment of times to repair.</figcaption></figure>

It is also noted in Figure 6 that costs and times to repair will take place at different times and discounting could be used to estimate a present value. However, the costs are not discounted to avoid introducing additional assumptions about the definition of the discount factor for each country.
## 3.3 Total repair cost per year per category of buildings

### 3.3.1 Overview

Figure 7 provides the flowchart to compute the total normalized repair costs per category. The main input data are: (i) the building characteristics, (ii) the number of buildings per category, and (iii) the total normalized costs per square meter. Components in green are new inputs that will be different for each location depending on the building characteristics density while the component in red is the main output of the previous stage. Components in blue are the main outputs. A brief information on buildings characteristics per category and the number of exposed buildings at a European level is presented in section 3.3.2. The proposed methodology for estimating the exposed building surface per category is described in section 3.3.2. The total normalized costs per category are finally estimated by using the cost determined in section 3.2 and the exposed building surface (section 3.3.2).

<figure><img src="image_11.png" /><figcaption>**Figure 7.** Flowchart to assess the total normalized repair costs per category.</figcaption></figure>

### 3.3.2 Assessment of the exposed building surface and related cost

Determining the number of buildings and their characteristics at European level is a complex task. These parameters will be estimated from a georeferenced database available at the JRC that was initially elaborated to assess seismic vulnerability of residential and commercial buildings at the EU scale. Further details on the building exposure and characteristics may be found in Chapter 5.

Only reinforced concrete structures are considered from the database. Six lateral load resisting systems are defined in the database for those structures. Three categories for height are also included (low, mid and high). The years of construction of buildings are not known, therefore, buildings were aggregated in two large groups for building age, 'old' (built before 2000) and 'recent' (built after 2000), using the database information on the date of enforcement of seismic design standards for these buildings.

The following information was available in a georeferenced basis for each category to estimate the exposed reinforced concrete surface:

* Number of RC buildings, $N_b$

* Average number of floors, $N_f$

* Total surface of dwellings, $S_d$

The average number of floors for the different categories is presented in Table 5.
## 3.3 Total repair cost per year per category of buildings

### 3.3.1 Overview

Figure 7 provides the flowchart to compute the total normalized repair costs per category. The main input data are: (i) the building characteristics, (ii) the number of buildings per category, and (iii) the total normalized costs per square meter. Components in green are new inputs that will be different for each location depending on the building characteristics density while the component in red is the main output of the previous stage. Components in blue are the main outputs. A brief information on buildings characteristics per category and the number of exposed buildings at a European level is presented in section 3.3.2. The proposed methodology for estimating the exposed building surface per category is described in section 3.3.2. The total normalized costs per category are finally estimated by using the cost determined in section 3.2 and the exposed building surface (section 3.3.2).

<figure><img src="image_12.png" /><figcaption>**Figure 7.** Flowchart to assess the total normalized repair costs per category.</figcaption></figure>

### 3.3.2 Assessment of the exposed building surface and related cost

Determining the number of buildings and their characteristics at European level is a complex task. These parameters will be estimated from a georeferenced database available at the JRC that was initially elaborated to assess seismic vulnerability of residential and commercial buildings at the EU scale. Further details on the building exposure and characteristics may be found in Chapter 5.

Only reinforced concrete structures are considered from the database. Six lateral load resisting systems are defined in the database for those structures. Three categories for height are also included (low, mid and high). The years of construction of buildings are not known, therefore, buildings were aggregated in two large groups for building age, 'old' (built before 2000) and 'recent' (built after 2000), using the database information on the date of enforcement of seismic design standards for these buildings.

The following information was available in a georeferenced basis for each category to estimate the exposed reinforced concrete surface:

* Number of RC buildings, $N_b$

* Average number of floors, $N_f$

* Total surface of dwellings, $S_d$

The average number of floors for the different categories is presented in Table 5.
<table><caption>**Table 5.** Categories for building height</caption><thead><tr><td>Category</td><td>Range for number of floors</td><td>Average number of floors</td></tr></thead><tbody><tr><td>Low rise</td><td>1 – 4</td><td>3</td></tr><tr><td>Mid rise</td><td>5 – 7</td><td>6</td></tr><tr><td>High rise</td><td>8 – 20</td><td>14</td></tr></tbody></table>

Source: Developed by authors.

As highlighted in the introduction of Chapter 3, the pervasive nature of carbonation damage is observed across the entire surface of façades. Therefore, to estimate the associated repair costs, this study considers all external façades to be exposed building surfaces and assumes that the replacement of all carbonated concrete on the entirety of the concrete façade will be necessary following corrosion initiation. Hence, in order to estimate the exposed concrete surfaces on façades, the volume of an ideal rectangular prism-shaped concrete building was taken into consideration.

Based on the information available in the buildings database, the average ground floor area per building, S<sub>s</sub>, (Figure 8) was estimated as:

$$S_s = f_{ca} \frac{S_d}{N_b N_f} \qquad (14)$$

where f<sub>ca</sub> is a factor to consider the existence of common areas in the building (e.g. f<sub>ca</sub> = 1.05).

<figure><img src="image_12.png" /><figcaption>**Figure 8.** Idealized reinforced concrete building for the estimation of the exposed building surface façades.</figcaption></figure>

For the idealized reinforced concrete building presented in Figure 8, its length, *l* could be determined as:

$$l = \sqrt{\frac{S_s}{2}} \qquad (15)$$

The exposed building surface per category, S<sub>e</sub>, becomes:

$$S_e = 6f_l f_w h l N_f N_b \qquad (16)$$

where f<sub>l</sub> is a factor that reduces the exposed area if the building is placed in a rural (f<sub>l</sub>=1) or urban (f<sub>l</sub>=0.75) area, as f<sub>l</sub>=0.75 indicates that only 3 façades are exposed; h is the average height of a floor (h=3m); and f<sub>w</sub> is a factor that reduces the exposed area to account for the presence of windows or the use of other construction materials for concrete frames with infill panels.
<table><caption>**Table 5.** Categories for building height</caption><thead><tr><td>Category</td><td>Range for number of floors</td><td>Average number of floors</td></tr></thead><tbody><tr><td>Low rise</td><td>1 – 4</td><td>3</td></tr><tr><td>Mid rise</td><td>5 – 7</td><td>6</td></tr><tr><td>High rise</td><td>8 – 20</td><td>14</td></tr></tbody></table>

Source: Developed by authors.

As highlighted in the introduction of Chapter 3, the pervasive nature of carbonation damage is observed across the entire surface of façades. Therefore, to estimate the associated repair costs, this study considers all external façades to be exposed building surfaces and assumes that the replacement of all carbonated concrete on the entirety of the concrete façade will be necessary following corrosion initiation. Hence, in order to estimate the exposed concrete surfaces on façades, the volume of an ideal rectangular prism-shaped concrete building was taken into consideration.

Based on the information available in the buildings database, the average ground floor area per building, S<sub>s</sub>, (Figure 8) was estimated as:

$$S_s = f_{ca} \frac{S_d}{N_b N_f} \qquad (14)$$

where f<sub>ca</sub> is a factor to consider the existence of common areas in the building (e.g. f<sub>ca</sub> = 1.05).

<figure><img src="image_13.png" /><figcaption>**Figure 8.** Idealized reinforced concrete building for the estimation of the exposed building surface façades.</figcaption></figure>

For the idealized reinforced concrete building presented in Figure 8, its length, *l* could be determined as:

$$l = \sqrt{\frac{S_s}{2}} \qquad (15)$$

The exposed building surface per category, S<sub>e</sub>, becomes:

$$S_e = 6f_l f_w h l N_f N_b \qquad (16)$$

where f<sub>l</sub> is a factor that reduces the exposed area if the building is placed in a rural (f<sub>l</sub>=1) or urban (f<sub>l</sub>=0.75) area, as f<sub>l</sub>=0.75 indicates that only 3 façades are exposed; h is the average height of a floor (h=3m); and f<sub>w</sub> is a factor that reduces the exposed area to account for the presence of windows or the use of other construction materials for concrete frames with infill panels.
Table 6 presents the values suggested for the $f_w$ factor. These values were estimated by considering usual structural component dimensions. Concerning the building lateral load resisting system, the highest values correspond to structures built using concrete load bearing walls where the material will be more exposed to carbonation. The value of $f_w$ also increases with the building height because the dimensions of the structural components increase for higher buildings.

<table><caption>**Table 6.** Reduction factor $f_w$ according to buildings lateral load resisting system and height.</caption><thead><tr><th rowspan="2">Lateral load<br>resisting system /<br>Height</th><th colspan="3">f<sub>w</sub></th></tr><tr><th>Low rise (3 floors)</th><th>Middle rise (6 floors)</th><th>High rise (14 floors)</th></tr></thead><tbody><tr><td>Dual frame-wall system</td><td>0.21</td><td>0.26</td><td>0.32</td></tr><tr><td>Infilled or filled frame</td><td>0.16</td><td>0.18</td><td>0.21</td></tr><tr><td>Load bearing wall</td><td>0.80</td><td>0.83</td><td>0.87</td></tr></tbody></table>

Source: Developed by authors.

Finally, the total normalized repair cost per category from the year of construction until 2100, $C_{T,cat}$ in €/year is:

$$C_{T,cat}=C_{T,m}^2S_e \qquad (17)$$

Each category of reinforced concrete buildings in equation (17) is defined as a function of number of stories, average soil surface per building, construction period and respective durability characteristics, settlement type and lateral load resisting system as explained in more in detail in Chapter 5.
Table 6 presents the values suggested for the $f_w$ factor. These values were estimated by considering usual structural component dimensions. Concerning the building lateral load resisting system, the highest values correspond to structures built using concrete load bearing walls where the material will be more exposed to carbonation. The value of $f_w$ also increases with the building height because the dimensions of the structural components increase for higher buildings.

<table><caption>**Table 6.** Reduction factor $f_w$ according to buildings lateral load resisting system and height.</caption><thead><tr><th rowspan="2">Lateral load<br>resisting system /<br>Height</th><th colspan="3">f<sub>w</sub></th></tr><tr><th>Low rise (3 floors)</th><th>Middle rise (6 floors)</th><th>High rise (14 floors)</th></tr></thead><tbody><tr><td>Dual frame-wall system</td><td>0.21</td><td>0.26</td><td>0.32</td></tr><tr><td>Infilled or filled frame</td><td>0.16</td><td>0.18</td><td>0.21</td></tr><tr><td>Load bearing wall</td><td>0.80</td><td>0.83</td><td>0.87</td></tr></tbody></table>

*Source: Developed by authors.*

Finally, the total normalized repair cost per category from the year of construction until 2100, $C_{T,cat}$ in €/year is:

$$C_{T,cat}=C_{T,m}^2S_e \qquad (17)$$

Each category of reinforced concrete buildings in equation (17) is defined as a function of number of stories, average soil surface per building, construction period and respective durability characteristics, settlement type and lateral load resisting system as explained in more in detail in Chapter 5.
# 4 Introduction to the climatic data

## 4.1 Overview

To model the concrete carbonation, in a climate change perspective, Saha and Eckelmann (2014) framed an approach relying on the assessment of the carbonation front depth. This approach requires as input only two atmospheric forcing variables: the ambient CO₂ concentration and the mean air temperature regulating the diffusion coefficient Dₐₓ(t). Both variables have to be represented as running averages between the year of interest and a reference starting point.

The selection of such datasets to investigate the potential impact of climate change shall consider some constraints: CO₂ concentration observational datasets for recent decades do not cover (spatially and temporally) Europe in adequate way while an harmonized greenhouse gas (GHG) concentration and emissions time series has been built (Meinshausen et al., 2011) homogenising several types of data sources to provide data reasonable over the entire Globe from preindustrial era (1765) up to now and future decades. In the same way, Meinshausen et al., 2011 provide estimations related to the other climate-altering gases. They are used as forcing for Global Climate Models (GCMs) to reconstruct the atmospheric patterns at global scale (section 4.2). Furthermore, for climate projections over the future time spans, several global concentration scenarios are selected as reference for the 5th Assessment Report of Intergovernmental Panel on Climate Change (2013) (section 4.2); Meinshausen et al., 2011). Nevertheless, a correction factor is proposed by Saha and Eckelmann (2014) to account for local increases (e.g., due to the urban or industrial settlements) in CO₂ concentration.

For what concern the temperature values for recent decades and future time spans over Europe, several other constraints have to be considered: the approach of Saha and Eckelmann (2014) requires absolute values of temperature and not simply expected anomalies due to the climate change; as the biases currently affecting raw climate projections prevent the use of absolute values, the temperature values are expected to be bias-adjusted by exploiting statistical approaches establishing ‘a statistical transfer function between model and observed data and applies this transfer function to post-process model data’ (Maraun and Widmann, 2018). Furthermore, the analysis should cover the entire Europe while it should permit the comparison with other potential impacts of climate change in different economic sectors. In this regard, the temperature values are provided by 11 bias-adjusted climate projections (Dosio, 2016) where the raw climate simulation chains are included in the ensemble of Regional Climate Models (RCMs) made available within the European branch of the Coordinated Downscaling Experiment (EURO-CORDEX) initiative (Jacob et al., 2014). All RCMs are run over the same spatial domain covering the European continent at a resolution of 0.11°. Historical runs, forced by observed natural and anthropogenic atmospheric composition, cover, at least, the period from 1950 to 2005; the projections (2006–2100) are forced by two Representative Concentration Pathways (RCP), namely, RCP4.5 and RCP8.5. RCMs’ outputs have been bias-adjusted following the procedure suggested by Piani (2010) as described in Dosio and Paruolo (2011) using the observational data set EOBSv10 (released in April 2014; Cornes et al., 2018). Beyond the mean temperature, the original dataset also includes minimum and maximum temperature, and precipitation.

The adoption of such a dataset has several advantages. It is well documented and peer reviewed (Dosio, 2016; Dosio, 2020). In recent years, it has also been used as reference dataset for the assessments included in the III and IV PESETA projects<fnref n="67" />,³³ by the JRC (Ciscar et al., 2018; Feyen et al., 2020). The full extension of the PESETA acronym is ‘Projection of Economic impacts of climate change in Sectors of the European Union based on bottom-up Analysis’. Thus, it enables an easy comparison with the impacts potentially induced by climate change in different sectors and for several —meteorological and hydrological hazards. Furthermore, the entire dataset is freely available enabling the development of a transparent and fully reproducible framework<fnref n="68" />. On the other side, the availability of only bias-corrected temperature values drives the choice of a simplified impact model for the assessment of corrosion dynamics following the approach in section 2.3 and preventing the adoption of more sophisticated parametrizations, for instance the consideration of relative humidity, despite E-OBS has been extended with relative humidity fields from the version 23.1e; March 2021.

In the following, a brief overview about the CO₂ concentration datasets and the climate simulation chains usually adopted for impacts assessment is given with a special focus on the bias-adjusted data presented in

<footnote n="67">⁶⁷ https://joint-research-centre.ec.europa.eu/peseta-projects_en</footnote>

<footnote n="68">⁶⁸ http://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/LISCOAST/10011/LATEST/</footnote>
# 4 Introduction to the climatic data

## 4.1 Overview

To model the concrete carbonation, in a climate change perspective, Saha and Eckelmann (2014) framed an approach relying on the assessment of the carbonation front depth. This approach requires as input only two atmospheric forcing variables: the ambient CO₂ concentration and the mean air temperature regulating the diffusion coefficient Dₐ₅₂(t). Both variables have to be represented as running averages between the year of interest and a reference starting point.

The selection of such datasets to investigate the potential impact of climate change shall consider some constraints: CO₂ concentration observational datasets for recent decades do not cover (spatially and temporally) Europe in adequate way while an harmonized greenhouse gas (GHG) concentration and emissions time series has been built (Meinshausen et al., 2011) homogenising several types of data sources to provide data reasonable over the entire Globe from preindustrial era (1765) up to now and future decades. In the same way, Meinshausen et al., 2011 provide estimations related to the other climate-altering gases. They are used as forcing for Global Climate Models (GCMs) to reconstruct the atmospheric patterns at global scale (section 4.2). Furthermore, for climate projections over the future time spans, several global concentration scenarios are selected as reference for the 5th Assessment Report of Intergovernmental Panel on Climate Change (2013) (section 4.2); Meinshausen et al., 2011). Nevertheless, a correction factor is proposed by Saha and Eckelmann (2014) to account for local increases (e.g., due to the urban or industrial settlements) in CO₂ concentration.

For what concern the temperature values for recent decades and future time spans over Europe, several other constraints have to be considered: the approach of Saha and Eckelmann (2014) requires absolute values of temperature and not simply expected anomalies due to the climate change; as the biases currently affecting raw climate projections prevent the use of absolute values, the temperature values are expected to be bias-adjusted by exploiting statistical approaches establishing ‘a statistical transfer function between model and observed data and applies this transfer function to post-process model data’ (Maraun and Widmann, 2018). Furthermore, the analysis should cover the entire Europe while it should permit the comparison with other potential impacts of climate change in different economic sectors. In this regard, the temperature values are provided by 11 bias-adjusted climate projections (Dosio, 2016) where the raw climate simulation chains are included in the ensemble of Regional Climate Models (RCMs) made available within the European branch of the Coordinated Downscaling Experiment (EURO-CORDEX) initiative (Jacob et al., 2014). All RCMs are run over the same spatial domain covering the European continent at a resolution of 0.11°. Historical runs, forced by observed natural and anthropogenic atmospheric composition, cover, at least, the period from 1950 to 2005; the projections (2006–2100) are forced by two Representative Concentration Pathways (RCP), namely, RCP4.5 and RCP8.5. RCMs’ outputs have been bias-adjusted following the procedure suggested by Piani (2010) as described in Dosio and Paruolo (2011) using the observational data set EOBSv10 (released in April 2014; Cornes et al., 2018). Beyond the mean temperature, the original dataset also includes minimum and maximum temperature, and precipitation.

The adoption of such a dataset has several advantages. It is well documented and peer reviewed (Dosio, 2016; Dosio, 2020). In recent years, it has also been used as reference dataset for the assessments included in the III and IV PESETA projects<fnref n="67" />,³³ by the JRC (Ciscar et al., 2018; Feyen et al., 2020). The full extension of the PESETA acronym is ‘Projection of Economic impacts of climate change in Sectors of the European Union based on bottom-up Analysis’. Thus, it enables an easy comparison with the impacts potentially induced by climate change in different sectors and for several —meteorological and hydrological hazards. Furthermore, the entire dataset is freely available enabling the development of a transparent and fully reproducible framework<fnref n="68" />. On the other side, the availability of only bias-corrected temperature values drives the choice of a simplified impact model for the assessment of corrosion dynamics following the approach in section 2.3 and preventing the adoption of more sophisticated parametrizations, for instance the consideration of relative humidity, despite E-OBS has been extended with relative humidity fields from the version 23.1e; March 2021.

In the following, a brief overview about the CO₂ concentration datasets and the climate simulation chains usually adopted for impacts assessment is given with a special focus on the bias-adjusted data presented in

<footnote n="67">⁶⁷ https://joint-research-centre.ec.europa.eu/peseta-projects_en</footnote>

<footnote n="68">⁶⁸ http://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/LISCOAST/10011/LATEST/</footnote>
Dosio (2016). Then, the potential differences in the gridded observational dataset E-OBS throughout its releases are discussed. Finally, an overview about expected variations of annual temperature over Europe is provided with a specific detail at the country scale.

## 4.2 Pan-European temperature values for recent decades and future projections

### 4.2.1 Climate simulation chains

The capabilities of the current generation of Global Climate Models (GCMs), albeit greatly improved thanks to the advancements in computing power and representation of the physical dynamics (Wilby, 2017), is widely recognized to not permit a proper characterization of weather patterns at regional and local scale.

To fill this gap, statistical and dynamical downscaling techniques are usually adopted. More specifically, statistical downscaling is based on statistical relationships developed between the historic observed climate data and the output of the climate model for the same historical period. Such relationship is then adopted in the projection mode. It is an expeditious approach, usually performed on limited areas but it requires the availability of reliable and long observed weather data series. On the other side, dynamical downscaling is based on the adoption of higher resolution climate models (Regional Climate Models, RCMs) nested, over the area of interest, on GCM from which they retrieve boundary conditions. RCMs are computationally intensive, but they permit a better characterization of the geomorphological features (e.g., orography, land-sea interaction, land-use effect) and to explicitly assess several weather patterns parametrized as sub-grid processes in GCMs. Currently, their resolution ranges between 10 and 50 km but an increasing number of cutting-edge experiments at very high explicit convection permitting resolution (up to 1-2 km) is being carried out (Ban et al., 2021; Kendon et al., 2021; Pichelli et al., 2021).

However, also after the adoption of downscaling approaches, two main aspects should be carefully considered and addressed for impact analysis:

1. characterizing the different sources of uncertainties affecting the future evaluations;

2. identifying and minimizing the biases in climate simulation chain potentially preventing the adoption of raw outputs as inputs for impact models.

For what concerns the first aspect, three main sources of uncertainties are generally recognized (Sanderson, 2018):

(a) 'the human boundary condition' intended as the effect of future human activities in terms of emissions of greenhouse gases or aerosol precursors, deforestation, or the irrigation of crops. In this regard, the emissions represent the forcing for the climate simulation chains. Within the Coupled Model Intercomparison 5 (CMIP5; Tailor et al., 2012⁶⁹) initiative and then in the 5th Assessment Report of Intergovernmental Panel on Climate Change (2013), four time-dependent projections of atmospheric greenhouse gas (GHG) concentrations, the Representative Concentration Pathways (RCPs), are introduced: RCP2.6, RCP4.5, RCP6.0 and RCP8.5 where the suffix stands for the expected increase in radiative forcing compared to the preindustrial era (i.e., 1750). They are "representative" for a broad range of forcing in 2100 assumed as a proxy of the combined effect of greenhouse gases, aerosols, and other factors directly influencing atmospheric heat trapping, but they are not directly related to any socioeconomic 'narratives'.

RCP2.6 represents the more optimistic scenario peaking at about 3 W/m² before 2100 and after declining, while RCP8.5 specifically selected as 'a high-end scenario' and inducing an increment in global temperature much larger than the target of 2°C in the Paris Agreement (2015⁷⁰). Recent authoritative comments (Hausfather & Peters, 2020) suggested to adopt it not as 'business as usual' but 'intended to explore an unlikely high-risk future'. Finally, RCP4.5 and RCP6.0 are assumed as intermediate 'stabilization pathways'. These scenarios provide variations in land use/land cover and evolutions of emissions and concentrations of the full suite of greenhouse gases, aerosols, and chemically active gases. In Figure 9, the evolutions

<footnote n="69">⁶⁹ https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip5</footnote>

<footnote n="70">⁷⁰ Furthermore, the 6th IPPC Assessment Report AR6 (2023) adopts as reference a different set of scenarios explicitly accounting for the narrative of socio-economic dynamics and known as Shared Socioeconomic Pathways (SSPs; Riahi et al., 2017)</footnote>
Dosio (2016). Then, the potential differences in the gridded observational dataset E-OBS throughout its releases are discussed. Finally, an overview about expected variations of annual temperature over Europe is provided with a specific detail at the country scale.

## 4.2 Pan-European temperature values for recent decades and future projections

### 4.2.1 Climate simulation chains

The capabilities of the current generation of Global Climate Models (GCMs), albeit greatly improved thanks to the advancements in computing power and representation of the physical dynamics (Wilby, 2017), is widely recognized to not permit a proper characterization of weather patterns at regional and local scale.

To fill this gap, statistical and dynamical downscaling techniques are usually adopted. More specifically, statistical downscaling is based on statistical relationships developed between the historic observed climate data and the output of the climate model for the same historical period. Such relationship is then adopted in the projection mode. It is an expeditious approach, usually performed on limited areas but it requires the availability of reliable and long observed weather data series. On the other side, dynamical downscaling is based on the adoption of higher resolution climate models (Regional Climate Models, RCMs) nested, over the area of interest, on GCM from which they retrieve boundary conditions. RCMs are computationally intensive, but they permit a better characterization of the geomorphological features (e.g., orography, land-sea interaction, land-use effect) and to explicitly assess several weather patterns parametrized as sub-grid processes in GCMs. Currently, their resolution ranges between 10 and 50 km but an increasing number of cutting-edge experiments at very high explicit convection permitting resolution (up to 1-2 km) is being carried out (Ban et al., 2021; Kendon et al., 2021; Pichelli et al., 2021).

However, also after the adoption of downscaling approaches, two main aspects should be carefully considered and addressed for impact analysis:

1. characterizing the different sources of uncertainties affecting the future evaluations;

2. identifying and minimizing the biases in climate simulation chain potentially preventing the adoption of raw outputs as inputs for impact models.

For what concerns the first aspect, three main sources of uncertainties are generally recognized (Sanderson, 2018):

(a) 'the human boundary condition' intended as the effect of future human activities in terms of emissions of greenhouse gases or aerosol precursors, deforestation, or the irrigation of crops. In this regard, the emissions represent the forcing for the climate simulation chains. Within the Coupled Model Intercomparison 5 (CMIP5; Tailor et al., 2012⁶⁹) initiative and then in the 5th Assessment Report of Intergovernmental Panel on Climate Change (2013), four time-dependent projections of atmospheric greenhouse gas (GHG) concentrations, the Representative Concentration Pathways (RCPs), are introduced: RCP2.6, RCP4.5, RCP6.0 and RCP8.5 where the suffix stands for the expected increase in radiative forcing compared to the preindustrial era (i.e., 1750). They are "representative" for a broad range of forcing in 2100 assumed as a proxy of the combined effect of greenhouse gases, aerosols, and other factors directly influencing atmospheric heat trapping, but they are not directly related to any socioeconomic 'narratives'.

RCP2.6 represents the more optimistic scenario peaking at about 3 W/m² before 2100 and after declining, while RCP8.5 specifically selected as 'a high-end scenario' and inducing an increment in global temperature much larger than the target of 2°C in the Paris Agreement (2015⁷⁰). Recent authoritative comments (Hausfather & Peters, 2020) suggested to adopt it not as 'business as usual' but 'intended to explore an unlikely high-risk future'. Finally, RCP4.5 and RCP6.0 are assumed as intermediate 'stabilization pathways'. These scenarios provide variations in land use/land cover and evolutions of emissions and concentrations of the full suite of greenhouse gases, aerosols, and chemically active gases. In Figure 9, the evolutions

<footnote n="69">⁶⁹ https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip5</footnote>

<footnote n="70">⁷⁰ Furthermore, the 6th IPPC Assessment Report AR6 (2023) adopts as reference a different set of scenarios explicitly accounting for the narrative of socio-economic dynamics and known as Shared Socioeconomic Pathways (SSPs; Riahi et al., 2017)</footnote>
of CO₂ concentrations (input for the selected impact model) are reported for historical times (up to 2005) and for the time span 2006-2100 under the RCP4.5 and RCP8.5 (selected to ensure the consistency with the temperature projections).

<figure><img src="image_13.png" /><figcaption>**Figure 9.** Global values of CO₂ concentrations for historic periods (1765-2005) and in scenario mode (2006-2100) under RCP4.5 and RCP8.5 scenarios; Source: elaboration by authors using data provided by https://tntcat.iiasa.ac.at/RcpDb/dsd?Action=htmlpage&page=about#intro.</figcaption></figure>

(b) 'the limits of predictability' where small errors in initial conditions of the highly chaotic atmospheric systems can grow exponentially over time. Such constrain results particularly significant for weather forecast where it is usually addressed by initial condition ensembles. On the other side, for century-scale climate projections, the timescales can be assumed an order of magnitude longer than the 'memory' of the ocean or atmosphere (Sanderson, 2018) and then the exact initial state of the system is 'uninformative'.

(c) the 'approximation of reality'; the finite spatial resolution of climate models implies approximations in the representation of the geomorphological features (e.g., orography) and the need of adopting parametrizations for all the atmospheric processes occurring on a scale smaller than the grid (sub-grid processes). It requires the selection of formulations based on several competing theories and of the associated parameters to be calibrated by exploiting theoretical constraints or observed data. To account for it, usually ensembles of climate models are adopted; nevertheless, the required time and computational resources prevent to carry out single model ensemble where the single parametrizations are varied in a controlled way starting from a single reference climate model but entails the adoption of multi-model ensembles. However, such ensembles should be viewed as 'ensemble of opportunity' (Gleckler et al., 2008) where the voluntary participation can prevent a comprehensive sample uncertainty (e.g., the climate simulation chains often share GCM or RCM, or the adopted parametrizations).

Over Europe, the most authoritative ensemble of RCMs is represented by the European branch of the Coordinated Downscaling Experiment (CORDEX) (Drobinski et al. 2014; Jacob et al. 2014; Giorgi and Gutowski 2015) of the World Climate Research Programme (WCRP), referred to as EURO-CORDEX<fnref n="71" />. In brief, the aim of the WCRP CORDEX is to provide an internationally coordinated framework to improve regional climate scenarios. This encompasses the harmonization of model evaluation activities and the generation of multi-model ensembles of regional climate projections for the land-regions worldwide. As part of the global CORDEX framework, the goal to the EURO-CORDEX initiative is to provide regional climate projections for Europe by

<footnote n="71">⁷¹ https://www.euro-cortex.net/</footnote>
of CO₂ concentrations (input for the selected impact model) are reported for historical times (up to 2005) and for the time span 2006-2100 under the RCP4.5 and RCP8.5 (selected to ensure the consistency with the temperature projections).

<figure><img src="image_14.png" /><figcaption>**Figure 9.** Global values of CO₂ concentrations for historic periods (1765-2005) and in scenario mode (2006-2100) under RCP4.5 and RCP8.5 scenarios; Source: elaboration by authors using data provided by https://tntcat.iiasa.ac.at/RcpDb/dsd?Action=htmlpage&page=about#intro.</figcaption></figure>

(b) 'the limits of predictability' where small errors in initial conditions of the highly chaotic atmospheric systems can grow exponentially over time. Such constrain results particularly significant for weather forecast where it is usually addressed by initial condition ensembles. On the other side, for century-scale climate projections, the timescales can be assumed an order of magnitude longer than the 'memory' of the ocean or atmosphere (Sanderson, 2018) and then the exact initial state of the system is 'uninformative'.

(c) the 'approximation of reality'; the finite spatial resolution of climate models implies approximations in the representation of the geomorphological features (e.g., orography) and the need of adopting parametrizations for all the atmospheric processes occurring on a scale smaller than the grid (sub-grid processes). It requires the selection of formulations based on several competing theories and of the associated parameters to be calibrated by exploiting theoretical constraints or observed data. To account for it, usually ensembles of climate models are adopted; nevertheless, the required time and computational resources prevent to carry out single model ensemble where the single parametrizations are varied in a controlled way starting from a single reference climate model but entails the adoption of multi-model ensembles. However, such ensembles should be viewed as 'ensemble of opportunity' (Gleckler et al., 2008) where the voluntary participation can prevent a comprehensive sample uncertainty (e.g., the climate simulation chains often share GCM or RCM, or the adopted parametrizations).

Over Europe, the most authoritative ensemble of RCMs is represented by the European branch of the Coordinated Downscaling Experiment (CORDEX) (Drobinski et al. 2014; Jacob et al. 2014; Giorgi and Gutowski 2015) of the World Climate Research Programme (WCRP), referred to as EURO-CORDEX<fnref n="71" />. In brief, the aim of the WCRP CORDEX is to provide an internationally coordinated framework to improve regional climate scenarios. This encompasses the harmonization of model evaluation activities and the generation of multi-model ensembles of regional climate projections for the land-regions worldwide. As part of the global CORDEX framework, the goal to the EURO-CORDEX initiative is to provide regional climate projections for Europe by

<footnote n="71">⁷¹ https://www.euro-cordex.net/</footnote>
dynamically downscaling the outputs of GCMs participating to the CMIP5 at 50 km (EUR-44) and 12.5 km (EUR11) resolution.

In this regard, Dosio (2016) selected a sub-set of EUR11 RCMs reported in Table 7. Historical runs, driven by observed natural and anthropogenic atmospheric composition, cover the period from 1950 to 2005. Conversely, climate projections regard the period from 2006 up to 2100 and are forced by the two hypotheses of greenhouse gas concentration trajectories, acknowledged as RCP4.5 and RCP8.5.

<table><caption>**Table 7.** List of climate model chains used in Dosio (2016)</caption><thead><tr><th>Institute</th><th>RCM I</th><th>Driving GCM (G)</th><th>CORDEX full name</th><th>Acronym (R-G)</th></tr></thead><tbody><tr><td rowspan="3">CLMcom</td><td rowspan="3">CCLM4.8-17</td><td>CNRM-CERFACS-CNRM-CM5</td><td>CNRM-CERFACS-CNRM-CM5_r1i1p1_CLMcom-CCLM4-8-17</td><td>R1-G1</td></tr><tr><td>ICHEC-EC-EARTH</td><td>ICHEC-EC-EARTH_r12i1p1_CLMcom-CCLM4-8-17</td><td>R1-G2</td></tr><tr><td>MPI-M-MPI-ESM-LR</td><td>MPI-M-MPI-ESM-LR_r1i1p1_CLMcom-CCLM4-8-17</td><td>R1-G3</td></tr><tr><td>DMI</td><td>HIRHAM5</td><td>ICHEC-EC-EARTH</td><td>ICHEC-EC-EARTH_r3i1p1_DMI-HIRHAM5</td><td>R2-G2</td></tr><tr><td>IPSL-INERIS</td><td>WRF331F</td><td>IPSL-IPSL-CM5A-MR</td><td>IPSL-IPSL-CM5A-MR_r1i1p1_IPSL-INERIS-WRF331F</td><td>R3-G4</td></tr><tr><td>KNMI</td><td>RACMO22E</td><td>ICHEC-EC-EARTH</td><td>ICHEC-EC-EARTH_r1i1p1_KNMI-RACMO22E</td><td>R4-G2</td></tr><tr><td rowspan="5">SMHI</td><td rowspan="5">RCA4</td><td>CNRM-CERFACS-CNRM-CM5</td><td>CNRM-CERFACS-CNRM-CM5_r1i1p1_SMHI-RCA4</td><td>R5-G1</td></tr><tr><td>ICHEC-EC-EARTH</td><td>ICHEC-EC-EARTH_r12i1p1_SMHI-RCA4</td><td>R5-G2</td></tr><tr><td>IPSL-IPSL-CM5A-MR</td><td>IPSL-IPSL-CM5A-MR_r1i1p1_SMHI-RCA4</td><td>R5-G4</td></tr><tr><td>MOHC-HadGEM2-ES</td><td>MOHC-HadGEM2-ES_r1i1p1_SMHI-RCA4</td><td>R5-G5</td></tr><tr><td>MPI-M-MPI-ESM-LR</td><td>MPI-M-MPI-ESM-LR_r1i1p1_SMHI-RCA4</td><td>R5-G3</td></tr></tbody></table>

Source: Developed by authors.

In what concerns the second issue, i.e., identifying and minimizing the biases in climate simulation, it is widely recognized that part of these biases arises from the parent GCM; others instead are related to, e.g., modelling formulation and physical parameterizations of the adopted RCM (Fowler et al., 2007). To cope with this issue, one of the most widespread strategies relies on the use of statistical bias correction approaches (hereinafter referred as 'bias-adjustment' or Model Output Statistics). In brief, Model Output Statistics (MOS) include 'any method that establishes a statistical transfer function between model and observed data and applies this transfer function to post-process model data' (Maraun and Widmann, 2018). Over recent years, many different
dynamically downscaling the outputs of GCMs participating to the CMIP5 at 50 km (EUR-44) and 12.5 km (EUR11) resolution.

In this regard, Dosio (2016) selected a sub-set of EUR11 RCMs reported in Table 7. Historical runs, driven by observed natural and anthropogenic atmospheric composition, cover the period from 1950 to 2005. Conversely, climate projections regard the period from 2006 up to 2100 and are forced by the two hypotheses of greenhouse gas concentration trajectories, acknowledged as RCP4.5 and RCP8.5.

<table><caption>**Table 7.** List of climate model chains used in Dosio (2016)</caption><thead><tr><th>Institute</th><th>RCM I</th><th>Driving GCM (G)</th><th>CORDEX full name</th><th>Acronym (R-G)</th></tr></thead><tbody><tr><td rowspan="3">CLMcom</td><td rowspan="3">CCLM4.8-17</td><td>CNRM-CERFACS-CNRM-CM5</td><td>CNRM-CERFACS-CNRM-CM5_r1i1p1_CLMcom-CCLM4-8-17</td><td>R1-G1</td></tr><tr><td>ICHEC-EC-EARTH</td><td>ICHEC-EC-EARTH_r12i1p1_CLMcom-CCLM4-8-17</td><td>R1-G2</td></tr><tr><td>MPI-M-MPI-ESM-LR</td><td>MPI-M-MPI-ESM-LR_r1i1p1_CLMcom-CCLM4-8-17</td><td>R1-G3</td></tr><tr><td>DMI</td><td>HIRHAM5</td><td>ICHEC-EC-EARTH</td><td>ICHEC-EC-EARTH_r3i1p1_DMI-HIRHAM5</td><td>R2-G2</td></tr><tr><td>IPSL-INERIS</td><td>WRF331F</td><td>IPSL-IPSL-CM5A-MR</td><td>IPSL-IPSL-CM5A-MR_r1i1p1_IPSL-INERIS-WRF331F</td><td>R3-G4</td></tr><tr><td>KNMI</td><td>RACMO22E</td><td>ICHEC-EC-EARTH</td><td>ICHEC-EC-EARTH_r1i1p1_KNMI-RACMO22E</td><td>R4-G2</td></tr><tr><td rowspan="5">SMHI</td><td rowspan="5">RCA4</td><td>CNRM-CERFACS-CNRM-CM5</td><td>CNRM-CERFACS-CNRM-CM5_r1i1p1_SMHI-RCA4</td><td>R5-G1</td></tr><tr><td>ICHEC-EC-EARTH</td><td>ICHEC-EC-EARTH_r12i1p1_SMHI-RCA4</td><td>R5-G2</td></tr><tr><td>IPSL-IPSL-CM5A-MR</td><td>IPSL-IPSL-CM5A-MR_r1i1p1_SMHI-RCA4</td><td>R5-G4</td></tr><tr><td>MOHC-HadGEM2-ES</td><td>MOHC-HadGEM2-ES_r1i1p1_SMHI-RCA4</td><td>R5-G5</td></tr><tr><td>MPI-M-MPI-ESM-LR</td><td>MPI-M-MPI-ESM-LR_r1i1p1_SMHI-RCA4</td><td>R5-G3</td></tr></tbody></table>

Source: Developed by authors.

In what concerns the second issue, i.e., identifying and minimizing the biases in climate simulation, it is widely recognized that part of these biases arises from the parent GCM; others instead are related to, e.g., modelling formulation and physical parameterizations of the adopted RCM (Fowler et al., 2007). To cope with this issue, one of the most widespread strategies relies on the use of statistical bias correction approaches (hereinafter referred as 'bias-adjustment' or Model Output Statistics). In brief, Model Output Statistics (MOS) include 'any method that establishes a statistical transfer function between model and observed data and applies this transfer function to post-process model data' (Maraun and Widmann, 2018). Over recent years, many different
methods were developed (Maraun et al., 2010; Teutschbein and Seibert, 2012; Maraun, 2016) and widely applied to post-process climate projections. Among these, Dosio (2016) selected the Piani et al. (2010) method. Operatively, such a method relies on the calculation of a monotonically increasing transfer function (TF) such that the marginal cumulative distribution function of the adjusted variable matches that of the observations on the chosen reference period.

As observational reference of precipitation and temperature for present-day period, Dosio (2016) used a daily gridded European land-only dataset, acknowledged as E-OBS (Haylock et al., 2008; Cornes et al., 2018). Such a dataset relies on the ‘blended’ time series of measurements from the station network of the European Climate Assessment & Dataset (ECA&D) project. Since 2008 (year of release of the version 1), the density of stations gradually increases. This allows E-OBS dataset to improve time coverage<sup>72</sup>, horizontal resolution<sup>73</sup>, and the number of atmospheric variables<sup>74</sup>.

Among the different released versions, Dosio (2016) adopted the version 10 (released in April 2014) on a 0.22° grid. Before applying the Piani et al. (2010) method, E-OBS data was firstly interpolated into the EURO-CORDEX RCMs grid (i.e., 0.11°) through the First-order Conservative Remapping (Jones, 1999) technique. Such an interpolation techniques better preserve integrals of the data, like flux, between the source and target grids, although it could induce a larger local interpolation error than other methods.

### 4.2.2 Insights about the selected bias correction method and observational dataset

As far as the bias correction procedure is concerned, when RCMs projections are post-processed to reduce their biases during a present-day calibration period, the resulting climate signal may be altered. This effect should be properly considered if bias-adjusted climate projections are used for the assessment of climate change on specific sectors. On this issue, some authors (e.g., Gobiet et al., 2015) stated that the bias adjustment improves the climate change signal, as this modification is caused by the removal of the intensity-dependent errors in the original models. Others (e.g., Cannon et al., 2015; Switanek et al., 2017) suggested conversely to adopt approaches explicitly preserving relative changes between bias-adjusted and non-bias-adjusted climate projections.

The bias correction method used by Dosio (2016) entails to induce potential changes in the trend in simulated climate data. Over the years, some authors (e.g., Hempel et al., 2013) tried to modify such a method introducing assumptions to preserve climate signal during bias correction. Albeit this issue, Casanueva et al. (2020) claimed that a climate signal alteration may be justified in some cases, for instance, for highly biased climate indices such as those defined using absolute thresholds, where the raw signal could be not reliable (e.g., Dosio, 2016). On the other hand, preserving the trends of the basic distributional statistics (mean and quantiles) is desirable in general if there are no physical mechanisms justifying a modification.

By looking at the observational dataset, E-OBS data may be affected by the same constraints and limitations (e.g., spatial, and temporal heterogeneities, large absolute and relative differences over regions where dense station networks do not exist, see Hofstra et al., 2009; under catching in mountain areas, see Lenderink, 2010) that are typical of observational gridded datasets (Isotta et al., 2014). In general, as most of the gridded dataset relies on a point-estimate of station observations, the produced data could not represent in some areas neither point estimates (due to a smoothing effects) nor areal mean values (due to limitation of the availability of stations). Moreover, the issue of the heterogeneous station density and other sources of uncertainty in E-OBS is also raised by van der Schrier et al. (2013) and Cornes et al. (2018). As an instance, Kysel'y and Plavcová (2010) highlighted for Czechia large biases of E-OBS, especially for minimum temperature at the tail of the probability density function, with respect to a high-density network of observations. Of course, the quality of gridded datasets is highly dependent on the number of stations per grid box. Some areas of Europe can be affected by a very sparse coverage (Cornes et al., 2018). Thus, the effective resolution could be much coarser than the nominal ones (0.22° and 0.1°) made available by E-OBS (Bandhauer et al. 2021). Even if high resolution national gridded datasets can be available for several countries, E-OBS is currently the most authoritative and largely validated pan-European gridded observational dataset, and it justifies its wide adoption in recent years.

<footnote n="72">72 Over the years, the E-OBS time coverage has been extended from 01/01/1950-31/12/2006 for version 1 up to 01/01/1950-31/12/2020 for its latest version, i.e., version 23.1e, released in March 2021</footnote>

<footnote n="73">73 Over the years, the E-OBS spatial resolution has been enhanced: originally delivered on a 0.25° and 0.5° regular lat-long grid as well as on a 0.22° and 0.44° rotated pole grid, with the north pole at 39.25°N-162°W; now it is available also on a finer 0.1° regular lat-long grid</footnote>

<footnote n="74">74 In addition to precipitation amount, mean/maximum/minimum temperature, and sea level pressure, other variables have been included such as surface shortwave downwelling radiation and relative humidity</footnote>
methods were developed (Maraun et al., 2010; Teutschbein and Seibert, 2012; Maraun, 2016) and widely applied to post-process climate projections. Among these, Dosio (2016) selected the Piani et al. (2010) method. Operatively, such a method relies on the calculation of a monotonically increasing transfer function (TF) such that the marginal cumulative distribution function of the adjusted variable matches that of the observations on the chosen reference period.

As observational reference of precipitation and temperature for present-day period, Dosio (2016) used a daily gridded European land-only dataset, acknowledged as E-OBS (Haylock et al., 2008; Cornes et al., 2018). Such a dataset relies on the ‘blended’ time series of measurements from the station network of the European Climate Assessment & Dataset (ECA&D) project. Since 2008 (year of release of the version 1), the density of stations gradually increases. This allows E-OBS dataset to improve time coverage<sup>72</sup>, horizontal resolution<sup>73</sup>, and the number of atmospheric variables<sup>74</sup>.

Among the different released versions, Dosio (2016) adopted the version 10 (released in April 2014) on a 0.22° grid. Before applying the Piani et al. (2010) method, E-OBS data was firstly interpolated into the EURO-CORDEX RCMs grid (i.e., 0.11°) through the First-order Conservative Remapping (Jones, 1999) technique. Such an interpolation techniques better preserve integrals of the data, like flux, between the source and target grids, although it could induce a larger local interpolation error than other methods.

### 4.2.2 Insights about the selected bias correction method and observational dataset

As far as the bias correction procedure is concerned, when RCMs projections are post-processed to reduce their biases during a present-day calibration period, the resulting climate signal may be altered. This effect should be properly considered if bias-adjusted climate projections are used for the assessment of climate change on specific sectors. On this issue, some authors (e.g., Gobiet et al., 2015) stated that the bias adjustment improves the climate change signal, as this modification is caused by the removal of the intensity-dependent errors in the original models. Others (e.g., Cannon et al., 2015; Switanek et al., 2017) suggested conversely to adopt approaches explicitly preserving relative changes between bias-adjusted and non-bias-adjusted climate projections.

The bias correction method used by Dosio (2016) entails to induce potential changes in the trend in simulated climate data. Over the years, some authors (e.g., Hempel et al., 2013) tried to modify such a method introducing assumptions to preserve climate signal during bias correction. Albeit this issue, Casanueva et al. (2020) claimed that a climate signal alteration may be justified in some cases, for instance, for highly biased climate indices such as those defined using absolute thresholds, where the raw signal could be not reliable (e.g., Dosio, 2016). On the other hand, preserving the trends of the basic distributional statistics (mean and quantiles) is desirable in general if there are no physical mechanisms justifying a modification.

By looking at the observational dataset, E-OBS data may be affected by the same constraints and limitations (e.g., spatial, and temporal heterogeneities, large absolute and relative differences over regions where dense station networks do not exist, see Hofstra et al., 2009; under catching in mountain areas, see Lenderink, 2010) that are typical of observational gridded datasets (Isotta et al., 2014). In general, as most of the gridded dataset relies on a point-estimate of station observations, the produced data could not represent in some areas neither point estimates (due to a smoothing effects) nor areal mean values (due to limitation of the availability of stations). Moreover, the issue of the heterogeneous station density and other sources of uncertainty in E-OBS is also raised by van der Schrier et al. (2013) and Cornes et al. (2018). As an instance, Kysel'y and Plavcová (2010) highlighted for Czechia large biases of E-OBS, especially for minimum temperature at the tail of the probability density function, with respect to a high-density network of observations. Of course, the quality of gridded datasets is highly dependent on the number of stations per grid box. Some areas of Europe can be affected by a very sparse coverage (Cornes et al., 2018). Thus, the effective resolution could be much coarser than the nominal ones (0.22° and 0.1°) made available by E-OBS (Bandhauer et al. 2021). Even if high resolution national gridded datasets can be available for several countries, E-OBS is currently the most authoritative and largely validated pan-European gridded observational dataset, and it justifies its wide adoption in recent years.

<footnote n="72">72 Over the years, the E-OBS time coverage has been extended from 01/01/1950-31/12/2006 for version 1 up to 01/01/1950-31/12/2020 for its latest version, i.e., version 23.1e, released in March 2021</footnote>

<footnote n="73">73 Over the years, the E-OBS spatial resolution has been enhanced: originally delivered on a 0.25° and 0.5° regular lat-long grid as well as on a 0.22° and 0.44° rotated pole grid, with the north pole at 39.25°N-162°W; now it is available also on a finer 0.1° regular lat-long grid</footnote>

<footnote n="74">74 In addition to precipitation amount, mean/maximum/minimum temperature, and sea level pressure, other variables have been included such as surface shortwave downwelling radiation and relative humidity</footnote>
4.2.3 Potential differences in the gridded observational dataset throughout its releases

The ongoing release of updated versions of the E-OBS dataset poses an issue on the presence of potential variations/inconsistencies between the different versions. Under such frame, this section aims to compare the E-OBS version 10 (E-OBSv10.0), on which the methodology proposed by Dosio (2016) is based, to the latest available version (version 23.1e, E-OBSv23.1), freely retrievable from the Copernicus Climate Datastore (CDS) platform<sup>75</sup>. Such a comparison is performed over a shared grid (i.e., a resolution equal to 0.25°) by assuming the annual mean temperature as a proxy variable, and a shared period between the versions (i.e., 1981-2010) as control period. The goal is to derive spatial and temporal indications. In this regard, even if E-OBSv23.1 makes available temperature datasets at 0.1° resolution, under the caveats explained in the previous section, the coarse resolution should be preferred to ensure that each grid box has an adequate number of stations associated.

Figure 10 shows the spatial distribution of annual temperature over 1981-2010 as provided by E-OBSv10.0 (Figure 10a) and its bias against E-OBSv23.1 for the same period (Figure 10b). Moreover, Figure 11 depicts the empirical cumulative distribution function (CDF) of the annual temperature given by E-OBSv10.0 against E-OBSv23-1.

<figure><img src="image_14.png" /><figcaption>**Figure 10.** Map of annual temperature provided by E-OBSv10.0 over 1981-2010 (a); bias of annual temperature in-between E-OBS v10.0 and E-OBS v23.1 over the same period (b). Source: Developed by authors.</figcaption></figure>

<footnote n="75"><sup>75</sup> https://cds.climate.copernicus.eu/cdsapp#/dataset/insitu-gridded-observations-europe?tab=overview</footnote>
4.2.3 Potential differences in the gridded observational dataset throughout its releases

The ongoing release of updated versions of the E-OBS dataset poses an issue on the presence of potential variations/inconsistencies between the different versions. Under such frame, this section aims to compare the E-OBS version 10 (E-OBSv10.0), on which the methodology proposed by Dosio (2016) is based, to the latest available version (version 23.1e, E-OBSv23.1), freely retrievable from the Copernicus Climate Datastore (CDS) platform<sup>75</sup>. Such a comparison is performed over a shared grid (i.e., a resolution equal to 0.25°) by assuming the annual mean temperature as a proxy variable, and a shared period between the versions (i.e., 1981-2010) as control period. The goal is to derive spatial and temporal indications. In this regard, even if E-OBSv23.1 makes available temperature datasets at 0.1° resolution, under the caveats explained in the previous section, the coarse resolution should be preferred to ensure that each grid box has an adequate number of stations associated.

Figure 10 shows the spatial distribution of annual temperature over 1981-2010 as provided by E-OBSv10.0 (Figure 10a) and its bias against E-OBSv23.1 for the same period (Figure 10b). Moreover, Figure 11 depicts the empirical cumulative distribution function (CDF) of the annual temperature given by E-OBSv10.0 against E-OBSv23-1.

<figure><img src="image_15.png" /><figcaption>**Figure 10.** Map of annual temperature provided by E-OBSv10.0 over 1981-2010 (a); bias of annual temperature in-between E-OBS v10.0 and E-OBS v23.1 over the same period (b). Source: Developed by authors.</figcaption></figure>

<footnote n="75"><sup>75</sup> https://cds.climate.copernicus.eu/cdsapp#/dataset/insitu-gridded-observations-europe?tab=overview</footnote>
<figure><img src="image_15.png" /><figcaption>**Figure 11.** Empirical Cumulative Distribution Function of annual temperature bias.</figcaption></figure>

As expected, annual temperature across Europe (Figure 10a) varies with latitude increasing from the northern part to the southern one and decreasing with altitude with low values over the Alps and the Carpathian Mountains chains and in the Southern part over the Pyrenean chain. By comparing different versions of E-OBS (Figure 10b), slight biases emerge. It could depend mainly on the increased availability of local stations on which the gridded dataset is built. In any case, such a bias is contained (± 0.5°C in the range 5th-95th percentile of the distribution; ± 1°C in the range 1st-99th percentile of the distribution, see Figure 11) and occurs mainly in those areas like North Africa or Turkey where the station density is scarce. By moving to the temporal scale, Figure 12 plots the evolution of annual temperature provided by E-OBSv10.0 and E-OBSv23.1 averaged over the shared domain for the period 1981-2010. E-OBSv10.0 follows the same trend of E-OBSv23.1, slightly varying in the last 5 years. This is in line with bias reported in Figure 10b.

To sum up, both the CDFs of Figure 11 and the temperature evolution of Figure 12 demonstrate that the latest E-OBS version slightly differs from the E-OBS version used by Dosio (2016), making the assumption of using for the scope of this work data relying on a past version of the E-OBS dataset consistent with the current state of the art.

<figure><img src="image_16.png" /><figcaption>**Figure 12.** Evolution of annual mean temperature derived from E-OBSv10.0 and E-OBSv23.1 over 1981-2010.</figcaption></figure>

# 4.3 Variations of annual temperature

## 4.3.1 Variations over Europe and European countries

Section 4.3.1 provides a general overview about variations of annual temperature over Europe investigating both on recent climate and on future one. The geographical domain of the work is illustrated in Figure 13 following the NUTSO definition of the Nomenclature of Territorial Units for Statistics in 2021, i.e., the administrative boundaries of European countries.
<figure><img src="image_16.png" /><figcaption>**Figure 11.** Empirical Cumulative Distribution Function of annual temperature bias.</figcaption></figure>

As expected, annual temperature across Europe (Figure 10a) varies with latitude increasing from the northern part to the southern one and decreasing with altitude with low values over the Alps and the Carpathian Mountains chains and in the Southern part over the Pyrenean chain. By comparing different versions of E-OBS (Figure 10b), slight biases emerge. It could depend mainly on the increased availability of local stations on which the gridded dataset is built. In any case, such a bias is contained (± 0.5°C in the range 5th-95th percentile of the distribution; ± 1°C in the range 1st-99th percentile of the distribution, see Figure 11) and occurs mainly in those areas like North Africa or Turkey where the station density is scarce. By moving to the temporal scale, Figure 12 plots the evolution of annual temperature provided by E-OBSv10.0 and E-OBSv23.1 averaged over the shared domain for the period 1981-2010. E-OBSv10.0 follows the same trend of E-OBSv23.1, slightly varying in the last 5 years. This is in line with bias reported in Figure 10b.

To sum up, both the CDFs of Figure 11 and the temperature evolution of Figure 12 demonstrate that the latest E-OBS version slightly differs from the E-OBS version used by Dosio (2016), making the assumption of using for the scope of this work data relying on a past version of the E-OBS dataset consistent with the current state of the art.

<figure><img src="image_17.png" /><figcaption>**Figure 12.** Evolution of annual mean temperature derived from E-OBSv10.0 and E-OBSv23.1 over 1981-2010.</figcaption></figure>

# 4.3 Variations of annual temperature

## 4.3.1 Variations over Europe and European countries

Section 4.3.1 provides a general overview about variations of annual temperature over Europe investigating both on recent climate and on future one. The geographical domain of the work is illustrated in Figure 13 following the NUTSO definition of the Nomenclature of Territorial Units for Statistics in 2021, i.e., the administrative boundaries of European countries.
<figure><img src="image_17.png" /><figcaption>**Figure 13.** Delimitation of NUTS0 Country (blue line). This area identifies in this work the European domain. Source: Developed by authors.</figcaption></figure>

The investigation is performed considering 30-year periods as prescribed by the World Meteorological Organization (WMO) as a standard reference to compare variations at a climate scale, accounting for the intrinsic inter-annual variability and reducing the effect of external forcing that may induce statistically significant trends and thus undermine the homogeneity of the data.

Figure 14 show the variation in annual temperature assessed over the recent climate by comparing E-OBSv10.0 data averaged over 1981-2010 (already presented in Figure 10a) with E-OBSv10.0 data averaged over 1951-1980.

<figure><img src="image_18.png" /><figcaption>**Figure 14.** Map of variation of annual temperature given by E-OBS v10.0 over 1981-2010 against 1951-1980. Source: Developed by authors.</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>**Figure 13.** Delimitation of NUTS0 Country (blue line). This area identifies in this work the European domain. Source: Developed by authors.</figcaption></figure>

The investigation is performed considering 30-year periods as prescribed by the World Meteorological Organization (WMO) as a standard reference to compare variations at a climate scale, accounting for the intrinsic inter-annual variability and reducing the effect of external forcing that may induce statistically significant trends and thus undermine the homogeneity of the data.

Figure 14 show the variation in annual temperature assessed over the recent climate by comparing E-OBSv10.0 data averaged over 1981-2010 (already presented in Figure 10a) with E-OBSv10.0 data averaged over 1951-1980.

<figure><img src="image_19.png" /><figcaption>**Figure 14.** Map of variation of annual temperature given by E-OBS v10.0 over 1981-2010 against 1951-1980. Source: Developed by authors.</figcaption></figure>
In general, annual temperature shows increases over Europe moving from 1951-1980 to 1981-2010, apart from some specific areas such as part of Turkey, Greece, and Romania. It should be stressed that precisely those areas feature a limited density of local stations forming the base for E-OBS dataset. By looking at some synthetic statistics like those reported in Table 8, the medium bias among the two periods is 0.7°C and it is in any case limited between -0.5°C and +1.2°C.

<table><caption>**Table 8.** Statistics about bias of annual temperature (1981-2010 vs 1951-1980) over Europe as provided by E-OBSv10.0.</caption><thead><tr><th>Area</th><th>1st %tile</th><th>5th %tile</th><th>25th %tile</th><th>50th %tile</th><th>75th %tile</th><th>95th %tile</th><th>99th %tile</th></tr></thead><tbody><tr><td>Europe</td><td>-0.5</td><td>-0.1</td><td>0.4</td><td>0.7</td><td>0.8</td><td>1.1</td><td>1.2</td></tr></tbody></table>

To have a clearer understanding about the statistical significance of the detected changes, the 2-Sample Kolmogorov-Smirnov test (Hodges, 1958) is performed comparing the yearly temperature values for the two periods to retrieve the areas where the variations can be recognised as statistically significant (with a significance level < 0.05). The null hypothesis for this test is that both samples come from a population with the same distribution. Figure 15 shows how it is rejected for the largest part of the Europe (green areas) while the statistical test failed to reject the null hypothesis in red areas, that have already been identified as affected by sparse observations.

Once analysed the recent climate, the next elaborations provide some insights about the potential expected variations due to climate change. Specifically, Figure 16 and Figure 17 show the annual temperature anomaly (2071-2100 against 1981-2010) computed by using the set of 11 bias-adjusted RCMs under the RCP4.5 and RCP8.5 scenarios, respectively.

For both RCPs, an increase in annual temperature is expected. This increase is more pronounced for RCP8.5 than for RCP4.5 in line with the severity of the scenario. Spatially, the projected increase is most evident in eastern and northern Europe, as well as in central Spain, northern Italy, and the Balkans.

<figure><img src="image_19.png" /><figcaption>**Figure 15.** Two sample Kolmogorov Smirnov test over Europe comparing yearly temperature values from E-OBSv10 for 1951-1980 and 1981-2010. Green areas reject the null hypothesis while red areas do not. Source: Developed by authors.</figcaption></figure>
In general, annual temperature shows increases over Europe moving from 1951-1980 to 1981-2010, apart from some specific areas such as part of Turkey, Greece, and Romania. It should be stressed that precisely those areas feature a limited density of local stations forming the base for E-OBS dataset. By looking at some synthetic statistics like those reported in Table 8, the medium bias among the two periods is 0.7°C and it is in any case limited between -0.5°C and +1.2°C.

<table><caption>**Table 8.** Statistics about bias of annual temperature (1981-2010 vs 1951-1980) over Europe as provided by E-OBSv10.0.</caption><thead><tr><th>Area</th><th>1st %tile</th><th>5th %tile</th><th>25th %tile</th><th>50th %tile</th><th>75th %tile</th><th>95th %tile</th><th>99th %tile</th></tr></thead><tbody><tr><td>Europe</td><td>-0.5</td><td>-0.1</td><td>0.4</td><td>0.7</td><td>0.8</td><td>1.1</td><td>1.2</td></tr></tbody></table>

To have a clearer understanding about the statistical significance of the detected changes, the 2-Sample Kolmogorov-Smirnov test (Hodges, 1958) is performed comparing the yearly temperature values for the two periods to retrieve the areas where the variations can be recognised as statistically significant (with a significance level < 0.05). The null hypothesis for this test is that both samples come from a population with the same distribution. Figure 15 shows how it is rejected for the largest part of the Europe (green areas) while the statistical test failed to reject the null hypothesis in red areas, that have already been identified as affected by sparse observations.

Once analysed the recent climate, the next elaborations provide some insights about the potential expected variations due to climate change. Specifically, Figure 16 and Figure 17 show the annual temperature anomaly (2071-2100 against 1981-2010) computed by using the set of 11 bias-adjusted RCMs under the RCP4.5 and RCP8.5 scenarios, respectively.

For both RCPs, an increase in annual temperature is expected. This increase is more pronounced for RCP8.5 than for RCP4.5 in line with the severity of the scenario. Spatially, the projected increase is most evident in eastern and northern Europe, as well as in central Spain, northern Italy, and the Balkans.

<figure><img src="image_20.png" /><figcaption>**Figure 15.** Two sample Kolmogorov Smirnov test over Europe comparing yearly temperature values from E-OBSv10 for 1951-1980 and 1981-2010. Green areas reject the null hypothesis while red areas do not. Source: Developed by authors.</figcaption></figure>
<figure><img src="image_20.png" /><figcaption>**Figure 17.** Map of annual temperature anomaly 2071-2100 vs 1981-2010 as derived from the Ensemble Mean of 11 bias-adjusted RCMs for RCP8.5 scenario. Source: Developed by authors.</figcaption></figure>

<figure><img src="image_21.png" /></figure>

Annual temperature is also investigated in terms of trend by yearly averaging the output from RCMs over Europe over the historical period and the two RCPs considered and adding the information provided by E-OBS over the 1951-2010 time span gridded observational dataset (Figure 18). To provide a measure of uncertainty, for historical data and future ones the shaded areas represent the variation in-between one standard deviation (±σ).

<figure><img src="image_22.png" /><figcaption>**Figure 18.** Trend of annual temperature over Europe. Shaded areas represent the inter-model variability assessed as ±σ.</figcaption></figure>

The trend reflects the expected increase in annual temperature at the end of 2100. By looking at observations and historical ensemble mean, it confirms the ability of bias-adjusted RCMs in reproducing the current period. By looking at future RCMs' projections, RCP8.5 trend overlaps RCP4.5 trend during the near time horizon (i.e., 2011-2040) and then it significantly diverges after 2050. In terms of variability, the selected set of RCMs returns an inter-model variability up to ±1°C.

After this preliminary screening at European scale, a more detailed analysis is performed at country level, reported in Table 9. Specifically, Table 9 presents for each country the 30-year annual temperature over 1981-2010 as reference for the current condition, and the anomalies over 2011-2040, 2041-2070, and 2071-2100 as indicative for the future conditions respectively for near, medium, and long-time horizons for both RCPs. In addition, the first row outlines the same values for Europe, being this latter assumed as a comparative reference for the expected variations of each country.
<figure><img src="image_21.png" /><figcaption>**Figure 17.** Map of annual temperature anomaly 2071-2100 vs 1981-2010 as derived from the Ensemble Mean of 11 bias-adjusted RCMs for RCP8.5 scenario. Source: Developed by authors.</figcaption></figure>

<figure><img src="image_22.png" /></figure>

Annual temperature is also investigated in terms of trend by yearly averaging the output from RCMs over Europe over the historical period and the two RCPs considered and adding the information provided by E-OBS over the 1951-2010 time span gridded observational dataset (Figure 18). To provide a measure of uncertainty, for historical data and future ones the shaded areas represent the variation in-between one standard deviation (±σ).

<figure><img src="image_23.png" /><figcaption>**Figure 18.** Trend of annual temperature over Europe. Shaded areas represent the inter-model variability assessed as ±σ.</figcaption></figure>

The trend reflects the expected increase in annual temperature at the end of 2100. By looking at observations and historical ensemble mean, it confirms the ability of bias-adjusted RCMs in reproducing the current period. By looking at future RCMs' projections, RCP8.5 trend overlaps RCP4.5 trend during the near time horizon (i.e., 2011-2040) and then it significantly diverges after 2050. In terms of variability, the selected set of RCMs returns an inter-model variability up to ±1°C.

After this preliminary screening at European scale, a more detailed analysis is performed at country level, reported in Table 9. Specifically, Table 9 presents for each country the 30-year annual temperature over 1981-2010 as reference for the current condition, and the anomalies over 2011-2040, 2041-2070, and 2071-2100 as indicative for the future conditions respectively for near, medium, and long-time horizons for both RCPs. In addition, the first row outlines the same values for Europe, being this latter assumed as a comparative reference for the expected variations of each country.
<table><caption>**Table 9.** 30-year annual temperature anomalies at country level; the blue-red palette compares the country anomalies with those assessed over Europe; blue (red) values mean areas colder (hotter) than the European average value.</caption><thead><tr><th rowspan="2">Country</th><th rowspan="2">Current Period<br>1981-2010</th><th rowspan="2">Scenario</th><th colspan="3">Future Periods (as anomaly against current period)</th></tr><tr><th>2011-2040</th><th>2041-2070</th><th>2071-2100</th></tr></thead><tbody><tr><td rowspan="2">Europe</td><td rowspan="2">9.0</td><td>RCP4.5</td><td>0.8</td><td>1.5</td><td>2.0</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.1</td><td>3.7</td></tr><tr><td rowspan="2">Austria</td><td rowspan="2">7.3</td><td>RCP4.5</td><td>0.8</td><td>1.4</td><td>1.9</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.0</td><td>3.7</td></tr><tr><td rowspan="2">Belgium</td><td rowspan="2">9.7</td><td>RCP4.5</td><td>0.7</td><td>1.2</td><td>1.7</td></tr><tr><td>RCP8.5</td><td>0.8</td><td>1.7</td><td>3.2</td></tr><tr><td rowspan="2">Bulgaria</td><td rowspan="2">11.0</td><td>RCP4.5</td><td>0.9</td><td>1.6</td><td>2.1</td></tr><tr><td>RCP8.5</td><td>1.0</td><td>2.3</td><td>4.1</td></tr><tr><td rowspan="2">Croatia</td><td rowspan="2">11.6</td><td>RCP4.5</td><td>0.8</td><td>1.5</td><td>1.9</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.1</td><td>3.8</td></tr><tr><td rowspan="2">Cyprus</td><td rowspan="2">18.1</td><td>RCP4.5</td><td>0.8</td><td>1.4</td><td>1.9</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.1</td><td>3.6</td></tr><tr><td rowspan="2">Czechia</td><td rowspan="2">8.0</td><td>RCP4.5</td><td>0.7</td><td>1.4</td><td>1.9</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.0</td><td>3.6</td></tr><tr><td rowspan="2">Denmark</td><td rowspan="2">8.6</td><td>RCP4.5</td><td>0.8</td><td>1.3</td><td>1.9</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>1.9</td><td>3.3</td></tr><tr><td rowspan="2">Estonia</td><td rowspan="2">6.0</td><td>RCP4.5</td><td>1.0</td><td>1.7</td><td>2.3</td></tr><tr><td>RCP8.5</td><td>1.1</td><td>2.3</td><td>4.0</td></tr><tr><td rowspan="2">Finland</td><td rowspan="2">1.6</td><td>RCP4.5</td><td>1.3</td><td>2.4</td><td>3.1</td></tr><tr><td>RCP8.5</td><td>1.6</td><td>3.2</td><td>5.3</td></tr><tr><td rowspan="2">France</td><td rowspan="2">11.5</td><td>RCP4.5</td><td>0.7</td><td>1.3</td><td>1.7</td></tr><tr><td>RCP8.5</td><td>0.8</td><td>1.9</td><td>3.4</td></tr><tr><td rowspan="2">Germany</td><td rowspan="2">8.8</td><td>RCP4.5</td><td>0.7</td><td>1.3</td><td>1.9</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>1.9</td><td>3.5</td></tr><tr><td rowspan="2">Greece</td><td rowspan="2">14.4</td><td>RCP4.5</td><td>0.8</td><td>1.5</td><td>2.0</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.2</td><td>4.0</td></tr><tr><td rowspan="2">Hungary</td><td rowspan="2">10.6</td><td>RCP4.5</td><td>0.7</td><td>1.4</td><td>1.8</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.0</td><td>3.6</td></tr><tr><td rowspan="2">Ireland</td><td rowspan="2">9.6</td><td>RCP4.5</td><td>0.6</td><td>0.9</td><td>1.3</td></tr><tr><td>RCP8.5</td><td>0.6</td><td>1.4</td><td>2.5</td></tr><tr><td rowspan="2">Italy</td><td rowspan="2">13.3</td><td>RCP4.5</td><td>0.7</td><td>1.4</td><td>1.8</td></tr><tr><td>RCP8.5</td><td>0.8</td><td>2.0</td><td>3.6</td></tr><tr><td rowspan="2">Latvia</td><td rowspan="2">6.4</td><td>RCP4.5</td><td>0.9</td><td>1.6</td><td>2.1</td></tr><tr><td>RCP8.5</td><td>1.0</td><td>2.2</td><td>3.8</td></tr><tr><td rowspan="2">Lithuania</td><td rowspan="2">6.8</td><td>RCP4.5</td><td>0.9</td><td>1.6</td><td>2.1</td></tr><tr><td>RCP8.5</td><td>1.0</td><td>2.1</td><td>3.7</td></tr><tr><td rowspan="2">Luxembourg</td><td rowspan="2">9.1</td><td>RCP4.5</td><td>0.7</td><td>1.3</td><td>1.8</td></tr><tr><td>RCP8.5</td><td>0.8</td><td>1.8</td><td>3.4</td></tr><tr><td rowspan="2">Malta</td><td rowspan="2">17.7</td><td>RCP4.5</td><td>1.0</td><td>1.8</td><td>2.4</td></tr><tr><td>RCP8.5</td><td>1.1</td><td>2.6</td><td>4.6</td></tr><tr><td rowspan="2">Netherlands</td><td rowspan="2">10.0</td><td>RCP4.5</td><td>0.7</td><td>1.2</td><td>1.7</td></tr><tr><td>RCP8.5</td><td>0.8</td><td>1.7</td><td>3.2</td></tr><tr><td rowspan="2">Poland</td><td rowspan="2">8.0</td><td>RCP4.5</td><td>0.7</td><td>1.4</td><td>1.9</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>1.9</td><td>3.5</td></tr><tr><td rowspan="2">Portugal</td><td rowspan="2">15.6</td><td>RCP4.5</td><td>0.6</td><td>1.2</td><td>1.6</td></tr><tr><td>RCP8.5</td><td>0.7</td><td>1.8</td><td>3.2</td></tr><tr><td rowspan="2">Romania</td><td rowspan="2">9.4</td><td>RCP4.5</td><td>0.8</td><td>1.5</td><td>2.0</td></tr><tr><td>RCP8.5</td><td>1.0</td><td>2.1</td><td>3.8</td></tr><tr><td rowspan="2">Slovakia</td><td rowspan="2">8.4</td><td>RCP4.5</td><td>0.7</td><td>1.5</td><td>1.9</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.0</td><td>3.7</td></tr><tr><td rowspan="2">Slovenia</td><td rowspan="2">9.3</td><td>RCP4.5</td><td>0.8</td><td>1.5</td><td>2.0</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.1</td><td>3.8</td></tr><tr><td rowspan="2">Spain</td><td rowspan="2">13.7</td><td>RCP4.5</td><td>0.7</td><td>1.4</td><td>1.8</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.1</td><td>3.7</td></tr><tr><td rowspan="2">Sweden</td><td rowspan="2">3.0</td><td>RCP4.5</td><td>1.1</td><td>1.9</td><td>2.6</td></tr><tr><td>RCP8.5</td><td>1.3</td><td>2.6</td><td>4.5</td></tr></tbody></table>

Temperature differences against European average value
-1.5 °C
+1.5 °C
Source: Developed by authors.
<table><caption>**Table 9.** 30-year annual temperature anomalies at country level; the blue-red palette compares the country anomalies with those assessed over Europe; blue (red) values mean areas colder (hotter) than the European average value.</caption><thead><tr><th rowspan="2">Country</th><th rowspan="2">Current Period<br>1981-2010</th><th rowspan="2">Scenario</th><th colspan="3">Future Periods (as anomaly against current period)</th></tr><tr><th>2011-2040</th><th>2041-2070</th><th>2071-2100</th></tr></thead><tbody><tr><td rowspan="2">Europe</td><td rowspan="2">9.0</td><td>RCP4.5</td><td>0.8</td><td>1.5</td><td>2.0</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.1</td><td>3.7</td></tr><tr><td rowspan="2">Austria</td><td rowspan="2">7.3</td><td>RCP4.5</td><td>0.8</td><td>1.4</td><td>1.9</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.0</td><td>3.7</td></tr><tr><td rowspan="2">Belgium</td><td rowspan="2">9.7</td><td>RCP4.5</td><td>0.7</td><td>1.2</td><td>1.7</td></tr><tr><td>RCP8.5</td><td>0.8</td><td>1.7</td><td>3.2</td></tr><tr><td rowspan="2">Bulgaria</td><td rowspan="2">11.0</td><td>RCP4.5</td><td>0.9</td><td>1.6</td><td>2.1</td></tr><tr><td>RCP8.5</td><td>1.0</td><td>2.3</td><td>4.1</td></tr><tr><td rowspan="2">Croatia</td><td rowspan="2">11.6</td><td>RCP4.5</td><td>0.8</td><td>1.5</td><td>1.9</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.1</td><td>3.8</td></tr><tr><td rowspan="2">Cyprus</td><td rowspan="2">18.1</td><td>RCP4.5</td><td>0.8</td><td>1.4</td><td>1.9</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.1</td><td>3.6</td></tr><tr><td rowspan="2">Czechia</td><td rowspan="2">8.0</td><td>RCP4.5</td><td>0.7</td><td>1.4</td><td>1.9</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.0</td><td>3.6</td></tr><tr><td rowspan="2">Denmark</td><td rowspan="2">8.6</td><td>RCP4.5</td><td>0.8</td><td>1.3</td><td>1.9</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>1.9</td><td>3.3</td></tr><tr><td rowspan="2">Estonia</td><td rowspan="2">6.0</td><td>RCP4.5</td><td>1.0</td><td>1.7</td><td>2.3</td></tr><tr><td>RCP8.5</td><td>1.1</td><td>2.3</td><td>4.0</td></tr><tr><td rowspan="2">Finland</td><td rowspan="2">1.6</td><td>RCP4.5</td><td>1.3</td><td>2.4</td><td>3.1</td></tr><tr><td>RCP8.5</td><td>1.6</td><td>3.2</td><td>5.3</td></tr><tr><td rowspan="2">France</td><td rowspan="2">11.5</td><td>RCP4.5</td><td>0.7</td><td>1.3</td><td>1.7</td></tr><tr><td>RCP8.5</td><td>0.8</td><td>1.9</td><td>3.4</td></tr><tr><td rowspan="2">Germany</td><td rowspan="2">8.8</td><td>RCP4.5</td><td>0.7</td><td>1.3</td><td>1.9</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>1.9</td><td>3.5</td></tr><tr><td rowspan="2">Greece</td><td rowspan="2">14.4</td><td>RCP4.5</td><td>0.8</td><td>1.5</td><td>2.0</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.2</td><td>4.0</td></tr><tr><td rowspan="2">Hungary</td><td rowspan="2">10.6</td><td>RCP4.5</td><td>0.7</td><td>1.4</td><td>1.8</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.0</td><td>3.6</td></tr><tr><td rowspan="2">Ireland</td><td rowspan="2">9.6</td><td>RCP4.5</td><td>0.6</td><td>0.9</td><td>1.3</td></tr><tr><td>RCP8.5</td><td>0.6</td><td>1.4</td><td>2.5</td></tr><tr><td rowspan="2">Italy</td><td rowspan="2">13.3</td><td>RCP4.5</td><td>0.7</td><td>1.4</td><td>1.8</td></tr><tr><td>RCP8.5</td><td>0.8</td><td>2.0</td><td>3.6</td></tr><tr><td rowspan="2">Latvia</td><td rowspan="2">6.4</td><td>RCP4.5</td><td>0.9</td><td>1.6</td><td>2.1</td></tr><tr><td>RCP8.5</td><td>1.0</td><td>2.2</td><td>3.8</td></tr><tr><td rowspan="2">Lithuania</td><td rowspan="2">6.8</td><td>RCP4.5</td><td>0.9</td><td>1.6</td><td>2.1</td></tr><tr><td>RCP8.5</td><td>1.0</td><td>2.1</td><td>3.7</td></tr><tr><td rowspan="2">Luxembourg</td><td rowspan="2">9.1</td><td>RCP4.5</td><td>0.7</td><td>1.3</td><td>1.8</td></tr><tr><td>RCP8.5</td><td>0.8</td><td>1.8</td><td>3.4</td></tr><tr><td rowspan="2">Malta</td><td rowspan="2">17.7</td><td>RCP4.5</td><td>1.0</td><td>1.8</td><td>2.4</td></tr><tr><td>RCP8.5</td><td>1.1</td><td>2.6</td><td>4.6</td></tr><tr><td rowspan="2">Netherlands</td><td rowspan="2">10.0</td><td>RCP4.5</td><td>0.7</td><td>1.2</td><td>1.7</td></tr><tr><td>RCP8.5</td><td>0.8</td><td>1.7</td><td>3.2</td></tr><tr><td rowspan="2">Poland</td><td rowspan="2">8.0</td><td>RCP4.5</td><td>0.7</td><td>1.4</td><td>1.9</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>1.9</td><td>3.5</td></tr><tr><td rowspan="2">Portugal</td><td rowspan="2">15.6</td><td>RCP4.5</td><td>0.6</td><td>1.2</td><td>1.6</td></tr><tr><td>RCP8.5</td><td>0.7</td><td>1.8</td><td>3.2</td></tr><tr><td rowspan="2">Romania</td><td rowspan="2">9.4</td><td>RCP4.5</td><td>0.8</td><td>1.5</td><td>2.0</td></tr><tr><td>RCP8.5</td><td>1.0</td><td>2.1</td><td>3.8</td></tr><tr><td rowspan="2">Slovakia</td><td rowspan="2">8.4</td><td>RCP4.5</td><td>0.7</td><td>1.5</td><td>1.9</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.0</td><td>3.7</td></tr><tr><td rowspan="2">Slovenia</td><td rowspan="2">9.3</td><td>RCP4.5</td><td>0.8</td><td>1.5</td><td>2.0</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.1</td><td>3.8</td></tr><tr><td rowspan="2">Spain</td><td rowspan="2">13.7</td><td>RCP4.5</td><td>0.7</td><td>1.4</td><td>1.8</td></tr><tr><td>RCP8.5</td><td>0.9</td><td>2.1</td><td>3.7</td></tr><tr><td rowspan="2">Sweden</td><td rowspan="2">3.0</td><td>RCP4.5</td><td>1.1</td><td>1.9</td><td>2.6</td></tr><tr><td>RCP8.5</td><td>1.3</td><td>2.6</td><td>4.5</td></tr></tbody></table>

Temperature differences against European average value
-1.5 °C
+1.5 °C
Source: Developed by authors.
For Europe, the 30-year annual temperature over 1981-2010 for both RCPs is 9.0 °C. Regarding the future time horizons, the RCP4.5 scenario projects an increase of 30-year annual temperature at values of 0.8 °C, 1.5 °C and 2.0 °C for near (2011-2040), medium (2041-2070), and long-time horizons (2071-2100), respectively; conversely, the RCP8.5 scenario projects for the same periods an increase of 0.9 °C, 2.1 °C and 3.7 °C. With respect to the European tendency, the countries in the Scandinavian and the Baltic area as well as Malta, and to a lesser extent Bulgaria, Greece and Croatia in the Balkans, are the ones projecting higher temperature increases; conversely, countries such as Ireland, and Portugal, as well as Belgium and the Netherlands, in proximity to the Atlantic Ocean and the North Sea are the ones projecting lower temperature increases. A detailed overview on the trend of projected annual temperature for both RCPs is reported in Figure 19 and Figure 20.

The dataset used for the analysis does not include data for Malta. Indeed, the version of E-OBS (v10; at 0.25°) used for bias correction of EURO-CORDEX data does not cover Malta, the climate projections also lack information about the area. However, in the study the calculations are based on the closest available data points in Sicily, assuming those could be reliable proxies for Malta. Although the distances between Malta and the closest temperature grids exceed 70 km, the analysis has been conducted using these grid-points for all calculations related to Malta, including average temperature, probabilities, damages, and costs. Despite this limitation, the approach taken provides a reasonable approximation for the temperatures in the region, ensuring consistency in the analysis.

<figure><img src="image_23.png" /><figcaption>**Figure 19.** Trend of annual temperature at country level (part 1). Shaded areas represent the inter-model variability assessed as ±σ.</figcaption></figure>
For Europe, the 30-year annual temperature over 1981-2010 for both RCPs is 9.0 °C. Regarding the future time horizons, the RCP4.5 scenario projects an increase of 30-year annual temperature at values of 0.8 °C, 1.5 °C and 2.0 °C for near (2011-2040), medium (2041-2070), and long-time horizons (2071-2100), respectively; conversely, the RCP8.5 scenario projects for the same periods an increase of 0.9 °C, 2.1 °C and 3.7 °C. With respect to the European tendency, the countries in the Scandinavian and the Baltic area as well as Malta, and to a lesser extent Bulgaria, Greece and Croatia in the Balkans, are the ones projecting higher temperature increases; conversely, countries such as Ireland, and Portugal, as well as Belgium and the Netherlands, in proximity to the Atlantic Ocean and the North Sea are the ones projecting lower temperature increases. A detailed overview on the trend of projected annual temperature for both RCPs is reported in Figure 19 and Figure 20.

The dataset used for the analysis does not include data for Malta. Indeed, the version of E-OBS (v10; at 0.25°) used for bias correction of EURO-CORDEX data does not cover Malta, the climate projections also lack information about the area. However, in the study the calculations are based on the closest available data points in Sicily, assuming those could be reliable proxies for Malta. Although the distances between Malta and the closest temperature grids exceed 70 km, the analysis has been conducted using these grid-points for all calculations related to Malta, including average temperature, probabilities, damages, and costs. Despite this limitation, the approach taken provides a reasonable approximation for the temperatures in the region, ensuring consistency in the analysis.

<figure><img src="image_24.png" /><figcaption>**Figure 19.** Trend of annual temperature at country level (part 1). Shaded areas represent the inter-model variability assessed as ±σ.</figcaption></figure>
<figure><img src="image_24.png" /><figcaption>**Figure 20.** Trend of annual temperature at country level (part 2). Shaded areas represent the inter-model variability assessed as ±σ.</figcaption></figure>

An analysis about the statistical significance of the trends is carried out by exploiting the Mann-Kendall test (Mann, 1945; Kendall, 1975). It reveals how the trends, for all the countries under both scenarios, can be assessed as statistically significant with a significance level much lower than 5%. It confirms how the increase signal can be assumed robust all over the Continent albeit the differences in the magnitude. Of course, analogous results are returned over all the Europe by performing the 2 sample Kolmogorov Smirnov test between the long-time horizon (2071-2100) and the reference period (1981-2010).

### 4.3.2 Variations over European capital cities

At a first stage, the carbonation damage is evaluated for the 27 capital cities of the EU Member States for climate change scenario projections forced by the two Representative Concentration Pathways RCP4.5 and RCP8.5 (see section 4.2.1). Subsequently, the calculation of corrosion costs is extended for all countries of the European Union (section 6.2).
<figure><img src="image_25.png" /><figcaption>**Figure 20.** Trend of annual temperature at country level (part 2). Shaded areas represent the inter-model variability assessed as ±σ.</figcaption></figure>

An analysis about the statistical significance of the trends is carried out by exploiting the Mann-Kendall test (Mann, 1945; Kendall, 1975). It reveals how the trends, for all the countries under both scenarios, can be assessed as statistically significant with a significance level much lower than 5%. It confirms how the increase signal can be assumed robust all over the Continent albeit the differences in the magnitude. Of course, analogous results are returned over all the Europe by performing the 2 sample Kolmogorov Smirnov test between the long-time horizon (2071-2100) and the reference period (1981-2010).

### 4.3.2 Variations over European capital cities

At a first stage, the carbonation damage is evaluated for the 27 capital cities of the EU Member States for climate change scenario projections forced by the two Representative Concentration Pathways RCP4.5 and RCP8.5 (see section 4.2.1). Subsequently, the calculation of corrosion costs is extended for all countries of the European Union (section 6.2).
The impact of climate change on carbonation damage in the European building stock is determined by the difference in the effect of future climate scenarios compared to two reference scenarios played out by two stationary temperature baselines.

Figure 21 illustrates the trend of annual temperature for the EU-27 northernmost and southernmost capital cities, Helsinki, Finland and Nicosia, Cyprus, respectively. Annex 1 brings together figures showing the temperature trends for the capital cities of the 27 EU Member States.

<figure><img src="image_25.png" /><figcaption>**Figure 21.** Trend of annual temperature in Helsinki (top figure) and Nicosia (bottom figure), observations, historical and projections scenarios.</figcaption></figure>

<figure><img src="image_26.png" /></figure>
The impact of climate change on carbonation damage in the European building stock is determined by the difference in the effect of future climate scenarios compared to two reference scenarios played out by two stationary temperature baselines.

Figure 21 illustrates the trend of annual temperature for the EU-27 northernmost and southernmost capital cities, Helsinki, Finland and Nicosia, Cyprus, respectively. Annex 1 brings together figures showing the temperature trends for the capital cities of the 27 EU Member States.

<figure><img src="image_26.png" /><figcaption>**Figure 21.** Trend of annual temperature in Helsinki (top figure) and Nicosia (bottom figure), observations, historical and projections scenarios.</figcaption></figure>

<figure><img src="image_27.png" /></figure>
In the previous figures and in Annex 1:

- The black-horizontal-dotted lines represent the two baseline-temperature scenarios assumed constant until 2100. The black bold-dotted line represents the observations averaged over a 15 years' time interval starting in 1965, while the black-thin-dotted line represents the observations averaged over a 30 years' time interval centred in 1995. Each one of the two reference-stationary temperature scenario, Tref, will be used as the current climate at the date of construction of each group of buildings acknowledged as old and recent, as described in section 3.1, together with two global constant values of CO₂ concentration obtained from Figure 1 at two instants, 1965 and 2000, when old and recent buildings were assumed uncorroded.

- the bold-back line represents annual mean temperature derived from E-OBSv10.0 for each capital city between 1965 and 1980.

- the very-thin-dashed lines, identified in the figure's legend by 'Single model val.', represent the bias-adjusted historical data (in red) and the bias-adjusted projections of the mean annual air temperature obtained for each of the 11 regional Climate Models and the two climate change scenarios, i.e., RCP4.5 in green and RCP8.5 in blue.

- the thin smoothed curves, identified in the figure's legend by 'Mov. Avg. RCP4.5' and 'Mov. Avg. RCP8.5', illustrate the yearly average air temperature calculated with a moving average over 30 years for each of the 11 regional Climate Models and the two climate change scenarios, i.e., RCP4.5 in green and RCP8.5 in blue.

- the three thicker lines, identified in the figure's legend by 'EM Hist.', 'EM RCP4.5' and 'EM RCP8.5' represent the ensemble mean (EM) obtained from the 11 moving average curves for the historical period (in red) and the two climate change scenarios, i.e., RCP4.5 in green and RCP8.5 in blue. The red-thick line is expected to reproduce, in a statistical way, the evolution of the corresponding E-OBS trends on the historic period between 1981 and 2100.

The same procedure illustrated for two capital cities in this section was applied all over Europe over a grid of 12.5 km, considering a sub-set of EUR11 regional climate projections (EUR 11) as reported in Table 7.

Among the 27 capital cities analysed, Nicosia, Athens, and Bucharest are the only ones showing an overlap in the average temperature from the earlier to the later baseline period. Note that the choice of the baseline period starting in year 1965 was governed by the availability of data and does not cover the usual 30-year average climatological period, which contributes to increased uncertainty in estimating climate averages. Furthermore, non-climatic drivers as local land use changes or significant variations in the availability of observations informing the dataset could play a relevant role.

On the contrary, Helsinki and most capital cities in Northern Europe, exhibit a sharp increase in the temperature average between the earlier and the later baseline periods. At the end of the XXI century, the ensemble mean for Helsinki is estimated to increase by 2.6°C and 4.7°C, for RCP4.5 and RCP8.5, respectively, compared to most recent baseline period, and the ensemble mean for Nicosia is estimated to increase by about 1.9°C and 4.2°C for the RCP4.5 and RCP8.5, respectively, compared with the most recent baseline period.

**Error! Reference source not found.** showcases the temperature variations (in °C) among the studied capital cities. It illustrates the differences under two RCP scenarios, RCP4.5 and RCP8.5, along with the relative changes from the two temperature baseline scenarios representative of the climate at the time of construction of old buildings (1965) and recent buildings (2000) to the years 2050 and 2085. To visualize these variations, a blue-red colour palette is used, where red indicates higher temperature differences and blue represents lower differences compared to the respective baseline temperature for the same period. The minimum and maximum values of the selected range by column are highlighted in bold, dark blue and black colour, respectively.
In the previous figures and in Annex 1:

- The black-horizontal-dotted lines represent the two baseline-temperature scenarios assumed constant until 2100. The black bold-dotted line represents the observations averaged over a 15 years' time interval starting in 1965, while the black-thin-dotted line represents the observations averaged over a 30 years' time interval centred in 1995. Each one of the two reference-stationary temperature scenario, Tref, will be used as the current climate at the date of construction of each group of buildings acknowledged as old and recent, as described in section 3.1, together with two global constant values of CO₂ concentration obtained from Figure 1 at two instants, 1965 and 2000, when old and recent buildings were assumed uncorroded.

- the bold-back line represents annual mean temperature derived from E-OBSv10.0 for each capital city between 1965 and 1980.

- the very-thin-dashed lines, identified in the figure's legend by 'Single model val.', represent the bias-adjusted historical data (in red) and the bias-adjusted projections of the mean annual air temperature obtained for each of the 11 regional Climate Models and the two climate change scenarios, i.e., RCP4.5 in green and RCP8.5 in blue.

- the thin smoothed curves, identified in the figure's legend by 'Mov. Avg. RCP4.5' and 'Mov. Avg. RCP8.5', illustrate the yearly average air temperature calculated with a moving average over 30 years for each of the 11 regional Climate Models and the two climate change scenarios, i.e., RCP4.5 in green and RCP8.5 in blue.

- the three thicker lines, identified in the figure's legend by 'EM Hist.', 'EM RCP4.5' and 'EM RCP8.5' represent the ensemble mean (EM) obtained from the 11 moving average curves for the historical period (in red) and the two climate change scenarios, i.e., RCP4.5 in green and RCP8.5 in blue. The red-thick line is expected to reproduce, in a statistical way, the evolution of the corresponding E-OBS trends on the historic period between 1981 and 2100.

The same procedure illustrated for two capital cities in this section was applied all over Europe over a grid of 12.5 km, considering a sub-set of EUR11 regional climate projections (EUR 11) as reported in Table 7.

Among the 27 capital cities analysed, Nicosia, Athens, and Bucharest are the only ones showing an overlap in the average temperature from the earlier to the later baseline period. Note that the choice of the baseline period starting in year 1965 was governed by the availability of data and does not cover the usual 30-year average climatological period, which contributes to increased uncertainty in estimating climate averages. Furthermore, non-climatic drivers as local land use changes or significant variations in the availability of observations informing the dataset could play a relevant role.

On the contrary, Helsinki and most capital cities in Northern Europe, exhibit a sharp increase in the temperature average between the earlier and the later baseline periods. At the end of the XXI century, the ensemble mean for Helsinki is estimated to increase by 2.6°C and 4.7°C, for RCP4.5 and RCP8.5, respectively, compared to most recent baseline period, and the ensemble mean for Nicosia is estimated to increase by about 1.9°C and 4.2°C for the RCP4.5 and RCP8.5, respectively, compared with the most recent baseline period.

**Error! Reference source not found.** showcases the temperature variations (in °C) among the studied capital cities. It illustrates the differences under two RCP scenarios, RCP4.5 and RCP8.5, along with the relative changes from the two temperature baseline scenarios representative of the climate at the time of construction of old buildings (1965) and recent buildings (2000) to the years 2050 and 2085. To visualize these variations, a blue-red colour palette is used, where red indicates higher temperature differences and blue represents lower differences compared to the respective baseline temperature for the same period. The minimum and maximum values of the selected range by column are highlighted in bold, dark blue and black colour, respectively.
<table><caption>**Table 10.** Temperature disparities between the reference baseline temperature for old (constructed 1965) and recent buildings (constructed 2000) and the years 2050 and 2085 in European capital cities analysed under RCP4.5 and RCP8.5 scenarios.</caption><thead><tr><th rowspan="3">CC scenarios<br>City /Year</th><th colspan="2">Old buildings</th><th colspan="4">Old buildings (constructed 1965)</th><th colspan="4">Recent buildings (constructed 2000)</th></tr><tr><th rowspan="2">Baseline Temp. (°C)</th><th rowspan="2">Baseline Temp. (°C)</th><th colspan="2">RCP 4.5</th><th colspan="2">RCP 8.5</th><th colspan="2">RCP 4.5</th><th colspan="2">RCP 8.5</th></tr><tr><th>2050</th><th>2085</th><th>2050</th><th>2085</th><th>2050</th><th>2085</th><th>2050</th><th>2085</th></tr></thead><tbody><tr><td>Amsterdam</td><td>9.4</td><td>10.2</td><td>1.8</td><td>2.3</td><td>2.2</td><td>3.9</td><td>1.1</td><td>1.6</td><td>1.5</td><td>3.2</td></tr><tr><td>Athens</td><td>17.4</td><td>17.4</td><td>1.2</td><td>1.9</td><td>1.8</td><td>3.8</td><td>1.3</td><td>1.9</td><td>1.8</td><td>3.8</td></tr><tr><td>Berlin</td><td>9.1</td><td>9.8</td><td>1.8</td><td>2.5</td><td>2.3</td><td>4.0</td><td>1.1</td><td>1.7</td><td>1.5</td><td>3.2</td></tr><tr><td>Bratislava</td><td>9.6</td><td>10.8</td><td>2.3</td><td>2.9</td><td>2.7</td><td>4.6</td><td>1.1</td><td>1.7</td><td>1.5</td><td>3.3</td></tr><tr><td>Brussels</td><td>10.1</td><td>10.8</td><td>1.7</td><td>2.3</td><td>2.1</td><td>3.7</td><td>1.0</td><td>1.5</td><td>1.3</td><td>3.0</td></tr><tr><td>Bucharest</td><td>10.9</td><td>11.0</td><td>1.3</td><td>1.9</td><td>1.8</td><td>3.7</td><td>1.2</td><td>1.8</td><td>1.7</td><td>3.6</td></tr><tr><td>Budapest</td><td>10.7</td><td>11.7</td><td>2.1</td><td>2.6</td><td>2.5</td><td>4.3</td><td>1.1</td><td>1.7</td><td>1.5</td><td>3.4</td></tr><tr><td>Copenhagen</td><td>8.6</td><td>9.2</td><td>1.7</td><td>2.3</td><td>2.1</td><td>3.7</td><td>1.1</td><td>1.7</td><td>1.5</td><td>3.1</td></tr><tr><td>Dublin</td><td>9.2</td><td>9.8</td><td>1.3</td><td>1.8</td><td>1.6</td><td>3.0</td><td>0.8</td><td>1.3</td><td>1.1</td><td>2.4</td></tr><tr><td>Helsinki</td><td>5.0</td><td>5.7</td><td>2.5</td><td>3.3</td><td>3.0</td><td>5.0</td><td>1.7</td><td>2.5</td><td>2.2</td><td>4.3</td></tr><tr><td>Lisbon</td><td>16.0</td><td>17.4</td><td>2.3</td><td>2.7</td><td>2.6</td><td>4.1</td><td>0.9</td><td>1.3</td><td>1.2</td><td>2.7</td></tr><tr><td>Ljubljana</td><td>8.8</td><td>10.4</td><td>2.9</td><td>3.5</td><td>3.3</td><td>5.4</td><td>1.3</td><td>1.9</td><td>1.7</td><td>3.8</td></tr><tr><td>Luxembourg</td><td>8.7</td><td>9.5</td><td>1.9</td><td>2.5</td><td>2.2</td><td>4.1</td><td>1.1</td><td>1.7</td><td>1.5</td><td>3.3</td></tr><tr><td>Madrid</td><td>13.9</td><td>15.0</td><td>2.5</td><td>3.0</td><td>3.0</td><td>5.0</td><td>1.4</td><td>1.9</td><td>1.9</td><td>3.9</td></tr><tr><td>Nicosia</td><td>18.5</td><td>18.4</td><td>1.2</td><td>1.8</td><td>1.7</td><td>3.6</td><td>1.2</td><td>1.8</td><td>1.8</td><td>3.7</td></tr><tr><td>Paris</td><td>11.5</td><td>12.3</td><td>1.8</td><td>2.3</td><td>2.1</td><td>3.9</td><td>1.0</td><td>1.5</td><td>1.4</td><td>3.1</td></tr><tr><td>Prague</td><td>8.8</td><td>9.5</td><td>1.7</td><td>2.3</td><td>2.2</td><td>4.0</td><td>1.1</td><td>1.7</td><td>1.5</td><td>3.3</td></tr><tr><td>Riga</td><td>6.1</td><td>7.2</td><td>2.5</td><td>3.2</td><td>3.0</td><td>4.7</td><td>1.4</td><td>2.1</td><td>1.8</td><td>3.6</td></tr><tr><td>Rome</td><td>15.2</td><td>15.9</td><td>1.8</td><td>2.2</td><td>2.1</td><td>3.8</td><td>1.1</td><td>1.5</td><td>1.4</td><td>3.1</td></tr><tr><td>Sofia</td><td>8.3</td><td>10.3</td><td>3.4</td><td>4.0</td><td>3.9</td><td>6.0</td><td>1.3</td><td>1.9</td><td>1.8</td><td>3.9</td></tr><tr><td>Stockholm</td><td>6.0</td><td>7.1</td><td>2.5</td><td>3.3</td><td>3.0</td><td>4.8</td><td>1.5</td><td>2.2</td><td>2.0</td><td>3.8</td></tr><tr><td>Tallinn</td><td>4.9</td><td>6.1</td><td>2.8</td><td>3.7</td><td>3.4</td><td>5.4</td><td>1.6</td><td>2.5</td><td>2.2</td><td>4.2</td></tr><tr><td>Valletta</td><td>16.1</td><td>17.9</td><td>3.3</td><td>4.0</td><td>3.9</td><td>6.2</td><td>1.4</td><td>2.2</td><td>2.1</td><td>4.3</td></tr><tr><td>Vienna</td><td>10.4</td><td>11.1</td><td>1.8</td><td>2.3</td><td>2.2</td><td>3.9</td><td>1.1</td><td>1.6</td><td>1.5</td><td>3.2</td></tr><tr><td>Vilnius</td><td>5.6</td><td>6.6</td><td>2.4</td><td>3.0</td><td>2.8</td><td>4.7</td><td>1.3</td><td>2.0</td><td>1.8</td><td>3.6</td></tr><tr><td>Warsaw</td><td>7.8</td><td>8.7</td><td>2.0</td><td>2.6</td><td>2.5</td><td>4.2</td><td>1.1</td><td>1.7</td><td>1.6</td><td>3.3</td></tr><tr><td>Zagreb</td><td>10.5</td><td>11.3</td><td>2.1</td><td>2.6</td><td>2.5</td><td>4.5</td><td>1.3</td><td>1.8</td><td>1.7</td><td>3.7</td></tr></tbody></table>

Source: Developed by authors.

Based on the analysis, the following observations can be made:

1. The most notable temperature differences occur between the reference scenario for old buildings and the 2085 projected RCP8.5 scenario.

2. Dublin consistently demonstrates lower temperature increases across all RCPs and year when compared to the two referenced scenarios. Nicosia follows a similar pattern, when considering the reference scenario for old buildings.

3. Under the reference scenario for old buildings Sofia and Valletta exhibit the most significant temperature rises across both analysed years and the two RCP scenarios. Similarly, Helsinki and Tallinn in Northern Europe, exhibit similar trends comparing the variations against the reference scenario for recent buildings
<table><caption>**Table 10.** Temperature disparities between the reference baseline temperature for old (constructed 1965) and recent buildings (constructed 2000) and the years 2050 and 2085 in European capital cities analysed under RCP4.5 and RCP8.5 scenarios.</caption><thead><tr><th rowspan="3">CC<br>scenarios<br>City /Year</th><th colspan="2">Old<br>buildings</th><th colspan="4">Old buildings<br>(constructed 1965)</th><th colspan="4">Recent buildings<br>(constructed 2000)</th></tr><tr><th rowspan="2">Baseline<br>Temp. (°C)</th><th rowspan="2">Baseline<br>Temp. (°C)</th><th colspan="2">RCP 4.5</th><th colspan="2">RCP 8.5</th><th colspan="2">RCP 4.5</th><th colspan="2">RCP 8.5</th></tr><tr><th>2050</th><th>2085</th><th>2050</th><th>2085</th><th>2050</th><th>2085</th><th>2050</th><th>2085</th></tr></thead><tbody><tr><td>Amsterdam</td><td>9.4</td><td>10.2</td><td>1.8</td><td>2.3</td><td>2.2</td><td>3.9</td><td>1.1</td><td>1.6</td><td>1.5</td><td>3.2</td></tr><tr><td>Athens</td><td>17.4</td><td>17.4</td><td>1.2</td><td>1.9</td><td>1.8</td><td>3.8</td><td>1.3</td><td>1.9</td><td>1.8</td><td>3.8</td></tr><tr><td>Berlin</td><td>9.1</td><td>9.8</td><td>1.8</td><td>2.5</td><td>2.3</td><td>4.0</td><td>1.1</td><td>1.7</td><td>1.5</td><td>3.2</td></tr><tr><td>Bratislava</td><td>9.6</td><td>10.8</td><td>2.3</td><td>2.9</td><td>2.7</td><td>4.6</td><td>1.1</td><td>1.7</td><td>1.5</td><td>3.3</td></tr><tr><td>Brussels</td><td>10.1</td><td>10.8</td><td>1.7</td><td>2.3</td><td>2.1</td><td>3.7</td><td>1.0</td><td>1.5</td><td>1.3</td><td>3.0</td></tr><tr><td>Bucharest</td><td>10.9</td><td>11.0</td><td>1.3</td><td>1.9</td><td>1.8</td><td>3.7</td><td>1.2</td><td>1.8</td><td>1.7</td><td>3.6</td></tr><tr><td>Budapest</td><td>10.7</td><td>11.7</td><td>2.1</td><td>2.6</td><td>2.5</td><td>4.3</td><td>1.1</td><td>1.7</td><td>1.5</td><td>3.4</td></tr><tr><td>Copenhagen</td><td>8.6</td><td>9.2</td><td>1.7</td><td>2.3</td><td>2.1</td><td>3.7</td><td>1.1</td><td>1.7</td><td>1.5</td><td>3.1</td></tr><tr><td>Dublin</td><td>9.2</td><td>9.8</td><td>1.3</td><td>1.8</td><td>1.6</td><td>3.0</td><td>0.8</td><td>1.3</td><td>1.1</td><td>2.4</td></tr><tr><td>Helsinki</td><td>5.0</td><td>5.7</td><td>2.5</td><td>3.3</td><td>3.0</td><td>5.0</td><td>1.7</td><td>2.5</td><td>2.2</td><td>4.3</td></tr><tr><td>Lisbon</td><td>16.0</td><td>17.4</td><td>2.3</td><td>2.7</td><td>2.6</td><td>4.1</td><td>0.9</td><td>1.3</td><td>1.2</td><td>2.7</td></tr><tr><td>Ljubljana</td><td>8.8</td><td>10.4</td><td>2.9</td><td>3.5</td><td>3.3</td><td>5.4</td><td>1.3</td><td>1.9</td><td>1.7</td><td>3.8</td></tr><tr><td>Luxembourg</td><td>8.7</td><td>9.5</td><td>1.9</td><td>2.5</td><td>2.2</td><td>4.1</td><td>1.1</td><td>1.7</td><td>1.5</td><td>3.3</td></tr><tr><td>Madrid</td><td>13.9</td><td>15.0</td><td>2.5</td><td>3.0</td><td>3.0</td><td>5.0</td><td>1.4</td><td>1.9</td><td>1.9</td><td>3.9</td></tr><tr><td>Nicosia</td><td>18.5</td><td>18.4</td><td>1.2</td><td>1.8</td><td>1.7</td><td>3.6</td><td>1.2</td><td>1.8</td><td>1.8</td><td>3.7</td></tr><tr><td>Paris</td><td>11.5</td><td>12.3</td><td>1.8</td><td>2.3</td><td>2.1</td><td>3.9</td><td>1.0</td><td>1.5</td><td>1.4</td><td>3.1</td></tr><tr><td>Prague</td><td>8.8</td><td>9.5</td><td>1.7</td><td>2.3</td><td>2.2</td><td>4.0</td><td>1.1</td><td>1.7</td><td>1.5</td><td>3.3</td></tr><tr><td>Riga</td><td>6.1</td><td>7.2</td><td>2.5</td><td>3.2</td><td>3.0</td><td>4.7</td><td>1.4</td><td>2.1</td><td>1.8</td><td>3.6</td></tr><tr><td>Rome</td><td>15.2</td><td>15.9</td><td>1.8</td><td>2.2</td><td>2.1</td><td>3.8</td><td>1.1</td><td>1.5</td><td>1.4</td><td>3.1</td></tr><tr><td>Sofia</td><td>8.3</td><td>10.3</td><td>3.4</td><td>4.0</td><td>3.9</td><td>6.0</td><td>1.3</td><td>1.9</td><td>1.8</td><td>3.9</td></tr><tr><td>Stockholm</td><td>6.0</td><td>7.1</td><td>2.5</td><td>3.3</td><td>3.0</td><td>4.8</td><td>1.5</td><td>2.2</td><td>2.0</td><td>3.8</td></tr><tr><td>Tallinn</td><td>4.9</td><td>6.1</td><td>2.8</td><td>3.7</td><td>3.4</td><td>5.4</td><td>1.6</td><td>2.5</td><td>2.2</td><td>4.2</td></tr><tr><td>Valletta</td><td>16.1</td><td>17.9</td><td>3.3</td><td>4.0</td><td>3.9</td><td>6.2</td><td>1.4</td><td>2.2</td><td>2.1</td><td>4.3</td></tr><tr><td>Vienna</td><td>10.4</td><td>11.1</td><td>1.8</td><td>2.3</td><td>2.2</td><td>3.9</td><td>1.1</td><td>1.6</td><td>1.5</td><td>3.2</td></tr><tr><td>Vilnius</td><td>5.6</td><td>6.6</td><td>2.4</td><td>3.0</td><td>2.8</td><td>4.7</td><td>1.3</td><td>2.0</td><td>1.8</td><td>3.6</td></tr><tr><td>Warsaw</td><td>7.8</td><td>8.7</td><td>2.0</td><td>2.6</td><td>2.5</td><td>4.2</td><td>1.1</td><td>1.7</td><td>1.6</td><td>3.3</td></tr><tr><td>Zagreb</td><td>10.5</td><td>11.3</td><td>2.1</td><td>2.6</td><td>2.5</td><td>4.5</td><td>1.3</td><td>1.8</td><td>1.7</td><td>3.7</td></tr></tbody></table>

<figure><img src="image_28.png" /></figure>

Source: Developed by authors.

Based on the analysis, the following observations can be made:

1. The most notable temperature differences occur between the reference scenario for old buildings and the 2085 projected RCP8.5 scenario.

2. Dublin consistently demonstrates lower temperature increases across all RCPs and year when compared to the two referenced scenarios. Nicosia follows a similar pattern, when considering the reference scenario for old buildings.

3. Under the reference scenario for old buildings Sofia and Valletta exhibit the most significant temperature rises across both analysed years and the two RCP scenarios. Similarly, Helsinki and Tallinn in Northern Europe, exhibit similar trends comparing the variations against the reference scenario for recent buildings
4. It is worth noting that, under RCP8.5, and by 2085, temperature variations exceeding 5.0°C are observed in cities like Valletta, Tallinn, Sofia, Madrid, Ljubljana, and Helsinki within the older buildings group reference scenario. Valletta and Sofia notably peak at 6.2°C and 6.0°C, respectively. These temperature differences consistently exceed 4.0°C when compared to the reference scenario of old and recent buildings in Valletta, Tallinn, and Helsinki in the South and Northern Europe by the year 2085.
4. It is worth noting that, under RCP8.5, and by 2085, temperature variations exceeding 5.0°C are observed in cities like Valletta, Tallinn, Sofia, Madrid, Ljubljana, and Helsinki within the older buildings group reference scenario. Valletta and Sofia notably peak at 6.2°C and 6.0°C, respectively. These temperature differences consistently exceed 4.0°C when compared to the reference scenario of old and recent buildings in Valletta, Tallinn, and Helsinki in the South and Northern Europe by the year 2085.
# 5 Characterization of the European building stock

## 5.1 Buildings inventory

The inventory of building stock was based on a database produced for a European pilot project led by the Joint Research Centre (JRC) called 'Integrated techniques for the seismic strengthening and energy efficiency of existing buildings' or simply REEBUILD (Gkatzogias et al., 2022). The database is georeferenced and encompasses the number of residential and commercial buildings for the EU-27 countries, grouped in classes established to describe the seismic performance of buildings.

The basis to produce the REEBUILD database was the seismic exposure model of the European Seismic Risk Model 2020 (Crowley et al., 2021), from the European Facilities for Earthquake Hazard and Risk (EFEHR<sup>76</sup>, © Eucentre Foundation, 2022) as detailed in Gkatzogias et al. (2022). The exposure model includes the spatial distribution of the number of residential and commercial buildings, dwellings, occupants, floor area, settlement type and replacement cost within the EU-27 for defined seismic performance classes. The model was originally developed to characterize seismic vulnerability of buildings to perform regional seismic risk assessments, therefore, not responding directly to the distinctive features of the present study. Nevertheless, it allows to perform an analysis based on the location of buildings in the EU-27 countries with focus on reinforced concrete (RC) buildings selected from the database. Furthermore, the database provides information not only about residential buildings, as in housing censuses, but also on commercial buildings. The building stock database was further extended to characterise the vulnerability to corrosion of reinforced concrete buildings at a regional scale (section 5.2).

The exposure model uses an updated version (Silva et al., 2022) of the Global earthquake Model (GEM) Building Taxonomy<sup>77</sup> (Brzev et al., 2013) that sorts buildings according to several attributes. The attributes used in this work were (i) number of stories of buildings, (ii) settlement type where buildings are located within each country, (iii) occupancy type, (iv) main construction material, (v) lateral load resisting system, (vi) seismic design code level, (vii) number of dwellings per building and (viii) average area per dwelling for certain building types. Table 11 describes the instances in the database of the first six attributes mentioned above.

The settlement type for residential buildings, i.e., urban, rural areas and big cities, is used to differentiate the exposed area in buildings and consequently the carbonation costs (see section 3.3.2). Overall, 62+ big cities were listed in the residential exposure model (Crowley et al, 2021).

Commercial buildings cover three types: offices, wholesale and retail trade, and hotels and restaurants. Mixed use buildings, i.e., buildings with both commercial and residential use, were thought to be part of the residential building stock data.

<table><caption>**Table 11.** Attributes in the exposure model (Crowley et al, 2021, Gkatzogias et al., 2022)</caption><thead><tr><td>Number of storeys</td><td>Settlement type</td><td>Occupancy type</td></tr></thead><tbody><tr><td>Low rise(1, 2, 3, 4 floors)</td><td>Rural</td><td>Residential</td></tr><tr><td>Mid rise (5, 6, 7 floors)</td><td>Urban</td><td>Commercial</td></tr><tr><td>High rise(8 -20 floors)</td><td>Big cities</td><td></td></tr></tbody></table>

Source: Developed by authors.

<footnote n="76"><sup>76</sup> http://risk.efehr.org</footnote>

<footnote n="77"><sup>77</sup> https://github.com/gem/gem_taxonomy</footnote>
# 5 Characterization of the European building stock

## 5.1 Buildings inventory

The inventory of building stock was based on a database produced for a European pilot project led by the Joint Research Centre (JRC) called 'Integrated techniques for the seismic strengthening and energy efficiency of existing buildings' or simply REEBUILD (Gkatzogias et al., 2022). The database is georeferenced and encompasses the number of residential and commercial buildings for the EU-27 countries, grouped in classes established to describe the seismic performance of buildings.

The basis to produce the REEBUILD database was the seismic exposure model of the European Seismic Risk Model 2020 (Crowley et al., 2021), from the European Facilities for Earthquake Hazard and Risk (EFEHR<sup>76</sup>, © Eucentre Foundation, 2022) as detailed in Gkatzogias et al. (2022). The exposure model includes the spatial distribution of the number of residential and commercial buildings, dwellings, occupants, floor area, settlement type and replacement cost within the EU-27 for defined seismic performance classes. The model was originally developed to characterize seismic vulnerability of buildings to perform regional seismic risk assessments, therefore, not responding directly to the distinctive features of the present study. Nevertheless, it allows to perform an analysis based on the location of buildings in the EU-27 countries with focus on reinforced concrete (RC) buildings selected from the database. Furthermore, the database provides information not only about residential buildings, as in housing censuses, but also on commercial buildings. The building stock database was further extended to characterise the vulnerability to corrosion of reinforced concrete buildings at a regional scale (section 5.2).

The exposure model uses an updated version (Silva et al., 2022) of the Global earthquake Model (GEM) Building Taxonomy<sup>77</sup> (Brzev et al., 2013) that sorts buildings according to several attributes. The attributes used in this work were (i) number of stories of buildings, (ii) settlement type where buildings are located within each country, (iii) occupancy type, (iv) main construction material, (v) lateral load resisting system, (vi) seismic design code level, (vii) number of dwellings per building and (viii) average area per dwelling for certain building types. Table 11 describes the instances in the database of the first six attributes mentioned above.

The settlement type for residential buildings, i.e., urban, rural areas and big cities, is used to differentiate the exposed area in buildings and consequently the carbonation costs (see section 3.3.2). Overall, 62+ big cities were listed in the residential exposure model (Crowley et al, 2021).

Commercial buildings cover three types: offices, wholesale and retail trade, and hotels and restaurants. Mixed use buildings, i.e., buildings with both commercial and residential use, were thought to be part of the residential building stock data.

<table><caption>**Table 11.** Attributes in the exposure model (Crowley et al, 2021, Gkatzogias et al., 2022)</caption><thead><tr><td>Number of storeys</td><td>Settlement type</td><td>Occupancy type</td></tr></thead><tbody><tr><td>Low rise(1, 2, 3, 4 floors)</td><td>Rural</td><td>Residential</td></tr><tr><td>Mid rise (5, 6, 7 floors)</td><td>Urban</td><td>Commercial</td></tr><tr><td>High rise(8 -20 floors)</td><td>Big cities</td><td></td></tr></tbody></table>

Source: Developed by authors.

<footnote n="76"><sup>76</sup> http://risk.efehr.org</footnote>

<footnote n="77"><sup>77</sup> https://github.com/gem/gem_taxonomy</footnote>
<table><thead><tr><td>Main construction material</td></tr></thead><tbody><tr><td>Reinforced concrete (RC)</td></tr><tr><td>Steel</td></tr><tr><td>Reinforced masonry</td></tr><tr><td>Confined masonry</td></tr><tr><td>Unreinforced masonry (concrete block, clay brick, dressed stone, rubble stone, adobe)</td></tr><tr><td>Wood</td></tr></tbody></table>

<table><thead><tr><td>Lateral load resisting system</td></tr></thead><tbody><tr><td>Dual frame-wall system</td></tr><tr><td>Infilled frame</td></tr><tr><td>Load bearing wall</td></tr><tr><td>Moment frame</td></tr><tr><td>Braced frame</td></tr><tr><td>Flat slab/plate or waffle slab</td></tr><tr><td>Post and beam</td></tr></tbody></table>

<table><thead><tr><td>Seismic design code level</td></tr></thead><tbody><tr><td>C-N - Pre-code (absence of seismic design)</td></tr><tr><td>C-L - Low code (buildings designed for lateral resistance using allowable stress method)</td></tr><tr><td>C-M - Moderate code (buildings designed for lateral resistance limit state method)</td></tr><tr><td>C-H - High code (buildings designed for lateral resistance coupled with target ductility requirements / capacity design)</td></tr></tbody></table>

Source: Developed by authors.

Although if REEBUILD database does not provide direct information on the date of construction of buildings, the age of a building is crucial, as different construction technologies and durability parameters are associated with different years of construction. The year of construction also indicates the share of existing buildings that have exhausted their design service life, which is considered to be 50 years for ordinary buildings. Based on the 2011 Population and Housing Census of the European Statistical System<sup>78</sup> and other database used<sup>79</sup>, recent 2023 REEBUILD reports (Gkatzogias et al., 2023 and Romano et al., 2023) revealed that nearly 80% of EU dwellings were built before 1990, with over 20% built before 1945. Residential buildings make up almost the entire segment of dwellings in Europe, accounting for 98.5% in the EU. Most dwellings, more than 50%, are in multi-family houses with at least three dwellings per building, while 40% of dwellings are in single-family houses. The study reveals that most of the EU-27 consists of masonry buildings, with Portugal, Cyprus, and Greece having a higher proportion of reinforced concrete (RC) constructions. Additionally, other countries also have significant shares of RC buildings. Timber buildings are predominantly found in Sweden, Finland, Germany, and Romania. The study highlighted that reinforced concrete buildings play a significant role in Europe's overall building landscape (Ozcebe et al., 2014, Romano et al., 2023).

For the purpose of this study, the construction period of the buildings can be roughly estimated from the dates of entry in force of seismic design standards in EU-27 countries. To make the analysis for all European countries simpler, the reinforced concrete buildings were aggregated in two major groups to reflect their age, called 'old' and 'recent', as explained in section 3.1. In each group, buildings durability is characterized in terms of recommendations of construction standards, literature review and expert knowledge (see section 5.2). As a reminder, the 'old buildings' group includes all reinforced concrete buildings constructed before 2000. Buildings

<footnote n="78"><sup>78</sup> European Statistical System (ESS) by means of the Census Hub web-channel, https://ec.europa.eu/CensusHub2</footnote>

<footnote n="79"><sup>79</sup> Other database for building inventory used REEBUILD: EPISCOPETABULA (https://episcope.eu/welcome/); World Housing Encyclopedia (http://db.world-housing.net); GED4GEM; US Geological Surveys (USGS) Prompt Assessment of Global Earthquakes for Response (PAGER) building inventory database (Jaiswal and Wald, 2008); GEM Building Taxonomy (https://github.com/gem/gem_taxonomy); INSPIRE (Birchall et al., 2014); ENTRANZE project specifically for Croatia (ENTRANZE and Enerdata, 2008a, b)</footnote>
<table><thead><tr><td>Main construction material</td></tr></thead><tbody><tr><td>Reinforced concrete (RC)</td></tr><tr><td>Steel</td></tr><tr><td>Reinforced masonry</td></tr><tr><td>Confined masonry</td></tr><tr><td>Unreinforced masonry (concrete block, clay brick, dressed stone, rubble stone, adobe)</td></tr><tr><td>Wood</td></tr></tbody></table>

<table><thead><tr><td>Lateral load resisting system</td></tr></thead><tbody><tr><td>Dual frame-wall system</td></tr><tr><td>Infilled frame</td></tr><tr><td>Load bearing wall</td></tr><tr><td>Moment frame</td></tr><tr><td>Braced frame</td></tr><tr><td>Flat slab/plate or waffle slab</td></tr><tr><td>Post and beam</td></tr></tbody></table>

<table><thead><tr><td>Seismic design code level</td></tr></thead><tbody><tr><td>C-N - Pre-code (absence of seismic design)</td></tr><tr><td>C-L - Low code (buildings designed for lateral resistance using allowable stress method)</td></tr><tr><td>C-M - Moderate code (buildings designed for lateral resistance limit state method)</td></tr><tr><td>C-H - High code (buildings designed for lateral resistance coupled with target ductility requirements / capacity design)</td></tr></tbody></table>

Source: Developed by authors.

Although if REEBUILD database does not provide direct information on the date of construction of buildings, the age of a building is crucial, as different construction technologies and durability parameters are associated with different years of construction. The year of construction also indicates the share of existing buildings that have exhausted their design service life, which is considered to be 50 years for ordinary buildings. Based on the 2011 Population and Housing Census of the European Statistical System<sup>78</sup> and other database used<sup>79</sup>, recent 2023 REEBUILD reports (Gkatzogias et al., 2023 and Romano et al., 2023) revealed that nearly 80% of EU dwellings were built before 1990, with over 20% built before 1945. Residential buildings make up almost the entire segment of dwellings in Europe, accounting for 98.5% in the EU. Most dwellings, more than 50%, are in multi-family houses with at least three dwellings per building, while 40% of dwellings are in single-family houses. The study reveals that most of the EU-27 consists of masonry buildings, with Portugal, Cyprus, and Greece having a higher proportion of reinforced concrete (RC) constructions. Additionally, other countries also have significant shares of RC buildings. Timber buildings are predominantly found in Sweden, Finland, Germany, and Romania. The study highlighted that reinforced concrete buildings play a significant role in Europe's overall building landscape (Ozcebe et al., 2014, Romano et al., 2023).

For the purpose of this study, the construction period of the buildings can be roughly estimated from the dates of entry in force of seismic design standards in EU-27 countries. To make the analysis for all European countries simpler, the reinforced concrete buildings were aggregated in two major groups to reflect their age, called 'old' and 'recent', as explained in section 3.1. In each group, buildings durability is characterized in terms of recommendations of construction standards, literature review and expert knowledge (see section 5.2). As a reminder, the 'old buildings' group includes all reinforced concrete buildings constructed before 2000. Buildings

<footnote n="78"><sup>78</sup> European Statistical System (ESS) by means of the Census Hub web-channel, https://ec.europa.eu/CensusHub2</footnote>

<footnote n="79"><sup>79</sup> Other database for building inventory used REEBUILD: EPISCOPETABULA (https://episcope.eu/welcome/); World Housing Encyclopedia (http://db.world-housing.net); GED4GEM; US Geological Surveys (USGS) Prompt Assessment of Global Earthquakes for Response (PAGER) building inventory database (Jaiswal and Wald, 2008); GEM Building Taxonomy (https://github.com/gem/gem_taxonomy); INSPIRE (Birchall et al., 2014); ENTRANZE project specifically for Croatia (ENTRANZE and Enerdata, 2008a, b)</footnote>
are assumed to be built in 1965, to model corrosion deterioration over their lifetime. The 'recent buildings' group includes the remaining buildings where ageing effects started to be considered in 2000. This simplified consideration of the age of concrete buildings aims to make an initial assessment of the significance of the corrosion phenomenon for the European climatic conditions. The same study can be repeated with more details about the buildings age for the countries the most impacted by the corrosion due to carbonation.

The resolution of the exposure model was similar to the administrative level at which the data were collected, thus being different for each country. Calculations were made using the highest resolution available in the REEBUILD database. The data and results in this report are listed or mapped mostly at a country level to allow general conclusions of the expected corrosion due to carbonation of the concrete cover.

Figure 22 presents the number of buildings per km² in the 27 EU member states, according to the REEBUILD database, and the distribution of buildings in each country into four classes: old residential, old commercial, new residential and new commercial buildings. Spain, Greece and Romania present the largest number of new residential buildings. The buildings inventory in Malta shows a big proportion of old commercial buildings.

It is important to note, that based on the information in the database, it was only possible to classify the building stock as part of the recent buildings group in seven countries. These countries are Austria, Germany, Greece, Hungary, Portugal, Romania, and Spain. The classification of buildings in the other countries does not take into account recent buildings. This data limitation will likely yield a conservative estimation in carbonation damage. Yet, as the durability parameters were known for all countries, it was possible to access the expected damage from corrosion to recent buildings and the time remaining to repair for these buildings across all European countries. However, when accounting for exposure, like in the calculation carbonation damage for a NUTS 3 level (see Figure 32) or in assessing repair costs, the estimation was made considering recent buildings only in the above mentioned seven countries.

<figure><img src="image_27.png" /><figcaption>**Figure 22.** Density of RC buildings in the 27 Member States; pie graphs showing the distribution of buildings occupancy type (residential or commercial buildings) crosswise with age group (old or recent) in each Member State.</figcaption></figure>
are assumed to be built in 1965, to model corrosion deterioration over their lifetime. The 'recent buildings' group includes the remaining buildings where ageing effects started to be considered in 2000. This simplified consideration of the age of concrete buildings aims to make an initial assessment of the significance of the corrosion phenomenon for the European climatic conditions. The same study can be repeated with more details about the buildings age for the countries the most impacted by the corrosion due to carbonation.

The resolution of the exposure model was similar to the administrative level at which the data were collected, thus being different for each country. Calculations were made using the highest resolution available in the REEBUILD database. The data and results in this report are listed or mapped mostly at a country level to allow general conclusions of the expected corrosion due to carbonation of the concrete cover.

Figure 22 presents the number of buildings per km² in the 27 EU member states, according to the REEBUILD database, and the distribution of buildings in each country into four classes: old residential, old commercial, new residential and new commercial buildings. Spain, Greece and Romania present the largest number of new residential buildings. The buildings inventory in Malta shows a big proportion of old commercial buildings.

It is important to note, that based on the information in the database, it was only possible to classify the building stock as part of the recent buildings group in seven countries. These countries are Austria, Germany, Greece, Hungary, Portugal, Romania, and Spain. The classification of buildings in the other countries does not take into account recent buildings. This data limitation will likely yield a conservative estimation in carbonation damage. Yet, as the durability parameters were known for all countries, it was possible to access the expected damage from corrosion to recent buildings and the time remaining to repair for these buildings across all European countries. However, when accounting for exposure, like in the calculation carbonation damage for a NUTS 3 level (see Figure 32) or in assessing repair costs, the estimation was made considering recent buildings only in the above mentioned seven countries.

<figure><img src="image_29.png" /><figcaption>**Figure 22.** Density of RC buildings in the 27 Member States; pie graphs showing the distribution of buildings occupancy type (residential or commercial buildings) crosswise with age group (old or recent) in each Member State.</figcaption></figure>
## 5.2 Durability parameters of RC buildings

The durability parameters of the European concrete buildings, used in this study, were established after exploring the prescriptions of construction standards as well as the historical evolution of national standards. In addition, specialised literature was analysed, particularly for the group of old buildings, and expert knowledge was considered. The durability parameters needed to implement the chosen model for assessment of corrosion induced by carbonation (see section 2.3) are: (i) concrete cover thickness, $c_t$, (ii) water/cement ratio, w/c, and (iii) minimum cement content, $C_e$.

The definition of the thickness of the concrete cover in this study is consistent with the clause 4.4.1.1(1) of EN 1992-1-1: 'The concrete cover is the distance between the surface of the reinforcement closest to the nearest concrete surface (including links and stirrups and surface reinforcement where relevant) and the nearest concrete surface'.

The JRC Eurocodes Nationally Determined Parameters (NDPs) Database was consulted for the concrete durability parameters, for instance regarding the national choices related to concrete cover in EN 1992-1-1:2004.

### Old buildings

A survey focused on the durability parameters for old RC buildings was carried out among participants of the JRC network on the adaptation of structural design to climate change and other experts. Furthermore, specialised literature was consulted (Kappos et al., 2003, Kyriakides and Chrysostomou, 2012, Masi et al., 2019, Vacareanu et al., 2004). The process gleaned insights into national practices and/or standards provisions for old RC buildings in Cyprus, France, Greece, Italy, the Netherlands, Portugal and Romania.

The left parts of Figure 23 to Figure 25 show the values of the durability parameters adopted in the current study for old buildings. In terms of the concrete cover, not enough information was found to differentiate between the UE-27 countries.

### Recent buildings

Durability parameters for the group of buildings aggregating recent RC construction have been established considering the provisions of EN 206-1:2000, and the information provided in CEN technical report CEN/TR 15868:2009. CEN technical report TR 15868, from 2009 provides a summary of national requirements used with EN 206-1:2000, i.e., a picture of the practical application of these standards. The provisions of EN 206:2013 and the respective implementation study CEN/TR 15868:2018 are deemed to provide less representative information for the considered period of construction of the recent buildings (after 2000), and were not taken into account. Moreover, this study focuses on the national provisions for the exposure class XC4, i.e., *cyclic and wet-dry* environmental conditions, for an intended working life of at least 50 years. CEN technical report CEN/TR 15868:2009 provides information on the national provisions for the minimum concrete cover $c_{min}$. The thickness of the concrete cover $c_t$ is then determined as the nominal concrete cover specified in EN 1992-1-1, clause 4.4.1.1(2). There, equation 4.1 implies adding to the minimum concrete cover $c_{min}$ an allowance in design for deviation, $\Delta c_{dev}$, whose recommended value is 10mm according to EN 1992-1-1, clause 4.4.1.3(1). In this study $\Delta c_{dev}$ is considered equal to 10mm for all countries.

The survey to prepare the report CEN/TR 15868:2009 was carried out among all CEN members, however only 14 out of 27 considered in this study countries responded. In this study the durability parameters for the countries on which no information was available, were taken to be the default values of the parameters specified in EN 206-1:2000 or in some cases - the values adopted by neighbouring countries, mainly when the default value was very different from the latter. Table 12 lists the concrete durability conditions assumed for the countries, for which no specific information was found.

<table><caption>**Table 12.** Default durability parameters.</caption><thead><tr><td>Parameter</td><td>Old buildings</td><td>Recent buildings</td></tr></thead><tbody><tr><td>c<sub>t</sub> (mm)</td><td>20</td><td>40</td></tr><tr><td>w/c</td><td>0.50</td><td>0.50</td></tr><tr><td>C<sub>e</sub> (kg/m<sup>3</sup>)</td><td>250-280</td><td>300</td></tr></tbody></table>

Source: Developed by authors.
## 5.2 Durability parameters of RC buildings

The durability parameters of the European concrete buildings, used in this study, were established after exploring the prescriptions of construction standards as well as the historical evolution of national standards. In addition, specialised literature was analysed, particularly for the group of old buildings, and expert knowledge was considered. The durability parameters needed to implement the chosen model for assessment of corrosion induced by carbonation (see section 2.3) are: (i) concrete cover thickness, $c_t$, (ii) water/cement ratio, w/c, and (iii) minimum cement content, $C_e$.

The definition of the thickness of the concrete cover in this study is consistent with the clause 4.4.1.1(1) of EN 1992-1-1: 'The concrete cover is the distance between the surface of the reinforcement closest to the nearest concrete surface (including links and stirrups and surface reinforcement where relevant) and the nearest concrete surface'.

The JRC Eurocodes Nationally Determined Parameters (NDPs) Database was consulted for the concrete durability parameters, for instance regarding the national choices related to concrete cover in EN 1992-1-1:2004.

### Old buildings

A survey focused on the durability parameters for old RC buildings was carried out among participants of the JRC network on the adaptation of structural design to climate change and other experts. Furthermore, specialised literature was consulted (Kappos et al., 2003, Kyriakides and Chrysostomou, 2012, Masi et al., 2019, Vacareanu et al., 2004). The process gleaned insights into national practices and/or standards provisions for old RC buildings in Cyprus, France, Greece, Italy, the Netherlands, Portugal and Romania.

The left parts of Figure 23 to Figure 25 show the values of the durability parameters adopted in the current study for old buildings. In terms of the concrete cover, not enough information was found to differentiate between the UE-27 countries.

### Recent buildings

Durability parameters for the group of buildings aggregating recent RC construction have been established considering the provisions of EN 206-1:2000, and the information provided in CEN technical report CEN/TR 15868:2009. CEN technical report TR 15868, from 2009 provides a summary of national requirements used with EN 206-1:2000, i.e., a picture of the practical application of these standards. The provisions of EN 206:2013 and the respective implementation study CEN/TR 15868:2018 are deemed to provide less representative information for the considered period of construction of the recent buildings (after 2000), and were not taken into account. Moreover, this study focuses on the national provisions for the exposure class XC4, i.e., *cyclic and wet-dry* environmental conditions, for an intended working life of at least 50 years. CEN technical report CEN/TR 15868:2009 provides information on the national provisions for the minimum concrete cover $c_{min}$. The thickness of the concrete cover $c_t$ is then determined as the nominal concrete cover specified in EN 1992-1-1, clause 4.4.1.1(2). There, equation 4.1 implies adding to the minimum concrete cover $c_{min}$ an allowance in design for deviation, $\Delta c_{dev}$, whose recommended value is 10mm according to EN 1992-1-1, clause 4.4.1.3(1). In this study $\Delta c_{dev}$ is considered equal to 10mm for all countries.

The survey to prepare the report CEN/TR 15868:2009 was carried out among all CEN members, however only 14 out of 27 considered in this study countries responded. In this study the durability parameters for the countries on which no information was available, were taken to be the default values of the parameters specified in EN 206-1:2000 or in some cases - the values adopted by neighbouring countries, mainly when the default value was very different from the latter. Table 12 lists the concrete durability conditions assumed for the countries, for which no specific information was found.

<table><caption>**Table 12.** Default durability parameters.</caption><thead><tr><td>Parameter</td><td>Old buildings</td><td>Recent buildings</td></tr></thead><tbody><tr><td>c<sub>t</sub> (mm)</td><td>20</td><td>40</td></tr><tr><td>w/c</td><td>0.50</td><td>0.50</td></tr><tr><td>C<sub>e</sub> (kg/m<sup>3</sup>)</td><td>250-280</td><td>300</td></tr></tbody></table>

Source: Developed by authors.
The adopted in the study durability parameters for recent buildings are illustrated in the right parts of Figure 23 to Figure 25. To note, that the durability parameters might be further detailed according to the structural class, when considering building stock with well-established characteristics. A summary of the considered durability parameters for exposure class XC4 and for an intended working life of at least 50 years, used in the calculations are shown in detail in Table 1 in Annex 4.

<figure><img src="image_28.png" /><figcaption>**Figure 24.** Water-to-cement (w/c) ratio, adopted for the 27 Member States; left: old buildings, right: recent buildings.</figcaption></figure>

<figure><img src="image_29.png" /></figure>
The adopted in the study durability parameters for recent buildings are illustrated in the right parts of Figure 23 to Figure 25. To note, that the durability parameters might be further detailed according to the structural class, when considering building stock with well-established characteristics. A summary of the considered durability parameters for exposure class XC4 and for an intended working life of at least 50 years, used in the calculations are shown in detail in Table 1 in Annex 4.

<figure><img src="image_30.png" /><figcaption>**Figure 24.** Water-to-cement (w/c) ratio, adopted for the 27 Member States; left: old buildings, right: recent buildings.</figcaption></figure>

<figure><img src="image_31.png" /></figure>
<figure><img src="image_30.png" /><figcaption>**Figure 25.** Cement content, C<sub>e</sub> (kg/m<sup>3</sup>), adopted for the 27 Member States; left: old buildings, right: recent buildings.</figcaption></figure>

Regarding the water-to-cement ratio, there is no difference between the considered values for old and recent buildings. However, for this variable, a grouping of the European countries into three clusters is visible, i.e., a group in the south-west (w/c=0.6), another in the south-east (w/c=0.5), and a third cluster of countries in northern Europe (w/c=0.55).

For every country and building group, the uncertainty related with the concrete cover thickness, used to evaluate the probability of corrosion initiation in equation (11), was assumed to be equal to ±0.5ΔC<sub>dev</sub>, where ΔC<sub>dev</sub> is the allowance in design for deviation, whose value is considered equal to 10mm for all countries. In other words, it was assumed c<sub>t,max</sub> – c<sub>t,min</sub> = 10 mm.

In summary, the old building group is representative of buildings designed and constructed prior to the date of enforcement of modern codes (e.g. EN 206-1:2000), assuming 1965 as the construction date for the corrosion assessment. The recent buildings group assumes a construction date of 2000 for assessing the corrosion process.
<figure><img src="image_32.png" /><figcaption>**Figure 25.** Cement content, C<sub>e</sub> (kg/m<sup>3</sup>), adopted for the 27 Member States; left: old buildings, right: recent buildings.</figcaption></figure>

Regarding the water-to-cement ratio, there is no difference between the considered values for old and recent buildings. However, for this variable, a grouping of the European countries into three clusters is visible, i.e., a group in the south-west (w/c=0.6), another in the south-east (w/c=0.5), and a third cluster of countries in northern Europe (w/c=0.55).

For every country and building group, the uncertainty related with the concrete cover thickness, used to evaluate the probability of corrosion initiation in equation (11), was assumed to be equal to ±0.5ΔC<sub>dev</sub>, where ΔC<sub>dev</sub> is the allowance in design for deviation, whose value is considered equal to 10mm for all countries. In other words, it was assumed c<sub>t,max</sub> – c<sub>t,min</sub> = 10 mm.

In summary, the old building group is representative of buildings designed and constructed prior to the date of enforcement of modern codes (e.g. EN 206-1:2000), assuming 1965 as the construction date for the corrosion assessment. The recent buildings group assumes a construction date of 2000 for assessing the corrosion process.
# 6 Climate change impact on the European reinforced concrete building stock

## 6.1 Overview of data and models used

This section recaps and summarizes the procedures and data used to assess the impact of climate change on the reinforced concrete European building stock, with specific references to the models and data presented throughout the report.

The models adopted for evaluating corrosion damage, and for the associated repair costs, are presented in Chapters 2 and 3, respectively. Chapter 4 describes the sources of climate data, while Chapter 5 focuses on the inventory of the reinforced concrete European building stock and the characterization of its durability parameters.

The simplified model adopted for calculating carbonation depth is presented by expression 6 in Chapter 2. In essence, the model delineates the progression of carbonation depth from an initial year, accounting for the durability characteristics of buildings and environmental conditions to which these buildings are exposed over time. It requires two atmospheric forcing variables as inputs: the atmospheric CO₂ concentration and mean air temperature, both of which change over time.

The two-step process for evaluating carbonation costs is outlined in Chapter 3. Firstly, the probability of corrosion initiation (as per equation (11)) is estimated, which is defined as the probability of carbonation depth reaching the concrete cover. The median of this probability distribution determines the year when the first structural repair due to carbonation is required. Secondly, the data of the building stock exposure and building characteristics is used to estimate the exposed building surface (expression (16)) and the respective total repair costs. The assessment of the cost of repair of the damage due to carbonation is performed under the assumption that the repair takes place before the loss of cross-sectional area of reinforcement bars becomes significant, i.e. the considered option is Repair 2 (corrective), described in section 3.2.2. It is assumed that only one repair takes place at the start of the corrosion, and for this reason the associated repair costs are considered as total, and not as cumulative. The study considers uniform price of repair of 293.6 €/m² for all countries. The calculated total repair costs are normalised by the time span from 2020 until the time for the repair, to evaluate the annual repair costs, which are compared to the Gross Domestic Product.

The study presents climate data sources in Chapter 4, derived from 11 bias-adjusted climate projections obtained through the EURO-CORDEX initiative. The employed Regional Climate Models (RCMs) are run over the European continent at a resolution of 0.11°. The projections until 2100 are forced by two Representative Concentration Pathways (RCP), specifically, RCP4.5 and RCP8.5, adjusted for bias using the observational data set E-OBSv10. Figure 9 in Chapter 4 illustrates the evolution of CO₂ atmospheric concentrations, considering historical and two climate-altering gases future scenarios, RCP4.5 and RCP8.5, spanning the historical data period from 1765 to 2005 and the two climate projections until 2100. The study evaluates the impact of climate change on the carbonation depth of European buildings by comparing future climate scenarios with baseline or reference scenarios, as detailed in section 4.3.2. This analysis includes variations in annual temperatures for these scenarios, illustrated for the 27 EU capital cities in Annex 1, and extends to all European Union countries with a 0.11° grid resolution, covering the entire region.

The inventory of the European building stock is based on a georeferenced database created for a European pilot project led by the Joint Research Centre, is presented in Chapter 5. The database encompasses information on the number of residential and commercial buildings in the 27 EU countries, categorized by their seismic performance and various attributes, such as the number of stories, settlement type, occupancy type, construction material, lateral load resisting system, seismic design code level, number of dwellings and average dwelling area. However, construction dates are absent from the database, so construction periods are roughly estimated based on the dates when seismic design standards came into force in the EU-27 countries. To simplify the analysis of this study, EU-27 reinforced concrete buildings are classified into two main groups: "old" buildings, constructed before 2000, assumed initially uncorroded in 1965 and "recent" buildings with ageing effects considered from 2000 onwards. Durability parameters for recent buildings are established based on standards, while for old buildings, a survey focused on durability parameters was conducted, supported by expert opinions and specialized literature. In short, the assessment of the effects of corrosion is carried out for two **groups** of buildings which are then subdivided into **categories** in order to assess the exposed area and the respective repair costs.
# 6 Climate change impact on the European reinforced concrete building stock

## 6.1 Overview of data and models used

This section recaps and summarizes the procedures and data used to assess the impact of climate change on the reinforced concrete European building stock, with specific references to the models and data presented throughout the report.

The models adopted for evaluating corrosion damage, and for the associated repair costs, are presented in Chapters 2 and 3, respectively. Chapter 4 describes the sources of climate data, while Chapter 5 focuses on the inventory of the reinforced concrete European building stock and the characterization of its durability parameters.

The simplified model adopted for calculating carbonation depth is presented by expression 6 in Chapter 2. In essence, the model delineates the progression of carbonation depth from an initial year, accounting for the durability characteristics of buildings and environmental conditions to which these buildings are exposed over time. It requires two atmospheric forcing variables as inputs: the atmospheric CO₂ concentration and mean air temperature, both of which change over time.

The two-step process for evaluating carbonation costs is outlined in Chapter 3. Firstly, the probability of corrosion initiation (as per equation (11)) is estimated, which is defined as the probability of carbonation depth reaching the concrete cover. The median of this probability distribution determines the year when the first structural repair due to carbonation is required. Secondly, the data of the building stock exposure and building characteristics is used to estimate the exposed building surface (expression (16)) and the respective total repair costs. The assessment of the cost of repair of the damage due to carbonation is performed under the assumption that the repair takes place before the loss of cross-sectional area of reinforcement bars becomes significant, i.e. the considered option is Repair 2 (corrective), described in section 3.2.2. It is assumed that only one repair takes place at the start of the corrosion, and for this reason the associated repair costs are considered as total, and not as cumulative. The study considers uniform price of repair of 293.6 €/m² for all countries. The calculated total repair costs are normalised by the time span from 2020 until the time for the repair, to evaluate the annual repair costs, which are compared to the Gross Domestic Product.

The study presents climate data sources in Chapter 4, derived from 11 bias-adjusted climate projections obtained through the EURO-CORDEX initiative. The employed Regional Climate Models (RCMs) are run over the European continent at a resolution of 0.11°. The projections until 2100 are forced by two Representative Concentration Pathways (RCP), specifically, RCP4.5 and RCP8.5, adjusted for bias using the observational data set E-OBSv10. Figure 9 in Chapter 4 illustrates the evolution of CO₂ atmospheric concentrations, considering historical and two climate-altering gases future scenarios, RCP4.5 and RCP8.5, spanning the historical data period from 1765 to 2005 and the two climate projections until 2100. The study evaluates the impact of climate change on the carbonation depth of European buildings by comparing future climate scenarios with baseline or reference scenarios, as detailed in section 4.3.2. This analysis includes variations in annual temperatures for these scenarios, illustrated for the 27 EU capital cities in Annex 1, and extends to all European Union countries with a 0.11° grid resolution, covering the entire region.

The inventory of the European building stock is based on a georeferenced database created for a European pilot project led by the Joint Research Centre, is presented in Chapter 5. The database encompasses information on the number of residential and commercial buildings in the 27 EU countries, categorized by their seismic performance and various attributes, such as the number of stories, settlement type, occupancy type, construction material, lateral load resisting system, seismic design code level, number of dwellings and average dwelling area. However, construction dates are absent from the database, so construction periods are roughly estimated based on the dates when seismic design standards came into force in the EU-27 countries. To simplify the analysis of this study, EU-27 reinforced concrete buildings are classified into two main groups: "old" buildings, constructed before 2000, assumed initially uncorroded in 1965 and "recent" buildings with ageing effects considered from 2000 onwards. Durability parameters for recent buildings are established based on standards, while for old buildings, a survey focused on durability parameters was conducted, supported by expert opinions and specialized literature. In short, the assessment of the effects of corrosion is carried out for two **groups** of buildings which are then subdivided into **categories** in order to assess the exposed area and the respective repair costs.