4.3. Identification of Urban Vulnerability Indicators

The developed analysis of urban vulnerability indicators (Section 3.1 and Appendix A) provided a comprehensive list of multidimensional indicators to be used to address urban vulnerability through a multidimensional approach. This list (Appendix A) has been developed to provide a repository for selecting the appropriate indicators according to the evaluation purpose and context. In this application, the evaluation demand concerns the identification of the most vulnerable urban areas for UHI in the city of Milan, concerning multiple dimensions. Therefore, social, environmental and infrastructure dimensions have been considered. The economic dimension has not been engaged according to the main evaluation demands. Therefore, the indicators included in the comprehensive list, referred to social, environmental and infrastructure dimensions, have been compared with the available data of the city of Milan, to identify the indicators to be used for developing vulnerability maps for UHI exposure. Table 7 lists the multidimensional indicators used for the application.

5. Results

5.1. Vulnerability maps

5.1.1. Social indicators

5.1.1.1 People over 75 years old and people under 14 years old

For the social indicators, the number of people over 75 years old and under 14 years old have been represented. Figure 5a and Figure 5b represent the spatial concentration of these population categories per NIL2 (Nucleo di Identità Locale), with the overlapping of the areas to be redeveloped. It can be seen from this overlay that many of the areas which need to be redeveloped fall within the NILs, with highest concentration of over-65 and under-14 population.

5.1.2. Environmental indicators

5.1.2.1 Urban Heat Island (UHI)

UHI effects and climate change are phenomena which severely affect citizens’ well-being and health, especially in summer (Olivieri et al., 2024). Figure 6 represents the Land Surface Temperature (LST) of the city of Milan. This map represents the average surface temperature trend in the city of Milan by combining the average value of the surface temperature of the summer months from 2013 to 2017, aggregated over the Urban Atlas zones. It can be noted that the higher temperatures are registered in the most urbanised areas, characterised by a greater quantity of impermeable soil. On the other hand, the more peripheral areas show lower temperatures due to the higher presence of green areas. It is also relevant to underline that the difference in temperature between peripheral and central areas is approximately 10 °C, which corresponds to the phenomenon of the UHI. Moreover, this map overlaps the distribution of the area which needs to be renovated with the UHI. As can be noticed, some areas fall in zones characterised by 36.8° and 38.8°.

5.1.2.2. Hydrogeologic risk map

Figure 7 represents the overlapping of areas to be regenerated with the different zones defined by the Hydrogeological Planning Plan (Lombardy Region, 2023). It is possible to notice that three levels of risk have been identified, lower, medium and high. It is possible to address that some mixed areas and free areas are located in the signaled flood areas.

5.1.3. Infrastructure indicators

5.1.3.1. Strategic infrastructures

According to the infrastructural dimension, the spatial distribution of strategic infrastructure has been investigated to identify if their location falls in those areas with the highest UHI and/or in the addresses flood areas. Figure 8 represents the mapping of schools, sanitary structures and hospitals. These types of structures have been analysed according to the fact that they host the most vulnerable population groups (i.e., population under-14 years old, population over-14 years old, and population with health assistance needs). Moreover, the spatial distribution of strategic infrastructure has been overlapped with the spatial distribution of areas to be regenerated to verify their potentials for regeneration intervention.

5.2. Intervention area identification

Figure 9 illustrates the framework used to identify the most urgent intervention area, which is based on the overlapping of the different developed maps (Section 5.1).

This procedure is based on the methodology proposed by McHarg (McHarg, 1995). He introduced the possibility of describing the territory according to different thematic maps, each representing an environmental or social variable by layers. The main objective is to overlap these different spatial levels to design harmoniously with nature, to support the planning decision to minimise environmental impact and maximise sustainability.

Following this methodology, the developed maps have been overlapped to identify the area where NBS implementation for UHI mitigation is most urgently needed, considering also the flood risk. Thus, the maps related to (1) population under-14 years old (Figure 5a), (2) population over 75 years old (Figure 5b), (3) the UHI (Figure 6), the PAI bands (Figure 7), and the distribution of the strategic infrastructures (schools, hospitals, and health facilities) (Figure 8) have been overlapped to the map of the abandoned areas in city of Milan (Figure 4).

To perform the overlapping of these different maps, it is necessary to make them homogeneous layers. Therefore, maps have been converted into maps with homogeneous values, ranging from 1 to 5, where 1 indicates a very low vulnerability level, and 5 a very high vulnerability level. Only the maps related to the spatial distribution of the areas to be regenerated (Figure 4) and the strategic infrastructures (Figure 8) have not been homogenized with these value scales, as the objective was to evaluate their position concerning the UHI and flood areas.

Table 8 illustrates the conversion of indicator values into homogeneous values.

Based on the overlap between homogeneous vulnerability maps and localisation data, the area in Via dei Canzi (33,367 m²) (Figure 9) has been identified as one of the most critical zones for UHI and flood risk mitigation through NBS. This site falls within a NIL classified as high-risk due to the significant presence of vulnerable population, particularly individuals over 75 and under 14 years of age. Additionally, the area is exposed to elevated LST (value equal to 4) and is situated near key public infrastructures, including schools and healthcare facilities. The urgency for intervention is further underscored by its classification as a medium-risk zone for flooding (value equal to 3). Consequently, the implementation of NBS in this area holds the potential to address both UHI effects and flood risk, aligned with the objectives of the Piano Clima plan.

5.3. Definition of alternative NBS scenarios

Once the intervention area has been selected, alternative scenarios of NBS implementation have been developed. Based on the objective of the evaluation, which gives great attention to UHI effects and flooding exposure to identify the most vulnerable area in which it is mostly urgent to intervene, a specific literature review has been carried out to identify the most suitable NBS intervention to be implemented for UHI mitigation and the water management support.

Table 9 illustrates the considered references to identify the most suitable NBS for addressing these stresses. From the performed analysis, it can be concluded that the most suitable NBS, according to the feature of the implementation area, are: (1) urban forestation and (2) bioswales/rain gardens. Furthermore, Table 9 specifies which mitigation measures and which vulnerabilities the different NBS are designed to address.

Three different implementation scenarios have been proposed by different combinations of the selected NBS. The proposed alternative scenarios are described in Table 10.

5.4. Comparative evaluation of NBS scenarios

The alternative scenarios have been evaluated through the proposed multi-step evaluation framework (Section 3), according to the implementation and maintenance costs and the provided ES.

5.4.1. Cost Evaluation

The proposed cost schedule has been used for cost estimation to address and describe the different working phases. In detail, the estimation of implementation and maintenance costs has been developed using the pricing list of Lombardy Region (2024), according to the location of the case study.

Table 11 represents the synthesis of the cost estimation for the three NBS scenarios according to the proposed schedule (for the detailed spreadsheet, please see Appendix B).

5.4.2. Ecosystem Services Assessment

Alternative scenarios have also been evaluated according to the provided ES. This evaluation has been carried out using SimulSoil software, which has allowed to perform the simulation of the different NBS scenarios in terms of ES supply. These scenarios have then been compared with the current state-of-the-art of the area (Business as Usual scenario – BAU) to address the difference in ES provision. The provided ES have been assessed both in biophysical and monetary terms (Fig. 10 and Tab. 12).

According to the cost estimation, Scenario 2 has the highest implementation expenditure and the lowest annual maintenance requirement. Scenario 1 is characterised by the lower implementation cost and the higher maintenance cost. On the other hand, Scenario 3 has a similar implementation cost compared to Scenario 2 and a maintenance cost equal to € 87,317.06 per year.

Regarding the evaluation of ES in biophysical terms, all scenarios are expected to improve the Carbon Sequestration (CS) and Habitat Quality (HQ) compared to the BAU scenario, although to varying degrees depending on their specific performances. Conversely, for the Sediment Delivery Ratio (SDR), only Scenario 2 is expected to enhance supply conditions relative to the BAU scenario. Scenario 3 is projected to maintain current performance levels, while Scenario 1 may result in a decrease.

6. Discussion

The proposed multi-step evaluation framework enables the assessment and comparison of different NBS scenarios concerning implementation and maintenance costs, and the provision of ES both in monetary and biophysical terms. Consequently, this approach can support the selection of the most suitable scenario by considering the trade-offs between costs and benefits (de Magalhães et al., 2019). Therefore, Decision-Makers (DMs) and public administrations can identify the most suitable solution under available financial resources for covering both implementation and maintenance costs, while aligning these with the provision of ES.

For instance, if the priority is given to the provision of ES, according to limited financial resources, the Scenario 2 should be the preferred option, as it improves the supply of all considered ES. Conversely, if the priority is the economic budget, Scenario 1 might be favored, given that it has the lowest implementation cost. Moreover, according to the fact that it is an ex-ante evaluation, it is possible to provide some changes to the proposed intervention to reduce maintenance costs.

Therefore, the proposed E-SDSS can be considered a suitable tool to support the spatial planning of NBS in the urban context, according to specific context-based analysis of urban vulnerability. Moreover, this context-based analysis is suitable to support the allocation of economic resources to intervene with NBS where it is most needed. Furthermore, this evaluation framework gives information about the implementation and maintenance costs of NBS intervention to select the most suitable one according to both expenses and benefits, or rather the supply of different ES.

According to this first attempt, one of the main strengths of the proposed E-SDSS is the cost estimation framework, which is based on a simplified and adapted version of the WBS methodology. This approach enables the estimation of both implementation and maintenance costs through a hierarchical and clear structure. Moreover, it allows the description of the various work entities and takes into account the geographical location of the intervention, which is one of the main influential factors affecting the overall costs.

Moreover, the integration of cost estimation with spatial vulnerability analysis and ES assessment can provide an innovative and suitable E-SDSS by giving a comprehensive evaluation of NBS intervention, supporting DMs in locating the limited economic resources where they are needed (Rossitti et al., 2023). Therefore, this E-SDSS can contribute in an operative way to the advancement of NBS design and implementation in the urban context by proposing an ex-ante and context-based assessment (Nesshöver et al., 2017; C. M. Raymond et al., 2017; Rossitti and Torrieri, 2021a), filling the gap of the absence of a suitable DSS for NBS implementation in urban contexts (Salm et al., 2023; Wild et al., 2024).

Furthermore, the proposed evaluation framework can be applied in other contexts according to the list of vulnerability indicators (Appendix A) which can be used as a repository to identify the suitable frameworks to address the context-based conditions, as well as the proposed cost sheet can be applied in other context and implemented with different price lists.

However, above these potentialities, some criticalities have to be addressed. The first issue concerns the availability of the data to develop the vulnerability maps. In this application, many of the listed indicators in Appendix A cannot be used due to the lack of availability for the city of Milan. Despite its user-friendly application for addressing ES through biophysical terms, SimulSoil software estimates the monetary values through absolute terms, which does not allow the assessment of annual economic benefits.

7. Conclusions

NBS are widely recommended as a strategic approach to support the sustainable and resilient transition of cities, as well as an effective solution to mitigate climate change effects and provide essential ES (Di Pirro et al., 2023). However, their implementation in the urban context is quite limited due to the lack of an evaluation framework to address both the implementation and the maintenance costs (Dumitru et al., 2020; Wild et al., 2020), which negatively affect the integration of NBS in strategic urban planning. Moreover, the issue of how to distribute NBS in the urban context has been recognised as one of the main crucial challenges in NBS urban implementation (Camacho-Caballero et al., 2024; Langemeyer et al., 2020). Therefore, to fill these gaps, this research proposed an E-SDSS to support (1) the identification of the most vulnerable areas where to intervene with respect to to multidimensional vulnerability maps and (2) the evaluation of NBS alternative scenarios according to implementation and maintenance costs and ES supply. More in detail, the maintenance cost has been included in the evaluation according to the fact that it should be a relevant burden for public administrations, despite the capacity of NBS to create healthier, safer, and more resilient environments, as well as to give new values to poor quality or abandoned landscapes (Giordano et al., 2020).

The application of the proposed E-SDSS to the city of Milan represents a preliminary test to address both strengths and weaknesses, to define the required future improvements to the proposed model. Among the obtained results, two main trajectories can be proposed for implementing the provided E-SDSS.

The first implementation concerns the integration of the Spatial Multi-Criteria Analysis (SMCA) to identify the most urgent areas according to a synthetic Vulnerability Index (VuI) (Oppio and Dell’Ovo, 2020; Rossitti and Torrieri, 2021b). Furthermore, SMCA also consider the different relative importance of the indicators, which allows to better addressing the context-based conditions. Secondly, a more in-depth analysis of the monetary values of ES provided will be implemented to develop a Cost Benefit Analysis (CBA) for the economic and financial feasibility of NBS interventions.

These further implementations will be relevant to exploring more in-depth the cost-effectiveness of NBS interventions. Furthermore, a better understanding of the economic value generated by NBS and the ES they provide supports the development of more effective policies and measures (Masiero et al., 2022). Moreover, future implementation will also encourage stakeholders’ participation in decision-making processes (Barton et al., 2018), strenghtening the importance of their inclusion in NBS planning (Kabisch et al., 2017).

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Appendix A

Appendix B