Keywords

FormalPara Highlights

  • Participatory and integrated tools provided a representations of NBS multi-dimensionality

  • Co-benefits were considered in many cases even more important than the reduction of water-related risks, and were used as the main elements for the co-design of the NBS.

  • In addition to socio-economic, technical and institutional barriers, NBS implementation claims to detect and overcome those related to the interaction between the various decision-actors.

5.1 Introduction and Conceptual Frame

Nature-based solutions (NBS) have become a valid alternative to grey infrastructures – i.e. hard, human-engineered structures (Palmer et al. 2015) – for coping with climate-related risks in urban and rural areas alike (Raymond et al. 2017; Calliari et al. 2019; Frantzeskaki 2019). The increasing success of NBS is due to their capacity to foster the functioning of ecosystems and to generate additional environmental, economic and social benefits that are considered as essential backbones of actions for climate-change mitigation and adaptation (Bain et al. 2016; Kabisch et al. 2016; Josephs and Humphries 2018). Nevertheless, the transition of the risk management system from the grey solutions toward NBS is still slow (Wihlborg et al. 2019). This is mainly due to the existence of several barriers to NBS implementation. Most of the works in the scientific literature demonstrate that physical barriers (i.e. the technical effectiveness of NBS in water-related risk reduction) are less important than those related to governance, socio-institutional and economic dimensions(O’Donnell et al. 2017; Calliari et al. 2019; Pagano et al. 2019; Giordano et al. 2020). Among the different barriers, this work focuses on two issues that need to be addressed in order to enable the NBS implementation, the low level of social acceptance and the collaboration barriers. The main scope of this work is to demonstrate the effectiveness of participatory modelling exercises in facilitating stakeholders’ engagement in NBS design and implementation as viable approach for overcoming the above-mentioned barriers.

Stakeholders’ needs and concerns represented the backbone of the adopted approach in the different NAIAD case studies. Therefore, efforts have been carried out to put stakeholders’ problem understanding, risk perceptions and preferences at the core of the adopted approach. Before describing the methodological approaches, a few definitions are needed. Problem understanding refers to the mental construction of a certain issue to be addressed, in terms of main causes, impacts, objectives to be achieved and actions to be applied. Risk perceptions refers to the fact that people construct their own reality and evaluate risks according to their subjective perceptions. This type of intuitive risk perception is based on how information on the source of a risk is communicated, the psychological mechanisms for processing uncertainty, and earlier experience of danger (Renn 1998).

Neglecting the differences among values and perceptions held by different stakeholders, which in many cases are not well represented in the decision-making process, may lead to conflict, hampering the effective implementation of NBS. Moreover, stakeholders’ engagement in participatory processes may be turned into an often controversial and futile process (Brugnach and Ingram 2012; Giordano et al. 2017a). Therefore, NBS design and implementation should be based on inclusive and equitable participatory processes, capable to ensure the active involvement of all different categories of stakeholders, and to reflect the diversity of meanings and interpretations that the inclusion of multiple actors brings (Brugnach and Ingram 2012; Cohen-Shacham et al. 2016).

Ambiguity analysis plays a key role in facilitating the stakeholders’ engagement. Ambiguity refers to the degree of confusion that exists among actors in a group for attributing different meaning to a problem that is of concern to all (Weick 1995). Ambiguity, which can be considered as a form of uncertainty and indeterminacy (Brugnach et al. 2011; van den Hoek et al. 2014), is ineradicable in complex decision-making processes (Jasanoff 2007). Figure 5.1 shows how a different stakeholder involvement affects the decision-making process, and helps highlighting how neglecting the role of stakeholders’ engagement in NBS design may lead to barriers to their implementation. On the one hand, ambiguity in problem understanding could cause discussions and conflicts in the initial stage of the participatory process, increasing the time required for making the decision, compared to the unilateral decision-making process. Nevertheless, addressing the ambiguity issues in the early phase of the process has a positive impact on the implementation phase, which is faster.

Fig. 5.1
A diagram of Stakeholder involvement. It has the following labels and arrows. Unilateral decision with arrow pointing right to another star and a wavy arrow pointing to another star to the right. Another star labeled public participation, has a dashed line to problem identified. A wavy arrow points to two stars connected by an arrow. A flower bracket around the first set on the left is labeled search for solution. The top arrow on the right is encased with a flower bracket labeled implementation. The second set of stars and arrows below on the right, has decision made, and another flower bracket labeled gain from public participation.

Impacts of the stakeholders’ involvement on the decision-making process

Full size image

Contrarily, neglecting the existence of different and equally valid problem framings (unilateral decision-making process) facilitates the identification of the most suitable solution according to one kind of knowledge – i.e. the technical knowledge – but conflicts will immediately arise, hampering the implementation phase and/or reducing the measure’s effectiveness (Giordano et al. 2007; Giordano et al. 2017a).

Starting from these premises, we aimed at enabling the stakeholders’ engagement, facilitating the dialogue, aligning divergences and reducing conflicts among different decision-makers due to the ambiguity in problem understanding and risk perception.

To this aim, a multi-steps process was applied in different NAIAD case studies. As shown in Fig. 5.2, the whole process was based on a continuous interaction with local stakeholders, and combined individual interactions and group discussion.

Fig. 5.2
A four-step approach depicts participatory process for N B S analysis and trade-offs assessment. The steps include individual risk perception, ambiguity analysis and co-benefits definition, dynamic scenario simulation and N B S co-design, and N B S trade-offs assessment and conflict analysis. The processes are either by individual interaction and group discussion.

Combination of individual and group interactions in NAIAD implementation

Full size image

5.2 Applied Tools and Methods

Figure 5.3 below shows the different phases of the applied approach and the methods used. Three main phases can be defined for handling ambiguity in risk perceptions through the stakeholders’ engagement in NBS design, i.e. (i) individual risk perception elicitation and analysis; (ii) detection of the main barriers to NBS co-design and implementation; (iii) trade-offs analysis and conflicts detection. Specifically, the analysis carried out in phases (i) and (ii) allowed to bring stakeholders and decision-makers in a participatory process whose main scope was to co-design effective interventions for reducing the water-related risks and producing the expected co-benefits. The methods applied in phase (iii) were meant to enhance the equity of the NBS implementation process.

Fig. 5.3
A flow diagram has three headings, methodological phases, analysis, and implemented methods. Methodological phases include individual risk perception, barriers hampering the N B S co-design, and Trade-offs analysis and conflict detection. Analysis includes problem structuring, co-benefits definition, networking intervention definition, integration of socio-institutional actions and N B S, and N B S based scenarios. Implemented methods include fuzzy cognitive maps, social network analysis, and dynamic scenario simulation.

Different phases of the applied methodology and methods used

Full size image

Prior to describing the different methods, it is worth mentioning that a key preliminary activity has to be carried out in order to guarantee the success of the whole process that is the selection of the stakeholders to be involved. This is due to different reasons. Firstly, because the knowledge elicited by interacting with them is at the basis of the whole process (Jetter and Kok 2014).

Therefore, their representativeness needs to be taken into account. Secondly, the stakeholders-driven process is quite long and requires the stakeholders to go through different phases of individual inputs and group discussion. Therefore, the stakeholders’ selection should also account for their willingness to commit themselves to the whole process. Efforts are required from the analyst in order to keep the stakeholders interested and motivated for the whole process duration. The “snow-ball” sampling approach demonstrated its usefulness in selecting the stakeholders. Basically, we started interacting with key stakeholders, characterized by a pretty high risk awareness and willing to cooperate. Then, other stakeholders were indicated by them during the interviews. In this way, we were capable to define gradually the set of representative stakeholders.

5.2.1 Individual Risk Perception and Co-benefits Definition

The first phase of the applied methodology aimed at collecting and structuring stakeholders’ risk perception and problem understanding, in order to support the co-design of the most suitable NBS. To this aim, Fuzzy Cognitive Mapping (FCM) approach was applied. FCM is part of the Problem Structuring Methods, based on the assumption according to which the most demanding and troublesome task in problem solving often consists in defining the nature of the problem, rather than its solutions (Rosenhead and Mingers 2001).

FCM are defined as a “mirror” of the causes and effects that are inside the mind of decision makers (Montibeller et al. 2008; Kok 2009). FCMs can simulate the cause – effect relationships between the main variables in the model. Semi-structured interviews involving local stakeholders were carried out in order to collect the diverse risk perceptions (Olazabal et al. 2018). The interviews aimed at gathering stakeholders’ understandings about: (i) the main elements affecting the water-related risks at local level; (ii) the direct and indirect expected impacts of the water-related risks; and (iii) the most important issues (social challenges) that need to be addressed in order to increase the effectiveness of the risk management actions and enhance the system conditions. Finally, stakeholders were required to specify the expected roles of the NBS in reducing water-related risks and addressing the social challenges.

The interviews were then analyzed in order to detect the keywords in the stakeholders’ argumentation – i.e. the concepts in the FCM – and the causal connections among them – i.e. the links in the FCM. Table 5.1 shows a series of examples. In order to facilitate the development of the individual FCM, the interviews were designed in such a way as to make the cause-effect relations immediately identifiable in the stakeholders’ argumentation. The collected knowledge was, hence, processed in order to obtain the individual FCM. The sentences were broken down into specific categories, i.e. (i) cause variables; (ii) effect variables; and (iii) relationships type (Kim and Andersen 2012). Table 5.1 shows an example of the stakeholders’ argumentation analysis, allowing to detect the structural relationships for FCM development.

Table 5.1 Examples of the analysis of the interviews for developing the structural relationships in the FCM
Full size table

A FCM is composed by interrelated variables and directional edges, i.e. connections – representing the causal relationships between variables (Kok 2009). The connections are defined by the strength of the causal relationship between two variables. The connection strength indicates the stakeholder’s perceived mutual influence of two variables (Ozesmi and Ozesmi 2003). The weight can be either positive or negative. A positive weight indicates an excitatory relation between two connected variables – i.e. the increase of one variable leads to the increase of the connected one – while a negative weight indicates an inhibitory connection – i.e. the increase of one variable leads to the decrease of the other. For more details about the process for building FCM from interviews, a reader could refer to (Santoro et al. 2019; Giordano et al. 2020; Gómez Martín et al. 2020). Figure 5.4 shows two examples of FCM developed for the Lower Danube case study.

Fig. 5.4
Two network diagrams labeled a and b. A. Flood, rainfall intensity, community well-being, building damages, lake ecosystem quality, and wetland restoration. B. Quality of the lake ecosystem, biodiversity, conservation status, fish species, fishery production, community well-being and agricultural productivity.

FCM developed using the stakeholders’ interviews for the Lower Danube case study: (a) Bistret Municipality; (b) WWF Romania. The connections are characterized by different width according to the weight assigned by each stakeholder. The polarity of the connections is also represented (Giordano et al. 2020)

Full size image

Once developed, the individual FCM were analyzed in order to identify the most central elements in the stakeholders’ risk perceptions by assessing the centrality degree measure, the so called “nub of the issue” (Eden 2004).

Then, FCM scenario analysis (Kok 2009) was applied in order to define the expected NBS impacts according to the stakeholders’ problem understanding. The NBS impacts on the variables in the FCM were defined in a semi-quantitative way, considering a change in the variables within the interval [−1, 1] (for further details refer to (Gray et al. 2015)).

In this work, the comparison between the value of the variables in case of NBS implementation and without NBS allowed us to assess the stakeholders’ expected impacts. Figure 5.5 shows the results of two FCM scenarios, i.e. with and without NBS. The larger is the difference between these two scenarios for the different variables, the stronger is the expected impact due to NBS implementation.

Fig. 5.5
A bar and waterfall graph gives values for B, N B S and B, No N B S in two different shades. Y axis has values from negative 80 to positive 1,00 in increments of 20.

Simulated change of the FCM variables due to the NBS implementation (Giordano et al. 2020)

Full size image

The proposed approach assumes that a stakeholder attributes a high importance to a certain variable if it is central in the FCM and if the NBS is expected to provoke a significant change in its state. The following Fig. 5.6 shows an example of the identification of the most important variables using the results of the FCM analysis.

Fig. 5.6
Three spider diagrams for centrality degree, impact degree, and Importance degree. It gives values for institutional cooperation, local development, biodiversity, agricultural productivity, community risk awareness, ecosystem state, river transportation, eco-tourism, and fish production.

Infogram showing the differences among the stakeholders’ problem understanding and risk perception. The centrality degree shows the most central variables in stakeholders’ FCM; The impacts degree shows the expected impacts due to NBS implementation; The importance degree shows the most important variables in the stakeholders’ FCM

Full size image

Figure 5.6 shows the most important variables according to the stakeholders’ problem understandings. These variables refer to: (i) barrier to risk management that need to be overcome (e.g. institutional cooperation and community risk awareness); (ii) socio-economic objectives (e.g. eco-tourism); (iii) ecosystem improvements (e.g. biodiversity). These variables represent the goals that, according to the stakeholders’ perceptions, should be achieved while implementing measures for reducing water-related risks. That is, these variables were used in this work to describe the stakeholders’ expected co-benefits. The results of this analysis were used for supporting the participatory modelling exercises for NBS co-design.

The experiences carried out in different case studies demonstrated that accounting for the different perspectives and problem understandings, rather than searching for the synthesis and consensus among participants since the beginning of the process, enhanced the richness, diversity and complexity of the knowledge collected for NBS design. Moreover, making the different decision-makers and stakeholders aware of the different, and equally valid, risk perceptions and problem understandings facilitated the debate among the participants and reduced the risk of conflicts.

5.2.2 Detection of the Barriers Hampering NBS Co-design and Implementation

This section describes the efforts carried out in NAIAD for overcoming the collaborative barriers. NBS implementation is a complex issue, which effectiveness does not depend exclusively on the capacity and resources of the involved decision-makers, but also on the number and quality of the relationships with each other. However, ambiguity in risk perceptions (see previous section) may lead to collaboration structures that encourage stakeholders and decision-makers to avoid each other, turning the participatory process into a controversial and futile process.

There is a wide support in decision and conflict analysis for distinguishing two categories of conflict, i.e. (i) those provoked by existing differences among decision-makers over problem perceptions and preferences; (ii) conflicts due to disharmonious relationships among decision-actors due to lack of trust, also regardless to the problem at stake. Correlations have been detected between these two kinds of conflict in multi-actors decision-making environment. That is, conflicts may not occur between decision-makers with a rather different problem frames, but with good relationships. Conversely, highly similar opinions cannot ensure the absence of conflicts between decision-makers if they distrust each other (Liu et al. 2019).

Starting from these premises, the work carried out in NAIAD aimed at demonstrating that effective network of interaction in multi-actors decision-making environment could contribute at reducing the level of conflict due to differences in problem understandings and, consequently, could enable collective actions for NBS design and implementation. Networking Interventions approach has been applied in this work to enhance the existing network of interactions involving the different decision-actors and stakeholders (Valente 2012). Network Interventions are based on the diffusion of innovations theory, which explains how new ideas and practices spread within and between communities.

To this aim, Social Network Analysis (SNA) was applied in order to map the complex network of interactions taking place among the different decision-makers and stakeholders. SNA allows to represent the interactions as a linear graph, characterized by nodes - agents - connected through links of different strength. A link represents a connection between two actors. Its strength describes the importance of this connection in terms of frequency, level of trust, etc. SNA could detect weaknesses in the interaction network – e.g. problems of coordination, lack of information sharing and knowledge transfer, isolated agents, etc. – and support the definition of interventions for improving the cooperation among decision-makers.

In NAIAD, SNA implementation aimed at detecting the main elements negatively affecting the efficiency of the interaction network related to the NBS implementation, and at identifying the leverage elements, i.e. those elements nodes in the network that can be used for implementing interventions aiming at enhancing the cooperation among different decision-makers. To this aim, a stakeholders-based process was applied in NAIAD case studies. Specifically, a participatory network mapping exercise was organized involving institutional and non-institutional decision-actors. During the mapping exercise, participants were requested to mention the tasks that each actor in the list was required to carry out in risk management and NBS implementation. Links were drawn connecting actors and tasks. Then participants were requested to specify with whom the different actors were supposed to cooperate in order to carry out the defined tasks. Finally, the information was introduced in the map. Participants connected the different kinds of information with the tasks this information was supposed to support (Information x Task network), and the actors owning/using the information (Agent x Information network). Once the map describing the Agents-Information-Tasks connections was developed, participants were requested to assign an importance degree to each link according to their own understanding. Three different values were used in this phase, i.e. “High importance” (+++ in the map), “Medium importance” (++ in the map), “Low importance” (+ in the map). Figure 5.7 shows an example of interaction map.

Fig. 5.7
A network diagram has the following labels P P E, C N G, L A , P A, C P A, F A A, W U A, I C, P A, and C N I.

Map of interactions among different actors in the Medina del Campo case study

Full size image

A reader interested in learning more about the SNA methodological approach could refer to (Giordano et al. 2017b). This work aims at describing the potentialities of the method in overcoming the collaboration barriers hampering the NBS implementation.

Four different maps were developed during the participatory mapping exercise:

  • the Agent X Agent map, describing the interactions among the different decision-makers and stakeholders;

  • the Agent X Knowledge map, connecting the different pieces of knowledge (e.g. groundwater state) with the agents owning/using such knowledge;

  • the Agent X Task map, connecting the different agents with the tasks they are required to carry out in water-risk management;

  • The Knowledge X Task map, connecting the different tasks with the pieces of knowledge used/needed for carrying out such tasks.

Graph theory measuresFootnote 1 were applied in order to analyze the network of interactions, to detect key vulnerabilities – that is, those elements that can hamper the effectiveness of the interactions among decision-makers – and to identify the key nodes for the interventions (Table 5.2). In this work, we assumed that a key vulnerability in the organization can be due to agents, information and tasks, or a combination of the three categories. The following graph theory measures were applied in the network analysis. For a more extensive description of the graph theory measures for the network analysis, a reader could refer to (Freeman 1978; Carley et al. 2007; Giordano et al. 2017b; Pagano et al. 2018).

Table 5.2 Graph theory measures for detecting key vulnerabilities in the network of interaction
Full size table

As shown in the Table 5.2, different measures were aggregated in order to detect the key vulnerabilities in the network of interactions. Specifically, an agent could be considered as a vulnerable element if she/he has a low centrality degree – that is, weak connections with the other actors – and a high number of tasks to be carried out in the collective process for risk management. In these conditions, the agent would not be able to cooperate with the others and, thus, there is the risk of not fulfilling the tasks. Similarly, an agent with high “most knowledge” degree has access to a high number of pieces of important knowledge. Nevertheless, a low centrality degree in the Agent X Agent network means that this agent is poorly connected in the network, reducing the effectiveness of the knowledge flow within the network.

A piece of knowledge could represent a vulnerability if it is central in the process – i.e. enable the access to other kinds of knowledge and/or allow the fulfillment of important tasks – and it is not effectively shared within the network. Finally, a task could represent a vulnerability if it is carried out by a single agent and if it plays a key role in activating other important tasks. In these conditions, if the exclusive agent would fail in carrying out this task, the whole process will be affected.

The results of this analysis allowed to detect the key vulnerabilities in the interaction network that need to be tackled through the design and implementation of the network interventions. Table 5.3 shows the barriers detected in the Medina del Campo case study.

Table 5.3 Key barriers to NBS implementation due to the network of interactions in the Medina Case Study
Full size table

These vulnerabilities of the interaction network could lead to barriers to the NBS implementation. As a way of example, we could refer to the Medina del Campo case study. The selected NBS was the Managed Aquifer Recharge, which main goal was to enhance the quality of the groundwater, protecting the resources from the effects of the over-exploitation for irrigation purposes. The above described SNA-based methodology was applied in order to detect the vulnerabilities in the network of interactions and to assess their impacts on NBS implementation. The first vulnerability detected through the SNA was related to the agent Water Users Association (WUA). As learned during the first phases of the process in Medina, the formation of WUA could have a positive impact on the control of the territory and, thus, on the over-exploitation of the groundwater. The second key vulnerability in the SNA is the task “Water rights management”, influenced by the farmers’ risk awareness, and affecting the capability of the River Basin Authority to reduce the volume of groundwater used for irrigation purposes. The third key vulnerability in the network of interactions is the technical support to farmers for enabling crop change, which depends on the reputation of the basin authority (which should be capable to provide effective information and technical support to select less water demanding crops), and affects the farmers’ capabilities to reduce groundwater exploitation. Finally, the fourth key vulnerability in the network of interactions was the “GW state information”. The availability of this information could enhance farmers’ risk awareness and, thus, reduce the exploitation of groundwater.

The results of the SNA analysis were used to inform the debate among the different decision-makers and stakeholders aiming at co-designing the interventions for overcoming the detected collaborative barriers, as shown in Table 5.4.

Table 5.4 Networking interventions for NBS co-design and implementation
Full size table

5.2.3 NBS Scenario Simulation and Trade-Offs Analysis

As stated previously, the work carried out in different NAIAD case studies aimed at demonstrating the suitability of Participatory Modelling approaches in enabling the stakeholders’ engagement for NBS co-design. Specifically, two modelling methods were applied in NAIAD case studies, i.e. System Dynamic Model (SDM) and Fuzzy Cognitive Map (FCM). According to (Sterman 2000) System Dynamics Modelling is a set of conceptual tools that enables an improved understanding of the structure and the dynamics of complex systems, as well as of rigorous modeling methods that enable building formal simulations of complex systems to design more effective policies. SDM is widely used to analyze complex (‘wicked’) problems over time, taking into account their multi-dimensionality through the integration of qualitative and quantitative, ‘hard’ (e.g. technical) and ‘soft’ (e.g. social) variables. Both SDM and FCM were selected for three main characteristics that made them suitable for addressing the complex issues related to NBS co-design and assessment. Firstly, both SDM and FCM are based on System Thinking approach – i.e. the evolution of the modelled system is affected by the structure of the interconnections among the different elements. Therefore, these two modelling approaches were considered suitable for mapping and analyzing the complex web of interactions involving physical, ecological and socio-economic factors affecting the NBS effectiveness. Secondly, these methods were selected due to their capabilities to simulate the dynamic evolution of the system, accounting for the time dimension, whereas many other modelling approaches provide simply “snapshot” of the system state. Thirdly, both SDM and FCM allow the integration of stakeholders’ and scientific knowledge and, in doing so, enhance the legitimacy of the developed model.

Several experiences were carried out in different NAIAD case studies using integrated modelling tools, in order to define a bottom-up procedure for co-designing NBS and for supporting their assessment. The basic idea behind the proposed approach is to focus on the identification, analysis and modelling of the co-benefits production, which is the key value added of NBS with respect to traditional grey infrastructures. The rationale of the proposed approach is the development of a sequence of individual and collective activities that should support assessing the effectiveness of strategies (i.e. a measure of a combination of multiple measures) in the production of the high-ranked benefits and co-benefits according to local stakeholders. The key advantage and element of innovation lies in the effort of proposing a solid procedure for eliciting and structuring local knowledge, collectively building a vision of the system under investigation, and simulating the effects of the most relevant scenarios. The collected and structured stakeholders’ knowledge was, then, integrated with scientific and expert knowledge for simulating NBS scenarios. Figure 5.8a shows an example of integrated model (SDM) developed for the Glinscica case study. Figure 5.8b shows the dynamic FCM developed for the Lower Danube case study.

Fig. 5.8
Two loop diagrams labeled a and b. In a, river renaturation, retention areas effectiveness, wetlands restoration, opening floodplains, river remeandering, and Barriers removal.

(a) Overview of the stock and flow model built in the Glinscica Case study (Pagano et al. 2019); (b) Aggregated dynamic FCM developed for the Lower Danube case study (Giordano et al. 2020)

Full size image

Both models were built, using a participatory approach, starting from the aggregation of individual mental models (discussed in Sect. 5.2.1). This process is not straightforward, given the ambiguity and the differences in problem framing, although several methods exist for the purpose. In this cases, stakeholders were directly invited to construct an aggregated map. The process started, with the support of the analysts, merging similar variables (e.g. the same concept expressed using different words). Then, a discussion between the stakeholders helped drawing the weighted connections among the variables and identifying potential additional variables and connections. The global structure was then further discussed to check for potential inconsistencies. Additional details are available in (Pagano et al. 2019; Gómez Martín et al. 2020; Coletta et al. 2021).

The stock and flow model (one of the most common SDM tools) represented in Fig. 5.8a can be interpreted as an evolution of FCM. It includes a multiplicity of mathematical expressions governing the system, incorporated via flow diagrams and finally transformed using a simulation environment. Basically, FCM’s variables and causal relationships are identified and translated into the common SDM sets, and hypotheses are formulated on the mathematical aspects, integrating multiple sources of information, such as expert consultation and scientific/grey literature. In the Glinscica river case it was adopted for a twofold reason: (i) the need to quantitatively integrate both ‘hard’ information (e.g. hydraulic models and data) and expert opinion on ‘soft’ variables (e.g. socio-economic aspects); (ii) the need to explicitly model the multi-dimensional implications of several NBS, in order to comparatively analyze their benefits and co-benefits with time.

The aggregated dynamic FCM (Fig. 5.8b) was used in the Lower Danube case study to develop a more simplified scenario analysis related to different sets of measures identified by the stakeholders. This analysis was highly relevant for a twofold reason: (i) to explicitly highlight the differences in stakeholders’ perception of benefits and co-benefits production with time (using delays and multiple time steps for analysis), identifying the potential onset of trade-offs; (ii) to help decision-makers in the timely identification of such trade-offs, thus helping in the timely identification of conflicts and facilitating NBS implementation.

As already mentioned, the integrated models were, then, used for simulating NBS scenarios and assessing NBS effectiveness in reducing the water-related risks and producing the expected co-benefits, as shown in Fig. 5.9a. Specifically, the focus is on how stakeholders might differently evaluate the co-benefits, which depends on the individuals’ benefits perception. Neglecting these differences and ignoring the consequences of trade-offs between values held by different stakeholders, which in many cases are not well represented in the decision-making process, may lead to conflict, and thus to policy resistance mechanisms.

Fig. 5.9
Four line graphs labeled a. It depicts Co-benefit, primary impacts, socio-institutional dynamics, and Lan use dynamics. A line graph depicts the scenario analysis. It gives values for flood risk, eco-tourism, institutional cooperation, agricultural productivity, river transportation, biodiversity, fish population and production, community well-being, community risk awareness, and depopulation.

Dynamic evolution of NBS benefits and co-benefits using the SDM in the Glinscica Case study (Pagano et al. 2019) (a) and the FCM in the Lower Danube Case study (b) (Giordano et al. 2020)

Full size image

The integrated models were also used for detecting and analyzing potential trade-offs among different beneficiaries. Starting from the ‘Importance degree’ that was attributed to specific co-benefits according to individual problem understanding, we assumed that there was a trade-off between two stakeholders if there was an unequal distribution of such co-benefits (i.e. an increase for a stakeholder, and a decrease for another). A stakeholder would therefore not fully capture NBS co-benefits if the value of her/his objective function associated to the NBS implementation would be lower than she/his expects. Figure 5.10 shows the comparison between the simulated and desirable (given individual risk perception and co-benefits analysis) NBS benefits for some of the stakeholders involved in the Lower Danube case study, based on the results of Fig. 5.9b. The axes in Fig. 5.10 plot the results for the ‘short’, ‘medium’ and ‘long’ term of analysis, which is useful to show how the difference between the simulated and perceived benefits and co-benefits may significantly change over time, in different stages of measures implementation.

Fig. 5.10
Three diagrams of triangle. Each diagram has three triangles one inside the other. The edges are marked as F short, F long, and F medium. The inside triangle is simulated and outside is desirable.

Comparison between desirable and simulated benefits due to NBS implementation for single stakeholders, based on the results of the FCM in Fig. 5.9b. This Figure refers to a subset of the results obtained in the Lower Danube case study. The three axes represent the stakeholders’ objective functions in the short-, medium and long-term

Full size image

Figure 5.10 shows that the simulated value is lower than the desirable one on the short-term axis for a subset of the involved stakeholders, which means that they all perceive a dis-benefit in the short term. This is mainly because the effectiveness of the selected strategy on variables such as community well-being and risk awareness is limited in the short term. In some cases, such as e.g. Corabia, the objective function is lower than the expected one in all time steps. This is mainly because these stakeholders gave a high importance degree to the co-benefits “agricultural productivity” and “river transportation”. The model simulation showed that: (i) the former is expected to decrease in the medium and long terms due to the increase of the natural protected areas; and (ii) the implemented NBS was supposed to have a limited impact on the river flow and, consequently, river transportation.

In many cases, the stakeholders perceived a high benefit from the strategy implementation in the long term because of its positive impact on the eco-tourism and community well-being. The analysis showed that most of the potential conflicts can occur in the long term, and could involve mainly the stakeholders that assigned a high value to the agricultural productivity.

The results of the trade-offs analysis can be used by decision-makers to prevent potential conflicts and to facilitate the NBS implementation. The results demonstrate also that all stakeholders need to be informed in the early stage of the project implementation, in order to make them aware of the time lag needed for producing the expected co-benefits.

5.3 Concluding Remarks

This section is meant to discuss to what extent the activities carried out in the NAIAD case studies allow to demonstrate the suitability of the applied approaches for overcoming two of the key barriers that could hamper the effective implementation of NBS, i.e. lack of stakeholders’ engagement and collaborative barriers among different decision-makers and stakeholders.

Participatory and integrated tools, such as SDM and FCM, helped overcoming one of the key limits of the existing frameworks for NBS assessment that is the lack of structured representations of their multi-dimensionality. Co-benefits were considered in many cases even more important than the reduction of water-related risks, and were used as the main elements for the co-design of the NBS. The NAIAD Case studies demonstrated the relevance of using participatory tools to increase social acceptance towards NBS, since the whole process helped breaking down some of the existing socio-institutional barriers, mainly related to the limited knowledge and to the partial involvement of the stakeholders in the discussion. The active participation of stakeholders throughout the process of NBS design is crucial in order to move beyond individual perception and problem understanding, and to support building a shared view of the problem under consideration. Additionally, defining a shared problem frame and group model facilitates interdisciplinary and cross-sectoral communication and collaboration.

The applied approaches for eliciting and structuring individual risk perceptions allowed to account for the diversities, and enabling an inclusive and equitable participatory process. Moreover, the experiences carried out in NAIAD demonstrated the importance of making explicit to the stakeholders how the knowledge they provided during the different phases was actually used for developing the model. This contributes to create a sense of ownership toward the developed model, facilitating the interaction with the stakeholders. Finally, by contributing to the model development, stakeholders became aware of this complexity and realized that NBS effectiveness is influenced by several elements, ranging from the physical to the socio-institutional ones. As result of this learning process, stakeholders and decision-makers selected several socio-institutional actions to be implemented as “supportive” measures for NBS effectiveness.

The approach based on SNA demonstrates that overcoming the barriers to collaboration and enhancing the effectiveness of the network of interactions, through the implementation of the networking interventions, could have positive impacts on the NBS implementation and effectiveness. Therefore, this work demonstrates that, in addition to socio-economic, technical and institutional barriers, NBS implementation claims to detect and overcome those related to the interaction between the various decision-actors. Methods for unravelling the complex network of interactions taking place among different decision-makers are needed.

Two key messages can be identified from the work carried out in the NAIAD case studies concerning the trade-off analysis. Firstly, differences among stakeholders concerning the definition of co-benefits to be produced through the NBS implementation and their importance could lead to trade-offs among different stakeholders. Therefore, the trade-offs analysis requires methods and tools capable to handle the diversity in problem frames among the different stakeholders, and suitable to simulate the dynamic evolution of complex systems. Secondly, trade-offs analysis claims for a clear understanding and modelling of the complex cause-effects chains affecting the NBS impacts on the system.

The results of the trade-offs analysis can be used for supporting decision-makers in the definition of actions/measures for reducing trade-offs and potential conflicts, supporting NBS acceptance. The work described in this chapter is mainly based on risk perceptions and problem understandings. Therefore, the results of the analysis could be useful for enhancing the communication with the stakeholders, affecting their perceptions and enabling learning processes. The described example shows the importance of raising stakeholders’ awareness about the great potentialities of the NBS implementation in creating new community development opportunities due to the eco-tourism. Therefore, the communication should emphasize the production of the co-benefits, rather than being simply focused on the reduction of the climate-related risks.

The analysis of the activities carried out in the case studies demonstrated also some drawbacks of the applied methodology. Capturing and processing stakeholders’ knowledge starting from individual inputs is time consuming and requires substantial efforts by skilled analysts for post-processing the information collected during the individual interviews. Moreover, efforts are required in order to avoid stakeholders’ fatigue in taking part to the different phases of the methodological approach, and keep them engaged. The qualitative nature of the modelling approaches also represented a limit of the applied methodology. Although the structure of the developed models, based on causal connections, was easily understandable by the stakeholders, and used for supporting the debate, many perplexities were mentioned by the participants concerning the results of the scenario simulations. The participants seemed inclined to prefer quantitative evaluation, rather than qualitative results, specifically when they were required to comment the NBS capability to reduce climate-related risks. Therefore, efforts are still required to integrate qualitative modelling approaches with more quantitative, physically based models.

Box 5.1 Key Lessons Learnt

  • Involving stakeholders in the development of integrated models could raise their awareness concerning risks and NBS effectiveness, affecting social acceptance;

  • The applied approaches should aim at enhancing the potential richness, diversity and complexity of the collected knowledge, rather than searching consensus among participants;

  • Dynamic approaches are required to assess NBS effectiveness, co-benefits production and trade-offs among different beneficiaries;

  • Ineffective interactions among different decision-actors – i.e. limited exchange of information – could create barriers to NBS co-design and implementation.

  • Enhancing communication on co-benefits production and potential trade-offs improves NBS acceptance.