D4.3 Quantifying long-term patterns between adaptation actions, socio-economic behaviours, and climate service information

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 Deliverable D4.3 Quantifying long-term patterns between adaptation actions, socio-economic behaviours, and climate service information 31-10-2024 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 Innovating Climate services through Integrating Scientific and local Knowledge Deliver able Title: Quantifying long - term patterns between adaptation actions, socio - economic behaviours, and climate service information Authors: Riccardo Biella, Charles N. Wamucii, Maurizio Mazzoleni, and Giuliano Di Baldassarre Contributing Authors: Lucia De Stefano, N uria Henrndez - Mora Zapata Date 31 - 10 - 2024 Suggested citation: Availability: ☒ PU: This report is public [Please select] ☐ CO: Confidential, only for members of the consortium (including the Commission Services) Document Revisions: Au thor Revision Date Biella et al. First draft 09 - 10 - 2024 Biella et al. Revised draft 18 - 10 - 2024 Biella et al. Final draft 24 - 10 - 2024 Micha Werner Review 26 - 10 - 2024 Biella et al. Final version 31 - 10 - 2024 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 Executive Summary This deliverable explores the complex interactions between climate services (CS), human adaptation behaviours, and socio-economic dynamics. It focuses on how CS can influence adaptation strategies to climate change while potentially leading to maladaptive outcomes. The research presented in the document investigates these dynamics through system archetypes across the various living labs (LLs) established in the context of the I-CISK project in Europe. This report expands on the work done in prior deliverables, establishing a framework for evaluating the potential maladaptive risks associated with CS. This includes examining the unintended consequences that may arise when CS are deployed, such as increased resource exploitation, social inequities, or fostering dependency on short-term solutions that compromise long-term sustainability. Two key archetypes are highlighted: i) “band-aid solutions”, which occur when short-term adaptive actions, supported by CS, delay more transformative, long-term solutions, potentially undermining sustainability; ii) “success to the successful”, which describes a situation where more resource-rich or organized sectors benefit disproportionately from CS, reinforcing social and economic inequities. Case studies from the LLs illustrate how these archetypes manifest in real-world settings. In the Spanish and Georgian LLs, CSs supporting agriculture can enable increased water use, addressing immediate challenges but contributing to long-term resource depletion. In the Italian and Dutch LLs, CSs supporting short-term irrigation practices, potentially delay necessary transitions to more sustainable water management practices. In the Greek LL, the tourism industry benefited more from CS than agriculture, exacerbating inequities between the two sectors. This report focuses on long-term patterns and it stresses the need for a balanced approach that integrates both short-term and long-term adaptation strategies. It warns that over-reliance on short-term CS can lead to maladaptation, where the very services designed to mitigate climate risks end up exacerbating vulnerabilities in the long run. The deliverable also provides an initial set of recommendations. CS developers and adaptation managers are encouraged to incorporate systemic perspectives when designing and implementing climate services, ensuring that long-term sustainability is prioritized. More inclusive and collaborative CS co-creation processes are needed to address power imbalances, ensuring equitable access and benefits across all stakeholders. The report calls for practical tools, such as decision frameworks, to help developers assess maladaptation risks. In closing, while climate services hold significant potential for improving adaptation strategies, they are not necessarily "no-regret" solutions. If not carefully designed and implemented, they can lead to unintended maladaptive consequences, reinforcing inequalities and promoting unsustainable practices. The report highlights the importance of a systemic, long-term approach to ensure that climate services truly support sustainable and equitable adaptation efforts. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 Table of Contents 1. Introduction ..............................................................................................................................................3 1.1. Potential maladaptive dynamics across living labs ..............................................................................3 1.2 Identifying maladaptation risk: a framework .............................................................................................4 2. Maladaptive processes across the LLs ......................................................................................................5 2.1 Fixes that fail: short-term solutions with long-term consequences .......................................................5 2.2 Band-aid solutions: tension between short-term and long-term adaptation ........................................6 2.3 Success to the successful: inequities in climate service benefits ...........................................................6 2.4 Identifying maladaptation ......................................................................................................................7 2.5 Significance of the study for the field of CSs ..........................................................................................8 3. Long-term patterns emerging from the success to the successful archetype .............................................9 Premise .............................................................................................................................................................9 Model description, definitions and assumptions .......................................................................................... 11 Preliminary results ......................................................................................................................................... 12 Sectoral adaptation efficiencies ................................................................................................................ 12 Impact on sectoral indices ........................................................................................................................ 12 Overall system resilience ........................................................................................................................... 14 Implications of the preliminary results ...................................................................................................... 17 4. Long-term patterns emerging from the band-aid solutions archetype .................................................... 18 Modelling maladaptation .............................................................................................................................. 18 Introducing CSs in the model ........................................................................................................................ 19 Future developments of the model............................................................................................................... 21 5. Conclusions ................................................................................................................................................ 22 References ..................................................................................................................................................... 23 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 1. Introduction While the benefits of climate services (CS) have been widely explored, it is still unclear how their introduction can potentially influence human adaptation to climate-related events, and generate unintended consequences, including the emergence of undesired risks or maladaptive dynamics. In the context of the I-CISK project, the work in WP4 (and WP2) has shown how the availability of (and the information provided by) CS can change the way in which individuals perceive climate-related risks and adapt their behaviours over time. Deliverable D4.2, for example, discussed how behavioural changes depend on individual and/or social norms, attitudes, risk preferences, and heuristics that are often neglected in climaterelated modelling. Moreover, it provided new insights about the interplay between climate and human systems mediated by the way in which societies respond to the availability of CS and established a conceptual framework for exploring the feedbacks between CS, human behaviour, and climate-related events. This framework was then applied and co-developed, together with local stakeholders from the different living labs (LL) of the I-CISK project, a number of system archetypes, which were further developed in a recent publication (Biella et al., 2024). In particular, we used system archetypes to show how climate services can play a role in both producing and preventing maladaptation. Despite scientific progress, the dynamics emerging from the feedbacks between climate change, CS information, and adaptation actions, remain poorly understood. The aim of this deliverable is to start unravelling these dynamics and assess long-term patterns generated by the system archetypes described in D4.2 and updated in Biella et al. (2024). To this end, we developed stylized models capturing the reciprocal effects between climate adaptation actions along temporal scales (short-term vs. long-term) and across sectors by focusing on two system archetypes: i) “band aid solutions”, where the benefits brought about in the short-term come at the expenses of delaying long-term adaptive actions; and ii) “success to the successful”, where some sectors increasingly benefit from climate services at the expenses of others. The next sections will briefly recap and update the work on system archetypes across LLs and describe long-term patterns emerging from human-climate interactions with reference to the success to the successful archetype and band-aid solutions. The deliverable concludes by providing recommendations to prevent maladaptation when designing and implementing climate services. 1.1. Potential maladaptive dynamics across living labs The work carried out for deliverable 4.2 (sections 1.2 and 1.3) was further expanded into a framework for the ex-ante assessment of maladaptation risk arising from the development and use of CSs. This framework is described extensively in the research paper "Thinking Systemically about Climate Services: Using Archetypes to Reveal Maladaptation" (Biella et al., 2024a) which explores how CSs can sometimes lead to unintended maladaptive outcome. This section reports the main outcomes. While CSs are often promoted as "no-regret" solutions that inherently improve adaptive capacity, we challenged this assumption by arguing for a more systemic approach that includes the potential for unintended consequences that have negative impacts on the long-term (Boon et al., 2021, 2022). Consequently, examples of maladaptation were identified across the LLs of I-CISK using our assessment framework based on system archetypes. These archetypes were then classified as maladaptive using a framework developed by Magna next al. (2016). A core contribution of the paper is the development of a maladaptation assessment framework to better understand and anticipate the unintended consequences of using CSs in complex socio-ecological systems (SES). The study lays the groundwork for novel methodologies for the ex-ante assessment of the risk of maladaptation arising through the use of CSs which aims to provide practical tools for adaptation managers and CS developers. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 1.2 Identifying maladaptation risk: a framework The research integrates system archetypes with the concept of maladaptation. We argue that the common "no-regret" assumption—where CSs are expected to only improve adaptive outcomes—is overly simplistic, as it overlooks the complex feedback mechanisms inherent in socio-ecological systems (Boon et al., 2021, 2022). Instead, we propose a more comprehensive approach that considers how CSs interact with various socioecological dynamics, potentially leading to maladaptive outcomes. The framework builds on the pathways to maladaptation described by Magnan et al. (2016), itself based in the mechanisms identified in a case study by Barnett and O’Neill (2010), which outlines five mechanisms through which adaptation measures can inadvertently increase vulnerability: 1. Increased Resource Exploitation: Adaptation measures that initially mitigate risks can encourage behaviours that intensify resource use, eventually leading to overexploitation and increased vulnerability. 2. Disproportionately Burdening the Most Vulnerable: Some adaptation measures may benefit certain groups while transferring risks to more vulnerable groups, thereby increasing inequalities. 3. High Opportunity Costs: The cost of implementing certain adaptation strategies may be higher than equivalent alternatives, making them inefficient in the long term. 4. Reduced Incentives to Adapt: Certain adaptive measures can create dependency on external support or discourage proactive adaptation, leading to stagnation. 5. Path Dependency: Adaptation strategies can create pathways that limit future adaptation options, locking systems into particular practices that may become maladaptive over time. We integrate these mechanisms with system archetypes, which are recurring patterns in systems dynamics that help conceptualize the interactions between CSs and adaptation/maladaptation processes (Meadows et al., 1972; Wolstenholme, 2003; Mirchi et al., 2012; Moallemi et al., 2022). This approach allows us to identify specific scenarios where CSs might inadvertently lead to maladaptive outcomes, providing a theoretical framework that links CSs, adaptation, and maladaptation. The methodology used in this study is designed to reveal the complexities of how CSs interact with socio-ecological systems and to assess the potential for maladaptive outcomes before they occur. It follows a grounded theory approach, involving iterative cycles of data collection, analysis, and hypothesis development, which are then tested and refined through engagement with stakeholders. This method emphasizes the importance of integrating theoretical insights with practical experiences, making it suitable for exploring the dynamic interactions between climate services and socioecological adaptation processes. The methodology consisted of four main steps: 1. Desk Research: The first step involved gathering data on the environmental, social, and economic characteristics from the I-CISK LLs (Masih, 2022). 2. Survey Development and Administration: Based on the desk research, the authors designed a survey aimed at identifying the presence of system archetypes in the case studies. 3. Interviews with LL Leaders: Following the survey, the authors conducted interviews with the LL leaders from LLs. These interviews allowed for a deeper understanding of the dynamics identified in the survey, validating or refining the hypothesized archetypes. 4. Maladaptation Assessment: The final step involved using the pathways to maladaptation framework by Magnan et al. (2016) to evaluate whether the processes identified in each case study constituted maladaptation. The authors also applied the typology of maladaptation developed by Juhola et al. (2016) to classify the observed maladaptive processes into three types: rebounding vulnerability; shifting vulnerability; and eroding the conditions for sustainable development. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 2. Maladaptive processes across the LLs The methodology was applied to all I-CISK LLs and was able to identify eight possible maladaptive processes corresponding to three system archetypes across five LLs, namely Spain, Italy, Georgia the Netherlands and Greece (Figure 1). Figure 1. Archetypes of the interaction between climate services and adaptation (Biella et al., 2024a) 2.1 Fixes that fail: short-term solutions with long-term consequences The fixes that fail archetype describes scenarios where short-term solutions address immediate symptoms of a problem but fail to tackle the underlying causes, ultimately worsening the situation over time. This pattern may emerge in the project LLs where CSs are being increasingly used to support agricultural practices based on potentially unsustainable use of water resources (Fig. 1a). It should be mentioned, however, that research work in I-CISK, especially in WP2, has shown that today’s decisions are primarily made reactively and based on past experience or local knowledge. Yet, it is expected that reliance on CS will plausibly increase in several places around Europe and beyond. In the Spanish LL, for instance, dairy farming has become a critical economic activity despite the increasing drought risk. Potentially, CSs like seasonal drought forecasting could provide valuable information that allows farmers to anticipate water shortages and adjust their practices accordingly. While this adaptation strategy improves resilience in the short term, it might lead to increased groundwater extraction, which depletes local water resources. Similarly, in the Alazani river basin in the Georgian LL, the use of seasonal streamflow forecasts supports the expansion of the region’s wine production industry. This economic development This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 strategy is heavily reliant on water-intensive irrigation systems, which the climate services help to optimize. However, this increased water use occurs in a region already vulnerable to desertification, raising concerns about long-term sustainability. As a result, the CSs enable a temporary improvement in resilience while fostering practices that may undermine the region’s ecological stability over time. In both cases, the “fixes that fail” archetype illustrates how heavy reliance on CSs can initially mask the unsustainable nature of resource use. These services provide short-term relief but may lead to greater vulnerability in the long-term as resource depletion worsens. The study suggests that CSs should provide a systemic perspective to identify and mitigate such risks, ensuring that short-term gains do not come at the expense of long-term sustainability. 2.2 Band-aid solutions: tension between short-term and long-term adaptation The band-aid solutions archetype is characterized by a tension between short-term solutions, which address immediate needs, and more fundamental long-term strategies that can achieve lasting adaptation. While short-term CSs like seasonal forecasts offer quick benefits, they can delay or inhibit the adoption of transformative adaptations that address the root causes of climate vulnerability (Fig. 1b). In this archetype, it is hypothesized that while seasonal forecasts have the potential of informing decisions about water resources use for intensive dairy farming during drought, the mitigation of drought-driven impacts may actually discourage the adoption of more sustainable strategies informed by long-term CSs. As a result, short-term gains undermine the potential for a more sustainable long-term adaptation. A similar dynamic exists in the Italian LL. Here, agricultural practices heavily depend on irrigation, which is supported by short-term CSs. While these services help farmers manage water use during droughts, they do not address the long-term ecological limits of the region’s water resources. The short-term focus of these services can prevent transition to more water-efficient practices or crop choices that might be needed to ensure sustainability under future climate conditions which would instead benefit from long-term climate services. In the Dutch LL, for example, the band-aid solutions archetype might manifest in the use of seasonal forecasts to manage saltwater intrusion affecting agriculture. Farmers use this information to adjust their practices without having to consider more transformative changes, such as switching to crops that are more tolerant of saltwater. This short-term adaptation offers immediate relief, but delays necessary adjustments that would be more effective in the long-term. Across these examples, the band-aid solutions archetype reveals the risk that short-term CSs may reinforce a reliance on short-term measures at the expense of long-term resilience, which would instead benefit from long-term CSs. The authors suggest that integrating both shortand long-term perspectives into climate services could help balance immediate needs with the necessity for transformative change, guiding adaptation strategies that are sustainable over time. 2.3 Success to the successful: inequities in climate service benefits The success to the successful archetype highlights how certain groups can gain disproportionate benefits from CSs, which can exacerbate existing social and economic inequalities and lead to maladaptation. This dynamic occurs when more resource-rich or organized stakeholders are better able to access and leverage climate information, while less privileged groups are left behind (Fig. 1c). In the Dutch LL, agricultural stakeholders, who are better organized and have more experience with using CSs, are able to influence the co-creation of these services more effectively than the largely unorganized recreationists. As a result, the CSs developed are more aligned with the needs of farmers, potentially sidelining other interests. In the Greek LL, a similar dynamic is observed between the tourism industry and agriculture. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 Although agriculture is the primary livelihood for many on the island, the tourism sector is better represented in the co-creation of climate services. This may lead to the development of CSs that prioritize the needs of tourism businesses, potentially at the expense of the agricultural community. As the tourism sector becomes more climate-resilient with the help of these services, the gap between the two sectors could widen, making it harder for agriculture to remain a viable livelihood, consequently losing its ability to adapt to changing socioecological and climate conditions. The study suggests that addressing this inequity requires more inclusive cocreation processes that actively involve all relevant stakeholders. Ensuring that diverse interests are represented can help prevent the development of "success to the successful" dynamics, where climate services serve only the interests of more powerful groups. 2.4 Identifying maladaptation Once the processes are described and evaluated using system archetypes, our methodology suggests the evaluation of such processes using the Pathways to Maladaptation Framework proposed by Magnan et al. (2016). Additionally, the identified maladaptive processes were classified according to the typology proposed by Juhola et al. (2016). Table 1 showcases how the framework was used for this evaluation. Table 1. Mechanisms and typologies of maladaptation identified in the various case studies. The five mechanisms are those described in the pathways to maladaptation framework by Magnan et al. (2016), while the typology is that provided by Juhola et al (2016). The acronyms in the first row meaning: Increased Resource Exploitation (IRE); Disproportionally burdening the most vulnerable (DBV); High opportunity costs (HOC); Reduced incentive to adapt (RIA); and Path Dependency (PD). Taken from (Biella et al., 2024). Country Archetype Pathways to maladaptation (Magnan et al. 2016) Typology of maladaptation (Juhola et al, 2016) IRE DBV HOC RIA PD Georgia Fixes that fail Rebounding vulnerability Greece Success to the successful #1 Shifting vulne rability Success to the successful #2 Shifting vulnerability Italy Band - aid solutions Rebounding vulnerability; Eroding sustainable development Netherlands Band - aid solutions Rebounding vulnerability; Eroding sustainable development Success to the successful Shifting vulnerability Spain Fixes that fail Rebounding vulnerability Band - aid solutions Rebounding vulnerability; Eroding sustainable development This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 Figure 4. Sectoral indices. Based on preliminary analysis of the selected sectors: (A) Food cost index, (B) Tourism water scarcity index, (C) energy scarcity index, and (D) road inaccessibility index, under the three scenarios: conventional scenario (Conv), upgrading agricultural sector to secondary level - AG(sec), and ideal scenario - All(Prm). The Y-axis represents values of indices (min = 0, and max 1). Overall system resilience To simulate the overall system resilience, the sectoral indices were used to define the overall attractiveness of the Island. In all the three scenarios, the results showed a decreasing pattern of the overall attractiveness of the Island for tourism over the simulation period (Fig 5). We observed that when the overall attractiveness value is relatively high (above a defined value), the model simulated higher number of tourists in the Island (Fig 6A). Hence, touristic pressure in the first 35 years, after which the Island attractiveness dropped below a threshold, reducing the number of tourists over time. The decreasing Island attractiveness coincided with increasing local permanent human population estimated from approximately 600,000 persons at the beginning of the simulation to over 2.5 million after 100 years (Fig 6B). (A) (B) (C) (D) (C) This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 Figure 5. Overall attractiveness of the Island for tourism, under the three scenarios: conventional scenario (Conv), upgrading agricultural sector to secondary level - AG(sec), and ideal scenario - All(Prm). Based on preliminary analysis. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 Figure 6. Dynamic human population: (A) estimated number of tourists, and (B) local permanent population. The Y-axis represents the population numbers. Based on the preliminary analysis. (A) (B) Touristic pressure This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 Implications of the preliminary results The preliminary results show that power imbalances between various stakeholders may have an influence on the sectoral adaptation efficiencies. The consistency observed on the primary sector’s adaption efficiency, may imply the success to the successful dynamic. We found a significant difference between the indices of the primary and non-targeted sectors. But this difference changes when non-targeted sector is upgraded into a secondary or a primary sector category. This implies that primary sectors are likely to continue benefiting at the expense of others due to the higher power, status and privileges. These preliminary results also indicate that some system outcomes may not be noticeable in the short term but negative outcomes can occur in the long run e.g. energy scarcity and road inaccessibility. This emphasizes the importance of system dynamic modelling which can be integrated in the co-production process of CS to give a system overview. Especially in evaluating and accounting for power imbalances among various stakeholders. Assuming that the number of tourists is affected by the overall status of the Island (i.e. attractiveness of the Island for tourism), the preliminary results revealed a decreasing pattern over time. This implies that the targeted sector can benefit from CS based decision-making in the short term but expect different results in the long run. This is because the overall status of the Island also depends with other sector indices (interlinkages) e.g. food costs, water availability, energy scarcity, road usability etc. This further implies a need for a system-thinking approach and not a sectoral-thinking approach in the co-designing and co-development of CS products. Lastly, these initial outcomes also reveal that increasing number of tourists in the short run, increases pressure on other sectors e.g. demand for energy, road use, water, food etc. The touristic pressure causes a higher sectoral variability especially in the first 35 years. This changes when the attractiveness of the Island falls below a particular threshold. However, towards the end of the simulation sectoral variability can also be seen, which can be linked to the increasing local permanent population over time. Therefore, in the designing and development of CS products, it would be important to consider the long-term dynamics of both the climate variability and human population pressures. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 4. Long-term patterns emerging from the band-aid solutions archetype To evaluate and assess the socio-ecological dynamics emerging from the use of CSs in the LLs, with particular focus on unintended consequences and maladaptive processes, the ex-ante maladaptation assessment framework is expanded upon into numerical system dynamic models. Specifically, our investigation focusses on the band-aid solutions archetype, where CSs play a role in the competition between short-term and longterm adaptation. We developed a simple synthetic model hypothesizing that reliance on short-term adaptive measures can perpetuate unsustainable practices and disincentivize the implementation of long-term adaptation and more fundamental adaptive transformation, as previously described in figure 1b (section 2). For the sake of generality, the model aims to be “as simple as possible, but not simpler” (Sterman, 2001). Hence, in this preliminary work, the model aims to describe the competition between two types of agricultural practices. One that is a more economically profitable but water-demanding, termed as "unsustainable production" (UP). The other is less economically profitable, but water-efficient, referred to as "sustainable production" (SP). The decision-making process between these types of agricultural practices is assumed to be influenced by different types of CSs. Short-term services, such as seasonal drought forecasting, can enhance the capacity of UP to mitigate the impact of seasonal hazards like droughts. This allows the continuous expansion of UP due to its higher immediate profitability. In contrast, long-term services, such as climate projections, have the potential to encourage a broader perspective, highlighting the need for sustainability and supporting a larger shift towards SP. Modelling maladaptation A simple model was developed to describe the dynamics resulting from two types of agricultural productions (UP and SP) linked by shared, and limited, water resources. For the purpose of generalization, we characterize the pool of water resources, with one variable being its height (h). This could be representative of a confined aquifer, where h is the level of the water table, or a reservoir, where h represents the water level in the reservoir. In the first equation, the change in accumulated wealth (W) over time is hypothesised to equal to the sum of the share (%) of unsustainable production (UP) multiplicated by a factor of the extracted water (h-h1). For the sake of simplicity, the share of sustainable production (SP = 1-UP) is assumed to not requiring any water. The parameters A and B indicate the profitability of each production type. UP is assumed to be more profitable: The second equation expresses the change in water height (h) over time that depends on the extraction for UP, and the total recharge (R): This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 The two differential equations can be written as a system of equations where wealth is progressively generated for each time step by using water, which is in turn expressed as changes in water resource height. The equation below describes this relation: These equations describe simple system where wealth generation and water availability are coupled and conditioned by the share of UP. Wealth can be quickly generated via UP as long as abundant water is available. However, if the production is larger than the recharge rate, the system collapses (e.g. aquifer depletion) and this leads to a drop-in wealth generation. Figure 2 shows initial results of the model for a scenario with 50% sustainable production and 50% unsustainable production. All variables are maintained constant and their exact values are not necessary for the understanding of the current model. Figure 7. Relation between accumulated wealth and water resource height as described above by the model for a scenario with 50% sustainable production and 50% unsustainable production. Introducing CSs in the model: Preliminary results Climate services are introduced in the model by informing which type of production will grow over time. We assume that if the system is informed by short-term CSs the UP can become more resilient to seasonal hazards and variability and therefore expand at the expenses of the SP. On the other hand, if the system is informed by long-term CSs, the SP will instead be favoured, leading to its relative expansion. Figure 3 describes the relation between the elements of the described system using a Causal Loop Diagram (CLD). The current version of the model includes CSs as informing the type of adaptation scenarios chosen, but, while theoretically linked to the changing precipitation, they are currently defined by a variable external to the model. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 Figure 8. Causal Loop Diagram of a system where climate services are used to inform the adaptation strategy chosen and where a sustainable and an unsustainable production compete over the use of water resource. In this version of the model, CSs are assumed to inform the shift in sustainable and unsustainable production (i.e. the “adaptation scenario”). The starting state of the system is 50% SP and 50% UP. The scenario is run for 150 years. To this end, five scenarios were devised: 1. Current State: CSs do not inform adaptation and no change is introduced over time. The percentage remains 50% SP and 50% UP all throughout the runtime. 2. Gradual Transition: long-term CSs progressively show the need to shift towards more SP. Gradually, the system shifts towards a bigger share of SP (0.5% /year). 3. Sudden Transition: The need to transform the production in the system is initially resisted and no change is made until the water resource drops below a certain height (here set as 2m). Once this threshold is passed, the system switches entirely to SP. 4. Sustainable Production only: This scenario offers a baseline where the system is entirely defined by SP (100%). 5. Unsustainable production only: Similar to the previous one, this scenario offers a baseline where the system is entirely defined by UP (100%). The model introduced earlier was used to run the above-described five scenarios. The results from the model show how the base scenario where UP has a more significant share of the total production have a more rapid growth of accumulated wealth, yet the water resource is also depleted quicker. Scenario 1 and 5 in particular also show that no changes in adaptation strategy will also lead to the complete depletion of the water resource with the consequent impossibility to further generate wealth and consequent system collapse (i.e. the complete depletion of the water resource). Scenarios that shifted towards more SP instead show the possibility to avoid the system collapse. Figure 4 describes the changes in wealth and water resource height over time for the 5 adaptation scenarios described above. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 Figure 9. Preliminary results: comparison of the five adaptation scenarios with regard of water resource height and accumulated wealth. The scenarios assume unrecoverable system collapse if the water resource is completely depleted (i.e. h=0). Hence, the scenario stops running and the wealth variable (W) stops changing. Future developments of the model The model presented in this deliverable constitutes a first step towards the numerical modelling of the effect of CSs on the competition between shortand long-term CSs on the socio-ecological system (i.e. the dynamics described by the band-aid solutions archetype). The model still needs to be refined before being able to be applied to specific LL. For example, CSs are currently described as an external variable to the systems, with their effect only theoretically assumed to inform the adaptation scenario. The final objective of our modelling effort is to instead introduce CSs as an endogenous variable to the system which is in turn informed by changes in the SES. Additionally, instead of using scenarios, the shifts in UP could be directly parameterized as a function of the information used, with a preference for shortor long-term adaptation being the driver for the direction of the shift. Moreover, as highlighted in I-CISK related work, the decision-making process is influenced by many factors, including local knowledge, in addition to CSs. The next step in the development of the model will be to tailor it to the LLs where this type of dynamic could be identified and evaluate it by using observational data. The resulting models will provide insights into the processes governing the sustainability of the system with respect to CSs. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037293 5. Conclusions The work conducted in WP4 reveals that CS may not always provide "no-regret" solutions in climate adaptation efforts. Instead, their development and use can potentially lead to unintended consequences, some of which may represent maladaptive practices (Biella et al., 2024). Our research highlights the critical need to factor in maladaptation risks when working with CSs in adaptation strategies. We offer theoretical insights, tools, and frameworks to support this approach. System archetypes— such as “fixes that fail,” “band-aid solutions,” and “success to the successful”—help explain how CSs can unintentionally promote unsustainable practices, impede transformative change, and deepen inequalities. These insights point to the complex relationship between adaptation and maladaptation, underscoring the importance of assessing potential maladaptation risks when designing and implementing CSs. The assumption that CSs always lead to "no-regret" solutions should be reconsidered in favour of a more nuanced view that accounts for trade-offs and unintended consequences. Additionally, the research stresses the importance of inclusive, collaborative processes that consider the interests of all stakeholders, helping to minimize disparities in CS access and avoid reinforcing success-to-success dynamics. We advocate for adaptation managers and CS developers to incorporate a systemic view that accounts for maladaptation and the potential for unintended consequences. This study demonstrates how linking CSs with both adaptation and maladaptation through system archetypes can serve as a foundation for creating tools to assess maladaptation risks in CS development and adaptation planning. To that end, we introduced an initial conceptual model showing how CSs interact with adaptation and maladaptation (Fig. 8). We also found system archetypes to be useful in this type of conceptualization and highlighted the pathways to maladaptation framework (Magnan et al., 2016) as a key tool for evaluating maladaptive processes. Future research should expand on these findings by incorporating more archetypes and developing practical tools, such as checklists, decision trees, and best-practice guidelines, to help CS developers and adaptation managers assess the risk of maladaptation. In summary, while CSs offer significant potential for enhancing climate adaptation, their design, implementation, and the interests they prioritize are crucial in determining whether they result in adaptive or maladaptive outcomes. 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