Public investments in Climate Change Adaptation (CCA) are progressing, and the private sector is being increasingly mobilised. Yet, the prioritisation of these investments remains a key challenge due to the scarcity of resources and the often long-term, uncertain nature of CCA benefits. As a result, academics are now turning to multi-criteria assessment to support decision-making by capturing multiple benefits of CCA investments.

Despite growing recognition of multiple CCA benefits, the economic return of CCA investments remains one of the least understood, representing a persistent gap in current research. This gap is critical given that studies have found that the economic and social co-benefits often deliver the greatest overall benefits, even more so than avoided losses.

This study provides the first quantitative macroeconomic assessment of CCA using a Multi-Region Input Output (MRIO) model to map the output and value-added effects of CCA investments that flow through the European economy. The analysis covers 19 sectors across 27 European countries for five distinct climate hazards and three reference years, employing a Global Value Chain (GVC) framework combined with a Global Sensitivity Analysis (GSA).

From a theoretical perspective, this study contributes by revealing pivotal CCA sectors, European sectoral and national value-added allocation, distinct GVC characteristics and economic return metrics that can enhance CCA prioritisation. From a societal perspective, the findings provide policy-relevant insights to guide more effective adaptation strategies. First, by identifying pivotal sectors that can be targeted for industrial policies. Second, by identifying economic spillovers across countries, this study aims to reopen discussions on EU-level coordination. Third, by identifying high-macroeconomic-return on CCA investments, this study aims to support the prioritisation of CCA investments.

Trust is key to the success of climate adaptation strategies. However, the level of trust varies between different information sources. Without trust in the source, information from a source or policy could be disregarded.

As the intensity of climate change is increasing, flood adaptation measures are not keeping up with rising flood risks. This study therefore analyses the influence of trust in information on floods and flood adaptation on different types of flood adaptation strategies. Based on a UK case study using survey data from the SCALAR-team within the European Research Council project, a descriptive analysis, a binary logistic regression and a clustering analysis have been performed to answer the research gap.

Results showed that rural areas tend to trust information sources less than urban areas, except when the information originates from family and friends. However, trust in information sources is not always associated with the intent to take flood adaptation measures. Sources such as family and friends, social media and the prime minister are associated with an increase in certain flood adaptation measures, whereas trust in a government representative is associated with a decrease in the intent to take certain flood adaptation measures. Moreover, the clustering analysis identified five distinct groups of respondents, showing the importance of area-specific policies as local trust characteristics vary.

It is advised to look into longitudinal studies using the UK survey data to further establish the relationship between trust and flood adaptation strategies. The results of this study are not generalisable, but the methods are transferable for future research. Acknowledging the power of trust is fundamental for designing successful flood adaptation policies.

This thesis estimates the economic effects of temperature, precipitation, and relative sea level rise (SLR) on regional economic growth in Europe from 1900 to 2015. A central starting point is the model developed by Burke, Hsiang, et al. (2015) (BHM), which provided the first global evidence that aggregate economic growth responds non‑linearly to temperature, with growth peaking at an annual average temperature of 13 °C and declining sharply at higher temperatures. Rising temperatures can reduce productivity and agricultural yields, especially in warmer regions (Somanathan et al., 2021). Shifts in precipitation patterns can cause longer dry periods or heavier rainfall, increasing the risk of floods and stressing water systems and infrastructure (Kotz et al., 2022; Malhi et al., 2021). SLR increases the risk of flooding and loss of land in low‑lying coastal areas (Cortés Arbués et al., 2024; Chatzivasileiadis et al., 2023).

This thesis addresses key gaps in the climate econometric literature by re‑analysing and extending the BHM model with a regional dataset and additional climate variables for over a century. In doing so, it moves beyond theory‑based models by relying on observed historical data to estimate the economic effects of climate variables with greater spatial and temporal detail. This research uses a quantitative approach, applying an econometric model to regional (NUTS‑2 level) climate data from the Climatic Research Unit, University of East Anglia (n.d.) and economic data by Rosés et al. (2021). Following Clemens (2017), it performs a re‑analysis and extension of the BHM model to examine whether its findings remain valid, testing the stability of the concave relationship between temperature and economic growth while also including precipitation and SLR to capture the combined effects of multiple climate variables.

The results from this research support the overall concave shape of the relation between temperature and economic growth, meaning that even over a longer time frame, at the regional level, and with the inclusion of an additional climate variable, temperature and economic growth are linked in a non‑linear way, with growth peaking at an optimal temperature of 11.8 °C. Temperature has the strongest effect on economic growth among the climate variables included in the model. Precipitation does not show a statistically significant effect on its own, but it is jointly significant when included with other climate variables. SLR has a smaller (compared to temperature) but statistically significant impact, suggesting that it plays a meaningful role alongside temperature in shaping economic outcomes. However, the estimated coefficients for temperature in this research are approximately six times larger than those in the model by BHM, indicating stronger temperature sensitivity at the regional level. The sensitivity analysis further shows that these coefficients are not stable, particularly across different benchmark years, suggesting that the estimated turning point is shaped by historical context and should not be interpreted as a fixed economic threshold.

Future research should build on this work by using micro‑level climate and economic data to examine the effects of seasonal variation, heatwaves, extreme rainfall, and other short‑term events (Kotz et al., 2024; Somanathan et al., 2021). This could be combined with models that include adaptation processes or delayed responses to climate change (Mérel et al., 2021). The use of only twelve benchmark years in this study limits the ability to identify such short‑run effects. Increasing temporal resolution would improve understanding of both short‑term shocks and long‑term structural patterns.

It will also be essential to better understand how climate variables interact. Temperature is a known driver of both precipitation and SLR, through its influence on atmospheric moisture and ice melt (Malhi et al., 2021). These dependencies should be explicitly modelled in future studies to avoid misattributing indirect effects or underestimating compound risks (Kotz et al., 2022). Finally, moving beyond the reduced‑form approach by BHM would allow for more flexible models that capture causal mechanisms and account for the possibility of multiple optima, thresholds, or plateaus that a fixed quadratic form may overlook. These steps are essential to ensure that econometric models can support meaningful and context‑specific design of climate policies.

This study explores the potential of nature-based solutions (NBS), specifically green roofs and walls, to mitigate extreme heat in arid urban environments like Riyadh. Using fieldwork, interviews, and remote sensing, a microclimate model is developed to assess the feasibility and effectiveness of these inter- ventions. Findings highlight that combined green roofs and walls provide the greatest cooling benefits, reducing temperatures by up to 1.31°C for the tested scenarios, but come with significant challenges, including high costs, structural limitations, water scarcity, and maintenance needs. Reflective coatings emerged as a cost-effective alternative, offering consistent but modest cooling. The study emphasizes the need for targeted, localized cooling efforts and raises concerns about the feasibility of widespread green infrastructure implementation in arid regions.

This research investigates the dynamic nature of place attachment and its critical role in shaping household-level climate change adaptation, particularly in the context of flood damage reduction. With the increasing frequency and intensity of flooding due to climate change, understanding the factors that drive households to adopt adaptation measures is essential for developing effective policies. While many studies have explored various psychological and social factors influencing household adaptation, this study uniquely focuses on place attachment—a concept that, despite its significance, has often been underexplored and treated as static in existing literature.

This thesis approaches place attachment as a multi-dimensional and dynamic factor that evolves over time and interacts with household adaptation decisions. By conceptualizing place attachment in this way, the research provides a more comprehensive understanding of its influence on flood resilience. The study employs an agent-based model (ABM) grounded in the Protection Motivation Theory (PMT) and is supported by statistical analysis, data analysis, and a review of existing literature. The case study is based in the Houston, Texas region, a coastal urban area prone to flooding, using survey data that includes specific measures of place attachment and household adaptation behaviors.

The research identifies two distinct dimensions of place attachment: active and traditional. Active place attachment involves engagement and participation in the community, while traditional place attachment reflects a more passive, sentimental connection to the place. These dimensions were found to be significant drivers of household adaptation measures such as elevation, wet-proofing, and dry-proofing. However, the study also reveals that the relationship between adaptation measures and place attachment is complex; certain adaptations can either strengthen or weaken place attachment, leading to mixed outcomes in terms of flood resilience.

Key findings include the synergistic effect of strong active and traditional place attachments, which together drive higher adaptation rates and more significant reductions in flood damage. The study also highlights the profound impact of flood experiences on household adaptation, which is heavily influenced by the state of place attachment. For example, if a flood weakens place attachment, the motivation to adapt decreases, leading to outcomes similar to those in scenarios where no flooding occurred. Additionally, the research underscores the importance of public protection measures, noting that while they can reduce flood risk, they may also inadvertently damage place attachment, thereby reducing household-level adaptation efforts.

From a policy perspective, the findings suggest that fostering both active and traditional place attachments can be a powerful tool in motivating households to implement flood adaptation measures. Policymakers should consider strategies that enhance place attachment as part of broader efforts to improve flood resilience.

Despite the valuable insights provided, the research acknowledges several limitations, including a small sample size in later survey waves and the reliance on limited literature for modeling dynamic place attachment. The absence of panel data to capture the evolution of place attachment over time is also noted as a limitation. Future research should aim to address these gaps by conducting larger, more targeted surveys and collecting longitudinal data to better understand the factors that influence place attachment and its role in household adaptation.

Climate change intensifies the frequency and severity of flooding in urban areas, such as Jakarta. While government-led adaptation measures are crucial, the limitation of planned adaptations in providing complete protection emphasises household-level strategies in mitigating risks. However, limited adaptive capacity and bounded rationality can lead to maladaptive behaviours. This study investigates the dynamics of household-level climate change adaptations in Jakarta, focusing on their impact on vulnerability and potential maladaptation. Employing a mixed-methods approach, the research analyses micro-level survey data and agent-based modelling to examine adaptation measures like elevation, dry-proofing, and wet-proofing. Findings reveal that socioeconomic factors, such as income and fatalistic attitudes, influence adaptation choices. Low-income households are particularly vulnerable to suboptimal strategies, like wet-proofing, which can exacerbate their risk. The study also highlights a discrepancy between these measures' perceived and actual efficacy, emphasising the need for targeted policies. Recommendations include promoting effective adaptation, providing targeted financial aid, and enhancing community and governmental support. By addressing these factors, policymakers can reduce the likelihood of maladaptation in urban Jakarta.

The increasing impacts of climate change are presenting numerous challenges globally, with one of the most pressing being the heightened risk of flooding due to rising sea levels and more intense rainfall patterns. However in many countries, among which the Netherlands, a significant portion of residential properties are at risk of flooding—a risk that is often underappreciated or overlooked by homeowners. The primary focus of this thesis is to explore the potential introduction of a flood risk disclosure policy, referred to as the "Climate Label," and its implications for real estate development practices.
By making flood information readily available, the policy aims to influence market values and guide investment decisions, potentially encouraging more resilient housing developments. The central question of this study examines how such a policy might affect the decision-making processes of real estate developers and, consequently, the distribution of flood risks across new residential constructions.

The exploration of this topic is timely and significant, given the scarcity of existing literature on the impact of flood risk information policies on housing supply and developer behavior. Real estate developers, particularly those in the private sector, play a crucial role in the construction of new housing and their profit-driven motives make their responses to such policies particularly insightful for understanding broader market dynamics.

This research employs an Agent-Based Modeling (ABM) approach to simulate the interactions within the housing market, developer decision-making, and assess the potential outcomes of implementing the Climate Label. Moreover, interviews with real-estate developers are conducted to achieve a deeper understanding of their decision-making, their current perspective of building flood-resilient housing, and how a Climate label would influence such.

The model results it can be concluded that a housing value shift due to flood risk disclosure can cause a change in the development patterns of new residential houses. However, the distribution of flood risks is highly dependent on the magnitude of the price shift, whether developers perceive it as a gain or loss, and the reference point they use in their project decision-making to assess gains and losses.

Amid the Anthropocene, the escalating threat of flooding, driven by extreme rainfall and sea-level rise, challenges societies worldwide. In the last two decades, floods have impacted billions and inflicted colossal economic losses. Concurrently, the global trend towards urbanization predicts that by 2050, about 70\% of the global population will inhabit urban areas. This demographic trend, heavily influenced by agglomeration forces, further underscores the vulnerability of these urban centers, many of which are precariously situated in flood-prone areas. Given the confluence of escalating climate risks and the surge in populations settling in vulnerable zones, a pressing question emerges: How will rapidly urbanizing coastal societies adapt to intensifying flood risks in the face of escalating climate-induced shocks and changing regional economic landscapes?

To address this multifaceted issue, this dissertation delves into the complex nexus between climate shocks, regional economic dynamics, and societal responses. Central to this exploration is the creation of innovative simulation tools tailored to incorporate the autonomous adaptation strategies of various actors within a regional economic framework. This thesis stands at the forefront of a new wave of computational models that encompass risk and embed resilience into complex adaptive systems.

I commence by examining the current advancements and gaps in employing Agent-Based Models to unravel the dynamics of flood risk and adaptation assessments. In this exploration, I underscore the pivotal role of human actions in shaping risks and resilience within flood-prone urban settings.

Building on this foundation, I introduce the Climate-Economy Regional Agent-Based (CRAB) model. The CRAB model employs an evolutionary perspective to provide a comprehensive view of the balances struck between the driving forces of economic agglomeration and the counteracting pressures of climate hazards. It focuses on the decision-making of heterogeneous agents, representing households and firms, as they navigate the choice of relocation between safer inland regions and hazard-exposed coastal zones.

Venturing further, I enhance the CRAB model to embody autonomous household adaptation behaviors, drawing from empirical data. Here, I challenge the traditional reliance on rational agents in sustainability models, unveiling a notable adaptation deficit when juxtaposed against boundedly-rational choices gleaned from real-world surveys. This nuanced exploration uncovers how varied adaptive capacities can potentially accentuate inequality and impede resilience.

Subsequently, I include in the CRAB model a layered risk strategy that encompasses an array of climate change adaptation measures. This refined model, enriched by extensive behavioral and flood data, bridges existing gaps in the current understanding of feedback loops and cascading effects triggered by flood shocks within a socio-economic system of boundedly-rational agents.

In conclusion, this dissertation pioneers a unique trajectory in understanding societal responses to the specter of flooding, offering invaluable insights and frameworks for devising future climate-resilient strategies.

Flooding, one of the costlier climate change disasters, has emerged as a pressing global challenge, growing in frequency and severity. The traditional reliance on government intervention alone to protect households from flooding is not enough. An essential shift in perspective underscores the need for households themselves to become proactive participants in multi-level protection strategies. To achieve this, drivers of vulnerability shaping household adaptation to flooding are explored, focusing on social, physical and perceived vulnerabilities. Furthermore, examining these vulnerabilities on a local scale is crucial and a knowledge gap, as it is at this level that household adaptation predominantly occurs.

Recognising the varying manifestations of these vulnerabilities depending on the flood-prone location, this study prioritises the examination of disparities in vulnerability profiles between two representative cities: Jakarta, representing the Global South, and Houston, representing the Global North. By doing so, this research contributes significantly to the ongoing scientific discourse on climate change adaptation, as well as assist in extrapolating gained insights to data-scarce regions. This research aims to assists in aligning flood management efforts more closely with the needs and concerns of the residents in each city, acknowledging the distinct perceptions of flood hazards and socio-economic disparities. This, in turn, lays the groundwork for the development of targeted support and interventions, fostering more inclusive and resilient communities capable of mitigating the impacts of flooding and adapting to future challenges. The primary research question driving this study is as follows: 'How are social, physical and perceived vulnerabilities that influence flood adaptation different among households in an urban space?' To answer this question comprehensively, the research employs a multi-faceted approach, examining each vulnerability dimension separately before exploring their interactions in the urban context.

Comparing the social, physical and perceived vulnerability maps for Jakarta in the context of flooding uncovered many discrepancies and complexities that contribute to the scientific debate surrounding flood adaptation. The disparities between these dimensions highlight the need for a comprehensive approach to vulnerability assessment and flood mitigation. By recognising that vulnerability is not solely determined by physical factors, but also influenced by social dynamics and individual perceptions, authorities can develop strategies that foster community resilience and enhance disaster preparedness. Therefore, this research recommends addressing mismatches in risk perception, understanding the nuanced distribution of vulnerabilities and implementing context-specific interventions in order to build a safer and more resilient world in the face of flooding and other environmental challenges.

Climate is changing. It is widely accepted that irrespective of the emission reductions efforts, adaptation to the already committed climate change is a must in the coming decades. Flooding is one of the most devastating climate-induced hazards, calling for adaptation across scales: from government-led adaptation (e.g. dikes), to personal household-led adaptation. There are numerous private adaptation measures, both structural and non-structural, that households can autonomously take to reduce damages and speed up own recovery, in case of the adverse event does occur. Worldwide household surveys provide the empirical evidence suggesting that the appraisal of fear, perceptions of own coping abilities and social influences affect individual decision to adapt. Especially the latter appear as a strong determinant of household-led adaptation. Yet, the role of social networks has not been studied systematically, let alone the exploration of the interplay between private households' adaptation and public policies in the presence of social influences. To increase the understanding of social networks' impact on private flood adaptation under various public policies, an empirical agent-based model is built. In addition to empirical flood maps, I employ Protection Motivation Theory and households' survey data from Houston (Texas, USA) to capture the household’s adaptation decisions, which are subject to social influence. To represent a range of social networks, I explore the effect of three random networks - Erdös-Renyi random network, Barabasi-Albert scale-free network, Watts-Strogatz small-world network -- on the diffusion of flood preparedness. These three networks could serve as a proxy of diverse types of social relationships that vary across cultures, with some being hierarchical and others egalitarian. Furthermore, I test how the diffusion of private adaptation among households evolves under four generic public policy strategies: protection by publicly-funded infrastructure, market-based policy such as subsidy, information policy and regulatory policy.
The thesis findings reveal that the type of social networks influence success of private adaptation decisions significantly. Notably, the effect of social norms depends on the network configuration, where more homogeneous networks lead to higher private adaptation uptake. Furthermore, the thesis quantifies the interaction effects between social networks and policies designed to support private climate change adaptation, for example communication campaigns affecting individual risk perceptions that in turn drive household-led adaptation. Therefore, information policies can be very effective in steering public opinions and, hence, the uptake of households' adaptation measures. Moreover, I find that in the presence of social networks, public adaptation policies interact with each other: a combination of subsidies with communication exhibit synergistic effects on the flood damage reduction from private adaptation. Policies that do not interact with households' perceptions, like infrastructure projects or laws, can be considered more robust - less sensitive to social networks. To conclude, networks can shape the success of climate change adaptation policies and should be accounted for in their design. Further research should focus on the endogenous evolution of social networks and the model behavior under changing flooding conditions and what this complex adaptive system's dynamics means for future cross-scale adaptation efforts.

Climate change intensifies the frequency and severity of floods - the most devastating and costly climate-induced hazard. Government adaptations such as dikes or beach nourishments are important, yet insufficient in the face of worsening hazards. They reduce the hazard probability, but the local actions at the household level determine the extent of damages and inequalities in its distributional impacts on various societal groups. To design effective policies and risk reduction strategies it is critical to quantify the speed and scope of the household adaptation uptake and the resulting damage prevention. This is especially the case for distributional impacts (i.e., how different societal household groups adapt), which are often neglected. Quantifying both aggregate and distributional impacts of household climate change adaptation (CCA) on flood risk fosters the design of tailored flood risk management policies which allocate resources to the societal groups that need them most. This thesis presents a state-of-the-art agent-based flood model in downtown Shanghai, China to understand the role of household CCA in reducing coastal flood risk both in its aggregate and distributional impacts. To model the households’ exposure to climate-induced floods we overlayed the geolocations of 18.039 residential buildings with 21 inundation maps that depict dike failures and dike overtopping under different climate-change scenarios. We further parameterized households using context-specific micro-level survey data from Shanghai and depicted households’ adaptation decisions using an extended version of the Protection Motivation Theory. Our results show that autonomous household adaptation (adaptation without government policy) to climate change plays an essential role in reducing flood damage in downtown Shanghai. However, despite the considerable adaptation uptake, the residual damages increase over time due to the effects of sea level rise and land subsidence. This shows that autonomous household adaptation alone is insufficient to keep pace with the increasing severity of climate-related flooding, as it is constrained by barriers in the form of adaptation measure costs and regulations. Thus, additional policies are needed to overcome these barriers. These policies should take into account differences in adaptation behaviour and damage prevention among societal household groups. Specifically, our results indicate that households with lower worry, self-efficacy, and income adapt measurably slower to climate-induced floods, which makes these household groups significantly more vulnerable to flooding.

Climate change will lead to more extreme climate and weather phenomena, and this includes the increased risk and severity of flooding. Flooding is one of the most destructive and common natural hazards globally, and rising sea levels and extreme precipitation mean that many human settlements will be situated in climate-sensitive areas.

However, the increased threat of floods in coastal cities is not currently sufficiently represented in the housing markets. Contemporary property prices are shaped by the locational advantages of coastal cities, and have a tendency to underestimate or ignore flood risk, even after experiencing a flood themselves. This increases the possibility of structural shifts in housing markets when the flood risk is realised by the public, leading to a depreciation of real-estate property values, and may cause cascading effects in other aspects of the economy, such as the collapse of the regional housing market and reduced economic attractiveness to the region. The Netherlands, for example, is arguably the best-protected delta in the world from flooding. Still, it is vulnerable to the "safe development paradox", where public flood protection measures motivate the continued investment and expansion of flood-risky areas. While Dutch safety standards are very high, they cannot guarantee absolute protection from floods; the country is still at risk from rare-but-severe flooding, or the occasional minor flood, both of which can influence the Dutch housing markets.

The deep uncertainty of future flood risk means that empirical research alone cannot sufficently describe possible responses to housing market shocks from flooding. Therefore, modelling and simulation is useful in testing potential variants of a housing market system, such as testing different dynamics of housing market actors and potential policy levers in various flood scenarios. This is further supported by an increase in publicly-available rich datasets, that allow for spatial and empirical representation of the housing market and climate-induced flood scenarios.

\noindent In this thesis, the city of Rotterdam is used as a case study, to explore the usage of empirical data and to model a housing market shocked by various plausible flood scenarios. This is done in two steps: firstly via a data exploration effort in publically-available datasets for the Netherlands, and then consolidating the data into a stylised agent-based model, simulating the transactions of the housing market while several districts experience flooding shocks from different flood scenarios.

Firstly, I have conducted the data exploration aspect using Dutch open data for flood scenarios, housing, local demographics, and empirically-estimated flood discounts. The data was judged on the suitability of the datasets to be incorporated into an empirical model, and on the presence of data gaps while linking between different data. The exploration highlights the potential of modelling the housing market using empirical demographic data based on income level, but only missing certain data to characterise how homebuyers would acquire mortgage financing. Additionally, on the flood damages side, I show the possibility of characterising flood discounts as a function of flood depth, and the depth-damage relation for the Netherlands.

In the modelling part of the thesis study, I have consolidated the data into an agent-based model with a stylised set of relationships for the housing market. The model was designed based on the empirical stylised trends regarding housing markets dynamics, like declining flood risk discounts in property prices over time. In short, the model simulates the purchase transactions of homebuyers, who are discouraged from flood-affected properties, thus leading to a growing demand and price premium for flood-safe properties. This model was then tested with a simple series of experiments, for single flooding and multiple flooding scenarios, based on empirically-grounded severe flood scenarios on Rotterdam. While the model results are limited in terms of prediction for policy purposes directly, the modelling process as a whole illustrates the value of exploratory modelling to refine the understanding of a system. Via this exploration, it was highlighted that there is a need to further characterise flood discounting behaviour around the action of housing market actors.

What exactly causes the emergence of the attitude-behaviour gap, and to what extent, is not fully understood. Previous academic research has shown that many barriers to adoption remain, and that difficulty lies in the heterogeneity of consumers. Through the use of a simulation model this thesis explores the emergence of the attitude-behaviour gap by analyzing the decision-making behaviour of heterogenous Dutch households, parametrized by real-world survey data. Ultimately, to close the gap by lowering the barriers to adoption with policy interventions. An agent-based simulation model (ABM) has been developed to address the main research question: To which extent can different psychological factors influence the emergence of the attitude-behaviour gap in household energy consumption, and what policy interventions can be employed to close the gap?

Climate change is happening and has an increasingly great impact on nature, ecosystems, biodiversity and human society. To reduce adverse consequences of hazards for current and future generations, one must understand how adaptation behavior of civil societies could develop itself over time, interact with its environment and effect its ecosystem. As the emergence of adaptation behavior is complex, differs locally and is influenced by lots of cultural, socio, environment and political factors, there are a lot of missing places on which more research on the emergence of adaptation behavior of civil societies can be done. This EPA master thesis will study is the emergence of adaptation behavior on a case-study about flood adaptation behavior in Jakarta. A modeling approach will be used to analyze the cumulative impact of autonomous bottom-up household adaptation decisions according to the Protection Motivation Theory on the overall flood resilience of Jakarta. The model will be used to explore the effect of policy interventions on household adaptation decisions, to ultimately give an advice on how to achieve a more resilient flood response. The results of this study aim to provide a better understanding on human behavior and its interactions within Jakarta’s environmentally complex situation, what could be of help for the government of Jakarta in the development of long-term flood adaptation strategies.