Amid escalating climate impacts, understanding private sector adaptation is critical. Here using data of actual adaptation expenditures from nearly 300,000 businesses in five coastal regions, we reveal variations in private sector adaptation across sectors and regions. The agriculture sector leads in adaptation efforts, while transport, construction and utilities—that is potential sources of system-wide cascading effects—lag. Small, medium and large businesses prioritize hard and soft measures, barely investing in ecosystem-based adaptations. Adding to the multifaceted discourse on adaptation effectiveness, our panel data reveal positive, although inelastic in the short run, relationships between private sector adaptations and aggregate regional economic performance. Business adaptations in the construction, transport and health sectors associate positively with regional economic performance, with the accommodation and food services sector yielding the highest return per euro invested in adaptation. Combining these findings with existing assessments of adaptation could support the development of societally effective adaptation strategies.

Climate policy faces increasingly complex challenges that span multiple human decision scales in nature-society systems. Contemporary climate policy models, while valuable and increasingly versatile in handling spatial and temporal scales, struggle to capture interacting multiscale decisions on the socioeconomic side. This perspective draws attention to the power of coupling among different modeling families, taking integrated assessment models (IAM), computable general equilibrium models (CGE), and agent-based models (ABM) as examples. Recent computational advances, maturity of models, availability of data, and interdisciplinary expertise make model coupling an increasingly feasible, effective, and useful tool for climate policy analysis. We examine the unique contributions of each modeling approach, highlight synergies from uniting their strengths, and discuss alternatives to and conditions for coupling. In addressing methodological challenges, we present examples of effective coupling of IAM-ABM-CGE, emphasizing the importance of maintaining model integrity while enhancing policy relevance. By bridging human decision scales and leveraging complementary strengths, coupled models can provide nuanced insights into climate-economy interactions, ultimately supporting effective and equitable-not just efficient and optimal-climate policies.

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.

Despite the growing calls to integrate realistic human behavior in sustainability science models, the representative rational agent prevails. This is especially problematic for climate change adaptation that relies on actions at various scales: from governments to individuals. Empirical evidence on individual adaptation to climate-induced hazards reveals diverse behavioral and social factors affecting economic considerations. Yet, implications of replacing the rational optimizer by realistic human behavior in nature-society systems models are poorly understood. Using an innovative evolutionary economic agent-based model we explore different framings regarding household adaptation behavior to floods, leveraging on behavioral data from a household survey in Miami, USA. We find that a representative rational agent significantly overestimates household adaptation diffusion and underestimates damages compared to boundedly rational behavior revealed from our survey. This "adaptation deficit" exhibited by a population of empirically informed agents is explained primarily by diverse "soft" adaptation constraints-awareness, social influences-rather than heterogeneity in financial constraints. Besides initial inequality disproportionally impacting low/medium adaptive capacity households post-flood, our findings suggest that even under a nearly complete adaptation diffusion, adaptation benefits are uneven, with late or less-efficient actions locking households to a path of higher damages, further exacerbating inequalities. Our exploratory modeling reveals that behavioral assumptions shape the uncertainty of physical factors, like exposure and objective effectiveness of flood-proofing measures, traditionally considered crucial in risk assessments. This unique combination of methods facilitates the assessment of cumulative and distributional effects of boundedly rational behavior essential for designing tailored climate adaptation policies, and for equitable sustainability transitions in general.

Climate change intensifies the likelihood of extreme flood events worldwide, amplifying the potential for compound flooding. This evolving scenario represents an escalating risk, emphasizing the urgent need for comprehensive climate change adaptation strategies across society. Vital to effective response are models that evaluate damages, costs, and benefits of adaptation strategies, encompassing non-linearities and feedback between anthropogenic and natural systems. While flood risk modeling has progressed, limitations endure, including inadequate stakeholder representation and indirect risks such as business interruption and diminished tax revenues. To address these gaps, we propose an innovative version of the Climate-economy Regional Agent-Based model that integrates a dynamic, rapidly expanding agglomeration economy populated by interacting households and firms with extreme flood events. Through this approach, feedback loops and cascading effects generated by flood shocks are delineated within a socio-economic system of boundedly-rational agents. By leveraging extensive behavioral data, our model incorporates a risk layering strategy encompassing bottom-up and top-down adaptation, spanning individual risk reduction to insurance. Calibrated to resemble a research-rich coastal megacity in China, our model demonstrates how synergistic adaptation actions at all levels effectively combat the mounting climate threat. Crucially, the integration of localized risk management with top-down approaches offers explicit avenues to address both direct and indirect risks, providing significant insights for constructing climate-resilient societies.

By 2050 about 70% of the world's population is expected to live in cities. Cities offer spatial economic advantages that boost agglomeration forces and innovation, fostering further concentration of economic activities. For historic reasons urban centers cluster along coasts, which are prone to climate-induced flooding and sea level rise. To explore trade-offs between agglomeration economies and hazards increasing with climate change, we develop an evolutionary agent-based model with heterogeneous boundedly-rational agents who learn and adapt to a changing environment. The model combines migration decision of both households and firms between safe Inland and hazard-prone Coastal regions with endogenous technological learning and economic growth. Flood damages affect Coastal firms hitting their labour productivity, capital stock and inventories. We find that the model is able to replicate a rich set of micro- and macro-empirical regularities concerning economic and spatial dynamics. Without climate-induced shocks, the model shows how lower transport costs favour the Coastal region fueling the self-reinforcing and path-dependent agglomeration processes. We then introduce five scenarios of floods characterized by different frequency and severity to study the complex interplay of hazards with agglomeration patterns affecting the performance of the overall economy. We find that when shocks are mild or infrequent, they negatively affect the economic performance of the economy. If strong flood hazards hit frequently the Coastal region before agglomeration forces trigger high levels of the waterfront urbanization, firms and households can timely adapt and migrate landwards, thus averting the adverse impacts of climate shocks on the whole economy. Conversely, in the presence of climate tipping points where the frequency and magnitude of flood hazards abruptly intensifies, we find that economic activities remain trapped in the hazard-prone region, generating lock-ins and leading to a harsh downturn of the overall economy.

By 2050 about 80% of the world’s population is expected to live in cities. Cities offer spatial economic advantages that create agglomeration forces and innovation that foster concentration of economic activities, but for historic reasons cluster along coasts and rivers that are prone to climate-driven flooding. To explore tradeoffs between agglomeration economies and the changing face of hazards we present an evolutionary economics model with heterogeneous agents. Without climate-induced shocks, the model demonstrates how advantageous transport costs that the waterfront offers lead to the self-reinforcing and path-dependent agglomeration process in coastal areas. The likelihood and speed of such agglomeration strongly depend on the transport cost and magnitude of climate-driven shocks. In particular, shocks of different size have non-linear impact on output growth and spatial distribution of economic activities.

Understanding social and behavioural drivers and constraints of household adaptation is essential to effectively address increasing climate-induced risks. Factors shaping household adaptation are commonly treated as universal, despite an emerging understanding that adaptations are shaped by social, institutional and cultural contexts. Using original surveys in the United States, China, Indonesia and the Netherlands (N = 3,789), we explore variations in factors shaping households’ adaptations to flooding, the costliest hazard worldwide. We find that social influence, worry, climate change beliefs, self-efficacy and perceived costs exhibit universal effects on household adaptations, despite countries’ differences. Disparities occur in the effects of response efficacy, flood experience, beliefs in governmental actions, demographics and media, which we attribute to specific cultural or institutional characteristics. Climate adaptation policies can leverage the revealed similarities when extrapolating best practices across countries yet should exercise caution, as context-specific socio-behavioural drivers may discourage or even reverse household adaptation motivation.