Urban housing markets increasingly face escalating flood risk alongside persistent scarcity and affordability constraints. While these pressures shape price dynamics, their effects depend critically on heterogeneity in household risk perceptions and preferences. This study develops a spatial agent-based model (ABM) to examine how price differentials and residential patterns emerge from bottom-up interactions in a flood-prone urban environment. Parameterised to represent a typical city in the Netherlands, the model conducts scenario experiments that vary (i) market demand pressure, (ii) spatially heterogeneous individual risk perception biases, and (iii) public flood defense systems, including traditional defenses and nature-based flood defenses with co-benefits. Results show that market pressure and biased risk perceptions are the primary drivers of price growth and income-based exclusion, emerging well before flood defenses are introduced. Traditional flood defenses narrow flood-related price discounts by reducing objective risk, with nature-based defenses further reinforcing existing patterns through amenity capitalization. In both cases, public flood protection mainly reallocate demand spatially without altering overall market participation or affordability.

Destructive climate-induced extreme events increasingly affect people and economies worldwide. Their impacts are widely studied using both empirical and simulation methods. Yet, the scientific debate on whether environmental shocks induce growth spurts, leave persistent scars on the economy, or barely have any long-term effects, remains unresolved. Here, we show how differences in aggregate economic dynamics can be explained by heterogeneity at the firm-level, specifically the distribution of damages among firms and different productivity level of affected firms. We employ a novel multi-regional economic agent-based model, where firms in one of the regions are struck by a climate-induced shock. We find that these firm-level heterogeneities have significant effects on aggregate economic dynamics, with long-run outcomes ranging from full recovery to modest growth, and even to persistent depression. Our results show that shocks to clusters of economic activity can have outsized impacts on regional economies compared to a representative distribution of impacts. This highlights fundamental problems with conventional aggregated analysis of physical climate risks and of overall costs of climate change, suggesting that policy-focused analysis could be misguided when omitting a granular representation of economic agents.

Adaptation to climate change has never been more urgent. The literature is full of examples when public (adaptation) action might create unintended consequences, for example, for individual risk perception and private actions. It is also well-known that people rarely understand risks objectively, implying that their choices are guided by biased risk perceptions. However, it remains unclear how these different effects interact and whether overall cumulative risks are reduced. This paper presents an agent-based model of a housing market to examine how alternative climate adaptation policies—traditional defenses and Nature-based Solutions (NbS)—interact with households’ biased risk perceptions, behavior, and affordability. We extend our existing housing market agent-based model (Filatova, 2015) to fit the Dutch context, enhancing it with a new negotiation algorithm, updated decision-under-risk behavioral model, and new empirical data on both housing prices and individual risk perceptions. Our simulations reveal that while NbS enhance resilience and environmental quality, they may also drive socioeconomic exclusion if access is not regulated. Traditional defenses reduce exposure but have a limited effect on spatial inequality. Our results unveil complex interactions between behavior and markets, highlighting that flood risk acts not only as a hazard but as a market signal shaped by individual perceptions and existing institutions. Equitable adaptation requires integrating physical protection with policies addressing affordability and behavioral dynamics.

Despite efforts to mitigate climate change, adaptation becomes critical. Among climate-induced hazards, flooding is the most costly and widespread, calling for adaptation across scales: from government-led to household-led adaptation. Private adaptation measures, if taken, reduce damages and speed up recovery. Empirical evidence suggests that among socio-behavioral factors, social norms and peer influence are crucial for households’ decisions to adapt. Yet, the role social networks play in household-level adaptation has not been studied systematically, even less so the interplay between private household adaptation and public information policies in the presence of social influences, the exchange of opinions and information within a social network that impacts individuals’ decisions. To improve the understanding of the impact of social influence on private household adaptation uptake, we build an empirically informed agent-based model of household behavior. We leverage household survey data collected in Harris County (Texas, USA) together with information on flood hazard scenarios to study the impact of private adaptation diffusion on flood damages under different social influence scenarios. Furthermore, we use the model to test how different information policy design choices—such as targeting specific households or all, and communicating different aspects of flood adaptation (e.g., flood damage, costs, or effectiveness of measures) --influence private adaptation diffusion and impact regional residual flood damage. We find that regardless of the structure of the social network, social influence triggers higher adaptation uptake by households, resulting in a 5–10% extra reduction in regional residual flood damage. Notably, the effect of social norms depends on the type of information exchanged within networks, where the opinion exchange on the effectiveness of measures and potential damages results in more private adaptation compared to the discussion on perceived costs of adaptation measures or the worry about flooding. Moreover, while information campaigns influence individual perceptions that facilitate household-led adaptation, the provision of information solely on expected damages is not as effective in steering public opinion and, thereby household adaptation, as its combination with information on coping measures that enable action. Our results demonstrate that social influence and information policy can shape the success of private action and highlight the importance of understanding the interaction among scales in climate change adaptation.

With growing consensus on the scale of climate change and its direct impacts on societies and economies, the debate on how direct climate damages cascade through interconnected economic systems eventually leading to profound indirect effects remains open amid limited quantitative evidence. The localised nature of climate risk means that regional economies may face stark differences in how they are impacted by the direct and indirect physical risks. In Europe, the world’s fastest warming continent, concentrated local damages spill over asymmetrically into tightly-interconnected regional markets, potentially leading to increased inter-regional inequalities despite the continent’s prioritisation of economic unity through its regional cohesion policy. Solid regionalized economy-wide analysis could identify if physical climate risks serve as structural drivers of future regional inequality, offering timely insights for policy interventions to curb exacerbating socio-economic adversities. Here, using an empirical dynamic computable general equilibrium model disaggregated to NUTS2 European regions, we explore a range of regional economic projections from two particularly costly climate-driven hazards: river flooding and sea-level rise. Our methodology captures complex economic feedbacks across granular regions and sectors to estimate both direct and indirect economic repercussions of combined sea-level rise and river flood events by 2100. We find that these climate-induced hazards represent an economically divergent force for the European regions. In contrast to aggregated studies, the resulting regional economic projections reveal wide heterogeneity in combined (direct and indirect) physical risks, with the most affected regions experiencing devastating declines in GDP up to 51% by 2100. Our disaggregated projections capture the effects of two climate-induced hazards with distinct geographical hotspots - coastal and inland - which occur simultaneously though unfold at different rates, enabling a detailed assessment of regional economic inequalities. Low income regions experience by far the highest proportional losses, leading to increases in both between and within-country regional inequality.

People's risk perceptions are crucial for climate change adaptation, influencing individual decisions and policy effectiveness. Although many studies highlight the importance of social influences and social norms in this context, the mechanisms through which they shape individual risk perceptions and adaptation behavior remain unclear. To address this gap, we analyze cross-country survey data (N = 1612) from coastal areas in the Netherlands, United Kingdom, and the USA with a focus on flood risk and adaptation behavior. Our statistical analysis reveals several important patterns in social interactions, and the ways in which these social interactions influence individual risk perceptions. First, we find limited social engagement regarding risks and adaptation, with a significant portion of respondents (50%) reporting no interactions with peers on these topics. Among those who do engage, social interactions on flood risk and adaptation appear infrequent (fewer than five times per year). Second, contrary to common assumptions, individuals who discuss flood risk and adaptation, rarely do so with neighbors. Moreover, homophily—shared socio-demographic characteristics—is not the primary determinant of who interacts on the topic. Third, we see that those with hazard experience and those with higher risk perceptions are more likely to interact with others on the topics of these risks and climate adaptation, confirming that social amplifications might be in place. These findings provide unique insights into the social dynamics underlying the evolution of individual risk perceptions, offering the potential to refine models of social influence in climate change and social tipping points. They also highlight potential synergies between communication strategies and policy tools to support timely and, possibly transformational, adaptation.

Managed realignment (MR) involves repositioning coastal or river flood defenses to re-establish tidal flooding and restore intertidal ecosystems in reclaimed areas. The restoration of intertidal ecosystems contributes to flood risk management and achieving nature conservation objectives. However, MR often faces resistance from local communities, potentially undermining its implementation. Previous qualitative studies have discussed the role of socio-psychological constructs in shaping public attitudes toward MR, but quantitative empirical assessments grounded in socio-psychological theory are scarce. In addition, the absence of comparative research across multiple cases limits the potential for generalization, making it difficult to apply findings to other contexts and populations. This study contributes to filling these research gaps by examining socio-psychological constructs that shape public support for MR in three case study areas in the Netherlands. We administered questionnaires among households (N = 324) and used multivariate and regression models to analyze the collected data. Results across the case studies point to three socio-psychological constructs that consistently explain public support for MR, including (i) trust in institutions, (ii) outcome expectancies and (iii) emotions. These constructs are intercorrelated, suggesting that they influence each other when collectively shaping support for MR. Strategies to enhance public support could be more effective when they address these constructs in an integrated manner. Moving forward, it is important to explore how public engagement and communication around MR policies could be tailored to leverage positive emotions better and how the design of MR can be aligned with location-specific priorities.

Understanding climate change adaptation constraints for different actors — governments, communities, individuals, and households — is essential, as adaptation turns into a matter of survival. Though rich qualitative research reveals constraints for diverse cases, methods to consolidate knowledge and elicit patterns in adaptation constraints for various actors are scarce. Therefore, this work analyzes associations between different adaptations and actors’ constraints to climate-induced floods and sea-level rise. Our novel approach derives textual data from peer-reviewed articles (published before February 2024) by using natural language processing, thematic coding books, and network analysis. The results show that social capital, economic factors, and government support are constraints shared among all actors.

Climate change is projected to adversely affect agriculture worldwide. This requires farmers to adapt incrementally already early in the twenty-first century, and to pursue transformational adaptation to endure future climate-induced damages. Many articles discuss the underlying mechanisms of farmers’ adaptation to climate change using quantitative, qualitative, and mixed methods. However, only the former is typically included in quantitative metanalysis of empirical evidence on adaptation. This omits the vast body of knowledge from qualitative research. We address this gap by performing a comparative analysis of factors associated with farmers’ climate change adaptation in both quantitative and qualitative literature using Natural Language Processing and generalized linear models. By retrieving publications from Scopus, we derive a database with metadata and associations from both quantitative and qualitative findings, focusing on climate change adaptation of farmers. We use the derived data as input for generalized linear models to analyze whether reported factors behind farmers’ decisions differ by type of adaptation (incremental vs. transformational) and across different global regions. Our results show that factors related to adaptive capacity and access to information and technology are more likely to be associated with transformational adaptation than with incremental adaptation. Regarding world regions, access to finance/income and infrastructure are uneven, with farmers in high-income countries having an advantage, whereas farmers in low- and middle-income countries require these the most for effective adaptation to climate change.

Agent-based modeling proliferates across applications and scientific disciplines. The downsides of this success are the plurality of code implementations and redundant solutions to recurring modeling tasks. It is especially critical for simulations concerned with modeling human behavior and social institutions. Reusable building blocks (RBBs) are seen as a solution due to their potential to foster standardization grounded in best practices, integration of domain knowledge (including qualitative social sciences) in code, and efficient model design. RBBs are compact code components representing mechanisms or processes useful across models and applications. RBBs have been extensively discussed in the agent-based community, with little progress in implementation. Here, we present an open-access online community platform – AGENTBLOCKS – designed to facilitate the sharing, comparison, review, reuse, and improvement of RBBs. As an international community effort, AGENTBLOCKS leverages lessons from past RBBs discussions and principles from other modeling communities that successfully apply modular, reusable code practices. The paper introduces the interface and structure of this repository, presents templates for RBBs documentation, provides tips to support aspiring users, and first examples. We highlight the need for alternative RBB implementations that share the same generic description. We also acknowledge that RBBs might represent different levels of interactions, starting from decisions concerning a single agent to interactions between multiple agents or agents and their environment. While initially designed to assist agent-based community, the platform can be utilized by other modelers (e.g. system dynamics, integrated assessment, equilibrium) who seek to improve the representation of human behavior, micro-level processes, heterogeneity, interactions, learning, and other complex dynamics. Naturally, the platform is only one element in the chain towards a successful adoption of best software development practices like RBBs. Future work should focus on populating the repository, refining review processes, and systematizing the variety of RBBs’ implementations including engagement with domain experts. Following this initial phase, we hope to further support technical improvements of the platform and widen its impact in and beyond the agent-based community.

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 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-induced hazards are becoming more frequent and severe, causing escalating economic losses worldwide. Consequently, climate change adaptation is increasingly necessary to protect people, nature and the economy. However, little is known about who is adapting and how much they spend on adaptation measures, especially in the private sector. This article focuses on firms—the backbone of economic development, yet understudied in climate adaptation research. Here we present insights from a unique panel dataset detailing businesses’ adaptation investments across 28 European countries (2018–2022), 5 hazard types, and 19 economic sectors. Our descriptive analysis reveals low but increasing adaptation investments across Europe (0.15–0.92% of national gross domestic product, annually increasing by 30.6–37.4%). Moreover, we highlight considerable differences in adaptation intensity across sectors, including low adaptation intensity in manufacturing and retail trade. Additionally, our econometric analysis indicates that public adaptation spending crowds in private investments in adaptation, highlighting opportunities to facilitate autonomous adaptation.

Despite the increasing use of standards for documenting and testing agent-based models (ABMs) and sharing of open access code, most ABMs are still developed from scratch. This is not only inefficient, but also leads to ad hoc and often inconsistent implementations of the same theories in computational code and delays progress in the exploration of the functioning of complex social-ecological systems (SES). We argue that reusable building blocks (RBBs) known from professional software development can mitigate these issues. An RBB is a submodel that represents a particular mechanism or process that is relevant across many ABMs in an application domain, such as plant competition in vegetation models, or reinforcement learning in a behavioural model. RBBs need to be distinguished from modules, which represent entire subsystems and include more than one mechanism and process. While linking modules faces the same challenges as integrating different models in general, RBBs are “atomic” enough to be more easily re-used in different contexts. We describe and provide examples from different domains for how and why building blocks are used in software development, and the benefits of doing so for the ABM community and to individual modellers. We propose a template to guide the development and publication of RBBs and provide example RBBs that use this template. Most importantly, we propose and initiate a strategy for community-based development, sharing and use of RBBs. Individual modellers can have a much greater impact in their field with an RBB than with a single paper, while the community will benefit from increased coherence, facilitating the development of theory for both the behaviour of agents and the systems they form. We invite peers to upload and share their RBBs via our website - preferably referenced by a DOI (digital object identifier obtained e.g. via Zenodo). After a critical mass of candidate RBBs has accumulated, feedback and discussion can take place and both the template and the scope of the envisioned platform can be improved.

Economic costs of climate change are conventionally assessed at the aggregated global and national levels, while adaptation is local. When present, regionalised assessments are confined to direct damages, hindered by both data and models’ limitations. This article goes beyond the aggregated analysis to explore direct and indirect economic consequences of sea level rise (SLR) at regional and sectoral levels in Europe. Using a dynamic computable general equilibrium model and novel datasets, we estimate the distribution of losses and gains across regions and sectors. A comparison of a high-end scenario against a no-climate-impact baseline suggests a GDP loss of 1.26% (€871.8 billion) for the whole EU&UK. Conversely our refined assessments show that some coastal regions lose 9.56–20.84% of GDP, revealing striking regional disparities. Inland regions grow due to the displaced demand from coastal areas, but the GDP gains are small (0–1.13%). While recovery benefits the construction sector, public services and industry face significant downturns. We show that prioritising recovery of critical sectors locally reduces massive regional GDP losses, at negligible costs to the overall European economy. Our analysis traces regional economic restructuring triggered by SLR, underscoring the necessity of region-specific adaptation policies that embrace uneven geographic impacts and unique sectoral profiles to inform resilient strategy design.

Sustainability outcomes are influenced by the laws and configurations of natural and engineered systems as well as activities in socio-economic systems. An important subset of human activity is the creation and implementation of institutions, formal and informal rules shaping a wide range of human behavior. Understanding these rules and codifying them in computational models can provide important missing insights into why systems function the way they do (static) as well as the pace and structure of transitions required to improve sustainability (dynamic). Here, we conduct a comparative synthesis of three modeling approaches— integrated assessment modeling, engineering–economic optimization, and agent-based modeling—with underexplored potential to represent institutions. We first perform modeling experiments on climate mitigation systems that represent specific aspects of heterogeneous institutions, including formal policies and institutional coordination, and informal attitudes and norms. We find measurable but uneven aggregate impacts, while more politically meaningful distributional impacts are large across various actors. Our results show that omitting institutions can influence the costs of climate mitigation and miss opportunities to leverage institutional forces to speed up emissions reduction. These experiments allow us to explore the capacity of each modeling approach to represent insitutions and to lay out a vision for the next frontier of endogenizing institutional change in sustainability science models. To bridge the gap between modeling, theories, and empirical evidence on social institutions, this research agenda calls for joint efforts between sustainability modelers who wish to explore and incorporate institutional detail, and social scientists studying the socio-political and economic foundations for sustainability transitions.