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.

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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.

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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.

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Transformational adaptation to climate change becomes increasingly urgent. Experiencing a severe hazard event is insufficient to enable a transformational response. Yet, there is no solid theoretical or empirical evidence on what policy and other social explanations enable transformational adaptation to climate-induced hazards. Our article addresses this gap by critically examining empirical evidence on theory-grounded explanations for transformational responses to the most costly and devastating climate-induced hazard: flooding. After systematically collecting empirical research, we compare transformational responses to floods, focuzing specifically on managed retreat and planned relocation. Our analysis combines qualitative data analysis and network analysis and covers 54 articles describing over 105 cases in more than 31 countries worldwide. By differentiating levels of change, we find that transformational adaptation is reported in the literature as occurring via various types of policy change: from incremental steps to a paradigm shift. Most studies pay attention to shocks like floods that trigger transformational adaptations (45 out of 54 articles). Notably, specific combinations of social explanations are reported to enable transformations as a series of steps (i.e. economic/financial and socio-behavioral factors facilitate first-order policy changes), complemented by changes in the legal system (for second-order policy changes). Empirical evidence confirms that the paradigmatic third-order policy change additionally necessitates policy entrepreneurs and advocacy coalitions. Our analysis calls for interdisciplinary efforts to link case-study insights with theoretically embedded explanations from policy and legal studies, and the economic and socio-behavioral domain to systematically reveal generic combinations of explanations that enable transformational adaptation.

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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.

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Climate change effects are not uniform and have disproportionate impacts among different groups of people within communities. It is therefore important to understand the underlying issues of intersectionality for climate change adaptation and human well-being. This paper aims to measure human capabilities and freedom of choice by analyzing perceived climate change impacts and current climate change adaptation ability among ethnic and non-ethnic communities in Bangladesh. This study applies a range of participatory rural appraisal tools and key informant interviews to assess impacts of climate change when considering gender and ethnicity. Women in the coastal regions have less access to resources and services because of social capital and cultural practices and this directly or indirectly influences their adaptation to climate change. Women have limited or no participation in decision-making processes at family or community levels and this impacts their vulnerability and well-being. In consequence, women’s capabilities must be focused on moderating their vulnerability and risk, and developing effective adaptation to the adverse impacts of climate change and natural hazards.@en

There has so far been no shared understanding of validity in agent-based simulation. We here conceptualise validation as systematically substantiating the premises on which conclusions from simulation analysis for a particular modelling context are built. Given such a systematic perspective, validity of agent-based models cannot be ensured if validation is merely understood as an isolated step in the modelling process. Rather, valid conclusions from simulation analysis require context-adequate method choices at all steps of the simulation analysis including model construction, model and parameter inference, uncertainty analysis and simulation. We present a twelve-step protocol to highlight the (often hidden) premises for methodological choices and their link to the modelling context. It is designed to aid modelers in understanding their context and in choosing and documenting context-adequate and mutually consistent methods throughout the modelling process. Its purpose is to assist reviewers and the community as a whole in assessing and discussing context-adequacy.@en

Reconstructions of the most severe historic flood events contribute to improved quantification of design discharges corresponding to large return periods. Reducing the uncertainty of design discharges has a great significance in constructing proper flood defences to protect the hinterland from future flooding. However, reconstructions of the peak discharges of such historic flood events are generally associated with large uncertainties, which arise from the accuracy of the historic topography, hydraulic roughness of the river channels and floodplains, and the historic hydrograph shape. This study sets up a one dimensional-two dimensional (1D-2D) coupled hydraulic model, stretching from the upstream of Bonn at Remagen to downstream of Düsseldorf, Germany, with the length of 113 km to reconstruct the maximum discharge of the 1374 flood event (Qmax,1374), which is considered to be the largest flood of the last millennium in the Lower Rhine catchment. An uncertainty analysis was performed by adopting different river bed levels and roughness values in order to estimate the influence of these uncertainties on the reconstructed peak discharge. The upstream discharge wave was varied corresponding to a wide range of peak discharges from 12,000 to 24,000 m3/s. The resulting Qmax,1374 was determined of between 14,400 and 18,500 m3/s, were then used in a flood frequency analysis to determine the design discharges corresponding to different return periods. Compared to the design discharge computed with previous estimations of the 1374 peak discharge, we found a significant reduction of 2,000 m3/s in the design discharge corresponding to a 100,000 year return period, which is the maximum safety standard adopted in the Dutch water policy for some downstream dike sections.@en

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.

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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.

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Nature-based solutions (NbS) are a cornerstone of climate change adaptation worldwide. Yet, evidence on their economic benefits is scarce, especially since the provided environmental amenities usually spatially correlate with climate-induced hazards, effects of which NbS aim to curb. This lack of empirical evidence creates obscurity regarding social acceptability of NS, hindering their uptake and upscaling. We apply hedonic price models to estimate homeowners' willingness-to-pay for NbS (like flood safety, environmental benefits), while controlling for spatio-temporal changes in capitalized flood risk discounts due to the 1993-1995 floods in the Limburg Province, the Netherlands. We reveal a pre-flood effect of 5.6% (discounting on average -€12,753 for flood-prone properties), which rises to 10.9% (−€24,691 on average) immediately after the floods. However, the effect is only transitory. The flood discount of home values price discount diminishes over time and eventually vanishes in 9–12 years, which coincides with the implementation of the largest and oldest NbS intervention in the Netherlands. Our analysis shows that NbS amenities provide a 15% (€33,687 on average) premium to nearby residential property prices. This evidence of the evolving flood risk discount and the stable NbS premium for individual homeowners could support the economic feasibility and wide acceptability of NbS for climate change adaptations.@en

Agent-based models are particularly suitable to reflect the dynamics of humans, nature, and their interactions, making them a crucial approach for understanding social-ecological systems. The formalisations of human decision-making are central to resulting model behaviours. Despite awareness of the complexity of human behaviour in social-ecological systems research, scholars tend to represent human decision-makers as simplified, perfectly informed rational optimisers, without explicitly considering the fit with decision context. Key reasons are a lacking uptake of social theories and insights. To advance, we need a practice of reflecting, sharing, and inquiring on the justification of the decision model fit with its context. This paper stimulates this practice by 1) supporting the justification of decision model (DM) fit by describing the DM landscape and providing guiding questions; and 2) by supporting researchers in considering alternative DMs through a survey-based impression of modeller practices, and through highlighting DM frontiers as inspiration for future research.@en

While agent-based modeling (ABM) has become one of the most powerful tools in quantitative social sciences, it remains difficult to explain their structure and performance. We propose to use artificial intelligence both to build the models from data, and to improve the way we communicate models to stakeholders. Although machine learning is actively employed for pre-processing data, here for the first time, we used it to facilitate model development of a simulation model directly from data. Our suggested framework, ML-ABM accounts for causality and feedback loops in a complex nonlinear system and at the same time keeps it transparent for stakeholders. As a result, beside the development of a behavioral ABM, we open the ‘blackbox’ of purely empirical models. With our approach, artificial intelligence in the simulation field can open a new stream in modeling practices and provide insights for future applications.@en

Ambitious climate mitigation policies face social and political resistance. One reason is that existing policies insufficiently capture the diversity of relevant insights from the social sciences about potential policy outcomes. We argue that agent-based models can serve as a powerful tool for integration of elements from different disciplines. Having such a common platform will enable a more complete assessment of climate policies, in terms of criteria like effectiveness, equity and public support. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models The Carbon Economy and Climate Mitigation > Policies, Instruments, Lifestyles, Behavior Policy and Governance > Multilevel and Transnational Climate Change Governance.@en

Physical tipping points have gained a lot of attention in global and climate change research to understand the conditions for system transitions when it comes to the atmosphere and the biosphere. Social tipping points have been framed as mechanisms in socio-environmental systems, where a small change in the underlying elements or behavior of actors triggers a large non-linear response in the social system. With climate change becoming more acute, it is important to know whether and how societies can adapt. While social tipping points related to climate change have been associated with positive or negative outcomes, overstepping adaptation limits has been linked to adverse outcomes where actors' values and objectives are strongly compromised. Currently, the evidence base is limited, and most of the discussion on social tipping points in climate change adaptation and risk research is conceptual or anecdotal. This paper brings together three strands of literature - social tipping points, climate adaptation limits and systemic risks, which so far have been separate. Furthermore, we discuss methods and models used to illustrate the dynamics of social and adaptation tipping points in the context of cascading risks at different scales beyond adaptation limits. We end with suggesting that further evidence is needed to identify tipping points in social systems, which is crucial for developing appropriate governance approaches.@en

Risk assessments are key for the effective management of potential environmental threats. Across probabilistic phenomena, climate change is an exemplar of paramount uncertainties. These uncertainties have been embraced in supporting governments’ decisions; yet receive scarce attention when studying individual behavior. Analyzing a survey conducted in the USA, China, Indonesia, and the Netherlands (N=6242), we explore socio-economic, psychological, and behavioral differences between individuals who can subjectively assess risks, and those who are risk-uncertain. We find that risk-uncertain individuals are more likely to belong to societal subgroups classically considered as vulnerable, and have reduced capacities and intentions to adapt to hazards—specifically floods. The distinctions between risk-aware and risk-uncertain individuals indicate that ignoring differences in individuals’ capacity to assess risks could amount to persistent vulnerability and undermine climate-resilience efforts. While we use floods emblematically, these finding have consequences for the standard practice of dropping or bootstrapping uncertain responses, irrespective of the hazard, with implications for environmental management.@en

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.@en

Human beings are an active component of every terrestrial ecosystem on Earth. Although our local impact on the evolution of these ecosystems has been undeniable and extensively documented, it remains unclear precisely how our activities are altering them, in part because ecosystems are dynamic systems structured by complex, non-linear feedback processes and cascading effects. We argue that it is only by studying human–environment interactions over timescales that greatly exceed the lifespan of any individual human (i.e., the deep past or longue durée), we can hope to fully understand such processes and their implications. In this article, we identify some of the key challenges faced in integrating long-term datasets with those of other areas of sustainability science, and suggest some useful ways forward. Specifically, we (a) highlight the potential of the historical sciences for sustainability science, (b) stress the need to integrate theoretical frameworks wherein humans are seen as inherently entangled with the environment, and (c) propose formal computational modelling as the ideal platform to overcome the challenges of transdisciplinary work across large, and multiple, geographical and temporal scales. Our goal is to provide a manifesto for an integrated scientific approach to the study of socio-ecological systems over the long term.@en