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.

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.

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.

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.

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.