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.

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.

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.