The scientific and policy needs to assess and manage climate change impacts have spawned new coupled, multi-scale integrated assessment model (IAM) frameworks that link global climate and economic processes with high-resolution data and models of human-environmental systems at local and meso scales (Fisher-Vanden and Weyant 2020). A central challenge is in accounting for the fundamental interdependence of people, firms, and economic activities across space at multiple scales. This requires modeling approaches that can incorporate the relevant spatial details at each scale while also ensure consistency with spatially varying feedbacks and interactions across scales—a condition economists refer to as spatial equilibrium. In this paper, we provide an overview of how economists think about and model spatial interactions, particularly those at the local level. We describe challenges and recent progress in accounting for greater spatial heterogeneity at individual (field, agent) scales and incorporating heterogeneous spatial interactions and dynamics into consistent IAM frameworks. We conclude that the most notable progress is in advancing global IAMs with spatial heterogeneity and dynamics embedded in spatial equilibrium frameworks and that less progress has been made in incorporating features of spatial equilibrium into highly detailed multi-scale IAMs.

Climate change by its very nature epitomizes the necessity and usefulness of the globalto-
local-to-global (GLG) paradigm. It is a global problem with the potential to affect local
communities and ecosystems. Accumulation of local impacts and responses to climate change
feeds back to regional and global systems creating feedback loops. Understanding these complex
impacts and interactions is key to developing more resilient adaptation measures and
designing more efficient mitigation policies. To this date, however, GLG interactions have
not yet been an integrative part of the decision-support toolkit. The typical approach either
traces the impacts of global action on the local level or estimates the implications of local
policies at the global scale. The first approach misses cumulative feedback of local responses
that can have regional, national or global impacts. In the second case, one undermines a
global context of the local actions most likely misrepresenting the complexity of the local
decision-making process. Potential interactions across scales are further complicated by the
presence of cascading impacts, connected risks and tipping points. Capturing these dimensions
is not always a straightforward task and often requires a departure from conventional
modeling approaches. In this paper, we review the state-of-the-art approaches to modeling
GLG interactions in the context of climate change. We further identify key limitations
that drive the lack of GLG coupling cases and discuss what could be done to address these
challenges.