A proposed method for analyzing historical adaptation pathways of coupled natural-human systems

Historical adaptation pathways (HAP) analyses identify sequences of multi-causal factors that shape climate change adaptation actions. Such analyses can be valuable for understanding why systems respond differently to climate risks, assessing important adaptation drivers and constraints, and identifying potential path dependencies. This paper synthesizes existing (and still emerging) HAP methods in order to present a more standardized and generalized approach to studying historic adaptations. The proposed method combines inductive and deductive approaches and draws on established practices from grounded theory to increase validity, including process tracing, memoing, construct definition, and member checking. This approach is designed to provide historical and contextual information that can be incorporated into a decision model or be shared with stakeholders and community members. In addition, future comparative studies based on this replicable approach could allow for theorization as to the casual mechanisms that engender successful adaptation. The approach is illustrated using a coastal adaptation case study in South Carolina, USA, with one of the main insights being that the island would not exist in its current form without the actions taken by concerned citizens, whose efforts ultimately helped combat the erosion caused (in part) by local jetties. Several areas for methodological improvement and theoretical development are also noted, as the aim of this work is both to enable cross-study comparisons of future HAP research – which can inform adaptation practice – and to provide a method that can be improved upon in future iterations. • An approach to assess historical adaptation pathways is described that combines both deductive and inductive components. • By standardizing the method, comparative analyses could be used to better understand adaptation drivers and constraints. • Through the methods application, one of the key causal mechanisms at work is the presence of motivated actors.