A network approach to climate change anxiety and its key related features. (January 2023) - Heeren, Alexandre; Mouguiama-Daouda, Camille; McNally, Richard J.
Abstract: Research has pointed to startling worldwide rates of people reporting considerable anxiety vis-à-vis climate change. Yet, uncertainties remain regarding how climate anxiety's cognitive-emotional features and daily life functional impairments interact with one another and with climate change experience, pro-environmental behaviors, and general worry. In this study, we apply network analyses to examine the associations among these variables in an international community sample (n = 874). We computed two network models, a graphical Gaussian model to explore network structure, potential communities, and influential nodes, and a directed acyclic graph to examine the probabilistic dependencies among the variables. Both network models pointed to the cognitive-emotional features of climate anxiety as a potential hub bridging general worry, the experience of climate change, pro-environmental behaviors, and the functional impairments associated with climate anxiety. Our findings offer data‐driven clues for the field's larger quest to establish the foundations of climate anxiety. Highlights: Uncertainties remain regarding how climate anxiety and its related features interact. We used network analysis to illuminate potent pathways at play among features of these variables. We computed a graphical Gaussian model (GGM) and a directed acyclic graph (DAG). Both GGM and DAG pointed to the cognitive-emotional features of climate anxiety as an especially potent mechanisms at play.