A Temporal Network Approach to the Emotional Responses to Climate Change

Recent research has indicated that emotional perception of climate change can lead to different emotional responses (aka, eco-emotions), which also correlated with general distress (e.g., depression or anxiety) and, in turn, may influence one’s behaviors. Unfortunately, despite great efforts, there is a lack of longitudinal research shedding light on the potential link between eco-emotions (i.e., eco-anxiety, eco-depression, eco-anger), general emotions (i.e., anxiety, depression, anger), and pro-environmental behaviors. The research question of this study is: how are eco-emotions, general distress and eco-responsible behaviors related over time? Given the lack of longitudinal studies, we aim to study the dynamic interplay over time between eco-emotions, general negative emotions and pro-environmental intentions and behaviors via intensive time-series data, collected via experience sampling method (ESM). An estimated sample of 40 individuals will report daily assessment of three negative eco-emotions and three general emotions, as well as, behaviors and intentions related to the environmental crisis over a 60-day period. Data is currently being collected and we will present the results from applying a temporal network approach, that is, a methodological tool derived from the network theory that allows us to apply multilevel autoregressive model to time-series data. This approach allows us to estimate models (i.e., network structure) including all the variables mentioned above and conceptualize them as a dynamic complex system of elements influencing each other at different points in time. We expect eco-emotions and general emotions to be embedded within a network system wherein they are strongly associated. Also, we expect them to be differently related with both eco-friendly intentions and behaviors. During the talk, a general model derived from this computational approach will be presented and discussed