Social connectedness as a pathway towards climate resilience: exploring climate change scepticism with machine learning approaches and mediation regression models

Climate resilience is vital for sustainable development, but vulnerable to doubt. Based on the data from the Chinese General Social Survey, this study systematically explores the correlates of climate change scepticism using various machine learning approaches and mediation regression models. The machine learning results show that social connectedness is a key factor negatively associated with lower climate change scepticism, and their relationship varies substantially across different dimensions of social connectedness. Furthermore, the mediation analyses reveal that the association between social connectedness and climate change scepticism is statistically accounted for by anthropocentrism belief, environmental risk awareness, and perception of environmental policies. Importantly, the mechanisms linking social connectedness to climate change scepticism are type-specific. Meanwhile, the association between social connectedness and climate change scepticism varies substantially across regions. Additionally, the results indicate that the patterns and roots of China's climate change scepticism may differ from those reported in Western literature. Overall, this study suggests that social connectedness is a subtle but important correlate of climate resilience. However, the prescription of social connectedness to counteract climate change scepticism should be tailored to the regional context.