Using Data Science to Assess the Impact of Disaster Event on Climate Change Belief: Case of Australian Bushfire Catastrophe
Keywords:Bushfires, Climate Change, Regression, Logistic Regression, McNemar's Test
Australia, vulnerable to bushfire incidents due to its unique climatic conditions, witnessed a transformative event in the 2019-2020 bushfire season. This research examines the impact of these bushfires on public perception of climate change. Leveraging robust statistical techniques, including McNemar's hypothesis testing and logistic regression, the study deciphers survey data collated pre and post these fires. The study's hypothesis that post-fire respondents are more likely to acknowledge climate change's role is confirmed. Factors such as education, political affiliation, and support for fossil fuel reduction are identified as influential predictors of climate change belief. The analysis also highlights the complex interplay of demographic characteristics and media exposure in shaping attitudes. Notably, direct firebush exposure showed a nuanced relationship with belief. The research underscores a significant shift in Australian attitudes toward climate change following the bushfires. These findings contribute to our understanding of public opinion dynamics and the role of experiential factors in climate change belief.