Artificial Intelligence in Predicting Public Attitudes towards Climate Change: Psychological and Behavioral Insights

This study is trying to assess the ability of AI to predict the opinion of people about climate change by giving importance to the psychological and behavioral facets of such opinions. The survey was conducted on 300 people in Pakistan by a self-administered questionnaire. The data analysis done in regard to these factors included the implementation of multiple statistical methods such as correlation, regression, and post-hoc analysis that would consider the complex interplay between several psychological factors in question. Some of the factors involved cognitive biases, emotional responses, and what the layperson in society thinks about climate change. The findings presented with this analysis clearly showed that the models in this study, driven by artificial intelligence, were far better than the traditional survey methods used for a long time. In this regard, the AI-driven models had an astonishing accuracy rate of 87%, whereas the accuracy rate of only 72% came with traditional approaches used in this study and was proven to be highly statistically significant, as indicated by the p-value being below 0.05. The regression analysis results showed that some psychological factors such as cognitive bias with a beta value of 0.75 and emotional responses at a beta value of 0.65 strongly and significantly predict the attitude of the people regarding climate change. Apart from the above primary analysis, post-hoc analysis brought a number of other insights to light, especially regarding the important role of social identity in forming a public perception related to climate change issues. The above findings depict promising scope for AI technology in refining and improving the strategy of climate change communication through an appropriate application of psychological knowledge within the predictive model.