We aim to bridge the public-academic gap in climate change predictions by presenting accurate information sourced from modern climate models. Our graphics and metrics are interpretable to laypeople while retaining the accuracy of the underlying predictions. To contextualize our findings, we algorithmically analyzed various socioeconomic factors to see whether they climate change impacts are discriminatory.
We believe our findings can inform macroeconomic policy design by identifying communities and regions in need of support against climate change. Prior literature suggests that these communities tend to be historically disadvantaged as well; our graphics present the future of climate change in this historical context, leading viewers to understand the interplay between the climate change crisis and socioeconomic factors. Additionally, we utilize interactive elements that allow users to explore how different climate scenarios could affect their local environments, enhancing engagement and facilitating a deeper understanding of the data.