Do political actors appeal to discrete emotions? In this study, I investigate how politicians adapt their emotional rhetoric to increased political conflict over climate change. To do so, I apply transformer-based machine learning classifier to a large dataset of text data coming from German Members of Parliament in order to measure discrete emotional appeals. Relying on staggered difference-in-difference models, I find robust results showing that local constructions of wind turbines cause the strongest opponents of climate change mitigation policies (radical-right MPs) to appeal to a specific negative moral emotion. Less robust evidence suggests a similar effect for the strongest proponents (Green MPs), however, appealing to a different discrete emotion. The effects range between 0.5 to 1.5 percentage points per additional wind turbine. These findings indicate the importance of distinct emotional framing in political communication with important implications for societal polarization and healthy political discourse.