Climate communication is becoming more abundant, but not necessarily more informative. This thesis investigates whether Natural Language Processing (NLP) can help structure climate-related discourse, distinguish substantive content from vague or rhetorical formulations, and support credibility assessment. By examining the literature on greenwashing and major datasets for climate-related NLP tasks, it highlights key limitations, including subjectivity, ambiguity, and noisy data. It then proposes ways to address these issues through annotation schemes and evaluation metrics designed for ambiguity, as well as methods for propagating uncertainty into downstream analyses. Overall, the thesis shows that NLP can make climate-related discourse more explicit and analyzable, while also emphasizing that progress depends not only on model performance, but also on task design, data quality, and uncertainty-aware evaluation.

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