Sustainability reports denote greenhouse gas emissions with heterogeneous unit strings, such as abbreviations, inconsistent formatting, and scale markers. In emission extraction pipelines, these units must be normalized to a common target, e.g. t CO2e, to enable reliable comparison and aggregation of emissions across companies, reporting years, scopes, and regions. We propose a hybrid normalization approach: If a raw unit string is not found in a curated dictionary, a machine learning classifier maps noisy unit strings to normalized emission-unit categories that can then be converted to the target unit. The classifier is trained using character-level TF-IDF, lightweight string features, and LLM-assisted data augmentation. On random-splits of the augmented dataset, an ensemble model achieves approximately 95% accuracy. Additional evaluation on rare real-world unit strings indicates that supervised fallback classifiers can handle out-of-dictionary variants in climate-related information extraction.