ClimateBERT-NetZero: Detecting and Assessing Net Zero and Reduction Targets
This addresses the challenge for public and private actors in managing vast sustainability information, though it is incremental as it builds on existing NLP methods.
The authors tackled the problem of assessing sustainability commitments by creating a tool to automatically detect net zero and reduction targets from text, using a 3.5K annotated dataset and a classifier, and demonstrated its use in analyzing target ambitions and communication patterns over time.
Public and private actors struggle to assess the vast amounts of information about sustainability commitments made by various institutions. To address this problem, we create a novel tool for automatically detecting corporate, national, and regional net zero and reduction targets in three steps. First, we introduce an expert-annotated data set with 3.5K text samples. Second, we train and release ClimateBERT-NetZero, a natural language classifier to detect whether a text contains a net zero or reduction target. Third, we showcase its analysis potential with two use cases: We first demonstrate how ClimateBERT-NetZero can be combined with conventional question-answering (Q&A) models to analyze the ambitions displayed in net zero and reduction targets. Furthermore, we employ the ClimateBERT-NetZero model on quarterly earning call transcripts and outline how communication patterns evolve over time. Our experiments demonstrate promising pathways for extracting and analyzing net zero and emission reduction targets at scale.