ClimateBERT-NetZero: Detecting and Assessing Net Zero and Reduction
  Targets
Tobias Schimanski Julia Bingler Camilla Hyslop Mathias Kraus Markus Leippold
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.