ClimateBERT-NetZero: Detecting and Assessing Net Zero and Reduction Targets

Tobias Schimanski, Julia Bingler, Mathias Kraus, Camilla Hyslop, Markus Leippold

Submission Type: Regular Short Paper

Submission Track: NLP Applications

Submission Track 2: Computational Social Science and Cultural Analytics

Keywords: ClimateBERT, BERT, climate change, net zero

TL;DR: This study develops a dataset and methods to detect and analyse emission reduction and net zero targets in texts.

Abstract: 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 and national 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.

Submission Number: 1027