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

Tobias Schimanski, Julia Bingler, Camilla Hyslop, Mathias Kraus and Markus Leippold
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Tobias Schimanski: University of Zurich
Julia Bingler: University of Oxford
Camilla Hyslop: University of Oxford
Mathias Kraus: University of Erlangen
Markus Leippold: University of Zurich; Swiss Finance Institute

No 23-110, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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, 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.

Keywords: Net Zero Targets; ClimateBERT; Transformers; NLP (search for similar items in EconPapers)
Pages: 12 pages
Date: 2023-11
New Economics Papers: this item is included in nep-big, nep-ene and nep-env
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp23110

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