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

TL;DR

This paper introduces ClimateBERT-NetZero, a tool that automatically detects and analyzes net zero and reduction targets in various texts, aiding stakeholders in assessing sustainability commitments at scale.

Contribution

The paper presents a new annotated dataset and a specialized classifier for identifying net zero and reduction targets in text, with applications in analyzing corporate and governmental sustainability communications.

Findings

ClimateBERT-NetZero achieves effective detection of targets.

The model enables analysis of communication patterns over time.

Promising results for large-scale extraction and assessment.

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.