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

arXiv: https://arxiv.org/abs/2310.08096 · HTML: https://mlsanity.com/paper/2310.08096/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 12 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [ClimateBert: A Pretrained Language Model for Climate-Related Text](https://mlsanity.com/paper/2110.12010/) (2021)
- [ClimaText: A Dataset for Climate Change Topic Detection](https://mlsanity.com/paper/2012.00483/) (2020)

## Built on by (most cited first, 17 of 17)

- [A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery](https://mlsanity.com/paper/2406.10833/) (2024)
- [Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey](https://mlsanity.com/paper/2312.03014/) (2023)
- [Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook](https://mlsanity.com/paper/2402.19348/) (2024)
- [WeatherQA: Can Multimodal Language Models Reason about Severe Weather?](https://mlsanity.com/paper/2406.11217/) (2024)
- [ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosures](https://mlsanity.com/paper/2406.09818/) (2024)
- [Explainable Natural Language Processing for Corporate Sustainability Analysis](https://mlsanity.com/paper/2407.17487/) (2024)
- [A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and Adaptation](https://mlsanity.com/paper/2504.17200/) (2025)
- [Omni-Weather: A Unified Multimodal Model for Weather Radar Understanding and Generation](https://mlsanity.com/paper/2512.21643/) (2025)
- [AIMS.au: A Dataset for the Analysis of Modern Slavery Countermeasures in Corporate Statements](https://mlsanity.com/paper/2502.07022/) (2025)
- [Judging It, Washing It: Scoring and Greenwashing Corporate Climate Disclosures using Large Language Models](https://mlsanity.com/paper/2502.15094/) (2025)
- [Climate-Eval: A Comprehensive Benchmark for NLP Tasks Related to Climate Change](https://mlsanity.com/paper/2505.18653/) (2025)
- [Identifying Climate Targets in National Laws and Policies using Machine Learning](https://mlsanity.com/paper/2404.02822/) (2024)
- [Climate AI for Corporate Decarbonization Metrics Extraction](https://mlsanity.com/paper/2411.03402/) (2024)
- [Earth Science Foundation Models: From Perception to Reasoning and Discovery](https://mlsanity.com/paper/2605.12542/) (2026)
- [What Transfers Under Source Shift? Definitions, Examples, and Fine-Tuning for Climate Disclosure Classification](https://mlsanity.com/paper/2607.17952/) (2026)
- [Execution-grounded evaluation reveals hidden failures in language-model calculations for environmental science](https://mlsanity.com/paper/2608.18726/) (2026)
- [Climate-ModernBERT: Revisiting Corpus Composition for Domain-Adaptive Continued Pretraining](https://mlsanity.com/paper/2609.07798/) (2026)

## Abstract (arXiv)

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.