English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In recent years there has been a growing demand from financial agents,especially from particular and institutional investors, for companies to reporton climate-related financial risks. A vast amount of information, in textformat, can be expected to be disclosed in the short term by firms in order toidentify these types of risks in their financial and non financial reports,particularly in response to the growing regulation that is being passed on thematter. To this end, this paper applies state-of-the-art NLP techniques toachieve the detection of climate change in text corpora. We use transferlearning to fine-tune two transformer models, BERT and ClimateBert -a recentlypublished DistillRoBERTa-based model that has been specifically tailored forclimate text classification-. These two algorithms are based on the transformerarchitecture which enables learning the contextual relationships between wordsin a text. We carry out the fine-tuning process of both models on the novelClima-Text database, consisting of data collected from Wikipedia, 10K FilesReports and web-based claims. Our text classification model obtained from theClimateBert fine-tuning process on ClimaText, outperforms the models createdwith BERT and the current state-of-the-art transformer in this particularproblem. Our study is the first one to implement on the ClimaText database therecently published ClimateBert algorithm. Based on our results, it can be saidthat ClimateBert fine-tuned on ClimaText is an outstanding tool within the NLPpre-trained transformer models that may and should be used by investors,institutional agents and companies themselves to monitor the disclosure ofclimate risk in financial reports. In addition, our transfer learningmethodology is cheap in computational terms, thus allowing any organization toperform it.

Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial risks