Cheap Talk in Corporate Climate Commitments: The Role of Active Institutional Ownership, Signaling, Materiality, and Sentiment

Bingler, Julia Anna; Kraus, Mathias; Leippold, Markus; Webersinke, Nicolas

doi:10.2139/ssrn.3998435

Cited by 7 publications

(10 citation statements)

References 73 publications

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“…At the time of this writing, the data for these two papers are not publicly available. For a comparison of dictionary-based analyses and approaches based on climateBERT in general, we refer toBingler et al (2023).13 Examples of initial bigrams for regulatory (transition) exposure are: carbon price, carbon tax, emission trade, greenhouse gas, EPA regulation, energy regulatory, etc. Examples for physical exposure are: coastal area, natural hazard, global warm, heavy snow, sea level.…”

mentioning

confidence: 99%

Stock Prices and the Russia-Ukraine War: Sanctions, Energy and ESG

2022

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confidence: 99%

Stock Prices and the Russia-Ukraine War: Sanctions, Energy and ESG

2022

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“…In the face of the expected growth of disclosure of climate-related risks by TFCD-supporting companies, the study concludes that said support is mostly cheap talk as firms cherry-pick in order to report primarily non-material climate risk information. They continue this study with a new paper, in which Bingler et al [19] conclude that "institutional ownership, targeted institutional investor engagement, materiality and downside risk disclosures are associated with a less cheap talk".…”

Section: Introductionmentioning

confidence: 90%

“…The growing importance of these disclosures, with their intrinsic characteristic of heterogeneity and dispersed features, make the task of studying and analyzing these type of financial and non-financial reports worthy of automation. As a result, in recent years, a growing literature has emerged that relies on AI for the identification of climate-related information [16][17][18][19][20][21][22][23][24][25][26][27], among others.…”

Section: Introductionmentioning

confidence: 99%

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

Garrido-Merchán,

González-Barthe,

Coronado-Vaca

2023

Preprint

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In recent years there has been a growing demand from financial agents, especially from particular and institutional investors, for companies to report on climate-related financial risks. A vast amount of information, in text format, can be expected to be disclosed in the short term by firms in order to identify these types of risks in their financial and non-financial reports, particularly in response to the growing regulation that is being passed on the matter. To this end, this paper applies state-of-the-art NLP techniques to achieve the detection of climate change in text corpora. We use transfer learning to fine-tune two transformer models, BERT and ClimateBert -a recently published DistillRoBERTa-based model that has been specifically tailored for climate text classification-. These two algorithms are based on the transformer architecture which enables learning the contextual relationships between words in a text. We carry out the fine-tuning process of both models on the novel “ClimaText” database, consisting of data collected from Wikipedia, 10K Files Reports, and web-based claims. Our text classification model obtained from the ClimateBert fine-tuning process on ClimaText, outperforms the models created with BERT and the current state-of-the-art transformer in this particular problem. Our study is the first one to implement the recently published ClimateBert algorithm on the ClimaText database. Based on our results, it can be said that ClimateBert fine-tuned on ClimaText is an outstanding tool within the NLP pre-trained transformer models that may and should be used by investors, institutional agents, and companies themselves to monitor the disclosure of climate risk in financial reports, as well as in other textual sources, such as policies, new legislation or directives passed on this matter. In addition, our transfer learning methodology is cheap in computational terms, thus allowing any organization to perform it.

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“…Moreover, we enhance the data with a variety of non-target text samples. All of these samples originate from annotated and reviewed datasets of previous projects in the climate domain (Stammbach et al, 2022;Webersinke et al 1).…”

Section: Datamentioning

confidence: 99%

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

Schimanski,

Bingler,

Kraus

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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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 expertannotated 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.

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Cheap Talk in Corporate Climate Commitments: The Role of Active Institutional Ownership, Signaling, Materiality, and Sentiment

Cited by 7 publications

References 73 publications

Stock Prices and the Russia-Ukraine War: Sanctions, Energy and ESG

Stock Prices and the Russia-Ukraine War: Sanctions, Energy and ESG

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

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

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