Global Tracking of Climate Change Adaptation Policy Using Machine Learning: a Systematic Map Protocol

Sietsma, Anne J.; Callaghan, Max; Biesbroek, Robbert; Theokritoff, Emily; Thomas, Adelle; Canosa, Iván Villaverde; Schleußner, Carl-Friedrich; Ford, James D.; Minx, Jan C.

doi:10.21203/rs.3.pex-1836/v1

Cited by 3 publications

(9 citation statements)

References 34 publications

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“…Language plays an important role in tackling the numerous effects of climate change. It facilitates precarious problem-solving during global political summits, it is used in scientific papers that must convince politicians to act, and it spreads the news to the general public [ 1 – 7 ].…”

Section: Introductionmentioning

confidence: 99%

Learning from climate change news: Is the world on the same page?

Eikelboom,

Esteve-Del-Valle,

Nissim

2024

PLoS ONE

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Climate change challenges countries around the world, and news media are key to the public’s awareness and perception of it. But how are news media approaching climate change across countries? With the problem of climate change and its solution being global, it is key to determine whether differences in climate change news reports exist and what they are across countries. This study employs supervised machine learning to uncover topical and terminological differences between newspaper articles on climate change. An original dataset of climate change articles is presented, originating from 7 newspapers and 3 countries across the world, and published in English during 26 Conference of the Parties (COP) meetings from the United Nations Framework Convention on Climate Change (UNFCC). Three aspects are used to discriminate between articles, being (1) countries, (2) political orientations, and (3) COP meetings. Our results reveal differences with regard to how newspaper articles approach climate change globally. Specifically, climate change-related terminology of left-oriented newspapers is more prevalent compared to their right-oriented counterparts. Also, over the years, newspapers’ climate change-related terminology has evolved to convey a greater sense of urgency.

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Section: Introductionmentioning

confidence: 99%

Learning from climate change news: Is the world on the same page?

Eikelboom,

Esteve-Del-Valle,

Nissim

2024

PLoS ONE

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“…Since its publication in October 2021, the pretained transformer model Cli-mateBert has only been applied so far in 6 other papers which have not been published yet (two of which are from ClimateBert's original authors): [17,4,30,7,13,36]. Hershcovich et al [17] analyze ClimateBert only regarding its energy consumption, in a context of awareness about the environmental impact that NLP pretrained models present.…”

Section: Introductionmentioning

confidence: 99%

“…Hershcovich et al [17] analyze ClimateBert only regarding its energy consumption, in a context of awareness about the environmental impact that NLP pretrained models present. Focusing on the policies being adopted by governments around the world, Sietsma et al [30] identify ClimateBert as a tool that allows real-time tracking of adaptation progress. Yu et al [36] present a database, cli-mateBUG, that provides a framework for detecting implicit information about how banks disclose their climate change-related activities.…”

Section: Introductionmentioning

confidence: 99%

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

Garrido-Merchán¹,

González-Barthe²,

Vaca³

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 stateof-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 "Clima-Text" 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 on the ClimaText database the recently published ClimateBert algorithm. Based on our results, it can be said that ClimateBert fine-tuned on Cli-maText 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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“…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%

“…Since its publication in October 2021, the pre-trained transformer model Cli-mateBert has only been applied so far in 6 other papers which have not been published yet (two of which are from ClimateBert's original authors): [35,19,21,[36][37][38] as well as in two published studies: [39] and the one published by ClimateBert's authors [20]. Hershcovich et al [35] analyze ClimateBert only regarding its energy consumption, in a context of awareness about the environmental impact that NLP pre-trained models present.…”

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

View full text Add to dashboard Cite

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.

show abstract

Global Tracking of Climate Change Adaptation Policy Using Machine Learning: a Systematic Map Protocol

Cited by 3 publications

References 34 publications

Learning from climate change news: Is the world on the same page?

Learning from climate change news: Is the world on the same page?

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

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

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