Applying unsupervised learning method to develop a regional risk model based on TCFD: A case study in the United States

Chiu, Ming‐Chuan; Wei, Chia-Jung; Wang, Yu-Ching; Kao, Meng-Chun

doi:10.1016/j.jclepro.2023.136669

Cited by 4 publications

(6 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prior research on ML for sustainability uses several unsupervised ML algorithms. Unsupervised learning involves training a model on unlabelled data, particularly when there is no prior knowledge about the data and the underlying structure of the data is undiscovered (Chiu et al , 2023). Prominent unsupervised learning algorithms include K-means clustering (Zhu et al , 2022), hierarchical clustering (Bao and Xie, 2022) and Gaussian Mixture Models (Balfaqih et al , 2022).…”

Section: Discussionmentioning

confidence: 99%

“…Prominent unsupervised learning algorithms include K-means clustering (Zhu et al , 2022), hierarchical clustering (Bao and Xie, 2022) and Gaussian Mixture Models (Balfaqih et al , 2022). Clustering techniques group similar data points together based on their inherent attributes (Abdella et al , 2020; Chiu et al , 2023). Further, dynamic topic modelling is an ML approach used to analyse the evolution of topics from a collection of documents over time (Kim et al , 2021).…”

Section: Discussionmentioning

confidence: 99%

“…To support their claims, Jin et al (2023) present empirical evidence demonstrating the alignment of urban climate adaptation with sustainable development, using LDA as their methodology. Similarly, Chiu et al (2023) concentrate on the application of unsupervised learning techniques to establish a regional risk model based on the Task Force on Climate-related Financial Disclosures framework. By using these unsupervised learning methods (Khodabandehlou and Rahman, 2017), the authors analyse data and identify patterns and relationships pertaining to climate risks at the regional level.…”

Section: Thematic Areasmentioning

confidence: 99%

“…Chiu et al (2023) demonstrated the application of unsupervised learning methods in developing a regional risk model for climate-related risks. Further research can explore the scalability and generalizability of such models by applying them to different geographical regions and considering a wider range of climate risk factors.…”

Section: Future Research Scopesmentioning

confidence: 99%

See 3 more Smart Citations

Machine learning for sustainable development: leveraging technology for a greener future

Kagzi,

Khanra,

Paul

2023

JSIT

View full text Add to dashboard Cite

Purpose From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries. Design/methodology/approach This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development. Findings ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals. Originality/value This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Thematic Areasmentioning

confidence: 99%

Section: Future Research Scopesmentioning

confidence: 99%

See 2 more Smart Citations

Machine learning for sustainable development: leveraging technology for a greener future

Kagzi,

Khanra,

Paul

2023

JSIT

View full text Add to dashboard Cite

show abstract

“…ESG is becoming increasingly important in the financial world, as there's a growing recognition that ESG factors can have a significant impact on a company's long-term value. [2] TCFD (Task Force on Climate-related Financial Disclosures) and [3] TNFD (Task Force on Nature-related Financial Disclosures) are both initiatives aimed at providing guidance and standards for companies to report on specific environmental risks (Dye et al, 2021 ; HOEKSTRA, 2022 ; Rudman et al, 2022 ; Chiu et al, 2023 ; Irvine-Broque and Dempsey, 2023 ; Lee et al, 2023 ; Adams et al, 2024 ).…”

Section: Detailed Data Requirements For Green Finance (And How Synthe...mentioning

confidence: 99%

Opportunities for synthetic data in nature and climate finance

Tkachenko

2024

Front. Artif. Intell.

View full text Add to dashboard Cite

This paper delves into the intricacies of synthetic data, emphasizing its growing significance in the realm of finance and more notably, sustainable finance. Synthetic data, artificially generated to simulate real-world data, is being recognized for its potential to address risk management, regulatory compliance, and the innovation of financial products. Especially in sustainable finance, synthetic data offers insights into modeling environmental uncertainties, assessing volatile social and governance scenarios, enhancing data availability, and protecting data confidentiality. This critical review attempts first ever classification of synthetic data production methods, when applied to sustainable finance data gaps, elucidates the methodologies behind its creation, and examines its assurance and controls. Further, it identifies the unique data needs of green finance going forward and breaks down potential risks tied to synthetic data utilization, including challenges from generative AI, input quality, and critical ethical considerations like bias and discrimination.

show abstract

Business strategies to counter climate change risks to long lived production assets

Tingey-Holyoak,

Cooper,

Crase

et al. 2024

Journal of Cleaner Production

View full text Add to dashboard Cite

Applying unsupervised learning method to develop a regional risk model based on TCFD: A case study in the United States

Cited by 4 publications

References 34 publications

Machine learning for sustainable development: leveraging technology for a greener future

Machine learning for sustainable development: leveraging technology for a greener future

Opportunities for synthetic data in nature and climate finance

Business strategies to counter climate change risks to long lived production assets

Contact Info

Product

Resources

About