Application of text mining to the analysis of climate-related disclosures
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DOI: 10.1016/j.irfa.2022.102307
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- Prianto Budi Saptono & Gustofan Mahmud & Intan Pratiwi & Dwi Purwanto & Ismail Khozen & Muhamad Akbar Aditama & Siti Khodijah & Maria Eurelia Wayan & Rina Yuliastuty Asmara & Ferry Jie, 2023. "Development of Climate-Related Disclosure Indicators for Application in Indonesia: A Delphi Method Study," Sustainability, MDPI, vol. 15(14), pages 1-25, July.
- Alessi, Lucia & Ossola, Elisa & Panzica, Roberto, 2023. "When do investors go green? Evidence from a time-varying asset-pricing model," International Review of Financial Analysis, Elsevier, vol. 90(C).
- Andrés Alonso-Robisco & José Manuel Carbó & José Manuel Marqués, 2023. "Machine Learning methods in climate finance: a systematic review," Working Papers 2310, Banco de España.
- Frankovic, Ivan & Kolb, Benedikt, 2024.
"The role of emission disclosure for the low-carbon transition,"
European Economic Review, Elsevier, vol. 167(C).
- Frankovic, Ivan & Kolb, Benedikt, 2023. "The role of emission disclosure for the low-carbon transition," Discussion Papers 33/2023, Deutsche Bundesbank.
- Daniel O. Beltran & Pinar Uysal, 2023. "What are Large Global Banks Doing About Climate Change?," International Finance Discussion Papers 1368, Board of Governors of the Federal Reserve System (U.S.).
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Keywords
Sustainability; Sustainability data gaps; Text mining; TCFD; Taxonomy and ontology management;All these keywords.
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