Climatebug: A Data-Driven Framework for Analyzing Bank Reporting Through a Climate Lens

15 Pages Posted: 20 Dec 2022

See all articles by Yinan Yu

Yinan Yu

Chalmers University of Technology

Samuel Scheidegger

Asymptotic AI

Jasmine Elliott

University of Gothenburg

Åsa Löfgren

Department of Economics, University of Gothenburg

Abstract

In this article we present a framework, climateBUG, that offers a framework to detect latent information about how banks discuss their activities related to climate change. The framework is based on a corpus of European banks’ annual reports, and it consists of an ingestion pipeline to extract information from the corpus data, a configurable database and a set of API’s. In addition, climateBUG provides two standalone components that can be used for customized analyses: 1. A unique annotated corpus with the foci of climate change and finance with approximately 1.1M (1,070,070) data points offered open access that can be used as training data for further model optimization. 2. A deep learning model adapted to the corpus. Benchmarking of the classification performance of the model shows that it outperforms other models with similar foci. The framework has been developed using an interdisciplinary approach with relevant domain knowledge from financial reporting, climate economics, and advanced computational linguistics natural language processing. The intended users of climateBUG include academic researchers, government representatives, managers from the the banking industry, and journalists.

Keywords: natural language processing, Annual reporting, climate change, sustainability, Finance & accounting

Suggested Citation

Yu, Yinan and Scheidegger, Samuel and Elliott, Jasmine and Löfgren, Åsa, Climatebug: A Data-Driven Framework for Analyzing Bank Reporting Through a Climate Lens. Available at SSRN: https://ssrn.com/abstract=4308287 or http://dx.doi.org/10.2139/ssrn.4308287

Yinan Yu

Chalmers University of Technology ( email )

Gothenburg
SE-412 96 Goteborg
Sweden

Samuel Scheidegger

Asymptotic AI ( email )

Guldpokalsgatan 3B
Göteborg, 41712
Sweden

HOME PAGE: http://www.asymptotic.ai

Jasmine Elliott

University of Gothenburg ( email )

Viktoriagatan 30
Göteborg, 405 30
Sweden

Åsa Löfgren (Contact Author)

Department of Economics, University of Gothenburg ( email )

Box 640
Vasagatan 1, E-building, floor 5 & 6
Göteborg, 40530
Sweden

HOME PAGE: http://economics.gu.se/english/staff/senior_lecturers-lecturers-_researchers/asa_lofgren/?languageId

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