RESEARCH

Informing climate risk analysis using textual information - A research agenda (2024)
With Malte Schierholz, Bolei Ma, Jacob Beck, Andreas Dimmelmeier, Hendrik Christian Doll, Maurice Fehr, Frauke Kreuter, and Alex Fraser. 

Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024), available at ACL 2024 Workshop ClimateNLP homepage.

This project is part of the larger research agenda GIST - Greenhouse Gas Insights and Sustainability Tracking, a research collaboration between Deutsche Bundesbank and LMU Munich to generate high-quality, granular firm-level emissions and sustainability data. More information can be found here. 

Abstract:
We present a research agenda focused on efficiently extracting, assuring quality, and consolidating textual company sustainability information to address urgent climate change decision-making needs. Starting from the goal to create integrated FAIR (Findable, Accessible, Interoperable, Reusable) climate-related data, we identify research needs pertaining to the technical aspects of information extraction as well as to the design of the integrated sustainability datasets that we seek to compile. Regarding extraction, we leverage technological advancements, particularly in large language models (LLMs) and Retrieval-Augmented Generation (RAG) pipelines, to unlock the underutilized potential of unstructured textual information contained in corporate sustainability reports. In applying these techniques, we review key challenges, which include the retrieval and extraction of CO2 emission values from PDF documents, especially from unstructured tables and graphs therein, and the validation of automatically extracted data through comparisons with human-annotated values. We also review how existing use cases and practices in climate risk analytics relate to choices of what textual information should be extracted and how it could be linked to existing structured data.

Extraction of CO2 emissions from corporate sustainability reports (2024)
With Malte Schierholz, Anna Steinberg, Jacob Beck, and Laia Domenech Burin. 

Solicited contribution to the 65th ISI World Statistics Congress 2025, The Hague, NL. 

This project is part of the larger research agenda GIST - Greenhouse Gas Insights and Sustainability Tracking, a research collaboration between Deutsche Bundesbank and LMU Munich to generate high-quality, granular firm-level emission and sustainability data. More information can be found here. 

Abstract:
Financial regulators and central banks are increasingly integrating sustainability aspects into their operations, but significant data gaps remain. The CSRD directive requires all large European enterprises to annually publish their greenhouse gas emissions (CO2-equivalents) in their management report, annual report, or sustainability report. The amount of information available, i.e., the value and unit for each scope, direct emissions (Scope 1), indirect energy-related emissions (Scope 2), and other indirect emissions (Scope 3), is immense, but the data are spread over thousands of PDF documents, published online on company websites, and historically often without abiding to official standards or guidelines. Until now, private companies extract carbon emissions and other indicators from these PDF documents and sell it in a structured, tabular data format to the Bundesbank and to other public authorities. However, despite little apparent difficulties in value extraction from PDF documents the reliability between values extracted by different companies is rather low. Given the current dim situation, we leverage Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) to build several fully automated data extraction pipelines, which are then being compared with data bought from private providers and evaluated using a specially curated gold standard dataset of our own. Open-source software is shared with the community which enables everyone to extract CO2-related indicators from company sustainability reports.

Houston, we have a problem: Can satellite information bridge the climate-related data gap? (2024)
With Andrés Alonso Robisco, José Manuel Carbó Martínez, and Elena Triebskorn.

Documento Ocasional No. 2428, available via Banco de España. 

Presentations: 

• 2024 CEMLA Conference
  (XXIX Meeting of the Central Bank Researchers Network, Center for Latin American Monetary Studies, Mexico City, México)

• 2024 YISF Annual Symposium
  (Yale Initiative on Sustainable Finance Annual Symposium, Yale School of Management, New Haven, CT)

• BIS-IFC Workshop on "Addressing climate change data needs: the global debate and central banks' contribution"
  (Central Bank of the Republic of Türkiye, Izmir, 2024)

Abstract:
Central banks and international supervisors have identified the difficulty of obtaining climate information as one of the key obstacles to the development of green financial products and markets. To bridge this data gap, the use of satellite information from Earth Observation (EO) systems may be necessary. To better understand this process, we analyse the potential of applying satellite data to green finance. First, we summarise the policy debate from a central banking perspective. We then briefly describe the main challenges for economists in dealing with the EO data format and quantitative methodologies for measuring its economic materiality. Finally, using topic modelling, we perform a systematic literature review of recent academic studies to identify the research areas in which satellite data are currently being used in green finance. We find the following topics: physical risk materialisation (including both acute and chronic risk), deforestation, energy and emissions, agricultural risk and land use and land cover. We conclude with a comprehensive analysis on the financial materiality of this alternative data source, a mapping of these application domains to new green financial instruments and markets under development, such as thematic bonds or carbon credits, and some key considerations for policy discussion. 

The climate data iceberg – A depth of information to integrate (2024)
With Hendrik Christian Doll, Susanne Walter, and Gabriela Alves Werb.

Forthcoming in Bulletin on the 12th biennial IFC Conference on "Statistics and beyond: new data for decision making in central banks", slides available via this download link.

Presentations:

• Conference of European Statistics Stakeholders (CESS) 2024
  (Paris, France)

• 12th biennial IFC Conference on "Statistics and beyond: new data for decision making in central banks"
  (BIS Basel, Switzerland)

Abstract:
Central banks need climate-related data to align evidence-based climate change considerations with their core tasks. While structured data from administrative and proprietary sources are limited and contain considerable gaps, a wealth of climate-related information is dispersed and lies below the surface in unstructured form, such as sustainability reports or satellite images. To characterise this situation, we introduce the image of the climate data iceberg. Information from unstructured sources can bridge current data gaps and enhance the usability of existing data by improving its accuracy, extending its scope, and reducing data sharing barriers. In this paper, we discuss the challenges and opportunities central banks and supervisors face in leveraging this unstructured information for climate analysis and research. We further investigate how innovative efforts between central banks and other institutions can help generate actionable and usable climate-related data, exemplified by our own experiences and early-stage learnings from such collaborations.

Extracting Data Citations with Large Language Models  (2024)
With Hendrik Christian Doll and Sebastian Seltmann.

Presentations: 

• Conference on Foundations and Advances of Machine Learning in Official Statistics
  (DESTATIS - Federal Statistical Office of Germany, Wiesbaden, 2024)

• 3rd IFC Workshop on Data Science in Central Banking 2023
  (Bank of Italy, Rome)

Abstract:
Empirical researchers and research data centers (RDCs) face challenges in efficiently understanding and categorizing data sources and methodologies used in scholarly papers. This process currently relies on human readers and is time-consuming and prone to errors. To address this, we explore the potential of using Large Language Models (LLMs), specifically GPT-3.5, to automate the identification and categorization of research data sources. We analyze the accuracy of GPT-3.5 in detecting and summarizing data sources and methods in economics and finance papers. By employing web-scraping techniques, we collect a comprehensive sample of research papers and create human-labeled validation datasets. We evaluate the detection and prediction accuracy and address the issue of false answers provided by the model. Additionally, we assess the pre-processing requirements of GPT-3.5 for cost-effective implementation. Our paper also provides a guide for implementing our proposed solution at research institutions and RDCs worldwide, aiming to enhance data analysis and research data provision services.

Includes upcoming presentations. Unpublished papers available upon request.