DeepAI AI Chat
Log In Sign Up

Automated Identification of Climate Risk Disclosures in Annual Corporate Reports

by   David Friederich, et al.
ETH Zurich

It is important for policymakers to understand which financial policies are effective in increasing climate risk disclosure in corporate reporting. We use machine learning to automatically identify disclosures of five different types of climate-related risks. For this purpose, we have created a dataset of over 120 manually-annotated annual reports by European firms. Applying our approach to reporting of 337 firms over the last 20 years, we find that risk disclosure is increasing. Disclosure of transition risks grows more dynamically than physical risks, and there are marked differences across industries. Country-specific dynamics indicate that regulatory environments potentially have an important role to play for increasing disclosure.


page 1

page 2

page 3

page 4


Evaluating TCFD Reporting: A New Application of Zero-Shot Analysis to Climate-Related Financial Disclosures

We examine climate-related disclosures in a large sample of reports publ...

Assessing and Improving Cybersecurity Maturity for SMEs: Standardization aspects

SMEs constitute a very large part of the economy in every country and th...

Analyzing Sustainability Reports Using Natural Language Processing

Climate change is a far-reaching, global phenomenon that will impact man...

Analysis of the evolution of agroclimatic risks in a context of climate variability in the region of Segou in Mali

In the Sahel region the population depends largely on rain-fed agricultu...

Evaluating the role of risk networks on risk identification, classification and emergence

Modern society heavily relies on strongly connected, socio-technical sys...