What Can Machine Learning Teach Us about Australian Climate Risk Disclosures?

Callan Harker (), Maureen Hassall, Paul Lant, Nikodem Rybak and Paul Dargusch
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Callan Harker: School of Earth and Environmental Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia
Maureen Hassall: School of Chemical Engineering, The University of Queensland, St. Lucia, QLD 4072, Australia
Paul Lant: School of Chemical Engineering, The University of Queensland, St. Lucia, QLD 4072, Australia
Nikodem Rybak: School of Chemical Engineering, The University of Queensland, St. Lucia, QLD 4072, Australia
Paul Dargusch: School of Earth and Environmental Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia

Sustainability, 2022, vol. 14, issue 16, 1-22

Abstract: There seems to be no agreed taxonomy for climate-related risks. The information in firms’ climate risk disclosures represents a new resource for identifying the priorities and strategies of Australian companies’ management of climate risk. This research surveys 839 companies listed on the Australian Stock Exchange for the presence of climate risk disclosures, identifying 201 disclosures on climate risk. The types of climate risks and the risk management strategies were extracted and evaluated using machine learning. The analysis revealed that Australian firms are focused on acute physical climate risks, followed by market and regulatory risks. The predominant management strategy for these risks was to use a risk reduction approach, rather than avoiding or transferring risk. The analysis showed that key Australian industry sectors, such as materials, banking, insurance, and energy are focusing on different mixtures of risk types, but they are all primarily managing risks through risk-reduction strategies. An underlying driver of climate risk disclosure was composed of the financial implications of climate risk, particularly with respect to acute physical risks. The research showed that emission reductions represent a primary consideration for Australian firms in their disclosures identifying how they are responding to climate risk. Further research using machine learning to evaluate climate risk disclosure should focus on analysing entire climate risk reports for key topics and trends over time.

Keywords: climate risk disclosure; climate risk types; risk management; machine learning; supervised classification; unsupervised classification (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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