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Paper Type
Complete
Description
The United Nations Climate Change (UNCC) report in 2022 warns that the likelihood of global warming exceeding 1.5°C by 2026 has surpassed 50%. Climate change is a global problem that requires collective action, and companies have a significant role to play in mitigating climate change. Companies have already started to incorporate climate change risk and liabilities in their annual reports. Monitoring a company's progress in transitioning to sustainability can help stakeholders make informed decisions. However, existing approaches for sustainability monitoring of annual reports are limited in scalability, mainly because of the manual steps involved. Therefore, we designed and evaluated a more scalable artifact using state-of-the-art natural language processing (NLP) techniques to monitor companies' sustainability targets based on their annual reports. This work presents a prototype that improves upon the current state of practice and contributes to the body of knowledge by outlining key design decisions.
Paper Number
1862
Recommended Citation
Rath, Oliver; Haase, Frederic; Erlacher, Nina Annika; and Schoder, Detlef, "Scalable Sustainability Monitoring of Financial Reports: A Design Science Artifact" (2023). AMCIS 2023 Proceedings. 18.
https://aisel.aisnet.org/amcis2023/sig_green/sig_green/18
Scalable Sustainability Monitoring of Financial Reports: A Design Science Artifact
The United Nations Climate Change (UNCC) report in 2022 warns that the likelihood of global warming exceeding 1.5°C by 2026 has surpassed 50%. Climate change is a global problem that requires collective action, and companies have a significant role to play in mitigating climate change. Companies have already started to incorporate climate change risk and liabilities in their annual reports. Monitoring a company's progress in transitioning to sustainability can help stakeholders make informed decisions. However, existing approaches for sustainability monitoring of annual reports are limited in scalability, mainly because of the manual steps involved. Therefore, we designed and evaluated a more scalable artifact using state-of-the-art natural language processing (NLP) techniques to monitor companies' sustainability targets based on their annual reports. This work presents a prototype that improves upon the current state of practice and contributes to the body of knowledge by outlining key design decisions.
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