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Out of Sight, Out of Reports: Generative AI in Sustainability Reporting

Ryan, Bianca LU and Carlsten, Klara LU (2026) INFM12 20261
Department of Informatics
Abstract
As generative AI becomes increasingly integrated into organisations, its negative environmental impact is also growing. Despite this, such impacts remain limited from sustainability reporting. This study examines how organisations navigate the lack of established information infrastructures for reporting the environmental impact of generative AI. Using a qualitative approach based on semi-structured interviews with seven interviews and nine respondents, combined with a document analysis of sustainability reports, the study applies information infrastructure theory and institutional isomorphism as analytical lenses. The findings show that the absence of reporting is not primarily due to organisational unwillingness, but rather a structural... (More)
As generative AI becomes increasingly integrated into organisations, its negative environmental impact is also growing. Despite this, such impacts remain limited from sustainability reporting. This study examines how organisations navigate the lack of established information infrastructures for reporting the environmental impact of generative AI. Using a qualitative approach based on semi-structured interviews with seven interviews and nine respondents, combined with a document analysis of sustainability reports, the study applies information infrastructure theory and institutional isomorphism as analytical lenses. The findings show that the absence of reporting is not primarily due to organisational unwillingness, but rather a structural vacuum. Existing frameworks such as the GHG Protocol and ESRS lack guidance on AI-related emissions, supplier data is insufficient, and IT and sustainability functions operate in organisational silos. Organisations act reactively and wait on regulatory signals and industry practices rather than taking proactive action. Furthermore, generative AI is consistently framed as a solution rather than a source of environmental impact, contributing to a legitimacy gap. The study concludes that the reporting gap is primarily structural and contributes with empirical insights to an emerging research area where reporting practices remain underdeveloped. (Less)
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author
Ryan, Bianca LU and Carlsten, Klara LU
supervisor
organization
course
INFM12 20261
year
type
H1 - Master's Degree (One Year)
subject
keywords
Generative AI, Sustainability reporting, Environmental impact, Information infrastructure, Scope 3
language
English
id
9241695
date added to LUP
2026-06-22 07:29:24
date last changed
2026-06-22 07:29:24
@misc{9241695,
  abstract     = {{As generative AI becomes increasingly integrated into organisations, its negative environmental impact is also growing. Despite this, such impacts remain limited from sustainability reporting. This study examines how organisations navigate the lack of established information infrastructures for reporting the environmental impact of generative AI. Using a qualitative approach based on semi-structured interviews with seven interviews and nine respondents, combined with a document analysis of sustainability reports, the study applies information infrastructure theory and institutional isomorphism as analytical lenses. The findings show that the absence of reporting is not primarily due to organisational unwillingness, but rather a structural vacuum. Existing frameworks such as the GHG Protocol and ESRS lack guidance on AI-related emissions, supplier data is insufficient, and IT and sustainability functions operate in organisational silos. Organisations act reactively and wait on regulatory signals and industry practices rather than taking proactive action. Furthermore, generative AI is consistently framed as a solution rather than a source of environmental impact, contributing to a legitimacy gap. The study concludes that the reporting gap is primarily structural and contributes with empirical insights to an emerging research area where reporting practices remain underdeveloped.}},
  author       = {{Ryan, Bianca and Carlsten, Klara}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Out of Sight, Out of Reports: Generative AI in Sustainability Reporting}},
  year         = {{2026}},
}