Can Sustainable Shopping Recommendations in Online Retail Help Reduce Global Warming?

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dc.contributor.authorHoffmann, Marja Lena
dc.contributor.authorNanevski, Ivana
dc.contributor.authorGossen, Maike
dc.contributor.authorBergener, Jens
dc.contributor.authorFlick, Alexander
dc.contributor.authorSantarius, Tilman
dc.contributor.authorBiessmann, Felix
dc.date.accessioned2024-04-25T15:24:23Z
dc.date.available2024-04-25T15:24:23Z
dc.date.issued2024-04-25
dc.description.abstractTwo dominant and contradictory narratives describe the apparent contribution of information and communication technology (ICT) to climate change. On the one hand, ICT can reduce global greenhouse gas (GHG) emissions by, for example, supporting energy efficiency or promoting sustainable consumption. On the other hand, the increased energy demands of emerging software components leveraging artificial intelligence or machine learning can be directly and indirectly responsible for GHG emissions. This makes it critical to assess whether ICT mitigates or exacerbates net climate impacts and the contributing factors. The impacts of software have received relatively little attention and require the development of new approaches to conduct such assessments. In particular, the net effect of complex real-world applications is frequently not measured. In this study, we provide a detailed step-by-step assessment to quantify the net global warming potential of an online shopping recommendation system that encourages users to make sustainable consumption decisions. We consider the energy consumed and associated GHG emissions in the development and use of the software and compare these to the potentially avoided GHG emissions associated with more sustainable recommended options. The results demonstrate that the software has the potential to indirectly avoid more emissions than it causes and that changes at different steps of the software can amplify this.
dc.description.sponsorshipThis publication has been funded by the Federal Ministry of Education and Research of Germany (BMBF) (grant no.: 16DII121, 16DII122, 16DII123, 16DII124, 16DII125, 16DII126, 16DII127, 16DII128 – “Deutsches Internet-Institut”).en
dc.identifier.citationHoffmann, M. L., Ivana Nanevski, Maike Gossen, Jens Bergener, Alexander Flick, Tilman Santarius, & Felix Biessmann. (2024). Can Sustainable Shopping Recommendations in Online Retail Help Reduce Global Warming? Assessing the Direct and Indirect Climate Impact of Modern Software. Weizenbaum Journal of the Digital Society, 4(1). https://doi.org/10.34669/WI.WJDS/4.1.2
dc.identifier.doihttps://doi.org/10.34669/wi.wjds/4.1.2
dc.identifier.issn2748-5625
dc.identifier.urihttps://www.weizenbaum-library.de/handle/id/582
dc.language.isoeng
dc.publisherWeizenbaum Institute
dc.rightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectSustainable AI
dc.subjectMachine learning
dc.subjectSustainable consumption
dc.subjectSoftware Global Warming Potential
dc.subjectICT
dc.subjectCarbon footprint
dc.titleCan Sustainable Shopping Recommendations in Online Retail Help Reduce Global Warming?
dc.typeArticle
dcmi.typeText
dcterms.bibliographicCitation.journaltitleWeizenbaum Journal of the Digital Society
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Vol. 4 No. 1 (2024)