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

Abstract

Two 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.

Keywords

• Sustainable AI
• Machine learning
• Sustainable consumption
• Software Global Warming Potential
• ICT
• Carbon footprint