Green software and generative AI : Can generative AI be used to implement green software?
Brusila, Tommi (2025)
2025
Tietojohtamisen DI-ohjelma - Master's Programme in Information and Knowledge Management
Johtamisen ja talouden tiedekunta - Faculty of Management and Business
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Hyväksymispäivämäärä
2025-06-11
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202506107065
https://urn.fi/URN:NBN:fi:tuni-202506107065
Tiivistelmä
The digitalization of our society has lead to increased emissions of the ICT field, as the energy required to run the constantly growing amount of ICT devices and IT software run on them is also increasing. These growing amounts of emissions are in direct contrast to the sustainability related goals of the modern economy that aims to be as energy efficient as possible. At the same time, a new technology is emerging that is also known to use vast amounts of energy: generative AI.
Though the focus of the current research and public discussion regarding generative AI and sustainability has been elsewhere, it has already been demonstrated in existing research that generative AI can be used to improve energy efficiency in different applications across multiple different industries. However, the research regarding generative AI and sustainability in the field of IT has mainly focused on the energy efficiency of generative AI itself. Though such research of improving energy efficiency of generative AI itself is clearly important, there seems to be a lack of research regarding the usage of generative AI for more sustainable IT solutions.
The goal of this study is to start filling the above mentioned research gap by investigating that can generative AI be used to implement more sustainable IT solutions, which is also known as green software. As the topic of green software is quite vast, this study only focuses on the code energy efficiency principle of green software. To achieve this goal design science research methodology is used to formulate an artefact that can be used to assess the capabilities of generative AI to implement green software. However, the study recognizes the rapid development of generative AI and hence doesn't limit itself to assessing the current capabilities of a specific generative AI tool or comparing different tools against each other as such research could likely be outdated in a short amount of time. This study also recognizes that software is developed for vastly different purposes, hence the importance and capability of implementing green software differ between different instances of software. As a result, this study proposes an high level greenness assessment methodology that can be used in evaluating the green code implementation capabilities of any generative AI tool with any type of software.
The findings of the study, achieved by demonstrating the artefact in a natural use case scenario, support the idea that generative AI can be used to implement green software. However, as the assessment methodology presented in this study is highly context dependant, the findings can't be used to state that any given generative AI tool can be used to implement green software in any given context. Still, the positive findings of this study should motivate additional research of the conjoint topic of generative AI and green software.
Though the focus of the current research and public discussion regarding generative AI and sustainability has been elsewhere, it has already been demonstrated in existing research that generative AI can be used to improve energy efficiency in different applications across multiple different industries. However, the research regarding generative AI and sustainability in the field of IT has mainly focused on the energy efficiency of generative AI itself. Though such research of improving energy efficiency of generative AI itself is clearly important, there seems to be a lack of research regarding the usage of generative AI for more sustainable IT solutions.
The goal of this study is to start filling the above mentioned research gap by investigating that can generative AI be used to implement more sustainable IT solutions, which is also known as green software. As the topic of green software is quite vast, this study only focuses on the code energy efficiency principle of green software. To achieve this goal design science research methodology is used to formulate an artefact that can be used to assess the capabilities of generative AI to implement green software. However, the study recognizes the rapid development of generative AI and hence doesn't limit itself to assessing the current capabilities of a specific generative AI tool or comparing different tools against each other as such research could likely be outdated in a short amount of time. This study also recognizes that software is developed for vastly different purposes, hence the importance and capability of implementing green software differ between different instances of software. As a result, this study proposes an high level greenness assessment methodology that can be used in evaluating the green code implementation capabilities of any generative AI tool with any type of software.
The findings of the study, achieved by demonstrating the artefact in a natural use case scenario, support the idea that generative AI can be used to implement green software. However, as the assessment methodology presented in this study is highly context dependant, the findings can't be used to state that any given generative AI tool can be used to implement green software in any given context. Still, the positive findings of this study should motivate additional research of the conjoint topic of generative AI and green software.