GHG impact of cloud IT solutions from Scania's commercial autonomous vehicles in use phase: Assessment, challenges, and possible recommendations to reduce GHG impact

Abstract: Sustainability study in the ever-growing Information technology (IT) sector is an emerging interdisciplinary research field. As one essential element in this sector, the development and implementation of cloud-based autonomous vehicles have the great potential to bring convenience to society and are defined as the climate change mitigation strategy. For instance, autonomous vehicles are able to fully utilize the eco-driving systems to reduce carbon emissions and reach high energy efficiency. Previous studies have shown that cloud IT service, one of the critical technologies for autonomous vehicles, is likely to yield novelties and advantages to the IT industry and reduce the greenhouse gas (GHG) emissions from other sectors. However, cloud services and their data center infrastructures consume plenty of electricity globally and cause GHG emission impacts. Robust methodologies to assess the environmental impacts related to cloud IT solutions are still lacking in academia and industry. In sum, there are knowledge gaps between empirical studies and general interest in software- supported and data-driven autonomous vehicles and their cloud service.  The purpose of this study is to investigate the possibilities and challenges connected to the assessment of the GHG impact related to cloud IT solutions in an autonomous vehicle set up. This study also aims to explore possible recommendations to reduce the GHG emission of cloud IT services. A qualitative in-depth case study is performed. The primary data is collected by semi-structured interview method, while the secondary data is collected by the scoping literature review method. The interviews are conducted with employees with different roles related to cloud services and/or sustainability at the case company.  The findings show the lack of transparent methodologies and calculation guidelines to assess cloud GHG emissions, both in the research community and industry. It shows the great opportunity and market demand for sound assessment methodologies and tools. Besides, six challenges to assessing cloud GHG emissions on the autonomous vehicle set up are identified: i) assessing system boundaries, ii) data quality and collection methods, iii) measurement methodologies, iv) calculation process, v) validation process, and vi) some other challenges. Additionally, five possible recommendations are developed to reduce the cloud GHG emissions: i) cloud GHG emission visualization and measurement tool, ii) better promotional schemes for user’s awareness and engagement, iii) investigations on both top-down and bottom-up approaches, iv) optimization through usage demand shaping, and v) optimization of the infrastructure services.