The contribution of data science applications to a green economy

Data science driven applications (e.g., big data and artificial intelligence) can support the transition to a green economy. However, this requires overcoming existing barriers and providing appropriate framework conditions. Based on an analysis of 295 German and US start-ups using data science to create positive environmental impacts, we identify six main obstacles to a greater use of data science for sustainable transformation, and propose six measures that can be used to formulate policy recommendations. This paper examines the intersections between the hoped-for shift toward a green economy and data science (various forms of big data analytics and artificial intelligence). It does so through an analysis of data science applications with environmental relevance developed or deployed by German and US start-ups. The majority of the data science applications identified seek to improve the efficiency of existing products and processes, or to provide information. Applications that support more fundamental transformations of existing production and consumption patterns are fewer in number. To increase the sustainability-related impact of data science, it seems necessary to adjust policy framework conditions. Based on our findings, recommendations for action are presented regarding sustainability-related changes of the legal and regulatory framework conditions.