Knowledge about the impact of NDR task characteristics is an enabler for adaptive takeover concepts. In addition, it might help regulators to make decisions on allowed NDR tasks during automated driving.

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Environmental aspects are more and more relevant for raw material policy-making and responsible sourcing strategies. This trend is partly based on growing public awareness of problems and impacts associated with extraction and processing of ores and minerals. Disaster events such as the tailing dam failures in Kolontár (Hungary, October 2010) and Bento Rodriguez (Brazil, November 2015) quite frequently highlight the fact that many mining and processing practices are associated with substantial environmental impacts and risks for the local and even regional environments. However, there is also increasing recognition that the rather devastating environmental performance of many past and current mining projects is a major reason for communities around the world to oppose both the development of new and the expansion of existing mines. Although mining companies constantly have to increase their efforts to secure the social license to operate, many scholars already point out that both environmental impacts and associated social and political reactions are emerging as a decisive factor determining current and future raw material supply. In light of these concerns, raw material policy-making requires solid information on environmental hot spots in mining, as well as on raw materials of particular concern. Whereas indicators and information systems are already well developed for geological, technical, structural, political, regulatory, and economic supply risks, there is currently no holistic method and information system for environmental concerns associated with the mining of raw materials. Although life cycle assessment can provide methodological support for various environmental aspects, it has substantial weaknesses in the fields of ecosystem degradation, impacts on fresh-and groundwater resources, and hazard potentials from episodic disaster events. This paper presents a methodology that aims to fill this gap. Our proposed method provides a system of 11 indicators allowing the identification of raw material-specific environmental hot spots and rankings of raw materials. Although the indicator system is qualitative in nature, its composition and aggregation cover the most relevant environmental concerns arising from mining and allow prioritizing of raw materials from a global environmental perspective.

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