The supply chains of raw materials and semi-finished products can create both positive and negative impacts affecting various stakeholders. Raw materials have also a strategic importance for enhancing the competitiveness of the European industry, and creating employment (EC - European Commission, 2017a). At European level, the secure and sustainable supply of raw materials from domestic sources and international markets are key objectives of the Raw Materials Initiative (EC - European Commission, 2008). The relationship between low security of supply and poor governance in supplier countries is acknowledged and captured in the list of Critical Raw Materials for the EU (EC - European Commission, 2017b). Internationally, many of the Sustainable Development Goals (UN General Assembly, 2015) address, directly or indirectly, the social dimension of sustainable development and, hence, are linked to the supply of raw materials, under several aspects (Mancini et al. 2018). In the context of sustainability assessment, Life Cycle Thinking is a well-known concept. Social Life Cycle Assessment (SLCA) evaluates social and socio-economic impacts along the life cycle of products (from the raw materials extraction, processing, manufacture, use, end of life) using a mix of generic and site specific data. Studies can be focused on a specific supply chain, or they can look at different sectors in an entire economy. Given that EU policy on raw materials aims at a sustainable supply of raw materials (both from domestic sources and from international markets), the selection of appropriate metrics for monitoring the sustainability at sector level is key. However, the task is particularly challenging for what concerns the social dimension of sustainability, which is less advanced from a methodological point of view (Boström, 2012). In this study, we used a SLCA database for assessing and comparing the social risks associated with the supply chain of raw materials sectors at the macro-scale in EU, and in a set of extra-EU countries. Negative social impacts are expressed in terms of potential risk to be exposed to negative social conditions while potential positive contributions are expressed using an opportunity evaluation. The economic sectors under investigation are mining and quarrying, forestry and logging, manufacture of basic metals, non-metallic minerals, paper and paper products, wood and of products of wood. A set of social aspects (called categories, or areas of concern) was selected from those available in the database, according to criteria of relevance, data quality, etc. These include aspects affecting workers (health and safety; freedom of association and collective bargaining; child labour; fair salary; working time), local communities (respect of indigenous rights and migration), actors in the value chain (corruption) and society as a whole (contribution to economic development). While the latter category include an indicator on a positive impact, the others are negative impacts occurring in the value chain. The results of the analysis compare social risk in the European raw materials supply chain with those of six extra-EU countries, for the set of selected social aspects. The contribution analysis shows social hotspots within a supply chain, highlighting sectors and locations that are mostly contributing to social risk. Data quality and sources of uncertainty are also discussed. Given the granularity of the data used to assess social aspects (mostly at country, or macro-sector level), specific features of raw materials sectors and sub-national variability are not captured in this analysis. This macro-scale assessment demonstrates the potential and the limitations of social data combined with input-output models for assessing social risk in supply chains. It provides a first-screening assessment of supply chains, which can be used for prioritizing areas for more detailed investigation. One of the strengths of this approach is to show social performance in various social categories and in different stakeholders, over the entire life cycle worldwide, thus has the capability of detecting trade-offs and burden shifting. Results, however, suggest that the current use of these models in e.g. policy analysis should be applied with some caution due to the uncertainty derived by the combination of input/output models with social data. As the governance indicators used in the criticality assessment, social risk results could be suitable for macro-scale assessment of material trade flows, in order to estimate, for instance, social implications of a high import reliance, or evaluate consequences of changes in trading partners for the EU.
