4.6 Article

Identifying methodological challenges in the social risk assessment of cellulosic ethanol value chains

Journal

JOURNAL OF INDUSTRIAL ECOLOGY
Volume 26, Issue 4, Pages 1233-1246

Publisher

WILEY
DOI: 10.1111/jiec.13287

Keywords

advanced biofuel; industrial ecology; risk assessment; Sustainable Development Goals (SDGs); social life cycle assessment (S-LCA); sustainability assessment

Funding

  1. Horizon 2020 Framework Programme [745012, 031B0935A]
  2. H2020 Societal Challenges Programme [745012] Funding Source: H2020 Societal Challenges Programme

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Social risk assessment using databases allows sustainability practitioners to quickly identify social hotspots in value chains. However, the regionalization of data and the accuracy of assumptions are crucial for reliable results, which need to be validated by external sources of information.
Social risk assessment using databases allows sustainability practitioners' rapid identification of social hotspots in value chains. The Soca database for sustainability assessment allows practitioners to perform a social risk assessment in addition to environmental assessment. As it is based on Ecoinvent, a database for environmental appraisal, the Soca database provides limited regionalization of data. This study aims to perform a regionalized assessment at country-level by following the Soca approach in combination with EXIOBASE 3 as a source of regional information on the origin of inputs and for the estimation of worker hours. This analysis uses a case study based on the production of advanced biofuels in Croatia to assess the shortcomings of the methodology. We show that the assumptions made during the study can lead to unspecific results and that results need to be validated by external sources of information. Data availability plays a crucial role in achieving usable results. Social hotspots result from the combination of worker hours, raw material requirements, and social conditions in the country where production takes place. These three factors and the assumptions made need to be presented in a clear and transparent way to facilitate the interpretation of results.

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