A Practical Approach for Social Life Cycle Assessment in the Automotive Industry

Identifying social impacts along the life cycle of their products is becoming increasingly important for companies. Social Life Cycle Assessment (SLCA) as a possible tool has not been conducted so far within industries with complex international supply chains using mainly company-specific data. As a...

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Bibliographic Details
Main Authors: Hannah Karlewski, Annekatrin Lehmann, Klaus Ruhland, Matthias Finkbeiner
Format: Article
Language:English
Published: MDPI AG 2019-08-01
Series:Resources
Subjects:
Online Access:https://www.mdpi.com/2079-9276/8/3/146
Description
Summary:Identifying social impacts along the life cycle of their products is becoming increasingly important for companies. Social Life Cycle Assessment (SLCA) as a possible tool has not been conducted so far within industries with complex international supply chains using mainly company-specific data. As a novelty, this work presents a practical SLCA approach along with the first case studies for the automotive industry, based on a previously developed indicator set and an extensive data collection. Social data was collected from companies along the life cycle of two specific car components, while analyzing data availability, validity and comparability. To obtain product references, both a top-down and a bottom-up approach for quantitative indicators based on time effort and data availability on the process level were devised. Also, two options were developed for how qualitative indicators (e.g., <i>written principles</i> for <i>Corruption</i>) can be applied together with quantitative performance indicators (e.g., <i>number of accidents</i>). The general practical applicability of the approach could be demonstrated by four quantitative and seven qualitative indicators. It is a first step towards analyzing the social performance of products with complex supply chains on a company level. Remaining challenges/limitations include social data availability and quality and obtaining data at the process level (allocation) and should be addressed in future studies.
ISSN:2079-9276