TU.2.C || The Role of Industry in Sustainable Supply Chains

Tietze, Ann-Carina; Schüler, Maximilian

At least since the Paris Climate Agreement, the transportation sector has found a new focus with sustainability and environmental protection being two of the most important topics of automotive production. In the life cycle of a vehicle, a large share of the greenhouse gas (GHG) emissions can be attributed to the supply chain (SC). For BEVs powered by green electricity, this share even increases due to the lower emissions in the use phase. Based on a material analysis, hotspots and reduction measures can be identified, but supplier specific data are necessary for accurate accounting of GHG reduction. Integrating these data in the life cycle assessment (LCA) necessitates GHG emissions to have been calculated using the same methodology and system models as the LCA they are to be integrated into. As long as no industry standard exists, this can be achieved by collecting supplier specific gate-to-gate data in order to allow companies to perform the balancing in-house by using consistent methods and allocation rules. Providing this raw data is challenging: Fear of IP loss and disadvantages in price negotiations lead to a time consuming effort in drafting bilateral, specific non-disclosure agreements. Secondly, profound process- and LCA expertise is necessary to provide meaningful data. The TED (transferring environmental data) approach is based on the VDA data collection format for LCA and focuses on gate-to-gate data retrieval from individual “hotspot”-suppliers. The idea is simple: analogous to a tax return software, the VDA sheet is “digitized” and step-by-step data collection is explained. Specific rules, e.g. a license agreement, allow for sharing the data only with the intended parties and for the intended purposes. TED thus provides an aid to confidently collect and share raw data in the SC to map specific, realistic vehicle LCAs. It will be further developed to share cradle-to-gate data in order to provide the best possible transparency along the entire SC.

File Type: pdf
Categories: LCM and Digitalization
Tags: Oral