SSELF: A Specific SEmiautomated Lifecycle Footprinting framework to go beyond generic data in LCA

Advancements in life cycle assessment (LCA) and environmentally extended input–output analysis enable quick generic estimations of the environmental footprint ofalmost any product and service. However, going beyond a generic estimate to anassessment based on actual, specific supply chain data remains costly and imprac-ticable and demands significant sharing of proprietary data between supply chainactors and the LCA practitioner. Achieving widespread specificity in LCA requires fun-damentally changing the way inventory and emission data are collected, stored, andexchanged. This research develops the SSELF (Specific SEmiautomated Lifecycle Foot-printing) framework to go beyond generic data in LCA in a way that can scale up, whilesafeguarding sensitive data. A key feature of the framework is decentralizing inventorycollection and footprint calculations. Thus, production functions remain private andupstream impacts are calculated using an iterative approach with a database of uniqueproduct identifiers and the footprints reported by other users, capturing changes inthe footprints of suppliers. Although this substantially reduces the effort of footprintassessments, implementing the framework in practice presents new challenges, whichare identified and discussed in this paper along with recommendations on how theycan be addressed and their implications. This work provides important insight intohow to get to a point where every product and service has its unique footprint. Broadaccess to footprints with more specificity is necessary to help consumers reduce theirconsumption-based impacts and make companies take accountability for, and reduce,their indirect impacts.

Mots clés

• big data
• carbon footprint
• climate change
• industrial ecology
• life cycle assessment
• supply chaindata