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

Area of Science:

• Environmental Science
• Industrial Ecology
• Sustainability Science

Background:

• Current life cycle assessment (LCA) methods often rely on generic data, limiting accuracy for specific products and services.
• Collecting specific supply chain data for LCA is costly, impractical, and requires extensive proprietary data sharing.
• Widespread adoption of specific LCA necessitates a paradigm shift in data collection, storage, and exchange.

Purpose of the Study:

• To develop a scalable framework for specific environmental footprint assessments in life cycle assessment (LCA).
• To address the challenges of cost, data sharing, and privacy in obtaining specific LCA data.
• To enable accurate, product-specific environmental footprinting for improved sustainability decision-making.

Main Methods:

• Development of the Specific Semiautomated Lifecycle Footprinting (SSELF) framework.
• Decentralization of inventory collection and footprint calculations within the SSELF framework.
• Iterative calculation of upstream impacts using a database of unique product identifiers and user-reported footprints.

Main Results:

• The SSELF framework allows for specific footprint assessments while protecting sensitive production data.
• Decentralized approach substantially reduces the effort required for footprint assessments.
• Identified practical implementation challenges and provided recommendations for addressing them.

Conclusions:

• The SSELF framework offers a scalable solution for generating specific environmental footprints for products and services.
• This approach facilitates greater accountability and reduction of indirect environmental impacts by companies.
• Enhanced specificity in footprint data empowers consumers to reduce consumption-based impacts.