Regionalized LCA in practice: the need for a universal shapefile to match LCI and LCIA

Abstract

The regionalization challenge Life cycle assessment (LCA) and environmental footprints studies have become prominent tools to assess environmental impacts of products, services, companies, and regions, including the impacts over global supply chains. Many impact categories require regional differentiation for life cycle impact assessment (LCIA), but also require regionally explicit life cycle inventory (LCI) data for emissions, water consumption, and land use. There have been many methodological developments to assess such impacts at a regional scale, such as for water scarcity on a watershed-level or land use impacts on an ecoregion-level (Jolliet et al. 2018). Furthermore, a broad range of regional life cycle inventory (LCI) data has been provided, e.g., for land, water, and air emissions (e.g.,

Globally Regionalized Monthly Life Cycle Impact Assessment of Particulate Matter

“…Classification into urban and rural regions was available from (44). Country and region polygons from (44), ecoinvent v3.6 region polygons from (45), ecoinvent v3.7 region polygons from (46), and the universal LCI-LCIA matching polygons from (47,48) were used to provide aggregated CFs in the SI.…”

“…Supporting materials. Extended descriptions of methods and additional results including aggregated CFs for countries, provinces, ecoinvent regions, universal LCI-LCIA matching polygons from (47,48), and the world are available in the SI. Case study data, source code for the model (R v3.6.0) (55), and characterization factor maps are available from http://dx.doi.org/10.17632/8jnj4vzbh6.1 (56).…”

“…The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.0c05691. Extended descriptions of methods and additional results including aggregated CFs for countries, provinces, ecoinvent regions, universal LCI-LCIA matching polygons from refs , , and the world; the case study data, the source code for the model (R v3.6.0), and characterization factor maps are available from ; and the case study data is fully compatible with the power plant data sets from refs , − (PDF) Supplementary Tables 1−16 (XLSX) …”

A Commodity Supply Mix for More Regionalized Life Cycle Assessments

“…Land occupation and transformation impacts require detailed information on biophysical conditions and spatial heterogeneity to quantify potential damage to biodiversity and ecosystem services. , Similarly, water consumption and degradation impacts are known to vary across river basins due to variability in human and ecosystem demand , or background chemical concentrations affecting human health or natural resources . More systematic regionalization in LCAs has been accompanied by methods to reduce uncertainty from spatial variability in both elementary flows and characterization factors, − methods for prioritizing regionalization, − software solutions, , and standardization of spatial boundaries …”

“…12 More systematic regionalization in LCAs has been accompanied by methods to reduce uncertainty from spatial variability in both elementary flows and characterization factors, 13−17 methods for prioritizing regionalization, 18−20 software solutions, 3,21 and standardization of spatial boundaries. 22 Regionalization has been a key consideration for the increasing number of LCAs of food products. 23−29 These LCAs require high-resolution information on regional practices, soil and water conditions for production, as well as environmental conditions for emissions to soil, air, and water to derive more regionalized impacts.…”