ABSTRACT
Life Cycle Assessment (LCA) is an established and standardized methodology to comprehensively assess environmental and public health metrics across industries and products (International Organization for Standardization, 2006). The United States Environmental Protection Agency (USEPA) is developing an open source LCA tool ecosystem (Ingwersen, 2019). The ecosystem includes tools to automate the creation of life cycle inventory (LCI) datasets, which account for flows to and from nature for steps across the life cycle of products or services, and tools for life cycle impact assessment (LCIA) to support classification and characterization of the cumulative LCI to potential impacts. Impacts are expressed via indicators, either midpoint or endpoint, corresponding to different points on the environmental cause-effect chain model (Frischknecht & Jolliet, 2016). This paper describes a USEPA LCA ecosystem tool 'LCIA formatter' that extracts LCIA information from original source methods and converts the data for interoperability with the rest of the USEPA LCA ecosystem tools.
ABSTRACT
INTRODUCTION: The flexibility of life cycle inventory (LCI) background data selection is increasing with the increasing availability of data, but this comes along with the challenge of using the background data with primary life cycle inventory data. To relieve the burden on the practitioner to create the linkages and reduce bias, this study aimed at applying automation to create foreground LCI from primary data and link it to background data to construct product system models (PSM). METHODS: Three experienced LCA software developers were commissioned to independently develop software prototypes to address the problem of how to generate an operable PSM from a complex product specification. The participants were given a confidential product specification in the form of a Bill of Materials (BOM) and were asked to develop and test prototype software under a limited time period that converted the BOM into a foreground model and linked it with one or more a background datasets, along with a list of other functional requirements. The resulting prototypes were compared and tested with additional product specifications. RESULTS: Each developer took a distinct approach to the problem. One approach used semantic similarity relations to identify best-fit background datasets. Another approach focused on producing a flexible description of the model structure that removed redundancy and permitted aggregation. Another approach provided an interactive web application for matching product components to standardized product classification systems to facilitate characterization and linking. DISCUSSION: Four distinct steps were identified in the broader problem of automating PSM construction: creating a foreground model from product data, determining the quantitative properties of foreground model flows, linking flows to background datasets, and expressing the linked model in a format that could be used by existing LCA software. The three prototypes are complementary in that they address different steps and demonstrate alternative approaches. Manual work was still required in each case, especially in the descriptions of the product flows that must be provided by background datasets. CONCLUSION: The study demonstrates the utility of a distributed, comparative software development, as applied to the problem of LCA software. The results demonstrate that the problem of automated PSM construction is tractable. The prototypes created advance the state of the art for LCA software.
ABSTRACT
National-scope environmental life cycle models of goods and services may be used for many purposes, not limited to quantifying impacts of production and consumption of nations, assessing organization-wide impacts, identifying purchasing hotspots, analyzing environmental impacts of policies, and performing streamlined life cycle assessment. USEEIO is a new environmentally-extended input-output model of the United States fit for such purposes and other sustainable materials management applications. USEEIO melds data on economic transactions between 389 industry sectors with environmental data for these sectors covering land, water, energy and mineral usage and emissions of greenhouse gases, criteria air pollutants, nutrients and toxics, to build a life cycle model of 385 US goods and services. In comparison with existing US models, USEEIO is more current with most data representing year 2013, more extensive in its coverage of resources and emissions, more deliberate and detailed in its interpretation and combination of data sources, and includes formal data quality evaluation and description. USEEIO is assembled with a new Python module called the IO Model Builder capable of assembling and calculating results of user-defined input-output models and exporting the models into LCA software. The model and data quality evaluation capabilities are demonstrated with an analysis of the environmental performance of an average hospital in the US. All USEEIO files are publicly available bringing a new level of transparency for environmentally-extended input-output models.
