Advanced Data Engineering for Life Cycle Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 17552

Special Issue Editor


E-Mail Website
Guest Editor
Environmental Decision Analytics Branch/Land Remediation and Technology Division, Center for Environmental Solutions and Emergency Response (CESER), US EPA Office of Research and Development, 61 Forsyth Street, SW, Atlanta, GA 30303, USA
Interests: life cycle assessment; environmental input-output; data harmonization; software development; model building automation

Special Issue Information

Dear Colleagues,

Data availability and compatibility have always been limiting factors in the ability to perform original analyses that track the environmental impacts and material demand across the life cycles of products. Maintaining, reviewing, and sharing the underlying models have also limited the transparency and reproducibility of life cycle models. However, recent years have seen explosive and exciting advances in the field of data science that offer great promise for helping to overcome this limitation. The rise of services for data and code sharing and collaboration can also make the data and computational tools used more transparent.

This Special Issue will highlight methods and tools that apply novel data engineering approaches to prepare, transform, publish, or otherwise make available data, models, and results for life cycle applications, such as life cycle assessment, footprinting studies, and environmental product declarations, or related fields such as material flow analysis, input–output analysis, and industrial systems. These methods and tools may be relevant anywhere from the process scale to economy-wide and global applications, and they may track anything from a single key material to a large portion of the total material, energetic, and monetary flows in an economy.

We seek papers that describe and illustrate new data engineering methods or tools related to data discovery, scraping of raw data, linking of data, rapid assembly of data, evaluation of data, description of data, publication of data, etc. We are receptive to a range of papers suitable to some aspect of data engineering that supports LCA or a related field; we just expect the method or its implementation to be data-driven and original, well-documented and described, and all the source code to be made available in a publicly accessible code repository (e.g., GitHub, Bitbucket). In addition to their normal duties in evaluating the manuscript, reviewers will be asked to test the code as part of the review process to verify that it functions as described in the manuscript.

Dr. Wesley Ingwersen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Data science
  • Open science
  • LCA
  • MFA
  • Input–output analysis
  • Environmental impact
  • Footprint

Published Papers (6 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

14 pages, 753 KiB  
Article
Life Cycle Data Interoperability Improvements through Implementation of the Federal LCA Commons Elementary Flow List
by Ashley N. Edelen, Sarah Cashman, Ben Young and Wesley W. Ingwersen
Appl. Sci. 2022, 12(19), 9687; https://doi.org/10.3390/app12199687 - 27 Sep 2022
Viewed by 1893
Abstract
As a fundamental component of data for life cycle assessment models, elementary flows have been demonstrated to be a key requirement of life cycle assessment data interoperability. However, existing elementary flow lists have been found to lack sufficient structure to enable improved interoperability [...] Read more.
As a fundamental component of data for life cycle assessment models, elementary flows have been demonstrated to be a key requirement of life cycle assessment data interoperability. However, existing elementary flow lists have been found to lack sufficient structure to enable improved interoperability between life cycle data sources. The Federal Life Cycle Assessment Commons Elementary Flow List provides a novel framework and structure for elementary flows, but the actual improvement this list provides to the interoperability of life cycle data has not been tested. The interoperability of ten elementary flow lists, two life cycle assessment databases, three life cycle impact assessment methods, and five life cycle assessment software sources is assessed with and without use of the Federal Life Cycle Assessment Commons Elementary Flow List as an intermediary in flow mapping. This analysis showed that only 25% of comparisons between these sources resulted in greater than 50% of flows being capable of automatic name-to-name matching between lists. This indicates that there is a low level of interoperability when using sources with their original elementary flow nomenclature, and elementary flow mapping is required to use these sources in combination. The mapping capabilities of the Federal Life Cycle Assessment Commons Elementary Flow List to sources were reviewed and revealed a notable increase in name-to-name matches. Overall, this novel framework is found to increase life cycle data source interoperability. Full article
(This article belongs to the Special Issue Advanced Data Engineering for Life Cycle Applications)
Show Figures

Figure 1

24 pages, 474 KiB  
Article
Incorporating New Technologies in EEIO Models
by Cindy G. Azuero-Pedraza, Valerie M. Thomas and Wesley W. Ingwersen
Appl. Sci. 2022, 12(14), 7016; https://doi.org/10.3390/app12147016 - 12 Jul 2022
Cited by 2 | Viewed by 2355
Abstract
We propose a methodology to add new technologies into Environmentally Extended Input–Output (EEIO) models based on a Supply and Use framework. The methodology provides for adding new industries (new technologies) and a new commodity under the assumption that the new commodity will partially [...] Read more.
We propose a methodology to add new technologies into Environmentally Extended Input–Output (EEIO) models based on a Supply and Use framework. The methodology provides for adding new industries (new technologies) and a new commodity under the assumption that the new commodity will partially substitute for a functionally-similar existing commodity of the baseline economy. The level of substitution is controlled by a percentage (%) as a variable of the model. In the Use table, a percentage of the current use of the existing commodity is transferred to the new commodity. The Supply or Make table is modified assuming that the new industries are the only ones producing the new commodity. We illustrate the method for the USEEIO model, for the addition of second generation biofuels, including naphtha, jet fuel and diesel fuel. The new industries’ inputs, outputs and value-added components needed to produce the new commodity are drawn from process-based life cycle inventories (LCIs). Process-based LCI inputs and outputs per physical functional unit are transformed to prices and assigned to commodities and environmental flow categories for the EEIO model. This methodology is designed to evaluate the environmental impacts of substituting products in the current US economy with bio-versions, produced by new technologies, that are intended to reduce negative environmental impacts. However, it can be applied for any new commodity for which the substitution assumption is reasonable. Full article
(This article belongs to the Special Issue Advanced Data Engineering for Life Cycle Applications)
Show Figures

Figure 1

20 pages, 2429 KiB  
Article
FLOWSA: A Python Package Attributing Resource Use, Waste, Emissions, and Other Flows to Industries
by Catherine Birney, Ben Young, Mo Li, Melissa Conner, Jacob Specht and Wesley W. Ingwersen
Appl. Sci. 2022, 12(11), 5742; https://doi.org/10.3390/app12115742 - 05 Jun 2022
Cited by 2 | Viewed by 2500
Abstract
Quantifying industry consumption or production of resources, wastes, emissions, and losses—collectively called flows—is a complex and evolving process. The attribution of flows to industries often requires allocating multiple data sources that span spatial and temporal scopes and contain varied levels of aggregation. Once [...] Read more.
Quantifying industry consumption or production of resources, wastes, emissions, and losses—collectively called flows—is a complex and evolving process. The attribution of flows to industries often requires allocating multiple data sources that span spatial and temporal scopes and contain varied levels of aggregation. Once calculated, datasets can quickly become outdated with new releases of source data. The US Environmental Protection Agency (USEPA) developed the open-source Flow Sector Attribution (FLOWSA) Python package to address the challenges surrounding attributing flows to US industrial and final-use sectors. Models capture flows drawn from or released to the environment by sectors, as well as flow transfers between sectors. Data on flow use and generation by source-defined activities are imported from providers and transformed into standardized tables but are otherwise numerically unchanged in preparation for modeling. FLOWSA sector attribution models allocate primary data sources to industries using secondary data sources and file mapping activities to sectors. Users can modify methodological, spatial, and temporal parameters to explore and compare the impact of sector attribution methodological changes on model results. The standardized data outputs from these models are used as the environmental data inputs into the latest version of USEPA’s US Environmentally Extended Input–Output (USEEIO) models, life cycle models of US goods and services for ~400 categories. This communication demonstrates FLOWSA’s capability by describing how to build models and providing select model results for US industry use of water, land, and employment. FLOWSA is available on GitHub, and many of the data outputs are available on the USEPA’s Data Commons. Full article
(This article belongs to the Special Issue Advanced Data Engineering for Life Cycle Applications)
Show Figures

Figure 1

21 pages, 2020 KiB  
Article
useeior: An Open-Source R Package for Building and Using US Environmentally-Extended Input–Output Models
by Mo Li, Wesley W. Ingwersen, Ben Young, Jorge Vendries and Catherine Birney
Appl. Sci. 2022, 12(9), 4469; https://doi.org/10.3390/app12094469 - 28 Apr 2022
Cited by 5 | Viewed by 2657
Abstract
useeior is an open-source R package that builds USEEIO models, a family of environmentally-extended input–output models of US goods and services used for life cycle assessment, environmental footprint estimation, and related applications. USEEIO models have gained a wide user base since their initial [...] Read more.
useeior is an open-source R package that builds USEEIO models, a family of environmentally-extended input–output models of US goods and services used for life cycle assessment, environmental footprint estimation, and related applications. USEEIO models have gained a wide user base since their initial release in 2017, but users were often challenged to prepare required input data and undergo a complicated model building approach. To address these challenges, useeior was created. In useeior, economic and environmental data are conveniently retrievable for immediate use. Users can build models simply from given or user-specified model configuration and optional hybridization specifications. The assembly of economic and environmental data and matrix calculations are automatically performed. Users can export model results to desired formats. useeior is a core component of the USEEIO modeling framework. It improves transparency, efficiency, and flexibility in building USEEIO models, and was used to deliver the recent USEEIO model. Full article
(This article belongs to the Special Issue Advanced Data Engineering for Life Cycle Applications)
Show Figures

Figure 1

16 pages, 1877 KiB  
Article
A System for Standardizing and Combining U.S. Environmental Protection Agency Emissions and Waste Inventory Data
by Ben Young, Wesley W. Ingwersen, Matthew Bergmann, Jose D. Hernandez-Betancur, Tapajyoti Ghosh, Eric Bell and Sarah Cashman
Appl. Sci. 2022, 12(7), 3447; https://doi.org/10.3390/app12073447 - 28 Mar 2022
Cited by 14 | Viewed by 3890
Abstract
The U.S. Environmental Protection Agency (USEPA) provides databases that agglomerate data provided by companies or states reporting emissions, releases, wastes generated, and other activities to meet statutory requirements. These databases, often referred to as inventories, can be used for a wide variety of [...] Read more.
The U.S. Environmental Protection Agency (USEPA) provides databases that agglomerate data provided by companies or states reporting emissions, releases, wastes generated, and other activities to meet statutory requirements. These databases, often referred to as inventories, can be used for a wide variety of environmental reporting and modeling purposes to characterize conditions in the United States. Yet, users are often challenged to find, retrieve, and interpret these data due to the unique schemes employed for data management, which could result in erroneous estimations or double-counting of emissions. To address these challenges, a system called Standardized Emission and Waste Inventories (StEWI) has been created. The system consists of four python modules that provide rapid access to USEPA inventory data in standard formats and permit filtering and combination of these inventory data. When accessed through StEWI, reported emissions of carbon dioxide to air and ammonia to water are reduced approximately two- and four-fold, respectively, to avoid duplicate reporting. StEWI will greatly facilitate the use of USEPA inventory data in chemical release and exposure modeling and life cycle assessment tools, among other things. To date, StEWI has been used to build the recent USEEIO model and the baseline electricity life cycle inventory database for the Federal LCA Commons. Full article
(This article belongs to the Special Issue Advanced Data Engineering for Life Cycle Applications)
Show Figures

Figure 1

11 pages, 657 KiB  
Communication
The LCA Commons—How an Open-Source Repository for US Federal Life Cycle Assessment (LCA) Data Products Advances Inter-Agency Coordination
by Ezra Kahn, Erin Antognoli and Peter Arbuckle
Appl. Sci. 2022, 12(2), 865; https://doi.org/10.3390/app12020865 - 15 Jan 2022
Cited by 6 | Viewed by 2775
Abstract
Life cycle assessment (LCA) is a flexible and powerful tool for quantifying the total environmental impact of a product or service from cradle-to-grave. The US federal government has developed deep expertise in environmental LCA for a range of applications including policy, regulation, and [...] Read more.
Life cycle assessment (LCA) is a flexible and powerful tool for quantifying the total environmental impact of a product or service from cradle-to-grave. The US federal government has developed deep expertise in environmental LCA for a range of applications including policy, regulation, and emerging technologies. LCA professionals from across the government have been coordinating the distributed LCA expertise through a community of practice known as the Federal LCA Commons. The Federal LCA Commons has developed open data infrastructure and workflows to share knowledge and align LCA methods. This data infrastructure is a key component to creating a harmonized network of LCA capacity from across the federal government. Full article
(This article belongs to the Special Issue Advanced Data Engineering for Life Cycle Applications)
Show Figures

Figure 1

Back to TopTop