The creation, management, and use of data quality information for life cycle assessment

Edelen, Ashley; Ingwersen, Wesley W.

doi:10.1007/s11367-017-1348-1

Cited by 48 publications

(45 citation statements)

References 22 publications

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“…For this work, scoring follows the recommendations of Edelen and Ingwersen (2016), with a flow reliability score of 1 (verified measurement) denoting the highest data quality and a score of 5 (undocumented estimate) representing the lowest data quality. When modeling multiple facilities, flow‐specific scores can be aggregated across facilities by averaging scores based on the quantity of each exchange (Rousseaux et al., 2001; Edelen, 2018). For the case studies, NEI, e‐GGRT, and TRI include a basis of estimate (i.e., how the value was derived) for each reported release that can be mapped to a flow reliability score as demonstrated in Cashman et al.…”

Section: Resultsmentioning

confidence: 99%

Improving the reliability of chemical manufacturing life cycle inventory constructed using secondary data

Meyer

Cashman

Gaglione

2020

J of Industrial Ecology

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This study proposes methods to improve data mining workflows for modeling chemical manufacturing life cycle inventory. Secondary data sources can provide valuable information about environmental releases during chemical manufacturing. However, the often facility‐level nature of the data challenges their utility for modeling specific processes and can impact the quality of the resulting inventory. First, a thorough data source analysis is performed to establish data quality scoring and create filtering rules to resolve data selection issues when source and species overlaps arise. A method is then introduced to develop context‐based filter rules that leverage process metadata within data sources to improve how facility air releases are attributed to specific processes and increase the technological correlation and completeness of the inventory. Finally, a sanitization method is demonstrated to improve data quality by minimizing the exclusion of confidential business information (CBI). The viability of the methods is explored using case studies of cumene and sodium hydroxide production in the United States. The attribution of air releases using process context enables more sophisticated filtering to remove unnecessary flows from the inventory. The ability to sanitize and incorporate CBI is promising because it increases the sample size, and therefore representativeness, when constructing geographically averaged inventories. Future work will focus on expanding the application of context‐based data filtering to other types and sources of environmental data.

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Section: Resultsmentioning

confidence: 99%

Improving the reliability of chemical manufacturing life cycle inventory constructed using secondary data

Meyer

Cashman

Gaglione

2020

J of Industrial Ecology

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“…The MTIs in Table 4 were 1 or 2 year intervals for unit processes that had undergone a recent development (waste generation, energy recovery), and 3 or 4 years for the unit processes that had been relatively stable for the last 10–15 years (flue gas cleaning). Existing pedigree criteria assign the worst data quality score for temporal deviations above 10 or 15 years (Edelen & Ingwersen, 2017; Laner et al., 2015; Weidema, 1998). Compared with the time intervals in Table 4, existing pedigree criteria correspond to the MTIs of the flue gas cleaning process.…”

Section: Discussionmentioning

confidence: 99%

“…Step 1 contains two data quality evaluation routes: 1a for data with relatively small importance, and 1b for data with relatively large importance (based on a comparison of the contribution of the data to the final results). In step 1a, a business‐as‐usual data quality screening is performed with the use of an existing pedigree matrix with pre‐defined criteria (such as Edelen & Ingwersen, 2017; Weidema, 1998). A data quality screening is sufficient because the data have a limited influence on the results, for example, due to a small input uncertainty combined with a small sensitivity in the specific model configuration.…”

Section: Methodsmentioning

confidence: 99%

“…For example, the pedigree in Laner et al. (2015) evaluated the level of similarity with the socioeconomic region of the study, Weidema (1998) evaluated the level of similarity with production conditions at the target location, and Edelen and Ingwersen (2017) evaluated the level of similarity with the geographical specificity of the study and whether the data were from a related area of the study. Regarding technological representativeness, Laner et al.…”

Section: Introductionmentioning

confidence: 99%

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Data representativeness in LCA: A framework for the systematic assessment of data quality relative to technology characteristics

Henriksen

Astrup

Damgaard

2020

J of Industrial Ecology

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A shortcoming in current data quality assessment schemes is that the data quality information is not used systematically to identify the critical data in a life cycle inventory (LCI) model. In addition, existing criteria employed to evaluate representativeness lack relevance to the specific context of a study. A novel framework is proposed herein for the evaluation of the representativeness of LCI data, including an analysis of the importance of the data and a modification of quality criteria based on unit process characteristics. Temporal characteristics are analyzed by identifying the technology shift, because data generated before this time are considered outdated. Geographical and technological characteristics are analyzed by defining a “related area” and a “related technology,” which is done by identifying a number of relevant geographical and technical factors, and then comparing the collected data with these factors. The framework was illustrated in a case study on household waste incineration in Denmark. The results demonstrated the applicability of the method in practice, and they provided data quality criteria unique to waste incineration unit processes, for example, different time intervals to evaluate temporal representativeness. However, the proposed method is time demanding, and thus sector‐level characteristic analyses are feasible instead of the user having to do the analyses.

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“…To gauge the quality of process inventories, Edelen and Ingwersen 49 described the value and limitations of data quality characteristics, emphasizing the need for a comprehensive methodology.…”

Section: Introductionmentioning

confidence: 99%

Applying Environmental Release Inventories and Indicators to the Evaluation of Chemical Manufacturing Processes in Early Stage Development

Smith

Tan

Ruiz-Mercado

2019

ACS Sustainable Chem. Eng.

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As manufacturing processes are developed through the early stages of technology readiness, various assessments can be used to evaluate their performance. Performance indicators describe processes by transforming attributes into scores that represent desirable objectives. One type of assessment is obtained by determining the life cycle inventories of inputs and outputs for processes. For a functional unit of product, the user finds the resources used and the releases to the environment, which can be compared to results for similar processes and/or combined with other processes in the life cycle. In this work, an expanded range of process inputs and releases is modeled, including forklift/loader, fugitive, storage, boiler, and cooling tower emissions. A generic scenario approach for the cooling tower releases provides a first approximation of emission and wastewater flows. These inventory values are used in performance indicators that can be placed on a scale between fixed best-and worst-case limits with the GREENSCOPE methodology, thus allowing comparisons across various technologies. The processes of interest are two conversion pathways for producing cellulosic ethanol from biomass via thermochemical and biochemical routes. The results can be used in risk assessments, decision making, evaluation of research, and in spurring future technology development.

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The creation, management, and use of data quality information for life cycle assessment

Cited by 48 publications

References 22 publications

Improving the reliability of chemical manufacturing life cycle inventory constructed using secondary data

Improving the reliability of chemical manufacturing life cycle inventory constructed using secondary data

Data representativeness in LCA: A framework for the systematic assessment of data quality relative to technology characteristics

Applying Environmental Release Inventories and Indicators to the Evaluation of Chemical Manufacturing Processes in Early Stage Development

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