Selection of the most appropriate life cycle inventory dataset: new selection proxy methodology and case study application

Meron, Noa; Blass, Vered

doi:10.1007/s11367-019-01721-8

Cited by 16 publications

(12 citation statements)

References 26 publications

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“…Machine learning (ML) models have become an important part of the data-driven approach in recent years. For example, Meron et al (2020) proposed a similarity-based method for proxy selection using a group of characteristics (e.g., regional population density, average waste composition index for a region-specific municipal solid waste [MSW] management process) to describe individual unit processes or inventory data (e.g., kg CO 2 emissions) contained in a data pool. These characteristics define a multidimensional space where each unit process is situated based on the values of its characteristics.…”

Section: Data-driven Approachmentioning

confidence: 99%

A review of inventory modeling methods for missing data in life cycle assessment

Zargar

Yao

2022

J of Industrial Ecology

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Missing data is the key challenge facing life cycle inventory (LCI) modeling. The collection of missing data can be cost-prohibitive and infeasible in many circumstances.Major strategies to address this issue include proxy selection (i.e., selecting a surrogate dataset to represent the missing data) and data creation (e.g., through empirical equations or mechanistic models). Within these two strategies, we identified three approaches that are widely used for LCI modeling: Data-driven, mechanistic, and future (e.g., 2050) inventory modeling. We critically reviewed the 12 common methods of these three approaches by focusing on their features, scope of application, underlying assumptions, and limitations. These methods were characterized based on the following criteria: "domain knowledge requirement" (both as a method developer and a user), "post-treatment requirement," "challenge in assessing data quality uncertainty," "challenge in generalizability," and "challenge in automation." These criteria can be used by LCA practitioners to select the suitable method(s) to bridge the data gap in LCI modeling, based on the goal and scope of the intended study. We also identified several aspects for future improvement for these reviewed methods.

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Section: Data-driven Approachmentioning

confidence: 99%

A review of inventory modeling methods for missing data in life cycle assessment

Zargar

Yao

2022

J of Industrial Ecology

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“…The information to be collected typically covers many topics, such as amounts of products, waste, and elementary flows that enter and exit the system, but also other aspects, for example, data quality and uncertainty distribution. LCA studies require a considerable volume of data [4] and their accuracy is crucial to assure the quality of the LCI and LCA study overall [5]. Therefore, data collection and life cycle modelling are widely recognized as challenging steps, for instance regarding multi-output processes and local technology representativeness [6].…”

Section: Background and Motivationmentioning

confidence: 99%

LCA database creation

Ciroth

Noi²,

Burhan³

et al. 2019

IJoLCAS

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LCA studies require a high volume of data and their quality has a direct influence on the quality of the Life Cycle Inventory (LCI) and Life Cycle Assessment (LCA) study overall. The use of LCA databases enables users to (i) reduce time, efforts, and resources for data collection and (ii) reflect supply chains they have no direct control over. On the other side, it creates the need to align own modeling of the foreground LCA study with the modeling in the database. In recent years, countries worldwide have been more and more motivated in supporting LCA studies by providing national databases that reflect their economy, energy mix, and disposal technologies. This article aims to give insights on the main needs, requirements, and challenges for the creation of an LCA database, with a special focus on national, reference databases. First, the article defines the main characteristics of LCA datasets and discusses data collection approaches. Secondly, LCA databases are defined, and the creation of LCA databases from developed datasets is addressed, including the case of national LCA databases. Finally, the existence of tools that could ease the LCA dataset and database creation process is investigated, namely the LCA Collaboration Server and the LCA Data-Machine. It is important that countries willing to create a national database are supported, for example with capacity-building workshops, by actors with a long tradition in the field, which is of mutual benefit: Countries with a long tradition in LCA will benefit from interactions with newcomers, for instance by discussing together unsolved methodological and interoperability issues; newcomers do not need to start from scratch but can benefit from gained experiences. Creating databases that provide specific data for various parts of the world supports LCA methodology and application in general, and it is not the least a chance for local LCA communities to bring in innovation into LCA, and benefit from existing experiences at the same time.

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“…For example, Olivetti et al put forward the structured underspecification and probabilistic triage framework, which quantifies the uncertainty of proxy data usage by categorizing an item within an aggregate group . This approach has been applied recently in the building sector and for electrical products. − Meron et al proposed a selection process based on the characteristic space among available and missing data . However, such methods still rely on data compiled via the semi-quantitative pedigree approach that depends in part on subjective evaluations to determine temporal and geographic representativeness.…”

Section: Introductionmentioning

confidence: 99%

“…27−29 Meron et al proposed a selection process based on the characteristic space among available and missing data. 30 However, such methods still rely on data compiled via the semi-quantitative pedigree approach that depends in part on subjective evaluations to determine temporal and geographic representativeness. Dai et al introduced multilevel modeling regression to address the temporal and geographical variations simultaneously and proposed to use the prediction interval for full quantification of uncertainty, but their model did not address proxy data usage.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Process Regression as a Replicable, Streamlined Approach to Inventory and Uncertainty Analysis in Life Cycle Assessment

Dai

Jordaan

Wemhoff

2022

Environ. Sci. Technol.

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Life cycle assessment plays a critical role in quantifying environmental impacts, but its credibility remains challenged when data and uncertainty analysis are lacking. In this study, we propose a data compilation framework to address these two issues. The framework first quantifies the correlations of production activities among existing data in temporal, geographical, and taxonomic dimensions. The framework then introduces covariance functions to convert these correlations to a similarity matrix, and the Gaussian process regression model is adopted to predict new data based on these covariance functions. The associated uncertainty is automatically characterized using the posterior distribution of predictions. The framework is demonstrated on the nitrogen fertilizer application rate for food productionan activity recognized for its environmental burdenwith results capable of reflecting temporal and geographical variations. By introducing the concept of phylogenetic distance as a correlation of taxonomy, the framework provides a quantitative basis for predictions in a proxy data usage scenario. The framework can be used in developing temporally and regionally representative life cycle inventories and databases and can facilitate consistent uncertainty quantification in future life cycle assessment methodologies.

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Selection of the most appropriate life cycle inventory dataset: new selection proxy methodology and case study application

Cited by 16 publications

References 26 publications

A review of inventory modeling methods for missing data in life cycle assessment

A review of inventory modeling methods for missing data in life cycle assessment

LCA database creation

Gaussian Process Regression as a Replicable, Streamlined Approach to Inventory and Uncertainty Analysis in Life Cycle Assessment

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