Implicit prioritization in life cycle assessment: text mining and detecting metapatterns in the literature

Grubert, Emily

doi:10.1007/s11367-016-1153-2

Cited by 18 publications

(18 citation statements)

References 32 publications

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“…More generally, concern about the appropriateness of a small, unrepresentative group of primarily natural scientists and engineers making value judgments for a wider population is valid. Societally based preference archetypes thus provide additional value by enabling comparison with expert preferences like those elicited by Gloria and colleagues () or implied by research focus areas (Grubert ), highlighting areas where experts and society appear to disagree.…”

Section: Introductionmentioning

confidence: 99%

“…Given the extreme sensitivity of LCA outcomes to risk attitude and preferences (see, e.g., De Schryver et al. ; Grubert ), careful consideration of these two decision elements is warranted.…”

Section: Introductionmentioning

confidence: 99%

“…Even though implicit weighting is observable in the literature (Grubert ), the community's reluctance to define and apply explicit weighting schemes is understandable. LCA's history as an industrial tool—and one explicitly used for policy making—means that concerns about liability remain salient (see, e.g., Ashford ; Guinée et al.…”

Section: Introductionmentioning

confidence: 99%

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The Need for a Preference‐Based Multicriteria Prioritization Framework in Life Cycle Sustainability Assessment

Grubert

2017

J of Industrial Ecology

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Summary Life cycle thinking is a valuable tool for integrated assessment of the environmental, social, and economic outcomes of human activities. The combination of the three as life cycle sustainability assessment (LCSA) is a powerful decision support tool, but it also presents important design challenges. Among the most important challenges is how to include subjective information necessary for defining the major elements of a decision: prospects to decide among, uncertainty, risk attitudes, and preferences. Previous work on values in life cycle methods has addressed prospects, uncertainty, and risk attitudes. This article builds on that work by arguing that given LCSA's broad scope, explicit and standardized intercategory preferences are especially important for improving its value for decision makers. Practitioners should not be solely responsible for the value judgments necessary to integrate impact categories within and across environmental life cycle assessment (E‐LCA), social LCA (S‐LCA), and life cycle costing evaluations for LCSA. Neither should this task fall entirely to decision makers, particularly as life cycle–grounded decisions are highly sensitive to value frames. Individuals are unlikely to be able to meaningfully interpret, evaluate, and determine trade‐offs without support. This article thus proposes that LCSA leverage its multiple paradigms to rigorously generate explicit, empirically grounded intercategory preference archetypes for use in evaluating decision robustness, much as cultural theory‐based archetypes are currently used to test robustness to risk attitudes. Proof‐of‐concept data from the United States illustrate this approach, named WELFARES.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Need for a Preference‐Based Multicriteria Prioritization Framework in Life Cycle Sustainability Assessment

Grubert

2017

J of Industrial Ecology

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“…We present two case studies of the use of text mining tools for literature review and analysis in the environmental sciences in this article, focusing on process and research development rather than results. More results-oriented descriptions can be found in Grubert (2016) and Siders (in prep). Both case studies represent 'large' datasets from a traditional review and meta-analysis perspective, analyzing 8239 and 275 texts respectively.…”

Section: Methodsmentioning

confidence: 99%

“…The first case study presented in this work focuses on the use of topic modeling in the LCA literature. It is intended to illustrate the suggestion that digital tools can aid hypothesis-driven literature reviews at a scale that cannot be performed manually: this case study investigates the titles and abstracts of about 8200 journal articles, essentially the complete English language LCA corpus published in the peer reviewed literature between 1995 and 2014 (Grubert 2016). This case study also illustrates the value of digital tools for enabling replicable testing of hypotheses developed from a more traditional reading program.…”

Section: Outcomes and Hypothesesmentioning

confidence: 99%

Benefits and applications of interdisciplinary digital tools for environmental meta-reviews and analyses

Grubert¹,

Siders²

2016

Environ. Res. Lett.

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Digitally-aided reviews of large bodies of text-based information, such as academic literature, are growing in capability but are not yet common in environmental fields. Environmental sciences and studies can benefit from application of digital tools to create comprehensive, replicable, interdisciplinary reviews that provide rapid, up-to-date, and policy-relevant reports of existing work. This work reviews the potential for applications of computational text mining and analysis tools originating in the humanities to environmental science and policy questions. Two process-oriented case studies of digitally-aided environmental literature reviews and meta-analyses illustrate potential benefits and limitations. A medium-sized, medium-resolution review (∼8000 journal abstracts and titles) focuses on topic modeling as a rapid way to identify thematic changes over time. A small, high-resolution review (∼300 full text journal articles) combines collocation and network analysis with manual coding to synthesize and question empirical field work. We note that even small digitally-aided analyses are close to the upper limit of what can be done manually. Established computational methods developed in humanities disciplines and refined by humanities and social science scholars to interrogate large bodies of textual data are applicable and useful in environmental sciences but have not yet been widely applied. Two case studies provide evidence that digital tools can enhance insight. Two major conclusions emerge. First, digital tools enable scholars to engage large literatures rapidly and, in some cases, more comprehensively than is possible manually. Digital tools can confirm manually identified patterns or identify additional patterns visible only at a large scale. Second, digital tools allow for more replicable and transparent conclusions to be drawn from literature reviews and meta-analyses. The methodological subfields of digital humanities and computational social sciences will likely continue to create innovative tools for analyzing large bodies of text, providing opportunities for interdisciplinary collaboration with the environmental fields.

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Water for energy: Characterizing co‐evolving energy and water systems under twin climate and energy system nonstationarities

Grubert

Marshall

2022

WIREs Water

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The energy system is a major water user, but understanding how much water is consumed and withdrawn for energy is a challenging empirical task: nonevaporative volumetric water use is not easily calculated from first principles. Water use also has impacts that differ in important ways depending on where water is abstracted and used, timing of use, and socioenvironmental context. Moreover, different decarbonization pathways and policy environments have very different water use implications. We currently face a crisis of twin nonstationarities in hydrology and energy systems that make understanding water use for energy critical for decision support as hydroclimate and energy systems change. Currently, water‐for‐energy data are highly uncertain and not centrally collected, which means researchers spend substantial effort collecting inventory data. Recent advances in impact assessment methods for water volumes focus largely on spatially resolved water scarcity evaluations, but robust conclusions can be elusive due to uncertain and low‐metadata inventory information. As water‐for‐energy quantification efforts progress, research should emphasize decision support for energy system design, incorporating crucial hydrologic dynamics. Beyond the location of water use, relative scarcity, and potential competing uses, these include sub‐daily to interannual temporal dynamics, the impacts of climate change on these dimensions, potential feedbacks between energy and water systems, and the impacts of hydrologic variability or change on policy‐based incentive structures. This article reviews prior US‐focused efforts to quantify water use for energy, highlights why these nonstationarities are analytically relevant with a brief policy case study, and highlights research needs for decision support under twin nonstationarities. This article is categorized under: Engineering Water > Sustainable Engineering of Water Engineering Water > Planning Water Engineering Water > Methods

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Implicit prioritization in life cycle assessment: text mining and detecting metapatterns in the literature

Cited by 18 publications

References 32 publications

The Need for a Preference‐Based Multicriteria Prioritization Framework in Life Cycle Sustainability Assessment

The Need for a Preference‐Based Multicriteria Prioritization Framework in Life Cycle Sustainability Assessment

Benefits and applications of interdisciplinary digital tools for environmental meta-reviews and analyses

Water for energy: Characterizing co‐evolving energy and water systems under twin climate and energy system nonstationarities

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