{"title":"Challenge Paper","authors":"P. Arbuckle, E. Kahn, Adam Kriesberg","doi":"10.1145/3106236","DOIUrl":null,"url":null,"abstract":"Life Cycle Assessment is a modeling approach to assess the environmental aspects and potential environmental impacts (e.g., use of resources and the environmental consequences of releases) throughout a product’s life cycle from raw material acquisition through production, use, end-oflife treatment, recycling and final disposal (i.e., cradle-to-grave) (ISO 14040). It has been employed in recent years by industry and governments to address growing interest about the true costs of resource use, environmental impact, and other externalities of economic activity. Inherently multidisciplinary, LCA draws and synthesizes information from the social and physical sciences. This breadth within LCA models (often referred to as “data” by the community of practitioners) can make collecting and synthesizing information the most expensive component of an analysis and drives the need for model reuse. However, the LCA community is faced with a major challenge in its capacity to produce sufficient documentation and metadata to determine representation of these models and to reuse them correctly, an issue broadly affecting researchers across disciplines. Tenopir et al. (2011, 2015) found in each of two surveys of scientific data management and sharing practices that researchers do not feel equipped to generate metadata to facilitate reuse of their data. Furthermore, some researchers reported limited knowledge of available standards to describe data. The challenge in capacity in the LCA community is driven by two factors: the nascent state of standardization in LCA modeling and the strong focus on research and results for funded LCA work. Standardization serves to create a foundational set of rules and guidelines to support","PeriodicalId":15582,"journal":{"name":"Journal of Data and Information Quality (JDIQ)","volume":"15 1","pages":"1 - 4"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Data and Information Quality (JDIQ)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3106236","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Life Cycle Assessment is a modeling approach to assess the environmental aspects and potential environmental impacts (e.g., use of resources and the environmental consequences of releases) throughout a product’s life cycle from raw material acquisition through production, use, end-oflife treatment, recycling and final disposal (i.e., cradle-to-grave) (ISO 14040). It has been employed in recent years by industry and governments to address growing interest about the true costs of resource use, environmental impact, and other externalities of economic activity. Inherently multidisciplinary, LCA draws and synthesizes information from the social and physical sciences. This breadth within LCA models (often referred to as “data” by the community of practitioners) can make collecting and synthesizing information the most expensive component of an analysis and drives the need for model reuse. However, the LCA community is faced with a major challenge in its capacity to produce sufficient documentation and metadata to determine representation of these models and to reuse them correctly, an issue broadly affecting researchers across disciplines. Tenopir et al. (2011, 2015) found in each of two surveys of scientific data management and sharing practices that researchers do not feel equipped to generate metadata to facilitate reuse of their data. Furthermore, some researchers reported limited knowledge of available standards to describe data. The challenge in capacity in the LCA community is driven by two factors: the nascent state of standardization in LCA modeling and the strong focus on research and results for funded LCA work. Standardization serves to create a foundational set of rules and guidelines to support