Challenge Paper

P. Arbuckle, E. Kahn, Adam Kriesberg
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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
挑战的论文
生命周期评估是一种建模方法,用于评估从原材料获取到生产、使用、生命周期结束处理、回收和最终处置(即从摇篮到坟墓)的整个产品生命周期中的环境因素和潜在的环境影响(例如,资源的使用和释放的环境后果)(ISO 14040)。近年来,工业和政府一直在利用它来解决人们对资源使用的真实成本、环境影响和经济活动的其他外部性日益增长的兴趣。LCA本质上是多学科的,它从社会科学和物理科学中汲取和综合信息。LCA模型中的这种广度(从业者社区通常将其称为“数据”)可以使收集和综合信息成为分析中最昂贵的组件,并推动模型重用的需求。然而,LCA社区面临着一个重大的挑战,那就是它是否有能力产生足够的文档和元数据来确定这些模型的表示并正确地重用它们,这是一个广泛影响跨学科研究人员的问题。Tenopir等人(2011年,2015年)在两项科学数据管理和共享实践调查中发现,研究人员认为自己没有能力生成元数据来促进数据的重用。此外,一些研究人员报告说,他们对描述数据的现有标准了解有限。LCA社区在能力方面的挑战是由两个因素驱动的:LCA建模标准化的新生状态,以及对资助LCA工作的研究和结果的强烈关注。标准化的作用是创建一套基本的规则和指导方针来支持
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