Overview and recommendations for regionalized life cycle impact assessment
Overview and recommendations for regionalized life cycle impact assessment
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区域化生命周期影响评估概述和建议
DOI：
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发表时间：
2018
期刊：
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通讯作者：
F. Verones
中科院分区：
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作者：
C. Mutel;Xun Liao;Laure Patouillard;J. Bare;P. Fantke;R. Frischknecht;M. Hauschild;O. Jolliet;Danielle Maia de Souza;Alexis Laurent;S. Pfister;F. Verones
目的区域化生命周期影响评估（LCIA）在过去十年中迅速发展，但其广泛应用、稳健性和有效性仍面临多重挑战。在 UNEP/SETAC 生命周期倡议的框架下，一个专门的区域化 LCIA 跨领域工作组旨在概述 LCIA 方法中区域化的现状。我们为 LCIA 方法、LCI 数据库和 LCA 软件的开发人员提供协调和支持 LCIA 区域化的指导和建议。方法对区域化 LCIA 方法开发人员的当前实践进行了调查。该调查包括有关所选方法的空间分辨率和规模、输入参数的空间分辨率、本地空间分辨率和限制的选择、可操作性和与生命周期清单数据的对齐、空间聚合方法、输入参数和模型结构的不确定性评估以及空间聚合引起的可变性的问题。建议是根据调查结果和作者的广泛讨论制定的。结果和讨论调查结果表明，大多数区域化 LCIA 模型具有全球覆盖范围。原生空间分辨率通常是根据全局输入数据的可用性来选择的。年度建模或测量的基本流量主要用于将特征因子（CF）聚合到更大的空间尺度，尽管有些使用代理，例如人口计数。汇总的 CF 主要在国家一级提供。尽管由于输入参数、模型结构和空间聚合导致的不确定性可用于某些 LCIA 方法，但它们很少用于 LCA 研究。到目前为止，对于更精细的原始空间分辨率是否是减少总体不确定性的最佳方法尚未达成一致。当空间差异化模型 CF 不易获得时，有时会开发原型模型。结论 应使用标准化数据格式将区域化 LCIA 方法作为透明且一致的数据和元数据集提供。区域化 CF 应包括不确定性和可变性。除了本地规模的 CF 之外，还应始终提供汇总 CF，并应根据可用的年流量作为权重，将其计算为组成 CF 的加权平均值。本文是提高区域化 LCIA 方法开发和应用的透明度、一致性和稳健性的重要一步。
PurposeRegionalized life cycle impact assessment (LCIA) has rapidly developed in the past decade, though its widespread application, robustness, and validity still face multiple challenges. Under the umbrella of UNEP/SETAC Life Cycle Initiative, a dedicated cross-cutting working group on regionalized LCIA aims to provide an overview of the status of regionalization in LCIA methods. We give guidance and recommendations to harmonize and support regionalization in LCIA for developers of LCIA methods, LCI databases, and LCA software.MethodsA survey of current practice among regionalized LCIA method developers was conducted. The survey included questions on chosen method’s spatial resolution and scale, the spatial resolution of input parameters, the choice of native spatial resolution and limitations, operationalization and alignment with life cycle inventory data, methods for spatial aggregation, the assessment of uncertainty from input parameters and model structure, and the variability due to spatial aggregation. Recommendations are formulated based on the survey results and extensive discussion by the authors.Results and discussionSurvey results indicate that majority of regionalized LCIA models have global coverage. Native spatial resolutions are generally chosen based on the availability of global input data. Annual modeled or measured elementary flow quantities are mostly used for aggregating characterization factors (CFs) to larger spatial scales, although some use proxies, such as population counts. Aggregated CFs are mostly available at the country level. Although uncertainty due to input parameter, model structure, and spatial aggregation are available for some LCIA methods, they are rarely implemented for LCA studies. So far, there is no agreement if a finer native spatial resolution is the best way to reduce overall uncertainty. When spatially differentiated model CFs are not easily available, archetype models are sometimes developed.ConclusionsRegionalized LCIA methods should be provided as a transparent and consistent set of data and metadata using standardized data formats. Regionalized CFs should include both uncertainty and variability. In addition to the native-scale CFs, aggregated CFs should always be provided and should be calculated as the weighted averages of constituent CFs using annual flow quantities as weights whenever available. This paper is an important step forward for increasing transparency, consistency, and robustness in the development and application of regionalized LCIA methods.