Purpose Using generic or country-specific life cycle inventory (LCI) datasets for life cycle assessments (LCAs) with no site-specific LCI datasets can result in inaccurate LCA results, whereas comprehensive site-specific LCI datasets require considerable time and effort. We use the term site-specific as a broad term, the site denoting a site, region, or technology. We propose a methodology for systematic selection of the most appropriate proxy dataset for a specific site, from the available LCI datasets for a specific background process. Methods The “Selection Proxy” methodology applies rigorous mathematical and statistical techniques. The selection process is based on the concept of a descriptive characteristics space. Each potential proxy dataset and the missing dataset are assigned coordinates in the characteristics space. The proxy is selected based on the “distance” between candidate LCI datasets and the known coordinates of the target dataset in the characteristics space. The dataset with the minimum distance is the “Selection Proxy” dataset. The methodology is demonstrated on water supply systems using a harmonized set of 22 LCA studies, focusing on climate change, with analysis of four additional impact categories. The methodology is corroborated on climate change of coal-fired electric power stations using a harmonized set of 100 LCA studies. Results and discussion The results that we have obtained for assessing climate change of water supply systems indicate the potentially high approximation power of our “Selection Proxy” methodology. The proposed method provides LCA impact scores that in most cases are considerably closer to “true values” at only a small fraction of the effort needed to create a comprehensive site-specific LCI dataset. The application of the methodology to the coal-fired power stations demonstrated the approximation power and cost-effectiveness of the methodology. The methodology incorporates a built-in improvement capability: every additional unique LCI dataset improves the accuracy of results. Conclusion The new methodology enables selection of a dataset that represents the missing dataset that leads in most cases to a much better approximation of environmental impacts than a dataset selected by default or by geographical proximity. The methodology is general and is applicable to various background processes. The models developed for water supply systems and coal-fired power stations are freely available upon request and can be used “as is” for LCAs in locations for which site-specific LCIs for these processes are not available.

中文翻译：

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选择最合适的生命周期清单数据集：新的选择代理方法和案例研究应用

目的 使用通用或国家特定生命周期清单 (LCI) 数据集进行生命周期评估 (LCA)，而没有特定站点 LCI 数据集可能会导致 LCA 结果不准确，而全面的特定站点 LCI 数据集需要大量时间和精力。我们使用术语站点特定作为广义术语，站点表示站点、区域或技术。我们提出了一种方法，用于从特定背景过程的可用 LCI 数据集中为特定站点系统地选择最合适的代理数据集。方法 “选择代理”方法应用严格的数学和统计技术。选择过程基于描述性特征空间的概念。每个潜在的代理数据集和缺失的数据集都在特征空间中分配了坐标。根据候选 LCI 数据集和特征空间中目标数据集的已知坐标之间的“距离”选择代理。距离最小的数据集是“选择代理”数据集。该方法在供水系统上使用了 22 项 LCA 研究的统一集，重点是气候变化，并分析了四个额外的影响类别。使用 100 项 LCA 研究的统一集，该方法在燃煤发电站的气候变化方面得到了证实。结果与讨论 我们为评估供水系统的气候变化而获得的结果表明，我们的“选择代理”方法具有潜在的高逼近能力。所提出的方法提供 LCA 影响分数，在大多数情况下，该分数非常接近“真实值”，而只需花费创建综合站点特定 LCI 数据集所需工作的一小部分。该方法在燃煤发电站的应用证明了该方法的近似功率和成本效益。该方法结合了一个内置的改进能力：每一个额外的独特 LCI 数据集都会提高结果的准确性。结论 新方法可以选择代表缺失数据集的数据集，这在大多数情况下比默认选择或地理邻近性选择的数据集更接近环境影响。该方法是通用的，适用于各种后台进程。