Despite the wide use of LCA for environmental profiling, the approach for determining the system boundary within LCA models continues to be subjective and lacking in mathematical rigor. As a result, life cycle models are often developed in an ad hoc manner, and are difficult to compare. Significant environmental impacts may be inadvertently left out. Overcoming this shortcoming can help elicit greater confidence in life cycle models and their use for decision making. This paper describes a framework for hybrid life cycle model generation by selecting activities based on their importance, parametric uncertainty, and contribution to network complexity. The importance of activities is determined by structural path analysis—which then guides the construction of life cycle models based on uncertainty and complexity indicators. Information about uncertainty is from the available life cycle inventory; complexity is quantified by cost or granularity. The life cycle model is developed in a hierarchical manner by adding the most important activities until error requirements are satisfied or network complexity exceeds user-specified constraints. The framework is applied to an illustrative example for building a hybrid LCA model. Since this is a constructed example, the results can be compared with the actual impact, to validate the approach. This application demonstrates how the algorithm sequentially develops a life cycle model of acceptable uncertainty and network complexity. Challenges in applying this framework to practical problems are discussed. The presented algorithm designs system boundaries between scales of hybrid LCA models, includes or omits activities from the system based on path analysis of environmental impact contribution at upstream network nodes, and provides model quality indicators that permit comparison between different LCA models.

中文翻译：

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基于不确定性和复杂性的混合生命周期评估模型设计

尽管 LCA 广泛用于环境分析，但在 LCA 模型中确定系统边界的方法仍然是主观的，缺乏数学严谨性。因此，生命周期模型通常是临时开发的，很难进行比较。可能会无意中忽略重大的环境影响。克服这一缺点有助于增强对生命周期模型及其在决策中的使用的信心。本文通过根据活动的重要性、参数不确定性和对网络复杂性的贡献来选择活动，描述了混合生命周期模型生成的框架。活动的重要性由结构路径分析确定，然后根据不确定性和复杂性指标指导生命周期模型的构建。关于不确定性的信息来自可用的生命周期清单；复杂性通过成本或粒度进行量化。生命周期模型是通过添加最重要的活动以分层方式开发的，直到满足错误要求或网络复杂度超过用户指定的约束。该框架应用于构建混合 LCA 模型的说明性示例。由于这是一个构建的示例，因此可以将结果与实际影响进行比较，以验证该方法。此应用程序演示了该算法如何依次开发可接受的不确定性和网络复杂性的生命周期模型。讨论了将该框架应用于实际问题的挑战。所提出的算法设计了混合 LCA 模型尺度之间的系统边界，