归因和后果生命周期评估对社会和决策支持的相关性

T. Schaubroeck
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引用次数: 1

摘要

生命周期评价(LCA)是评价产品系统对环境影响的重要工具。主要有两种类型:归因LCA (ALCA),它评估产品生命周期的全球影响份额,以及结果性LCA (CLCA),它评估决策的结果性影响。在我们的分析中,我们探讨了这些类型与社会的相关性以及它们帮助决策的能力。这种分析建立在现有文献的基础上,并结合了两个意识形态标准和三个实用标准。首先,当涉及到可持续发展背景下所需的现实建模时,理论上,CLCA试图实现现实建模,而ALCA由于概念规则(例如人为拆分副产物过程)而在一定程度上存在不足。关于与伦理一致的第二个标准,CLCA完全与结果性伦理一致,即根据其后果来判断行为。CLCA的这种一致性使其在我们的目标是在未来获得有利结果的世界中毫无疑问具有相关性,例如,实现可持续发展目标。ALCA只是部分后果,因为它受到与义务论伦理有关的概念规则的限制,例如,涵盖了产品的相对过去。由于义务伦理学(即根据行为是否符合规则来判断行为)与现代人类社会普遍相关,因此ALCA和CLCA之间在伦理相关性方面存在互补的空间。然而,ALCA的概念规则(如可加性)及其相关性尚未被社会所接受。因此,对ALCA的道德接受仍然是必需的。在决策支持的背景下,CLCA评估决策的后果,而ALCA包括批准和分担与决策相关的产品在整个生命周期内对环境影响的潜在责任。我们还强调了组织ALCA的独特价值,这需要负责产品的组织的上述方面。关于这三项实际标准,得出了以下结论。虽然ALCA在标准方面受到了最多的关注,但目前只有CLCA能够始终如一地可靠地进行。这是因为目前应用于ALCA的生命周期影响评价方法尚未划分环境多输入过程。在标准中应更加突出CLCA。此外,CLCA与建模相关的复杂性和不确定性通常只比ALCA稍微高一些,这主要是由于考虑到决策导致的变更。然而,ALCA和CLCA建模可能同样复杂,并且具有同样高的不确定性,因为这两种方法都包含过去和/或未来的预测(例如,对未来背景过程的预测)。最后,ALCA模型可以看作是CLCA的实用近似,但目前的CLCA模型更适合于研究后果效应。随着CLCA建模和数据库的不断改进,这种区别将变得更加明显。
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Relevance of attributional and consequential life cycle assessment for society and decision support
Life cycle assessment (LCA) is an essential tool for assessing the environmental impact of product systems. There are two main types: attributional LCA (ALCA), which assesses the global impact share of a product's life cycle, and consequential LCA (CLCA), which evaluates the consequential impact of a decision. In our analysis, we explored the relevance of these types for society and their ability to aid decision-making. This analysis builds upon existing literature and incorporates two ideological and three pragmatic criteria. First, when it comes to realistic modeling as desired in the context of sustainable development, in theory, CLCA attempts to model realistically, whereas ALCA falls short to a certain degree because of conceptual rules, e.g., artificial splitting of co-product processes. Concerning the second criterion of alignment with ethics, CLCA completely aligns with consequential ethics, where an action is judged based on its consequences. This alignment of CLCA makes it undoubtedly relevant in a world where we aim to obtain favorable consequences in the future, e.g., meeting sustainability goals. ALCA is only partially consequential, as it is restricted by conceptual rules relating to deontological ethics and, for example, covers the relative past of the product. Since deontological ethics, i.e. judging an action based on its alignment with rules, is generally relevant for our modern human society, there is room for complementarity in ethical relevance between ALCA and CLCA. However, the conceptual rules of ALCA (e.g., additivity) and their relevance have not been accepted by society. As a result, ethical acceptance of ALCA is still required. In the context of decision support, CLCA evaluates the consequences of decisions, while ALCA encompasses the approval and sharing of potential responsibility for the environmental impact throughout the life cycle of the product associated with the decision. We also highlight the unique valorization of Organizational ALCA, which entails the aforementioned aspects for the organizations responsible for the product. Concerning the three practical criteria, the following conclusions were drawn. Although ALCA has received the most attention in terms of standards, only CLCA can currently be consistently conducted in a reliable manner. This is because the current life cycle impact assessment methods applied in ALCA do not yet partition environmental multi-input processes. CLCA should be given greater prominence in standards. Furthermore, the complexity and uncertainty associated with modeling may often be only slightly higher for CLCA than for ALCA, mainly due to the consideration of change resulting from a decision. However, both ALCA and CLCA modeling may be similarly complex and have equally high levels of uncertainty as both methods encompass past and/or future projections (e.g., prediction of future background processes). Finally, ALCA modeling may be viewed as a practical approximation of CLCA, but the current CLCA models are more suitable for studying consequential effects. As CLCA modeling and databases continue to improve, this distinction will become even more pronounced.
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