Integrating knowledge co-production with life cycle assessment
Integrating knowledge co-production with life cycle assessment
复制标题
将知识共同生产与生命周期评估相结合
DOI：
10.1016/j.resconrec.2022.106650
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Miller, Shelie A.
中科院分区：
文献类型：
--
作者：
Mo, Weiwei;Hart, David;Ashcraft, Catherine M.;Chester, Mikhail;Cucurachi, Stefano;Lu, Zhongming;Miller, Shelie A.
在过去的十年中，生命周期评估方法有了很大的进步，能够进行更现实的影响模拟和预测，并考虑到空间和时间因素。然而，通过生命周期评估工作创造的知识仍然主要用作信息来源，而不是作为一个过程，使利益攸关方参与实施建议，并促进迅速和适应性决策和实现可持续性的变化，例如（达文波特和弗里德曼，2022年）。人们还对LCA的易用性及其促进沟通、公开讨论和公众参与的能力提出了担忧（Cowell等人，2002年）。将知识与行动联系起来是一个共同的挑战，被称为“装卸码头”问题。它描述了知识从研究界向利益攸关方的单向转移，以及由此造成的科学知识在实际决策中的有限使用。装卸码头问题是可持续发展科学中特别关注的问题，因为解决“邪恶”问题通常需要利益相关者和研究团体之间的富有成效的合作。LCA需要在我们如何纳入和参与利益相关者方面进行范式转变。传统上，LCA社区将自己定位为一个诚实的信息经纪人，专注于提出缺乏特定偏见的“事实”主张。根据ISO 14040/44，推动利益攸关方选择的背景价值被认为“没有科学依据”。这种观点可以在很大程度上解释为什么LCA社区没有接受决策科学，政治科学或行为经济学等学科。然而，LCA本质上是有价值的。例如，目标和范围通常由委托研究的利益相关者定义，这可能与受LCA研究影响或希望使用LCA研究结果的其他利益相关者的观点和价值观一致或不一致。在此，我们概述了一些可能阻碍LCA知识与行动联系的障碍。·代表和参与复杂性：自成立以来，LCA一直吹嘘其“系统”的方法来解决可持续性问题，作为传统简化方法的替代方案。生命周期评估所处理的问题往往涉及跨界物质和能源流动，涉及不同时间尺度的多个管辖区和地理环境。研究的系统通常是动态的，非线性的，并由反馈控制。这些复杂的环境为LCA提出了挑战：1）模型和场景表示，2）沟通和促进利益相关者的参与。尽管生命周期评价最近取得了一些进展，但它在捕捉复杂的人类-环境动态方面的能力仍然有限。生命周期评估的结果可能会受到影响，因此需要根据利益相关者的决定/行动产生的新情况进行更新。理想情况下，LCA将有助于参与复杂性（Chester等人，2021年）。然而在实践中，研究人员必须对模型的复杂性、系统边界以及地理空间和时间分辨率做出选择。研究人员也很难传达复杂性。让利益相关者参与解决复杂问题需要所有各方都具有一定的能力和对系统思维的兴趣，以便能够合作解决问题。
Over the last decade, the life cycle assessment (LCA) methodology has significantly advanced to enable more realistic impact simulations and predictions with spatial and temporal considerations. Nevertheless, knowledge created through LCA efforts is still largely used as an information source, rather than as a process to engage stakeholders with the implementation of recommendations and to foster prompt and adaptive decision-making and changes towards sustainability, see for example (Davenport & Friedman, 2022). Concerns have also been raised regarding LCA’s ease-of-use as well as its capability in fostering communication, open discussion, and public participation (Cowell et al., 2002). Linking knowledge with actions is a common challenge that was coined the “loading dock” problem. It describes the one-way transfer of knowledge from research communities to stakeholders, and the resulting limited use of scientific knowledge in actual decision-making. The loading dock problem is a particular concern in sustainability science, as tackling “wicked” problems often requires productive collaborations between stakeholders and research communities. LCA needs a paradigm shift in how we include and engage stakeholders. Traditionally, the LCA community has positioned itself as an honest broker of information focusing on making “factual” claims that lack specific bias. The contextual values that drive stakeholders’ choices have been considered “not scientifically based”, according to ISO 14040/44. This perspective could largely explain why the LCA community has not embraced disciplines such as decision science, political science, or behavioral economics. Nevertheless, LCA is inherently value laden. For example, the goal and scope are often defined by stakeholders that commission the study, which may or may not align with the perspective and values of other stakeholders who are impacted by or wish to use the results of an LCA study. Here we outline some barriers that may hinder linking LCA knowledge with actions.• Representing and Engaging with Complexity: Since its inception, LCA has boasted its “systematic” approach to sustainability problems as an alternative to the traditional reductionist approach. The problems LCA addresses often involve transboundary material and energy movements that involve multiple jurisdictions and geographical contexts at different time scales. The systems studied are commonly dynamic, non-linear, and governed by feedback. These complex environments raise challenges for LCA: 1) in model and scenario representation, and 2) communicating and fostering stakeholder engagement. Despite LCA’s recent developments, its capability in capturing the complex human-environment dynamics remains limited. LCA results can be influenced and hence need to be updated based on new circumstances arising from stakeholders’ decisions/actions. Ideally, LCA will facilitate engaging with complexity (Chester et al., 2021). Yet in practice, researchers must make choices about model complexity, system boundaries, and geospatial and temporal resolutions. It is also inherently difficult for researchers to communicate complexity. Engaging stakeholders with complex problems require all parties to have a certain level of capacity and interest in systems thinking to allow for collaborative problem solving.