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Analyzing stakeholder's perceptions of uncertainty to advance collaborative sustainability science: Case study of the watershed assessment of nutrient loads to the Detroit River project

机译:分析利益相关者对不确定性的看法,以推进协作式可持续发展科学:底特律河项目营养分担的分水岭评估案例研究

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摘要

The topic of uncertainty is of growing interest in the impact assessment (IA) field, due to increases in contextual uncertainty and the awareness of the complexity of advanced analysis. IA practitioners can now draw on maturing theoretical frameworks to manage uncertainty, but questions remain about whether these frameworks align with stakeholder concerns and how their use can benefit IA projects. This article reports on an empirical application of the leading framework for organizing IA uncertainty proposed by Walker et al. in 2003. Twenty-two stakeholders involved in a large water quality modeling project in the U.S. Great Lakes region were interviewed, and their uncertainty-related statements were categorized according to the Walker dimensions. Overall, the frameworks three primary dimensions performed well and allowed for the analysis of differences in uncertainty perceptions among the stakeholder groups. In addition, the analysis resulted in useful insights for the project, such as identifying top scenario uncertainties to use for modeling as well as highlighting specific concerns about the assumptions, data, and modeling approach for further exploration. In addition to encompassing the variety of uncertainty concerns raised in the case, the paper illustrates how the Walker framework can support IA practices like stakeholder collaboration and scenario construction which may improve IA outcomes.
机译:由于上下文不确定性的增加以及对高级分析的复杂性的认识,不确定性主题在影响评估(IA)领域中的兴趣日益浓厚。 IA的从业者现在可以利用成熟的理论框架来管理不确定性,但是仍然存在疑问,即这些框架是否符合利益相关者的关注,以及它们的使用如何使IA项目受益。本文报道了由Walker等人提出的用于组织IA不确定性的领先框架的经验应用。 2003年,我们采访了22个参与美国大湖区大型水质模型项目的利益相关者,并根据Walker维度对与不确定性有关的陈述进行了分类。总体而言,框架的三个主要方面表现良好,并且可以分析利益相关者群体之间对不确定性看法的差异。此外,分析还为项目提供了有用的见解,例如,确定了用于建模的主要方案不确定性,并突出了对假设,数据和建模方法的特定关注,以进行进一步的探索。除了涵盖案例中提出的各种不确定性问题外,本文还说明了Walker框架如何支持IA实践,例如利益相关者协作和场景构建,这可能会改善IA结果。

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