Are Global Environmental Uncertainties Inevitable? Measuring Desertification for the SDGs
Are Global Environmental Uncertainties Inevitable? Measuring Desertification for the SDGs
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DOI：
10.3390/su14074063
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发表时间：
2022-03
期刊：
影响因子：
3.9
通讯作者：
A. Grainger
中科院分区：
文献类型：
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作者：
A. Grainger
对目前全球环境变化现象的严重程度的持续不确定性限制了对人类对地球影响的科学理解，也限制了就如何应对这些现象向决策者提供科学咨询的质量。然而，为什么全球环境的不确定性如此之大，为什么它们持续存在，它们的程度如何从一种现象到另一种现象有所不同，以及它们是否可以减少，人们对此知之甚少。为了解决这些问题，一个新的工具，不确定性评估框架（UAF），提出了建立在以前的研究，将环境不确定性的来源分为与现象中固有的功能，以及概念化和测量现象的能力不足的类别。应用UAF表明，基于其规模、复杂性、区域变异性和周转时间，荒漠化是最具内在不确定性的全球环境变化现象之一。目前荒漠化的不确定性也很高，而且持续存在：全球荒漠化程度五项估计的时间序列的不确定性得分显示变化有限，平均值为6.8，范围从0到8，基于四个概念化不确定性的存在（术语困难、规格不足、结构化不足和使用代用品）和四种测量不确定性（随机误差、系统误差、标量缺陷和使用主观判断）。这表明，联合国可持续发展目标（SDG）15（“陆地生命”）中土地退化零增长（LDN）目标15.3的实现将难以在干旱地区进行监测。根据UAF，仅通过统计方法进行评价是适当的，时间序列中的估计值均不具有2的不确定性评分。这支持了统计方法在评估非常不确定的现象时具有局限性的说法。全球环境的不确定性可以通过制定更好的规则来减少，以构建全球环境信息，将概念化和测量结合起来。这里应用了一套来自UAF的七条规则来说明如何衡量荒漠化，表明荒漠化的不确定性并非不可避免。最近的评论文章主张使用“大数据”来填补监测LDN和其他SDG 15目标的国家数据空白，但使用UAF对三项示例性研究的样本进行评估仍然给出了4.7的平均不确定性得分，因此这种方法并不简单。
Continuing uncertainty about the present magnitudes of global environmental change phenomena limits scientific understanding of human impacts on Planet Earth, and the quality of scientific advice to policy makers on how to tackle these phenomena. Yet why global environmental uncertainties are so great, why they persist, how their magnitudes differ from one phenomenon to another, and whether they can be reduced is poorly understood. To address these questions, a new tool, the Uncertainty Assessment Framework (UAF), is proposed that builds on previous research by dividing sources of environmental uncertainty into categories linked to features inherent in phenomena, and insufficient capacity to conceptualize and measure phenomena. Applying the UAF shows that, based on its scale, complexity, areal variability and turnover time, desertification is one of the most inherently uncertain global environmental change phenomena. Present uncertainty about desertification is also very high and persistent: the Uncertainty Score of a time series of five estimates of the global extent of desertification shows limited change and has a mean of 6.8, on a scale from 0 to 8, based on the presence of four conceptualization uncertainties (terminological difficulties, underspecification, understructuralization and using proxies) and four measurement uncertainties (random errors, systemic errors, scalar deficiencies and using subjective judgment). This suggests that realization of the Land Degradation Neutrality (LDN) Target 15.3 of the UN Sustainable Development Goal (SDG) 15 (“Life on Land”) will be difficult to monitor in dry areas. None of the estimates in the time series has an Uncertainty Score of 2 when, according to the UAF, evaluation by statistical methods alone would be appropriate. This supports claims that statistical methods have limitations for evaluating very uncertain phenomena. Global environmental uncertainties could be reduced by devising better rules for constructing global environmental information which integrate conceptualization and measurement. A set of seven rules derived from the UAF is applied here to show how to measure desertification, demonstrating that uncertainty about it is not inevitable. Recent review articles have advocated using ‘big data’ to fill national data gaps in monitoring LDN and other SDG 15 targets, but an evaluation of a sample of three exemplar studies using the UAF still gives a mean Uncertainty Score of 4.7, so this approach will not be straightforward.