大陆尺度上城市和城郊自然的高分辨率舒适度评价

Roland Löwe , Martina Viti , Karsten Arnbjerg-Nielsen , Jacob Ladenburg
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引用次数: 0

摘要

在城市中,空间是一项价值很高的资产。这就是为什么基于自然的水管理解决方案(NBS)通常被认为比传统的灰色解决方案更昂贵的一个关键原因。然而,空间的分配也提供了相对于灰色解决方案的好处。在成本效益驱动的规划范式中,国家统计局的实施需要以标准化和易于应用的方式量化这些效益的方法。基于114项关于城市地区自然的国家偏好评估研究和发达国家公开的地理数据,我们开发了一个城市自然总效益价值的预测元模型,该模型涵盖了面积从0.5公顷到90万公顷的NBS类型的整个范围。使用交叉验证程序,我们比较了840万个模型排列的预测性能,这些排列考虑了场地属性、地形和周围社会经济特征的不同组合作为输入。研究发现,自然区域的总效益值是由自然区域的规模和周围的人口密度决定的。有明确的证据表明,现有的自然区域会降低购买新自然的意愿,从而产生替代效应。除了对面积的依赖,几乎没有证据表明对自然类型进行区分。经济价值确实取决于一个地点的平均收入,但这些变化完全由购买力修正反映出来。我们的价值估计与相关文献一致,范围在150至40万美元/公顷/年之间。本文提供了我们元模型的Python实现,它以100m的空间分辨率生成欧洲任何地方的预测值地图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Amenity valuation of urban and peri‑urban nature in high resolution on continental scale
Space is a highly valued asset in cities. This is a key reason why nature-based solutions (NBS) for water management are often perceived to be more expensive than traditional grey solutions. However, the allocation of space also provides benefits relative to grey solutions. In a planning paradigm driven by cost-effectiveness, NBS implementation requires methods for quantifying these benefits in a standardized and easily applicable manner. Based on 114 stated-preference valuation studies of nature in urban areas and openly available geographic data from the developed world, we develop a predictive metamodel for the aggregate benefit value of urban nature, covering the entire range of NBS types with sizes from 0.5 to 900,000 ha. Using a cross-validation procedure, we compare the predictive performance of 8.4 million model permutations that consider different combinations of site properties and topographic and socio-economic characteristics of the surroundings as input. We find that the aggregate benefit value is determined by the size of the nature areas and population densities in their surroundings. There is clear evidence for substitution effects where available nature areas reduce the willingness to pay for new nature. Beyond the dependency on area, there is little evidence for making distinctions between nature types. Economic values do depend on the average income at a site, but these variations are entirely captured by purchase power corrections. Our value estimates are aligned with related literature and range between 150 and 400,000 USD/ha/year. A Python implementation of our metamodel is provided alongside this paper, which generates maps of the predicted values for any place in Europe in a spatial resolution of 100m.
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