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Downscaling a human well-being index for environmental management and environmental justice applications in Puerto Rico
Applied Geography ( IF 4.732 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.apgeog.2020.102231
Susan H Yee 1 , Elizabeth Paulukonis 1 , Kyle D Buck 1
Affiliation  

Abstract Human well-being is often an overarching goal in environmental decision-making, yet assessments are often limited to economic, health, or ecological endpoints that are more tangible to measure. Composite indices provide a comprehensive approach to measuring well-being in terms of multi-dimensional components, such as living standards, health, education, safety, and culture. For example, the Human Well-Being Index (HWBI) framework, initially developed for the U.S. fifty states, was recently applied to quantify human well-being for Puerto Rico. However, the paucity of data at spatial scales finer than state or county levels, particularly for social metrics, poses a major limitation to quantifying well-being at neighborhood-scales relevant to decision-making. Here we demonstrate a spatial interpolation method to fill in missing fine-scale data where coarser-scale data is available. Downscaling from municipio (i.e., county-equivalent) to census-tract revealed a greater range of variability in well-being scores across Puerto Rico, in particular, a larger proportion of low well-being scores. Furthermore, while some components of wellbeing (e.g., Education, Health, Leisure Time, Safety and Security, Social Cohesion) showed consistent improvement over time from 2000 to 2017 across Puerto Rico, others (e.g., Connection to Nature, Cultural Fulfillment, Living Standards) were variable among census tracts, increasing for some but declining for others. We use a case study in the San Juan Bay estuary watershed to illustrate how approaches to quantify baseline levels of well-being can be used to explore potential impacts of management actions on communities, including to identify environmental justice inequalities among neighborhoods. Spatial clustering analysis was used to identify statistically significant cold or hot spots in well-being. This study demonstrates how indicators of well-being, coupled with interpolation methods to overcome limitations of data availability, can help to monitor long-term changes over time and to better communicate the potential value of ecosystem restoration or resource management.

中文翻译:

降低波多黎各环境管理和环境正义应用的人类福祉指数

摘要 人类福祉通常是环境决策中的首要目标,但评估通常仅限于经济、健康或生态终点,这些终点更易于衡量。综合指数提供了一种综合方法,可以根据生活水平、健康、教育、安全和文化等多维组成部分衡量幸福感。例如,最初为美国五十个州开发的人类幸福指数 (HWBI) 框架最近被用于量化波多黎各的人类幸福。然而,在比州或县级更精细的空间尺度上,尤其是对于社会指标而言,数据的缺乏对在与决策相关的社区尺度上量化福祉构成了主要限制。在这里,我们演示了一种空间插值方法,用于在可用的粗尺度数据的情况下填充缺失的细尺度数据。从 municipio(即相当于县)到人口普查区的规模缩小显示波多黎各的福祉得分差异更大,特别是低福祉得分的比例更大。此外,虽然福祉的某些组成部分(例如,教育、健康、休闲时间、安全和保障、社会凝聚力)在 2000 年至 2017 年间在波多黎各显示出持续改善,但其他部分(例如,与自然的联系、文化实现、生活标准) ) 在人口普查区域中是可变的,有些增加,而另一些则减少。我们使用圣胡安湾河口流域的案例研究来说明如何使用量化基线幸福水平的方法来探索管理行动对社区的潜在影响,包括确定社区之间的环境正义不平等。空间聚类分析用于确定幸福感中具有统计学意义的冷点或热点。本研究展示了福祉指标与克服数据可用性限制的插值方法如何有助于监测长期变化并更好地传达生态系统恢复或资源管理的潜在价值。空间聚类分析用于确定幸福感中具有统计学意义的冷点或热点。本研究展示了福祉指标与克服数据可用性限制的插值方法如何有助于监测长期变化并更好地传达生态系统恢复或资源管理的潜在价值。空间聚类分析用于确定幸福感中具有统计学意义的冷点或热点。本研究展示了福祉指标与克服数据可用性限制的插值方法如何有助于监测长期变化并更好地传达生态系统恢复或资源管理的潜在价值。
更新日期:2020-10-01
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