Downscaling a human well-being index for environmental management and environmental justice applications in Puerto Rico

摘要

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.