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- All Subjects: Land use
- Creators: Kinzig, Ann
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To examine what drives LaSLA, I used country level data from 2005 to 2013 on economic conditions, natural resources, business practices, and governance to estimate LaSLA models. I find that LaSLA increases with increasing government effectiveness, land prices, and the ease of doing business, and decreases with stronger regulatory regimes. To assess LaSLA's impacts on local people, I conducted a comparative case study in Tanzania. I compare changes in peoples' livelihood between treatment villages (those experiencing LaSLA) and control villages (those without LaSLA projects). The results show that under current practices, the risks of LaSLA outweigh the benefits to local livelihoods, yet there are potential benefits if LaSLA is implemented correctly.
To philosophically examine whether LaSLA can be considered just and ethical, I apply John Rawls' theory of justice. The analysis indicates that from both procedural and distributive justice perspective, LaSLA currently fails to satisfy Rawlsian principles of justice. From these analyses, I conclude that if implemented correctly, LaSLA can produce a win-win outcome for both investors and host countries. I suggest that strong governance, rigorous environmental and social impact assessment, and inclusion of local people at all levels of LaSLA decision making are critical for sustainable and equitable outcomes.
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Background:
The evidence that heat waves can result in both increased deaths and illness is substantial, and concern over this issue is rising because of climate change. Adverse health impacts from heat waves can be avoided, and epidemiologic studies have identified specific population and community characteristics that mark vulnerability to heat waves.
Objectives:
We situated vulnerability to heat in geographic space and identified potential areas for intervention and further research.
Methods:
We mapped and analyzed 10 vulnerability factors for heat-related morbidity/mortality in the United States: six demographic characteristics and two household air conditioning variables from the U.S. Census Bureau, vegetation cover from satellite images, and diabetes prevalence from a national survey. We performed a factor analysis of these 10 variables and assigned values of increasing vulnerability for the four resulting factors to each of 39,794 census tracts. We added the four factor scores to obtain a cumulative heat vulnerability index value.
Results:
Four factors explained > 75% of the total variance in the original 10 vulnerability variables: a) social/environmental vulnerability (combined education/poverty/race/green space), b) social isolation, c) air conditioning prevalence, and d) proportion elderly/diabetes. We found substantial spatial variability of heat vulnerability nationally, with generally higher vulnerability in the Northeast and Pacific Coast and the lowest in the Southeast. In urban areas, inner cities showed the highest vulnerability to heat.
Conclusions:
These methods provide a template for making local and regional heat vulnerability maps. After validation using health outcome data, interventions can be targeted at the most vulnerable populations.