BACKGROUND: In the past decade urbanization has increased, with over half the world’s population and 83% of the total United States population, residing in urban areas as of 2021. Urbanization has the potential to decrease connectivity between nature and humans, impacting health and exacerbating urban heat islands. Previous research has found that the natural environment (i.e., trees and bluespace) provide vital ecosystem services through both direct and indirect pathways that positively impact health. Objective: The objective of my dissertation was to assess the current state of the natural environment in urban areas and gain a better understanding of the relationship between exposure to nature, health, and heat. METHODS: Research aims were addressed by focusing on publicly available data and a range of metrics used in previous literature to characterize exposure to greenness and natural landscapes and their associations with health and land surface temperature. In Chapter 2, I report on a nationwide quantitative health impact assessment to estimate the reduction in all-cause mortality in populations aged 65 and older in the largest metropolitan areas (n=35) in the United States associated with an increase in greenness in selected years across twenty years (2000, 2010, and 2019). Census tract mean seasonal greenness exposure was estimated by using the Normalized Difference Vegetation Index (NDVI) from Landsat 30 m 16-day satellite imagery from April to September. All-cause mortality estimates were downscaled from county to tract-level from the CDC WONDER Portal. Mortality reduction was estimated using a recently published exposure-response function. Chapter 3 focuses on urban census tracts in Massachusetts (n=525), in order to assess the cumulative exposure of the urban natural environment. Thirty candidate, validated measures of the natural landscape space were derived from data published between 2016 and 2019. Principal component analysis was used to reduce the number of candidate items and confirmatory factor analysis was used to test the underlying hypothesized latent structure of three sub-domains: green space, parks and recreation and blue space. Items hypothesized to measure each latent structure were summed using equal weights and each sub-domain was rescaled from 0–100. The tract-level cumulative exposure index was the sum of the three sub-domains. Lastly, in Chapter 4 using generalized linear models, I investigated the relationship between urban heat and nature using the natural landscape index created in Chapter 3 and mean five-year Land Surface Temperature (LST). The City of Boston was then used as a case-study to identify vulnerable tracts with low nature and high heat to inform planning for climate adaptation strategies and public health planning. RESULTS: In Chapter 2, overall greenness in the largest metropolitan areas in the US increased from 3% between 2000 and 2010 to 11% between 2010 and 2019. Approximately 34,000 to 38,000 all-cause mortality deaths could have been prevented with an increase in 0.1 NDVI units from 2000 to 2019. In developing the cumulative natural landscape index, in Chapter 3, we found that greenness measures (NDVI, tree canopy, percent impervious, etc.) explained the most variance among all of the items hypothesized to measure the cumulative natural landscape index. The three sub-domains explained 69% of the total variance, with greenness accounting more than half the total variance explained. Lastly, in Chapter 4, we report that with an interquartile shift in overall nature, LST decreased by 0.8°C. More specifically, higher overall greenness, higher percent tree canopy, and higher percent impervious surfaces resulted in the largest change in LST. With an increase in greenness and tree canopy we found a decrease in LST by over 1°C. In contrast, an increase in impervious surfaces resulted in an increase in LST by 1.31°C. Within the City of Boston, we found that 9% of the total population resides in tracts that were within the highest quartile of LST and lowest quartile of overall nature. CONCLUSION: Overall, this work finds that large urban areas in the United States saw a small increase in overall greenness across the past two decades. With a trend of increasing greenness there is also the potential to reduce all-cause mortality in those 65 years and older. Going beyond overall greenness, we created a cumulative natural landscape index to capture simultaneous exposures to the urban natural environment. Greenness measures such as tree canopy, NDVI, open space, and impervious surfaces explained the most variance in the cumulative nature index and was the most associated with LST as compared to parks and recreation and bluespace. We assessed the impacts of the natural landscape on temperature, and found that an increase in nature resulted in a significant decrease in LST with greenness measures having the largest impact. Results from these studies can be used to support climate action plans and greening initiatives to show the co-benefits of increased exposure to the natural environment and help identify areas that are lacking in these exposures to better implement initiatives.

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Centre of African Ecology Animal, Plants and the Environmental Sciences University of the Witwatersrand, Johannesburg. / Arid African savannas experience seasonal, variable rainfall, resulting in unpredictable patterns in vegetation distribution. Understanding the spatio-temporal variability in primary productivity and the resulting behavioural responses of native herbivores is essential for the analysis of the vulnerability of savanna ecosystems to climatic and human-induced threats. The Central Kalahari Game Reserve (CKGR), Botswana, is open to free-ranging wildlife to its south and west. The mostly homogeneous dune landscape is interspersed with valley and pan systems, which deviate considerably from dune regions in their soil and vegetation structures. I assessed the phenology of green vegetation across the pan-valley and dune habitats of the northern CKGR, using Normalized Difference Vegetation Index (NDVI) imagery, and related variations in greenness to the ecology of gemsbok (Oryx gazella), a herbivore species that is highly adapted to arid conditions. Eight female gemsbok were collared in the northern CKGR, and their patterns of habitat selection and responses to three greenness measures (NDVI, ΔNDVI and Relative Greenness) were assessed using logistic regression models. Gemsbok 12-hour displacement distances for each herd were compared seasonally to assess whether gemsbok in the northern CKGR differ in their movement strategies depending on the prevailing environmental conditions at that point in time and space. The northern CKGR experiences high inter-annual variability in NDVI greenness and phenology. Pan-valley and dune habitats did not have significantly different rates of green-up or green season durations, but dune habitats had higher NDVI levels. Patches with the highest greenness levels showed little spatial persistence from year to year. Gemsbok did not select for higher NDVI or ΔNDVI, but they selected for categories of relative greenness that were higher than the lowest relative greenness level. Gemsbok selected pan-valleys over dunes during the green season, but were not selective during the brown season, probably as a result of the loss of green grasses in pan-valley habitats during this period. Finally, gemsbok had no specific general trend in seasonal displacement distances. Gemsbok in the CKGR are likely to be opportunistic feeders, and herds probably made varying behavioural decisions based on their immediate environmental conditions.

The objective of this paper is to provide a robust understanding of the magnitude and direction of effect of urban green space on human health outcomes. This relationship has been studied using a variety of health and green space measures and multiple approaches to collect health data. Due to the various approaches used to study the relationship between green space and health, it is difficult to clearly understand the general relationship. In order to have a robust and clear understanding of the relationship between green space and human health, it is necessary to do a meta-analysis that considers all the approaches to assessing green space, health, and health data. Overall, the average estimated effect shows that surrounding urban green space improves human health by 1.14 fold (~u=0.13 (95% CI: (0.07-0.19)). The magnitude of effect increased when accounting for perceived health and when using greenness as the urban green space measure (~u=0.29 (95% CI: (-0.06-0.63)). In conclusion we can now objectively imply that health can be improved with increased surrounding green space. Further, that the magnitude depends on how studies assess green space and health measures, as well as how health data is collected.

Indiana University-Purdue University Indianapolis (IUPUI) / Research on the association between health and the built environment often delineates environmental exposure using different spatial forms and distances surrounding points of interest, such as residences or schools. Examples from the literature include Euclidian and network buffers, administrative and census boundaries, and other arbitrary geographies, such as grid cells. There is a lack, however, of reports that describe the justifications or implications for using different methods. This research compares different forms and distances for measuring environmental variables surrounding residential locations in the context of adult walking behavior in Marion County, Indiana. Walkability index and vegetation greenness variables were evaluated within 400-meter, 1-kilometer, and 2-kilometer Euclidian and network buffers, census block groups and tracts, and 805- X 805-meter grid cells. Results of analyses using each of these methods to test walkability and greenness as correlates of self-reported walking behavior were compared. Significant differences were observed in measurements of environmental variables as a function of both size and form. There were also significant differences between spatial measure methods when measuring components of walkability and NDVI. Census geographies, widely used in the public health literature, yielded environmental variable measurements differently than did similarly-sized residence-based measure methods. In logistic regressions, the walkability index did not exhibit a significant relationship with self-reported walking behavior. NDVI exhibited a negative relationship with self-reported walking, although the relationship was reversed and significant when stratifying by residential density.

Global sea surface temperature (SST) anomalies have a demonstrable effect on terrestrial climate dynamics throughout the continental U.S. SST variations have been correlated with greenness (vegetation densities) and precipitation via ocean-atmospheric interactions known as climate teleconnections. Prior research has demonstrated that teleconnections can be used for climate prediction across a wide region at sub-continental scales. Yet these studies tend to have large uncertainties in estimates by utilizing simple linear analyses to examine chaotic teleconnection relationships. Still, non-stationary signals exist, making teleconnection identification difficult at the local scale. Part 1 of this research establishes short-term (10-year), linear and non-stationary teleconnection signals between SST at the North Atlantic and North Pacific oceans and terrestrial responses of greenness and precipitation along multiple pristine sites in the northeastern U.S., including (1) White Mountain National Forest - Pemigewasset Wilderness, (2) Green Mountain National Forest - Lye Brook Wilderness and (3) Adirondack State Park - Siamese Ponds Wilderness. Each site was selected to avoid anthropogenic influences that may otherwise mask climate teleconnection signals. Lagged pixel-wise linear teleconnection patterns across anomalous datasets found significant correlation regions between SST and the terrestrial sites. Non-stationary signals also exhibit salient co-variations at biennial and triennial frequencies between terrestrial responses and SST anomalies across oceanic regions in agreement with the El Nino Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) signals. Multiple regression analysis of the combined ocean indices explained up to 50% of the greenness and 42% of the precipitation in the study sites. The identified short-term teleconnection signals improve the understanding and projection of climate change impacts at local scales, as well as harness the interannual periodicity information for future climate projections. Part 2 of this research paper builds upon the earlier short-term study by exploring a long-term (30-year) teleconnection signal investigation between SST at the North Atlantic and Pacific oceans and the precipitation within Adirondack State Park in upstate New York. Non-traditional teleconnection signals are identified using wavelet decomposition and teleconnection mapping specific to the Adirondack region. Unique SST indices are extracted and used as input variables in an artificial neural network (ANN) prediction model. The results show the importance of considering non-leading teleconnection patterns as well as the known teleconnection patterns. Additionally, the effects of the Pacific Ocean SST or the Atlantic Ocean SST on terrestrial precipitation in the study region were compared with each other to deepen the insight of sea-land interactions. Results demonstrate reasonable prediction skill at forecasting precipitation trends with a lead time of one month, with r values of 0.6. The results are compared against a statistical downscaling approach using the HadCM3 global circulation model output data and the SDSM statistical downscaling software, which demonstrate less predictive skill at forecasting precipitation within the Adirondacks.

Accumulating evidence of the beneficial effects of contact with nature on children’s mental and physical health suggests that exposure to green spaces can support learning and enhance academic performance in children. A limited number of studies exploring the relationship between vegetation in the school environment and academic outcomes has found positive associations. However, most studies use students’ contextual data and outcomes aggregated at the school level, and therefore are unable to adequately control for individual characteristics of students known to influence academic performance. Furthermore, these studies have been carried out in developed countries of North America and Europe, and therefore the extent to which their findings are generalizable to different socio-economic, cultural, and ecological contexts is not well understood. In this dissertation, we advance our understanding on the use of remote sensing data to assess greenness exposure, in order to evaluate the association between greenness in the school environment and student-level academic outcomes in a country of the Global South. We first estimated the influence of spatial resolution of normalized difference vegetation index (NDVI) on greenness exposure misclassification. We assessed residential greenness for a large cohort of children in Boston, Massachusetts using NDVI at different spatial resolutions and multiple buffer sizes, following methods commonly used in the environmental health literature. Using a reliability assessment framework, we compared continuous and categorical exposure estimates and found that exposure misclassification was higher with coarser spatial resolution, smaller buffers, and greater number of exposure quantiles. We conclude that greenness exposure assessment is sensitive to spatial resolution of NDVI, aggregation area, and number of exposure quantiles. Then, we linked satellite-derived greenness estimates for 2,931 schools in urban areas in Chile to administrative records of standardized test scores and contextual information at the individual and school level for 541,171 elementary-aged students. We evaluated the association between greenness in the school environment and individual-level academic performance in elementary-aged students in Santiago, Chile. Higher school greenness levels were associated with improved individual-level academic performance, with greater magnitude and strength of associations for students in public schools. Finally, we evaluated the individual-level association between greenness in the school environment and academic performance of students in schools in 16 cities across different climate zones in Chile. School greenness was positively associated with test scores and odds of attaining learning standards in mathematics and reading, although associations held only in public schools. The magnitude of associations varied widely across climate regions, decreasing from the desert region in the north to the more vegetated regions in the south. In light of the growing interest on health effects from urban green spaces, this dissertation provides insights on methodological decisions regarding greenness exposure assessment in environmental health research. Furthermore, it expands the evidence on a beneficial association between school-greenness and academic performance to different cultural, demographic, and climate settings. Our results highlight the nuanced nature of this association and the importance of considering students’ social and ecological contexts in urban greenspace management around schools to provide green spaces that effectively support learning. / 2024-12-05T00:00:00Z

As mobile devices are nowadays used regularly and everywhere, their energy consumption has become a central concern for their users. However, mobile applications often do not consider energy requirements and users have to install and try them to reveal information on their energy behavior. In this paper, we compare mobile applications from two domains and show that applications reveal different energy consumption while providing similar services. We define microbenchmarks for emailing and web browsing and evaluate applications from these domains. We show that non-functional features such as web page caching can but not have to have a positive influence on applications' energy consumption.

Submitted by Raphael Anauate Ferraz de Sampaio (raphaelsampaio@gmail.com) on 2018-07-18T00:36:28Z No. of bitstreams: 1 tese.pdf: 374374 bytes, checksum: 3132e669937f24138723b9fc6e638772 (MD5) / Rejected by Thais Oliveira (thais.oliveira@fgv.br), reason: Boa tarde, Raphael! Para que possamos aprovar seu trabalho, serão necessárias as seguintes alterações: - Na capa, na parte Superior, 3cm acima do seu nome, deve conter o nome da escola "FUNDAÇÃO GETULIO VARGAS" abaixo "ESCOLA DE ECONOMIA DE SÃO PAULO"; - Retirar a página em branco; - Retirar local e ano (São Paulo 2018) da página que contém a Banca Examinadora. Por gentileza, alterar e submeter novamente. Para qualquer dúvida, entre em contato. Thais Oliveira - SRA mestradoprofissional@fgv.br - 3799-7764 on 2018-07-18T17:39:35Z (GMT) / Submitted by Raphael Anauate Ferraz de Sampaio (raphaelsampaio@gmail.com) on 2018-07-20T01:47:16Z No. of bitstreams: 1 tese.pdf: 373526 bytes, checksum: 848ea54d6d9e831e1e2a35d565efc3b9 (MD5) / Approved for entry into archive by Thais Oliveira (thais.oliveira@fgv.br) on 2018-07-24T16:05:25Z (GMT) No. of bitstreams: 1 tese.pdf: 373526 bytes, checksum: 848ea54d6d9e831e1e2a35d565efc3b9 (MD5) / Approved for entry into archive by Suzane Guimarães (suzane.guimaraes@fgv.br) on 2018-07-24T16:23:54Z (GMT) No. of bitstreams: 1 tese.pdf: 373526 bytes, checksum: 848ea54d6d9e831e1e2a35d565efc3b9 (MD5) / Made available in DSpace on 2018-07-24T16:23:54Z (GMT). No. of bitstreams: 1 tese.pdf: 373526 bytes, checksum: 848ea54d6d9e831e1e2a35d565efc3b9 (MD5) Previous issue date: 2018-06-26 / Whether firms benefit from socially responsible actions is under debate over the last few decades. Real estate is an important class of assets on investors portfolios and also largely contributes to waste generation, energy consumption and has a significant impact on health, because people spend several hours of their days at the office or at home. In this paper I explore whether brazilian FIIs (funds that invest in real estate just like american REITs) with greener portfolios show better stock returns and operating efficiency. I extract properties owned by each fund and its financials from CVM3 and LEED scores of each property from USGBC4 . I run a set of fixed-effects regressions of stock return, asset and equity turnovers on the share of green properties for each REIT over the 2001–2017 period and detect an approximate 1% increase in stock return given a 1% increase in portfolio greenness, but find no evidence of increased operating performance. Another model, accounting for endogeneity of past returns is developed and shows no relationship between greeness and returns or operating performance. / Se empresas que exercem práticas socialmente responsáveis se beneficiam em relação a suas concorrentes é tema sob debate, no mercado e na academia, já há algumas décadas. O mercado imobiliário em particular constitui uma importante classe de ativos, pois ocupa fatia considerável da carteira de investidores e traz consigo relevantes impactos socio-ambientais. São exemplos destes a geração de lixo, o consumo energético e impactos no sistema de saúde: com o avanço da urbanização e do terceiro setor na economia, as pessoas passam cada vez mais horas de seus dias em escritórios ou em casa. Nesse artigo, exploro a seguinte questão: fundos de investimento imobiliários (FIIs) investindo em propriedades ambientalmente certificadas têm melhor performance financeira e/ou operacional? Extraio as propriedades e dados financeiros da CVM 1 e verifico na base dados USGBC 2 se há certificação para cada propriedade e qual foi a pontuação obtida. Rodo um conjunto de regressões de efeito fixo relacionando performance financeira (retorno contábil das cotas) e performance operacional (razão entre fluxo de caixa operacional e ativo/patrimônio líquido) à porcentagem de propriedades certificadas de cada fundo no período 2001-2017. Detecto um aumento de aproximadamente 1% no retorno financeiro dado 1% mais greenness, mas não encontro evidência de melhora operacional. Outro modelo, levando em consideração um potencial problema de endogeneidade de retornos passados é desenvolvido e não mostra qualquer relação significativa entre greenness e performance financeira ou operacional.

Crop improvement is necessary for food security as the global population is expected to exceed 9 billion by 2050. Limitations in water resources and more frequent droughts and floods will make it increasingly difficult to manage agricultural resources and increase yields. Therefore, we must improve our ability to monitor agronomic research plots and use the information they provide to predict impacts of moisture stress on crop growth and yield. Towards this end, agronomists have used reductions in leaf expansion rates as a visible ‘plant-based’ indicator of moisture stress. Also, modeling researchers have developed crop models such as AquaCrop to enable quantification of the severity of moisture stress and its impacts on crop growth and yield. Finally, breeders are using Unmanned Aircraft Systems (UAS) in field-based High-Throughput Phenotyping (HTP) to quickly screen large numbers of small agronomic research plots for traits indicative of drought and flood tolerance. Here we investigate whether soybean canopy cover and color time series from high-resolution UAS ortho-images can be collected with enough spatial and temporal resolution to accurately quantify and differentiate agronomic research plots, pinpoint the timing of the onset of moisture stress, and constrain crop models such as AquaCrop to more accurately simulate the timing and severity of moisture stress as well as its impacts on crop growth and yield. We find that canopy cover time series derived from multilayer UAS image ortho-mosaics can reliably differentiate agronomic research plots and pinpoint the timing of reductions in soybean canopy expansion rates to within a couple of days. This information can be used to constrain the timing of the onset of moisture stress in AquaCrop resulting in a more realistic simulation of moisture stress and a lower likelihood of underestimating moisture stress and overestimating yield. These capabilities will help agronomists, crop modelers, and breeders more quickly develop varieties tolerant to moisture stress and achieve food security.