By Jorge De la Roca, Ingrid Gould Ellen, Justin Steil
Journal for Housing Economics
2017
We estimate the effects of residential racial segregation on socio-economic outcomes for native-born Latino young adults over the past three decades. Using individual public use micro-data samples from the Census and a novel instrumental variable, we find that higher levels of metropolitan area segregation have negative effects on Latino young adults’ likelihood of being either employed or in school, on the likelihood of working in a professional occupation, and on income. The negative effects of segregation are somewhat larger for Latinos than for African Americans. Controlling for Latino and white exposure to neighborhood poverty, neighbors with college degrees, and industries that saw large increases in high-skill employment explains between one half and two thirds of the association between Latino-white segregation and Latino-white gaps in outcomes.
By Julien Emile-Geay, Kim M. Cobb, Matthieu Carré, Pascale Braconnot, Julie Leloup, Y. Zhou, S. P. Harrison, Thierry Corrège, H. V. Mcgregor, Matthew Collins, R. Driscoll, M. Elliot, B. Schneider, A. Tudhope
The El Niño/Southern Oscillation (ENSO) is the leading mode of interannual climate variability. However, it is unclear how ENSO has responded to external forcing, particularly orbitally induced changes in the amplitude of the seasonal cycle during the Holocene. Here we present a reconstruction of seasonal and interannual surface conditions in the tropical Pacific Ocean from a network of high-resolution coral and mollusc records that span discrete intervals of the Holocene. We identify several intervals of reduced variance in the 2 to 7 yr ENSO band that are not in phase with orbital changes in equatorial insolation, with a notable 64% reduction between 5,000 and 3,000 years ago. We compare the reconstructed ENSO variance and seasonal cycle with that simulated by nine climate models that include orbital forcing, and find that the models do not capture the timing or amplitude of ENSO variability, nor the mid-Holocene increase in seasonality seen in the observations; moreover, a simulated inverse relationship between the amplitude of the seasonal cycle and ENSO-related variance in sea surface temperatures is not found in our reconstructions. We conclude that the tropical Pacific climate is highly variable and subject to millennial scale quiescent periods. These periods harbour no simple link to orbital forcing, and are not adequately simulated by the current generation of models.
By Rema Hanna, Paulina Oliva
The Future of Children, Climate Change Issue
2016
Climate change may be particularly dangerous for children in developing countries. Even today, many developing countries experience a disproportionate share of extreme weather, and they are predicted to suffer disproportionately from the effects of climate change in the future. Moreover, developing countries often have limited social safety nets, widespread poverty, fragile health care systems, and weak governmental institutions, making it harder for them to adapt or respond to climate change. And the fact that many developing countries have high birth rates and high ratios of children to adults (known as high dependency ratios) means that proportionately more children are at risk there than in the developed world. In this article, Rema Hanna and Paulina Oliva delve into climate change’s likely implications for children in developing countries. Such children already face severe challenges, which climate change will likely exacerbate. In particular, most people in developing countries still depend primarily on agriculture as a source of income, and so anything that reduces crop yields—such as excessive heat or rain—is likely to directly threaten the livelihoods of developing-country families and their ability to feed their children. Poor nutrition and economic disruption are likely to lower children’s scholastic achievement or even keep them out of school altogether. Children in developing countries also face more-severe threats from both air and water pollution; from infectious and parasitic diseases carried by insects or contaminated water; and from possible displacement, migration, and violence triggered by climate change. How can we temper the threat to children in developing countries? Hanna and Oliva write that we should design and fund policies to shield children in developing nations from the harm caused by climate change. Such policies might include developing new technologies, inventing more-weather-resistant crops, improving access to clean water, increasing foreign aid during disasters, and offering more assistance to help poor countries expand their safety net programs.
By Pouya Vahmani, Fengpeng Sun, Alex Hall, George Ban-Weiss
Environmental Research Letters
2016
The climate warming effects of accelerated urbanization along with projected global climate change raise an urgent need for sustainable mitigation and adaptation strategies to cool urban climates. Our modeling results show that historical urbanization in the Los Angeles and San Diego metropolitan areas has increased daytime urban air temperature by 1.3 °C, in part due to a weakening of the onshore sea breeze circulation. We find that metropolis-wide adoption of cool roofs can meaningfully offset this daytime warming, reducing temperatures by 0.9 °C relative to a case without cool roofs. Residential cool roofs were responsible for 67% of the cooling. Nocturnal temperature increases of 3.1 °C from urbanization were larger than daytime warming, while nocturnal temperature reductions from cool roofs of 0.5 °C were weaker than corresponding daytime reductions. We further show that cool roof deployment could partially counter the local impacts of global climate change in the Los Angeles metropolitan area. Assuming a scenario in which there are dramatic decreases in greenhouse gas emissions in the 21st century (RCP2.6), mid- and end-of-century temperature increases from global change relative to current climate are similarly reduced by cool roofs from 1.4 °C to 0.6 °C. Assuming a scenario with continued emissions increases throughout the century (RCP8.5), mid-century warming is significantly reduced by cool roofs from 2.0 °C to 1.0 °C. The end-century warming, however, is significantly offset only in small localized areas containing mostly industrial/commercial buildings where cool roofs with the highest albedo are adopted. We conclude that metropolis-wide adoption of cool roofs can play an important role in mitigating the urban heat island effect, and offsetting near-term local warming from global climate change. Global-scale reductions in greenhouse gas emissions are the only way of avoiding long-term warming, however. We further suggest that both climate mitigation and adaptation can be pursued simultaneously using ‘cool photovoltaics’.
By Gale M. Sinatra, Barbara K. Hofer
Federation of Associations in Behavioral & Brain Sciences
2016
The need for public understanding of science is especially critical in today’s society when citizens frequently confront complex, conflicting information on challenging topics. This article presents research on challenges for public understanding of science: In addition to increased scientific literacy (knowledge), people may need to shift epistemic cognition (beliefs about the nature of knowledge) and epistemic trust (beliefs about source credibility) to accept scientific perspectives. The article suggests how educators, media specialists, and scientists who communicate about their work might help address these challenges. Educational implications include (a) teach scientific processes, (b) teach for deeper understanding, (c) promote epistemic cognition, and (d) use instructional scaffolds. Policy recommendations include (a) fund educational research on thinking, (b) emphasize how to think over what to think, (c) support malleable psychological skills and dispositions, (d) avoid presenting “balanced perspectives” when there is scientific consensus, and (e) demand more rigorous teacher preparation standards. All these develop an informed citizenry.
By Kyle Konis
Building and Environment
2016
This paper extends the applicability of emerging frameworks for evaluating the non-visual effects of light through the development of a novel area-based daylighting metric addressing goals of human circadian stimulus and entrainment in buildings. Procedures using annual, climate-based daylight modeling of eye-level light exposures are developed to analyze and map indoor environments in regard to spatial and seasonal changes in the availability of a circadian-effective daylight stimulus. Because the biological effects of light exposure are not instantaneous, a novel approach is developed to assess the duration of an effective stimulus on a daily basis, as well as the frequency an effective stimulus is present over the course of a year. Results can be used to identify and visually examine building zones where long-term occupancy may lead to disruption of the circadian system in the absence of supplemental electrical lighting capable of effective circadian stimulus. The metric and visualization techniques are implemented in a parametric, simulation-based workflow utilizing publicly available software tools. The workflow can be used to assess and differentiate the performance of various daylighting strategies during the design phases of a project, or to examine existing spaces. The applicability of the workflow is demonstrated using two example models: a portable school classroom, and a generic open-plan commercial office floor plate.
By Rose Yu, Andrew Gelfand, Suju Rajan, Cyrus Shahabi, Yan Liu
Proceedings of the Ninth ACM International Conference on Web Search and Data Mining
2016
Discovering latent structures in spatial data is of critical importance to understanding the user behavior of location based services. In this paper, we study the problem of geographic segmentation of spatial data, which involves dividing a collection of observations into distinct geo-spatial regions and uncovering abstract correlation structures in the data. We introduce a novel, Latent Poisson Factor (LPF) model to describe spatial count data. The model describes the spatial counts as a Poisson distribution with a mean that factors over a joint item-location latent space. The latent factors are constrained with weak labels to help uncover interesting spatial dependencies. We study the LPF model on a mobile app usage data set and a news article readership data set. We empirically demonstrate its effectiveness on a variety of prediction tasks on these two data sets.
