The Experts below are selected from a list of 596340 Experts worldwide ranked by ideXlab platform
Karim Ben Khediri - One of the best experts on this subject based on the ideXlab platform.
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financial development and Environmental Quality in uae cointegration with structural breaks
Renewable & Sustainable Energy Reviews, 2016Co-Authors: Lanouar Charfeddine, Karim Ben KhediriAbstract:This study extends the recent work of Shahbaz et al. (2014) by implementing recent unit root tests with multiple structural breaks and regime-switching cointegration techniques considering for one and two unknown regime shifts to investigate the relationship between carbon dioxide emissions, electricity consumption, economic growth, financial development, trade openness and urbanization for the UAE over the period spanning from 1975 to 2011. Our results confirm the existence of Environmental Kuznets Curve (EKC). Moreover, we find an inverted U-shaped relationship between financial development and CO2 emissions. We also find that electricity consumption, urbanization and trade openness contribute to improve Environmental Quality.
Chen Ping - One of the best experts on this subject based on the ideXlab platform.
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analysis on the constitution of Environmental Quality standard system for surface water in japan
Environmental Monitoring in China, 2011Co-Authors: Chen PingAbstract:This article briefly introduced the current constitution of Environmental Quality standard system for surface water in Japan,mainly included the content and function of environment Quality standards system for surface water,as well as monitoring and investigating method,and evaluation method.
David R Jacobs - One of the best experts on this subject based on the ideXlab platform.
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improving indoor Environmental Quality for public health impediments and policy recommendations
Environmental Health Perspectives, 2007Co-Authors: David R Jacobs, Clifford S Mitchell, D Miller, Meryl H KarolAbstract:BackgroundPeople in modern societies spend more than 90% of their time indoors. Hence, indoor Environmental Quality (IEQ) has a significant impact on public health. In this article we describe heal...
Danelle T Lobdell - One of the best experts on this subject based on the ideXlab platform.
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county level cumulative Environmental Quality associated with cancer incidence
Cancer, 2017Co-Authors: Jyotsna S Jagai, Lynne C Messer, Kristen M Rappazzo, Christine L Gray, Shannon Grabich, Danelle T LobdellAbstract:BACKGROUND Individual Environmental exposures are associated with cancer development; however, Environmental exposures occur simultaneously. The Environmental Quality Index (EQI) is a county-level measure of cumulative Environmental exposures that occur in 5 domains. METHODS The EQI was linked to county-level annual age-adjusted cancer incidence rates from the Surveillance, Epidemiology, and End Results (SEER) Program state cancer profiles. All-site cancer and the top 3 site-specific cancers for male and female subjects were considered. Incident rate differences (IRDs; annual rate difference per 100,000 persons) and 95% confidence intervals (CIs) were estimated using fixed-slope, random intercept multilevel linear regression models. Associations were assessed with domain-specific indices and analyses were stratified by rural/urban status. RESULTS Comparing the highest quintile/poorest Environmental Quality with the lowest quintile/best Environmental Quality for overall EQI, all-site county-level cancer incidence rate was positively associated with poor Environmental Quality overall (IRD, 38.55; 95% CI, 29.57-47.53) and for male (IRD, 32.60; 95% CI, 16.28-48.91) and female (IRD, 30.34; 95% CI, 20.47-40.21) subjects, indicating a potential increase in cancer incidence with decreasing Environmental Quality. Rural/urban stratified models demonstrated positive associations comparing the highest with the lowest quintiles for all strata, except the thinly populated/rural stratum and in the metropolitan/urbanized stratum. Prostate and breast cancer demonstrated the strongest positive associations with poor Environmental Quality. CONCLUSION We observed strong positive associations between the EQI and all-site cancer incidence rates, and associations differed by rural/urban status and Environmental domain. Research focusing on single Environmental exposures in cancer development may not address the broader Environmental context in which cancers develop, and future research should address cumulative Environmental exposures. Cancer 2017;123:2901–8. © 2017 American Cancer Society.
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construction of an Environmental Quality index for public health research
Environmental Health, 2014Co-Authors: Lynne C Messer, Jyotsna S Jagai, Kristen M Rappazzo, Danelle T LobdellAbstract:Background: A more comprehensive estimate of Environmental Quality would improve our understanding of the relationship between Environmental conditions and human health. An Environmental Quality index (EQI) for all counties in the U.S. was developed. Methods: The EQI was developed in four parts: domain identification; data source acquisition; variable construction; and data reduction. Five Environmental domains (air, water, land, built and sociodemographic) were recognized. Within each domain, data sources were identified; each was temporally (years 2000–2005) and geographically (county) restricted. Variables were constructed for each domain and assessed for missingness, collinearity, and normality. Domain-specific data reduction was accomplished using principal components analysis (PCA), resulting in domain-specific indices. Domain-specific indices were then combined into an overall EQI using PCA. In each PCA procedure, the first principal component was retained. Both domain-specific indices and overall EQI were stratified by four rural–urban continuum codes (RUCC). Higher values for each index were set to correspond to areas with poorer Environmental Quality. Results: Concentrations of included variables differed across rural–urban strata, as did within-domain variable loadings, and domain index loadings for the EQI. In general, higher values of the air and sociodemographic indices were found in the more metropolitan areas and the most thinly populated areas have the lowest values of each of the domain indices. The less-urbanized counties (RUCC 3) demonstrated the greatest heterogeneity and range of EQI scores (�4.76, 3.57) while the thinly populated strata (RUCC 4) contained counties with the most positive scores (EQI score ranges from �5.86, 2.52). Conclusion: The EQI holds promise for improving our characterization of the overall environment for public health. The EQI describes the non-residential ambient county-level conditions to which residents are exposed and domain-specific EQI loadings indicate which of the Environmental domains account for the largest portion of the variability in the EQI environment. The EQI was constructed for all counties in the United States, incorporating a variety of data to provide a broad picture of Environmental conditions. We undertook a reproducible approach that primarily utilized publically-available data sources.
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data sources for an Environmental Quality index availability Quality and utility
American Journal of Public Health, 2011Co-Authors: Danelle T Lobdell, Jyotsna S Jagai, Kristen M Rappazzo, Lynne C MesserAbstract:Objectives. An Environmental Quality index (EQI) for all counties in the United States is under development to explore the relationship between Environmental insults and human health. The EQI is potentially useful for investigators researching health disparities to account for other concurrent Environmental conditions. This article focused on the identification and assessment of data sources used in developing the EQI. Data source strengths, limitations, and utility were addressed.Methods. Five domains were identified that contribute to Environmental Quality: air, water, land, built, and sociodemographic environments. An inventory of possible data sources was created. Data sources were evaluated for appropriate spatial and temporal coverage and data Quality.Results. The overall data inventory identified multiple data sources for each domain. From the inventory (187 sources, 617 records), the air, water, land, built environment, and sociodemographic domains retained 2, 9, 7, 4, and 2 data sources for inclu...
Neha Khanna - One of the best experts on this subject based on the ideXlab platform.
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the demand for Environmental Quality and the Environmental kuznets curve hypothesis
Ecological Economics, 2004Co-Authors: Neha Khanna, Florenz PlassmannAbstract:Abstract Household demand for better Environmental Quality is the key factor in the long-term global applicability of the Environmental Kuznets Curve (EKC) hypothesis. We argue that, for given consumer preferences, the threshold income level at which the EKC turns downwards or the equilibrium income elasticity changes sign from positive to negative depends on the ability to spatially separate production and consumption. We test our hypothesis by estimating the equilibrium income elasticities of five pollutants, using 1990 data for the United States. We find that the change in sign occurs at lower income levels for pollutants for which spatial separation is relatively easy as compared to pollutants for which spatial separation is difficult. Our results suggest that even high-income households in the United States have not yet reached the income level at which their demand for better Environmental Quality is high enough to cause the income–pollution relationship to turn downwards for all the pollutants that we analyzed.
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measuring Environmental Quality an index of pollution
Ecological Economics, 2000Co-Authors: Neha KhannaAbstract:Abstract This paper develops an index of pollution based on the epidemiological dose-response function associated with each pollutant, and the welfare losses due to exposure to pollution. The probability of damage is translated into welfare losses, which provides the common metric required for aggregation. Isopollution surfaces may then be used to compare Environmental Quality over time and space. An Air Pollution Index (API) is computed using 1997 data for the criteria pollutants under the Clean Air Act (CAA). The results are compared with the EPA's Pollutant Standards Index (PSI). Two significant differences emerge: unlike the PSI, the API facilitates a detailed ranking of regions by air Quality and API values may contradict PSI results. Some regions with PSI values of 100–200 are considered less polluted under the proposed methodology than those with PSI values between 50 and 100. The key reason for the difference is that PSI values are determined entirely by the gas with the highest relative concentration whereas the API value is based on the ambient concentrations of all pollutants.
