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Copyright of Canadian Foreign Policy Journal (CFPJ) is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Empirical studies of the EKC hypothesis may be very sensitive to datasets, specifications, and functional forms. The aim of this paper is to investigate the long-run relationship among CO 2 emissions, real GDP, and energy consumption using a panel of 9 advanced economies from 1870 to 2008 using both parametric and semi-parametric additive models. While at the panel level the results provide support to the Environmental Kuznets Curve (EKC) only in the post-1950s period, at the individual country level the inverted U-shaped relationship between CO 2 and real GDP is validated for a subset of countries only. However, when a semi-parametric regression framework is applied an inverse U-shaped pattern becomes clear for all countries of the sample, except Canada. Empirical findings indicate that relaxing the restrictions associated with parametric regression models may be critical for the question of investigating the existence of the EKC. • Panel data results provide support to the EKC only in the post-1950s period. • Semi-parametric regressions reveal an inverse U-shaped pattern for all countries, except Canada • An effective policy to reduce CO 2 emissions should encourage innovation activities in low-emission technologies. [ABSTRACT FROM AUTHOR]

The decrease and increase in temperature between neighboring days will have obvious influence on air quality and public health. Based on generalized additive model, this paper aims to examine the seasonal effects of temperature fluctuations on air quality index (AQI) and respiratory disease (RD) during 2008-2012 in Beijing. The results show that the impact of decrease and increase in temperature on AQI and RD varies in different seasons. A large decrease in temperature results in the increase in AQI and RD only in the cold season. At the same time, compared with cold season, larger increased effects of small increase in temperature are observed on AQI and RD in warm season. For a large increase in temperature, a larger impact on AQI is observed in the warm season compared with cold season, while a larger impact on RD is observed in the cold season contrarily. Furthermore, extremely large decrease in temperature (>7 °C) results in the similar impact on AQI in the warm and cold season. Extremely large increase in temperature (>7 °C) has the similar influence on AQI and RD for both warm and cold season. Compared with small and large increase in temperature, extremely large increase in temperature (>7 °C) results in the largest influence on AQI and RD. Our results suggest that the air quality and public health in Beijing are significantly influenced by decrease and increase in temperature in different seasons. [ABSTRACT FROM AUTHOR]

Our objective was to characterize the association between body mass index (BMI) and childhood asthma while adjusting for individual and neighborhood socioeconomic factors. Data were obtained from 3,804 students 10 to 11 years of age in Nova Scotia, Canada. Asthma was defined as parent-reported doctor-diagnosed asthma or bronchitis. Smoothed curves suggested a linear association between BMI and asthma with a 6 % increase in prevalence per unit increase of BMI. This association was independent of allergies, sex, and socioeconomic factors. Girls from socioeconomically disadvantaged neighborhoods were less likely to be asthmatic as were boys from well-educated and wealthy families. [ABSTRACT FROM AUTHOR]

To investigate the health effects of air pollution via a partially linear model, one must choose an appropriate amount of smoothing for accurate estimation of the linear pollution effects. This choice is complicated by the dependencies between the various covariates and by the potential residual correlation, Most existing approaches to making this choice are inadequate, as they neither target accurate estimation of the linear pollutant effects nor handle residual correlation. In this paper, we illustrate two new adaptive and objective methods for determining an appropriate amount of smoothing. We construct valid confidence intervals for the linear pollutant effects, intervals that account for residual correlation. We use our inferential methods to investigate the same-day effects of PM10 on daily mortality in two data sets for the period 1994 to 1996: one collected in Mexico City, an urban area with high levels of air pollution, and the other collected in Vancouver. British Columbia. an urban area with low levels of air pollution. For Mexico City, our methodology does not detect a PM10 effect. In contrast, for Vancouver, a PM10 effect corresponding to an expected 2.4% increase (95% confidence interval ranging from 0.0% to 4,7% in daily mortality for every 10.µg/m³ increase in PM10 is identified. [ABSTRACT FROM AUTHOR]

Abstract: We demonstrate the potential of using least-squares regression, generalized additive model, tree-based model, and neural network model on layers of environmental data grids for mapping site index in a case study. Grids of numerical environmental variables represented layered data, and a sparse site index plot network was located in the grids. Site index data were based on stem analysis (observed height at the index age of 50 years) of 431 lodgepole pine trees in 88 sample plots. The plots were established in a 17,460km2 boreal mixedwood forest of Alberta, Canada dominated by mature and over-mature stands. The generalized additive model presented a better fit and better adaptability to extreme data (i.e., mature stands) than the least squares nonlinear and other nonparametric techniques, such as the tree-based model and neural network model. Among the four models tested, nonlinear regression is of the data modeling culture, which assumes a stochastic data to relate productivity to environmental variables, and such models are optimized for estimation. Other three models belong to the algorithm modeling culture, which treat the relationship between productivity and independent variables as an unknown black box and try to find a function between them; therefore, these models are more suitable for prediction purpose. Implications for biophysical site index modelling with extreme data are discussed. [Copyright &y& Elsevier]

Environmental noise can cause important cardiovascular effects, stress and sleep disturbance. The development of appropriate methods to estimate noise exposure within a single urban area remains a challenging task, due to the presence of various transportation noise sources (road, rail, and aircraft). In this study, we developed a land-use regression (LUR) approach using a Generalized Additive Model (GAM) for LA eq (equivalent noise level) to capture the spatial variability of noise levels in Toronto, Canada. Four different model formulations were proposed based on continuous 20-min noise measurements at 92 sites and a leave one out cross-validation (LOOCV). Models where coefficients for variables considered as noise sources were forced to be positive, led to the development of more realistic exposure surfaces. Three different measures were used to assess the models; adjusted R 2 (0.44-0.64), deviance (51-72%) and Akaike information criterion (AIC) (469.2-434.6). When comparing exposures derived from the four approaches to personal exposures from a panel study, we observed that all approaches performed very similarly, with values for the Fractional mean bias (FB), normalized mean square error (NMSE), and normalized absolute difference (NAD) very close to 0. Finally, we compared the noise surfaces with data collected from a previous campaign consisting of 1-week measurements at 200 fixed sites in Toronto and observed that the strongest correlations occurred between our predictions and measured noise levels along major roads and highway collectors. Our validation against long-term measurements and panel data demonstrates that manual modifications brought to the models were able to reduce bias in model predictions and achieve a wider range of exposures, comparable with measurement data., (Copyright © 2018 Elsevier Inc. All rights reserved.)