A dynamic analysis of air pollution emissions in China: Evidence from nonparametric additive regression models

PM_2.5 emissions not only have serious adverse health effects, but also impede transportation activities, especially in air and highway transport. As a result, PM_2.5 emissions have become a public policy concern in China in recent years. Currently, the vast majority of existing researches on PM_2.5 are based on natural science perspective. Very few economic studies on the subject have been conducted with linear models. This paper adopts provincial panel data from 2001 to 2012, and uses the STIRPAT model and nonparametric additive regression models to examine the key driving forces of PM_2.5 emissions in China. The results show that the nonlinear effect of economic growth on PM_2.5 emissions is consistent with the Environmental Kuznets Curve (EKC) hypothesis. The nonlinear impact of urbanization exhibits an inverted “U-shaped” pattern due to the rapid development of urban real estate in the early stages and the strengthening of environmental protection measures in the latter stage. Coal consumption follows an inverted “U-shaped” relationship with PM_2.5 emissions owing to massive coal consumption at the beginning and efforts to optimize the energy structure as well as technological progress in clean energy in the latter stages. The nonlinear inverted “U-shaped” impact of private vehicles may be due to the different roles of scale, structural and technical effects at different stages. However, energy efficiency improvement follows a positive “U-shaped” pattern in relation to PM_2.5 emissions because of differences in the scale of the economy and the speed of technological progress at different times. As a result, the differential dynamic effects of the driving forces of PM_2.5 emissions at different times should be taken into consideration when initiating policies to reduce PM_2.5 emissions in China.     

Negative binomial additive models

The generalized additive model is extended to handle negative binomial responses. The extension is complicated by the fact that the negative binomial distribution has two parameters and is not in the exponential family. The methodology is applied to data involving DNA adduct counts and smoking variables among ex-smokers with lung cancer. A more detailed investigation is made of the parametric relationship between the number of adducts and years since quitting while retaining a smooth relationship between adducts and the other covariates.     

Bayesian nonparametric quantile process regression and estimation of marginal quantile effects

Flexible estimation of multiple conditional quantiles is of interest in numerous applications, such as studying the effect of pregnancy-related factors on low and high birth weight. We propose a Bayesian nonparametric method to simultaneously estimate noncrossing, nonlinear quantile curves. We expand the conditional distribution function of the response in I-spline basis functions where the covariate-dependent coefficients are modeled using neural networks. By leveraging the approximation power of splines and neural networks, our model can approximate any continuous quantile function. Compared to existing models, our model estimates all rather than a finite subset of quantiles, scales well to high dimensions, and accounts for estimation uncertainty. While the model is arbitrarily flexible, interpretable marginal quantile effects are estimated using accumulative local effect plots and variable importance measures. A simulation study shows that our model can better recover quantiles of the response distribution when the data are sparse, and an analysis of birth weight data is presented.     

Bayesian nonparametric functional data analysis through density estimation

In many modern experimental settings, observations are obtainedin the form of functions and interest focuses on inferencesabout a collection of such functions. We propose a hierarchicalmodel that allows us simultaneously to estimate multiple curvesnonparametrically by using dependent Dirichlet process mixturesof Gaussian distributions to characterize the joint distributionof predictors and outcomes. Function estimates are then inducedthrough the conditional distribution of the outcome given thepredictors. The resulting approach allows for flexible estimationand clustering, while borrowing information across curves. Wealso show that the function estimates we obtain are consistenton the space of integrable functions. As an illustration, weconsider an application to the analysis of conductivity andtemperature at depth data in the north Atlantic.