ABSTRACT
This study focuses on the sustainability efficiency of the Chinese transportation system by investigating the relationship between CO2 emission levels and the respective freight and passenger turnovers for each transportation mode from January 1999 to December 2017. A novel Robust Bayesian Stochastic Frontier Analysis (RBSFA) is developed by taking carbon inequality into account. In this model, the aggregated variance/covariance matrix for the three classical distributional assumptions of the inefficiency term-Gamma, Exponential, and Half-Normal-is minimized, yielding lower Deviance Information Criteria when compared to each classical assumption separately. Results are controlled for the impact of major macro-economic variables related to fiscal policy, monetary policy, inflationary pressure, and economic activity. Results indicate that the Chinese transportation system shows high sustainability efficiency with relatively small random fluctuations explained by macro-economic policies. Waterway, railway, and roadway transportation modes improved sustainability efficiency of freight traffic while only the railway transportation mode improved sustainability efficiency of passenger traffic. However, the air transportation mode decreased sustainability efficiency of both freight and passenger traffic. The present research helps in reaching governmental policies based not only on the internal dynamics of carbon inequality among different transportation modes, but also in terms of macro-economic impacts on the Chinese transportation sector.
Subject(s)
Carbon , Transportation , Bayes Theorem , Socioeconomic FactorsABSTRACT
This paper proposes a new method for calibration transfer, which was specifically designed to work with isolated variables, rather than the full spectrum or spectral windows. For this purpose, a univariate procedure is initially employed to correct the spectral measurements of the secondary instrument, given a set of transfer samples. A robust regression technique is then used to obtain a model with low sensitivity with respect to the univariate correction residuals. The proposed method is employed in two case studies involving near infrared spectrometric determination of specific mass, research octane number and naphthenes in gasoline, and moisture and oil in corn. In both cases, better calibration transfer results were obtained in comparison with piecewise direct standardization (PDS). The proposed method should be of a particular value for use with application-targeted instruments that monitor only a small set of spectral variables.
ABSTRACT
El análisis de datos de extinción en experimentos de miedo condicionado ha involucrado, tradicionalmente, el uso de modelos lineales estándar, primordialmente ANOVA de diferencias entre grupos de sujetos sometidos a diferentes protocolos de extinción, manipulaciones farmacológicas o algún otro tratamiento. Aún cuando algunos estudios reportan diferencias individuales en indicadores como tasas de supresión o porcentajes de congelamiento, esas diferencias no son incluidas en el análisis estadístico. Los patrones de respuesta intra-sujeto son entonces promediados usando ventanas temporales de baja resolución, las cuales pueden ignorar esta dinámica del desempeño individual. Este trabajo ilustra un procedimiento analítico alternativo que consta de 2 pasos: estimación de la tendencia para los datos intra-sesión y el análisis de las diferencias entre-grupo usando la tendencia como variable de respuesta. Este procedimiento se pone a prueba usando datos reales de extinción de miedo condicionado, comparando estimaciones de tendencia robusta vía Mínimos Cuadrados Medianos con Mínimos Cuadrados Ordinarios, y comparando las diferencias de grupo usando la pendiente robusta versus la mediana del porcentaje de congelamiento como variable dependiente.
Traditionally , the analysis of extinction data in fear conditioning experimentshas involved the use of standard linear models, mostly ANOVA of between-group differences of subjects that have undergone different extinction protocols, pharmacological manipulations or some other treatment. Although some studies reportindividual differences in quantities such as suppression rates or freezing percentages, these differences are not included in the statistical modeling. Withinsubject response patterns are then averaged using coarse-grain time windows which can overlook these individual performance dynamics. Here we illustrate an alternative analytical procedure consisting of 2 steps: the estimation of a trend for within-session data and analysis of group differences in trend as main outcome. This procedure is tested on real fear-conditioning extinction data, comparing trend estimates via Ordinary Least Squares (OLS) and robust Least Median of Squares (LMS) regression estimates, as well as comparing between-group differences and analyzing mean freezing percentage versus LMS slopes as outcomes.