Support vector regression （SVR） method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study...Support vector regression （SVR） method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study presents four SVR models by selecting linear, radial basis, spline, and polynomial functions as kernels, respectively for the prediction of urban dust fall levels. The inputs of the models are identified as industrial coal consumption, population density, traffic flow coefficient, and shopping density coefficient. The training and testing results show that the SVR model with radial basis kernel performs better than the other three both in the training and testing processes. In addition, a number of scenario analyses reveal that the most suitable parameters （insensitive loss function e, the parameter to reduce the influence of error C, and discrete level or average distribution of parameters σ） are 0.001, 0.5, and 2 000, respectively.展开更多
The present study aims to better understand the relationship between energy intensity and its determinants including energy price, technological progress, economic structure, and energy mix using the autoregressive di...The present study aims to better understand the relationship between energy intensity and its determinants including energy price, technological progress, economic structure, and energy mix using the autoregressive distributed lag(ARDL) bounds approach and vector error correction model technique. Based on China's time series over 1985-2014, the ARDL bounds approach yields empirical evidence that confirms the existence of long run relationship between energy price, technological progress, economic structure, energy mix, and energy intensity. The results show that technological progress is an important driver for the declining energy intensity in short and long run. Energy price has not been demonstrated as an important role in decreasing energy intensity in the short run. The high share of coal use in total energy use may be responsible for China's high energy intensity.However, the relative change in economic sectors plays a minor role in energy intensity reduction during the past years. In the long run, technological progress, energy mix and energy prices Granger cause energy intensity, but not vice versa except for the energy mix.展开更多
基金Projects(2007JT3018, 2008JT1013, 2009FJ4056) supported by the Key Project in Hunan Science and Technology Program, ChinaProject(20090161120014) supported by the New Teachers Sustentation Fund in Doctoral Program, Ministry of Education, China
文摘Support vector regression （SVR） method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study presents four SVR models by selecting linear, radial basis, spline, and polynomial functions as kernels, respectively for the prediction of urban dust fall levels. The inputs of the models are identified as industrial coal consumption, population density, traffic flow coefficient, and shopping density coefficient. The training and testing results show that the SVR model with radial basis kernel performs better than the other three both in the training and testing processes. In addition, a number of scenario analyses reveal that the most suitable parameters （insensitive loss function e, the parameter to reduce the influence of error C, and discrete level or average distribution of parameters σ） are 0.001, 0.5, and 2 000, respectively.
文摘The present study aims to better understand the relationship between energy intensity and its determinants including energy price, technological progress, economic structure, and energy mix using the autoregressive distributed lag(ARDL) bounds approach and vector error correction model technique. Based on China's time series over 1985-2014, the ARDL bounds approach yields empirical evidence that confirms the existence of long run relationship between energy price, technological progress, economic structure, energy mix, and energy intensity. The results show that technological progress is an important driver for the declining energy intensity in short and long run. Energy price has not been demonstrated as an important role in decreasing energy intensity in the short run. The high share of coal use in total energy use may be responsible for China's high energy intensity.However, the relative change in economic sectors plays a minor role in energy intensity reduction during the past years. In the long run, technological progress, energy mix and energy prices Granger cause energy intensity, but not vice versa except for the energy mix.
