No abstract

No abstract

No abstract

No abstract

No abstract

No abstract

While previous study has confirmed significant correlation between infrastructure construction and air quality, little is known about the nature of the relationship. In this paper, we intend to fill this gap by using the Panel Smooth Transition Regression (PSTR) model to discuss the nonlinear relationship between transportation infrastructure construction and air quality. The panel data includes 280 cities in China for the period 2000-2017. We find that the transportation infrastructure investment is positively correlated to the air quality when the GDP per capita is below RMB 7151 or the number of motor vehicle population per capita is below 37 (vehicles per 10,000 persons) where the model is in the lower regime, and that the transportation infrastructure investment is negatively correlated to the air quality when the GDP per capita is greater than RMB 7151 or the number of motor vehicle population per capita is larger than 37 (vehicles per 10,000 persons) where the model is in the upper regime. The empirical results of the three sub-samples, including eastern, western and central regions, are similar to that of the national level. Furthermore, increasing transportation infrastructure investment is conducive to improving air quality. Urban bus services, green area, population density, wind speed and rainfall are also conducive to reducing air pollution, but the role of environmental regulation is not significant. After adding the instrumental variable (urban built-up area), the conclusions are further supported. Finally, relevant policy recommendations for reducing air pollution are proposed based on the empirical results.

In this paper, we establish VAR model to study the issue of household energy consumption (HEC) in China with status quo analysis of that from 1980 to 2009. Firstly, based on the previous literature of domestic and foreign, the main factors affecting HEC are derived. Besides, result from testing suggests that consuming capacity, population and structure are the leading power to determine HEC. Further, BVAR model is also introduced into the analytical framework to overcome the exceeding sample-size and overfitting existing in VAR. What's more, changes in energy consumption in the period of "the 12th five year plan" are forecasted that way; meanwhile, we do the same work via ARIMA with the historical data information itself. At the end of this analysis, comparison is made between the results from both the BVAR and ARIMA models to justify the reasonableness of BVAR in this paper.