Presently, the total supply of crude oil is sufficient, but short-term supply and demand imbalances and regional imbalances still exist. The effect of crude oil supply security and price impact cannot be ignored. As the world's largest oil importer, China is highly dependent on foreign oil. Therefore, the fluctuation of international oil prices may impact the development of China's various industries in a significant and differential way. However, because the available data have different frequencies, much of the recent research that addresses the effect of oil prices on industry development need to replace, split, or merge the original data, resulting in loss of the information from the original data. Using the mixed data sampling model (MIDAS(m,K,h)-AR(1)) with the first-order lag autoregressive terms of the interpreted variables, this study builds a mixed data model to investigate the effect of oil price volatility on the output of China's industries. This study expands the extant research by financial market fluctuations and macroeconomic analysis, and at the same time makes short-term predictions on the output of China's seven main industries. The analysis results show that the mixed data regression model brings the original information contained in different frequency data into the model analysis, and utilizes the latest high frequency data of the explanatory variables to perform real-time short-term prediction of low-frequency interpreted variables. This method improves the timeliness of forecasting macroeconomic indicators and the accuracy of short-term forecasts. The empirical results show that the spot price of international crude oil has a significant and differential impact on the outputs of the seven industries in China. Among them, oil price fluctuation has the greatest impact on the output of China's financial industry.

Reducing transportation CO 2 emissions and addressing population characteristic changes are two major challenges facing China, involving various requirements for sustainable economic development. Due to the interdependence of population characteristics and transportation, human activities have become a significant cause of the increase in greenhouse gas levels. Previous studies mainly focused on evaluating the relationship between one-dimensional or multi-dimensional demographic factors and CO 2 emissions, while few studies have reported on the effect of multi-dimensional demographic factors on CO 2 emissions in transportation. Analyzing the relationship between transportation CO 2 emissions is the foundation and key to understanding and reducing overall CO 2 emissions. Therefore, this paper used the STIRPAT model and panel data from 2000 to 2019 to investigate the effect of population characteristics on CO 2 emissions of China’s transportation sector, and further analyzed the effect mechanism and emission effect of population aging on transportation CO 2 emissions. The results show that (1) population aging and population quality restrained CO 2 emissions from transportation, but the negative effects of population aging were indirectly caused by economic growth and transportation demand. And with the aggravation of population aging, the influence on transport CO 2 emissions changed and presented a U-shape. (2) Population living standard on transportation CO 2 emissions exhibited an urban–rural difference, and urban living standard was predominant in transportation CO 2 emissions. Additionally, population growth is under a weakly positive effect on transportation CO 2 emissions. (3) At the regional level, the effect of population aging on transportation CO 2 emissions showed regional differences. In the eastern region, the CO 2 emission coefficient of transportation was 0.0378, but not significant. In central and western regions, the influence coefficient of transportation was 0.6539 and 0.2760, respectively. These findings indicated that policy makers should make relevant recommendations from the perspective of coordinating population policy and energy conservation and emission reduction policy in transportation.

Selection and peer-review under responsibility of the scientific committee of the 13th Int. Conf. on Applied Energy (ICAE2021).