Hydropower, which is the most extensively used renewable energy, is sensitive to the change of streamflow under the great impact of precipitation. According to the relationship between the hydropower station generation and local precipitation, the impact of precipitation on hydropower can be analyzed. In this paper, the global climate model and regional climate model simulations in precipitation are compared firstly, and the high-resolution precipitation data are then calculated by stepwise clustering analysis statistical downscaling method. Secondly, based on the hydropower potential (HP), the hydropower response model driven by precipitation is established. Finally, the simulated generation of a hydropower station in Dadu River basin is used as a case to validate this proposed model. The results show that precipitation will increase by around 42% from May to August in study region, while it will decrease by 40% in other months in RCP4.5. For different periods of reservoir scheduling, the precipitation will increase by about 40% in the Neutral I and Wet period, while it will decrease by around 30% in other periods, which will lead to the shortening of the peak period of hydropower generation and the peak value will be decreased. Correspondingly, the results show power generation will decrease by around 12% from June to December and increase by around 4% in the rest months. On the other hand, owing to the changes in precipitation, the future power generation will increase by 25% in Neutral I and decrease by 13.5% in other periods, but the total hydropower generation will remain. The results can provide some decision support for future water resources management in Dadu river basin, especially for the planning and operation of hydropower stations.

China’s transport sector is responsible for approximately 10% of national CO₂emissions. In the process of industrialization and urbanization of China, emissions from transport sector would continuously increase. In order to investigate the emissions and reduction potential and provide the policy guidance for policymakers in China’s transport sector, this study decomposed the CO₂emissions using the Kaya identity, calculated the contribution based on the Logarithmic Mean Divisia Index (LMDI) method to explore the underlying determinants of emissions change, and then constructed different scenarios to predict the emissions and estimate the potential of emission reduction in the future. Results indicated that carbon emissions in China’s transport sector have increased from 123.14 Mt in 1995 to 670.76 Mt in 2012. Income effect is the dominant factor that results in the increase of emissions while energy intensity effect is the main driving force to lower carbon emissions. The transportation modal shifting, transportation intensity change, and population growth have the positive but relatively minor impact on emissions. The accumulated emission reduction is expected to be 1825.97 Mt, which is 3 times more than the emissions in 2010. Policy recommendations are thus put forward for future emission reduction.

Since China claimed to achieve carbon emission peak around 2030 in the “China-U.S. Joint Presidential Statement on Climate Change,” whether or not the target can be accomplished has become the focus of discussion. Thus, the aim of this study is to forecast the carbon emissions peak of Hebei Province in China (as a case study) for the period of 2016-2030 through the historical data of 1990-2015 using the STIRPAT model and GA-BP (BP neural network based on genetic algorithm) model. We choose the proportion of coal consumption, population, urbanization rate, energy intensity, per capita GDP (replaced by GDP in the GA-BP model) and the proportion of services as the independent variables, and set 9 scenarios in the light of different increment speeds of these variables during 2016-2030. Results show that the ranges of estimated carbon emission peaks are 784.1635-1,007.2901 million tons in the STIRPAT model and 702.7465- 702.8144 million tons in the GA-BP model, with corresponding peak years all in or before 2030. Moreover, a comparative study of the STIRPAT and GA-BP models reveals that the GA-BP model estimates carbon emissions more accurately than STIRPAT; however, the STIRPAT model is more precise on the prediction of carbon emission peak years.