This paper uses the super-efficiency DEA model to study the super-efficiency energy efficiency values of 30 provinces in China from 2006 to 2018, compares the differences between different regions, and verifies the impact of different environmental regulatory tools on energy efficiency. The results show that the regions with the highest energy efficiency in China’s provinces are, in order, the eastern region, the central region, and the western region. At the same time, the impact of different environmental regulatory tools on energy efficiency is quite different. The number of environmental administrative penalties has a negative impact on China’s energy efficiency, that is, increased number of environmental administrative penalties will reduce China's energy efficiency; market-encouraging environmental regulations tools impact on China's energy efficiency show a positive correlation, which means that market-encouraging environmental regulation tools are conducive to achieving a win-win goal of environmental regulation and energy efficiency in China. The voluntary environmental regulation tool has a negative correlation with energy efficiency, but it is not statistically significant, indicating that it is not the main factor affecting energy efficiency. In terms of other influencing variables, the level of economic development and technological progress are positively related to energy efficiency. The proportion of the secondary industry in regional GDP is positively related to energy efficiency, the relationship between energy consumption structure and energy efficiency is negative correlation, the degree of opening to the outside world has a negative correlation to China's energy efficiency.

Based on the energy consumption data of the logistics industry in 30 provinces and cities in China, this paper uses the carbon emission accounting method of IPCC to estimate the total carbon emissions of the logistics industry in China from 2010 to 2019, and introduces the carbon emission Theil index. The Logarithmic Mean Divisia Index (LMDI) model is used to measure the regional differences in carbon emissions of China’s logistics industry and decompose the influencing factors. The research results show that the total carbon emissions of China’s logistics industry have increased significantly, and the total carbon emissions and growth rates of the eastern region are significantly higher than those of the central and western regions. On the whole, the inter-provincial differences in carbon emissions of China’s logistics industry have not changed substantially and have shown a downward trend. Intra-regional differences are the main reason for the overall difference in carbon emissions in China’s logistics industry, and inter-regional differences have little impact on the overall difference. Among the three major regions, the eastern region has the largest inter-provincial carbon emissions difference, followed closely by the central region, with the western region having the smallest difference. Furthermore, the difference in carbon emissions between the eastern, central, and western regions is expanding, the results of the LMDI model show that the economic development effect is the most important positive effect of the logistics industry’s carbon emissions in terms of the national average. The population scale effect is second, and the energy intensity effect is the main negative effect, followed by the energy structure effect. In terms of regional differences, the energy intensity effect is the most obvious in the eastern region, and the population scale effect has a relatively greater impact on the western region. The effect of economic development on the eastern and western regions is higher than in the central region.

Three Gorges project; cascade hub; navigation organization; intelligent navigation; environmental-friendly maintenance

Recently, various machine learning techniques have been applied to solve online portfolio optimization (OLPO) problems. These approaches typically explore aggressive strategies to gain excess returns due to the existence of irrational phenomena in financial markets. However, existing aggressive OLPO strategies rarely consider the downside risk and lack effective trend representation, which leads to poor prediction performance and large investment losses in certain market environments. Besides, prediction with a single model is often unstable and sensitive to the noises and outliers, and the subsequent selection of optimal parameters also become obstacles to accurate estimation. To overcome these drawbacks, this paper proposes a novel kernel-based aggregating learning (KAL) system for OLPO. It includes a two-step price prediction scheme to improve the accuracy and robustness of the estimation. Specifically, a component price estimator is built by exploiting additional indicator information and the nonstationary nature of financial time series, and then an aggregating learning method is presented to combine multiple component estimators following different principles. Next, this paper conducts an enhanced tracking system by introducing a kernel-based increasing factor to maximize the future wealth of next period. At last, an online learning algorithm is designed to solve the system objective, which is suitable for large-scale and time-limited situations. Experimental results on several benchmark datasets from diverse real markets show that KAL outperforms other state-of-the-art systems in cumulative wealth and some risk-adjusted metrics. Meanwhile, it can withstand certain transaction costs.