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There is now widespread agreement that the world community must actively combat climate change and advance green and low-carbon development. In order to deal with the issue of carbon leakage caused by the rising cost of industrial production as a result of policies to reduce greenhouse gas (GHG) emissions, the EU intends to implement the Carbon Border Adjustment Mechanism (CBAM) in its entirety starting in 2026, the pilot phase of which will begin in 2023. This shows the progressive emergence of a new international trade system driven by “climate change actions”, “carbon peaking”, and “carbon neutrality”, which will have a broad and far-reaching impact on China’s foreign trade industry. As more industries are being covered by the EU’s CBAM, it will exert a negative impact on the social welfare and export of China, the largest trading partner of the EU, even though the existing mechanism has only limited economic impact on China’s energy industry. This paper presents policy proposals to actively address the issues and effects of the EU’s “carbon tariff” by methodically analyzing the EU CBAM’s operation process and, via the development of models, determining the mechanism’s influence on social welfare, carbon emissions, and China’s exports.

Aerosol lidar network can play an important role on revealing structural characteristics of atmospheric boundary layer, urban heat island effect, spatial distribution of aerosols, especially monitoring atmospheric pollution in a megacity. To fulfill the need of monitoring and numerical forecast of atmospheric pollution, an aerosol lidar network is proposed by China Meteorological Administration, which serves as an important part of "MegaCity Experiment on Integrated Meteorological Observation in China" (MEMO). To ensure high standard of data quality and traceability of measurement error, an inter-comparison campaign, dedicated for quality assessment of lidar systems from different institutes and manufacturers, was designed and performed at Beijing Southern Suburb Observatory in September 2021. Six Mie-Rayleigh lidar systems at 1064 nm were involved in this campaign. The strategies for lidar self-evaluations and inter-comparisons were predefined. A lidar system at 1064 nm, which was developed by Atmospheric Remote Sensing group at Wuhan University, was selected as the reference lidar system after passing all self-evaluations quality checks in a strict way. The reference lidar system serves as the corner stone for evaluating the performance of other lidar systems. After using the self-test of Rayleigh fit and signalto- noise evaluation for each individual lidar system to fast check the data quality, the range-corrected signal and backscatter coefficient obtained from all the lidar systems were inter-compared with a reference lidar system. In the end, the lidar systems were quality assured, of which the standard deviation of range-corrected signal can be controlled within 5 % at 500–2000 m while 10 % at 2000–5000 m. For the derived aerosol backscatter coefficients, standard deviations can be controlled within 10 % at 500–2000 m and 2000–5000 m. The quality assurance strategy lays down a solid basis for atmospheric lidar at near-infrared wavelength and will be applied in Chinese lidar network development.

A compact polarization Raman lidar has been designed and constructed for using it for atmospheric correction measurements during satellite optical sensor calibration in areas with high altitude and extremely low aerosol loading. The parameters of this lidar, such as laser wavelength, telescope diameter and interference filter bandwidth, were simulated and optimized for the best observation performance. The instrument has low weight, is small in size, and requires air cooling instead of commonly used water-cooling of the laser. Thus, the instrument is suitable for autonomous operation in remote sites. The lidar prototype was installed in Lijiang (26°43’ N, 100°01’ E), China, a potential observation site for calibrations of optical sensors of satellites. This observation site has been shown to be an appropriate place for remote sensing and satellite calibration activities with low aerosol loading, thin air and a comparably high proportion of cloud-free days. A field campaign carried out between November 2019 and April 2020 allowed for thoroughly testing the instruments. The results of test observations show that complete overlap between emitted laser beam and field-of-view of the receiver unit is achieved at relatively low heights above ground. The measurement accuracy is comparably high. Thus, this instrument is suitable for operating in areas with relatively clean atmospheric conditions.

Aiming at the problem of data imbalance when in using deep learning model for traffic recognition tasks, a method of using Wasserstein Generative Adversarial Network (WGAN) to generate minority samples based on the image of the original traffic data packets is proposed to achieve a small number of data categories expansion to solve the problem of data imbalance. Firstly, through data preprocessing, the original traffic PCAP data in the dataset is segmented, filled, and mapped into grayscale pictures according to the flow unit. Then, the balance of dataset is achieved by using traditional random over sampling, WGAN confrontation network generation technology, ordinary GAN generation technology and synthetic minority oversampling technology. Finally, public datasets USTC- TFC2016 and CICIDS2017 are adopted to classify the unbalanced dataset and the balanced dataset on classic deep model CNN, and three evaluation indicators of precision, recall, and f1 are applied to evaluate classification effect. Experimental results show that the dataset balanced by the WGAN model is better than the ordinary GAN generation method, traditional oversampling method and the synthesis of the minority class sampling technique method under the same classification model.