Regional allocation of carbon emission quotas in China under the total control target.

The allocation of provincial carbon emission quotas under total amount control is an effective way for China to achieve its carbon peak and neutrality targets. Firstly, in order to study the factors influencing China's carbon emissions, the expanded STIRPAT model was constructed; and combined with the scenario analysis method, the total of national carbon emission quota under the peak scenario was predicted. Then, the index system of regional carbon quota allocation is constructed based on the principles of equity, efficiency, feasibility, and sustainability; and the allocation weight is determined by the grey correlation analysis method. Finally, the total carbon emission quota under the peak scenario is distributed in 30 provinces of China, and the future carbon emission space is also analyzed. The results show that: (1) only under the low-carbon development scenario, can China reach the peak target by 2030, with a peak carbon of about 14,080.31 million tons; (2) under the comprehensive allocation principle, China's provincial carbon quota allocation is characterized by high levels in the west and low in the east. Among them, Shanghai and Jiangsu receive fewer quotas, while Yunnan, Guangxi, and Guizhou receive more; and (3) the future carbon emission space for the entire country is modestly surplus, with regional variations. Whereas Hainan, Yunnan, and Guangxi have surpluses, Shandong, Inner Mongolia, and Liaoning have significant deficits.

Analysis on the influencing factors of carbon emission in China's logistics industry based on LMDI method.

Logistics industry, as one of the industries with high carbon emissions, has become the focus of all parties while developing rapidly. Based on panel data, the total carbon emissions of logistics industry in China from 2000 to 2016 were calculated by using IPCC method. On this basis, the LMDI decomposition model is used to decompose the influencing factors of carbon emission from five aspects: carbon emission coefficient, energy intensity, energy structure, economic level and population size. Finally, using MATLAB tools to analyze the data. Results show that economic growth is the main factor to promote carbon emissions in logistics industry, followed by the positive impact of population size and energy structure. The carbon factor effect is negligible, energy intensity is the main restraining factor. The effect of various factors on carbon emissions varies from time to time. Therefore, we should speed up the adjustment of energy structure, reduce the dependence on high­carbon emissions such as coal, optimize transportation system and improve logistics efficiency; at the same time, strengthen the cooperation between the government and enterprises, formulate feasible policies and measures, and do a good job in top-level planning and design of logistics, make our logistics industry embark on the road of low-carbon development.