Logistic regression model is widely used in ecology and in the analysis of social economic systems, because of its good adaptability. In order to improve the measurement accuracy of logistic model, this paper proposes a new method. A compound grey-logistic model is developed to carry out the grey transformation of the original data. Practice shows that the grey transformation data has better simulation accuracy; at the same time, grey transformation can reduce the observation noise of the original data. Mean absolute percentage error index has been used to evaluate the accuracy of prediction model, and information entropy can be used to evaluate the change of information entropy of forecasting data. In this paper, three cases are used to verify the applicability of grey-logistic model. From the perspective of the type of original data, the three cases represent three different data conditions: sufficient data, insufficient data, and fragmentary data. The cases represent different related fields: market share data, economic growth data, and R&D output data. The results show that the proposed grey-logistic method can effectively carry out the population growth analysis.
This paper selected the data from 2010 to 2020 to measure the carbon emissions of the logistics industry in different regions of China, decomposed the influencing factors of carbon emissions in China’s logistics industry based on the LMDI model, and, finally, conducted a decoupling analysis of carbon emissions and the development of the logistics industry. The conclusions are as follows: (1) China’s carbon emission levels vary greatly from region to region, with the highest distribution pattern in the east and the lowest in the west, while the growth rate in the east is also the highest. (2) The level of economic development has the greatest impact on carbon emissions, and it has the effect of promoting carbon emissions in three regions; logistics development effects have the characteristics of first driving and then restraining emissions in the three major regions. The effect of energy intensity has great volatility. The effect of intensity in the eastern region dropped sharply in 2015, with negative effects after that year. Development of the logistics industry has limited the inhibition of carbon emissions in the central and western regions. Although the effect of the energy structure is negative, it failed the significance test. The effects of the energy structure began to show a downward trend in three regions after 2015. (3) The decoupling analysis showed that only 3 provinces are strongly decoupled, 20 provinces are weakly decoupled, and the regional carbon emissions are quite different.
From a global perspective, carbon emissions are a global problem that needs to be solved urgently. At present, 61% of countries have committed to achieving net zero emissions. Compared with industry and construction, the transportation sector has become the focus and challenge for countries to achieve carbon neutrality due to the characteristics of strong mobility, scattered emission sources, and complex social behaviors. Therefore, the issue of carbon emissions in the transportation industry has become the focus of academic attention. This paper first calculates the carbon emission efficiency (CEE) of the regional transportation industry through the super-efficiency SBM model and then evaluates its regional differentiation characteristics through the Theil index, which has important practical significance for reducing regional carbon emissions. The results show that the national transportation CEE average value is 0.612, a relatively low level. The spatial distribution of China’s transportation CEE shows an obvious characteristic of “east highest and west lowest”. The regional differences in the transportation industry CEE are larger than those between regions. The differences in the transportation industry CEE among the eastern, central, and western regions are on the downward trend as a whole, and intra-regional differences are greater than inter-regional. The intra-regional differences cause the overall differences in transportation industry CEE; the eastern region contributed the most to the Theil index, while the central contributed the least. The biggest factor affecting the transportation industry CEE is the regional energy structure, and the smallest factor is the per capita GDP. This research has important reference significance on the target of carbon neutrality.
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