Ningbo University of Finance & Economics, College of Digital Technology and Engineering, Ningbo, Zhejiang, 315175, China
As an important industry in China's national economy, the logistics industry has an increasing proportion of carbon emissions year by year. In order to achieve China's carbon emission reduction target, it is urgent to study the emission reduction path of the logistics industry. In this paper, GM and ARIMA are combined in a weighted way to improve the prediction accuracy, uses the GM-ARIMA method to select 12 influencing factors such as population, per capita GDP, and per capita urban road area to establish a carbon emission prediction model for China's logistics industry. And take the carbon emission data from 2000 to 2020 as an example to conduct empirical research. The results show that: Compared with the GM (1,1), the GM-ARIMA prediction has a 4.33% reduction in error, which has a better prediction effect on the carbon emissions of the logistics industry. The carbon emission of China's logistics industry will peak at 967.45Mt CO2 in 2028. To achieve the carbon peaking target, China should introduce more emission reduction policies related to logistics.
Logistics Industry, Carbon Peak, Carbon Emission, GM - ARIMA Model
Junhao Dong. Influencing Factors and Forecasting of Carbon Emissions in China's Logistics Industry——Based on GM-ARIMA Model. Academic Journal of Environment & Earth Science (2022) Vol. 4 Issue 2: 1-4. https://doi.org/10.25236/AJEE.2022.040201.
 Xu Li, Qu Jiansheng, Li Hengji, Zeng Jingjing, Zhang Hongfen. Current Situation Analysis and Forecast of Carbon Emissions from Chinese Residents' Energy Consumption [J]. Ecological Economy, 2019,35(01):19-23+29.
 Liu Longzheng, Pan Zhaoan. Research on the driving factors of carbon emissions in China's logistics industry [J]. Business Research, 2012(07): 189-196. DOI: 10.13902/j.cnki.syyj. 2012.07.022.
 Du Qiang, Chen Qiao, Lu Ning. Prediction of China's future carbon emissions based on the improved IPAT model [J]. Journal of Environmental Science, 2012, 32(09): 2294-2302. DOI: 10.13671/j.hjkxxb. 2012.09.020.
 Song Jiekun, Zhang Yu. Prediction of my country's carbon emission scenarios based on BP neural network [J]. Science, Technology and Engineering, 2011, 11(17): 4108-4111+4116
 Ma Yueyue, Wang Weiguo. Analysis of carbon emission characteristics and influencing factors of China's logistics industry - based on LMDI decomposition technology [J]. Practice and Understanding of Mathematics, 2013, 43(10): 31-42.