Scenario prediction of carbon peaking in China's logistics industry based on SVR
DOI:
https://doi.org/10.54097/hset.v47i.8220Keywords:
support vector regression, sparrow search optimization algorithm, logistics industry, carbon peaking, scenario analysis.Abstract
As a strategic, basic and leading industry for the development of China's national economy, the logistics industry shoulders the important mission of implementing the national "dual carbon" goal. It is urgent to keep up with the pace of the times and accelerate the green and low-carbon process of the logistics industry. Based on the relevant data from 1998 to 2020, this paper selects 8 indicators such as fixed asset investment in the logistics industry, freight turnover, passenger turnover, freight volume, the number of trucks, PM2.5 concentration and per capita GDP. The scenario analysis method is combined with the SSA-SVR model to establish a scenario prediction model for China's logistics industry's carbon peaking. The research results show that: under the baseline scenario, the carbon emissions of China's logistics industry will peak in 2047; under the low-carbon scenario, carbon emissions will peak in 2038; under the enhanced low-carbon scenario, carbon emissions will peak in 2029. 55.94 million tons. Combined with the trend analysis results under different scenarios, China should continue to strengthen the construction of the carbon trading market, optimize the industrial structure, and accelerate the progress of carbon peaking.
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References
Qi Yu, Xu Chaoyi. (2022).Research on the influencing factors of carbon emissions in the logistics industry in Henan Province based on LMDI [J]. Journal of Luoyang Institute of Technology (Social Science Edition) ,37(01):47-53.
Jiang Leping.(2022). Carbon emission measurement and influencing factors of logistics industry in Anhui Province based on LMDI [J]. Logistics Technology and Application, 27(06):173-177.
Xu Weigan.(2021). Research on carbon emission efficiency evaluation of my country's provincial logistics industry under low carbon background [D]. Jiangnan University.
Wu Zhenjia.(2021). "One Belt One Road" domestic logistics industry carbon emission efficiency measurement and control strategy research [D]. Kunming University of Science and Technology.
Xu Xiangyang, Hu Jianan.(2022). Prediction of peak carbon emissions in China's paper industry under multiple scenarios: Based on a scalable stochastic environmental assessment model [J]. Forestry Economics. 44(05):5-22.
Zhao Ci,Song Xiaocong,Liu Xiaoyu,Shen Peng,Chen Chen,Liu Lei. (2022).Prediction and Analysis of Peak Carbon Emissions in Zhejiang Province Based on STIRPAT Model[J]. EcologicalEconomy,38(06):29-34.
Hu Maofeng, Zheng Yibin, Li Yuhan. (2022).Prediction of peak carbon emissions from transportation in Hubei Province under multiple scenarios [J]. Journal of Environmental Science, 42(04): 464-472.
Zhang Jinliang, Jia Fan.(2022).Multi-model carbon peaking scenario prediction for China's thermal power industry [J]. Electric Power Construction, 43(05):18-28.
Ma Yan. (2020).Research on the linkage development path and promotion strategy of manufacturing and logistics industry in Shanxi Province [D]. Shanxi Normal University.
Yang Shuxia, Zhang Yang.(2021). Research on Green Transformation of Logistics Industry Considering Energy Efficiency [J]. Journal of North China Electric Power University (Social Science Edition), (04):40-52
Yan Chengliang, Yuan Liang.(2018). Research on the Interaction between Regional Logistics and Regional Economic Development in Hubei Province [J]. Value Engineering,37(15):7-10.
Wang Huiru, Feng Xuejun, Shen Jinxing, Zhang Yan, Wang Haipeng.(2022). Prediction of carbon emissions peaking in Jiangsu's transportation industry based on SVR [J]. Environmental Science and Technology,35(03):58-63.
Li Yunjie. (2021). Research on the efficiency of low-carbon logistics in the Beijing-Tianjin-Hebei region under the coordinated development strategy [D]. Shijiazhuang Railway University,.
Bao Yu.(2019). Analysis on the Efficiency of the Linked Development of Manufacturing and Logistics Industry in Anhui Province [D]. Hefei University of Technology.
Zhou Peng.(2022). Short-term gas load prediction based on SSA-SVR [J]. Shandong Chemical Industry,51(05):154-158.
Liu Yulin.(2021). Research on the prediction model of coal gangue subgrade settlement based on SSA-SVR [J]. Journal of Hebei University of Geosciences, 44(06):99-104.
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