陕西省物流业碳排放因素分解与情景预测

Decomposition of Carbon Emission Factors and Scenario Prediction of Logistics Industry in Shaanxi Province

为研究物流业碳排放影响因素和变化趋势,文中利用LMDI分解法对各影响因素进行分解,构建扩展的STIRPAT模型得出拟合方程,运用通径分析法计算各因素对陕西省物流业碳排放的直接及间接影响,并预测不同情景下的碳排放趋势。通过梳理陕西省物流业2000-2020年碳排放量变化得出结果。结果表明:人口总量和经济增长对碳排放有促进作用,电力占比、能源强度、产业结构对碳排放增长有抑制作用;物流业碳排放各影响因素的总间接影响均显著大于直接影响,经济增长是主要中介因素;不同情景下物流业碳排放存在明显差异,加速电气化情景最符合未来发展趋势。该情景于2035年到达峰,碳排放量为1484万吨。

In order to study the influencing factors and change trends of carbon emissions in logistics industry,this paper decomposes each influencing factor by using LMDI decomposition method,constructs the extended STIRPAT model to derive the fitting equations,calculates the direct and indirect influences of each factor on carbon emissions of logistics industry in Shaanxi Province by using the through path analysis method,and predicts the carbon emission trends under different scenarios.The results were obtained by sorting out the changes of carbon emissions in the logistics industry in Shaanxi Province from 2000 to 2020.The results show that:total population and economic growth have a catalytic effect on carbon emissions,while electricity share,energy intensity,and industrial structure have a suppressive effect on carbon emissions growth;The total indirect effects of all factors influencing carbon emissions in logistics industry are significantly greater than the direct effects,and economic growth is the main mediating factor;carbon emissions in logistics industry under different scenarios are significantly different,and the accelerated electrification scenario is most consistent with the future development trend.This scenario reaches its peak in 2035 with 14.84 million tons of carbon emissions.