基于STIRPAT拓展模型的交通运输业碳排放测算与情景预测

The Calculation and Scenario Prediction of Transportation Carbon Emissions in China Based on Extended STIRPAT Model

摘要: 随着我国双碳目标的提出，对交通运输行业碳排放进行测算并制定科学合理的发展路径对提前实现碳达峰有至关重要的作用。本文使用“自顶向下”的方法对2001~2019年中国交通运输碳排放进行测算，通过建立扩展的STIRPAT模型，对影响碳排放的几个主要因素作回归分析，结果表明人口规模、人均GDP、单位周转能耗对碳排放有正向影响，清洁能源占比和第三产业占比对碳排放有负向影响。最后考虑未来社会经济发展情况，对人口规模、经济水平和技术条件分别作高速发展和低速发展的预测，分别计算8种情景下未来交通运输碳排放情况。结果表明不同情景下的碳排放存在差异，保持人口低速增长，经济稳定增长和技术条件稳步提升可能是最适合中国交通低碳发展的道路。

Abstract: With the introduction of China’s double carbon target, it is crucial to measure carbon emissions in the transportation industry and formulate a scientific and reasonable development path to achieve the carbon peak ahead of schedule. This paper uses a “top-down” approach to measure China’s transportation carbon emissions from 2001 to 2019. By establishing an extended STIRPAT model, the regression analysis of several major factors affecting carbon emissions shows that population size, GDP per capita, and energy consumption per unit of turnover have a positive effect on carbon emissions, while the share of clean energy and the share of tertiary industry have a negative effect on carbon emissions. Finally, considering the future socio-economic development, high and low development speeds are made for population size, economic level and technological conditions, and future transportation carbon emissions are calculated under eight scenarios respectively. The results show that there are differences in carbon emissions under different scenarios, and that maintaining low population growth, stable economic growth and steady improvement of technological conditions may be the most suitable path for low-carbon transportation development in China.