中国环境科学

中国道路交通碳排放时空特征及影响因素

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杨文卓 ¹^,²^,³ , 王远 ¹^,²^,³^,^* , 陈晗璐 ¹^,²^,³ , 董战峰 ⁴

中国环境科学 | 碳排放控制 2026,46(3): 1710-1724

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中国环境科学 |碳排放控制 2026 , 46 (3) : 1710 -1724

中国道路交通碳排放时空特征及影响因素

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杨文卓¹^,²^,³, 王远¹^,²^,³^,^*, 陈晗璐¹^,²^,³, 董战峰⁴

作者信息

^1.福建师范大学，湿润亚热带生态地理过程教育部重点实验室，福建福州 350117

^2.福建师范大学地理科学学院，碳中和未来技术学院，福建福州 350117

^3.福建师范大学地理研究所，福建福州 350117

^4.生态环境部环境规划院，北京 100044

通讯作者:

^* 责任作者，教授，y.wang@fjnu.edu.cn

作者简介:

杨文卓（2000-），男，湖北襄阳人，福建师范大学硕士研究生，主要从事环境经济地理研究．发表论文2篇．1902857342@qq.com.

Analysis of spatial and temporal characteristics and influencing factors of carbon emissions from road transportation in China

Wen-zhuo YANG¹^,²^,³, Yuan WANG¹^,²^,³^,^*, Han-lu CHEN¹^,²^,³, Zhan-feng DONG⁴

Affiliations

^1.Key Laboratory for Humid Subtropical Eco-gergraphical Processes of the Ministry of Education, Fujian Normal University, Fuzhou 350117, China

^2.Carbon Neutral and Future Technology Institute, School of Geographic Sciences, Fujian Normal University, Fuzhou 350117, China

^3.Institute of Geography, Fujian Normal University, Fuzhou 350117, China

^4.Chinese Academy for Environmental Planning, Ministry of Ecology and Environment, Beijing 100044, China

出版时间: 2026-03-20

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摘要

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本文在核算中国31省份2003~2022年道路交通碳排放量基础上，运用slope值、泰尔指数并融合XGBoost-SHAP构建可解释的机器学习模型分析中国及东、中、西部道路交通碳排放的时间特征、空间差异及影响因素．结果表明：小微型客车与重型货车碳排放合计占比超80%，轻微型货车占比先升后稳，中型货车占比持续下降．中大型客车、公交车及出租车排放占比较低且呈下降态势．中国道路交通碳排放量呈现“快速增长—增速放缓”的阶段性特征．东部地区前期增速较快，但后期增速持续放缓；中部地区后期增速反超东部；西部地区基数低但增速最快．中国道路交通碳排放总体呈现“东扩-中升-西稳”的空间格局，东部沿海及中部腹地依次形成高排放连绵带．区域间道路交通碳排放差异逐渐降低，区域内差异显著上升．东、西部内部各省份差异显著，中部差异最小且保持稳定．全国层面，公路货运周转量为首要因素且对道路交通碳排放具有显著的推动作用，其次为人口总量与每万人机动车保有量，且各显著因素呈现协同增排效应．区域层面，东、西部地区公路货运周转量仍最为显著，但在中部地区不显著．每万人机动车拥有量与人均GDP贡献率中部较东、西部显著提升．

关键词

道路交通 / 碳排放 / 时空特征 / XGBoost-SHAP / 影响因素

Abstract

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With the continuous increase in carbon emissions from road transportation and significant regional differences, understanding the spatio-temporal evolution patterns and influencing factors of carbon emissions from road transportation in China is crucial for energy conservation and carbon reduction in this sector. Based on the calculation of carbon emissions from road transportation in 31 provinces of China from 2003 to 2022, this study uses slope values, Theil index, and integrates XGBoost-SHAP to construct an interpretable machine learning model to analyze the temporal characteristics, spatial differences, and influencing factors of carbon emissions from road transportation in China and its eastern, central, and western regions. The results show that: The combined carbon emissions from small and medium-sized passenger cars and heavy-duty trucks account for over 80%, while the proportion of light-duty trucks first increases and then stabilizes, and that of medium-duty trucks continuously decreases. The proportions of medium and large-sized passenger cars, buses, and taxis are relatively low and show a downward trend. The carbon emissions from road transportation in China exhibit a phased characteristic of "rapid growth-decelerating growth". The eastern region had a relatively fast growth rate in the early stage, but it has been continuously decelerating in the later stage; the central region's growth rate surpassed that of the eastern region in the later stage; the western region has a low base but the fastest growth rate. The overall spatial pattern of carbon emissions from road transportation in China is "expansion in the east-increase in the central-stability in the west", with high-emission continuous belts formed successively in the eastern coastal areas and the central hinterland. The differences in carbon emissions from road transportation between regions are gradually decreasing, while the differences within regions are significantly increasing. The differences among provinces within the eastern and western regions are significant, while those in the central region are the smallest and remain stable. (4) At the national level, the turnover volume of road freight is the primary factor and has a significant driving effect on carbon emissions from road traffic, followed by the total population and the number of motor vehicles per 10,000people, moreover, each significant factor shows a synergistic effect of increasing emissions. At the regional level, the turnover volume of road freight remains the most significant factor in the eastern and western regions, but it is not significant in the central region. The contribution rate of motor vehicles per 10,000people and per capita GDP in the central region has significantly increased compared to the eastern and western regions.

Key words

road traffic / carbon emissions / spatio-temporal characteristics / XGBoost-SHAP / influencing factors

引用本文

杨文卓, 王远, 陈晗璐, 董战峰. 中国道路交通碳排放时空特征及影响因素. 中国环境科学, 2026 , 46 (3) : 1710 -1724 .

Wen-zhuo YANG, Yuan WANG, Han-lu CHEN, Zhan-feng DONG. Analysis of spatial and temporal characteristics and influencing factors of carbon emissions from road transportation in China[J]. China Environmental Science, 2026 , 46 (3) : 1710 -1724 .

基金

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福建省自然科学基金资助重点项目(2021J02030)
福建省社科基金资助项目(FJ2021B042)

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2026年第46卷第3期

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文章信息

接收时间：2025-07-25
首发时间：2026-10-08
出版时间：2026-03-20

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出版历史

收稿日期：2025-07-25

基金

福建省自然科学基金资助重点项目(2021J02030)

福建省社科基金资助项目(FJ2021B042)

作者信息

^1.福建师范大学，湿润亚热带生态地理过程教育部重点实验室，福建福州 350117

^2.福建师范大学地理科学学院，碳中和未来技术学院，福建福州 350117

^3.福建师范大学地理研究所，福建福州 350117

^4.生态环境部环境规划院，北京 100044

通讯作者:

^* 责任作者，教授，y.wang@fjnu.edu.cn

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https://castjournals.cast.org.cn/joweb/zghjkx/CN/1314923065046295402

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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