• 关键词：交通运输碳排放  影响因素  时空差异  ESDA  GWR

• 基金项目：国家自然科学基金（No.51278057）；霍英东教育基金（No.151075）

• 张诗青
• 长安大学, 经济与管理学院, 西安 710064

• 王建伟
• 长安大学, 经济与管理学院, 西安 710064

• 郑文龙
• 长安大学, 经济与管理学院, 西安 710064

• 摘要：为减少交通运输二氧化碳排放量，采用ESDA方法对交通运输碳排放时空分布格局进行研究，同时考虑了空间联系作用，构建GWR模型对碳排放影响因素进行时空差异分析.研究发现：12000—2013年中国省域交通碳排放空间聚类特征随时间变化不大，存在显著的高值、低值聚类特征.京津冀地区、辽宁、山东、山西、陕西、河南地区为高值聚类区，新疆、青海地区一直处于低值聚类区.2碳排放影响因素在相邻地区差异较小.其中城镇化率、交通运输结构为主要推动因素，能源强度则起到关键抑制作用.应对碳排放聚类区域施行协同减排目标，同时，应根据因素影响作用差异，分区域制定针对性减排政策.

• Abstract：In order to decrease transportation carbon emission, ESDA is used to study the spatio-temporal distribution of transportation carbon emission, and GWR model is built to analyze spatio-temporal difference among provinces of transportation carbon emission influencing factors. The results show that:1 In 2000-2013, the spatial cluster characteristics of transportation carbon emission in China were comparatively stable, and there were significant high and low-value cluster characteristics in provinces. Beijing-Tianjin-Hebei Region, Liaoning, Shandong, Shanxi, Shaanxi, Henan were in high-value cluster areas, and Xinjiang, Qinghai were in low-value cluster areas. 2 There was relatively small difference among influencing factors in adjacent provinces. Urbanization and transportation structure were the main influencing factors, and energy intensity played a key negative role in carbon emission. It is suggested government should implement the coordinated emission reduction plans in specific cluster areas. Meanwhile, according to the difference among provinces of influencing factors, the government should formulate targeted emission reduction policies in different regions.

• Key words：transportation carbon emission  influencing factors  spatio-temporal difference  ESDA  GWR

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