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中国交通运输碳排放强度时空交互特征及跃迁机制
摘要点击 746  全文点击 127  投稿时间:2023-10-25  修订日期:2023-12-18
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中文关键词  交通运输业  碳排放强度  时空交互  跃迁机制  分位数回归  地理探测器
英文关键词  transportation industry  carbon intensity  spatiotemporal interaction  transition mechanism  quantile regression  geographic detector
作者单位E-mail
李健 天津理工大学管理学院, 天津 300384
天津大学管理与经济学部, 天津 300372 
lijian631219@163.com 
刘舒琪 天津理工大学管理学院, 天津 300384  
王晓祺 天津理工大学管理学院, 天津 300384 wangxiaoqi@email.tjut.edu.cn 
中文摘要
      采取多种空间分析的方法,对2002~2020年交通运输业碳排放强度的时空交互特征进行剖析,通过得到的时空跃迁类型与面板分位数模型进行嵌套来探究其跃迁机制,最后根据不同的跃迁机制引入地理探测器模型来考察影响交通运输业碳排放强度的不同因素之间的交互作用效应.结果表明: ①中国30个省区的交通运输业碳排放强度整体呈波动下降态势,在空间上的聚集水平也相对稳定.②ESTDA的时空交互特征表明,西北地区和周边邻接空间单位的关系不稳定,变化和波动较大.而东部沿海城市等经济发达地区已经形成了成熟的交通运输网络,因此局部空间格局也相对稳定,但仍有部分地区存在时空竞争性.③交通运输业碳排放强度时空跃迁可分为4类驱动或制约模式(人口-经济-城镇化制约模式;人口-经济-城镇化-设施制约模式;技术-消费-产业驱动模式和技术-产业-规制驱动模式).大部分省份受低分位制约和高分位驱动两种模式的影响,仅有少部分省份受高分位制约和低分位驱动的作用影响,且绝大多数属于西北或西南地区.④根据得出的交通运输业碳排放强度跃迁机制进一步引入地理探测器模型,注重多因子的协调发展,加强区域间协同治理.
英文摘要
      This research was conducted using many spatial analysis approaches to dissect the spatiotemporal interactive characteristics of carbon emission intensity within the transportation sector from 2002 to 2020. An in-depth exploration of their transition mechanisms was conducted by nesting the obtained timewarp types with the panel quantile model. Finally, the geodetector model aligned with different transition mechanisms was employed to investigate and analyze the interaction effects among various factors influencing carbon intensity in the transportation sector. The results indicated that:① The carbon emission intensity of the transportation sector in 30 provinces and regions of China showed an overall downward trend with fluctuations, and the spatial clustering level was relatively stable. ② The spatiotemporal interactive features of ESTDA revealed that the relationship between the northwest region and its adjacent spatial units was unstable, with significant variations and fluctuations. In contrast, economically developed areas such as coastal cities in the eastern part had established mature transportation networks, resulting in a relatively stable local spatial pattern, though a few areas still exhibited spatiotemporal competitiveness. ③ The spatiotemporal transition of carbon intensity in the transportation sector could be categorized into four driving or constraining modes(the population economy urbanization constraint model, population economy urbanization facility constraint model, technology consumption industry-driven model, and technology industry regulation-driven model). Most provinces were influenced by the low quantile constraint and high quantile drive modes, with only a few affected by the high quantile constraint and low quantile drive modes, the majority of which were located in the northwest or southwest regions. ④ Further, we introduced the geographical detector model based on the identified mechanism of carbon emission intensity transition in the transportation sector, emphasizing the coordinated development of multiple factors and strengthening inter-regional collaborative governance.

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