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基于地统计分析的中国省域交通运输系统碳排放时空特征研究
引用本文:宋京妮,吴群琪,袁长伟,张帅,包旭,杜凯. 基于地统计分析的中国省域交通运输系统碳排放时空特征研究[J]. 气候变化研究进展, 2017, 13(5): 502-511. DOI: 10.12006/j.issn.1673-1719.2016.234
作者姓名:宋京妮  吴群琪  袁长伟  张帅  包旭  杜凯
作者单位:1.长安大学经济与管理学院, 西安 710064;2.江苏省交通运输与安全保障重点实验室, 淮安 223003;3.长安大学电子与控制工程学院, 西安 710064
基金项目:高等学校博士学科点专项科研基金(20130205110001);陕西省科技工业攻关项目(2015GY033);中央高校基本业务费项目(310823165017,2014G6231001,2014G6231003);江苏省交通运输与安全保障重点建设实验室开放基金项目(TTS2015-04)
摘    要:以30省域为研究单元,基于能源消耗测算了中国省域2003-2014年交通运输系统的碳排放量,探究了中国省域交通运输系统碳排放的时空分布特征及演变规律。结果表明:中国交通运输系统碳排放量持续快速增长,空间上表现为东高西低,南北方向呈"倒U"型的特征,且区域间相对差异逐渐减小。新疆、青海、甘肃这3省均为冷点地区,热点地区主要分布于东部沿海,历年交通运输系统碳排放重心基本位于河南省南部偏东地区,呈现东北-西南的方向格局,并向正北转变。不同时期交通运输系统碳排放数据变异的随机成分不同,且结构化差异呈减弱态势,而整体空间效应范围不断增大,溢出效应逐渐增强。

关 键 词:交通运输  碳排放  地统计分析  空间变异函数  时空演变  
收稿时间:2016-12-04
修稿时间:2017-02-27

Spatial-Temporal Characteristics of China Transport Carbon Emissions Based on Geostatistical Analysis
Song Jingni,Wu Qunqi,Yuan Changwei,Zhang Shuai,Bao Xu,Du Kai. Spatial-Temporal Characteristics of China Transport Carbon Emissions Based on Geostatistical Analysis[J]. Progressus Inquisitiones DE, 2017, 13(5): 502-511. DOI: 10.12006/j.issn.1673-1719.2016.234
Authors:Song Jingni  Wu Qunqi  Yuan Changwei  Zhang Shuai  Bao Xu  Du Kai
Affiliation:1.School of Economics and Management, Chang'an University, Xi'an 710064, China;2.Key Laboratory for Traffic and Transportation Security of Jiangsu Province, Huai'an 223003, China;3.School of Electronic and Control Engineering, Chang'an University, Xi'an 710064, China
Abstract:Measurement and calculation were performed on carbon emissions produced by the transportation system in 30 provinces of China from 2003 to 2014 based on energy consumption by taking the provinces as a research unit. The temporal-spatial evolution of China's provincial transport carbon emissions was explored. Results show that:carbon emissions grew rapidly, and the eastern regions were higher than the western, space characteristics of north-south direction showed an inverted-U curve, and the difference between regions tended to be slight; Xinjiang, Qinghai and Gansu were cold spots, while the hot spots were mainly distributed in the eastern coastal regions, and the gravity center of carbon emissions was concentrated in the southeast of Henan province, with the directional pattern of northeast-southwest and a trend towards the north; random factors of data variation in carbon emissions during different periods were different, and the structural difference was weakened with an increasing overall spatial effect range and a gradually enhanced spillover effect.
Keywords:transportation  carbon emissions  geostatistical analysis  semivariogram function  spatial-temporal evolution  
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