中国交通运输碳排放空间聚类与关联网络结构分析
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摘要
基于中国29个省域1996、2005和2014年交通运输碳排放数据及公路交通网,采用社会网络分析法(SNA),从网络密度、空间异质性、凝聚子类、中介性等角度分析中国29个省域的网络结构演变、空间分布及聚集特征、中介性及角色。结果表明:(1)从时间趋势上看,1996—2014年中国交通运输碳排放关联网络密度不断提升,网络结构呈现复杂化态势,同时碳排放关联强度存在明显差异性;(2)通过交通运输碳排放网络TOP1、TOP5、TOP10及凝聚子类分析发现中国交通运输碳排放大体可以分为6大区域:东北地区、西南地区、华南及华中华东部分地区、长江三角洲地区、北方地区、西北部分地区;(3)在不同的区域中,不同省份扮演着不同的地位和作用,其中上海、江苏、浙江、安徽、河南、山东、贵州、广东、湖北等省份存在着明显的"空间溢出效应",同时与区域内部省份间存在着较强的空间关联特性,扮演着"中间人"角色,其余省份主要在区域内具有较强的空间关联特性。
To explore the network structure and the clustering features of China's provincial transport carbon emissions in the geographical space, based on the data of 29 provincial transportation carbon emissions and road traffic networks in1996, 2005 and 2014 years, this paper uses the social network analysis(SNA) to analyze regional network structure evolution, spatial distribution characteristics, regional division, intermediary and role from the perspective of network density, spatial heterogeneity, agglomeration subclass and mediation. The results show that: 1) From the point of time trend, the density of China's transportation carbon emissions network has been increasing from 1996 to 2014, and the network structure has been complicated. The development process of network has been extending from the east to the middle, southwest and northwest. At the same time, the intensity of carbon emission present the spatial polarization phenomenon; 2) It is found that China's transportation carbon emissions can be divided into six regions: Northeast,Southwest, South and some central and eastern regions, Yangtze River Delta, North, Parts of the northwest by the TOP1,TOP5, and TOP10 transportation carbon emission network and agglomeration subclass analysis; 3) In different regions,different provinces play different roles, including Shanghai, Jiangsu, Zhejiang, Anhui, Henan, Shandong, Guizhou,Guangdong, Hubei play an important internal, external connectivity intermediary and "middleman" role, the rest of the provinces assume the internal interoperability role.
To explore the network structure and the clustering features of China's provincial transport carbon emissions in the geographical space, based on the data of 29 provincial transportation carbon emissions and road traffic networks in1996, 2005 and 2014 years, this paper uses the social network analysis(SNA) to analyze regional network structure evolution, spatial distribution characteristics, regional division, intermediary and role from the perspective of network density, spatial heterogeneity, agglomeration subclass and mediation. The results show that: 1) From the point of time trend, the density of China's transportation carbon emissions network has been increasing from 1996 to 2014, and the network structure has been complicated. The development process of network has been extending from the east to the middle, southwest and northwest. At the same time, the intensity of carbon emission present the spatial polarization phenomenon; 2) It is found that China's transportation carbon emissions can be divided into six regions: Northeast,Southwest, South and some central and eastern regions, Yangtze River Delta, North, Parts of the northwest by the TOP1,TOP5, and TOP10 transportation carbon emission network and agglomeration subclass analysis; 3) In different regions,different provinces play different roles, including Shanghai, Jiangsu, Zhejiang, Anhui, Henan, Shandong, Guizhou,Guangdong, Hubei play an important internal, external connectivity intermediary and "middleman" role, the rest of the provinces assume the internal interoperability role.
引文
[1]陈露露,赵小凤,赖力.江苏省交通运输业碳排放预测及减排情景分析研究[J].环境科学与管理,2015,40(10):13-17.
[2] IEA. CO2Emissions from Fuel Combustion Highlights[R].2010.
[3] IEA. Technology Roadmap:Biofuels for Transport[R]. 2011.
[4] Lesiv M,Bun R. Geoinformation Technologies and Spatial Analysis of Carbon Dioxide Transport through Border Line[J].2010(4):322-329.
[5] Jr W.J.R,Koutrakis P,Roig H.L. Spatial distribution of vehicle emission inventories in the Federal District,Brazil[J]. Atmo‐spheric Environment,2015(112):32-39.
[6] Shu Y,Lam N. S. N. Spatial disaggregation of carbon dioxide emissions from road traffic based on multiple linear regression model[J]. Atmospheric Environment,2011,45(3):634-640.
[7] Eicker M.O.D,Zah R,Trivi?o R,et al. Spatial accuracy of a simplified disaggregation method for traffic emissions applied in seven mid-sized Chilean cities[J]. Atmospheric Environ‐ment,2008,42(7):1 491-1 502.
[8] Huang Z,Cao F,Jin C,et al. Carbon emission flow from selfdriving tours and its spatial relationship with scenic spots-A traf‐fic-related big data method[J]. Journal of Cleaner Production,2016(142):946-955.
[9]杨文越,李涛,曹小曙.中国交通CO2排放时空格局演变及其影响因素——基于2000—2012年30个省(市)面板数据的分析[J].地理科学,2016(4):491-501.
[10]张陶新,曾熬志.中国交通碳排放空间计量分析[J].城市发展研究,2013,20(10):14-20.
[11]袁长伟,芮晓丽,武大勇,等.基于地理加权回归模型的中国省域交通碳减排压力指数[J].中国公路学报,2016,29(6):262-270.
[12]袁长伟,张帅,焦萍,等.中国省域交通运输全要素碳排放效率时空变化及影响因素研究[J].资源科学,2017,39(4):687-697.
[13]高洁,张晓明,王建伟,等.中国碳排放与交通运输碳排放重心演变及对比分析[J].生态学报,2013(8):36-40.
[14]余跃武,李晔,包磊,等.中国公路货运运输碳排放重心转移轨迹分析[J].生态经济,2016,32(2):117-121.
[15]杨彬,宁小莉.新型城镇化视角下中国交通运输碳排放测度及其空间格局分布[J].内蒙古师范大学学报:自然科学汉文版,2015(5):667-673,679.
[16]刘华军,刘传明,孙亚男.中国能源消费的空间关联网络结构特征及其效应研究[J].中国工业经济,2015(5):83-95.
[17] Stanley Wasserman,Katherine Faust. Social Network Analysis:Method and Application[M]. New York:Sage publication,2011.
[18]吕康娟,付旻杰.我国区域间产业空间网络的构造和结构测度[J].经济地理,2010,30(11):1 785-1 791.
[19]王茂军,田丽英,杨雪春.山东省城镇网络结构与城镇网络角色识别——基于民国时期土货/洋货流通网络的分析[J].地理研究,2011,30(9):1 621-1 636.
[20]周一星,胡志勇.从航空运输看中国城市体系的空间网络结构[J].地理研究,2003,21(3):276-286.
[21]李亚婷,潘少奇,苗长虹.中原经济区县际经济联系网络结构及其演化特征[J].地理研究,2014,33(7):1 239-1 250.
[22] Taaffe E J. The urban hierarchy:An air passage definition[J].Economic Geography,1962,38(1):1-14.
[23] IPCC. 2006 IPCC Guidelines for national greenhouse gas inventories[R]. Japan:the Institute for Global Environmental Strategies(IGES),2006.
[24]冷炳荣,杨永春,李英杰,等.中国城市经济网络结构空间特征及其复杂性分析[J].地理学报,2011,66(2):199-211.
(1)该数据由作者自行计算得到。
(2)交通碳排放联系体现为非对称性,因而本文参考刘华军等的研究[16],利用kij来修正重力模型,以突出网络联系的有向性。
(3)1996、2005及2014年中国交通公路基础设施状况分别处于不同的阶段:1996年,全国普通公路网络基本建成;2005年,高速公路、铁路网高速发展;2014年,全国公路、高速公路、铁路高度网络化。因而,利用公路网旅行时间代表距离能够真实反映各地区间距离及其动态变动。
[2] IEA. CO2Emissions from Fuel Combustion Highlights[R].2010.
[3] IEA. Technology Roadmap:Biofuels for Transport[R]. 2011.
[4] Lesiv M,Bun R. Geoinformation Technologies and Spatial Analysis of Carbon Dioxide Transport through Border Line[J].2010(4):322-329.
[5] Jr W.J.R,Koutrakis P,Roig H.L. Spatial distribution of vehicle emission inventories in the Federal District,Brazil[J]. Atmo‐spheric Environment,2015(112):32-39.
[6] Shu Y,Lam N. S. N. Spatial disaggregation of carbon dioxide emissions from road traffic based on multiple linear regression model[J]. Atmospheric Environment,2011,45(3):634-640.
[7] Eicker M.O.D,Zah R,Trivi?o R,et al. Spatial accuracy of a simplified disaggregation method for traffic emissions applied in seven mid-sized Chilean cities[J]. Atmospheric Environ‐ment,2008,42(7):1 491-1 502.
[8] Huang Z,Cao F,Jin C,et al. Carbon emission flow from selfdriving tours and its spatial relationship with scenic spots-A traf‐fic-related big data method[J]. Journal of Cleaner Production,2016(142):946-955.
[9]杨文越,李涛,曹小曙.中国交通CO2排放时空格局演变及其影响因素——基于2000—2012年30个省(市)面板数据的分析[J].地理科学,2016(4):491-501.
[10]张陶新,曾熬志.中国交通碳排放空间计量分析[J].城市发展研究,2013,20(10):14-20.
[11]袁长伟,芮晓丽,武大勇,等.基于地理加权回归模型的中国省域交通碳减排压力指数[J].中国公路学报,2016,29(6):262-270.
[12]袁长伟,张帅,焦萍,等.中国省域交通运输全要素碳排放效率时空变化及影响因素研究[J].资源科学,2017,39(4):687-697.
[13]高洁,张晓明,王建伟,等.中国碳排放与交通运输碳排放重心演变及对比分析[J].生态学报,2013(8):36-40.
[14]余跃武,李晔,包磊,等.中国公路货运运输碳排放重心转移轨迹分析[J].生态经济,2016,32(2):117-121.
[15]杨彬,宁小莉.新型城镇化视角下中国交通运输碳排放测度及其空间格局分布[J].内蒙古师范大学学报:自然科学汉文版,2015(5):667-673,679.
[16]刘华军,刘传明,孙亚男.中国能源消费的空间关联网络结构特征及其效应研究[J].中国工业经济,2015(5):83-95.
[17] Stanley Wasserman,Katherine Faust. Social Network Analysis:Method and Application[M]. New York:Sage publication,2011.
[18]吕康娟,付旻杰.我国区域间产业空间网络的构造和结构测度[J].经济地理,2010,30(11):1 785-1 791.
[19]王茂军,田丽英,杨雪春.山东省城镇网络结构与城镇网络角色识别——基于民国时期土货/洋货流通网络的分析[J].地理研究,2011,30(9):1 621-1 636.
[20]周一星,胡志勇.从航空运输看中国城市体系的空间网络结构[J].地理研究,2003,21(3):276-286.
[21]李亚婷,潘少奇,苗长虹.中原经济区县际经济联系网络结构及其演化特征[J].地理研究,2014,33(7):1 239-1 250.
[22] Taaffe E J. The urban hierarchy:An air passage definition[J].Economic Geography,1962,38(1):1-14.
[23] IPCC. 2006 IPCC Guidelines for national greenhouse gas inventories[R]. Japan:the Institute for Global Environmental Strategies(IGES),2006.
[24]冷炳荣,杨永春,李英杰,等.中国城市经济网络结构空间特征及其复杂性分析[J].地理学报,2011,66(2):199-211.
(1)该数据由作者自行计算得到。
(2)交通碳排放联系体现为非对称性,因而本文参考刘华军等的研究[16],利用kij来修正重力模型,以突出网络联系的有向性。
(3)1996、2005及2014年中国交通公路基础设施状况分别处于不同的阶段:1996年,全国普通公路网络基本建成;2005年,高速公路、铁路网高速发展;2014年,全国公路、高速公路、铁路高度网络化。因而,利用公路网旅行时间代表距离能够真实反映各地区间距离及其动态变动。