我国旅游业碳排放的空间关联性及其影响因素
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摘要
为全面厘清我国旅游业碳排放的空间网络结构特征,基于2000—2015年我国各省(自治区、直辖市)旅游业碳排放相关数据(不含西藏自治区和港澳台地区数据,下同),结合修正的引力模型,构建旅游业碳排放的空间关联关系;采用社会网络分析法,深入剖析我国旅游业碳排放的空间关联性及其影响因素.结果表明:①2000—2015年,我国旅游业碳排放的空间网络关联度始终为1,其网络关系数与网络密度持续增加,而网络等级度与网络效率平稳下降.②上海市、浙江省、江苏省、北京市、天津市等东部经济发达地区在网络中处于核心位置,对旅游业碳排放空间关联性的影响显著;而海南省、云南省、广西壮族自治区、青海省、吉林省等在网络中居于边缘位置,对旅游业碳排放空间关联性的影响微弱.③广西壮族自治区、贵州省、新疆维吾尔自治区等归为"净溢出"板块,河北省、甘肃省、陕西省等处于"经纪人"板块,北京市、天津市、内蒙古自治区等归为"双向溢出"板块,江苏省、浙江省、上海市等属于"净受益"板块.④空间邻接关系、城镇化水平差异在1%的显著性水平上对旅游业碳排放空间关联性起正向促进作用,旅游消费水平差异和产业结构差异分别在5%和10%的显著性水平上与旅游业碳排放空间关联性呈正相关,能源消耗差异在1%的显著性水平上与其呈负相关.研究显示,我国整体旅游业碳排放空间关联性愈趋紧密,但仍存在较大改良空间,各板块间的空间关联性有待进一步加强.
In order to fully clarify the spatial network structure characteristics of carbon emissions in China's tourism industry,based on the data of carbon emissions( excluding the data of Tibet Autonomous Region,Hongkong,Macao and Taiwan,the rest is the same) from the tourism industry of various provinces,autonomous regions and municipalities directly under the central government in China from 2000 to 2015,and by combining them with the improved gravity model,this paper constructs the spatial network correlation of carbon emissions in tourism industry. Social network analysis method is applied to the analysis of the spatial correlation of carbon emissions in tourism industry and its influencing factors in China. The results show that:( 1) During the investigation period,the spatial network correlation degree of carbon emissions in the tourism industry is always 1,the number of its network relations and the network density continue to rise,while the network hierarchy and efficiency steadily decline.( 2) The eastern developed regions such as Shanghai City,Zhejiang Province,Jiangsu Province,Beijing City,Tianjin City,etc. are at the core position of the network,which have a significant impact on the spatial correlation of carbon emissions in tourism industry. Relatively backward regions such as Hainan Province,Yunnan Province,Guangxi Zhuang Autonomous Region,Qinghai Province,Jilin Province,etc. are at the edge position of the network,and have little effect on the spatial correlation of carbon emissions in the tourism industry.( 3) Regions such as Guangxi Zhuang Autonomous Region,Guizhou Province and Xinjiang Uygur Autonomous Region are classified as ‘net spillover plate'; Hebei Province,Gansu Province,Shaanxi Province and so on are in the ‘agent plate '; Beijing City,Tianjin City and Inner Mongolia Autonomous Region are classified as‘bidirectional spillover plate'; Jiangsu Province,Zhejiang Province as well as Shanghai City belong to the‘net benefit plate'.( 4) The spatial adjacency relation and urbanization level disparity have a positive effect on the spatial correlation of tourism carbon emissions at a significant level of 1%; The differences of tourism consumption level and industrial structure are positively correlated with the spatial correlation of tourism carbon emissions at the significant level of 5% and 10%; The energy consumption disparity has a negative correlation with the spatial correlation of tourism carbon emissions at the significant level of 1%. The research shows that the spatial correlation of carbon emissions in tourism industry is closely related,yet there is still much space for improvement. The spatial correlation between the plates needs to be further strengthened.
In order to fully clarify the spatial network structure characteristics of carbon emissions in China's tourism industry,based on the data of carbon emissions( excluding the data of Tibet Autonomous Region,Hongkong,Macao and Taiwan,the rest is the same) from the tourism industry of various provinces,autonomous regions and municipalities directly under the central government in China from 2000 to 2015,and by combining them with the improved gravity model,this paper constructs the spatial network correlation of carbon emissions in tourism industry. Social network analysis method is applied to the analysis of the spatial correlation of carbon emissions in tourism industry and its influencing factors in China. The results show that:( 1) During the investigation period,the spatial network correlation degree of carbon emissions in the tourism industry is always 1,the number of its network relations and the network density continue to rise,while the network hierarchy and efficiency steadily decline.( 2) The eastern developed regions such as Shanghai City,Zhejiang Province,Jiangsu Province,Beijing City,Tianjin City,etc. are at the core position of the network,which have a significant impact on the spatial correlation of carbon emissions in tourism industry. Relatively backward regions such as Hainan Province,Yunnan Province,Guangxi Zhuang Autonomous Region,Qinghai Province,Jilin Province,etc. are at the edge position of the network,and have little effect on the spatial correlation of carbon emissions in the tourism industry.( 3) Regions such as Guangxi Zhuang Autonomous Region,Guizhou Province and Xinjiang Uygur Autonomous Region are classified as ‘net spillover plate'; Hebei Province,Gansu Province,Shaanxi Province and so on are in the ‘agent plate '; Beijing City,Tianjin City and Inner Mongolia Autonomous Region are classified as‘bidirectional spillover plate'; Jiangsu Province,Zhejiang Province as well as Shanghai City belong to the‘net benefit plate'.( 4) The spatial adjacency relation and urbanization level disparity have a positive effect on the spatial correlation of tourism carbon emissions at a significant level of 1%; The differences of tourism consumption level and industrial structure are positively correlated with the spatial correlation of tourism carbon emissions at the significant level of 5% and 10%; The energy consumption disparity has a negative correlation with the spatial correlation of tourism carbon emissions at the significant level of 1%. The research shows that the spatial correlation of carbon emissions in tourism industry is closely related,yet there is still much space for improvement. The spatial correlation between the plates needs to be further strengthened.
引文
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[14]PETTERS P,SCHOUTEN F. Reducing the ecological footprint of inbound tourism and transport to Amsterdam[J]. Journal of Sustainable Tourism,2006,14(2):157-171.
[15]El HANANDEH A. Quantifying the carbon footprint of religious tourism:the case of Hajj[J]. Journal of Cleaner Production,2013,52(16):53-60.
[16]孙瑞红.基于碳排放清单的九寨沟自然保护区碳足迹及碳管理研究[D].上海:上海师范大学,2013:69-75.
[17]BALEENTIS T,BALEENTIS A. Rural tourism development in Lithuania(2003-2010):a quantitative analysis[J]. Tourism Management Perspectives,2012,15(6):1-6.
[18]SUN Yanyen.Decomposition of tourism greenhouse gas emissions:revealing the dynamics between tourism economic growth,technological efficiency,and carbon emissions[J]. Tourism Management,2016,55(2):326-336.
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[20]贺腊梅,于萌,查建平.基于BML生产率指数的中国旅游业能源效率评价与影响因素研究[J].长江流域资源与环境,2017,26(12):1991-2002.HE Lamei,YU Meng,ZHA Jianping. Energy efficiency evaluation and influencing factors of the tourism industry in China based on BML productivity index[J]. Resources and Environment in the Yangtze Basin,2017,26(12):1991-2002.
[21]李强谊,钟水映,徐飞.中国旅游业二氧化碳排放的地区差异与收敛性研究[J].经济问题探索,2017(8):28-38.
[22]罗芬,王怀採,钟永德.旅游者交通碳足迹空间分布研究[J].中国人口·资源与环境,2014,24(2):38-46.LUO Fen,WANG Huaicai,ZHONG Yongde.Tourists'transportation carbon footprint spatial distribution[J].China Population Resources and Environment,2014,24(2):38-46.
[23]王凯,夏莉惠,陈勤昌,等.基于空间聚类分析的中国旅游业碳排放效率[J].环境科学研究,2018,31(3):419-427.WANG Kai,XIA Lihui,CHEN Qinchang,et al. Carbon emission efficiency in China's tourism industry by spatial clustering analysis[J].Research of Environmental Sciences,2018,31(3):419-427.
[24]王凯,邵海琴,周婷婷,等.中国旅游业碳排放效率及其空间关联特征[J].长江流域资源与环境,2018,27(3):473-482.WANG Kai,SHAO Haiqin,ZHOU Tingting,et al. A study on carbon emissions efficiency of tourism and its spatial correlation characteristics in China[J]. Resources and Environment in the Yangtze Basin,2018,27(3):473-482.
[25]BORG E A. The marketing of innovations in high-technology companies:a network approach[J].European Journal of Marketing,2009,43(3/4):364-370.
[26]CASSI L,MORRISON A,Ter WAL A L J. The evolution of trade and scientific collaboration networks in the global wine sector:a longitudinal study using network analysis[J].Economic Geography,2012,88(3):311-334.
[27]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,2015,13(5):83-95.LIU Huajun,LIU Chuanming,SUN Yanan. Spatial correlation network structure of energy consumption and its effect in China[J].China Industrial Economics,2015,13(5):83-95.
[28]黄杰.中国能源环境效率的空间关联网络结构及其影响因素[J].资源科学,2018,40(4):759-772.HUANG Jie.The spatial network structure of energy-environmental efficiency and its determinants in China[J]. Resources Science,2018,40(4):759-772.
[29]杨桂元,吴齐,涂洋.中国省际碳排放的空间关联及其影响因素研究:基于社会网络分析方法[J].商业经济与管理,2016,38(4):56-68.YANG Guiyuan,WU Qi,TU Yang. Researchs of China's regional carbon emission spatial correlation and its determinants:based on the method of social network analysis[J]. Journal of Business Economics,2016,38(4):56-68.
[30]李敬,陈澍,万广华,等.中国区域经济增长的空间关联及其解释:基于网络分析方法[J].经济研究,2014,49(11):4-16.LI Jing,CHEN Shu,WAN Guanghua,et al. Study on spatial correlation and explanation of regional economic growth in China:based on analytic network processs[J]. Economic Research Journal,2014,49(11):4-16.
[31]WASSERMAN S,FAUST K. Social network analysis:methods and applications[M]. London:Cambridge University Press,1994:40-186.
[32]魏艳旭,孙根年,马丽君,等.中国旅游交通碳排放及地区差异的初步估算[J].陕西师范大学学报,2012,40(2):76-84.WEI Yanxu,SUN Gennian,MA Lijun,et al. Estimating the carbon emissions and regional differences of tourism transport in China[J].Journal of Shaanxi Normal University,2012,40(2):76-84.
[33]王俊,徐金海,夏杰长.中国区域旅游经济空间关联结构及其效应研究:基于社会网络分析[J].旅游学刊,2017,32(7):15-26.WANG Jun,XU Jinhai,XIA Jiechang. Study on the spatial correlation structure of China's tourism economic and its effect:based on social network analysis[J]. Tourism Tribune,2017,32(7):15-26.
[34]GOSSLING S,SCOTT D,HALL C M. Inter-market variability in CO2emission-intensities in tourism:implications for destination marketing and carbon management[J]. Tourism Management,2015,46(2):203-212.
[35]WANG Yanan,CHEN Wei,KANG Yanqing,et al. Spatial correlation of factors affecting CO2emission at provincial level in China:a geographically weighted regression approach[J].Journal of Cleaner Production,2018,184(5):929-937.
[36]XU Helian,CHENG Long. The QAP weighted network analysis method and its application in international services trade[J].Physica A:Statistical Mechanics and Its Applications,2016,448(4):91-101.
[37]CELA E,DEINEKO V G,WOEGINGER G J.Well-solvable cases of the QAP with block-structured matrices[J]. Discrete Applied Mathematics,2015,186(5):56-65.
[2]潘植强,梁保尔.旅游业碳排放强度分布及其驱动因子的时空异质研究:基于30个省(市、区)2005—2014年的面板数据分析[J].人文地理,2016,31(6):152-158.PAN Zhiqiang,LIANG Baoer.Research on space-time heterogeneity of tourism industry carbon emission intensity distribution and influencing factors:analysis of panel data from 30 provinces(cities and districts)from 2005 to 2014[J]. Human Geography,2016,31(6):152-158.
[3]GOSSLING S. Sustainable tourism development in developing countries:some aspects of energy-use[J]. Journal of Sustainable Tourism,2000,8(5):410-425.
[4]GOSSLING S,GARROD B,AALL C,et al. Food management in tourism:reducing tourism's carbon foodprint[J]. Tourism Management,2010,32(3):534-543.
[5]BECKEN S.Analyzing international tourist flows to estimate energy use associated with air travel[J]. Journal of Sustainable Tourism,2002,10(2):114-131.
[6]BECKEN S,FRAMPTON C,SIMMONS D G. Energy consumption patterns in the accommodation sector:the New Zealand case[J].Ecological Economics,2001,39(3):371-386.
[7]GOSSLING S. Global environmental consequences of tourism[J].Global Environmental Change,2002,12(6):283-302.
[8]EIJGELAAR E,THAPER C,PEETERS P. Antarctic cruise tourism:the paradoxes of ambassadorship,‘last chance tourism’and greenhouse gas emissions[J]. Journal of Sustainable Tourism,2010,18(3):337-354.
[9]DUBOIS G,CERON J P.Tourism/leisure greenhouse gas emissions forecasts for 2050:factors for change in France[J]. Journal of Sustainable Tourism,2006,14(2):172-191.
[10]石培华,吴普.中国旅游业能源消耗与CO2排放量的初步估算[J].地理学报,2011,66(2):235-243.SHI Peihua,WU Pu.A rough estimation of energy consumption and CO2emission in tourism sector of China[J]. Acta Geographica Sinica,2011,66(2):235-243.
[11]NIELSEN S P,SESARTIC A,STUCKI M. The greenhouse gas intensity of the tourism sector:the case of Switzerland[J].Environmental Science&Policy,2009,13(2):131-140.
[12]JONES C. Scenarios for greenhouse gas emissions reduction from tourism:an extended tourism satellite account approach in a regional setting[J].Journal of Sustainable Tourism,2013,21(3):458-472.
[13]谢园方,赵媛.长三角地区旅游业能源消耗的CO2排放测度研究[J].地理研究,2012,31(3):429-438.XIE Yuanfang,ZHAO Yuan. Measuring carbon dioxide emissions from energy consumption by tourism in Yangtze River Delta[J].Geographical Research,2012,31(3):429-438.
[14]PETTERS P,SCHOUTEN F. Reducing the ecological footprint of inbound tourism and transport to Amsterdam[J]. Journal of Sustainable Tourism,2006,14(2):157-171.
[15]El HANANDEH A. Quantifying the carbon footprint of religious tourism:the case of Hajj[J]. Journal of Cleaner Production,2013,52(16):53-60.
[16]孙瑞红.基于碳排放清单的九寨沟自然保护区碳足迹及碳管理研究[D].上海:上海师范大学,2013:69-75.
[17]BALEENTIS T,BALEENTIS A. Rural tourism development in Lithuania(2003-2010):a quantitative analysis[J]. Tourism Management Perspectives,2012,15(6):1-6.
[18]SUN Yanyen.Decomposition of tourism greenhouse gas emissions:revealing the dynamics between tourism economic growth,technological efficiency,and carbon emissions[J]. Tourism Management,2016,55(2):326-336.
[19]GOSSLING S,PEETERS P,CERON J P,et al.The eco-efficiency of tourism[J].Ecological Economics,2005,54(4):417-434.
[20]贺腊梅,于萌,查建平.基于BML生产率指数的中国旅游业能源效率评价与影响因素研究[J].长江流域资源与环境,2017,26(12):1991-2002.HE Lamei,YU Meng,ZHA Jianping. Energy efficiency evaluation and influencing factors of the tourism industry in China based on BML productivity index[J]. Resources and Environment in the Yangtze Basin,2017,26(12):1991-2002.
[21]李强谊,钟水映,徐飞.中国旅游业二氧化碳排放的地区差异与收敛性研究[J].经济问题探索,2017(8):28-38.
[22]罗芬,王怀採,钟永德.旅游者交通碳足迹空间分布研究[J].中国人口·资源与环境,2014,24(2):38-46.LUO Fen,WANG Huaicai,ZHONG Yongde.Tourists'transportation carbon footprint spatial distribution[J].China Population Resources and Environment,2014,24(2):38-46.
[23]王凯,夏莉惠,陈勤昌,等.基于空间聚类分析的中国旅游业碳排放效率[J].环境科学研究,2018,31(3):419-427.WANG Kai,XIA Lihui,CHEN Qinchang,et al. Carbon emission efficiency in China's tourism industry by spatial clustering analysis[J].Research of Environmental Sciences,2018,31(3):419-427.
[24]王凯,邵海琴,周婷婷,等.中国旅游业碳排放效率及其空间关联特征[J].长江流域资源与环境,2018,27(3):473-482.WANG Kai,SHAO Haiqin,ZHOU Tingting,et al. A study on carbon emissions efficiency of tourism and its spatial correlation characteristics in China[J]. Resources and Environment in the Yangtze Basin,2018,27(3):473-482.
[25]BORG E A. The marketing of innovations in high-technology companies:a network approach[J].European Journal of Marketing,2009,43(3/4):364-370.
[26]CASSI L,MORRISON A,Ter WAL A L J. The evolution of trade and scientific collaboration networks in the global wine sector:a longitudinal study using network analysis[J].Economic Geography,2012,88(3):311-334.
[27]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,2015,13(5):83-95.LIU Huajun,LIU Chuanming,SUN Yanan. Spatial correlation network structure of energy consumption and its effect in China[J].China Industrial Economics,2015,13(5):83-95.
[28]黄杰.中国能源环境效率的空间关联网络结构及其影响因素[J].资源科学,2018,40(4):759-772.HUANG Jie.The spatial network structure of energy-environmental efficiency and its determinants in China[J]. Resources Science,2018,40(4):759-772.
[29]杨桂元,吴齐,涂洋.中国省际碳排放的空间关联及其影响因素研究:基于社会网络分析方法[J].商业经济与管理,2016,38(4):56-68.YANG Guiyuan,WU Qi,TU Yang. Researchs of China's regional carbon emission spatial correlation and its determinants:based on the method of social network analysis[J]. Journal of Business Economics,2016,38(4):56-68.
[30]李敬,陈澍,万广华,等.中国区域经济增长的空间关联及其解释:基于网络分析方法[J].经济研究,2014,49(11):4-16.LI Jing,CHEN Shu,WAN Guanghua,et al. Study on spatial correlation and explanation of regional economic growth in China:based on analytic network processs[J]. Economic Research Journal,2014,49(11):4-16.
[31]WASSERMAN S,FAUST K. Social network analysis:methods and applications[M]. London:Cambridge University Press,1994:40-186.
[32]魏艳旭,孙根年,马丽君,等.中国旅游交通碳排放及地区差异的初步估算[J].陕西师范大学学报,2012,40(2):76-84.WEI Yanxu,SUN Gennian,MA Lijun,et al. Estimating the carbon emissions and regional differences of tourism transport in China[J].Journal of Shaanxi Normal University,2012,40(2):76-84.
[33]王俊,徐金海,夏杰长.中国区域旅游经济空间关联结构及其效应研究:基于社会网络分析[J].旅游学刊,2017,32(7):15-26.WANG Jun,XU Jinhai,XIA Jiechang. Study on the spatial correlation structure of China's tourism economic and its effect:based on social network analysis[J]. Tourism Tribune,2017,32(7):15-26.
[34]GOSSLING S,SCOTT D,HALL C M. Inter-market variability in CO2emission-intensities in tourism:implications for destination marketing and carbon management[J]. Tourism Management,2015,46(2):203-212.
[35]WANG Yanan,CHEN Wei,KANG Yanqing,et al. Spatial correlation of factors affecting CO2emission at provincial level in China:a geographically weighted regression approach[J].Journal of Cleaner Production,2018,184(5):929-937.
[36]XU Helian,CHENG Long. The QAP weighted network analysis method and its application in international services trade[J].Physica A:Statistical Mechanics and Its Applications,2016,448(4):91-101.
[37]CELA E,DEINEKO V G,WOEGINGER G J.Well-solvable cases of the QAP with block-structured matrices[J]. Discrete Applied Mathematics,2015,186(5):56-65.