| 中国旅游业碳减排潜力的空间关联网络结构及其影响因素 |
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| 引用本文： | 王凯,余芳芳,胡奕,甘畅.中国旅游业碳减排潜力的空间关联网络结构及其影响因素[J].地理科学,2022,42(6):1034-1043. |
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| 作者姓名： | 王凯 余芳芳 胡奕 甘畅 |
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| 作者单位： | 湖南师范大学旅游学院，湖南 长沙 410081 |
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| 基金项目： | 湖南省国内一流培育学科建设项目（5010002）资助 |
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| 摘 要： | 基于2000—2018年中国省际面板数据，利用“自下而上”法和Super-SBM模型测度30个省（区、市）的旅游业碳减排潜力，借助修正的引力模型和社会网络分析方法探究其空间关联网络特征及影响因素。结果表明：①中国旅游业碳减排潜力的空间关联日趋紧密，网络密度和网络关联数呈增长态势，网络效率和网络等级度呈下降态势；②东部区域在空间关联网络中居于核心位置，对降低旅游业碳减排潜力所需要素的掌控与支配能力较强；西部区域在网络中居于边缘位置，难以影响和控制其他省（区、市）；③北京、天津、江苏和上海属于“净受益”板块，广东、浙江和福建属于“经纪人”板块，吉林、内蒙古等23省（区、市）属于“净溢出”板块；④空间邻接关系、经济发展水平差异、产业结构差异、技术创新水平差异和旅游业人数规模差异共同驱动着中国旅游业碳减排潜力空间关联网络结构的形成与演化。
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| 关 键 词： | 旅游业碳减排潜力 空间网络结构 社会网络分析 |
| 收稿时间： | 2021-06-15 |
| 修稿时间： | 2021-12-23 |
Spatial Correlation Network Structure of Tourism Carbon Emission Reduction Potential and the Determinants in China |
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| Wang Kai,Yu Fangfang,Hu Yi,Gan Chang.Spatial Correlation Network Structure of Tourism Carbon Emission Reduction Potential and the Determinants in China[J].Scientia Geographica Sinica,2022,42(6):1034-1043. |
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| Authors: | Wang Kai Yu Fangfang Hu Yi Gan Chang |
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| Institution: | College of Tourism, Hunan Normal University, Changsha 410081, Hunan, China |
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| Abstract: | Based on Chinese provincial panel data from 2000-2018, the ‘bottom-up’ method and Super-SBM model were used to measure tourism carbon emission reduction potential of 30 provinces. Besides, the modified gravity model and social network analysis method were applied to explore the spatial correlation network characteristics and its determinants of tourism carbon emission reduction potential in China. The results showed that: 1) The spatial correlation degree of tourism carbon emission reduction potential was strengthened during the study period. In addition, the network density and the number of network relations maintained a fluctuating upward trend, while the network efficiency and network hierarchy declined gradually. 2) The eastern provinces were at the center of spatial correlation network, which had strong ability to control and dominate the elements needed to reduce the tourism carbon emission reduction potential. The western less developed provinces were at the edge position and had difficulty in influencing and controlling other provinces. 3) Beijing, Tianjin, Jiangsu and Shanghai belonged to ‘net benefit spillover’; Guangdong, Zhejiang and Fujian belonged to ‘agent plate’; Jilin and other 22 provinces were classified as ‘net spillover plate’. 4) Spatial adjacency relation, differences in economic development level, industrial structure, technological innovation level and tourist scale drove the formation and evolution of spatial correlation network structure of tourism carbon emission reduction potential in China. |
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| Keywords: | tourism carbon emission reduction potential spatial network structure social network analysis |
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