中国旅游业碳排放效率的空间网络结构及其效应研究

doi:10.13249/j.cnki.sgs.2020.03.002

地理科学 ›› 2020, Vol. 40 ›› Issue (3): 344-353.doi: 10.13249/j.cnki.sgs.2020.03.002

中国旅游业碳排放效率的空间网络结构及其效应研究

王凯¹, 张淑文¹, 甘畅¹, 杨亚萍¹, 刘浩龙²

^1. 湖南师范大学旅游学院, 湖南长沙410081
^2. 中国科学院地理科学与资源研究所/中国科学院陆地表层格局与模拟重点实验室, 北京100101

收稿日期:2019-05-07 修回日期:2019-08-10 出版日期:2020-03-10 发布日期:2020-05-13
作者简介:王凯（1969-）,男,湖南新宁人,教授,博导,主要从事低碳经济、区域旅游发展规划研究。E-mail: kingviry@163.com
基金资助:
湖南省自然科学基金项目(2018JJ2259);国家社会科学基金项目(18BJY191);湖南省国内一流培育学科建设项目资助(5010002)

Spatial Network Structure of Carbon Emission Efficiency of Tourism Industry and Its Effects in China

Wang Kai¹, Zhang Shuwen¹, Gan Chang¹, Yang Yaping¹, Liu Haolong²

^1. College of Tourism, Hunan Normal University, Changsha 410081, Hunan, China
^2. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Received:2019-05-07 Revised:2019-08-10 Online:2020-03-10 Published:2020-05-13
Supported by:
Natural Science Foundation of Hunan Province, China(2018JJ2259);National Social Science Foundation of China(18BJY191);The Construction Program for First-class Disciplines of Hunan Province, China(5010002)

摘要/Abstract

摘要：

基于中国30个省区2001-2016年面板数据,采用SBM模型测度各省区旅游业碳排放效率,借助修正的引力模型和社会网络分析方法,厘清中国旅游业碳排放效率的空间网络结构及其效应。研究表明：① 中国旅游业碳排放效率的空间关联渐趋紧密,网络发育程度日益完善,但距理想状态仍有差距;② 各省区网络中心性指标分异性逐步减小,上海、北京、江苏等省区排名稳居前列,重庆、福建、内蒙古等省区排名波动上升,宁夏、青海、山西等省区排名相对滞后;③ 网络整体呈核心区由东部沿海向中部及西南地区持续扩展,而边缘区范围逐步收缩态势;④ 网络密度与旅游业碳排放效率呈正相关,与旅游业碳排放效率差异构成负相关关系,网络等级度和网络效率则与之相反,网络中心性各指标的提升均能显著增强旅游业碳排放效率。

关键词: 旅游业碳排放效率, 社会网络析, 核心-边缘结构, 网络结构效应

Abstract:

The carbon emission efficiency of China's tourism industry presents complex spatial correlation characteristics for the interaction of multiple factors. It is of great significance for energy saving and emission reduction of tourism industry to clarify the comprehensive structure scenario of carbon emission efficiency in China's tourism industry. Based on panel data of 30 provinces in mainland China from 2001 to 2016, this article uses the SBM model to measure the carbon emission efficiency of tourism industry. Then, the method of social network analysis and the modified gravity model are applied to examine the characteristics of spatial network and its effects on carbon emission efficiency in China's tourism industry. The results show that: 1) The network relationship number and network density of tourism carbon emission efficiency fluctuate upwards. While the network grade and network efficiency decline gradually. The spatial correlation of carbon emission efficiency in China's tourism industry has been significantly strengthened. The network structure tends to be mature, but there is still much space for improvement from the ideal state. 2) From the analysis results of network centrality, the diversity of network centrality indicators in various provinces gradually decreases. Shanghai, Beijing, Jiangsu and other provinces rank steadily in the forefront, Chongqing, Fujian, Inner Mongolia etc. present a fluctuant rising trend, Ningxia, Qinghai, Shanxi and others rank relatively backward. 3) According to the analysis results of core-periphery structure, the network as a whole shows the trend of core districts expansion from eastern coastal areas to central and southwestern China, while the scope of the periphery shrinks gradually. 4) The network density is positively proportional to the tourism carbon emission efficiency and negatively correlated with the difference of tourism carbon emission efficiency. While the network grade and network efficiency are contrary to network density. The promotion of individual centrality indicators including degree, closeness and betweenness can significantly enhance the carbon emission efficiency of tourism industry. In order to promote regional cooperative emission reduction in tourism industry, targeted policies should be formulated according to the effects of network structure.

Key words: tourism carbon emission efficiency, social network analysis, core-periphery structure, network structure effects

中图分类号:

王凯, 张淑文, 甘畅, 杨亚萍, 刘浩龙. 中国旅游业碳排放效率的空间网络结构及其效应研究[J]. 地理科学, 2020, 40(3): 344-353.

Wang Kai, Zhang Shuwen, Gan Chang, Yang Yaping, Liu Haolong. Spatial Network Structure of Carbon Emission Efficiency of Tourism Industry and Its Effects in China[J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(3): 344-353.

图/表 7

图1

图2

表1

中国旅游业碳排放效率网络中心性分析"

省区	2001年			2006年			2011年			2016年
省区	C_AB	C_Ap	C_AD	C_AB	C_Ap	C_AD	C_AB	C_Ap	C_AD	C_AB	C_Ap	C_AD
安徽	10.345	51.786	0.018	13.793	51.786	0.035	20.690	51.786	0.102	27.586	58.000	0.601
北京	68.966	76.316	8.849	75.862	78.378	11.566	65.517	72.500	9.769	72.414	78.378	6.127
重庆	24.138	56.863	0.441	37.931	61.702	1.048	48.276	65.909	2.834	55.172	69.048	1.733
福建	17.241	54.717	0.112	55.172	69.048	3.851	44.828	63.043	1.186	62.069	72.500	2.530
甘肃	24.138	56.863	0.274	27.586	58.000	0.553	37.931	61.702	1.351	41.379	63.043	1.083
广东	58.621	63.043	8.812	58.621	70.732	5.137	51.724	59.184	4.469	48.276	61.702	2.554
广西	13.793	53.704	0.186	20.690	55.769	0.318	31.034	59.184	1.112	37.931	61.702	0.691
贵州	20.690	55.769	0.274	27.586	58.000	0.299	31.034	59.184	0.884	37.931	61.702	0.691
海南	10.345	52.727	0.040	17.241	54.717	0.113	27.586	51.786	0.488	34.483	60.417	0.347
河北	17.241	54.717	0.076	24.138	56.863	0.154	31.034	56.863	0.655	37.931	60.417	0.462
河南	27.586	58.000	0.576	41.379	63.043	1.461	37.931	61.702	0.993	48.276	65.909	1.088
黑龙江	13.793	52.727	0.030	13.793	53.704	0.033	24.138	54.717	0.891	51.724	67.442	1.975
湖北	20.690	55.769	0.274	34.483	60.417	0.469	37.931	61.702	1.377	44.828	64.444	1.443
湖南	24.138	56.863	0.607	37.931	61.702	1.126	41.379	63.043	1.772	51.724	67.442	1.316
吉林	17.241	53.704	0.030	17.241	54.717	0.095	13.793	48.333	0.000	41.379	63.043	1.184
江苏	65.517	74.359	9.120	58.621	70.732	5.384	72.414	78.378	11.292	72.414	78.378	6.183
江西	17.241	54.717	0.201	20.690	55.769	0.186	34.483	58.000	0.986	44.828	64.444	1.312
辽宁	17.241	53.704	0.030	17.241	54.717	0.199	27.586	55.769	1.379	37.931	61.702	0.393
内蒙古	13.793	52.727	0.000	27.586	58.000	0.446	65.517	72.500	7.002	75.862	80.556	6.745
宁夏	20.690	55.769	0.274	27.586	58.000	0.427	24.138	56.863	0.449	27.586	58.000	0.283
青海	20.690	55.769	0.274	27.586	58.000	0.553	27.586	58.000	0.581	31.034	59.184	0.411
山东	17.241	54.717	0.191	20.690	55.769	0.214	20.690	53.704	0.242	37.931	60.417	0.680
山西	27.586	58.000	0.795	27.586	58.000	0.369	27.586	55.769	0.407	31.034	58.000	0.406
陕西	24.138	56.863	0.441	27.586	58.000	0.438	31.034	59.184	0.930	41.379	63.043	1.044
上海	96.552	96.667	29.300	93.103	93.548	21.309	65.517	74.359	7.766	68.966	76.316	5.267
四川	24.138	56.863	0.441	31.034	59.184	0.487	34.483	60.417	1.080	31.034	59.184	0.396
天津	65.517	74.359	7.663	68.966	74.359	8.355	48.276	63.043	3.658	65.517	74.359	3.834
新疆	27.586	58.000	1.120	34.483	60.417	0.789	48.276	65.909	3.526	58.621	70.732	3.839
云南	20.690	55.769	0.274	27.586	58.000	0.299	34.483	60.417	1.112	41.379	63.043	0.710
浙江	55.172	69.048	6.120	55.172	69.048	3.746	51.724	65.909	2.396	55.172	69.048	2.060
均值	29.425	59.363	2.561	35.632	61.671	2.315	38.621	60.962	2.356	47.126	65.720	1.913

表1

图3

表2

表3

表4

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	2001年		2006年		2011年		2016年
	核心区	边缘区	核心区	边缘区	核心区	边缘区	核心区	边缘区
核心区	0.687	0.156	0.761	0.142	0.809	0.199	0.783	0.246
边缘区	0.208	0.263	0.306	0.316	0.335	0.410	0.424	0.477

被解释变量	旅游业碳排放效率均值			旅游业碳排放效率标准差
模型	（1）	（2）	（3）	（4）	（5）	（6）
常数项	-3.729^***	2.140^***	4.915^***	-2.354^***	1.786^***	1.752^***
网络密度	3.885^***	—	—	-1.087^***	—	—
网络等级度	—	-1.030^***	—	—	0.511^***	—
网络效率	—	—	-5.203^***	—	—	4.300^***
R²	0.744	0.601	0.719	0.749	0.668	0.659

模型	（1）	（2）	（3）
常数项	2.520^***	2.625^***	8.132^***
度数中心度	0.869^***	—	—
接近中心度	—	1.994^***	—
中间中心度	—	—	0.207^***
R²	0.562	0.697	0.675
Hausman统计量	7.202^**	5.745^*	7.265^*
FE/RE	FE	RE	FE

中国旅游业碳排放效率的空间网络结构及其效应研究

Spatial Network Structure of Carbon Emission Efficiency of Tourism Industry and Its Effects in China

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