基于模糊物元模型的中国旅游生态安全评价及障碍因子诊断研究

doi:10.13249/j.cnki.sgs.2021.01.004

摘要/Abstract

摘要：

基于DPSIR模型构建旅游生态安全评价指标体系，利用模糊物元模型对中国31个省（市、区）（不含港澳台）旅游生态安全进行动态评价，并运用空间自相关分析空间格局演化特征。同时借助障碍度模型，对旅游生态安全的障碍因子进行诊断。结果表明:① 旅游生态安全呈现出波动增长的“W”型演化趋势。2013—2016年中国旅游生态安全在空间上呈现出“东部高、西部低”的分布格局，但随着时间推移，出现“北部高、南部低；中间高、两边低”的格局变化；② 从全局来看，2013—2017年中国旅游生态安全在空间上呈现出显著的空间集聚分布的特点，但集聚程度有逐渐下降的态势。从局部关联来看，各省旅游生态安全空间分布存在着空间集聚特征和省域间的空间依赖性；③ 各省旅游生态安全障碍因子存在显著差异，障碍度高于10%的障碍因子较多，主要的障碍因子有旅游经济密度、游客密度、烟尘排放、生活垃圾处理率和污水处理率等。因此，在大力发展旅游业时，需充分考虑经济发展程度和旅游生态安全提升能力；同时还要注意旅游政策与环境保护政策的协调性。

关键词: 旅游生态安全, 障碍因子, DPSIR模型, 模糊物元模型

Abstract:

Based on the DPSIR model, a tourism ecological safety evaluation index system is constructed, and the dynamic evaluation of tourism ecological safety in 31 provinces (municipalities and districts) of China (excluding Hong Kong, Macao and Taiwan) is carried out using the fuzzy object element model, and the spatial autocorrelation is applied to analyze the evolutionary characteristics of the spatial pattern. At the same time, the obstacle factors of tourism ecological safety were diagnosed with the help of the obstacle degree model. The results show that: 1) Tourism ecological safety shows a “W”-shaped evolutionary trend, generally in a good trend of fluctuation weakening and steady improvement. From 2013 to 2016, each province showed a spatial distribution pattern of “high in the east and low in the west”, with an overall gradient distribution pattern from coastal to inland. However, over time, the pattern of “high in the north, low in the south; high in the middle, low on both sides” has changed, with the overall Euclidean proximity of tourism ecological safety in each province increasing and the gap between regions narrowing. 2) From a global perspective, the spatial distribution of tourism ecological safety in China from 2013 to 2017 is characterized by significant spatial clustering, but the spatial correlation of tourism ecological safety in each province has weakened and the degree of clustering has gradually decreased. In terms of local correlations, the spatial distribution of tourism ecological safety in China’s 31 provinces (municipalities and districts) is characterized by spatial agglomeration and inter-provincial spatial dependence. Most of the provinces show a strong local autocorrelation in tourism ecological safety, with a relatively stable overall pattern. A small number of provinces show negative correlation and significant spatial heterogeneity. 3) There are significant differences in tourism ecological safety obstacle factors among provinces, with more obstacle factors having an obstacle degree higher than 10%. The main obstacle factors are tourism economic density, tourist density, smoke and dust emission, domestic waste disposal rate and sewage disposal rate. The tourism ecosystem is a complex and integrated system composed of natural, social and economic elements. Therefore, when vigorously developing tourism, the degree of economic development and the ability to enhance tourism ecological safety need to be fully considered; attention should also be paid to the coordination between tourism policies and environmental protection policies.

Key words: tourism ecological safety, barrier factors, DPSIR model, fuzzy object element model

中图分类号:

F590

徐少癸, 左逸帆, 章牧. 基于模糊物元模型的中国旅游生态安全评价及障碍因子诊断研究[J]. 地理科学, 2021, 41(1): 33-43.

Xu Shaogui, Zuo Yifan, Zhang Mu. Evaluation of Tourism Ecological Security and Diagnosis of Obstacle Factors in China Based on Fuzzy Object Element Model[J]. SCIENTIA GEOGRAPHICA SINICA, 2021, 41(1): 33-43.

图/表 6

表1

旅游生态安全评价指标体系"

维度	序号	评价指标	具体含义	测量单位	指标属性
驱动力（Driver）	D1	旅游收入增长率	反映旅游业收益的增长速度	%	-
	D2	游客增长率	反映旅游业发展规模	%	-
	D3	第三产业增长率	反映旅游地第三产业增长速度	%	-
	D4	人口自然增长率	反映旅游地人口增长速度	‰	-
	D5	人均国内生产总值	反映旅游地社会经济发展水平	元	-
	D6	城市化率	反映旅游地城市化建设水平	%	-
压力（Pressure）	P1	人口密度	表示人口与土地承载力之间的关系，反映社会压力	人/hm²	-
	P2	游客密度	表示游客与土地承载力之间的关系，反映社会压力	人/hm²	-
	P3	人均日生活用水量	表示用水需求量，反映资源压力	L	-
	P4	废水排放	表示旅游地废水排放量，反映环境压力	万t	-
	P5	烟尘排放	表示旅游地烟尘排放量，反映环境压力	万t	-
状态（State）	S1	人均公园绿地面积	反映城市整体环境水平和居民生活质量	m²	+
	S2	建成区绿化覆盖率	反映城市建成区生态环境的可持续性	%	+
	S3	第三产业在国内生产总值中的比重	反映产业结构的合理性	%	+
	S4	旅游业总收入占GDP的比重	反映旅游地发展旅游业的潜力和空间	%	+
	S5	人均耕地面积	反映土地资源的利用情况	hm²	+
影响（Impact）	I1	旅游资源利用强度	反映游客对旅游资源的消耗	-	-
	I2	旅游经济密度	反映旅游收入和区域土地承载力之间的关系	元/km²	-
	I3	人均旅游收入	反映旅游对旅游地居民经济收益的影响	万元/人	+
	I4	游客接待量和人口比例	反映旅游地的旅游渗透程度	%	-
	I5	第三产业员工人数	反映从业人员结构的合理性	万人	+
响应（Response）	R1	污水处理率	反映旅游地水环境治理水平	%	+
	R2	生活垃圾处理率	反映旅游地生活环境质量	%	+
	R3	环保投入占GDP的比重	反映政府对生态环境保护的资金支持力度	%	+
	R4	普通高等学校学生数	反映人才供给情况，体现旅游服务的专业性和精细化	人	+

表1

表2

2013—2017年中国旅游生态安全评价结果"

省份	2013年		2014年		2015年		2016年		2017年
省份	欧式贴近度	排名	欧式贴近度	排名	欧式贴近度	排名	欧式贴近度	排名	欧式贴近度	排名
注:未包括港澳台数据。
北京	0.510	1	0.499	1	0.485	4	0.502	3	0.516	2
天津	0.417	26	0.393	24	0.385	29	0.367	29	0.412	24
河北	0.430	20	0.410	15	0.415	25	0.413	22	0.438	20
山西	0.457	9	0.436	12	0.462	10	0.504	2	0.492	4
内蒙古	0.500	3	0.495	2	0.540	1	0.551	1	0.583	1
辽宁	0.485	5	0.449	8	0.481	5	0.490	4	0.502	3
吉林	0.433	16	0.402	20	0.467	8	0.465	10	0.450	17
黑龙江	0.471	8	0.461	4	0.454	14	0.473	7	0.462	12
上海	0.307	31	0.290	31	0.333	31	0.274	31	0.286	31
江苏	0.481	6	0.451	7	0.486	3	0.460	12	0.451	15
浙江	0.471	7	0.453	6	0.456	12	0.473	8	0.459	13
安徽	0.509	2	0.467	3	0.490	2	0.486	5	0.490	5
福建	0.420	24	0.388	27	0.397	27	0.392	25	0.406	27
江西	0.424	23	0.397	23	0.452	15	0.440	15	0.478	8
山东	0.489	4	0.447	9	0.476	6	0.460	11	0.468	11
河南	0.428	21	0.397	22	0.418	23	0.434	16	0.469	10
湖北	0.432	17	0.410	16	0.435	18	0.454	14	0.451	14
湖南	0.410	27	0.389	25	0.439	17	0.405	24	0.408	26
广东	0.424	22	0.405	19	0.424	19	0.427	18	0.399	29
广西	0.431	18	0.406	18	0.447	16	0.433	17	0.418	23
海南	0.437	14	0.388	26	0.423	20	0.383	27	0.399	28
重庆	0.447	11	0.409	17	0.417	24	0.411	23	0.439	19
四川	0.439	13	0.420	14	0.462	11	0.478	6	0.479	6
贵州	0.418	25	0.399	21	0.420	21	0.359	30	0.419	22
云南	0.451	10	0.439	10	0.466	9	0.421	21	0.448	18
西藏	0.378	29	0.374	29	0.387	28	0.389	26	0.411	25
陕西	0.431	19	0.423	13	0.455	13	0.468	9	0.476	9
甘肃	0.404	28	0.380	28	0.419	22	0.424	19	0.479	7
青海	0.368	30	0.339	30	0.380	30	0.374	28	0.375	30
宁夏	0.434	15	0.455	5	0.470	7	0.458	13	0.419	21
新疆	0.446	12	0.436	11	0.402	26	0.422	20	0.451	16
均值	0.438		0.416		0.440		0.435		0.446

表2

图1

图2

图3

表3

参考文献 33

[1]	Tan F, Lu Z. Assessing regional sustainable development through an integration of nonlinear principal component analysis and Gram Schmidt orthogonalization[J]. Ecological Indicators, 2016, 63 (4): 71-81
[2]	Ruan W, Li Y, Zhang S et al. Evaluation and drive mechanism of tourism ecological security based on the DPSIR-DEA model[J]. Tourism Management, 2019, 75 (11): 609-625
[3]	Petrosillo I, Zurlini G, Grato E et al. Indicating fragility of socio-ecological tourism-based systems[J]. Ecological Indicators, 2006, 6 (1): 104-113 doi: 10.1016/j.ecolind.2005.08.008
[4]	Jurado E N, Tejada M T, García F A et al. Carrying capacity assessment for tourist destinations: Methodology for the creation of synthetic indicators applied in a coastal area[J]. Tourism Management, 2012, 33 (6): 1337-1346 doi: 10.1016/j.tourman.2011.12.017
[5]	Gari S R, Newton A, Icely J D. A review of the application and evolution of the DPSIR framework with an emphasis on coastal social-ecological systems[J]. Ocean & Coastal Management, 2015, 103 (1): 63-77
[6]	Peña-Alonso C, Ariza E, Hernández-Calvento L et al. Exploring multi-dimensional recreational quality of beach socio-ecological systems in the Canary Islands (Spain)[J]. Tourism Management, 2018, 64 (2): 303-313
[7]	吕君. 旅游生态安全思想的缘起及其安全状态诊断[J]. 内蒙古财经学院学报, 2007, 28 (5): 35-38
	Lv Jun. Origins of the idea of tourism ecological safety and its safety state diagnosis. Journal of Finance and Economics Theory, 2007, 28 (5): 35-38
[8]	吕君, 于相贤, 刘丽梅. 旅游发展生态安全的动力学机制探讨[J]. 干旱区资源与环境, 2009, 23 (4): 146-1149
	Lv Jun, Yu Xiangxian, Liu Limei. Dynamic mechanism of ecological security in tourism development. Journal of Arid Land Resources and Environment, 2009, 23 (4): 146-1149
[9]	章锦河, 张捷, 王群. 旅游地生态安全测度分析——以九寨沟自然保护区为例[J]. 地理研究, 2008, 27 (2): 449-458
	Zhang Jinhe, Zhang Jie, Wang Qun. Measuring the ecological security of tourist destination: Methodology and a case study of Jiuzhaigou. Geographical Research, 2008, 27 (2): 449-458
[10]	曹新向. 基于生态足迹分析的旅游地生态安全评价研究——以开封市为例[J]. 中国人口·资源与环境, 2006, 16 (2): 70-75
	Cao Xinxiang. Ecological security evaluation of tourism destination based on ecological footprint analysies—A case of Kaifeng City. China Population, Resources and Environment, 2006, 16 (2): 70-75
[11]	秦晓楠, 程钰. 中国旅游城市生态安全系统评估与类型划分研究——基于节点权重约束网络DEA模型[J]. 地理科学, 2019, 39 (1): 156-163
	Qin Xiaonan, Cheng Yu. Evaluation and type classification on Chinese tourist urban ecological security system: Based on the network DEA Model with node weight. Scientia Geographica Sinica, 2019, 39 (1): 156-163
[12]	杜忠潮, 李志霞. 基于生态足迹模型的旅游地生态安全评价研究——以咸阳市为例[J]. 西北师范大学学报(自然科学版), 2011, 47 (03): 110-115
	Du Zhongchao, Li Zhixia. Study on the ecological safety evaluation for tourist destination based on the ecological footprint model—A case study of Xianyang City. Journal of Northwest Normal University (Natural Science), 2011, 47 (03): 110-115
[13]	Tang C, Wu X, Zheng Q et al. Ecological security evaluations of the tourism industry in ecological conservation development areas: A case study of Beijing’s ECDA[J]. Journal of Cleaner Production, 2018, 197 (1): 999-1010
[14]	Hua Y, Yan M, Limin D. Land ecological security assessment for Bai autonomous prefecture of Dali based using PSR model—With data in 2009 as case[J]. Energy Procedia, 2011, 5 (8): 2172-2177
[15]	Ehara M, Hyakumura K, Kurosawa K et al. Addressing maladaptive coping strategies of local communities to changes in ecosystem service provisions using the DPSIR framework[J]. Ecological Economics, 2018, 149 (7): 226-238
[16]	Cai W, Guo K, Huang Y. Matter element analysis and value engineering[J]. Journal of Guangdong University of Technology, 1984, 1 (1): 7-17
[17]	Qiu W. Management decision entropy and its applications[M]. Beijing: China Electric Power Press, 2011,
[18]	Deng X, Xu Y, Han L et al. Assessment of river health based on an improved entropy-based fuzzy matter-element model in the Taihu Plain, China[J]. Ecological Indicators, 2015, 57 (10): 85-95
[19]	Li B, Yang G, Wan R et al. Dynamic water quality evaluation based on fuzzy matter-element model and functional data analysis: A case study in Poyang Lake[J]. Environmental Science and Pollution Research, 2017, 24 (23): 19138-19148 doi: 10.1007/s11356-017-9371-0
[20]	Chen J, Zhang F, Yang C et al. Factor and trend analysis of total-loss marine casualty using a fuzzy matter element method[J]. International Journal of Disaster Risk Reduction, 2017, 24 (1): 383-390
[21]	Han H, Li H, Zhang K. Urban water ecosystem health evaluation based on the improved fuzzy matter-element extension assessment model: Case study from Zhengzhou City, China[J]. Mathematical Problems in Engineering, 2019, 2019 (1): 1-14
[22]	Xu W, Dong Z, Hao Z et al. River health evaluation based on the fuzzy matter-element extension assessment model[J]. Polish Journal of Environmental Studies, 2017, 26 (3): 1353-1361 doi: 10.15244/pjoes/67369
[23]	Xiao Q, He R, Ma C et al. Evaluation of urban taxi-carpooling matching schemes based on entropy weight fuzzy matter-element[J]. Applied Soft Computing, 2019, 81 (1): 1-10
[24]	张桂颖. 基于模糊物元模型的土地流转农户可持续生计评价方法[J]. 统计与决策, 2019, 35 (5): 121-125
	Zhang Guiying. Evaluation method of farmers’ sustainable livelihood in land transfer based on fuzzy matter-element model. Statistics & Decision, 2019, 35 (5): 121-125
[25]	Ord J K, Getis A. Testing for local spatial autocorrelation in the presence of global autocorrelation[J]. Journal of Regional Science, 2001, 41 (3): 411-432 doi: 10.1111/0022-4146.00224
[26]	Fotheringham A S. “The problem of spatial autocorrelation” and local spatial statistics[J]. Geographical Analysis, 2009, 41 (4): 398-403 doi: 10.1111/j.1538-4632.2009.00767.x
[27]	Chen Y, Zhu M, Lu J et al. Evaluation of ecological city and analysis of obstacle factors under the background of high-quality development: Taking cities in the Yellow River Basin as examples[J]. Ecological Indicators, 2020, 118 (11): 1-10
[28]	Wang Y, Feng Y, Zuo J et al. From “Traditional” to “Low carbon” urban land use: Evaluation and obstacle analysis[J]. Sustainable Cities and Society, 2019, 51 (11): 1-9
[29]	Shao Z, Ding L, Li D et al. Exploring the relationship between urbanization and ecological environment using remote sensing images and statistical data: A case study in the Yangtze River Delta, China[J]. Sustainability, 2020, 12 (14): 1-28
[30]	孙振杰, 袁家冬, 刘继斌. 中国典型旅游城市生活服务水平评价及障碍因素研究[J]. 地理科学, 2020, 40 (2): 289-297
	Sun Zhenjie, Yuan Jiadong, Liu Jibin. Evaluationand obstacle factors of life services level of typical tourism cities in China. Scientia Geographica Sinica, 2020, 40 (2): 289-297
[31]	李彩惠, 霍海鹰, 李雅洁, 等. 基于突变级数模型的城市低碳竞争力评价及障碍因子诊断分析[J]. 资源科学, 2015, 37 (7): 1474-1481
	Li Caihui, Huo Haiying, Li Yajie et al. Evaluation of low-carbon city competitiveness and its obstacle indicators analysis in Shandong Province. Resources Science, 2015, 37 (7): 1474-1481
[32]	中华人民共和国国家统计局. 中国统计年鉴[M]. 北京: 中国统计出版社, 2014-2018.
	National Bureau of Statistics of the People’s Republic of China. China statistical yearbook. Beijing: China Statistics Press, 2014-2018.
[33]	Cheniti H, Cheniti M, Brahamia K. Use of GIS and Moran’s I to support residential solid waste recycling in the city of Annaba, Algeria[J]. Environmental Science and Pollution Research, 2020, 27 (35): 1-15