RESEARCH ON THE CARRYING CAPACITY OF TOURISM ENVIRONMENT IN THE YELLOW RIVER DELTA

ZHI Liehui; ZHANG Zhe; BAI Junhong; LI Xiaowen

doi:10.13205/j.hjgc.202301016

Volume 41 Issue 1

Jan. 2023

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2023 > 41(1): 132-140,163

ZHI Liehui, ZHANG Zhe, BAI Junhong, LI Xiaowen. RESEARCH ON THE CARRYING CAPACITY OF TOURISM ENVIRONMENT IN THE YELLOW RIVER DELTA[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(1): 132-140,163. doi: 10.13205/j.hjgc.202301016

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ZHI Liehui, ZHANG Zhe, BAI Junhong, LI Xiaowen. RESEARCH ON THE CARRYING CAPACITY OF TOURISM ENVIRONMENT IN THE YELLOW RIVER DELTA[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(1): 132-140,163. doi: 10.13205/j.hjgc.202301016

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RESEARCH ON THE CARRYING CAPACITY OF TOURISM ENVIRONMENT IN THE YELLOW RIVER DELTA

doi: 10.13205/j.hjgc.202301016

1. School of Earth Sciences and Engineering, Hebei University of Engineering, Handan 056038, China;
2. School of Environment, Beijing Normal University, Beijing 100875, China;
3. School of Ecological Environment, Hebei University, Baoding 071002, China

Received Date: 2022-10-11
Available Online: 2023-03-23

Abstract

Abstract

The development of tourism should pay attention to the coordination and order of ecology and social economy. Ecotourism takes into account regional economic development and ecological protection, conforms to the development strategy of efficient ecological economy, and is an important part of the industrial structure of the efficient ecological economic zone in the Yellow River Delta. However, tourism started late and developed slowly in the efficient ecological economic zone of the Yellow River Delta. At the same time, the fragile ecological environment and reclamation activities make the importance of ecological protection in tourism development and tourism activities of this region particularly prominent. This study evaluated the ecological, spatial, facility and social environmental carrying capacities by building a multi-level evaluation model of tourism environmental carrying capacity and relying on the minimum limiting factor method, in order to identify the major limiting factors of the tourism environmental carrying capacity of the study area and to diagnose the major issues affecting the carrying capacity of the area. The result showed that:1) the tourism carrying capacity between 1995 and 2020 was severely overloaded, and the tourism environmental capacity of the high-efficiency ecological economic zone in the Yellow River Delta increased from -2885700 people per day to 116000 people per day in 2020. 2) Sewage treatment was the minimum limiting factor in 1995, and transportation facilities was the minimum limiting factor in 2020. Only the two indicators were overloaded, other indicators were weak load; all other indications were either not loaded or only slightly loaded. 3) In 1995, the carrying capacity of tourism environment was mainly limited by the capacity of ecological environment, while in 2020, space>social>ecological>facilities were the secondary indicators of the Yellow River Delta's high-efficiency ecological economic zone's environmental capacity. 4) The overcrowding of tourism facilities in the Yellow Delta contradicted the underutilization of resources and the weak loading of the ecological and spatial environments, including wetland ecological indicators. However, there was still enough of potential for tourist expansion from appropriate loading. The carrying capacity of the biological environment had decreased during the past 25 years, despite a general increase in the capacity of the tourism environment due to a more rapid increase in visitor numbers. In this paper, we clarified the tourism development space that wetland ecology could support, identified the main limiting factors of tourism development, and analyzed the tourism development status of high-efficiency ecological economic zones in the Yellow River Delta from the perspective of tourism environmental carrying capacity. The research results also provided scientific basis and data support for solving the problem of environmental overload and weak load, realizing the rational use of resources and promoting the orderly and coordinated development of regional eco-economy.
- environmental capacity,
- scenic area,
- ecological tourism,
- wetland,
- multilevel evaluation

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References

[1]	翁钢民, 李建璞, 杨秀平, 等.近20年国内外旅游环境承载力研究动态[J].地理与地理信息科学, 2021, 37(1):106-115.
[2]	赵勇为, 贺小荣, 黄静波.休闲旅游城市旅游环境承载力评价研究——以郴州市为例[J].湘南学院学报, 2017, 38(6):21-27 , 52.
[3]	翁钢民, 杨秀平, 李慧盈.国内外旅游环境承载力研究的发展历程与展望[J].生态经济, 2015, 31(8):129-132.
[4]	LIME D W, STANKEY G H. Carrying capacity:maintaining outdoor recreation quality:Recreation symposium proceedings[C].USDA Forest Service Syracuse, New York, 1971:173-179.
[5]	ELIO C, PAOLO C.Tourist carrying capacity:a fuzzy approach Annals of Tourism Research, 1991(18):295-311.
[6]	TONY P.Modeling carrying capacity for national parks[J].Ecological Economics, 2001(39):321-331.
[7]	STEVEN R L, ROBERT E, WILLIAN A.Proactive monitoring and adaptive management of social carrying capacity in Arches National Park:an application of computer simulation modeling[J].Environment Management, 2003(68):305-313.
[8]	刘晓冰, 保继刚.旅游开发的环境影响研究进展[J].地理研究, 1996(4):92-100.
[9]	保继刚. 颐和园旅游环境容量研究[J].中国环境科学, 1987, 7(2):32-38.
[10]	刘玲. 旅游环境承载力研究[M].中国环境科学出版社, 2000:418.
[11]	赖明州, 薛怡珍.雪霸公园雪山主峰线之承载量研究[J].生态学杂志, 2003, 22(1):94-96.
[12]	张广海, 刘佳. 山东半岛城市群旅游环境承载力地域差异与功能分区[J].地域研究与开发, 2008, 27(4):77-80.
[13]	中华人民共和国国务院新闻办公室.黄河三角洲高效生态经济区发展规划[EB\\OL].http://www.scio.gov.cn/ztk/xwfb/04/4/Document/542283/542283.html.
[14]	孙宏瑗.黄河三角洲每年吸引近600万只鸟类停靠择"地"而栖[EB/OL]. http://www.sd.chinanews.com.cn/2/2020/0921 /74954.html.
[15]	山东省文化和旅游厅. 山东省A级旅游景区名录[EB/OL].http://whhly.shandong.gov.cn/art/2020/4/24/art_100526_9032316.html.
[16]	中华人民共和国文化和旅游部. 旅游行业标准LB/T034-2014景区最大承载量核定工作导则[EB/OL]. https://zwgk.mct.gov.cn/zfxxgkml/hybz/202012/t20201224_920037.html.
[17]	中华人民共和国生态环境部.生活污染源排污系数手册[EB/OL].https://www.mee.gov.cn/.
[18]	连云港市生态环境局. 中国城市生活垃圾管理状况评估报告[EB/OL].http://hbj.lyg.gov.cn/lygshbj/gnhbdt/content/42ff4fc7-ef09-40f4-a17e-8622ff1d1d18.html.
[19]	中华人民共和国建设部.《风景名胜区规划规范》(GB 50298-1999)[EB/OL]. http://ylj.fuzhou.gov.cn/zz/zwgk/fgzd/ylfg/201601/P020170327419887997593.doc.
[20]	中华人民共和国住房和城乡建设部.风景名胜区总体规划标准[EB/OL]. https://www.mohurd.gov.cn/gongkai/fdzdgknr/tzgg/201903/20190320_239842.html.
[21]	国家统计局.中国统计年鉴[J].北京:中国统计出版社, 1996.
[22]	国家统计局.中国统计年鉴[J].北京:中国统计出版社, 2021.
[23]	山东省统计局.山东省统计年鉴[J].北京:中国统计出版社, 1996.
[24]	山东省统计局.山东省统计年鉴[J].北京:中国统计出版社, 2021.
[25]	东营市统计局.东营市统计年鉴[J].北京:中国统计出版社, 1996.
[26]	东营市统计局.东营市统计年鉴[J].北京:中国统计出版社, 2021.
[27]	滨州市统计局.滨州市统计年鉴[J].北京:中国统计出版社, 1996.
[28]	滨州市统计局.滨州市统计年鉴[J].北京:中国统计出版社, 2021.
[29]	潍坊市统计局.潍坊市统计年鉴[J].北京:中国统计出版社, 1996.
[30]	潍坊市统计局.潍坊市统计年鉴[J].北京:中国统计出版社, 2021.
[40]	德州市统计局.德州市统计年鉴[J].北京:中国统计出版社, 1996.
[41]	德州市统计局.德州市统计年鉴[J].北京:中国统计出版社, 2021.
[42]	乐陵市统计局.乐陵市统计年鉴[J].北京:中国统计出版社, 2021.
[43]	烟台市统计局.烟台市统计年鉴[J].北京:中国统计出版社, 1996.
[44]	烟台市统计局.烟台市统计年鉴[J].北京:中国统计出版社, 2021.
[45]	莱州市统计局.莱州市统计年鉴[J].北京:中国统计出版社, 2021.
[46]	淄博市统计局.淄博市统计年鉴[J].北京:中国统计出版社, 1996.
[47]	淄博市统计局.淄博市统计年鉴[J].北京:中国统计出版社, 2021.
[48]	高青县统计局.高青县统计年鉴[J].北京:中国统计出版社, 2021.
[49]	马田田. 黄河三角洲受损滨海湿地修复系统优化调控研究[D].北京:北京师范大学, 2019.
[50]	智烈慧, 李心, 马田田, 等. 辽河三角洲土地利用变化轨迹、驱动过程及生态系统服务时空演变[J]. 环境科学学报, 2022, 42:141-150.
[51]	李艳茹.山东全省建成城市污水处理厂304座, 污水处理能力1370万吨/日[EB/OL].https://www.h2o-china.com/news/253414.html.
[52]	吴正奎.官鹅沟国家地质公园旅游环境容量测评及发展策略探讨[D].兰州:西北师范大学, 2015.
[53]	澎湃新闻网, 国家林业局:中国人均湿地面积仅为世界1/5, 亟需恢复扩大[EB/OL].https://www.thepaper.cn/newsDetail_forward_1518804.
[54]	赵焕焱.中国住宿业三千年[EB/OL].http://www.gxhma.cn/page158?article_id=342&pagenum=2.
[55]	林丽花.林芝地区生态旅游环境容量研究[D].拉萨:西藏大学, 2009.
[56]	刘博. 黄河三角洲典型滨鸟食性特征的时空差异性研究[D].山东:烟台大学, 2019.
[57]	吕丽. 黄河三角洲湿地鸟类多样性及其生境选择[D].山东:山东农业大学, 2019.
[58]	葛海燕.黄河三角洲自然保护区湿地恢复对鸟类的影响[J].山东林业科技, 2012, 42(5):30-33 , 21.
[59]	ZHI L H, LI X W, BAI J H, et al. Seawall-induced impacts on large river delta wetlands and blue carbon storage under sea level rise[J]. Science of the Total Environment, 2022, 159891.
[60]	山东省自然资源厅.山东治理荒漠化沙化成效显著[EB/OL].https://www.eco.gov.cn/news_info/46012.html.
[61]	方荣辉.黄河三角洲旅游城市空间结构的联动发展研究[J].石家庄学院学报, 2014, 16(3):50-54.
[62]	东营市交通运输局.东营市综合交通运输中长期发展规划[EB/OL]. http://dyjt.dongying.gov.cn/art/2021/1/8/art_37006_10290058.html.
[63]	任建成, 王明涛, 郑宝枝.滨州市旅游资源评价及开发策略研究[J].中国人口. 资源与环境, 2016, 26(增刊2):283-286.
[64]	邢倩, 李仁杰, 郭风华, 等.一种新的客源地集中指数(tourCI)与实证分析[J].地理科学进展, 2022, 41(7):1261-1273.

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