|Table of Contents|

A Comparative Study on the Efficiency and Influencing Factors of China's Low-Carbon Tourism Development: A Case Study of Seven Provinces and Cities in Eastern China(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2022年04期
Page:
82-92
Research Field:
管理科学与工程
Publishing date:

Info

Title:
A Comparative Study on the Efficiency and Influencing Factors of China's Low-Carbon Tourism Development: A Case Study of Seven Provinces and Cities in Eastern China
Author(s):
Feng Yue1; Zhou Nianxing2; 3; Wang Huadi1
(1.School of Education Science,Nanjing Normal University,Nanjing 210097,China)
(2.School of Geography,Nanjing Normal University,Nanjing 210023,China)
(3.Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application,Nanjing Normal University,Nanjing 210023,China)
Keywords:
efficiency of low-carbon tourism development; spatio-temporal evolution; influencing factors; seven Eastern China provinces and cities
PACS:
F590.8
DOI:
10.3969/j.issn.1672-1292.2022.04.011
Abstract:
As an index of total factor carbon productivity of tourism,low-carbon tourism development efficiency is an important tool to measure the relationship between low-carbon tourism development and tourism economic growth. This paper takes seven provinces and cities in the Beijing-Tianjin-Hebei region,Yangtze River Delta and Pearl River Delta region in Eastern China as the research object,uses the super-efficiency DEA model to measure the development efficiency of low-carbon tourism in these provinces and cities from 2008 to 2019,and analyzes the evolution characteristics of the development efficiency of low-carbon tourism from static and dynamic perspectives. To further explore the growth evolution characteristics and influencing factors of Total Factor Carbon Productivity(TFCP)in regional tourism industry,the Malmquist-Luenberger(ML)index is used to calculate the TFCP and its decomposition from 2008 to 2019,and the influencing factors of low-carbon tourism development efficiency are explored with the help of geographical detectors. From 2008 to 2019,the static low-carbon tourism efficiency values of Beijing-Tianjin-Hebei,Yangtze River Delta and Pearl River Delta regions represented by the seven provinces and cities were 0.764,0.807 and 0.971,respectively,which belong to three different efficiency levels: medium efficiency,good efficiency and good efficiency(basically close to effective efficiency). The dynamic TFCP values are:1.005,1.136,and 1.158,and the contribution of technical efficiency to the Beijing-Tianjin-Hebei region is more significant,and the Yangtze River Delta and Pearl River Delta regions are affected by the combination of technological progress and technological efficiency. In general,the development efficiency of low-carbon tourism is formed with the interaction among the dominated effect of the level of technological progress,energy consumption structure and tourism resource endowment,the inducing effect of economic development level,industrial structure,urbanization level,and the driving effect of tourism reception scale and traffic level.

References:

[1]龚艳,张阳,唐承财. 长江经济带旅游业效率测度及影响因素研究[J]. 华东经济管理,2016,30(9):66-74.
[2]LENZEN M,SUN Y Y,FATURAY F,et al. The carbon footprint of global tourism[J]. Nature Climate Change,2018,8(6):522-529.
[3]FARRELL M J. The measurement of productive efficiency[J]. Journal of Royal Statistical Society,1957,120(3):253-278.
[4]PERCH N S,SESARTIC A,STUCKI M,et al. The greenhouse gas intensity of the tourism sector:The case of Switzerland[J]. Environmental Science & Policy,2010,13(2):131-140.
[5]刘军,问鼎,童昀,等. 基于碳排放核算的中国区域旅游业生态效率测度及比较研究[J]. 生态学报,2019,39(6):1979-1992.
[6]李姝晓,程占红. 山西省阳泉市旅游业碳均衡分析与低碳发展研究[J]. 陕西师范大学学报(自然科学版),2021,49(6):64-74.
[7]金准. 碳达峰、碳中和与旅游业高质量转型[J]. 旅游学刊,2021,36(9):3-5.
[8]刘文君,刘秀春. 基于SBM-DEA模型的工业碳减排潜力与影响因素分析[J]. 中南林业科技大学(社会科学版),2019,13(2):26-34.
[9]冯凌,陈达,高珊,等. 应对气候变暖:中国旅游业的政策和行动(英文)[J]. Journal of Resources and Ecology,2019,10(1):94-103.
[10]查建平. 中国低碳旅游发展效率、减排潜力及减排路径[J]. 旅游学刊,2016,31(9):101-112.
[11]DICKINSON J E,ROBBINS D,LUMSDON L. Holiday travel discourses and climate change[J]. Journal of Transport Geography,2010,18(3):482-489.
[12]涂建军. 评《低碳旅游产业发展模式研究》[J]. 经济地理,2020,40(4):231.
[13]肖岚,孟利宁. 低碳城市系统构建—基于全域旅游视角的仿真研究[J]. 现代财经(天津财经大学学报),2019,39(8):87-99.
[14]查建平. 低碳经济视角下中国旅游经济发展模式研究[J]. 旅游学刊,2015,30(11):63-73.
[15]唐承财. 基于4E系统的旅游地旅游业低碳发展模式研究[J]. 地理与地理信息科学,2014,30(3):114-119.
[16]王坤,黄震方,曹芳东. 中国旅游业碳排放效率的空间格局及其影响因素[J]. 生态学报,2015,35(21):7150-7160.
[17]陶玉国,黄震方,吴丽敏,等. 江苏省区域旅游业碳排放测度及其因素分解[J]. 地理学报,2014,69(10):1438-1448.
[18]周年兴,黄震方,梁艳艳. 庐山风景区碳源、碳汇的测度及均衡[J]. 生态学报,2013,33(13):4134-4145.
[19]保罗·萨缪尔森,威廉·诺德豪斯. 经济学[M]. 高鸿业,译. 北京:经济科学出版社,1991:41.
[20]中国旅游研究院. 中国旅游景区发展报告(2015)[M]. 北京:旅游教育出版社,2015.
[21]FIDEL M R,GONZALEZR,XOSE A. Occupancy level and productivity in rural tourism establishments:the case of Galicia,Spain[J]. Tourism Economics,2006,12(2):279-289.
[22]Wang H,Xu S,Xie Q,et al. Study on the temporal and spatial differentiation of provincial tourism efficiency in Eastern China and influencing factors[J/OL]. Complexity,2021:4580917[2022-10-21]. https://doi.org/10.1155/2021/4580917.
[23]邵海琴,王兆峰. 长江经济带旅游业碳排放效率的综合测度与时空分异[J]. 长江流域资源与环境,2020,29(8):1685-1693.
[24]蒋莉,邵海琴,王凯. 中国旅游业效率及其影响因素的时空异质性研究[J]. 旅游研究,2018,10(5):20-30.
[25]王坤,黄震方,曹芳东,等. 泛长江三角洲城市旅游绩效空间格局演变及其影响因素[J]. 自然资源学报,2016,31(7):1149-1163.
[26]刘佳,徐文强. 碳排放约束下中国沿海地区旅游产业绿色创新效率时空分异与形成机制[J]. 资源开发与市场,2022,38(5):538-545,553.
[27]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.
[28]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.
[29]陈飞,诸大建. 低碳城市研究的理论方法与上海实证分析[J]. 城市发展研究,2009,16(10):71-79.
[30]CHARNES A,COOPER W W,RHODES E. Measuring efficiency of decision making units[J]. European Journal of Operational Research,1978,6(2):429-444.
[31]DU J,LIANG L,ZHU J. A Slacks-based measure of super-efficiency in data envelopment analysis:A comment[J]. European Journal of Operational Research,2002,(1):32-41.
[32]CHUNG Y H,FARE R,GROSSKOPF S. Productivity and un-desirable outputs:a directional distance function approach[J]. Journal of Environmental Management,1997,51(3):229-240.
[33]王劲峰,徐成东. 地理探测器:原理与展望[J]. 地理学报,2017,72(1):116-134.
[34]李佳洺,陆大道,徐成东,等. 胡焕庸线两侧人口的空间分异性及其变化[J]. 地理学报,2017,72(1):148-160.
[35]马占新. 数据包络分析模型与方法[M]. 北京:科学出版社,2010.
[36]查建平,舒皓羽,李园园,等. 中国旅游业碳排放及其影响因素研究—来自2005～2015年省级面板数据的证据[J]. 旅游科学,2017,31(5):1-16.
[37]杜立民. 我国二氧化碳排放的影响因素:基于省级面板数据的研究[J]. 南方经济,2010(11):20-33.

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Last Update: 2022-12-15