Measurement and empirical analysis of eco-efficiency in tourism destinations based on a Slack-based Measure-Data Envelopment Analysis model

Hongsong, 彭红松 Peng; Jinhe, 章锦河 Zhang; Ya, 韩娅 Han; Guorong, 汤国荣 Tang; Yu, 张宇 Zhang

doi:10.5846/stxb201507311616

Cited by 9 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The current mainstream research paradigm of the TGPE follows the approach of "efficiency measurement-spatial evolution-reason analysis," with data envelopment analysis (DEA) being the primary method for measuring efficiency. In particular, there is a tendency to employ the Slacks-Based Measure model (SBM) (Peng et al, 2017) in current literature, which takes into account non-radial and nonangular outputs and incorporates unexpected outputs (Wang et al, 2022a). Cheng et al (2023) used the Super-SBM to calculate the TGPE of 12 cities in the Hanjiang River Basin from 2010 to 2019 and measured its spatiotemporal evolution, and found that the distribution of TGPE exhibited obvious spatial clustering and dependence.…”

Section: Literature Reviewmentioning

confidence: 99%

Study of the space–time transition and spatial spillover effects of tourism green production efficiency in the Yangtze River Delta—a reanalysis from the perspective of tourism carbon sinks

Shi,

Long,

Yao

et al. 2023

Front. Environ. Sci.

View full text Add to dashboard Cite

Tourism green production efficiency serves as the foundation for assessing the mutual coupling performance of the tourism economy and the ecological environment. In this paper, the tourism carbon sink is included in the measurement framework, and the TGPE of 41 cities in the Yangtze River Delta region from 2011 to 2019 is estimated by the Super-SBM model. Furthermore, kernel density estimate, spatial autocorrelation, Markov chain and spatial Durbin model are further integrated to explore its spatio-temporal evolution process, spatial effects and influencing factors. The results show that 1) TGPE in the Yangtze River Delta has been increasing during the study period. The high-efficiency and low-efficiency areas of the TGPE have a bipolar pattern characterized by “low–low convergence” and “high–high convergence.” 2) There is considerable spatial variation in TGPE from north to south. The number of hot spots and sub-hot spots increases in volatility, whereas the number of sub-cold spots and cold spots decreases. 3) Although cities with low levels of TGPE have a higher probability of moving to the next level, grade transformation across hierarchies is difficult to attain. When considering the factor of adjacent types and the influence of spatial lag on the transfer probability. 4) The positive spatial spillover effects of TGPE is significant. At the same time, economic development level, transport accessibility and tourism industry agglomeration have positive spillover effects on neighboring cities. Conversely, urbanization level and openness level have negative spillover effects.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Study of the space–time transition and spatial spillover effects of tourism green production efficiency in the Yangtze River Delta—a reanalysis from the perspective of tourism carbon sinks

Shi,

Long,

Yao

et al. 2023

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…With the change of social concerns, the research topics gradually shift from the tourism economic efficiency of destinations [20] to tourism environmental efficiency [21] , tourism ecological efficiency [22] , tourism internal and external circulation efficiency [23] and other aspects. The estimation methods are also constantly being optimized, mainly using the traditional DEA model [24] and stochastic frontier analysis (SFA) [25] to improve the DEA [26] and Super-SBM model [27] and other research methods. In the study of the influencing factors of tourism efficiency, convergence analysis [28] , modified gravity model [29] , multiple regression model [10] , generalized (SYS-GMM) moment model [30] , impulse response function [31] and other methods are used to organize the hair.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Influencing Factors Analysis of Urban Tourism Efficiency in Taihang Mountain Region

2023

tmte

View full text Add to dashboard Cite

The level of tourism efficiency directly affects the level of tourism industry quality, to improve tourism efficiency is an important measure to achieve quality upgrading of tourism. With the Taihang Mountain region as the research object and 18 cities within the region as the decision-making unit, the tourism efficiency of the region during 2016-2020 was comprehensively measured based on the DEA-Malmquist index model, and the influencing factors were analyzed. The results show that: (1) From 2016 to 2020, the comprehensive tourism efficiency of Taihang Mountain region shows a general trend of fluctuation, and the development level of tourism development of each city is uneven. (2) The total factor production efficiency of regional tourism showed an obvious downward trend from 2016 to 2020, mainly due to limited investment scale and no obvious progress in technology; The index of regional total factor production efficiency ranked East Taihang > West Taihang > South Taihang. (3) Industrial structure, transportation convenience and government intervention in tourism economy have significant influences on improving tourism efficiency, while economic development level and scientific and technological innovation level have weak influences.

show abstract

“…Existent studies about tourism eco-efficiency mainly focused on the definition of tourism eco-efficiency [7,8] , quantitative measurement [9,10] , influencing factors and mechanisms [11,12] , and related recommendations [13,14] . Research methods mainly include Ecological Footprint [15] , Life Cycle Assessment [16] , Ecological Multiplier Measurement Model [17] , and Data Envelopment Analysis (DEA) [18,19] . Most scholars used the radial angle DEA to calculate the directional distance function in their empirical studies to incorporate pollutant emissions into the efficiency evaluation framework [20][21][22] .…”

Section: Introductionmentioning

confidence: 99%

Spatial-temporal Evolution and Its Influencing Factors of Tourism Eco-efficiency in China

Liu

2022

j. of geogr. res.

View full text Add to dashboard Cite

Eco-efficiency is an invaluable indicator for the measurement of the relationship between production activities and environmental depletion. This study measures the tourism eco-efficiency of 30 provinces in China from 2005 to 2020 based on the super-efficiency SBM model, and explores its spatial-temporal evolution characteristics using the kernel density function, standard deviation ellipse, and center of gravity model. Then, the influencing factors of the tourism eco-efficiency in China are analyzed by Tobit regression model. The results show that the tourism eco-efficiency of China is generally fluctuating upwards, but has not yet reached the maximum production possibility frontier. The kernel density curve shows a unimodal-bimodal-unimodal pattern, while the inter-provincial differences have been decreasing and becoming more balanced. The center of gravity of tourism eco-efficiency is located at the junction of Henan and Hubei province and generally moves to the south (slightly to the southwest). Meanwhile, it is revealed that the level of economic development and the tourism eco-efficiency has a significant inverted U-shaped relationship. The level of economic openness, traffic conditions, and tourism eco-efficiency is positively correlated. The environmental regulations and industrial structure have a negative but limited impact on tourism eco-efficiency. Finally, recommendations and suggestions for policy formulation to promote quality and sustainable development of the tourism industry are put forward, such as increasing investment in ecological protection and governance in tourism development, improving capacity-building in allocating green and low-carbon technologies and resources, strengthening tourism infrastructure construction, and enhancing environmental governance systems and mechanisms.

show abstract

Measurement and empirical analysis of eco-efficiency in tourism destinations based on a Slack-based Measure-Data Envelopment Analysis model

Cited by 9 publications

References 0 publications

Study of the space–time transition and spatial spillover effects of tourism green production efficiency in the Yangtze River Delta—a reanalysis from the perspective of tourism carbon sinks

Study of the space–time transition and spatial spillover effects of tourism green production efficiency in the Yangtze River Delta—a reanalysis from the perspective of tourism carbon sinks

Evaluation and Influencing Factors Analysis of Urban Tourism Efficiency in Taihang Mountain Region

Spatial-temporal Evolution and Its Influencing Factors of Tourism Eco-efficiency in China

Contact Info

Product

Resources

About