The Measurement and Temporal and Spatial Evolution of Tourism Poverty Alleviation Efficiency in the Liupan Mountain Area of Gansu Province, China
Abstract
:1. Introduction
2. Research Area and Research Methods
2.1. Overview of the Study Area
2.2. Research Methods
2.2.1. Super-SBM Model
2.2.2. Coefficient of Variation
2.2.3. Malmquist Index
2.2.4. Spatial Trend Surface Analysis
2.2.5. Spatial Autocorrelation Analysis
- (1)
- Global spatial autocorrelation analysis
- (2)
- Local spatial autocorrelation analysis
2.3. Construction of Indicators System
2.4. Data Sources
3. Results and Analysis
3.1. Measurement of the Tourism Poverty Alleviation Efficiency
3.2. Time Series Evolution
3.2.1. The Difference in the Efficiency of Tourism Poverty Alleviation
3.2.2. MI and Its Dynamic Analysis
3.3. Evolution of Space Sequence
3.3.1. Spatial Distribution of Tourism Poverty Alleviation Efficiency
3.3.2. Analysis of the Spatial Trend of Tourism Poverty Alleviation Efficiency
- (1)
- From 2009 to 2018, the poverty alleviation efficiency in the Liupan Mountain area showed a spatial trend pattern of high in the east and low in the west, high in the north and low in the south. In the east–west and the north–south directions, the trend surface transitions from steep to gentle indicate that the difference of tourism poverty alleviation efficiency is gradually shrinking. It is consistent with the analysis results of the coefficient of variation.
- (2)
- In the east–west direction, from 2009 to 2018, the distribution pattern of tourism poverty alleviation efficiency gradually evolved from high at both ends and low in the middle to low at both ends and high in the middle. In the north–south direction, from 2009 to 2018, the tourism poverty alleviation efficiency of the Liupan Mountain area has gradually evolved from an inverted U-shaped pattern of low in the north and south and high in the middle to a linear distribution pattern with narrowing differences between the north and the south.
3.3.3. Spatial Correlation Analysis of Tourism Poverty Alleviation Efficiency
4. Conclusions and Enlightenment
4.1. Conclusions
- (1)
- The efficiency of tourism in the alleviation of poverty in poverty-stricken areas and counties in the Liupan Mountain area of Gansu Province had a high starting point and the average value rose from 0.76 in 2009 to 0.85 in 2018. Tourism development promoted the alleviation of poverty, but there is still room for improvement in most areas.
- (2)
- The time evolution of tourism poverty alleviation efficiency of districts and counties in the Liupan Mountain area of Gansu Province has a certain law. The coefficient of variation showed a gradual downward trend, indicating that the difference in tourism poverty alleviation efficiency started to diminish. The difference in tourism poverty alleviation efficiency in different regions showed a trend of northern area > south area > eastern area. The MI was less than 1, indicating that the change in factor productivity in the Liupan Mountain area of Gansu Province continues to decline. The decomposition efficiency analysis of MI showed that the main constraints on the improvement of tourism poverty alleviation efficiency were the lack of technological progress and lack of innovation.
- (3)
- The spatial distribution law of tourism poverty alleviation efficiency in Liupan Mountain area of Gansu Province shows that the spatial differentiation of tourism poverty alleviation efficiency from 2009 to 2018 presents a spatial trend of east area > west area and shows some continuity and spatial inertia. The trend analysis shows that the tourism poverty alleviation efficiency of poor areas and counties presents a spatial trend pattern of high in the east and low in the west, high in the north and low in the south. The east and north areas are the advantageous areas of tourism poverty alleviation efficiency. The spatial autocorrelation of tourism poverty alleviation efficiency is not significant, indicating the lack of a growth pole leading role and insufficient regional linkage. The local heterogeneity of H–H, L–L, H–L, L–H agglomeration shows a certain trend of core development.
- (1)
- In terms of improving the efficiency of tourism poverty alleviation, new tourism formats can be cultivated and related industries can be promoted. The government should give full play to its comprehensive leading role in tourism poverty alleviation and should guide multiple subjects to participate in tourism development. All districts and counties should seize the opportunities of the times of tourism development, dig deep into background resources, scientifically formulate tourism development plans, and cultivate new forms of business suitable for the local area.
- (2)
- In terms of improving the level of technology, new technologies should be fully utilized, and industry changes should be carried out. Tourism practitioners must strengthen technological innovation, increase the use of network virtual technologies and multi-functional travel apps, promote the efficient development of the tourism industry, and bring tourists a more comfortable travel experience.
- (3)
- In terms of solving the imbalance in regional development, the core growth pole should be cultivated, and regional cooperation should be strengthened. When formulating tourism industry policies, local governments should pay attention to strengthening regional exchanges and collaborations, coordinating tourism resources and tourist attractions, and maximizing spatial spillover effects. Counties with low tourism poverty alleviation efficiency should learn from the useful experience of advanced counties and should carry out more in-depth and all-round exchanges and cooperation with advanced counties. Administrative barriers should be broken, true centralized contiguous development should be realized, and industrial upgrading should be promoted. Multiple measures should be taken to improve the efficiency of tourism poverty alleviation in the concentrated and contiguous areas of the Liupan Mountain area in Gansu Province so as to solve the overall regional poverty, reduce the rate of return to poverty, and achieve the goal of common prosperity.
4.2. Enlightenment
- (1)
- The selection of indicators needs to be improved. The measurement of the efficiency of tourism poverty alleviation is complicated. When selecting input and output indicators, this article considered the availability of data and selected some alternative indicators, which may have led to deviations in the results. Factors such as the endowment of tourism resources and the local government’s emphasis on tourism development will affect the development of the local tourism industry and the improvement of tourism poverty alleviation efficiency. In the future, the construction of the index system can be improved.
- (2)
- The research content needs to be expanded. In this paper, the tourism poverty alleviation efficiency was measured by yearbook data and analyzed from a macro perspective. There was a lack of research on the real beneficiaries of tourism poverty alleviation. At the same time, researchers should study the mechanism of the temporal and spatial evolution of regional tourism poverty alleviation efficiency and the underlying factors behind it. Such studies will provide a more practical theoretical reference for policymaking.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Name of Cities | Name of Counties | Amount |
---|---|---|
Lanzhou | Yongdeng County, Gaolan County, Yuzhong County | 3 |
Baiyin | Jingyuan County, Huining County, Jingtai County | 3 |
Tianshui | Qingshui County, Qin’an County, Gangu County, Wushan County, Zhangjiachuan County and Maiji District | 6 |
Wuwei | Gulang County | 1 |
Pingliang | Kongtong District, Jingchuan County, Lingtai County, Zhuanglang County, Jingning County | 5 |
Qingyang | Qingcheng County, Huanxian County, Huachi County, Heshui County, Zhengning County, Ningxian County, Zhenyuan County | 7 |
Dingxi | Anding District, Tongwei County, Longxi County, Weiyuan County, Lintao County, Zhangxian county and Minxian County | 7 |
Linxia Hui Autonomous Prefecture | Linxia city, Linxia County, Kangle County, Yongjing County, Guanghe County, Hezheng County, Dongxiang County and Jishishan County | 8 |
Total | 40 |
Indicators | Indicators and Meanings | Unit | |
---|---|---|---|
Input | Tourism income per capita | Characterizing the development of tourism in poverty-stricken districts and counties | CNY/person |
The number of tourists per capita | Measuring the catalytic and comprehensive effects of the tourism industry | person-time | |
Output | GDP per capita | CNY/person | |
Disposable income of urban residents | The output of tourism poverty alleviation in poor areas in the economic dimension | CNY/person | |
Per capita net income of rural residents | CNY/person | ||
Number of secondary school students | The output of tourism poverty alleviation in poor areas in the dimension of Education | Number of people | |
Number of hospital beds per thousand people | The output of tourism poverty alleviation in poor areas in the medical dimension | Number of beds | |
Proportion of internet users | The output of tourism poverty alleviation in poor areas in the dimension of life | Percentage |
Year | effch | techch | pech | sech | tfpch |
---|---|---|---|---|---|
2009–2010 | 0.926 | 1.002 | 1.046 | 0.886 | 0.929 |
2010–2011 | 1.582 | 0.433 | 0.911 | 1.737 | 0.685 |
2011–2012 | 1.122 | 0.794 | 0.990 | 1.133 | 0.891 |
2012–2013 | 1.090 | 0.748 | 1.060 | 1.028 | 0.816 |
2013–2014 | 1.076 | 0.795 | 1.013 | 1.061 | 0.855 |
2014–2015 | 1.039 | 0.867 | 1.039 | 1.000 | 0.900 |
2015–2016 | 0.972 | 0.851 | 1.003 | 0.969 | 0.827 |
2016–2017 | 1.073 | 0.768 | 1.098 | 0.977 | 0.824 |
2017–2018 | 1.052 | 0.870 | 1.056 | 0.997 | 0.915 |
The average | 1.091 | 0.775 | 1.023 | 1.067 | 0.846 |
Year | Positive Spatial Correlation | Percentage | Negative Spatial Correlation | Percentage | ||
---|---|---|---|---|---|---|
H–H | L–L | H–L | L–H | |||
2010 | 0 | 2 | 5% | 1 | 0 | 2.5% |
2012 | 2 | 1 | 7.5% | 0 | 1 | 2.5% |
2014 | 0 | 1 | 2.5% | 0 | 1 | 2.5% |
2016 | 0 | 1 | 2.5% | 1 | 0 | 2.5% |
2018 | 1 | 0 | 2.5% | 2 | 1 | 7.5% |
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Wang, Y.; Song, Y.; Chen, G.; Huang, S.; Wang, M.; Pan, Y. The Measurement and Temporal and Spatial Evolution of Tourism Poverty Alleviation Efficiency in the Liupan Mountain Area of Gansu Province, China. Sustainability 2021, 13, 12637. https://doi.org/10.3390/su132212637
Wang Y, Song Y, Chen G, Huang S, Wang M, Pan Y. The Measurement and Temporal and Spatial Evolution of Tourism Poverty Alleviation Efficiency in the Liupan Mountain Area of Gansu Province, China. Sustainability. 2021; 13(22):12637. https://doi.org/10.3390/su132212637
Chicago/Turabian StyleWang, Yaobin, Ying Song, Guangfeng Chen, Shihua Huang, Meizhen Wang, and Yinggang Pan. 2021. "The Measurement and Temporal and Spatial Evolution of Tourism Poverty Alleviation Efficiency in the Liupan Mountain Area of Gansu Province, China" Sustainability 13, no. 22: 12637. https://doi.org/10.3390/su132212637