Spatio-temporal evolutionary analysis of ecological economic resilience in typical Chinese tourist cities

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Introduction
At present, the center of gravity of the world economy has been shifted to city clusters, and city clusters are playing a particularly crucial role in the rapid urbanization stage of China's development [1][2][3].The issue of regional economic differences within city clusters has also become a hot spot in academic research.However, the existing research on economic differences in city clusters focuses on analyzing them from the perspective of socio-economic indicators, mainly ignoring the critical role of ecological and economic theories in the sustainable development of city clusters [4][5][6].In April 2015, the China Development and Reform Commission (NDRC) officially released the relevant planning program for the development of city clusters in the middle reaches of the Yangtze River, clarifying the direction of city cluster development [7][8][9].In January 2016, the Yangtze River Economic Belt Development Forum put forward to promote its green development, the city cluster in the middle reaches of the Yangtze River, as one of its three significant trans-regional city cluster support, the strategic position is particularly prominent, the road of green development is imperative [10].
Zhu, Y. et al. combined the SMM-desired and DEA-Malmquist models to measure the industrial ecoefficiency of the Huaihe River Economic Zone from 2009 to 2018.ArcGIS and GeoDA were used as measurement tools to analyze the spatial structural characteristics of industrial eco-efficiency in the Huaihe River Economic Zone.The Tobit panel regression model was used to analyze the ecoefficiency influencing factors and discuss the mechanism of industrial eco-efficiency development [11].Sun, Y. et al. constructed a model of eco-efficiency evaluation indexes for tourism cities based on the panel data of the three major urban agglomerations in China from 2008 to 2017.The epsilon measure and the Super-EBM mixed distance model were applied to 63 Chinese cities to measure tourism eco-efficiency.They compared the spatio-temporal evolution characteristics of these three city clusters [12].Zhou, Y. et al. used data envelopment analysis to analyze the urban eco-efficiency of 48 cities in China's Bohai Sea Rim.Based on the super-unexpected relaxation model, the ecoefficiency variability among cities was assessed.
Meanwhile, using the Moran coefficient, the spatial autocorrelation of the 48 cities was identified to analyze the spatial aggregation characteristics of the cities [13].Luo, R. et al. designed an evaluation index system for the eco-health of urban tourism in terms of five dimensions, namely, vitality, organizational structure, resilience, and eco-service function, and using the coupling and coordination measurement model, they analyzed the spatio-temporal evolution characteristics and driving factors of the Yangtze River Economic Belt, focusing on the evaluation from five-dimensional aspects, to provide data support for the sustainable development of urban tourism economy [14].Qiu, G. et al.In order to solve the problem of conflicting land resource utilization brought about by China's urbanization and to accelerate the coordinated development of people and the environment, taking the SuxiChang area as an example, they calculated the land use of that area from three aspects of the spatial regional division, namely, the complexity, the vulnerability, and the stability.They calculated the land use of that area in terms of complexity, vulnerability, and stability.Aspects of calculating the spatial and temporal characteristics of land use conflicts in the region [15].This paper first constructed the ecological economic toughness measurement index system of tourism cities according to the principles of scientificity and relevance.After determining the weights of different indexes by using the entropy value method, the temporal evolution characteristics and spatial evolution characteristics of the ecological economic toughness of tourism cities were analyzed from 2014 to 2023, respectively.In the analysis of the temporal evolution of ecological and economic toughness, the evolution of the overall ecological and economic toughness of the typical tourist cities in China during this period, as well as the changes in the ecological toughness level of the typical tourist cities in the east, north, central and southwest were calculated respectively.On the spatial evolution, the global spatial correlation was analyzed by using the global Moran's I index.Then the spatial clustering relationship of ecological and economic resilience of Chinese cities was analyzed by using the Spatial Statistic cold hotspot of Arcgis 10.2 software.Different types of cities receive targeted sustainable development suggestions based on the results of the coupling coordination degree calculation.
2 Evaluation method of ecological and economic resilience of tourist cities

Construction of Ecological Economic Resilience Measurement Index System
Based on the connotation of eco-economic resilience of tourism cities, on the basis of relevant research at home and abroad, combined with the development characteristics of the economic belt, this paper constructs an evaluation index system of eco-economic resilience of typical tourism cities in China from four aspects: eco-economic resilience, social resilience, ecological resilience, and infrastructure resilience, and strictly follows the principles of availability, scientificity, and relevance, and selects 28 evaluation indicators, including 7 ecological resilience indicators.One-fourth of cities' economic resilience is due to ecological resilience.Table 1 shows the evaluation index system for eco-economic resilience of tourism cities.

Entropy method modeling
The entropy value method is essentially an objective assignment method, mainly based on the degree of difference between the indicator data to determine the greater each, and, conversely, the smaller the degree of difference between the data, the smaller the information entropy weight.The entropy method can objectively and genuinely reflect the degree of importance of a single indicator in the evaluation system, and to a certain extent, it can avoid the bias brought about by the influence of subjective factors.For this reason, it is widely utilized in the evaluation research of regional social and economic development.This study uses the entropy method model, which is mainly used to measure the weights of the indicators in the two sets of evaluation systems of urban tourism ecological, economic resilience and high-quality development of tourism, and combined with the comprehensive evaluation method.The main calculations are as follows: 1) Standardize the raw data to eliminate differences in magnitude: Positive indicators: Negative indicators: 2) Calculate the weight ij w of the i st indicator under research unit j : 3) Calculate the entropy value of the i rd indicator j e : ( ) 1

Spatial autocorrelation methods
Spatial autocorrelation analysis is a technique that reveals the spatial distribution and association status of an object, encompassing both global and local spatial autocorrelation analysis.When variable data is distributed, the non-stationary phenomenon will occur in the local area due to the randomness of the data.Local spatial autocorrelation needs to be introduced more on the basis of global spatial autocorrelation analysis.The spatial agglomeration characteristics of urban tourism eco-economic resilience and high-quality development of tourism are examined by using global and local spatial autocorrelation analyses in this study.
1) The spatial global autocorrelation analysis is mainly used to determine the distribution characteristics of the spatial data manifested in the whole system, which is generally measured by the global Moran's I index, with the following specific formula: ) ( ) Where i x and j x are the observed values of attribute feature x on , ij spatial units, x is the mean value of all observations, and 6 ij w is the corresponding spatial weight value.
2) Local spatial autocorrelation is mainly used to determine the spatial clustering status of data with spatial global correlation, which is generally measured by the local Moran's i I index with the following formula.

(
) ( ) means that there is a positive correlation between the i nd study area and the neighboring areas, and it is straightforward to form a high agglomeration or low, low agglomeration state in space, and if 0 i I  , it means that there is a big difference between the i th study area and the neighboring areas.It is more likely to form a low, high agglomeration, or high-low agglomeration state in space.

Modeling coupled coordination
The significant coupling characteristics of tourism economic growth and tourism environment construction measure the coordination capacity of the urban tourism economy.On the one hand, the construction of a tourism environment can provide a fundamental guarantee for the development of tourism activities.On the other hand, tourism, as a comprehensive emerging industry with a solid driving force, has a vital role in promoting the construction of urban infrastructure.Coordination ability can reflect the development status and degree of connection within the urban tourism economic system, reflecting the resistance of the tourism economic system when it is subject to external shocks, which is an essential indicator for measuring the resilience of the tourism ecological economy, and also a meaningful way to realize the sustainable growth of the tourism economy.In recent years, with the extensive development and in-depth promotion of tourism activities, the phenomena of rapid expansion of tourist destinations, a massive influx of tourists, overloaded urban infrastructure, and excessive development of resources and environment have gradually appeared.The contradiction between the demand for tourism economic growth on tourism urban ecology and the imbalanced and insufficient supply of tourism environment has become increasingly prominent, which puts the development of urban tourism in the midst of all kinds of uncertainties, risks, and pressures.The city's tourism development is facing various uncertain risks and pressures.In order to assess the coordination capacity of the urban tourism economy, the coupled coordination model of tourism economy and tourism ecology is constructed by drawing on the capacity coupled system model in physics, and the calculation formula is as follows: ( ) Where D is the degree of coupling coordination, C is the degree of coupling, M is the comprehensive measurement index of the two systems of urban tourism economy and tourism environment, and a and b are the coefficients to be determined.

Characterization of the temporal evolution of ecological and economic resilience
In order to objectively and accurately evaluate the resilience of tourism cities and compare the ecological resilience of tourism cities in different years, this paper adopts the entropy value method.It adds the time variable so as to determine the weights of the resilience index system of tourism cities.Table 2 displays the calculations for the weight coefficients of each indicator.The rate of increase is more significant but the score is less contribution to the overall toughness of the overall degree is not high.However, the overall tourism city's ecological and economic toughness construction is still facing a higher space for improvement.Figure 1 shows the overall ecological and economic resilience of key tourism cities in China from 2014 to 2023.Among them, Guangzhou City in the eastern region, Xi'an City and Changsha City in the central region, and Chengdu City in the southwestern region have fluctuating and declining eco-economic resilience levels.In contrast, the eco-economic resilience performance of other regions is basically stable.Clearly, the ecological economic level of Xi'an and Kunming will stay above the 0.6 level during 2014-2023, making them one of the cities with the highest degree of ecological economic resilience in the country.

Global spatial autocorrelation analysis
The global spatial correlation coefficient Moran's I index of the ecological and economic resilience of urban tourism at the national level from 2014 to 2023 was calculated using GeoDa software.The Monte Carlo simulation method was chosen to test its significance level.Table 3 shows the global autocorrelation Moran's I index of urban tourism economic resilience, where *, ** & *** denote significance at 10%, 5%, and 1% levels, respectively.The global spatial autocorrelation of the economic resilience of a typical tourism ecosystem on a national scale is measured based on the geographic distance weight matrix, focusing on the solid or weak role of spatial geographic distance.
The results show that the global Moran's I index and Z-value are favorable, and all of them, except for 2014, passed the significance test at the P < 0.1 level, indicating that the ecological economic resilience of typical tourism cities in China is not randomly spatially distributed in the complete sense of the term.In late 2018, under the influence of multiple factors such as national policies, infrastructure, and market changes, the ability of spatial cooperation in the whole region was once again emphasized by urban tourism development, plotting the positive driving effect of inter-city resilience, forming a "centralized-decentralized-centralized The evolution process of "centralized-decentralizedconcentrated."

Cold hot spot analysis
In order to further analyze in depth the spatial characteristics of the resilience of typical tourism cities in China, the hotspot analysis (Getis-Ord Gi*) module in the Spatial Statistics tool of Arcgis 10.2 software was used to carry out the cold hotspot analysis of the resilience of tourism cities in the Yangtze River Economic Belt for the years 2014, 2017, 2020 and 2023 calculation.It classified their scores into five types based on the natural breakpoint grading method, which are cold spot zone, subcold spot zone, random distribution zone, cold hot spot zone, and hot spot zone.Hot spot zones reflect the spatial clustering of cities with higher tourism, cold spot zones reflect the spatial clustering of cities with lower tourism, and random distribution zones indicate that there is no apparent spatial clustering.
Table 4 shows the provincial distribution of cold and hot spots of ecological and economic difficulty in typical tourist cities in China.The outcomes are as follows: 1) China's tourism city resilience hotspots in 2014-2023 have been distributed in Shanghai, Guangdong, Fujian and Jiangsu, all of which are located in the eastern region.The eastern region is particularly well-off in terms of socio-economic conditions, and the resilience of tourism cities in the provinces it emanates from is at a high level.
2) The sub-hotspot areas are distributed around the hotspot areas with a certain regularity, and all of them are distributed in Zhejiang, Guangxi, and Anhui from 2014 to 2023.
3) The subcold spot areas are mainly concentrated in the southwest region, distributed in Hubei, Chongqing, Guizhou, and Yunnan in 2014.In 2017, the sub-cold spot areas were reduced to three and shifted to Sichuan, Chongqing, and Yunnan.In 2020, The sub cold spot areas continue to be reduced to two, distributed in Chongqing and Guizhou.In 2023 Yunnan enters into the subcold spot areas again, and the subcold spot areas are expanded to three.
4) Cold spot zones show a fluctuating expansion trend, with only Hunan in 2011 and cold spot zones expanding to three in 2014, mainly in Hubei, Hunan, and Guizhou, and Guizhou transforming into a sub-cold spot zone after 2017.Overall, the toughness of China's tourism cities has a certain degree of spatial robustness, with hotspots and sub-hotspots mainly distributed in the east and coldspots and sub-coldspots mainly distributed in the west, which makes the distribution pattern of the toughness of China's tourism cities in the east, high school, and west even more significant, and is basically consistent with the spatial distribution characteristics of the four types of tourism city toughness.These cities should continue to strengthen their cities and scenic spots on an existing basis to improve their overall strength and provide advanced experience and templates for surrounding cities.

2) Cities with poor coupling coordination
The coupling coordination degree is not high for the city is divided into three cases.The first coupling coordination degree is less than 0.8.This kind of city, Luoyang and Taiyuan, as a representative, has exceptionally high-quality tourism resources.However, the city construction is relatively backward, becoming an important reason to constrain the degree of coordination of the coupling, so we should continue to strengthen the infrastructure construction.The infrastructure toughness of city's toughness accounted for the most significant proportion of the city's infrastructure.The city suffered from impacts in all aspects.Infrastructure resilience accounts for the largest share of urban resilience, and the role of infrastructure in the process of urban shocks is reflected in all aspects, which should not be ignored.Strengthening infrastructure construction should firstly increase its coverage and population so that more residents can use it in a timely manner when necessary, and secondly, increase the maintenance of infrastructure during weekdays so as to avoid the lack of maintenance, which leads to the inability of infrastructure to function correctly at critical moments.Increase the strategic reserve simultaneously.The local government's financial resources and individuals' ability to handle risk are both influenced by the impact of economic strength on the city's resilience.The first step is to increase the ability to stockpile goods, to extend the time of stockpiling and to increase the quantity of stockpiles.The second step is to increase the ability to secure personal savings.
Secondly, the degree of coupling coordination is more significant than 0.8 and less than 1.2, with Jinan, Xi'an, and Changsha as the representative cities, and the level of urban resilience and highquality tourism development is relatively balanced with no obvious shortcomings, but the overall level is not high.These cities are in a low level of steady state, and they need to lay a solid economic foundation, provide sufficient financial and human support for urban resilience and tourism development, and then find their breakthroughs to take the lead in breaking out of the steady state in terms of urban resilience or high-quality tourism development.
The final coupling coordination degree is more significant than 1.2, and such cities are represented by Guangzhou, Shenzhen, Zhongshan, and other cities, which have a high level of urban resilience.However, the level of high-quality development of tourism lags seriously.These cities often have comprehensive solid strength but lack high-quality tourist attractions, resulting in a large amount of financial and human resources being invested with little effect.In this regard, firstly, we should make full use of the advantage of mobile Internet in the information age, use big data and other technologies to optimize the level of tourism reception and service, and provide personalized service for tourists.Secondly, we should optimize the level of management of scenic spots, and thirdly, we should increase the training efforts and training level of tourism professionals.

Conclusion
This paper starts from the connotation of ecological economic toughness of tourism cities, determines the evaluation index system of urban ecological economic toughness measurement, first uses the entropy weight method to measure the time dimension, then uses the spatial autocorrelation method to analyze the spatial evolution characteristics of ecological economic toughness of tourism cities, and analyzes the enhancement of tourism ecological economic toughness enhancement paths by using the coupling coordination model to come up with the following conclusions: 1) Overall, the urban ecological and economic toughness of crucial tourist cities in China and the toughness of each sub-component showed an upward trend from 2014 to 2023, and the speed was relatively stable.The overall toughness of the city increased from 0.2129 in 2014 to 0.2807 in 2023, a more obvious growth rate, indicating that the city's urban toughness has generally improved to a certain extent after 10 years of construction and development.
2) The global spatial correlation coefficient Moran's I index reached a low value of 0.0532 in 2015, a peak value of 0.0951 in 2020, and then decreased steadily to 0.0738 after 2020, which indicates that the strengthening of the role of global spatial correlation is evident in the stage of 2015-2020.From 2020 to 2023, the global Moran's I index shows a decreasing development trend and spatial dependence decreases, forming an evolutionary process of "concentration-dispersion-concentration."During the period 2020-2023, the global Moran's I index shows a decreasing development trend, and the spatial dependence relationship decreases, forming an evolutionary process of "concentration-dispersion-concentration." 3) The toughness of China's tourism cities has certain spatial robustness, with hotspots and subhotspots mainly distributed in the east and coldspots and sub-coldspots mainly distributed in the west, making the distribution pattern of the toughness of China's tourism cities in the east, high school, and west even more significant.
4) Based on the coupling coordination degree (D-value) of different cities' ecological and economic resilience and the level of urban tourism development, different suggestions are given: cities with a D-value less than 0.8 should strengthen infrastructure construction, cities with a D-value greater than 0.8 and less than 1.2 should develop the economy to provide the basis for the enhancement of the city's ecological economic resilience, and cities with D-value greater than 1.2 should increase inputs in the tourism industry.

4 ) 5 ) 6 )
Calculate the utility value of the j st indicator j Calculate the weight of the j st indicator j w : Calculate the composite score i S :

Figure 1
Figure1shows the overall urban eco-economic resilience of key tourism cities in China from 2014-2023.Overall, the urban toughness of key tourism cities in China and the toughness of each subcomponent shows an upward trend from 2014 to 2023, with a relatively stable rate.The overall urban toughness went from 0.2129 in 2014 to 0.2807 in 2023, with a more obvious growth rate, indicating that the urban toughness of the city has generally improved to a certain extent after 10 years of construction and development.Further analysis of the subsections reveals that ecological economic resilience increased from a score of 0.0332 in 2014 to 0.04424 in 2023, making the most significant contribution to overall urban resilience improvement.Ecological economic toughness has a fluctuating upward trend score from 0.0995 in 2014 to 0.1327 in 2023.The rate of increase is more significant but the score is less contribution to the overall toughness of the overall degree is not high.However, the overall tourism city's ecological and economic toughness construction is still facing a

Figure 1 .
Figure 1.Overall Urban Resilience of Key Tourist Cities in China, 2014-2023 The evaluation index system's measurement results indicate that the time-series changes in tourism ecological and economic resilience in each city in the country are generally stable.The ecological resilience level of typical tourism cities in China is shown in Figure 2. (a)~(d) is the ecological resilience level of typical tourism cities in eastern, northern, central, and southwestern China, respectively.

Figure 2 .
Figure 2. The ecological economic toughness of a typical tourist city in China In terms of temporal evolution, the global Moran's I index shows a fluctuating trend of alternating increases.It decreases, with the Moran's I index reaching a low of 0.0532 in 2015 and a peak of 0.0951 in 2020.In the 2015-2020 period, the strengthening of the role of global spatial correlation is apparent.2015-2020 stage, the strengthening of the role of global spatial correlation is apparent, and the process of synergistic development of different regions advances, gradually transforming from the situation of near-isolated development in the previous period to a new pattern of integrated 2014 is not only the need for the rapid development of the urban economy in the early stage but also the objective result produced by the development of China's tourism industry.The mature tourism economic network continues to expand the spatial cooperation main body, driving the deepening of the cooperation of related industries.During the period of 2020-2023, the global Moran's I index shows a decreasing development trend, and the spatial dependence relationship has declined, which is related to the individual positioning and pattern of tourism development in different cities that are inextricably linked.China has gradually developed tourism destinations, led by the eastern region, especially Guangdong Province, which has an unusually prominent tourism position.

Table 1 .
Tourism City Resilience Evaluation Index System

Table 2 .
Tourism City Resilience Evaluation Index System

Table 3 .
Global autocorrelation Moran's I index

Table 4 .
Distribution Pattern of Resilient Cold Hot Spots in China's Tourist Cities

Analysis of the path to improve the level of development of tourist cities 4.1 Coupled analysis of the level of high-quality development of typical tourist cities in ChinaTable 5
shows the changes in the coupled coordination degree of ecological and ecological economic resilience and tourism high-quality development system of key tourism cities in China from 2014 to 2023.Using the formula of the coupling coordination model, the coupling coordination degree of ecological and ecological economic resilience and tourism high-quality development of China's key tourism cities in 2014-2023 is calculated, and according to Table5, it can be learned that the coupling coordination degree is generally high in 2014-2023, with little difference between cities.It indicates that the urban resilience and high-quality tourism development levels of key tourism cities in China are high.

Enhancement Recommendations Based on Coupling Harmonization Degree
Based on the above conclusions, specific countermeasures and suggestions are proposed to enhance the ecological, economic resilience, and high-quality tourism development level of typical tourist cities in China.The following aspects require countermeasures for cities with different characteristics.1)Cities with a high degree of coupling coordination Chengde and Hohhot in North China, Changchun in Northeast China, Suzhou, Wuxi, Huangshan, and Jiujiang in East China, Wuhan in Central China, Guilin in South China, and Guiyang and Chongqing in Southwest China have a better state of coupling coordination and are on an overall upward trend.