sustainability

Article Selected Environmental Assessment Model and Spatial Analysis Method to Explain Correlations in Environmental and Socio-Economic Data with Possible Application for Explaining the State of the Ecosystem

Junnan Xiong 1,2, Wei Li 1, Hao Zhang 1,3,4,* , Weiming Cheng 2,4,5 , Chongchong Ye 1 and Yunliang Zhao 1 1 School of Civil Engineering and Architecture, Southwest Petroleum University, 610500, 2 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, 100101, China 3 Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China 4 University of Chinese Academy of Sciences, Beijing 100049, China 5 Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, 210023, China * Correspondence: [email protected]

 Received: 18 July 2019; Accepted: 28 August 2019; Published: 2 September 2019 

Abstract: Regional ecosystem health is the basis for regular regional exploration, ecological protection, and sustainable development. This study explored ecosystem health at the southern end of the Hu Line ( and provinces) using the pressure–state–response model and examined the spatial evolution of ecosystem health. The proportion of unhealthy and morbid cities decreased from 45.9% in 2000 to 35.1% in 2016. The imbalance of ecosystem health among cities has gradually increased since 2006, but more high-quality cities have emerged (Z of Moran’s Index < 1.96, p > 0.05). Overall, the regional ecosystem on the southeast side of the Hu Line was healthier than that on the northwest side. Differences in ecosystem health on both sides of the Hu Line showed decreasing trends over time except for the pressure score. The spatial pattern of ecosystem health moved along the Hu Line because the pressure and state scores of ecosystems were mainly determined by the natural environmental conditions. Based on the county-level assessment, the grade of imbalance within cities was divided, and those that were lagging were identified. To correct regional imbalances, a comprehensive and proactive policy framework for a smart development model was put forward in Sichuan and Yunnan.

Keywords: ecosystem health; spatial evolution; PSR model; Hu Line;

1. Introduction The dramatic increase in the range and intensity of human activity has rapidly changed the global ecosystem, and poses a severe threat to the survival and development of human society [1,2]. The contemporary world is increasingly industrializing and urbanizing, especially in developing countries [3]. China, which is the largest developing country in the world, has shown remarkable progress in its social economy. However, ecosystems and social development have a limited capacity to manage environmental pressure, and when such pressure exceeds threshold levels there may be adverse effects on local ecosystems. In such cases, regional climate, hydrological conditions, and biological diversity will undergo appreciable changes, profoundly affecting regional ecological

Sustainability 2019, 11, 4781; doi:10.3390/su11174781 www.mdpi.com/journal/sustainability Sustainability 2019, 11, 4781 2 of 26 process [4–6]. The improvement and maintenance of healthy ecosystems is essential to achieving sustainable socioeconomic development, as natural ecosystems provide necessary materials and ecological services for survival and development of human beings [7]. Ecosystem health assessment is identified as the foundation of environmental management, and reasonable and effective evaluation of ecosystem health can identify environmental or economic crises to avoid serious deterioration of ecosystem health because of rapid socioeconomic development [7–9]. The concept of ecosystem health, which originated in the 1980s, has grown rapidly in recent years, and it is now a common perspective applied to ecosystem assessment [10,11]. Many assessment frameworks have been developed to examine ecosystem health on single-ecosystem scales, such as farmland [12], forests [13], grasslands [14], wetlands [8], marine systems [15], cities [16], and mines [17]. The exploration of better means for quantifying the interaction between human activities and ecosystems and recovery mechanisms such ecological services has been a long-term objective to enable the existence and sustainable development of human beings [18]. Comprehensive investigation of the combination of micro-level and macro-level is an ideal method for evaluating ecosystem health. Indicator taxa [19–21] and indices [8,22] are commonly used to conduct such investigations. Many ecosystem health assessment endeavors have employed the vigor-organization-resilience (VOR) model and its derivatives, which are often associated with benign operation of any life system at any scale, and can be used to construct systems for assessment of urban ecosystem health from different aspects [23,24]. The vigor is measured in terms of activity, metabolism or productivity; organization can be assessed as the diversity and amount of interactions between system components; resilience is measured in terms of a system’s capacity to maintain structural stability in the presence of stress [25]. Unlike the VOR model, which has the inherent confines due to lack of socio-economic and human health factors, the pressure–state–response (PSR) model emphasizes that human beings are a part of the ecosystem and play a pivotal role in determining the environmental state. Here, pressure indicators describe the pressures on ecosystem health exerted by human activities, including resource pressures and social pressures. State indicators reflect the status quo of ecosystem health, such as the vigor, organization, and resilience of an ecosystem. Response indicators show the response degree to the changes of ecosystem health conditions, including changes from humans and the ecosystem itself [26]. The comprehensive and dynamic features of this model makes it more informatory [27]. is largely lagging behind in their own development, and most of the economic activity in this region is concentrated on the southeastern side of the Hu Line [28], which is a classic theories of Chinese geography in which there is a hypothetical line dividing eastern and western China into roughly two parts [29,30]. To the southeast side of the line, 36% of the territory is inhabited by 96% of China’s population [31]. Correcting this regional imbalance has become one of the primary tasks of the Chinese government, which launched the Development of the West Region program in 1999 and the National New Urbanization Plan in 2014 [28,32]. Yunnan and Sichuan, located at the southern end of the Hu Line, has many complex characteristics including their landforms, human activity, climate change, and ecosystems. Simultaneously, they are in an important ecologically functional zone in China, with species diversity that has been recognized by the World Wildlife Fund and the International Union for Conservation of Nature [33,34]. Ecosystem health is not only the basis for ecological protection and sustainable development, but also a key point that must be addressed when breaking through the Hu Line during regional economic development. Hence, it is necessary to systematically explore the ecosystem health and its spatial evolution and regional differences in the western region, especially in the transitional zone traversed by the Hu Line. Therefore, this study was conducted to (1) evaluate the ecosystem health from 2000 to 2016 at the prefecture level using the PSR model; (2) measure the spatial temporal variation of ecosystem health using primacy ratio, coefficients of variation, spatial autocorrelation analysis, spatial gravity center model and standard deviational ellipse; (3) diagnose the ecosystem health at county dimension based on the statistical downscaling model; (4) conduct a difference analysis of regional ecosystem health to determine the strengths or weaknesses of typical counties in social development. The findings SustainabilitySustainability2019 2019, 11, 11, 4781, x FOR PEER REVIEW 33 ofof 26 25

findings from this study can provide references for policy-making regarding the regional economy fromand environmental this study can protection. provide references for policy-making regarding the regional economy and environmental protection. 2. Materials and Methods 2. Materials and Methods 2.1. Study Area 2.1. Study Area The study area is located at the southwestern end of Hu Line, which was further narrowed to The study area is located at the southwestern end of Hu Line, which was further narrowed to Yunnan and Sichuan province (21°08’–34°19´ N and 97°21´–108°33´ E), which are the first and second Yunnan and Sichuan province (21 08’–34 19´ N and 97 21´–108 33´ E), which are the first and second terrain transition zones of China◦ (Figure◦ 1). Our study◦ area included◦ 37 prefecture-level cities and terrain transition zones of China (Figure1). Our study area included 37 prefecture-level cities and 312 312 counties (Appendixes A and B). The southern end of the Hu Line is an ecologically fragile area counties (AppendicesA andB). The southern end of the Hu Line is an ecologically fragile area that is that is also a hot spot for economic development and population growth. Located in Sichuan and also a hot spot for economic development and population growth. Located in Sichuan and Yunnan Yunnan provinces, the southeastern part of the Tibetan Plateau is fragile and vulnerable to climate provinces, the southeastern part of the Tibetan Plateau is fragile and vulnerable to climate change [35]. change [35]. is in the northeastern part of the study area, where the terrain is relatively Sichuan Basin is in the northeastern part of the study area, where the terrain is relatively flat and the flat and the soil is fertile. The Yunnan–Guizhou Plateau is located in the southern part of the study soil is fertile. The Yunnan–Guizhou Plateau is located in the southern part of the study area, which area, which belongs to a subtropical humid zone subjected to significant climate differences [36]. belongs to a subtropical humid zone subjected to significant climate differences [36]. Sichuan and Sichuan and Yunnan Provinces have the most abundant biodiversity in China, and Hengduan Yunnan Provinces have the most abundant biodiversity in China, and Hengduan Mountain in the west Mountain in the west is even a global biodiversity hotspot [32]. The study area, in which the highest is even a global biodiversity hotspot [32]. The study area, in which the highest altitude is over 7000 altitude is over 7000 m, has environmental and socioeconomic differences among the plain, mountain, m, has environmental and socioeconomic differences among the plain, mountain, and plateau areas. and plateau areas. When compared with eastern China, Sichuan and Yunnan have not undergone When compared with eastern China, Sichuan and Yunnan have not undergone the rapid urbanization, the rapid urbanization, and its land use change is slow but marked. and its land use change is slow but marked.

FigureFigure 1. 1.The The study study area. area. 2.2. Ecosystem Health Assessment Framework 2.2. Ecosystem Health Assessment Framework A framework for information collection that embodies socio-economic, ecological and resource A framework for information collection that embodies socio-economic, ecological and resource aspects and considers the interactions among these components is needed [37]. This framework aspects and considers the interactions among these components is needed [37]. This framework must must ascertain both human activities that exert pressure on the environment and the impacts of ascertain both human activities that exert pressure on the environment and the impacts of environmental change on human well-being [8,38]. Moreover, health is not the opposite of disability, environmental change on human well-being [8,38]. Moreover, health is not the opposite of disability, while ecosystem health is an embodiment of ecological carrying capacity [39]. The deficiency of while ecosystem health is an embodiment of ecological carrying capacity [39]. The deficiency of ecosystem services and management would lead to the decline of ecological carrying capacity, thus ecosystem services and management would lead to the decline of ecological carrying capacity, thus

Sustainability 2019, 11, 4781 4 of 26 reducing the level of ecosystem health [1]. In this study, we set a desired indicator system for regional ecosystem health based on the PSR framework [8,27,40,41]. We then introduced indicators of human attributes into the assessment framework together with indicators of natural qualities. The developed indicator system consists of 17 indicators that reflect the pressure, state, and response in the assessment region to produce the ecosystem health scores. More details are shown in Table1.

Table 1. Indicator system used to evaluate ecosystem health at different scales.

Weight Weight 1st-Level 2nd-Level 3rd-Level Indicator (Prefecture (County Indicator Indicator Level) Level) Planting area of crops (I1) 0.316 0.382 Agriculture Fertilizer application amount (I2) 0.126 0.170 Population density (I3) 0.205 0.071 Pressure Population Natural population growth rate (I4) 0.181 0.062 Percentage of temperature anomaly (I5) 0.055 0.064 Nature Percentage of precipitation anomaly (I6) 0.116 0.251 Total output of agriculture, forestry, animal husbandry and 0.196 0.207 Agriculture fishery (I7) Grain yield per unit of cultivated land (I8) 0.226 0.289 State Per capita GDP (I9) 0.198 0.353 Economy Rural per capita net income (I10) 0.228 - Nature Normalized difference vegetation index (NDVI) (I11) 0.152 0.151 Irrigation area (I12) 0.107 0.178 Agriculture Total power of agricultural machinery (I13) 0.245 0.220 Per capita investment in fixed assets of the whole society (I14) 0.193 0.144 Response Per capita local government budget expenditures (I15) 0.174 0.168 Society Total mileage of highway (I16) 0.191 0.154 Tertiary industry proportion (I17) 0.089 0.135

Human activities, unsustainable resource consumption, or unreasonable economic structures exert stress on the natural environment, changing the quality and quantity of natural resources. But these changes would lead to responses in human organized behavior that adopt rational economic policies to restore or ameliorate the health of the environment and to mitigate or prevent ecosystem degradation.

2.3. Data Acquisition and Processing To quantify ecosystem health changes, we collected many relevant data at the city and county level from 2000 to 2016 from Sichuan and Yunnan statistical yearbooks and regional economy statistical yearbooks. These societal, economic, agricultural, and ecological data were used to derive or calculate ecosystem health indexes for ecosystem health assessment. Chinese statistical yearbooks at the county-level were used to supplement some missed data not available in the former two types of yearbooks, while the rest of the anomalies or missed data (0.93%) could be revised and calculated by regression equations. Temperature and precipitation data were provided by the China Meteorological Administration [35]. The meteorological data at a 5 km 5 km resolution were obtained by using × the kriging method to interpolate data from 109 meteorological stations around and within the Sichuan and Yunnan province. The moderate resolution imaging spectroradiometer (MODIS) data was downloaded from the NASA website [32]. The monthly NDVI dataset was compiled by the maximum value composite method to minimize the impacts of atmosphere scan angle, solar zenith angle, and cloud contamination.

2.3.1. Climate Change Index Climate change is associated with species abundances and distributions, as well as one species-level extinction [42]. Natural disasters such as floods and droughts pose serious threats to the environment, social development, and human life [43,44]. Sichuan and Yunnan are often the sites of natural disasters and have been specified as a global biodiversity hotspot [32]. The percentage of precipitation anomalies Sustainability 2019, 11, 4781 5 of 26 can intuitively reflect anomalies of precipitation and are commonly used to evaluate drought events [45]. Therefore, using the percentage of temperature anomaly and precipitation anomaly, we evaluated the natural pressure for the study area caused by climate change as follows:

P P Pa = − 100% (1) P ×

T T Ta = − 100% (2) T × where Pa and Ta are the percentage of temperature anomaly and precipitation anomaly, respectively; P and T are the total precipitation and mean temperature of a year, respectively; and P and T are the long-term mean annual precipitation and temperature, respectively. The term was from 2000 to 2016.

2.3.2. Data Acquisition at the County Level Ecosystem assessments require some socio-economic data of spatial and temporal resolutions, which are currently not available from remote sensing images. Because yearbook data are missing or given in low resolution, it is important to downscale them to the required spatial resolution in practical case studies. The indicators used in the assessment process at the county level were consistent with those used at the city level. The data acquisition methods and approaches mentioned above were used to collect most of the data, but some indicators lack data of some or a large number of counties (Table2).

Table 2. Ratio of missing indicator data.

Indicator Missing Ratio I2 58.7% I7 41.3% I12 41.3% I13 16.7%

There is usually a demand to translate information from a large spatial scale to finer geographic scales while keeping consistency with the raw dataset [46]. This process is called spatial downscaling, and it is a common method to use existing data with correlation to estimate unknown data [47,48]. Based on the correlation analysis of the indicator data with land cover data and socio-economic data, a multiple linear regression equation was established to calculate the indicator data that were missing. Moreover, the indicator of planting area of crops was replaced by the indicator of sown area of grain crops. Rural per capita net income data were not available directly or indirectly; therefore, this indicator was excluded. The missing data at the county level were calculated by the formula:

Y = a1A1 + a2A2 + ... + amAm (3)

Ycity Ycounty = Y (4) · Pn Y i=1 where Y is the coefficient of a county-level indicator, A is the socio-economic indicator or area of land cover type of the county, and a is the coefficient of the corresponding indicator. Additionally, Ycounty is the indicator data needed to acquire the county; Ycity is the total value of the city in which the county is located, and n is the number of counties in the city. Regression analysis was calculated using SPSS 16.0 and the results are shown in Table3. A1 is the population of the county; A2 is the GDP of the county; A3 is crop area of the county; A4 is the gross value of primary industry of the county; A5 is the impervious area of the county; and A6 is the area of Bareland. To verify the accuracy of the data, scatter Sustainability 2019, 11, 4781 6 of 26 plot analysis was performed with the calculated data and the actual data. Although the correlation coefficient of the total power of agricultural machinery was lowest, its missing ratio was also the lowest (Tables2 and3).

Table 3. Relationships between county-level indicators and other data.

R2 (Regression R2 (Fitted Indicator Regression Model Equation) Equation) I2 Y = 49010.2A 37950.7A + 19857.5 A 292.3 0.72 0.87 1 − 2 3 − I7 Y = 795200A4 + 0.067 0.97 0.98 I12 Y = 17288.7A3 + 39586.6A4 + 9099.1A5 + 1255.7 0.86 0.92 I13 Y = 33.538A + 23.88A + 25.428A 17.429A + 7.149 0.60 0.82 3 4 5 − 6

2.3.3. Comprehensive Assessment Positive indicators denote that the ecosystem health score is declining when indicator values decrease; negative ones indicate that the ecosystem health score is improving when indicator values decrease. In this study, planting areas of crops and all state and response indicators were positive. The extremum difference method was used to normalize each index [49]. Entropy is an objective method of measuring the information uncertainty by probability theory that implements quantitative analysis for indicators [50]. In this study, we used the entropy weight method to express the decision information of each indicator and assign the weight for indicators. The pressure, state, and response scores were found by weighted overlaying corresponding index layers, and the composite ecosystem health assessment result was obtained by overlaying these three criterion. The formula is as follows:

uv t ki X 2 S = W R (5) i ij ij j=1 v tu X3 2 H = Si (6)

i where Si is the assessment score of the i items (pressure, state, and response); ki is the number of assessment indicators in the items i; Wij is weight of indicator j in i items; Rij is the normalized value of each index; and factor H is the ecosystem health scores.

2.4. Analysis of Overall Evolution Characteristics Based on assessment of the ecosystem health in the study area at the city level from 2000 to 2016, its overall evolution characteristics were analyzed. The primacy ratio, coefficients of variation, spatial autocorrelation analysis, and Herfindahl coefficient methods [51–53] were introduced to explore the distribution, equilibrium degree, spatial autocorrelation trends, and agglomeration degree of ecosystem health in the entire region over the study period. Because there are many references [51–53] describing how to use these methods, only the spatial autocorrelation analysis is covered here. Global Moran’s Index, which is a commonly used method of spatial autocorrelation analysis, was used in this study to examine the spatial relationship with ecosystem health in each region. Global Moran’s Index was greater than 0, indicating that the ecosystem health of each region had a positive spatial autocorrelation. A smaller index indicated a stronger spatial dispersion of the assessment results. The formula was as follows: Pn Pn   Wij(xi x) xj x i=1 j=1 − − = I n n (7) 2 P P S Wij i=1 j=1 Sustainability 2019, 11, 4781 7 of 26

n 1 X S = (x x)2 (8) n i − i=1 where n is the number of spatial units (prefecture-level cities); xi and xj are the health scores for units i and j, respectively; and W is the spatial weight matrix. If units i and j are adjacent, then W = 1, otherwise, W = 0. The global Moran’s Index was tested for significance using the following formula:

I E(I) Z(I) = p− (9) Var(I)

2.5. Analysis of Local Evolution Characteristics We obtained the annual ecosystem health distributions over the last 20 years, after which the spatial change rate of ecosystem health was obtained by using the least squares method: P P P n xy x y slope = P − (10) n x2 (x)2 − where n is the number of years, and x and y are the year and ecosystem health scores (or pressure, state, and response scores), respectively. The concept of a gravity center is derived from physics, and the gravity model has been widely utilized in the fields of economic geography, land use science, urban planning, and ecosystem services value, etc. [54]. The variation track of the gravity center of assessment result value can well reflect the regional difference of the results in changes [55]. The migration process of ecosystem health status in space was revealed using the gravity model and the gravity center coordinate of ecosystem health is given as follows:

Pn Pn Hi Xi Hi Yi i=1 × i=1 × X = , Y = (11) Pn Pn Hi Hi i=1 i=1 where n is the number of small areas (spatial units); Xi and Yi are the center coordinate of area i; and Hi is an attribute value (health, pressure, states or response scores) of area i. A standard deviation ellipse was used to reflect the spatial distribution of ecosystem health and identify changes in average position and the moving direction [36]. The standard deviation ellipse consists of three elements: average location, orientation, and dispersion (or concentration). The long axis of the ellipse delineates the direction and trend of spatial distribution of ecosystem health. In ArcGIS10.4, three standard deviations can be used to describe the standard deviation ellipse and contain about 68%, 95% or 99% centroids of all input features, respectively. We chose the first one to explore the evolution characteristics of ecosystem health at the prefecture level.

3. Results

3.1. Global Features of Ecosystem Health Condition at the City Level in the Study Area The overall characteristics of ecosystem health at the prefecture level of the study area from 2000 to 2016 are shown in Figures2 and3. In 2000, the average ecosystem health score was 0.60, which increased by 15% to 0.69 by 2016 (Y = 0.0058X–11.01, R2 = 0.95, p < 0.0001) (Figure2). Specifically, the average pressure scores of the ecosystem showed a fluctuating trend, and they generally slowly increased (Y= 0.0008X–0.92, R2 = 0.70, p < 0.0001). The highest pressure period in Sichuan and Yunnan was in 2000, when the score was 0.720, while the lowest was in 2014, when there was a score of 0.735. Both the state score (Y = 0.0089X–0.17.34, R2 = 0.92, p < 0.0001) and response score (Y = 0.0089X–17.34, R2 = 0.96, p < 0.0001) of the study area increased rapidly with a slope of 0.0089 per year. These findings indicate that three aspects of the ecosystem were improving, as was the overall ecosystem health score. Sustainability 2019, 11, x FOR PEER REVIEW 8 of 25 SustainabilitySustainability2019 2019, 11, 11, 4781, x FOR PEER REVIEW 8 ofof 2625

Figure 2. Changes in the ecosystem health condition (including pressure, state, and response scores) Figure 2. Changes in the ecosystem health condition (including pressure, state, and response scores) in inFigure the study 2. Changes area from in the2000 ecosystem to 2016. health condition (including pressure, state, and response scores) thein the study study area area from from 2000 2000 to 2016. to 2016.

FigureFigure 3. 3. OverallOverall characteristics characteristics of of the the spatial spatial evolution evolution of of ecosystem ecosystem health health condition. condition. Figure 3. Overall characteristics of the spatial evolution of ecosystem health condition. AnalysisAnalysis of of the the primacy ratio, variation coeffici coefficient,ent, Herfindahl Herfindahl coefficient, coefficient, and and Moran‘s Moran‘s Index Index indicatedindicatedAnalysis that that there thereof the was was primacy a a tipping tipping ratio, point point variation in in the the evolution evolutioncoefficient, of of Herfindahlecosystem ecosystem health healthcoefficient, at at the the andyear year Moran‘s 2005 2005 or or 2007,Index 2007, sincesinceindicated which which that all all therevalues was except a tipping Moran’s point Index in the have evolution shown of increasingecosystem trendshealth at (Figure the year3 3).). 2005 AA Moran’sMoran’s or 2007, IndexIndexsince value valuewhich greater greaterall values than than except 0.15 0.15 appearedMoran’s appeared Index before before have 2005, 2005, shown reflecting reflecting increasing a a clustering trends distribution(Figure distribution 3). A trend trendMoran’s in in ecosystemecosystemIndex value health, health, greater and and thanthe the p p 0.15valuevalue appeared and and ZZ valuevalue before of of Moran’s Moran’s2005, reflecting index index were were a clustering less less than than distribution0.05 0.05 and and larger larger trend than in 1.96,ecosystem respectively health, (Figure and the 33d).d). p value Moran’sMora n’sand IndexIndex Z value showedshowed of Moran’s aa reversereverse index patternpattern were ( (lessZ > 1.96,than1.96, p0.05p << 0.05)0.05) and afterlarger after 2005, 2005, than with1.96, a respectively decrease turning (Figure to a 3d). random Mora n’s distribution. Index showed a reverse pattern (Z > 1.96, p < 0.05) after 2005, with a decrease turning to a random distribution. 3.2. Spatio-Temporal Pattern Evolution of Ecosystem Health Condition at the City Level 3.2. Spatio-Temporal Pattern Evolution of Ecosystem Health Condition at the City Level 3.2.1.3.2. Spatio-Temporal Spatio-Temporal Pattern Pattern Evolution Evolution of Ecosystem Health Condition at the City Level 3.2.1. Spatio-Temporal Pattern Evolution 3.2.1.The Spatio-Temporal pressure, state, Pattern and response Evolution scores as well as the spatial change rate (slope) of each region wereThe calculated. pressure, The state, results and response were divided scores into as well four as categories the spatial using change the rate natural (slope) breaks of each method region in The pressure, state, and response scores as well as the spatial change rate (slope) of each region wereArcGIS calculated. 10.4. The The natural results breaks were method divided is into designed four categories to determine using the the natural natural clustering breaks ofmethod attribute in ArcGISwere calculated. 10.4. The natural The results breaks were me thoddivided is designed into four to categories determine using the natural the natural clustering breaks of method attribute in ArcGIS 10.4. The natural breaks method is designed to determine the natural clustering of attribute

Sustainability 2019, 11, 4781 9 of 26 Sustainability 2019, 11, x FOR PEER REVIEW 9 of 25 valuesvalues throughthrough seekingseeking toto minimizeminimize averageaverage deviationdeviation withinwithin thethe classclass whilewhile maximizingmaximizing averageaverage deviationdeviation amongamong thethe classes,classes, andand thethe methodmethod hashas goodgood adaptabilityadaptability andand highhigh precisionprecision inin dividingdividing geographicalgeographical environmentenvironment unitsunits [[56,57].56,57]. This division of relative results isis stillstill validvalid forfor longlong timetime series,series, becausebecause allall unitsunits wouldwould notnot havehave essentialessential changeschanges oror leapsleaps atat thethe samesame timetime duringduring thethe studystudy period.period. TheThe spatial distribution ofof pressurepressure andand statestate scoresscores acrossacross thethe studystudy areaarea showedshowed lowerlower scoresscores onon thethe northwestnorthwest side side of of the the Hu Hu Line Line than than on on the the southeast southeast side side (Figure (Figure4Aa,Ab). 4Aab). TheThe highesthighest pressurepressure scoresscores (to(to withstandwithstand minimumminimum pressure)pressure) werewere distributeddistributed inin ,Nanchong, ,Mianyang, ,Dazhou, andand LiangshanLiangshan inin Sichuan,Sichuan, andand QujingQujing andand WenshanWenshan inin Yunnan.Yunnan. TheThe spatialspatial distributiondistribution ofof thethe responseresponse scoresscores waswas notnot obvious,obvious, andand scoresscores ofof thethe provincialprovincial capitalcapital (Chengdu(Chengdu andand )Kunming) werewere slightlyslightly higherhigher thanthan those of other areas areas (Figure (Figure 44Ac).Ac). Analys Analysisis of of ecosystem ecosystem health health changes changes over over the the past past 20 20years years revealed revealed that that the the negative negative slope slope of of pressure pressure scores scores was was spread spread over over the Sichuan Basin, whilewhile thethe positivepositive slopeslope waswas spreadspread overover Yunnan,Yunnan, with with values values ranging ranging from from 0.0036−0.0036 yr yr1−1 toto 0.00620.0062 yryr−11. − − − (Figure(Figure4 4Ba).). Higher Higher slopes slopes of of state state scores scores were were found found inin thethe provincialprovincial capitalcapital andand surroundingsurrounding areas,areas, allall onon thethe southeasternsoutheastern sideside ofof thethe HuHu LineLine (Figure(Figure4 4Bb).Bb). The The lowerlower slope slope of of responseresponse scores scores mimicked mimicked thethe distributiondistribution ofof lowerlower responseresponse scoresscores andand thethe slopeslope ofof responseresponse scoresscores inin SichuanSichuan waswas superiorsuperior toto thatthat inin YunnanYunnan inin general general (Figure (Figure4 Bc).4Bc).

FigureFigure 4.4. Ecosystem scoresscores andand theirtheir changechange raterate (slope).(slope). GraphsGraphs ((AA)) andand ((BB)) representrepresent thethe scoresscores andand slope,slope, respectively.respectively. GraphsGraphs ((aa),), ((bb),), andand( (cc)) representrepresent the the pressure, pressure, state, state, and and response, response, respectively. respectively.

InIn 2000,2000, nearlynearly 50%50% ofof thethe prefecture-levelprefecture-level citiescities inin thethe studystudy areaarea werewere belowbelow thethe unhealthyunhealthy levellevel (Figure(Figure5 a).5a). The The ecosystem ecosystem health health of fourof four cities cities were were at a morbidat a morbid level, almostlevel, almost all of which all of were which located were onlocated the northwest on the northwest side of the side Hu of Line. the Hu The Line. areas Th ofe healthareas of level health were level Chengdu, were Chengdu, Mianyang, Mianyang, Dazhou, andDazhou, Nanchong and Nanchong in Sichuan in Basin. Sichuan In 2016,Basin. the In number2016, the of number areas below of areas the unhealthybelow the levelunhealthy decreased level bydecreased four and by the four number and the of number morbid of cities morbid decreased cities decreased by 50% (Figure by 50%5b). (Figure Although 5b). Although the health the of health cities onof cities the northwest on the northwest side of the side Hu of Line the Hu have Line improved, have improved, the region the is region still unhealthy, is still unhealthy, especially especially andLijiang Nujiang. and Nujiang.

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Figure 5. Ecosystem health in 2000 (a) and 2016 (b). FigureFigure 5.5. Ecosystem health in 2000 (a)) andand 20162016 ((bb).). There were differences in ecosystem health on both sides of the Hu Line, and the ecosystem healthThereThere on thewerewere southeast di differencesfferences side in in ecosystemof ecosystemthe Hu healthLine health was on bothonbetter both sides than sides of that the of Hu onthe Line,the Hu northwest andLine, the and ecosystem sidethe ecosystem(Figure health 6). onhealthHowever, the southeaston thethe southeastscores side of for the sidedifferent Hu of Line the 1st-level was Hu betterLine indicato was than better thatrs showed on than the northwestthat different on the sidetrends northwest (Figure over6 time.).side However, (FigureFrom 2000 the 6). scoresHowever,to 2016, for the di theff stateerent scores scores 1st-level for differentgap indicators (Y = 1st-level0.001X–1.99, showed indicato di Rff² erent=rs 0.53) showed trends among overdifferent regions time. trends Fromon the 2000over two totime. sides 2016, From increased the state2000 scorestocontinuously 2016, gap the (Y state= and0.001X–1.99, scoresthe difference gapR (Y2 = =in0.53) 0.001X–1.99, the amongpressure regions R scores² = 0.53) on (Y the =among − two0.0008X+1.67, sides regions increased on R² =the 0.44) continuously two and sides health increased and scores the dicontinuously(Yff =erence −0.00038X in the and + pressure0.63, the difference R² = scores 0.38) inamong (Y the= pressure0.0008X the two + scoressides1.67, declined.(YR2 == −0.0008X+1.67,0.44) There and healthwas R² a tipping scores= 0.44) (Yand point= health 0.00038Xin the scores year+ − − 0.63,(Y2007, = −R 0.00038Xwith2 = 0.38) the differences + among 0.63, R the² = in0.38) two response sidesamong declined.scores the two increa Theresidessing declined. was before a tipping then, There and point was rapidly a in tipping the decreasing year point 2007, in after. withthe year The the di2007,supportfferences with of the inthe response differences area on scoresthe in no responserthwest increasing sidescores before of increathe then, Husing Line and before rapidlywas apparentlythen, decreasing and rapidly stepped after. decreasing Theup. support after. of The the areasupport on the of the northwest area on sidethe no ofrthwest the Hu Lineside wasof the apparently Hu Line was stepped apparently up. stepped up.

Figure 6. Disparity in ecosystem health between regions on the southeast and northwest sides of the HuFigure . over Disparity the time. in ecosystem health between regions on the southeast and northwest sides of the FigureHu Line 6. overDisparity the time. in ecosystem health between regions on the southeast and northwest sides of the 3.2.2.Hu Results Line over of the the Spatial time. Gravity Center Model and Standard Deviation Ellipse 3.2.2.The Results gravity of centerthe Spatial of ecosystem Gravity healthCenter for Model the entire and Standard study area Deviation was located Ellipse to the east of Liangshan, 3.2.2. Results of the Spatial Gravity Center Model and Standard Deviation Ellipse and movedThe gravity 6.64 km center to the of southwest ecosystem from health 2000 for to 2016 the (Figureentire 7study). The area gravity was center located of theto ecosystemthe east of pressureLiangshan,The (pressuregravity and moved center scores) 6.64of moved ecosystem km to 11.14 the kmsouthwesthealth to the for northeast fromthe entire 2000 (southwest) tostudy 2016 area(Figure along was 7). the located The Hu gravity Line. to the From center east 2005 ofof toLiangshan,the 2007 ecosystem it moved and pressure moved 5.72 km, 6.64(pressure which km wasto scores) the much southwest moved farther 11.14from than km2000 in other to to the 2016 years. northeast (Figure Similarly, (southwest) 7). The the gravity along center the Hu ofof thetheLine. ecosystemecosystem From 2005 statepressure to 2007 scores (pressureit moved 5.72scores) 11.43 km, km moved which along 11.14was the directionmuch km to farther the of northeast Hu than Line. in (southwest)other The gravity years. alongSimilarly, center the of theHuthe responseLine.gravity From center scores 2005 of movedto the 2007 ecosystem northit moved 5.21 state5.72 km km, (3.77scores which km move along wasd much11.43 the Hu kmfarther Line along and than the 3.65 in direction other km perpendicular years. of HuSimilarly, Line. to HuThethe gravitygravity center of thethe responseecosystem scores state moved scores northmove d5.21 11.43 km km (3.77 along km alongthe direction the Hu Lineof Hu and Line. 3.65 The km gravity center of the response scores moved north 5.21 km (3.77 km along the Hu Line and 3.65 km

Sustainability 2019, 11, 4781 11 of 26 Sustainability 2019, 11, x FOR PEER REVIEW 11 of 25 perpendicularLine). In general, to Hu the Line). gravity In centergeneral, of thethe ecosystemgravity center health, of the including ecosystem pressure health, scores including and state pressure scores, scoresmoved and almost state exclusively scores, moved along almo thest Hu exclusively Line. along the Hu Line.

FigureFigure 7. 7. TrajectoriesTrajectories of of movement movement of of gravity gravity center center and and standard standard deviational deviational ellipse ellipse of of ecosystem ecosystem healthhealth condition condition from from 2000 2000 to to 2016. 2016.

TheThe regions regions with with healthier healthier ecosystems ecosystems were were main mainlyly distributed distributed in in the the Si Sichuanchuan Basin Basin and and eastern eastern Yunnan.Yunnan. Furthermore, thethe directiondirection and and trend trend of of standard standard deviation deviation ellipses ellipses were were dominated dominated by these by theseregions. regions. The center The center of standard of standard deviation deviation ellipses ellipses was at awas central at a location central betweenlocation thebetween two provincial the two provincialcapitals (Chengdu capitals (Chengdu and Kunming). and Kunming). 3.3. Ecosystem Health at the County Level in Sichuan and Yunnan 3.3. Ecosystem Health at the County Level in Sichuan and Yunnan Because of the large number of counties in the study area (number = 312), the ecosystem health Because of the large number of counties in the study area (number = 312), the ecosystem health evaluation results of each county were divided into six categories using the natural breaks method in evaluation results of each county were divided into six categories using the natural breaks method ArcGIS 10.4 (Figure8). The distribution of ecosystem health of the study area as of 2016 is shown in in ArcGIS 10.4 (Figure 8). The distribution of ecosystem health of the study area as of 2016 is shown Figure8. There was a significant di fference between counties on the southeast and northwest sides of in Figure 8. There was a significant difference between counties on the southeast and northwest sides Hu Line with regards to the intensity of ecosystem pressure, ecosystem state, and ecosystem health. of Hu Line with regards to the intensity of ecosystem pressure, ecosystem state, and ecosystem health. On the southeastern side of the Hu Line, the pressure, state, and health performance was significantly On the southeastern side of the Hu Line, the pressure, state, and health performance was significantly higher than that on the northwestern side, while areas with high response scores were scattered higher than that on the northwestern side, while areas with high response scores were scattered throughout the study area. The overall health based on the county-level evaluation is consistent with throughout the study area. The overall health based on the county-level evaluation is consistent with that of the city-level evaluation. In 2016, the county with the highest ecosystem health index was that of the city-level evaluation. In 2016, the county with the highest ecosystem health index was (in the southeastern Hu Line area) of , while Dege of Ganzi (in the northwestern Hu Xuanwei (in the southeastern Hu Line area) of Qujing, while Dege of Ganzi (in the northwestern Hu Line area) had the lowest score. Moreover, 16.2% of the prefecture-level cities were unbalanced in their Line area) had the lowest score. Moreover, 16.2% of the prefecture-level cities were unbalanced in development, and the ecosystem health condition levels of each county in these cities differed greatly their development, and the ecosystem health condition levels of each county in these cities differed (Table4). Overall, 13.5% of the prefecture-level cities had a balanced development, and the ecosystem greatly (Table 4). Overall, 13.5% of the prefecture-level cities had a balanced development, and the health levels of the counties were basically the same. The ecosystems of some cities were healthy as a ecosystem health levels of the counties were basically the same. The ecosystems of some cities were whole, but with that of one county lagging behind. For example, the ecosystem of Pidu was healthy as a whole, but with that of one county lagging behind. For example, the ecosystem of Pidu the unhealthiest county in Chengdu, indicating that greater attention needs to be given to this county. District was the unhealthiest county in Chengdu, indicating that greater attention needs to be given to this county.

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FigureFigure 8.8. Ecosystem pressure scores scores ( (aa),), state state scores scores (b (b),), response response scores scores (c), (c and), and health health conditions conditions (d) (dfor) for each each county county in the in the study study area area in in2016. 2016.

Table 4. Equilibrium degree of internal development within prefecture-level cities. Typical prefecture- Table 4. Equilibrium degree of internal development within prefecture-level cities. Typical level cities have counties leading in ecosystem health or lagging in ecosystem health. prefecture-level cities have counties leading in ecosystem health or lagging in ecosystem health. Balance Lagging Leading Balance Degree ProportionProportion Overall Overall Health Health Typical Typical Cities CitiesLagging Counties Leading Counties Degree Healthy Counties Counties Balanced (<3) * 13.50% RelativeHealthy healthy Bazhong, Suining Ziyang

Balanced (<3) * 13.50% RelativeUnhealthy healthy Nujiang, , Ganzi Suining Danling HealthyUnhealthy Nujiang, Ganzi Chengdu Pidu Meishan Danling Relatively balanced RelativeHealthy healthy Mianyang Santai 70.2% Chengdu Pidu Relatively(>2, <5) Chuxiong Chuxiong Relative healthy Mianyang Santai balanced 70.2% Unhealthy Diqing Weixi Shangri-La Chuxiong Chuxiong (>2, <5) Aba Lixian HealthyUnhealthy Qujing Diqing Malong Weixi Shangri-La Unbalanced (>4) 16.2% LiangshanAba Lixian Relative healthy HealthyDali QujingJianchuan Malong Unbalanced * Numbers indicate16.2% how many types of ecosystem health Liangshanlevels there are in the region. There Xichang were no (>4) Relative healthy extremes in our study area. The healthiest ecosystem and Dalithe unhealthiest Jianchuan ecosystem will not be *located Numbers in indicate a region how at many the typessame of time ecosystem if the health region levels contains there are only in the two region. levels There of wereecosystem no extremes health. in ourTherefore, study area. we The considered healthiest ecosystem regions andthat the contained unhealthiest only ecosystem one or will two not ecosystem be located inhealth a region levels at the to same be time if the region contains only two levels of ecosystem health. Therefore, we considered regions that contained onlybalanced. one or two ecosystem health levels to be balanced.

4. Discussion

4.1. Assessment Methodology

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4. Discussion

4.1. Assessment Methodology Human society is bound to ecosystems, and the health of ecosystems underpins human sustainability [58]. When human activities exert pressure on ecosystems, the state of the ecosystem does not change immediately. Their interactions, which combines the complex cumulative effects of temporal and spatial patterns, are highly dynamic [37]. Owing to the complexity of ecosystems, it is not easy to develop an integrated framework and model that equally blends both natural and human factors to assess ecosystem health. Accordingly, a common and unifying approach to the process is critical [59,60]. The PSR model emphasizes that human beings are a part of the ecosystem and that human activities play a pivotal role in determining the environmental state. These comprehensive and dynamic features makes it a more informatory model [27], as indicated by the results of studies in China and other countries [61,62]. Determining the indicator and its weight are two important steps in the evaluation process [63]. In actual application, it is impossible to incorporate all factors affecting regional ecosystem health in an assessment model. In this study, the indicator system for regional ecosystem health was built based on the PSR framework. Human impacts and natural factors were introduced as indicators into the assessment framework. Overall, this framework consisted of 17 indicators reflecting the pressure, state, and response of each region to produce ecosystem health scores: (1) Our study area has limited land resources with over 90% mountain cover [32]. Farmland has been shrinking for various reasons since 1957 [64]. In 2013, the per capita cultivated land area of 12 cities, such as Chengdu, , and , was lower than the critical level issued by the United Nations (per capita cultivated land should be no less than 0.80 mu). Food supplies require continuous soil fertilization; however, the application of chemical fertilizers can cause environmental pollution and soil nutrient imbalance, such as an increase in heavy metals and toxic elements, decrease in soil microbial activity, and soil acidification. The northwest part of the study area is part of the Tibetan Plateau, which has a fragile environment that is sensitive to climate change [35]. Continuous drought also occurs in many places in Yunnan Province. Precipitation anomalies commonly cause drought events [45]. After 1949, the population of the study area increased substantially, with the permanent population exceeding 130 million in 2016. These factors of the study area are represented by the pressure indicator of the planting area of crops, fertilizer application amount, percentage of temperature anomaly, percentage of precipitation anomaly, population density, and natural population growth rate. (2) Agriculture production, economic situation, and ecological condition were selected as components of the state indicator to reflect ecosystem function and environmental status. Agriculture is a mainstay for human survival and development and, globally, the advances in agriculture is seen as an important means of economic prosperity and human well-being. Land resource vitality serve as a valuable indicator for measuring primary productivity and ecosystem activity [65]. Therefore, agricultural state, such as total output values for agriculture, forestry, animal husbandry, and fisheries and grain yield per unit of cultivated land, were selected as components of the state assessment indicators. Economic state was reflected in the use of rural per capita net income and per capita GDP as state indicators. To a certain extent, social and economic activities can improve people’s quality of life, enhance environmental protection awareness, and promote the coordinated development of humans and land. Healthy ecosystems are more resilient to adverse effects, such as disturbance from excessive human activities or natural disasters. For instance, a good forest ecosystem can regulate climate and preserve water and soil, and NDVI has been successfully used to reflect ecosystem vitality and monitor habitat degradation [66]. The NDVI is also commonly used as an assessment indicator of ecosystem state [8,67–69]. Generally, the higher the vegetation coverage (NDVI), the better the quality of the regional environment and the ability of the ecosystem to self-regulate. (3) Ecosystem responses to pressures can adjust a system’s state and agricultural and social countermeasures were used to indicate this response that was transformed into specific outcomes acting on the social and natural environment. Sustainability 2019, 11, 4781 14 of 26

For example, local government expenditure is an important measure for governments to coordinate regional economic development. In this study, total power of agricultural machinery, irrigation area, per capita local government budget expenditures, per capita investment in fixed assets of the whole society, tertiary industry proportion, and total mileage of highway were selected from social and agricultural responses to assess the responsiveness of the ecosystem. By combining a range of driving forces of ecosystem changes with the health status and responsiveness of the ecosystem, the PSR model was able to reflect the nature of ecosystem evolution more comprehensively. In addition to the traditional evaluation framework reflecting ecosystem quality, such as pressure, state, and response, health assessment is also related to biophysical processes and human ecological services. The responsibility for each indicator of ecosystem pressure, state, and response should be highlighted. The ownership of each index is non-definitiveness and subjective decision, whereas related areas are prone to confusion, especially ecosystem state and ecosystem response. As such, it is essential to monitor the state and sources of risk as well as to enforce timely regulation of probable sources of stress.

4.2. Dynamics of Ecosystem Health in Sichuan and Yunnan Evidence shows that many human-dominated ecosystems have become highly intense [70]. Regional ecosystems have been under such tremendous stress that it was difficult to obtain higher pressure scores. The average pressure scores of ecosystems in Sichuan and Yunnan have slowly increased at a decreasing rate since 2000 (Figure2). Development of the west region promoted the economic development of the western provinces through a large amount of state capital investment, which was a great strategic idea that began in 2000. Because macro-development was adjusted and controlled by the public policies [71], both the average state and response scores of the ecosystem have improved significantly, which were sensitive to administrative polices. As a result, even under low pressure scores, the average health scores of the ecosystem in the study area increased gradually. Increases in the primacy ratio and coefficient of variation indicated that the regional development imbalance has been further aggravated and the areas with higher health scores showed an expansion of health advantages after 2006 (Figure3a,b). As shown in Figure3d, there has been a more random spatial distribution of healthy ecosystems across the study area since then. The Chengdu Economic Circle, with its prosperous regional economy, took the lead in realizing rapid economic development by taking advantage of its innate strengths [72]. Traditional industries spread all over mountainous areas, and their initial economic growth had a lot to do with the exploitation and utilization of resources [73]. This has led to tremendous pressures on the ecosystems of regions undergoing rapid economic development. Therefore, the regional health balance in the study area increased before 2006 (before implementation of the resource protection policies). The Moran Index shows that more regions in the study area were well developed, but they were far less developed than the core regions, failing to correct regional imbalances. There were large differences in landforms, climate, ecology, and populations among the two sides of the Hu Line [30,74]. Overall, the pressure, state, response, and comprehensive health scores were all poorer on the northwest side of the Hu Line than the southeast (Figures4 and5). The distinct topography and climate on the southeast side creates a harsh and fragile geographical environment [35], which makes regional resources scarce and slows social and economic development. Although the superior natural conditions of the Sichuan basin brought about rapid social and economic development, the area has faced severe challenges such as intensive farmland reclamation, overexploitation of forest and mineral resources, river pollution, and habitat destruction. These regions had lower and lower pressure scores over time (Figure4a). Anthropogenic determinants involving social responses and concerns about environmental change have a strong impact on response scores [27]. Government decision-making focuses on supporting some core or hotspot regions, thereby improving their response scores. The ecosystem health has improved in most areas, but the situation in Lijiang appears to be very serious (Figure5). Other studies have also shown that the ecosystem health in much of the Sustainability 2019, 11, 4781 15 of 26 region is deteriorating [1]. , , , and Yangbi County were found to be the four worst counties in the Dali ecosystem (Figure8), which is in accordance with the results of previous study [40]. The vegetable industry in is well developed, and it has suffered from agricultural non-point source pollution for a long time due to the excessive application of fertilizer, and the ecosystem of Pidu District was the unhealthiest county in Chengdu (Table4). With the passage of time, the di fference in ecosystem response scores between the southeast and northwest sides of the Hu Line have narrowed (Figure6), indicating that more attention has been paid to ecologically fragile areas [75]. The gap in the pressure and health scores has been narrowing as the economy of the region on the southeast side of the Hu Line has grown. The long-term existence of the Hu Line is conditional on the comprehensive natural geographical conditions, which will not change in the near future [76]. This means that the center of gravity and spatial pattern of ecosystem health can only move along the Hu Line (Figure7).

4.3. Suggestions and Implications China, the world’s second-largest economy, has achieved amazing economic progress, with an average annual growth of 9.8% over the past 30 years. However, there is a huge disparity in social development and ecosystem health between the southeast and northwest sides of the Hu Line in China. In 2013, Premier Li Keqiang of the State Council questioned whether the pattern of the Hu Line could be broken. In the same year, the National New-Style Urbanization Plan (2014–2020) was promulgated and implemented. Accordingly, it is necessary to determine a method through which eco-sustainable development can be applied to the areas on both sides of the Hu Line in the Sichuan and Yunnan regions. To develop a smart model for breakthrough of the Hu Line suitable for Sichuan and Yunnan, it is essential to seek a relationship between geographical conditions and economic development, and to adjust social development to local conditions. At the national level, it is necessary to make overall arrangements for the ecological sustainable development of the entire country. The state should make construction of ecological civilization the goal, and resolve various practical conflicts, especially those associated with multisectoral management. Provinces or regions should guide and coordinate the ecological construction of various cities and counties. For example, areas with better institutional capacity can provide aid to resource-poor areas, and the ecologically sound places should undertake more population residence and economic construction through centralized resettlement. Prefecture-level cities should make detailed implementation and arrangements for the requirements of their superiors, but they must be tailored to local conditions. The northwest side of the Hu Line makes full use of local advantages, further developing ecotourism, promoting a distinctive culture, and improving access to domestic and foreign international markets. Furthermore, full use should be made of the economic resources on the southeast side of Hu Line, while developing technical innovations to foster green energy use and avoid excessive resource consumption and environmental pollution. The key management counties should be divided to identify those with weak and superior ecosystem health in each city (Table4). Counties with poor ecosystem health need to be emphatically managed, while those leading in ecosystem health should play a leading role. In practical terms, strategies should include promoting regional ecosystem health, extending green industries in towns and townships in the western region, and elevating the self-supporting capability and ecological protection consciousness of local residents through basic education and professional training. Finally, urban and rural integration strategies should be implemented to achieve rural revitalization. The mind map shown in Figure9 provides insightful suggestions to enable decision makers to achieve a smart breakthrough in the Hu Line that leads to long-term stability and prosperity. Sustainability 2019, 11, 4781 16 of 26 Sustainability 2019, 11, x FOR PEER REVIEW 16 of 25

Figure 9. Mind map for breakthrough in the Hu Line. 5. Conclusions 5. Conclusions This study systematically revealed the spatial evolution of regional ecosystem health at the south This study systematically revealed the spatial evolution of regional ecosystem health at the south end of the Hu Line (Sichuan and Yunnan) over the past 20 years. The ecosystem health of most end of the Hu Line (Sichuan and Yunnan) over the past 20 years. The ecosystem health of most prefecture-level cities has improved from 2000 to 2016, and the number of unhealthy cities has fallen prefecture-level cities has improved from 2000 to 2016, and the number of unhealthy cities has fallen by 25%, but the situation in Lijiang appeared to be unsatisfactory. Overall, the gap between cities by 25%, but the situation in Lijiang appeared to be unsatisfactory. Overall, the gap between cities was was widening, but the Moran Index results indicated that more high-quality cities have emerged. widening, but the Moran Index results indicated that more high-quality cities have emerged. Because Because the pressure on and state scores of the regional ecosystems was highly dependent on natural the pressure on and state scores of the regional ecosystems was highly dependent on natural geographical conditions, the comprehensive health conditions of the ecosystem southeast of the Hu geographical conditions, the comprehensive health conditions of the ecosystem southeast of the Hu Line were slightly better than those in the northwest. The spatial pattern and change direction of Line were slightly better than those in the northwest. The spatial pattern and change direction of ecosystem health were consistent with the direction of the Hu Line. At the county level of the ecosystem ecosystem health were consistent with the direction of the Hu Line. At the county level of the health evaluation, the imbalance in the development of the cities was divided, and counties within ecosystem health evaluation, the imbalance in the development of the cities was divided, and cities that were lagging were identified, such as Pidu District in Chengdu and Danling in Meishan. counties within cities that were lagging were identified, such as Pidu District in Chengdu and To correct regional imbalances, a comprehensive and proactive policy framework for development of a Danling in Meishan. To correct regional imbalances, a comprehensive and proactive policy smart breakthrough model of the Hu Line in Sichuan and Yunnan was put forward. Our study focused framework for development of a smart breakthrough model of the Hu Line in Sichuan and Yunnan on the spatial evolution of ecosystem health and contributed to the reasonable planning of ecological was put forward. Our study focused on the spatial evolution of ecosystem health and contributed to and environmental protection with the goal of identifying a path toward breakthrough in the Hu Line. the reasonable planning of ecological and environmental protection with the goal of identifying a However, this paper has no substantial contribution to the innovation of methods and the path toward breakthrough in the Hu Line. improvement of models. Future regional ecosystem health assessment requires a comprehensive However, this paper has no substantial contribution to the innovation of methods and the system based on different ecological and biological information scales that also takes into account improvement of models. Future regional ecosystem health assessment requires a comprehensive human health and cultural factors. Although the NDVI has been selected in many studies, there may system based on different ecological and biological information scales that also takes into account be other reasonable results without using NDVI. Many areas, such as plateau areas, have low scores of human health and cultural factors. Although the NDVI has been selected in many studies, there may ecosystem. Perhaps the ecological environment is not unhealthy, it may be fragile or have low carrying be other reasonable results without using NDVI. Many areas, such as plateau areas, have low scores capacity. It may be unreasonable or inappropriate to apply a gravity model to ecosystem health of ecosystem. Perhaps the ecological environment is not unhealthy, it may be fragile or have low research in this paper, although it is really used in economic geography and has sense. Other suitable carrying capacity. It may be unreasonable or inappropriate to apply a gravity model to ecosystem indicators need to be explored or the assessment model and analytical method need to be improved. health research in this paper, although it is really used in economic geography and has sense. Other Authorsuitable Contributions: indicators needConceptualization, to be explored J.X.or the and assessment H.Z.; formal model analysis, and W.L.; analytical data and method resources, need H.Z. to and be Y.Z.;improved. writing—original draft preparation, H.Z. and W.L.; writing—review and editing, W.C. and C.Y.; funding acquisition, J.X. Funding:Author Contributions:This research wasConceptualization, supported by the J.X. Strategic and H.Z.; Priority formal Research analysis, Program W.L.; of data Chinese and Academyresources, of H.Z. Sciences and (XDA20030302),Y.Z.; writing—original the Science draft and preparation, Technology H.Z. Project and ofW.L. Xizang; writing—review Autonomous Regionand editing, (Grant W.C. No. XZ201901-GA-07),and C.Y.; funding Openacquisition, Subject J.X. of Big Data Institute of Digital Natural Disaster Monitoring in Fujian (NDMBD2018003), Southwest Petroleum University of Science and Technology Innovation Team Projects (2017CXTD09), and National Flash FloodFunding: Investigation . This research and Evaluation was supported Project by (SHZH-IWHR-57). the Strategic Priority Research Program of Chinese Academy of ConflictsSciences of(XDA20030302), Interest: The authorsthe Science declare an nod Technology conflict of interest. Project of Xizang Autonomous Region (Grant No. XZ201901-GA-07), Open Subject of Big Data Institute of Digital Natural Disaster Monitoring in Fujian (NDMBD2018003), Southwest Petroleum University of Science and Technology Innovation Team Projects (2017CXTD09), and National Flash Flood Investigation and Evaluation Project (SHZH-IWHR-57).

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SustainabilityConflicts of2019 Interest:, 11, 4781 The authors declare no conflict of interest. 17 of 26

Appendix A Appendix A

FigureFigure A1.A1. City-level administrative divisionsdivisions inin studystudy area.area. Appendix B Appendix B Table A1. Ecosystem health of 312 counties in the study area. Table A1. Ecosystem health of 312 counties in the study area. City (Prefecture-Level Healthy ProvinceProvinc City (Prefecture-Level County PressurePressur Stat StateRespons ResponseHealth Healthy Healthy City) County Condition e City) e e e y Condition Sichuan Bazhong Bazhou 0.743 0.704 0.543 0.667 Critical Sichuan Bazhong Bazhou 0.743 0.704 0.543 0.667 Critical Enyang 0.76 0.694 0.533 0.667 Critical Enyang 0.76 0.694 0.533 0.667 Critical Nanjiang 0.774 0.696 0.525 0.677 Average Nanjiang 0.774 0.696 0.525 0.677 Average Pingchang 0.769 0.688 0.561 0.683 Average Pingchang 0.769 0.688 0.561 0.683 Average Tongjiang 0.783 0.691 0.512 0.678 Average Tongjiang 0.783 0.691 0.512 0.678 Average Chengdu Chenghua 0.715 0.932 0.621 0.707 Healthy Chengdu Chenghua 0.715 0.932 0.621 0.707 Healthy 0.742 0.778 0.562 0.699 Benign Chongzhou 0.742 0.778 0.562 0.699 Benign Dayi 0.73 0.79 0.536 0.687 Benign Dayi 0.73 0.79 0.536 0.687 Benign 0.713 0.803 0.542 0.691 Benign Dujiangyan 0.713 0.803 0.542 0.691 Benign Jinniu 0.711 0.898 0.612 0.688 Healthy JintangJinniu 0.7870.711 0.898 0.749 0.612 0.55 0.688 0.704 Healthy Benign Jintang 0.787 0.749 0.55 0.704 Benign Jinjiang 0.715 0.942 0.638 0.714 Healthy LongquanyiJinjiang 0.7270.715 0.942 0.888 0.638 0.511 0.714 0.722 Healthy Healthy

PengzhouLongquanyi 0.7320.727 0.888 0.804 0.511 0.558 0.722 0.708 Healthy Benign

PengzhouPidu 0.732 0.71 0.804 0.819 0.558 0.548 0.708 0.693 Benign Benign

PujiangPidu 0.7210.71 0.819 0.736 0.548 0.548 0.693 0.653 Benign Average

QingbaijiangPujiang 0.721 0.74 0.736 0.846 0.548 0.530.653 0.709 Average Benign Qingbaijian Qingyang 0.710.74 0.846 0.911 0.53 0.626 0.709 0.697 Benign Healthy Qionglaig 0.746 0.779 0.566 0.699 Benign

ShuangliuQingyang 0.7470.71 0.911 0.825 0.626 0.589 0.697 0.727 Healthy Healthy WenjiangQionglai 0.7130.746 0.779 0.903 0.566 0.578 0.699 0.724 Benign Healthy WuhouShuangliu 0.747 0.71 0.825 0.851 0.589 0.612 0.727 0.668 Healthy Healthy XinduWenjiang 0.7390.713 0.903 0.85 0.578 0.557 0.724 0.719 Healthy Healthy XinjinWuhou 0.730.71 0.851 0.873 0.612 0.569 0.668 0.719 Healthy Healthy Dazhou DachuanXindu 0.7990.739 0.85 0.721 0.557 0.543 0.719 0.704 Healthy Benign DazhuXinjin 0.8280.73 0.873 0.728 0.569 0.539 0.719 0.715 Healthy Benign Kaijiang 0.761 0.722 0.527 0.673 Average

Sustainability 2019, 11, 4781 18 of 26

Table A1. Cont.

City (Prefecture-Level Healthy Province County Pressure State Response Healthy City) Condition Quxian 0.831 0.693 0.535 0.708 Benign Tongchuan 0.733 0.748 0.515 0.667 Average 0.758 0.726 0.534 0.677 Average Xuanhan 0.82 0.715 0.569 0.723 Benign 0.755 0.82 0.542 0.709 Benign Jingyang 0.742 0.837 0.565 0.713 Benign Luojiang 0.721 0.805 0.553 0.678 Benign 0.731 0.805 0.542 0.693 Benign 0.707 0.837 0.538 0.694 Benign Zhongjiang 0.858 0.749 0.578 0.752 Healthy Ganzi Batang 0.65 0.637 0.52 0.598 Morbid Baiyu 0.677 0.625 0.482 0.604 Morbid Danba 0.701 0.658 0.508 0.623 Unhealthy Daofu 0.711 0.606 0.533 0.618 Morbid Daocheng 0.612 0.631 0.588 0.601 Morbid Derong 0.614 0.657 0.557 0.6 Morbid Dege 0.672 0.595 0.475 0.593 Morbid Ganzi 0.726 0.62 0.507 0.622 Morbid Jiulong 0.663 0.701 0.489 0.624 Morbid 0.667 0.665 0.523 0.625 Morbid Litang 0.678 0.619 0.518 0.613 Morbid Luhuo 0.734 0.615 0.516 0.625 Unhealthy Luding 0.665 0.634 0.5 0.598 Morbid Seda 0.713 0.607 0.537 0.627 Morbid Shiqu 0.663 0.596 0.477 0.594 Morbid Xiangcheng 0.63 0.653 0.542 0.601 Morbid Xinlong 0.708 0.626 0.508 0.622 Morbid Yajiang 0.68 0.638 0.563 0.629 Unhealthy Guang’an Guangan 0.787 0.731 0.574 0.7 Benign 0.758 0.741 0.533 0.677 Average Linshui 0.83 0.715 0.552 0.71 Benign Qianfeng 0.755 0.798 0.534 0.7 Benign Wusheng 0.792 0.738 0.536 0.696 Benign Yuechi 0.838 0.729 0.563 0.723 Benign Cangxi 0.783 0.722 0.617 0.712 Benign Chaotian 0.746 0.669 0.497 0.641 Unhealthy Jiange 0.792 0.715 0.646 0.717 Benign Lizhou 0.74 0.734 0.516 0.668 Average Qingchuan 0.734 0.661 0.484 0.634 Unhealthy Wangcang 0.754 0.728 0.505 0.672 Average Zhaohua 0.749 0.71 0.522 0.657 Critical Ebian 0.722 0.656 0.493 0.628 Unhealthy Emeishan 0.729 0.745 0.552 0.672 Average Jiajiang 0.731 0.733 0.571 0.672 Average Qianwei 0.777 0.768 0.547 0.7 Benign Jinkouhe 0.701 0.678 0.5 0.623 Unhealthy Jingyan 0.776 0.729 0.582 0.681 Benign Shizhong 0.745 0.777 0.546 0.689 Benign Mabian 0.756 0.63 0.486 0.63 Unhealthy Muchuan 0.762 0.688 0.513 0.652 Critical Shawan 0.728 0.851 0.534 0.706 Benign Wutongqiao 0.754 0.744 0.51 0.669 Average Liangshan Butuo 0.756 0.642 0.465 0.63 Unhealthy Dechang 0.753 0.747 0.534 0.674 Average Ganluo 0.712 0.638 0.476 0.616 Morbid Huidong 0.761 0.749 0.526 0.679 Average Huili 0.774 0.772 0.573 0.706 Benign Jinyang 0.737 0.625 0.471 0.618 Morbid Leibo 0.763 0.674 0.483 0.649 Critical Meigu 0.741 0.636 0.464 0.625 Morbid Sustainability 2019, 11, 4781 19 of 26

Table A1. Cont.

City (Prefecture-Level Healthy Province County Pressure State Response Healthy City) Condition Mianning 0.733 0.723 0.501 0.658 Critical Muli 0.646 0.628 0.52 0.613 Morbid Ningnan 0.739 0.716 0.534 0.653 Critical Puge 0.761 0.646 0.489 0.637 Unhealthy Xichang 0.789 0.79 0.613 0.733 Healthy Xide 0.751 0.635 0.479 0.628 Unhealthy Yanyuan 0.751 0.654 0.546 0.664 Critical Yuexi 0.737 0.658 0.477 0.632 Unhealthy Zhaojue 0.761 0.681 0.486 0.653 Critical Luzhou Gulan 0.818 0.638 0.504 0.672 Critical Hejiang 0.813 0.742 0.545 0.713 Benign Jiangyang 0.733 0.839 0.541 0.704 Benign Longmatan 0.713 0.822 0.549 0.686 Benign Luxian 0.776 0.777 0.56 0.718 Benign Naxi 0.742 0.765 0.539 0.674 Average Xuyong 0.781 0.663 0.536 0.67 Critical Meishan Danleng 0.722 0.757 0.564 0.665 Average Dongpo 0.762 0.796 0.601 0.724 Healthy Hongya 0.71 0.771 0.529 0.671 Average Pengshan 0.932 0.728 0.572 0.744 Healthy Qingshen 0.743 0.754 0.525 0.671 Average Renshou 0.72 0.765 0.608 0.731 Benign Mianyang Anzhou 0.739 0.769 0.572 0.684 Benign Beichuan 0.704 0.647 0.475 0.613 Morbid Fucheng 0.715 0.816 0.55 0.691 Benign 0.746 0.777 0.616 0.714 Benign Pingwu 0.718 0.626 0.474 0.614 Morbid Santai 0.861 0.722 0.604 0.754 Healthy Yanting 0.763 0.71 0.549 0.673 Average Youxian 0.735 0.768 0.554 0.682 Average Zitong 0.747 0.737 0.565 0.672 Average Nanchong Gaoping 0.77 0.72 0.502 0.672 Average Jialing 0.798 0.699 0.503 0.676 Average 0.776 0.725 0.548 0.691 Average Nanbu 0.84 0.712 0.556 0.72 Benign Pengan 0.774 0.726 0.528 0.682 Average Shunqing 0.756 0.761 0.53 0.673 Average Xichong 0.808 0.695 0.528 0.683 Average Yilong 0.793 0.725 0.534 0.701 Benign Yingshan 0.763 0.725 0.519 0.681 Average Dongxing 0.764 0.719 0.511 0.673 Average 0.76 0.745 0.537 0.683 Average Shizhong 0.737 0.729 0.503 0.658 Critical Weiyuan 0.796 0.765 0.544 0.705 Benign Zizhong 0.832 0.691 0.522 0.706 Benign Ngawa Aba 0.671 0.587 0.521 0.597 Morbid Heishui 0.707 0.654 0.496 0.622 Morbid Hongyuan 0.679 0.673 0.511 0.622 Morbid Jinchuan 0.723 0.655 0.527 0.635 Unhealthy 0.668 0.656 0.521 0.617 Morbid Lixian 0.678 0.734 0.53 0.648 Critical Barkan 0.722 0.661 0.547 0.644 Unhealthy Maoxian 0.687 0.673 0.499 0.617 Morbid Rangtang 0.715 0.604 0.535 0.62 Morbid Ruoergai 0.638 0.649 0.499 0.596 Morbid Songpan 0.679 0.66 0.515 0.623 Morbid Wenchuan 0.671 0.717 0.484 0.631 Unhealthy Xiaojin 0.685 0.625 0.526 0.61 Morbid Panzhihua Dongqu 0.695 0.902 0.512 0.685 Benign Miyi 0.728 0.814 0.538 0.694 Benign Sustainability 2019, 11, 4781 20 of 26

Table A1. Cont.

City (Prefecture-Level Healthy Province County Pressure State Response Healthy City) Condition Renhe 0.718 0.795 0.51 0.68 Average Xiqu 0.698 0.844 0.528 0.656 Benign Yanbian 0.736 0.751 0.525 0.672 Average Suining Anju 0.8 0.692 0.51 0.681 Average Chuanshan 0.749 0.741 0.543 0.667 Average Daying 0.774 0.718 0.513 0.673 Average Pengxi 0.786 0.714 0.512 0.68 Average 0.818 0.724 0.516 0.702 Benign Yaan Baoxing 0.631 0.707 0.508 0.612 Morbid Hanyuan 0.683 0.66 0.517 0.618 Morbid Lushan 0.668 0.725 0.52 0.634 Unhealthy Mingshan 0.712 0.705 0.531 0.641 Critical Shimian 0.677 0.739 0.497 0.64 Unhealthy Tianquan 0.668 0.741 0.514 0.637 Unhealthy Yingjing 0.626 0.722 0.504 0.617 Morbid Yucheng 0.681 0.763 0.55 0.661 Average Cuiping 0.749 0.817 0.541 0.704 Benign Gaoxian 0.769 0.718 0.537 0.675 Average Zongxian 0.758 0.717 0.526 0.665 Average Jiangan 0.748 0.764 0.513 0.679 Average Junlian 0.772 0.719 0.505 0.668 Average Nanxi 0.739 0.772 0.589 0.69 Benign Pingshan 0.772 0.675 0.53 0.655 Critical Xingwen 0.758 0.721 0.517 0.67 Average Yibin 0.823 0.74 0.52 0.714 Benign Changning 0.752 0.762 0.554 0.683 Benign Daan 0.733 0.755 0.483 0.663 Critical 0.782 0.769 0.518 0.706 Benign Gongjing 0.738 0.754 0.494 0.662 Average Rongxian 0.81 0.747 0.548 0.71 Benign Yantan 0.735 0.756 0.493 0.665 Average Ziliujing 0.728 0.816 0.52 0.683 Benign Ziyang Anyue 0.889 0.705 0.604 0.755 Healthy Jianyang 0.922 0.695 0.6 0.761 Healthy Lezhi 0.819 0.709 0.553 0.7 Benign Yanjiang 0.878 0.72 0.567 0.735 Healthy Yunnan Baoshan Changning 0.739 0.702 0.579 0.676 Average Longling 0.706 0.684 0.529 0.642 Unhealthy Longyang 0.738 0.761 0.569 0.711 Benign Shidian 0.706 0.668 0.545 0.642 Unhealthy 0.769 0.695 0.579 0.697 Average Chuxiong Chuxiong 0.77 0.76 0.601 0.716 Benign Dayao 0.766 0.689 0.521 0.663 Critical Lufeng 0.738 0.709 0.546 0.67 Critical Mouding 0.751 0.667 0.499 0.644 Unhealthy Nanhua 0.763 0.687 0.512 0.658 Critical Shuangbai 0.759 0.666 0.554 0.658 Critical Wuding 0.74 0.678 0.531 0.654 Critical Yaoan 0.753 0.716 0.514 0.661 Critical Yongren 0.739 0.685 0.532 0.647 Critical Yuanmou 0.726 0.698 0.538 0.651 Critical Dali Binchuan 0.742 0.758 0.581 0.692 Benign Dali 0.738 0.811 0.569 0.714 Benign Eryuan 0.729 0.743 0.514 0.67 Average Heqing 0.734 0.694 0.521 0.653 Critical Jianchuan 0.706 0.643 0.502 0.619 Morbid Midu 0.756 0.757 0.506 0.682 Average Nanjian 0.764 0.68 0.501 0.652 Critical Weishan 0.752 0.682 0.503 0.653 Critical Xiangyun 0.756 0.734 0.56 0.686 Average Sustainability 2019, 11, 4781 21 of 26

Table A1. Cont.

City (Prefecture-Level Healthy Province County Pressure State Response Healthy City) Condition Yangbi 0.728 0.684 0.507 0.636 Unhealthy Yongping 0.719 0.684 0.512 0.633 Unhealthy Yunlong 0.718 0.672 0.488 0.633 Unhealthy Dehong Lianghe 0.707 0.663 0.512 0.627 Unhealthy Longchuan 0.745 0.707 0.581 0.667 Average Mangxian 0.734 0.702 0.585 0.676 Average 0.747 0.743 0.595 0.68 Benign Yingjiang 0.76 0.702 0.563 0.676 Average Diqing Deqin 0.622 0.673 0.612 0.634 Unhealthy Weixi 0.665 0.635 0.54 0.616 Morbid Shangri-la 0.622 0.725 0.602 0.657 Critical Honghe 0.74 0.727 0.548 0.671 Average Hekou 0.741 0.767 0.576 0.671 Benign Honghe 0.73 0.668 0.483 0.637 Unhealthy Jianshui 0.738 0.678 0.574 0.672 Critical Jinping 0.749 0.657 0.486 0.644 Unhealthy Kaiyuan 0.709 0.748 0.59 0.677 Average Luxi 0.687 0.688 0.571 0.65 Critical Lvchun 0.723 0.652 0.487 0.629 Unhealthy Mengzi 0.764 0.681 0.574 0.674 Average Mile 0.708 0.72 0.573 0.677 Average Pingbian 0.762 0.642 0.504 0.64 Unhealthy Shiping 0.75 0.679 0.539 0.66 Critical Yuanyang 0.756 0.66 0.483 0.646 Unhealthy Kunming Anning 0.72 0.842 0.574 0.711 Benign Chenggong 0.683 0.738 0.55 0.658 Critical Dongchuan 0.719 0.658 0.499 0.632 Unhealthy Fumin 0.702 0.725 0.5 0.645 Critical Guandu 0.688 0.868 0.582 0.704 Benign Jinning 0.721 0.708 0.542 0.657 Critical Luquan 0.754 0.667 0.542 0.665 Critical Panlong 0.694 0.747 0.524 0.656 Critical Shilin 0.688 0.687 0.548 0.638 Unhealthy Songming 0.689 0.69 0.546 0.643 Unhealthy Wuhua 0.691 0.861 0.5 0.697 Benign Xishan 0.697 0.744 0.546 0.661 Critical Xundian 0.739 0.646 0.522 0.647 Unhealthy Yiliang 0.689 0.726 0.537 0.656 Critical Lijiang Gucheng 0.689 0.718 0.554 0.648 Critical Huaping 0.73 0.65 0.518 0.632 Unhealthy Ninglango 0.687 0.589 0.492 0.6 Morbid Yongsheng 0.748 0.669 0.534 0.661 Critical Yulong 0.678 0.647 0.529 0.62 Morbid Cangyuan 0.751 0.648 0.524 0.641 Unhealthy Fengqing 0.757 0.656 0.523 0.655 Critical Gengma 0.739 0.692 0.567 0.662 Critical Linxiang 0.752 0.675 0.526 0.656 Critical Shuangjiang 0.746 0.642 0.526 0.638 Unhealthy Yongde 0.745 0.627 0.539 0.643 Unhealthy Yunxian 0.788 0.654 0.52 0.667 Critical Zhenkang 0.722 0.632 0.532 0.628 Unhealthy Nujiang Fugong 0.67 0.607 0.497 0.595 Morbid Gongshan 0.64 0.633 0.533 0.599 Morbid Lanping 0.707 0.615 0.507 0.615 Morbid 0.708 0.628 0.503 0.617 Morbid Puer Jiangcheng 0.72 0.656 0.514 0.628 Unhealthy Jingdong 0.788 0.664 0.53 0.672 Critical Jinggu 0.78 0.692 0.537 0.68 Average Lancang 0.809 0.621 0.531 0.678 Critical Menglian 0.742 0.66 0.531 0.638 Critical Sustainability 2019, 11, 4781 22 of 26

Table A1. Cont.

City (Prefecture-Level Healthy Province County Pressure State Response Healthy City) Condition Mojiang 0.765 0.638 0.519 0.653 Unhealthy Ninger 0.754 0.656 0.528 0.649 Critical Simao 0.74 0.705 0.543 0.656 Critical Ximeng 0.756 0.617 0.516 0.633 Unhealthy Zhenyuan 0.771 0.663 0.537 0.659 Critical Qujing Fuyuan 0.753 0.706 0.508 0.674 Critical Huize 0.828 0.665 0.505 0.696 Average Luliang 0.718 0.706 0.558 0.672 Critical Luoping 0.718 0.74 0.536 0.68 Average Malong 0.702 0.639 0.524 0.621 Unhealthy Qilin 0.717 0.809 0.569 0.708 Benign Shizong 0.692 0.71 0.532 0.653 Critical Xuanwei 0.945 0.66 0.613 0.774 Healthy Zhanyi 0.775 0.715 0.55 0.688 Average Wenshan Funing 0.722 0.647 0.499 0.636 Unhealthy Guangnan 0.793 0.625 0.543 0.679 Critical Malipo 0.742 0.628 0.508 0.632 Unhealthy Maguan 0.784 0.641 0.514 0.656 Critical Qiubei 0.751 0.631 0.524 0.65 Unhealthy Wenshan 0.765 0.685 0.544 0.673 Average Xichou 0.738 0.623 0.509 0.626 Unhealthy Yanshan 0.773 0.65 0.598 0.683 Average Xishuangbanna 0.725 0.754 0.572 0.687 Average Menghai 0.74 0.739 0.552 0.686 Average Mengla 0.682 0.746 0.552 0.651 Critical 0.688 0.741 0.535 0.654 Critical Eshan 0.747 0.746 0.57 0.686 Benign Hongta 0.737 0.938 0.504 0.75 Healthy Huaning 0.691 0.72 0.553 0.65 Critical Jiangchuan 0.702 0.748 0.523 0.662 Critical Tonghai 0.71 0.713 0.573 0.663 Critical Xinping 0.761 0.73 0.549 0.687 Average Yimen 0.749 0.725 0.52 0.667 Average Yuanjiang 0.753 0.717 0.525 0.667 Average Daguan 0.782 0.617 0.494 0.642 Unhealthy Ludian 0.766 0.644 0.526 0.653 Critical Qiaojia 0.778 0.657 0.498 0.66 Critical 0.751 0.708 0.476 0.652 Critical Suijiang 0.749 0.639 0.498 0.632 Unhealthy Weixin 0.793 0.641 0.486 0.653 Critical Yanjin 0.786 0.636 0.496 0.651 Critical Yiliang 0.773 0.666 0.488 0.659 Critical Yongshan 0.796 0.642 0.49 0.658 Critical Zhaoyang 0.794 0.715 0.54 0.699 Average Zhenxiong 0.89 0.622 0.524 0.707 Benign

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