Comprehensive Assessment of Sustainable Development Goal 11 at the Sub-City Scale: A Case Study of Guilin City

Abstract

Quantifying the progress and interactions of the 11 indicators of Sustainable Development Goal 11 plays a crucial role in improving urban living and promoting urban prosperity. SDG 11, focused on sustainable cities and communities, employs forward-thinking strategies to address challenges arising from urban prosperity and development, such as land scarcity and resource shortages. This paper positions the indicators of SDG 11, analyzing the patterns, trends, dynamics, and issues of urbanization development in Guilin using a combination of geospatial satellite resource data and categorical statistical data. The study introduces a framework and positioning method for assessing sustainable development at the city–county scale, exploring the current state, spatial aggregation, synergies, and trade-offs in the development of Guilin City. The study introduces a framework and positioning method for assessing sustainable development at the city–county scale. Utilizing a localized evaluation system, it explores the developmental status of Guilin City. The application of Moran’s Index observes spatial aggregation among entities. By investigating Spearman’s rank correlation coefficient, it delves into the interplay of synergies and trade-offs within the studied region. Ultimately, it reveals significant disparities in the developmental landscape of the evaluated area, with a comprehensive spatial distribution indicating higher levels of development in the central and western regions and lower levels in the southeastern part. Strengthened cross-leverage and coordination are imperative to address the interconnections and harmonization of the developmental trends of the six synergistic indicators and nine trade-off indicators during the developmental process. The sustainable development of Guilin lays the groundwork for urban planning, construction, conservation, and management, positioning it as a potential model for successful sustainable development practices.

1. Introduction

Cities emerge and grow within a defined natural space, evolving as both an ecological system and an economic–social system along with economic development. However, the rapid expansion of cities has brought a series of challenges to human society. The United Nations, with unanimous agreement from all member states in 2015, established the “2030 Agenda for Sustainable Development”, which outlines 17 Sustainable Development Goals along with a comprehensive framework of global indicators for specific sustainable targets [1]. The proposed Sustainable Development Goal 11 (SDG 11) is identified as “Sustainable Cities and Communities”, and encompasses resources, transportation, population, and public spaces, with the aim of creating safe, inclusive, and habitable human settlements. It provides an important indicator system for analyzing urban development levels and societal needs [2,3,4]. SDG 11 sets the direction for global urban development by 2030 and offers specific indicators for assessing urban sustainability, serving as a crucial basis for evaluation and comparative research of different cities within a unified framework [5,6].

Many scholars in the research of urban sustainable development have focused on studying specific individual indicators or a few selected ones [7,8,9]. Zhou et al. discussed the urban land use and population changes in the Beijing-Tianjin-Hebei region, analyzing the efficiency changes in urban land use from 2000 to 2020. Roberta Ravanelli and her team utilized the Google Earth Engine to monitor the impact of land cover changes on surface urban heat islands (SUHI) for long-term spatiotemporal monitoring, along with their correlation with land cover changes. Some researchers are concerned about the impact of sub-indicators on sustainable urban development [10,11,12]. Some scholars are not confined to traditional data methodologies, and they also extensively harness remote sensing techniques in tandem with the Sustainable Development Goals (SDGs) to conduct in-depth investigations into individual indicators. This approach possesses distinctive characteristics, notably its rapid detection capabilities, adeptness in capturing long temporal sequences of evolving trends, and the ability to conduct research across multiple spatial scales. Wu et al. [10] combined the use of remote sensing methods with SDG 11.4 to scientifically evaluate the ecological environmental quality of natural heritage sites, quantifying the development of SDG 11.4. Li et al. and Wang et al. [11,12] combined the SDG 11.3.1 indicator with Earth observation data to statistically analyze cities in China with a population of over 300,000. They found that the lack of coordination between population mobility and land expansion in China is increasing.

Furthermore, scholars have conducted systematic research on the urbanization process from various perspectives, such as economic, population, social, regional, and spatial dimensions [13]. This comprehensive approach has enabled the assessment of the sustainability of urban development [14,15,16]. They provide a comprehensive and quantitative understanding of urban sustainability. The “Sustainable Development Goal Index and Dashboard”, co-authored by the United Nations Sustainable Development Solutions Network (SDSN) and the Bertelsmann Foundation, pioneered a set of measurement standards for SDGs at the national level. It establishes a specific set of indicators for each of the 17 SDGs, providing a groundbreaking framework [17]. Ishtiaque et al. conducted a comprehensive assessment and analysis of Bangladesh’s forests, wetlands, erosion, and landslides with respect to SDG 15, tracking the current research trends [18]. Chen et al. conducted a pilot study on the SDGs’ comprehensive assessment in Deqing County, Zhejiang Province, combining statistical and geographic information [19]. They developed the “Progress Report on Implementing the 2030 Sustainable Development Agenda in Deqing County” and constructed an online public service system, providing a demonstration of such research for the international community. Some studies assess the trends, correlations, and influencing factors of various indicators through data analysis, statistical methods, quantitative models, etc., with a focus on evaluating the effectiveness of policies and measures for implementing SDGs. Wang et al. developed an open framework for urban sustainability assessment based on SDG 11, which integrates point-to-area data using statistical regression methods and combines geospatial observation data with remote sensing data [20]. Gao et al. proposed a conceptual framework and evaluation indicator system for the “Beautiful China” based on the SDGs, using multi-source data, including earth big data, network data, and statistical data, as support [21]. Zhang et al. assessed the sustainability of Hainan Province using SDG 11 indicators, providing reference for evaluating urban and county-level SDGs [22].

In general, both domestic and international research have focused on the localization of sustainable development indicator frameworks at the city level and the relationships with comprehensive assessment cases. As the implementation of the United Nations’ SDGs progresses, the emphasis has gradually shifted from constructing indicator frameworks to monitoring and evaluating processes, and ultimately, to policy implementation [23,24]. The application of geographic information data and geospatial statistical data in assessing and monitoring SDGs is undergoing vigorous development, with numerous cases emerging at various levels of scale. However, in China, there has yet to be a widespread and systematic assessment of sustainable development at the provincial and city levels using SDG indicators [19,25].

Guilin possesses unique natural landscapes and resources, including the Li River and Yangshuo, which are popular tourist attractions. These attractions attract many tourists and investments, providing more opportunities for the development of Guilin. This enables Guilin to have a certain level of sustainability and the ability to innovate its development according to urban needs, making it a powerful engine for urban construction [17,26]. As one of the first batches of innovation demonstration zones in China, Guilin will provide a decision-making basis for China’s sustainable development agenda. However, research on the integration of SDGs and localized indicators for a comprehensive assessment of sustainable development in Guilin is still in need of further improvement. Therefore, this study takes Guilin as the research area and provides monitoring, measurement, and quantitative research on the comprehensive assessment indicators for sustainable development of “Beautiful Guilin with Exquisite Landscapes” based on the sub-city level sustainable development evaluation framework and positioning method, focusing on SDG 11.

2. Study Area and Datasets

2.1. Study Area

Guilin City is located in the northeastern part of Guangxi Zhuang Autonomous Region, China. It stretches 236 KM from north to south and 189 KM from east to west, covering a total area of 27,800 square kilometers. It lies between 109°36′50″E to 111°29′30″E longitude and 24°15′23″N to 26°23′30″N latitude. Guilin’s area accounts for 11.74% of the total area of Guangxi Zhuang Autonomous Region. The study area includes 6 districts and 11 counties in Guilin City. Among them, there is 1 county-level city, 8 counties, and 2 autonomous counties. In this study, the adjacent and relatively smaller areas, such as Xiufeng, Diecai, Xiangshan, and Qixing, are combined into the urban area (Figure 1).

2.2. Datasets

In this study, the geospatial observation data utilized primarily encompass remote sensing data from the Google Earth Engine database and the Atmospheric Composition Analysis Group database, as well as point of interest (POI) data and other related sources. The term “built-up area”, as referred to here, indicates the concentrated and contiguous urban sections within the city, as well as urban construction land with basic and well-developed municipal facilities. The collection and classification of SDG 11 data may involve certain uncertainties and subjectivity. Considering the specific context and background of Guilin City, the quantity and evaluation methods of relevant data may not fully align with these indicators. Therefore, when using these data, it is necessary to assess them in conjunction with the specific circumstances and background of Guilin City. With the advantage of comprehensive coverage and dynamic real-time updates provided by big data, an evaluation and characterization of the sustainable development connotation of Guilin City, as depicted by these evaluation indicators, can be conducted (

Table 1. Research data related to SDG 11 in Guilin City.

SDG 11 Indicators [1,27]	Temporal Interval	Data	Data Sources
11.1.1 Proportion of urban population living in slums, informal settlements, or inadequate housing	2010–2020	Number of subsistence allowances	Statistical data [17]
11.2.1 The proportion of the population with convenient access to public transportation	2010–2020	Passenger volume	Statistical data [17]
	2018–2020	Percentage of population using public transportation	Percentage of population [17]
11.3.1 Ratio of land consumption rate to population growth rate	2010–2020	Urban population and built-up area	Remote sensing data [28]
11.5.1 Number of deaths, missing persons, and directly affected persons attributed to disasters per 100,000 population	2010–2020	Total population of the city and urban population affected by disasters	Statistical data [17]
	2010–2020	Total urban population and the number of deaths and missing persons affected by disasters	Statistical data [17]
11.5.2 Direct economic loss in relation to global GDP, damage to critical infrastructure, and number of disruptions to basic services, attributed to disasters	2010–2020	GDP and economic losses caused by disasters	Statistical data [17]
11.6.1 Proportion of municipal solid waste collected and managed in controlled facilities out of total municipal waste generated by cities	2010–2020	Domestic garbage clearance volume	Statistical data [17]
11.6.2 Annual mean levels of fine particulate matter (e.g., PM2.5 and PM10) in cities (population weighted)	2010–2020	PM2.5 remote sensing data	Remote sensing data [29]
11.7.1 Average share of the built-up area of cities that is open space for public use for all, by sex, age, and persons with disabilities	2010–2020	Per capita park green area	Remote sensing data [28]

Note: Due to the availability of more comprehensive point of interest (POI) data starting from the year 2018, the time series for SDG 11.2.1 covers the years 2018 to 2020.

).

3. Methodology

3.1. Construction of Sustainable Development Indicator Framework and Methods for Localization

Based on the regional characteristics of Guilin City, this study analyzed and improved the practical significance and usage of each indicator of the Sustainable Development Goals, constructing a localized sustainable development indicator framework [30]. The research combined data conditions to specify the methods and data used in the indicators, employing calculations such as statistical data ratios or proportions, rates of change, and indices [27]. Geographic spatial data were also used, utilizing methods such as spatial density calculation and coverage extraction. By integrating statistical data with geographic spatial information and considering factors such as accessibility, coverage, and spatial relationships, spatial analysis of the statistical data was conducted (

Table 2. Localization methods for SDG 11 indicator data in Guilin.

SDG 11 Indicators	Methodology
SDG 11.1.1 [31]	${\mathrm{X}}_1=\frac{\mathrm{Number}\mathrm{of}\mathrm{subsistence}\mathrm{allowances}}{\mathrm{Total}\mathrm{population}}\times 100\%$
SDG 11.2.1 [32]	Percentage of population
SDG 11.3.1 [33,34]	${\mathrm{X}}_2=\mathrm{LCRPGR}=\frac{\mathrm{LCR}}{\mathrm{PGR}}=\frac{\ln ({\mathrm{U}}_{\mathrm{r}}{\mathrm{b}}_{\mathrm{t}+\mathrm{n}}/{\mathrm{U}}_{\mathrm{r}}{\mathrm{b}}_{\mathrm{t}})}{\ln ({\mathrm{P}}_{\mathrm{o}}{\mathrm{p}}_{\mathrm{t}+\mathrm{n}}/{\mathrm{P}}_{\mathrm{o}}{\mathrm{p}}_{\mathrm{t}})}\times 100\%$
SDG 11.5.1 [35]	${\mathrm{X}}_3=\frac{\mathrm{Number}\mathrm{of}\mathrm{victims}}{\mathrm{Total}\mathrm{population}}\times 100\%$ ${\mathrm{X}}_4=\frac{\mathrm{Number}\mathrm{of}\mathrm{dead}\mathrm{and}\mathrm{missing}}{\mathrm{Total}\mathrm{population}}\times 100\%$
SDG 11.5.2 [36,37]	${\mathrm{X}}_5=\frac{\mathrm{Direct}\mathrm{economic}\mathrm{loss}\mathrm{in}\mathrm{disasters}}{\mathrm{Gross}\mathrm{domestic}\mathrm{product}}\times 100\%$
SDG 11.6.2 [36,37]	Remote sensing data used to calculate the local average PM2.5 concentration/statistics
SDG 11.7.1 [38,39]	${\mathrm{X}}_6=\mathrm{NDVI}=\frac{\mathrm{NIR} - \mathrm{RED}}{\mathrm{NIR} + \mathrm{RED}}$ ${\mathrm{X}}_7=\mathrm{SAVI}=\frac{(1 + \mathrm{L}\left)\right(\mathrm{NIR}-\mathrm{RED})}{\mathrm{NIR} + \mathrm{R} + \mathrm{L}}$

).

3.2. Indicator System for Urban Sustainable Development Based on SDG 11

3.2.1. Calculation of Single-Indicator Sustainable Development Index

When comparing single indicators between cities, we normalized the data to eliminate the influence of different dimensions or extreme values. The threshold values in the data scores were derived from the three highest values to obtain the maximum threshold and from the three lowest values to obtain the minimum threshold. After normalizing all the initial data, the range of all scores was between 0 and 100. The detailed calculation formula is as follows (1):

$${\mathrm{x}}^{\prime }=\frac{\mathrm{x}{ - \mathrm{x}}_{\min }}{{\mathrm{x}}_{\max }{ - \mathrm{x}}_{\min }}\times 100$$

(1)

If a smaller value of the sub-indicator represents a higher level of sustainability, the formula would be as follows (2):

$${\mathrm{x}}^{\prime }=100 - \frac{\mathrm{x} - {\mathrm{x}}_{\min }}{{\mathrm{x}}_{\max }{ - \mathrm{x}}_{\min }}\times 100$$

(2)

In the equation, $\mathrm{x}$ represents the original data, ${\mathrm{x}}_{\min }/{\mathrm{x}}_{\max }$, respectively, represent, after eliminating anomalies, the minimum and maximum values of the data, and ${\mathrm{x}}^{\prime }$ represents the sub-indicator score, which is the normalized value.

The trend of a single indicator was derived based on the calculation method used for obtaining the single-indicator score. A higher index value indicates higher sustainability. The growth rate can be obtained using the growth rate Formula (3):

$${\mathrm{X}}_{\mathrm{GR}}={\left(\frac{\mathrm{x}}{{\mathrm{x}}_{\mathrm{n}}}\right)}^{\frac{1}{\mathrm{n}-1}}\times 100\%$$

(3)

If a higher index indicates lower sustainability, the growth rate can be obtained using Formula (4):

$${\mathrm{X}}_{\mathrm{GR}}={\left(\frac{{\mathrm{x}}_{\mathrm{n}}}{\mathrm{x}}\right)}^{\frac{1}{\mathrm{n}-1}}\times 100\%$$

(4)

where $\mathrm{x}$ is the data value of this year, ${\mathrm{x}}_{\mathrm{n}}$ is the data value of n years ago, and ${\mathrm{X}}_{\mathrm{GR}}$ is the growth rate.

The method for calculating the development trend of the Sustainable Development Goal scores is based on Formula (5):

$$X_{GR}={\left(\frac{X}{X_N}\right)}^{\frac{1}{N-1}}\times 100\%$$

(5)

where $X_{GR}$ represents the growth rate of the Sustainable Development Goal scores, $X_N$ is the Sustainable Development Goal score from N years ago, and $X$ is the Sustainable Development Goal score for the current year.

3.2.2. Comprehensive Index Calculation

Due to the limitations of evaluating individual indicators, which may not reflect the overall relative development level of the entire city, it is necessary to establish a comprehensive scoring system. We calculated the average value of all indicators for Guilin City each year using equal weights, and ultimately obtained the city’s comprehensive score through the allocation of scores based on weight distribution [40]. The following formula was used to calculate the composite index (6):

$${\mathrm{I}}_{\mathrm{i}}\left({\mathrm{N} }_{\mathrm{i}},{\mathrm{N}}_{\mathrm{IJ}},{\mathrm{I}}_{\mathrm{ijk}}\right)=\frac{1}{\mathrm{N}}\sum _{\mathrm{i}=1}^{{\mathrm{N}}_{\mathrm{i}}}\frac{1}{\mathrm{N}}\sum _{\mathrm{k}=1}^{{\mathrm{N}}_{\mathrm{ij}}}{\mathrm{I}}_{\mathrm{ijk}}$$

(6)

In the equation, ${\mathrm{I}}_{\mathrm{i}}$ represents the comprehensive SDG 11 index of city i, ${\mathrm{N}}_{\mathrm{i}}$ represents the number of sub-goals for city i, ${\mathrm{N}}_{\mathrm{ij}}$ represents the number of indicators under sub-goal j for city i, N represents the total number of indicators, and ${\mathrm{N}}_{\mathrm{ijk}}$ represents the value of the indicator under sub-goal j for city i.

4. Results

4.1. Quantifying the Development Progress of Specific Goals

According to the calculation method of single indicators, the development level of a city can be visualized using a traffic light system, with green, yellow, orange, and red colors [41]. Based on the sub-indicator scores for ranking, among the 13 cities and counties, those ranked 1–4 are considered green indicators, those ranked 5–9 are considered yellow indicators, and those ranked 10–13 are considered orange indicators. The presence of a higher number of “green lights” in Lingui District, Lingchuan County, and the urban area indicates their excellent sustainability performances among the 13 cities and counties in Guilin. Lingchuan County consistently ranks high in each indicator, surpassing most cities and counties in terms of housing conditions, public transportation convenience, urbanization, air quality, and public green spaces. Yangshuo County and Longsheng Various Ethnic Autonomous County have the highest number of “red lights”, with Yangshuo primarily facing challenges in urbanization and urban public green spaces, while Longsheng Various Ethnic Autonomous County has issues in urban housing, urban transportation, and urban public green spaces (Figure 2).

There are also differences in the development of different regions in Guilin (Figure 3). For example, Quanzhou County is an important agricultural area with a wide variety of agricultural products. Lingui County, on the other hand, is an industrial area with a well-established industrial system, focusing on industries such as machinery manufacturing and electronic information. Yangshuo County is a famous tourist destination, known for its beautiful natural landscapes and rich historical and cultural heritage. Overall, the development of each county in Guilin is relatively dispersed, with its own strengths. In the future, measures such as further economic system reform and optimizing the industrial structure can be taken to accelerate the development of weaker areas and improve the overall development level of the region.

From the perspective of the city, the overall trend of key indicators in Guilin is gradually improving (Figure 4). As the central area of Guilin, the urban district boasts a well-developed transportation network and commercial facilities, exhibiting better performance in indicators such as accessible public transportation, land utilization, and urban public green spaces. Lingchuan County serves as the seat of the Guilin Municipal Government and plays a crucial role as an important industrial base and transportation hub. It leads in terms of sustainable development compared to other counties in the region. The development of various industries, such as transportation and manufacturing, has propelled the process of urbanization. The indicators of the urban areas, primarily led by the city center, have shown improvements in their development trends, indicating a rising trajectory in the early stages of sustainable development.

4.2. Single-Indicator Analysis

4.2.1. SDG 11.1.1

The localized interpretation of slum concept, SDG 11.1.1, is evaluated by using the proportion of the population receiving social assistance. After standardization, it was found that compared to 2010, Yangshuo County, Xing’an County, Yongfu County, Quanzhou County, Pingle County, Ziyuan County, and Lipu County all experienced varying degrees of increased housing pressure. During the period from 2010 to 2020, the urban area had the best housing situation compared to other counties. Overall, the living conditions in the urban area were better than in other counties. By 2020, the proportion of inadequate housing in the urban population was only 0.39%. Additionally, except for Ziyuan County, the number of people receiving social assistance for housing in other counties had decreased to varying degrees, indicating an overall improvement in their living conditions. By 2020, only Yongfu County, Longsheng Autonomous County, and Ziyuan County had a proportion of the population living in inadequate housing exceeding 0.7%.

4.2.2. SDG 11.2.1

In 2020, the proportion of the population with access to convenient public transportation showed varying degrees of growth. The proportion of the population with access to convenient public transportation has significantly increased in Yongfu County, Lingchuan County, and Linyi County. Yongfu County had an increase of 3.2% compared to 2010, while Linyi County had an increase of 1.77% and Lingchuan County had an increase of 1.6%. Since 2014, the road passenger transportation in Guilin City has been affected by the opening of high-speed railways, resulting in a continuous low level of tourism travel. As a city with “one city and two high-speed railways”, Guilin City has multiple high-speed railway stations, providing convenient and efficient travel options that greatly satisfy the public’s safe and efficient travel needs. High-speed rail passenger transportation has had a significant impact on road passenger transportation in Guilin City. With the development of public transportation, the proportion of the population with access to convenient public transportation has steadily increased over the years. Quanzhou County, Xing’an County, Lingchuan County, Lingui District, the urban area, Yongfu County, and Yangshuo County have all experienced a gradual rise in the proportion of people benefiting from accessible public transportation. This pattern also indicates improved accessibility from the northeast to the southwest of Guilin.

4.2.3. SDG 11.3.1

To illustrate the relationship between the land consumption rate and population growth, the land consumption rate per population growth rate (LCRPGR) was calculated, enhancing the clarity of spatial distribution [42]. In the process of actively promoting urbanization, the built-up areas of each county and city in Guilin have expanded. After standardization, a higher corresponding value indicates better coordinated development between land and population urbanization. Compared to 2010, the average value of LCRPGR had increased from 1.015 to 1.324. However, not all cities had improved their land use efficiency, as both Guanyang County and the urban area had experienced varying degrees of decline in LCRPGR (

Table 3. LCRPGR classification in Guilin City.

Classification	County
LCRPGR > 4	Ziyuan County
1 ≤ LCRPGR ≤ 4	Yangshuo County, Quanzhou County, Longsheng Autonomous County, Gongcheng Yao Autonomous County, Lipu City
LCRPGR < 1	Lingui County, Lingchuan County, Xing’an County, Yongfu County, Guanyang County, Pingle County, Urban Area

). They are classified into different categories according to the following standards [43].

4.2.4. SDG 11.5.1

By quantifying the indicators, such as the number of affected people per 100,000, the number of injured or missing people per 100,000, and the direct economic losses as a percentage of the regional GDP, the disaster situation in Guilin City was quantified and its spatiotemporal distribution characteristics were analyzed. The analysis of urban disasters in Guilin City from 2010 to 2020 shows that the urban population affected by disasters is relatively high in Quanzhou County, Ziyuan County, and Gongcheng Yao Autonomous County. Among them, Ziyuan County had the highest percentage of direct economic losses as a percentage of the regional GDP, reaching 22.59% in 2019.

4.2.5. SDG 11.6.2

The United Nations defines the urban environmental impact using air quality, and accordingly, we used the annual average PM_2.5 concentration (SDG 11.6.2) to represent the air environment conditions in Guilin City [36,43]. A higher value of SDG 11.6.2 indicates worse air quality. The average value of SDG 11.6.2 in Guilin City decreased from 43.91 mg/m³ to 26.26 mg/m³. In 2020, the cities and counties in Guilin City with SDG 11.6.2 < 26 mg/m³ were Lingui District, Lingchuan County, Yongfu County, Longsheng Various Nationalities Autonomous County, Ziyuan County, and Gongcheng Yao Autonomous County. The cities and counties with SDG 11.6.2 > 26 mg/m³ are mainly distributed in the central-eastern part of Guilin. The air quality has improved in all cities and counties, especially in Quanzhou County and the urban area, where it has decreased by 29.22 mg/m³ (Figure 5).

4.2.6. SDG 11.7.1

Public green spaces were used as a localized indicator for SDG 17.1. The construction of urban parks and green spaces is an important foundation for creating a beautiful and livable modern urban space. In 2020, the greening coverage in the urban built-up areas reached 41.24%. From 2010 to 2020, the per capital park and green space area in Guilin City increased year by year. The construction of parks and green spaces reflects the unique landscape features of Guilin. The city has gradually increased the number of existing parks and has developed large-scale urban green space projects, such as the Two Rivers and Four Lakes Central Park, Seven Star Park, and Elephant Trunk Hill Park, creating a new national landscape tourism city.

4.3. Analysis of Time Evolution and Spatial Patterns

In 2010, 2015, and 2020, the top five cities in terms of ranking were Lingui County, Urban District, Guanyang County, Lingchuan County, and Pingle County. Additionally, the scores of other cities were generally between 55 and 65 (Figure 6). Due to the uneven development of cities, Quanzhou County, Lipu County, and Ziyuan County had lower scores and ranked lower in terms of sustainable development. Yangshuo County, Lingchuan County, and Pingle County showed significant growth in their scores, indicating rapid development and favorable conditions. Ziyuan County had a relatively weak industrial foundation and experienced a small growth rate in its scores from 2010 to 2020, indicating slower development during this decade and the need for continuous improvement in infrastructure construction.

Guilin’s development is unevenly distributed, with the central and western regions scoring higher and the southeastern region scoring lower (Figure 7). The central area of Guilin, including Lingui District and Lingchuan County, is an important agricultural and industrial development zone with abundant mineral resources. These regions have been progressively advancing agricultural modernization and industrial transformation and upgrading, achieving the highest level of sustainable development, with annual comprehensive scores exceeding 70. The major tourist cities, such as Yangshuo County and Longsheng Autonomous County, have shown good performance in terms of the urban sustainable development indicators. However, Lipu County and Pingle County in the southeast region have exhibited poor performance in the sustainable development indicators, characterized by low urbanization rates and lower levels of urban modernization, resulting in relatively lower levels of urban sustainable development. Resource County, on the other hand, faces significant challenges in terms of transportation and urbanization development, lagging behind other areas.

4.4. Spatio-Temporal Clustering Analysis

According to Global Moran’s I statistic, the spatial autocorrelation of the SDG 11 composite index was measured from 2010 to 2020, considering the spatial distribution and attribute values of the features [44]. The results indicate a strong spatial clustering effect in the composite index in 2019 and 2020 (

Table 4. Global Moran’s I of the sustainable development level in Guilin.

Year	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
Moran’s I	−0.030	−0.060	−0.035	−0.144	−0.055	0.050	−0.140	0.098	−0.020	0.094	0.183

). Overall, the level of Moran’s I in Guilin is relatively low, suggesting that sustainable development has not generated comprehensive impacts. Many cities have followed distinct development trajectories, with limited interaction and minimal spillover effects on neighboring urban areas. In 2010, 2011, and 2017, the Guilin urban cluster, led by Longsheng Various Nationalities Autonomous County, Quanzhou County, and Lipu City, had a negative impact on the surrounding areas, exhibiting specific patterns of low–low clustering and low–high clustering. In 2012, Lingui District experienced high–high clustering, which drove the development of the surrounding counties and cities, but comprehensive development had not yet been achieved. In 2019 and 2020, both Lingchuan District and the urban area exhibited high–high clustering, indicating the initiation of development in surrounding cities and counties. It is evident that urban sustainable development is influenced by a combination of natural, economic, and cultural factors (Figure 8).

4.5. The Sustainable Development Goal Indicators of SDG 11 Exhibit Synergies and Trade-Offs

Spearman correlation analysis was conducted using Stata for the five SDG 11 indicators in Guilin City for the years 2010, 2015, and 2020, resulting in a correlation coefficient matrix [40,44]. The absolute values of the coefficients indicate the magnitude of the correlation, with positive values indicating a positive correlation, and negative values indicating the opposite. A range of −1 to 1 is used to evaluate the correlation between the SDG indicators. Among them, SDG 11.3 showed the strongest negative correlation with SDG 11.7, with a coefficient of −0.905. SDG 11.2 and SDG 11.5 exhibited the weakest negative correlation, with a coefficient of 0.016. The strongest positive correlation was observed between SDG 11.7 and SDG 11.6, with a coefficient of 0.485, while the weakest positive correlation was found between SDG 11.1 and SDG 11.5, with a coefficient of 0.079 (Figure 9).

Based on the correlation coefficient matrix, an interactive network of SDG 11 indicators was constructed (Figure 10) [45]. If the correlation coefficient is negative, it indicates a trade-off effect between the SDG 11 indicators (represented by red lines). If the correlation coefficient is positive, it indicates a synergistic effect between the indicator pairs (represented by green lines). Thicker lines indicate stronger correlation coefficients. Six pairs of indicators were identified as exhibiting synergies, primarily concentrated between SDG 11.1 and SDG 11.3. Additionally, there were nine pairs of indicators showing trade-offs, mainly concentrated between SDG 11.6 and SDG 11.7. The results suggest that regions with higher urbanization have better housing conditions and stronger synergies. However, they also tend to have fewer urban public green spaces and poorer living environments, indicating significant trade-offs. Regions with high urbanization and good housing conditions also face higher levels of urban pollution, indicating trade-offs between these factors.

5. Discussion

Building upon the framework provided by the United Nations’ Sustainable Development Goals (SDGs) and utilizing an internationally recognized indicator system, a meticulous quantitative evaluation of urban sustainability at the county level within Guilin has been executed [17,46]. This assessment process involved the adaptation of data to Guilin’s specific developmental context. Due to its unique environmental and cultural characteristics, Guilin City primarily relies on the tourism industry as its sole economic structure. The city’s development lacks the driving force of high-tech industries. Various counties within Guilin have made initial progress in achieving sustainable development, but there are also numerous pressing issues that need to be addressed. The level of development significantly varies among these counties. Economically developed cities possess well-established infrastructure but also face housing pressures. Cities with more comprehensive urbanization exhibit widespread coverage of public services, yet they also encounter other challenges [47]. Economic development and ecological environmental protection are mutually constraining factors. There are significant disparities in the development level among different counties in Guilin City, but the phenomenon of “good leading to bad” has emerged in cities with better development levels, which needs to be addressed and strengthened. Therefore, it is necessary for Guilin City to implement the concept of sustainable development, further enhance the quality of economic, environmental, and industrial aspects in each county, and establish a comprehensive indicator system for sustainable development oriented toward SDG 11.

The main reason for the varying states of sustainable development among different cities and counties lies in the alignment of Guilin’s development philosophy and policies with the path of sustainable development. Guilin City is currently in a phase of positive development across various sustainable development indicators, and the sustainable development centered around the urban area of Guilin serves as a commendable model for the entire Guilin region. However, certain cities in the minority autonomous regions exhibit comparatively weaker performance in terms of urban infrastructure and economic foundations. Relying solely on a single industrial structure is insufficient to drive rapid urban development. In conjunction with Guilin’s local concept of green development, it is essential to formulate corresponding policies to enhance development efforts in areas that are relatively distant from achieving the sustainable development goals by 2030.

The methodology proposed in this paper conducted a comprehensive assessment of urban development in various districts and counties of Guilin City from six aspects of SDG 11. The study employed quantitative data, enhancing the practicality of the research and improving comparability among the districts and counties in Guilin. However, sustainable development is a complex concept that requires extensive data support, encompassing numerous diverse factors and interconnected objectives. The research methodology may encounter challenges in capturing this complexity [48,49]. During the research process, it was found that Earth observation data have better timeliness compared to statistical data, and they provide more standardized evaluation criteria for cities at different levels. Therefore, in future research, it is important to strengthen the use of Earth observation data and, under appropriate circumstances, consider their suitable substitution for statistical data [50,51]. Methods such as urban performance assessment, system dynamics modeling, and ecological footprint analysis are also applicable for assessing the comprehensive development of cities [52]. These methods offer in-depth insights from various perspectives and levels, contributing to a holistic understanding of a city’s sustainability and overall development status. In future research, integrating these methods can provide a more comprehensive assessment of urban comprehensive development. This will aid urban planners and policymakers in gaining a better understanding of a city’s challenges and opportunities.

The next step of this study will integrate the “Urban Monitoring Framework” (UMF) approved by the United Nations Statistical Commission and review the City Prosperity Index framework as a monitoring tool [53]. The UMF framework, along with the indicators of the New Urban Agenda, will be used to reorganize the indicator system in Guilin City. The utilization of big data and technologies, such as Earth observation and geospatial information, will enable the quantification, monitoring, and evaluation of SDGs. By combining SDGs with the UMF approach, new frameworks and indicators will be localized for Guilin City. Comparative studies of the two evaluation systems will be conducted in Guilin City, and the monitoring and comprehensive assessment of urban sustainability may present a diversified landscape.

6. Conclusions

This study analyzed the spatial and temporal distribution of the six indicators and the composite index of SDG 11 in Guilin City. It examined their development status and trends and quantitatively determined the synergies and trade-offs among them at the sub-city scale from 2010 to 2020 using geospatial big data. (1) The performance of SDG 11 indicators improved during the period from 2010 to 2020. The southwestern region, represented by Lingchuan County, Lingui District, and the urban area, showed better performance, but a comprehensive development pattern has not yet formed. (2) With the influence of the overall planning policies in Guilin City, the level of sustainable development has rapidly improved. Overall, the sustainability of cities and counties in Guilin showed an upward trend, with a spatial distribution pattern of higher development levels in the central and western regions and lower development levels in the southeastern region. (3) Among all indicators regarding Guilin City, trade-offs were more prevalent than synergies. Specifically, at a significance level of 0.05, six pairs of indicators exhibited synergistic effects, while nine pairs of indicators showed trade-off effects.