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Abstract

Assessing the sustainable development level of the Qinghai–Tibet Plateau (QTP) is of great significance for China’s sustainable development. With rich natural resources, a fragile ecological environment, and a relatively low level of economic development, the QTP is typical of regions in China where conflict between socio-economic development and environmental protection is prominent. However, there is a lack of assessment of spatiotemporal progress in achieving the Sustainable Development Goals (SDGs) on the QTP. To address this gap in knowledge, cluster analysis was used to quantitatively assess spatiotemporal changes in SDG indicators and sustainable development levels in different counties of the QTP from 2000 to 2020. The results show that the sustainable development level of the QTP is increasing; the growth rate of the sustainable development in 2010–2020 was three times that of 2000–2010. The level of sustainable development in the eastern region is significantly higher than that in the western region. However, while the level of sustainable environmental development is high, the levels of sustainable social and economic development remain low. Certain factors, including precipitation, slope, population density, and cultivated area, are strongly positively correlated with the sustainable development of the QTP. The findings of our study can serve as a valuable reference for other jurisdictions seeking to evaluate their evolution towards the SDGs.

Keywords

Sustainable Development Goals spatiotemporal evolution county scale driving factors Qinghai–Tibet Plateau

Introduction

In 2015, the United Nations (UN) adopted the 2030 Agenda for Sustainable Development, which includes 17 social, economic and environmental Sustainable Development Goals (SDGs) to end poverty, end hunger, ensure healthy lives, ensure equitable quality education, promote sustainable economic growth, take action to combat climate change, protect terrestrial ecosystems, and promote peaceful societies (Nilsson et al., 2016). To implement this global political agenda, the UN has developed a series of measures and actions. For example, the 2030 Agenda for Sustainable Development High-level Group on Statistical Partnerships, Coordination, and Capacity Building (HLG–PCCB) was established as a global partnership for sustainable development data. The Inter-Agency Expert Group on Sustainable Development Goal Indicators (IAEG–SDGs) was established to develop and implement a global indicator framework (Wei et al., 2022). Accurate assessment of changes in SDG indicators is essential for achieving the SDGs (Schmidt-Traub et al., 2017; Xu et al., 2020). In July 2017, the United Nations General Assembly adopted the Sustainable Development Goals Global Indicator Framework (SGIF) proposed by the IAEG–SDGs, which includes 232 specific indicators and provides a global framework for the quantitative assessment and regular monitoring of a country or region’s sustainable development level. Using this framework, a number of countries have conducted studies based on their national conditions (Ding et al., 2022; Fu et al., 2019).

As the world’s largest developing country and second-largest economy, China is increasingly concerned about domestic socio-economic development and environmental protection (Fu et al., 2019). In September 2016, China officially issued the National Plan for the Implementation of the 2030 Agenda for Sustainable Development, which integrated the 17 SDGs with China’s specific national conditions, and released annual reports on China’s progress in implementing the 2030 Agenda in 2017, 2019, 2021, and 2023 (Ni et al., 2022). Scholars have explored the localization of the SDGs from different perspectives, assessing China’s progress in different aspects of sustainable development at various levels (national, provincial, and municipal), including sustainable transportation (Liu and Yuan, 2023), the ecological environment (Wei et al., 2023b), and land use (Wei et al., 2023a). However, many previous studies relied primarily on statistical data and made less use of geospatial information, resulting in relatively few investigations of spatiotemporal changes in sustainable development. Assessing past and present situations, monitoring the progress of SDGs, clarifying the spatial change characteristics of SDGs, and accurately assessing the drivers of SDG indicators are key to avoiding the development of isolated policies between sectors (Weitz et al., 2018), successfully implementing the 2030 Agenda for Sustainable Development, and promoting policy coherence (Singh et al., 2018).

Scholars have used different methods and models to explore the driving factors of development goals. In China, the factor detection of the GeoDetector model was used to explore the main driving factors that cause spatial differentiation in the sustainable development of urban human settlements in China (Cong et al., 2021). The log-mean Dix index method (LMDI) was used to study key drivers affecting the sustainable development of wetlands in Northeast China; the results suggest that measures such as increasing wetland area and developing renewable energy can improve the sustainable development level of wetlands (Song et al., 2020). Researchers have applied gray correlation analysis and principal component analysis to investigate the development levels and influencing factors of 13 national sustainable development pilot zones in Shandong Province; differences in the key influencing factors among various pilot zones resulted in disparate development levels (Li et al., 2021). Some scholars have conducted research on the factors affecting the sustainable development of agriculture and used a geographically weighted regression model (GTWR) to determine the main factors affecting the agricultural sustainable development ability of countries along the Belt and Road (Li et al., 2019). The spatial scales of interactive studies on SDGs are primarily concentrated globally (Guo et al., 2022; Wu et al., 2022a; Xiao et al., 2024), regionally (Paulo et al., 2022; Qiao et al., 2023), or at the domestic state scale (Hu et al., 2023; Zhang et al., 2022). Studies have also been conducted in urban areas with high degrees of economic development and intensive land use (Dolley et al., 2020; Nyamekye et al., 2020). However, few SDGs studies have been conducted in sparsely populated and environmentally fragile regions.

Changes in the climate and ecological environment of the Qinghai–Tibet Plateau (QTP) not only affect local social progress, resource development, and economic construction, but also the ecological security of China and Asia (Teng et al., 2018), as it is an important ecological barrier in the region (Liu et al., 2023b). Most studies on the sustainable development of the QTP have focused on the sustainability of one certain aspect. Zhuang et al. (2021) discussed the sustainability of household energy. Wang et al. (2023) discussed the sustainable development of arable land utilization on the QTP based on a cultivated land use model. Other scholars have explored the contribution of ecosystem services to SDGs from the perspective of ecological engineering (Dai et al., 2024). However, there is less spatiotemporal variation in the exploration of the SDGs from social, economic, and environmental perspectives. The levels of social and economic development on the QTP are relatively low, and conflict between economic development and environmental protection is prominent; therefore, there is an urgent need for a robust basis on which to guide sustainable development of the region. Identifying the drivers that affect the sustainable development of a region can better reduce trade-offs and coordinate the relationships among society, economy, and environment (Adams et al., 2022; Hua et al., 2021). In summary, most existing studies focus on specific aspects of sustainable development over large spatial scales; in contrast, there are few studies on the spatiotemporal changes and influencing factors of SDGs in remote areas, such as the QTP, from the perspectives of society, economy, and environment. This has severely hindered local governments’ abilities to formulate plans and policies for achieving the SDGs on the QTP.

There are 233 counties on the QTP, including 139 counties in Tibet and Qinghai Province (National Basic Geographic Information Center, 2019). Counties are the most basic administrative units in China, and the sustainable development of the county economy and society is an important basis for its sustainable development. The county is the key to the realization of the SDGs, and the development of county economies is important for realizing regional sustainable development. Therefore, the county unit has important research significance (Wang et al., 2022). In this study, we investigated temporal changes in SDG progress at the county level. We addressed three major questions: (a) How has the level of sustainable development on the QTP changed over time? (b) What are the different levels of sustainable development in different counties of the QTP? (c) What factors affect the level of sustainable development on the QTP? We used annual county-level time-series data relevant to the SDGs from 2000 to 2020 and calculated SDG scores. The evaluation index was designed using nine SDGs, including 21 specific indicators. Using 21-year county-level SDG data from 2000 to 2020, we monitored spatiotemporal changes in SDG scores for 116 counties (from 2000 to 2012 there were 115 counties). Then, we used cluster analysis to group counties with similar characteristics and explored the temporal and spatial dynamics of these groups. Finally, we explored the drivers of the SDG groups.

Materials and methods

Study area

The QTP (73°19′~104°47′E, 26°00′~39°47′N) is located in southwest China (Figure 1). It is approximately 2800 km long from east to west and 1000 km wide from north to south, with a total area of 2.5 million km² (Liu et al., 2020), accounting for 26.8% of China’s total land area. With average annual precipitation of 413.6 mm and an average annual temperature of 1.61°C, the QTP is the largest plateau in China and the highest in the world, with an average altitude of >4000 m (Liu et al., 2023b). The QTP covers the whole of Tibet and Qinghai Province, as well as parts of the Xinjiang Uygur Autonomous Region, Sichuan Province, Yunnan Province, and Gansu Province. Tibet covers an area of 1.2 million km² with jurisdiction over six prefecture-level cities and one region. With a total area of 0.7 million km², Qinghai Province has jurisdiction over two prefecture-level cities and six autonomous prefectures. By the end of 2022, the resident populations of Tibet and Qinghai Province were 3.64 and 5.95 million, respectively. In this study, 116 counties (from 2000 to 2012 there were 115 counties) in Tibet and Qinghai Province were taken as representatives to study spatiotemporal changes in development goals on the QTP and to explore their drivers.

Figure 1.

The study area.

Data sources

SDG and socio-economic environmental driver (SED) datasets, including statistical data, meteorological data, digital elevation models (DEM), land use data, remote sensing data, and other multisource datasets, were included (Table 1). All the raster data with different resolutions in Table 1 were resampled to consistent spatial (1 × 1 km) and time (annual) resolutions to quantify the SDGs and their socio-economic and environmental drivers. The data for indicators such as PM_2.5, carbon dioxide emissions, temperature, and soil conservation were averaged on a monthly basis to obtain the corresponding annual averages. In contrast, the data for precipitation and net primary productivity were calculated by summing the monthly values to determine the annual total. As for the annual data for the normalized difference vegetation index, they were compiled by selecting the maximum value from each month. The slope was calculated from DEM data.

Table 1.

Summary of primary data.

Data type	Application	Data format/resolution	Time range	Data source
Socio-economic statistical data	SDG, SED	Spreadsheet, County	2000–2020	Qinghai statistical yearbookTibet statistical yearbookChina county statistical Yearbook(https://www.stats.gov.cn/)
Atmospheric particulate matter (PM_2.5)	SDG	Raster, 0.01°	2000–2020	Washington University in St. Louis (https://wustl.edu/)
Carbon dioxide emissions	SDG	Raster, 0.1°	2000–2020	EDGAR: The Emissions Database for Global Atmosphere Research (https://edgar.jrc.ec.europa.eu/)
Normalized difference vegetation index	SDG	Raster, 500 m	2000–2020	National Aeronautics and Space Administration (NASA) (https://www.nasa.gov/)
Soil conservation	SDG	Raster, 1 km	2000–2020	Food and Agriculture Organization of the United Nations (https://www.fao.org/home/en)
Net primary production	SDG	Raster, 1 km	2000–2020	National Aeronautics and Space Administration (NASA) (https://www.nasa.gov/)
Digital elevation model (DEM)	SED	Raster, 30 m	—	European Space Agency (https://panda.copernicus.eu/panda)
Temperature and precipitation	SED	Raster, 0.1°	2000, 2020	The ERA5-Land dataset released by organizations such as the European Union and the European Centre for Medium-Range Weather Forecasts (https://cds.climate.copernicus.eu/)
Land use	SED	Raster, 30 m	2000, 2020	Resource and Environment Science and Data Center (https://www.resdc.cn/)
Grazing quantity	SED	Raster, 300 m	2000, 2020	National Tibetan Plateau/Third Pole Environment Data Center (https://data.tpdc.ac.cn/home)

Note: SDG, Sustainable Development Goals; SED, socio-economic environmental driver.

Sustainable development index

The formulation of the global indicator framework is based on a global perspective. Many indicators are not applicable to the real conditions in China, and direct use to evaluate the sustainable development level of the QTP will inevitably be biased. Therefore, it is necessary to adjust and supplement the indicators of SDGs in combination with China’s national conditions, county conditions, and the sustainable development status of the QTP based on its regional characteristics, and explore the establishment of a universal, monitorable, assessable, and localized sustainable development evaluation model.

Scholars have divided the 17 SDGs into three pillars: economic, social, and environmental. They believe that there are four goals mainly related to the economic category (SDGs 8, 9, 10, and 12), eight mainly related to the social category (SDGs 1, 2, 3, 4, 5, 7, 11, and 16), and four mainly related to the environmental category (SDGs 6, 13, 14, and 15). SDG 17 represents the practical means and a guarantee throughout the whole process (Gao et al., 2021). Sustainable development is the coordinated development of the three pillars (economy, society, and environment). These three pillars are not independent variables, but interdependent and mutually restrictive; social and economic development must be combined with environmental protection. Therefore, to realize global sustainable development and the prosperity of mankind, it is necessary to cover all three categories when constructing an index system.

Based on the theoretical connotations of sustainable development, this paper takes the Global Indicators Framework for Sustainable Development Goals (SGIF) as the basic framework, and refers to the ‘Qinghai–Tibet Plateau Ecological Environment Protection and Sustainable Development Plan,’ ‘China SDG Index Construction and Progress Assessment Report: 2018’ and other relevant literature. A SDGs-oriented sustainable development index system for QTP counties was established from the three categories of economy, society, and environment (Table 2), and the constructed index system follows the principles of data representativeness, scientificity, and availability (Zhao et al., 2022).

Table 2.

SDG index evaluation framework for the Qinghai–Tibet Plateau.

Category	SDG	Title	Local indicator (direction)	SDG indicators
Society	SDG 1	No Poverty	Household savings deposit balance (+)	1.4.1 Proportion of population living in households with access to basic services
	SDG 1	No Poverty	Public expenditure (+)	1.a.2 Proportion of total government spending on essential services
	SDG 2	Zero Hunger	Total output value of the agricultural, forestry, animal husbandry, and fishery industries (+)	2.3.1 Volume of production per labor unit by classes of farming/pastoral/forestry enterprise size
			Total meat production (+)
			Grain total output (+)
			Value added of the primary industry (+)	2.3.2 Average income of small-scale food producers, by sex and indigenous status
	SDG 3	Good Health & Well-Being	Number of beds in 10,000 hospitals and health centers (+)	3.c.1 Health worker density and distribution
			Number of receiving units of various social welfare (+)	3.8.1 Coverage of essential health services
			Number of beds in various social welfare units (+)	3.8.1 Coverage of essential health services
	SDG 4	Quality Education	Primary school students (+)	4.1.2 Completion rate (primary education, lower secondary education, upper secondary education)
	SDG 4	Quality Education	Ordinary middle school students (+)
	SDG 11	Sustainable Cities & Communities	PM_2.5 (−)	11.6.1 Proportion of municipal solid waste collected and managed in controlled facilities out of total municipal waste generated, by cities
Economy	SDG 8	Decent Work & Economic Growth	GDP per capital (+)	8.1.1 Annual growth rate of real GDP per capita
			Rural practitioners (+)	8.6.1 Proportion of youth (aged 15–24 years) not in education, employment, or training
			Added value of secondary industries (+)	8.4.2 Domestic material consumption, domestic material consumption per capita, and domestic material consumption per GDP
	SDG 9	Industry, Innovation, & Infrastructure	Landline subscriber (+)	9.c.1 Proportion of population covered by a mobile network, by technology
	SDG 9	Industry, Innovation, & Infrastructure	Balance of loans from financial institutions at the end of the year (+)	9.3.2 Proportion of small-scale industries with a loan or line of credit
Environment	SDG 13	Climate Action	Carbon dioxide emissions (−)	13.2.2 Total greenhouse gas emissions per year
	SDG 15	Life on Land	NDVI (+)	15.2.1 Progress towards sustainable forest management
			Soil conservation (+)	15.3.1 Proportion of land that is degraded over total land area
			Net primary production (+)

Note: “+” indicates a positive indicator; that is, the larger the value, the better. “−” indicates a negative indicator; that is, the smaller the value, the better. GDP, Gross Domestic Product; NDVI, Normalized Difference Vegetation Index; PM, Particulate Matter; SDG, Sustainable Development Goals.

Economic Category. Economic development forms the foundation for sustainable development. Utilizing the SDGs as a guide, this study chose five key indicators to assess the regional economic climate, residents’ economic status, and economic progress. Specifically, GDP per capita, the number of rural practitioners, and the added value of the secondary sector align with SDG 8.1.1, 8.6.1, and 8.4.1, respectively, to gauge economic expansion and local employment rates. Additionally, the landline subscribers and the balance of loans from financial institutions correspond to indicators 9.c.1 and 9.3.2, respectively, thereby indicating the region’s infrastructure and industrial sustainability development levels.

Social Category. Advancing social well-being is both the goal and a critical measure of sustainable development, reflecting the quality of life and lifestyle of the local population. This study employed a set of 12 indicators to capture social integration and standards of living. Household savings deposit balances and public spending are compared to SDG 1.4.1 and 1.a.2, respectively, to assess the accessibility of essential public services. Ensuring a stable food supply is crucial to social stability, hence the selection of the total output value of the agriculture, forestry, animal husbandry, and fishing industries, as well as grain and meat production, aligning with SDG 2.3.1 and 2.3.2 to indicate sustainable farming practices. The number of hospital and health center beds per 10,000 individuals, along with the figures for social welfare recipients and facilities, correspond to SDG 3.c.1 and 3.8.1, respectively, providing insight into health care security. The enrolment rates for primary and general secondary education, as per SDG 4.1.2, reflect the pursuit of inclusive and equitable quality education. Lastly, as part of creating sustainable cities, the study looked at PM_2.5 levels (SDG 11) to evaluate the public-space environment.

Environmental Category. Environmental conservation was not only a prerequisite for achieving sustainable development but also a key indicator of environmental governance. This study employed four indicators to assess the state of the environment. Carbon dioxide emissions were measured against SDG 13.2.2 to gauge the air quality within the region. To depict the status of land conservation, the normalized difference vegetation index was utilized, aligning with SDG 15.2.1, which is directly associated with terrestrial ecological conditions. Furthermore, soil conservation and net primary production were examined through SDG 15.3.1 to indicate the region’s commitment to sustainable land management.

The indices of the different SDGs are not comparable owing to unit differences and changing attributes; therefore, dimensionless processing was required for the original values of each SDG index. The max–min normalization method was used for data standardization as follows:

If the influence is a positive indicator:

$S = \frac{s - \min (s)}{\max (s) - \min (s)}$ (1)

If the influence is negative indicator:

$S = \frac{\max (s) - s}{\max (s) - \min (s)}$ (2)

where $S$ is the standardized value, $\min (s)$ and $\max (s)$ are the minimum and maximum values of the original data, respectively. After processing, the data were always in the range of [0,1] (Fang et al., 2023).

The SDG scores of counties on the QTP from 2000 to 2020 were calculated using the mean value method, and the scores were analyzed using the natural breakpoint classification method. The target scores were divided into three levels—low, medium and high—according to their values, and the results were spatially visualized to analyze the evolution characteristics of the spatial pattern of sustainable development on the QTP.

Identification of SDG clusters

The function k-means cluster (Wu et al., 2022b) was used to identify counties with similar SDG relationships over the entire time series. Based on the elbow method, three clusters were selected as the optimal number of clusters. After identifying the SDG clusters, the characteristics of each cluster were summarized. According to Vinuesa et al. (2020), we divided the nine SDGs into three categories: economy (SDGs 8 and 9), society (SDGs 1, 2, 3, 4, and 11), and environment (SDGs 13 and 15). We then calculated the total number of counties in each cluster for each year and used a Sankey diagram to visualize the changes from one cluster to another over time. Finally, we mapped the SDG clusters for each year to show spatial distribution changes over time.

Identification of SDG cluster drivers

Redundancy analysis (RDA) is a direct gradient analysis method, an extension of multiple linear regression, primarily exploring the relationships between univariate or multivariate variables from a statistical perspective. In this study, RDA was conducted using Canoco 5.0, exploring the relationships between SDG clusters (the dependent variable) and socio-economic environmental factors (the independent variables). During the RDA process, the sampling Monte Carlo permutation test was used to assess the significant impact of influencing factors on the SDG clusters. Only explanatory variables that passed the Monte Carlo test (p < 0.05) were retained for further analysis. The RDA ordination plot displays the relationships between different indicators, with angles greater than 90° between the arrows of the explanatory and dependent variables indicating a negative correlation; angles less than 90° indicating a positive correlation; and angles of exactly 90° suggesting no correlation between the two. Additionally, the longer the line segment representing the explanatory variable, the greater the impact of the explanatory variable on the SDG clusters.

Based on a previous study (Huang et al., 2023) , we selected potential factors and used the variance inflation factor (VIF) to examine the multicollinearity between them and ensured a VIF < 4 (in this process, the temperature and rural settlement factors were eliminated). RDA combined with a forward selection procedure (Blanchet et al., 2008) ensured a better model for associating the factors in the SDG clusters. Finally, nine explanatory variables (Lu et al., 2019; Wu et al., 2022b)—GDP per capita, population density, precipitation, slope, DEM, grazing quantity, cultivated area, road length, and urban land–were selected, and explained 55.4% of the variance (adjusted explained variation of 53.1%). The variance inflation factors of all variables were <2.0.

Among the explanatory variables, precipitation, slope, and DEM constituted the first geographical conditions, making them the most fundamental geographical elements with a decisive impact on the survival and development of organisms in the QTP. The remaining six explanatory variables fall under the category of socio-economic factors. Among these, GDP per capita can directly reflect the average economic welfare level in the local area, while population density reveals the density of the population distribution. Additionally, grazing quantity, cultivated area, road length, and urban land reflect the specific impact of human activities on the region’s sustainable development capabilities.

Results

Spatiotemporal changes in SDG scores

SDG scores on the QTP increased from 2000 to 2020 (Figure 2). The county SDG scores increased by approximately 22.8%, from 0.241 in 2000 to 0.296 in 2020. From the perspective of provincial change, the sustainable development scores of Qinghai and Tibet showed an upward trend. Qinghai Province SDG scores increased by approximately 7.9%, from 0.245 in 2000 to 0.324 in 2020. Tibet SDG scores increased by approximately 16.3%, from 0.240 in 2000 to 0.279 in 2020. However, the SDG score of Qinghai Province was higher than that of Tibet every year, and the difference between them increased over time. In 2000, the difference between the two was 0.005 points, while in 2020, the difference was 0.045 points. Overall, the change in SDG scores at the provincial level was similar to the change trend at the QTP level, with a growth rate of 16.3% in the decade after 2010, which was three times that of the previous decade.

Figure 2.

Change in SDG scores from 2000 to 2020.

From the perspective of spatial change, from 2000 to 2020, the number of low SDG scores decreased, whereas the numbers of medium and high SDG scores increased (Figure 3). The level of sustainable development at the county level varied, showing a pattern of ‘high in the east and low in the west’. High-level sustainable development was roughly distributed in southeastern Tibet and eastern Qinghai Province, including Chamdo and Xining City. Low-level sustainable development was mainly distributed in western and southern Tibet, including Shigatse and Ngari. Qinghai Province changed from one high-level sustainable development district (Datong Hui Indigenous Autonomous County) in 2000 to two in 2005, four in 2010, six in 2015, and 20 in 2020. In comparison, Tibet had its first high-level sustainable development region in 2011, increasing to three in 2015 and 14 in 2020. These numbers indicate that the sustainable development level of Qinghai Province was higher than that of Tibet, and the sustainable development speed of the Qinghai–Tibet Plateau accelerated from 2000 to 2020. The sustainable development level of the QTP exhibited certain spatial agglomeration characteristics; high-level sustainable development areas were spatially clustered, and low-level sustainable development areas were spatially clustered. In summary, these results show that the level of sustainable development in the QTP generally presents an upward trend.

Figure 3.

Spatial pattern of SDG scores from 2000 to 2020.

SDG cluster characteristics and changes

Cluster analysis divided the SDG scores of 116 counties in 2000, 2005, 2010, 2015, and 2020 into three categories (Table 3) . Cluster 1 (C1, the cluster of counties with higher environmental scores) was mainly distributed in the southeast, accounted for the largest proportion each year, and was characterized by high SDG scores. Cluster 2 (C2, the collection of counties with higher economic scores) was mainly distributed in the southwest and accounted for the smallest proportion, reaching only 10.3% in 2020; it was characterized by the highest average SDG score (0.349), although the economic development of this region still requires improvement. Cluster 3 (C3, the collection of counties with poor performance in all SDG scores) was mainly distributed in the west and north, had the lowest mean SDG score (0.235), and was characterized by poor performance across all SDGs.

Table 3.

SDG cluster statistics from 2000 to 2020.

Cluster	2000	2005	2010	2015	2020	SDG score
C1	61	58	58	58	68	0.27
C2	0	6	7	9	12	0.35
C3	54	51	50	48	35	0.24

Over time, the spatial distribution of sustainable development clusters on the QTP has changed (Figure 4). Although the proportions of C1 and C3 in each year were relatively large, the proportion of C2 showed an increasing trend while that of C3 showed a decreasing trend (Figure 4a), indicating that the sustainable development level of the QTP improved overall. Some of the districts ranked C3 in 2000 increased to C1 and C2 in 2020, reflecting varying degrees of improvement in different regions. However, the spatial distribution pattern of the clusters was relatively stable, and the overall level of sustainable development on the QTP was not high; in particular, the economic and social SDGs require more attention.

Figure 4.

Dynamics of SDG clusters. (a) County cluster trajectories from 2000 to 2020. (b) Spatial distributions of the SDG clusters in 2000, 2005, 2010, 2015, and 2020.

Driving factors for SDG clusters

Driving factors for the sustainable development level of the QTP are shown in Table 4. All driving factors were significantly correlated with the level of sustainable development (p < 0.05). Population density had the greatest influence on the level of sustainable development in 2000 and 2020 (explaining 20.2%), followed by precipitation (13.9%). Grazing quantity, slope, urban land, cultivated land area, DEM, and GDP per capita had minor influences on the sustainable development level (2.0%–6.0%), while road length had the least influence (1%).

Table 4.

Explanatory power of driving factors on SDG clusters.

Drivers	Percentage explained (%)	P
POP	20.2	0.002
PRE	13.9	0.002
GRA	5.1	0.002
SLO	4.1	0.002
URB	3.8	0.002
CUL	2.3	0.002
DEM	2.2	0.002
GDP	2.1	0.010
ROA	1.0	0.002

CUL: cultivated land area; DEM: digital elevation model; GDP: gross domestic product per capita; GRA: grazing quantity; POP: population density; PRE: precipitation; ROA: road length; SLO: slope; URB: urban land.

Sustainable environmental development exhibited a positive correlation with precipitation and slope and a negative correlation with other driving factors (Figure 5a). The per capita GDP, population density, urban land, and cultivated land area drivers were positively correlated with the social and economic dimensions of sustainable development. In 2000 and 2020, DEM exhibited a strong negative correlation with sustainable development. In Figure 5a, the arrows of SDGen (environmental sustainable development) and SDGec (economic sustainable development) are longer, indicating that the selected natural and socio-economic factors have greater impacts on sustainable environmental and economic development, respectively; the arrows of the SDGso (social sustainable development) are short, which indicates that the driving factors had less influence on the social dimension.

Figure 5.

RDAs for (a) the relationship between drivers and SDGs in 2000 and 2020 and (b) the relationship between drivers and SDG clusters in 2000 and 2020.

County SDG clusters were related to the socio-economic and environmental attributes of the region (RDAs were applied to all counties, R² = 0.53; Figure 5b). Counties with high environmental scores (C1) exhibited intermediate precipitation and slopes. Counties in C2 (high economic scores) had a high GDP per capita, population density, urban land, and cultivated area. Counties with poor economic and environmental performance (C3) had high DEM, grazing quantity, and road length.

Discussion

SDGs progress on the QTP

The level of sustainable development on the QTP increased from 2000 to 2020, at which point it was similar to the overall level of sustainable development in China (Han et al., 2023). However, at the city level, the sustainable development scores are higher in the eastern regions of China, particularly in the coastal areas of Guangdong, Zhejiang, and Jiangsu Provinces, while the scores of cities in the western regions are relatively lower, especially those in Tibet and Qinghai Province (Liu et al., 2023a). This reflects the unique socio-economic development level and natural characteristics of the QTP, which jointly affect temporal and spatial changes in QTP SDG scores. In particular, the rugged terrain, coupled with its distance from the coast, complicates transportation within the region and to and from other regions. Therefore, the overall sustainable development level of the QTP was low in around 2000. To mitigate this underdevelopment, the Western Development Strategy was implemented in the late 20th century, which gradually ameliorated regional environmental and socio-economic conditions (Ortuño-Padilla et al., 2017). In 1999, only 29% of China’s fiscal transfers went to Western China, but in 2010, this had increased to 39.4%. Under the Western Development Strategy, infrastructure development and ecological protection on the QTP have greatly improved, and SDG scores have gradually increased.

The sustainable development level of Tibet was generally lower than that of Qinghai Province, as the SDG scores of the southwest regions, such as Ngari Prefecture and Xigaze, were lower, while those of northeast Xining and Haidong were higher. The QTP is sparsely populated, with a distribution characteristic of ‘dense southeast and sparse northwest’. Rural settlements increased from 1980 to 2015 and are mainly concentrated in eastern and southern Qinghai Province and southern Tibet. The northeast of the QTP is low in altitude, has natural conditions suitable to support human life, and is economically and industrially developed (Fan and Fang, 2022). The population distribution is reflected in the sustainable development level. Studies on land use/land cover, population density, road distribution, and grazing density have found that the degree of interference from human activities on the QTP was generally low before 2010 but has gradually increased with time (Li et al., 2018b); the main driving factors for the growth of human activities in Tibet are grazing and road construction (Li et al., 2018a). Our results are consistent with these studies.

Targeted actions for sustainable development

By identifying the strengths and weaknesses of counties in achieving the SDGs and selecting priority development targets (Atie et al., 2023), cooperation between different counties can be promoted. In 2020, 59.1% of counties had a C1 classification (i.e., a high environmental score), indicating that implementation of policies to return farmland to forest and grassland has successfully improved the ecological environment. Although the number of C2 counties (i.e., high economic scores) has gradually increased (Figure 4a), they only accounted for 10.3% in 2020, indicating that the region remains in urgent need of economic development to promote sustainable development. C3 counties (low SDG scores) must implement aid policies and seek help from rapidly developing areas in the central and eastern regions. However, this will require the support of national policies. The QTP should focus on the socio-economic dimensions of development, including economic growth, healthcare, education, and sustainable communities. The optimal solution would be to use ecosystem services to promote socio-economic development while protecting the environment (Deng et al., 2023), address trade-offs between SDGs in different dimensions, and make full use of coordination effects, which will require decision-makers to innovate practical methods. In recent years, China has sought to balance the relationship between ecological protection and economic development on the QTP through ecological economic initiatives. However, considerable effort is still required to fully achieve the SDGs in the region.

In 2014, the built-up area accounted for more than half of the total land area of Qinghai Province, indicating that the economic development of Qinghai Province has mainly been due to rapid urbanization, especially industrialization, which is supported by Qinghai Province’s rich natural resources (Wei et al., 2019). However, because of outdated technology, resource utilization efficiency in Qinghai Province remains low, resulting in substantial production of wastewater, waste gas, and solid waste during manufacturing and processing. This necessitates that Qinghai Province actively pursue the development of a circular economy and enhanced resource efficiency through the exchange of byproducts and the reuse of solid waste (Geng et al., 2016). In recent years, the urbanization process in Tibet has developed rapidly, but not as fast as that in Qinghai Province (Wei et al., 2019). Tibet is an important ecological protection area in China and environmental protection should be a priority. Tibet should continue to implement a policy of providing rewards and subsidies (GRS) for grassland ecological conservation, which has been implemented by the Chinese government since 2009. The policy aims to promote the protection and restoration of grassland ecology through measures such as the prohibition of grazing and sustainable grass storage, which will help to promote the sustainable development of grassland ecology and realize the SDGs. Simultaneously, to achieve the SDGs worldwide, the QTP should actively engage in cooperation and leverage transboundary synergies with neighboring countries (Xiao et al., 2024).

Limitations and future prospects

This study has certain limitations and requires further evaluation. First, owing to data constraints, some indicators that are difficult to obtain or quantify (SDG 5, 6, 7, 10, and 12) can only be substituted or directly discarded, resulting in the imperfect selection of indicators in the evaluation system for sustainable development on the QTP and some limitations in the indicator system (Huan et al., 2022). Second, our study assesses the spatiotemporal evolution of long-term SDGs on the QTP, but ignores the internal trade-offs and coordination relationships among the goals. Third, further research is needed on the degree of coupling and coordination between different subsystems within the region, and on analyzing the spillover benefits of policy measures in one area on other regions in China and across international borders for sustainable development.

Conclusions

This study has developed a localized county-level SDG assessment framework for the QTP based on a global indicator framework. The framework is capable of reflecting the development levels of economic, social, and environmental systems, serving as a practical tool for policymakers to formulate sustainable development strategies and achieve SDGs. Consequently, it holds significant reference value for assessing the sustainable development levels of key ecological vulnerable regions in China. Moreover, by utilizing county-level data from Tibet and Qinghai Province from 2000 to 2020, we were able to clearly track and monitor the long-term spatiotemporal changes in sustainable development at the county scale on the QTP. This is extremely important for the monitoring and evaluation of county-level sustainable development levels in China.

The main findings of this study are as follows: First, the sustainable development level of the QTP region showed a fluctuating upward trend from 2000 to 2020, with the score increasing from 0.241 in 2000 to 0.296 in 2020, an increase of 22.8%. There is still considerable room for improvement, especially in the western QTP region. There are gaps in the level of sustainable development among different counties, with Tibetan counties scoring lower and serving as the weak link in the system that constrains the current level of sustainable development of the QTP. Second, the economic development level of the QTP region still lags behind the level of environmental sustainable development. It is necessary to strengthen high-quality economic development and continuously improve the level of infrastructure to promote sustainable development in the economic and social dimensions. Lastly, the level of sustainable development on the QTP is influenced by various factors, among which precipitation, slope, population density, and cultivated land area are key driving factors.

Overall, the sustainable development level of county regions in the QTP has gradually improved from 2000 to 2020, indicating a positive trend for future sustainable development. Sustainable development is not achieved overnight, but it is a long-term goal. To meet the requirements of the SDGs, in addition to government guidance at the macro level and active participation from various regions, it is essential to establish a correct understanding of sustainable development across society. This will create a virtuous cycle to achieve sustainable development in the QTP region and promote coordinated development across all sectors of the Plateau.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This study is financially support by the National Natural Science Foundation of China (No. 42301113),Key R&D Program of Tibet Autonomous Region (XZ202401ZY0089) and the Science and Technology Planning Project of Lhasa (LSKJ202316,LSKJ202408 and LSKJ202320).

ORCID iD

Xian Li

Author biographies

Erfu Dai is a Professor in the Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences. His research interests include land use and ecosystem services,Sustainable Development Goals.

Xian Li is a Master Student in the School of Geography and Tourism,Qufu Normal University. Her research interests include sustainable development.

Lizhi Jia is an assistant research fellow in the Lhasa Plateau Ecosystem Research Station,Key Laboratory of Ecosystem Network Observation and Modeling,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences. His research interests include the ecosystem services and sustainability.

Zhongxu Zhao is a doctoral candidate at the Institute of Geographic Sciences and Natural Resources Research at the Chinese Academy of Sciences. His research interests include regional sustainable development and ecosystem services assessment.

Lin Zhao is an Associate Professor in the School of Geography and Tourism,Qufu Normal University. His research interests include regional sustainable development.

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