1.1 Study Area Overview

The paper selects county-level administrative units in Ningxia as the study area (Figure 1). This includes 10 counties such as Yongning County and Helan County, as well as 2 county-level cities of Lingwu City and Qingtongxia City, covering 65.7% of the region's total land area and 45.7% of its population. The area is primarily divided into three regions: northern, central, and southern Ningxia. Helan County, Yongning County, and others adjacent to the Yellow River belong to the northern Yellow River irrigation area; Yanchi County and Tongxin County belong to the central arid zone; and areas such as Xiji County and Pengyang County with complex terrain belong to the southern mountainous region. Over the past decade, relying on resource endowments and the poverty alleviation strategy, Ningxia's county economies have developed steadily and people's lives have improved significantly. However, challenges remain, such as large urban-rural gaps, single industrial structures, and prominent contradictions between socioeconomic progress and ecological environmental protection. How to promote county economic development and achieve common prosperity for all people represents a key focus for future development.

1.2 Data Sources and Processing

The paper uses panel data from Ningxia county areas from 2006 to 2022 to measure and analyze the development level of common prosperity in Ningxia counties. Data sources include the Ningxia Statistical Yearbook, statistical data from various cities and counties, and government bulletins. The comprehensive evaluation index is calculated using the EWM-CRITIC combination weighting method. Exploratory spatiotemporal data analysis employs ArcGIS 10.6 and Geoda software, while Thiel index, baseline regression, and robustness tests use Stata 16.

1.3 Indicator System Construction

Referencing relevant studies and considering Ningxia's regional characteristics and data availability, the paper adheres to scientific and comprehensive principles to construct a comprehensive evaluation index system comprising 4 dimensions and 20 indicators: development, sharing, and sustainability (Table 1). The development dimension includes indicators such as per capita GDP, economic stability, urban-rural employment rate, and regional development disparity coefficient to examine economic development levels and regional coordination during common prosperity advancement. The sharing dimension includes numbers of students at all school levels, medical beds, per capita transportation area, and other indicators reflecting the allocation of infrastructure and public service resources at the county level. The sustainability dimension encompasses social, economic, and ecological sustainable development, represented by indicators such as overall social labor productivity, per capita disposable income of all residents, and unit GDP exhaust emissions, primarily examining the sustainable development capacity of various regions in achieving common prosperity. Compared with national-level research, long time-series data for Ningxia counties are somewhat incomplete, significantly affecting the selection of environmental sustainability indicators. Therefore, unit GDP exhaust emissions are used as a substitute indicator for examination.

1.4.1 EWM-CRITIC Combination Weighting Method

Weight Calculation. The Entropy Weight Method (EWM) allocates weights based on the degree of information entropy chaos, primarily considering internal differences among indicators. The CRITIC method (Criteria Importance Through Intercriteria Correlation) allocates weights based on variability and conflict among indicators, which can reduce the impact of correlations between indicators. Combining these two methods can balance indicator differences and correlations, improving the scientific nature of weight calculation. Referencing Wang Fanglei et al. [25], the paper uses the EWM-CRITIC combination weighting method to calculate indicator weights.

Comprehensive Index Calculation. The range method is used to standardize raw data to eliminate differences in magnitude and dimension that could affect the comprehensive evaluation value. Based on standardized values and indicator weights, the linear weighted summation method is employed to calculate the comprehensive evaluation index of common prosperity for each county.

1.4.2 Thiel Index

The Thiel index is widely used to measure regional disparities or the fairness of resource utilization. This method can decompose overall regional differences into within-group and between-group differences and reveal the contribution rates causing these changes. The Thiel index is selected to analyze regional differences in common prosperity levels in Ningxia county areas [26]. Its basic expression is:

$$
T = \frac{1}{n}\sum_{i=1}^{n}\frac{y_i}{\bar{y}}\ln\left(\frac{y_i}{\bar{y}}\right)
$$

where: $T$ is the regional development Thiel index; $n$ is the number of counties; $y_i$ is the comprehensive evaluation index of common prosperity for each county; and $\bar{y}$ is the mean of common prosperity development indices for Ningxia counties.

All counties are divided into three regions (northern, central, and southern Ningxia), so the decomposed form of the Thiel index is:

$$
T = T_{\text{between}} + T_{\text{within}} = \sum_{k=1}^{m}Y_k\ln\left(\frac{Y_k}{n_k/n}\right) + \sum_{k=1}^{m}Y_k\sum_{l\in G_k}\frac{y_l}{Y_k}\ln\left(\frac{y_l/Y_k}{1/n_k}\right)
$$

where: $Y_l$ is the proportion of county $l$'s common prosperity index to the total sum of all counties; $Y_k$ is the proportion of the sum of common prosperity indices of counties in group $k$ to the total sum of all regions; $n_k$ is the number of counties in group $k$; $y_l$ is the common prosperity index of county $l$ in group $k$; $T_{\text{between}}$ represents differences between regions; and $T_{\text{within}}$ represents differences within regions. This allows calculation of the contribution rates of within-group and between-group differences to the overall Thiel index.

1.4.3 Exploratory Spatial Data Analysis

The global Moran's I index is selected to examine the overall spatial clustering characteristics of common prosperity levels in the study area, while the local Moran's I index measures the evolution of spatial clustering characteristics of common prosperity levels.

1.4.4 Baseline Regression Model

To investigate the influencing factors of common prosperity levels in Ningxia county areas and referencing relevant studies [27], panel data regression analysis is employed to construct the following baseline regression model:

$$
\text{Cop} = W_{ij}\ln(\text{...})
$$

where: $\text{Cop}$ represents the common prosperity level at the county level; $\lambda$ is the constant term; $u$ is the random disturbance term; $X_i$ is the independent variable; $n$ is the number of independent variables; $W_{ij}$ is the spatial weight coefficient of independent variables; and $i$ and $j$ represent the positions of study counties in the spatial weight matrix, where $i$ is the row number and $j$ is the column number. Due to the close relationship between economic level and common prosperity, an economic distance spatial weight matrix is used here.

2.1.1 Temporal Evolution Characteristics by Region

From 2006 to 2022, the overall common prosperity level in Ningxia county areas showed a stable growth trend (Figure 2), with an average annual growth rate of 6.3%, and the evaluation index increased from 0.23 to 0.58. This indicates that since the "Eleventh Five-Year Plan," Ningxia has placed greater emphasis on gradually achieving common prosperity for all people. Through implementing comprehensive deepening reforms, poverty alleviation, and high-quality economic development strategies, people's living standards in county areas have improved, regional gaps have narrowed, and common prosperity construction has achieved a good start. From the perspective of regional index evolution, the common prosperity levels of all three regions showed upward trends from 2006 to 2022. Specifically, the common prosperity index in northern Ningxia increased from 0.32 to 0.73, ranking first among the three regions; central Ningxia increased from 0.21 to 0.54, ranking second; and southern Ningxia, due to relatively weak economic foundations and regional development differences, increased from 0.18 to 0.46, remaining at a lower level. However, in terms of average annual growth rate, southern Ningxia ranked first, followed by central Ningxia, with northern Ningxia ranking lowest, indicating that Ningxia's county common prosperity development levels and growth rates have heterogeneous characteristics.

2.1.2 Temporal Evolution Characteristics by Dimension

The evaluation values of each dimension of the county common prosperity index also showed growth trends (Figure 3). Among them, the development index increased from 0.21 to 0.59, indicating that Ningxia's county productivity levels have continuously improved in recent years, with social progress bringing wealth growth, stable regional economic operation, and regional development paths gradually moving toward coordinated development. The sharing index increased from 0.19 to 0.52, primarily because the victory in poverty alleviation tasks and the achievement of the first centenary goal have led to varying degrees of improvement in education, medical care, and public infrastructure resources, which have continuously extended to counties and townships, creating a good foundation for common prosperity advancement. The sustainability index increased from 0.29 to 0.63, indicating that during the study period, Ningxia's county economy and ecological environment continued to develop positively, labor productivity continuously improved, residents' income gradually increased, environmental pollution was effectively controlled, and sustainable development capacity was gradually enhanced.

2.1.3 Evolution Characteristics of Regional Differences

The Thiel index can reveal not only overall differences in common prosperity levels in Ningxia county areas but also, through decomposition, further explore the contributions of within-group and between-group differences to overall differences. Analysis reveals (Table 2) that regional disparities in Ningxia counties have always existed, but the overall Thiel index decreased from 0.047 to 0.031, indicating that the overall gap in common prosperity levels among counties is gradually narrowing. In terms of contribution rates, the within-group disparity contribution decreased from 63.67% to 18.49% from 2006 to 2022, while the between-group disparity contribution increased from 36.33% to 81.51%, with the growth rate of between-group disparity contribution far exceeding that of within-group disparity. This shows that the key to solving the development gap in common prosperity in Ningxia counties in the short term lies in narrowing regional gaps, which is consistent with Ningxia's socioeconomic development reality. Northern and southern Ningxia have significant differences in terrain conditions, while economic foundations, resource endowments, and talent structures are severely unbalanced, causing regional development differences to gradually expand in recent years. Within each region, stable and coordinated development patterns have initially formed due to similar development paths, with Thiel indices continuously decreasing.

Further analysis of Thiel index distribution across the three regions reveals that northern, central, and southern Ningxia all show narrowing trends, but with significant regional differentiation characteristics, generally manifesting as: central > northern > southern. Specifically: (1) The Thiel index in northern Ningxia has always been at a relatively high level, with the main reasons for development disparities being resource allocation efficiency and urban-rural development differences. Lingwu City, Qingtongxia City, Helan County, and Yongning County are adjacent to Yinchuan City and can effectively undertake the relief of non-core functions from the central city, with relatively high economic income and infrastructure investment, resulting in high common prosperity indices. In contrast, Pingluo County has experienced narrowing economic development paths and severe population outflow in recent years, with continuously expanding urban-rural gaps and a relatively low common prosperity index. From 2006 to 2022, Pingluo County's urban-rural employment rate decreased from 87.3% to 78.6%, and the Thiel index of urban-rural residents' income increased from 0.12 to 0.21, indicating severe urban-rural development imbalance. (2) Yanchi County in central Ningxia has good industrial advantages and locational conditions; Tongxin County has achieved remarkable results in green and low-carbon transformation and development in recent years, placing these two counties at the forefront of common prosperity levels. Haiyuan County has a small economic aggregate and relatively weak industrial foundations; Zhongning County, despite having a characteristic goji berry industry, has a relatively single industrial structure, resulting in relatively low common prosperity indices for these two counties. Due to development differences among counties in central Ningxia, its Thiel index is relatively high. (3) The Thiel index in southern Ningxia has always remained at a relatively low level because the southern mountainous region has large terrain fluctuations, low transportation accessibility, and Xiji County, Longde County, Pengyang County, and Jingyuan County face similar development challenges, resulting in small development differences and a low Thiel index.

2.2.1 Global Spatial Evolution Characteristics

Selecting 2006, 2014, and 2022 as study time points, the common prosperity index is divided into 4 levels (Figure 4). The results show that the common prosperity level in Ningxia county areas has overall transitioned to a higher level, with significant regional heterogeneity in spatial evolution, generally presenting a "patchy distribution" spatial pattern. Areas with high common prosperity indices are located in counties adjacent to Yinchuan City because northern Ningxia has open terrain, developed transportation, and substantial support in terms of capital, technology, and industry, resulting in relatively high common prosperity levels. Areas with low common prosperity indices are located in Xiji County and adjacent counties, primarily because Xiji County, Pengyang County, Jingyuan County, and Longde County are geographically distant from Ningxia's central city, receive little radiation-driven effect, and the southern mountainous region is constrained by economic foundations and natural conditions, resulting in relatively low common prosperity levels. Simultaneously, examination of the spatial clustering characteristics of common prosperity levels in Ningxia county areas reveals that the spatial distribution pattern evolved from random to clustered, meaning that counties with high common prosperity levels are adjacent to each other, as are counties with low common prosperity levels. Overall, the spatial dependence of county common prosperity levels has continuously increased, presenting significant spatial clustering characteristics (Table 3).

2.2.2 Local Spatial Evolution Characteristics

Similarly selecting 2006, 2014, and 2022 as study time points, the evolution characteristics of local spatial clustering of common prosperity levels in Ningxia county areas are examined (Figure 5). The results show that the spatial distribution is primarily characterized by homogeneous clustering, with heterogeneous clustering as a secondary feature. High-high clustering counties are mainly distributed in Helan County, Lingwu City, and Yanchi County. These counties generally have relatively high economic development levels, coordinated industrial structures, and relatively affluent people's lives, with low spatial differentiation from neighboring counties and similar development states. Low-low clustering counties are mainly distributed in Xiji County, Longde County, Pengyang County, and Jingyuan County in southern Ningxia. These counties are overall in a state of underdevelopment, lack economic vitality, and have low common prosperity levels and similar low values in neighboring areas, showing significant homogeneous low-value clustering characteristics. Heterogeneous (low-high or high-low) clustering counties show negative spatial correlation, with Haiyuan County as a representative case, exhibiting high-low clustering characteristics. Haiyuan County is located in the central arid zone of Ningxia, with a relatively high common prosperity index and stable change trend, but it is adjacent to counties with lower development levels such as Xiji County and Tongxin County, resulting in a certain degree of spatial fragmentation in the region.

2.3.1 Variable Selection

Combining existing research with the actual development of Ningxia counties, this paper uses a baseline regression model to analyze the driving factors of common prosperity levels in Ningxia county areas. Since socioeconomic elements have significant impacts on common prosperity advancement such as increasing people's income levels and industrial efficiency growth, comprehensive factors of socioeconomic development are selected as the main driving factors for analysis (Table 4). After tests such as F-test and Hausman test, a two-way fixed effects model is ultimately selected for panel data regression analysis.

2.3.2 Baseline Regression

Regression results show (Table 5) that the regression coefficients for economic level, education level, industrial structure, and infrastructure construction are 0.421, 0.103, 0.086, and 0.137, respectively, all significant at the 1% level, indicating that these four factors play positive roles in improving common prosperity levels in Ningxia county areas. Among them, the regression coefficients for education level and industrial structure are relatively low, indicating that these two factors have smaller promoting effects on common prosperity development. The possible reasons are that educational resources in Ningxia counties are relatively scarce, with overall backward education levels, and the dominant industries in counties remain primarily agricultural, thus their contribution to common prosperity advancement is slightly insufficient. The regression coefficient for urbanization level is -0.203, significant at the 1% level, indicating that during the study period, the improvement of urbanization levels in Ningxia counties was not conducive to common prosperity development. The possible explanation is that in the early stages of urbanization, large numbers of people migrated from rural and suburban areas, leading to highly concentrated populations and industries. However, the "people-land挂钩" (people-land挂钩 is a Chinese policy term meaning "linking people with land") policy has not been fully implemented, making it difficult to break the urban-rural dual structure in a short time, with large income gaps between residents, thus having a stage-inhibitory effect on common prosperity development.

Based on the regional division of Ningxia counties above, subsample regression tests were conducted. Results show that the regression coefficients for northern, central, and southern Ningxia regions are consistent with the full-sample regression coefficients in direction (Table 5), indicating that economic level, education level, industrial structure, and infrastructure construction also have positive effects on common prosperity development in different regions, while urbanization level also has a stage-inhibitory effect.

2.3.3 Analysis of Driving Factor Mechanisms

Different driving factors have significantly different regulatory effects on the advancement of common prosperity in Ningxia county areas.

Economic Development Level. Economic prosperity is the key engine for advancing common prosperity and has a significant driving effect on achieving common prosperity for all people. From 2006 to 2022, Ningxia's county economic levels grew steadily, driving accelerated infrastructure construction, improved public service quality, gradually improved people's living standards, and alleviated poverty and income distribution inequality, effectively promoting the county common prosperity process. Simultaneously, economic prosperity can also promote the sharing of development achievements by the people, narrowing gaps between regions, between urban and rural areas, and between resident groups, thereby accelerating the achievement of common prosperity for all people.

Infrastructure Construction. Currently, the problem of unbalanced and insufficient development in Ningxia counties remains prominent. For relatively lagging counties, imperfect infrastructure and public service facility construction have become important bottlenecks restricting regional economic development and improving people's living standards. Taking southern Ningxia as an example, due to natural geographical environment and other factors, some counties lack modern transportation facilities, high-level medical facilities, and other public service and infrastructure systems, with insufficient extension from county towns to rural areas, affecting the county common prosperity process to some extent. Promoting equalization of basic public services between urban and rural areas, balancing infrastructure construction, and promoting smooth flow of resources and information among various regions can bring new development opportunities, thereby promoting regional coordinated development and gradually achieving common prosperity for all people.

Industrial Structure. A reasonable industrial structure can improve production efficiency, promote technological innovation and labor employment, enable sustained and healthy economic growth, and is of great significance for achieving common prosperity for all people. Currently, the differences in industrial structure among Ningxia counties are not obvious, contributing relatively little to common prosperity advancement. However, with the transformation of new quality productive forces, some counties have made breakthroughs in industrial structure evolution, and their promoting effect on common prosperity development will be enhanced.

Education Level. Education is the foundation of a nation and the prerequisite and basis for human capital improvement. Currently, the overall education level in Ningxia counties is relatively low, with an insignificant promoting effect on common prosperity levels. However, with the improvement of overall education levels in counties, human capital quality will be effectively enhanced, better adapting to social development needs. Simultaneously, it can promote residents' living needs to shift from single material needs to joint development of material and spiritual aspects to some extent. This transformation will drive the development of more industries, thereby accelerating the common prosperity process.

Urbanization Level. The urbanization level has stage-specific characteristics in promoting common prosperity development. In the past, Ningxia's county urbanization was dominated by rapid urbanization that consumed large amounts of land resources and was industry-centered, leading to continuous aggregation of population, economy, and industry in urban areas. The core elements of rural development gradually drained away, producing certain negative impacts on resources and environment, with continuously expanding urban-rural gaps. This stage had an inhibitory effect on common prosperity advancement. When county urbanization develops to the middle and later stages, the new-type urbanization and rural revitalization strategies enable some capital, talent, and industry elements to flow back to rural areas, effectively driving rural development. The urban-rural economic structure transforms toward green, low-carbon, and civilized healthy patterns, and at this time, the improvement of urbanization levels helps achieve common prosperity for all people.

2.3.4 Robustness Tests

The paper selects alternative regression models to test the robustness of regression results (Table 6). The regression results show that the coefficients of each factor are consistent with the baseline regression results in direction, indicating that economic level, education level, industrial structure, and infrastructure construction all have positive effects on common prosperity development, with economic development level having the most significant impact, and urbanization level also has stage-specific characteristics. Therefore, the regression results are basically robust.

3 Conclusions

From 2006 to 2022, the common prosperity level in Ningxia county areas has been effectively improved. Although there remains a significant gap compared with national and eastern regions, the growth rate of common prosperity levels in Ningxia counties is relatively fast, laying a foundation for common prosperity advancement. From the perspective of different regional growth rates, the order is southern Ningxia > central Ningxia > northern Ningxia; from the perspective of different dimensional growth rates, the order is sharing > development > sustainability. Unbalanced and insufficient development is the main challenge facing common prosperity advancement in Ningxia counties.

According to the Thiel index and its decomposition, the overall gap in common prosperity levels among Ningxia counties is gradually narrowing, but the contribution rate of between-group disparities shows a significant growth trend. Therefore, the key to solving differences in common prosperity levels in Ningxia in the short term lies in narrowing development gaps among northern, central, and southern Ningxia regions.

From 2006 to 2022, the common prosperity level in Ningxia county areas has overall transitioned to a higher level, presenting a "patchy distribution" spatial pattern with significant spatial clustering characteristics. High values are concentrated in counties adjacent to Yinchuan City, low values are concentrated in Xiji County and adjacent counties, and Haiyuan County exhibits heterogeneous clustering characteristics due to its location in a transitional zone.

Economic level, education level, industrial structure, and infrastructure construction have positive driving effects on the common prosperity process in Ningxia county areas. The promoting effect of economic development level is most significant, while the promoting effects of education level and industrial structure are relatively small. The urbanization level has stage-specific inhibitory characteristics.

Compared with central and eastern provinces, the common prosperity process in Ningxia county areas is relatively slow. Priority should be given to improving overall economic development levels, expanding infrastructure construction, accelerating the new-type urbanization process, optimizing county industrial structures, increasing county education investment, and improving education quality. Simultaneously, it is necessary to strengthen cooperation and connections with the Yellow River "几" character bay metropolitan area, the Yellow River basin, and other regions to promote regional coordinated development and opening up.

References

干 旱 区 地 理 中国市域城乡收入差距时空演化及影响因素 , 2024, 47(1): 147- 157. [Jiang Yuekun, Shi Pengjuan. Spatiotemporal evolution and its affecting factors of urban rural income gap at the city level scale in China[J]. Arid Land Geography, 2024, 47(1): 147-157. ] 共同富裕新论——基于平等理论的分析 2014(1): 22- 26. [Jiang Zheng. New theory of common prosperity: An analysis based on equality theory[J]. Lingnan Journal, 2014(1): 22-26. ] 县域共同富裕与中国式现代化的路径模式研究——来自浙江山区共同富裕示范区县域样板缙云 中国软科学 , 2024( 1): 441-453. [Liu Qing, Lu Fengjun, Li Baoming, et al. Research on the path model of county common prosperity and Chinese path to modernization: Evidence from Jinyun County, a model county in the demonstration area of common prosperity in mountainous areas of Zhejiang Province[J]. China Soft Science, 2024(Suppl. 1): 441-453. ] 宁夏县域土地利用碳排放空间分异与风险研究 干旱区地理 , 2023, 46(11): 1757-1767. [Jia Keli, Li Xiaoyu, Wei Huimin, et al. Spatial differentiation and risk of land use carbon emissions in county region of Ningxia[J]. Arid Land Geography, 2023, 46(11): 1757-1767. ] 共同富裕评价指标体系构建与实证测度 , 2024, 40(2): 73-77. [Feng Yuan, Nie Changfei. Evaluation index system construction and empirical measurement of common prosperity[J]. Statistical and Decision Sciences, 2024, 40(2): 73-77. ] 新时代共同富裕的新要求——学习习近平关于共同富裕的重要论述 毛泽东邓小平理论研究 , 2018(4): 24- 29, 107. [Yang Jing, Lu Shucheng. New requests for common prosperity in the new era: Studying Xi Jinping's important discussions on common prosperity[J]. Studies on Mao Zedong and Deng Xiaoping Theories, 2018(4): 24-29, 107. ] 高质量发展促进共同富裕的内在逻辑与路径选择 重庆大学学报(社会科学版), 2022, 28(4): 39- 52. [Xu Zheng, Zheng Linhao. Internal logic and path choice of high quality development to promote common prosperity[J]. Journal of Chongqing University (Social Science Edition), 2022, 28(4): 39-52. ] 中国市域尺度共同富裕水平格局及其影响因素 , 2023, 43(1): 44-54, 132. [Peng Gang, Yang Delin, Yang Lin. Distribution pattern and influence factors of city-scale common prosperity in China[J]. Economic Geography, 2023, 43(1): 44-54, 132. ] 新型城镇化对共同富裕的影响效应及作用机制 , 2023, 39(12): 126- 131. [Han Jianyu, Chu Haitao. The effect of new urbanization on common prosperity and its mechanism[J]. Statistical and Decision Sciences, 2023, 39(12): 126-131. ] 重庆三峡库区共同富裕水平时空分异特征与影响因素分析 地理与地理信息科学 , 2023, 39(4): 54-62. [Liao Yangyi, Su Weici. Spatio temporal differentiation characteristics and influencing factors of the common prosperity level in Chongqing section of Three Gorges Reservoir area[J]. Geography and Geo-Information Science, 2023, 39(4): 54-62. ] 长江经济带共同富裕水平的区域差异、分布动态与收敛性 , 2023, 39(11): 13-21. [Cheng Jing, Hao Wenli, Du Zhen, et al. The common prosperity level of the regional differences, distribution dynamics and convergence in the Yangtze River Economic Belt[J]. Ecological Economy, 2023, 39(11): 13-21. ] 共同富裕统计监测评价探讨 , 2022, 39(2): 3-17. [Li Jinchang, Yu Wei. Discussion on statistical monitoring and evaluation of common prosperity[J]. Statistical Research, 2022, 39(2): 3-17. ] 中国共同富裕发展的时代背景与"提低"路径 江苏大学学报(社会科学版), 2022, 24(4): 1-7, 34. [Huang Zuhui, Zhang Shuping. The background and increasing income path of China's promotion of common prosperity[J]. Journal of Jiangsu University (Social Science Edition), 2022, 24(4): 1-7, 34. ] 文旅融合发展促进共同富裕的理论逻辑与实现路径 , 2023, 43(11): 198-207. [Su Lujun, Ye Chengzhi, He Xuehuan. Theoretical logic and realization path of integrated development of culture and tourism promoting common prosperity[J]. Economic Geography, 2023, 43(11): 198-207. ] 实现农村共同富裕的数字化路径研究 , 2024(1): 128-129. [Li Min. Research on the digital path to realize common prosperity in rural areas[J]. Agricultural Economy, 2024(1): 128-129. ] 生态环境保护推进共同富裕的时代内涵、价值意蕴与实现路径 广西社会科学 , 2023(9): 26-33. [Zhang Ruiping. Promoting common prosperity through ecological environment protection: Era connotation, value connotation and implementation path[J]. Guangxi Social Science, 2023(9): 26-33. ] 教育促进共同富裕的独特作用——理论应然与实证现实 华东师范大学学报(教育科学版), 2023, 41(10): 12-26. [Huang Bin, Li Bo, Fang Chao. The unique role of education in promoting common prosperity: Theoretical oughtness and empirical reality[J]. Journal of East China Normal University (Educational Sciences Edition), 2023, 41(10): 12-26. ] 中国式现代化下的共同富裕: 理论内涵与实践路径 当代经济管理 , 2024, 46(1): 1-9. [Jiang Jianping. Common prosperity under Chinese path to modernization: Theoretical connotation and practical path[J]. Contemporary Economic Management, 2024, 46(1): 1-9. ] 中国共产党的共同富裕: 理论演进与实现路径 , 2021(3): 115-120. [Chen Yan. Common prosperity of the communist party of China: Theoretical evolution and realization path[J]. Scientific Socialism, 2021(3): 115-120. ] 论中国共同富裕测度指标和阶段性进展 经济研究 , 2023, 58(9): 79- 97. [Chen Zongsheng, Yang Xilei. On the measurement indicators of common prosperity and the phased progress in China[J]. Economic Research Journal, 2023, 58(9): 79-97. ] 原连片特困地区共同富裕能力评价及其演变跃迁 经济地理 , 2022, 42(8): 154-164. [Luo Rong, He Huangqi, Chen Shuang. Original contiguous destitute areas common prosperity capability evaluation and evolution transition[J]. Economic Geography, 2022, 42(8): 154-164. ] 中国共同富裕的时空演进、区域差异及收敛性研究 经济与管理研究 , 2022, 43(12): 65-84. [Feng Yuan, Nie Changfei. Spatial temporal evolution, regional differences and convergence of common prosperity in China[J]. Research on Economics and Management, 2022, 43(12): 65-84. ] 中国区域共同富裕实践中乡村旅游作用机制 自然资源学报 , 2023, 38(2): 387-402. [Zhang Guanghai, Liu Erlian, Dong Yuelei. The mechanism of rural tourism in the practice of regional common prosperity in China[J]. Journal of Natural Resources, 2023, 38(2): 387-402. ] 中国共同富裕推进能力的演化特征与空间关系 统计与信息论坛 , 2024, 39(1): 103-117. [Li Shunyi, Zhang Hongfeng. Evolution characteristics and spatial relationship of China's ability to promote common prosperity[J]. Journal of Statistics and Information, 2024, 39(1): 103-117. ] 基于EWM-CRITIC-TOPSIS模型的新疆典型自然景观评估 自然资源遥感 , 2025, 37(1): 94-101. [Wang Fanglei, Zhang Lei, Zhai Fuxiang. Evaluation of typical natural landscape in Xinjiang based on EWM-CRITIC-TOPSIS model[J]. Remote Sensing for Natural Resources, 2025, 37(1): 94-101. ] 城镇化高质量发展测度及其时空差异研究 , 2019, 35(22): 46-50. [Wang Bin. Study on the measurement of quality development of urbanization and its spatio temporal differences[J]. Statistical and Decision Sciences, 2019, 35(22): 46-50. ] 中国省域新型城镇化水平与绿色发展水平的区域特征及其影响机制 经济地理 , 2023, 43(9): 1- 9. [Zhao Yang, Wang Jing, Pan Weihua. Regional characteristics and impact mechanisms of new urbanization and green development level in China at the provincial level[J]. Economic Geography, 2023, 43(9): 1-9. ] 新型城镇化促进共同富裕的效应与机制研究——基于新型城镇化综合试点政策的准自然实验 郑州大学学报(哲学社会科学版), 2023, 56(1): 48-55. [Zhang Mingdou, Qiao Xuebei. The effect and mechanism of new urbanization promoting common prosperity: Quasi natural experiment based on the comprehensive pilot policy of new urbanization[J]. Journal of Zhengzhou University (Philosophy and Social Sciences Edition), 2023, 56(1): 48-55. ] 山西省县域高质量发展与生态系统服务耦合的时空演变特征 干旱区研究 , 2022, 39(4): 1234-1245. [Liu Hailong, Tang Fei, Ding Yanan, et al. Temporal and spatial evolution characteristics of the coupling between county quality development and ecosystem services in Shanxi Province[J]. Arid Zone Research, 2022, 39(4): 1234-1245. ]

Spatiotemporal Differentiation Pattern and Driving Factors of Common Prosperity Level in Ningxia County Areas

WANG Xin1, ZHENG Fang1,2, HE Lingyao1, HE Haoliang1, HOU Ying1,2

(1. College of Geography and Planning, Ningxia University, Yinchuan 750021, Ningxia, China; 2. Ningxia (China Arab) Key Laboratory of Resource Assessment and Environment Regulation in Arid Region, Yinchuan 750021, Ningxia, China)

Abstract: This study describes the gradual achievement of common prosperity as a goal and a path of Chinese-style modernization. Using panel data from Ningxia county areas from 2006 to 2022, we develop a comprehensive evaluation index system for assessing common prosperity at the county level. We employ exploratory spatial data analysis, the Thiel index, and baseline regression analysis to investigate the spatiotemporal differentiation patterns and driving factors affecting the common prosperity levels in Ningxia counties. The results show the following. (1) From 2006 to 2022, the level of common prosperity in Ningxia county areas generally exhibited an upward trend; however, the growth rates in northern, central, and southern regions of Ningxia varied significantly across different dimensions of the index. (2) According to the Thiel index, disparities in common prosperity development have regularly existed in Ningxia county areas, but the overall inequality has been gradually decreasing. Notably, the growth rate of the disparity between groups has far exceeded that of the disparity within groups. (3) The level of common prosperity in Ningxia county areas has transformed overall to a higher level, presenting a "patchy distribution" spatial pattern with significant clustering characteristics. (4) The progression toward common prosperity in Ningxia county areas has been relatively slow, driven positively by factors such as economic development, education levels, industrial structure, and infrastructure. By contrast, the urbanization process has shown a stage-inhibitory effect on the advancement of common prosperity in these areas.

Keywords: county areas; common prosperity; spatiotemporal pattern; driving factors; Ningxia