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Journal of Resources and Ecology >

2024 , Vol. 15 >Issue 4: 937 - 950

DOI: https://doi.org/10.5814/j.issn.1674-764x.2024.04.015

Resource Economy

The Imbalanced Pattern of Population and Economy and Its Influencing Factors in the Wumeng Mountain Area

  • CHEN Xuan ,
  • LI Xudong , *
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  • School of Geography and Environmental Science, Guizhou Normal University, Guiyang 550025, China
* LI Xudong, E-mail:

CHEN Xuan, E-mail:

Received date: 2023-09-11

  Accepted date: 2023-12-21

  Online published: 2024-07-25

Supported by

The National Natural Science Foundation Project(41261039)

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Abstract

Based on the population and economic data of the Wumeng Mountain Area from 2000 to 2020, this study explored the imbalanced spatiotemporal patterns of population and economy in the area using methods such as the geographic concentration, gravity model, imbalance index, and inconsistency index. The study also analyzed the influencing factors using geodetectors and spatiotemporal geographically weighted regression models. The results show four key aspects of this phenomenon. (1) The spatial distributions of the population and economic geographic concentrations deviate from their ideal distributions. The population distribution shows a spatial pattern of being higher in the northeast and lower in the southwest, while the economic distribution shows a spatial pattern of being higher in the south and lower in the north. (2) The population and economic gravity centers have shifted toward the northeast and south relative to the geometric center of the mountain area, respectively, and the economic center has shifted more than the population center. (3) The degree of imbalance between the population and economic distributions is decreasing, but regional development disparities still exist. The region with inconsistent spatial distributions between population and economy remains stable, showing a "north high, south low" pattern, with an increase in the number of counties dominated by the economy and reductions in the numbers of counties in other categories. (4) Economic power, social consumption level, industrial structure, urban development level, government regulation capacity, and health care infrastructure are the main factors affecting the inconsistent distributions of population and economy in Wumeng Mountain Area, and the effects of these factors are reflected in the promotion of economic development.

Key words： population; economy; imbalance pattern; Wumeng Mountain Area

Cite this article

CHEN Xuan , LI Xudong . The Imbalanced Pattern of Population and Economy and Its Influencing Factors in the Wumeng Mountain Area[J]. Journal of Resources and Ecology, 2024 , 15(4) : 937 -950 . DOI: 10.5814/j.issn.1674-764x.2024.04.015

1 Introduction

Population and economy are intricately linked, with both playing significant roles in social development. In the new era, with changes in regional development strategies and shifts in population policies, the acceleration of the urbanization and industrialization processes has led to a continuous reshaping of the distribution patterns of both population and economy. Studies on the relationship between the population and economic distributions has gradually become an important research direction in fields such as economics, demography, and sociology. Relevant studies can be traced back to the late 19th century, when the renowned British economist Thomas Robert Malthus proposed the famous Malthusian Population Theory, which suggests that rapid population growth has detrimental effects on economic development. More recently, the Italian economist Vilfredo Pareto discussed the relationship between population and economic growth in his work, The Mind and Society: A Treatise on General Sociology (Vifredo, 2016), French economist Esther Duflo focused on the relationship between population and poverty (Abhijit and Esther, 2019), and Abolade (2018), Tomoko (2020) and Chu et al. (2023) conducted research at the national level which suggested that there are regional disparities in population and economic development. Scholars both domestically and internationally have conducted abundant research on the relationship between population and economic distributions. In terms of research scale, this includes the provincial (Zhang and Cai, 2013), municipal (Wang et al., 2022a) and county-level (Yang et al., 2022) scales. In terms of research regions, recent studies encompass the more economically developed eastern and central regions (Li and Luo, 2017; Zhou and Tan, 2021; Wang, 2022a; Wang, 2022b;Wei et al., 2023). In terms of research content, this research encompasses the impacts of population decline (Li et al., 2023; Zhai and Jin, 2023), population aging (Pan and Chang, 2021), and population mobility (Dietz et al., 2023) on the economy, as well as the coordinated relationship between population and economy (Li et al., 2022; Wang et al., 2022b). From the perspective of research methods, with the flourishing development of geographic information systems (GIS) and remote sensing (RS) technologies, spatial statistical analysis and spatial representation have been widely applied in the study of population distribution patterns (Lu et al., 2019). Scholars have employed various methods, such as the gravity model (Huang and Zhao, 2016; Bawazir et al., 2020), Gini coefficient (Deng et al., 2022), spatial autocorrelation (Zhang et al., 2022), and hotspot analysis (Xing et al., 2022), to study the relationship between population and economic spatial patterns. In investigating the factors influencing the spatial relationship between the two, apart from a small number of scholars using multiple regression models (Yan et al., 2018) and geographic detector (Xiao et al., 2023) for quantitative research, most scholars have primarily engaged in qualitative analysis (Wang and Xu, 2020). In summary, scholars have conducted extensive research on the relationships between population and economics, as well as the factors influencing them, at different administrative scales. However, relatively less attention has been given to the long-term evolutionary characteristics of the imbalance in the population and economic patterns in natural geographic units. Moreover, there is a lack of research using quantitative methods to study the influencing factors in this context.
The terrain and landforms in the western mountainous regions of China are complex, and they have a relatively underdeveloped economies and prominent contradictions between the people and the land. Over time, the government has introduced relevant policies to strongly support the development of the western mountainous areas. In the context of promoting the consolidation of poverty alleviation efforts and effective rural integration, research on the evolution of population and economic patterns in the western mountainous regions through population and economic data is not only essential for gaining a clear understanding of the demographic and economic situations and grasping the new characteristics in population and economic development, but it also helps relevant departments to deeply identify the relevant social issues, thereby providing a reference basis for the formulation and implementation of rational and scientifically valid policies. Based on these considerations, this study comprehensively applies various quantitative analysis methods such as the imbalance index, geographical concentration, gravity model, and inconsistency index. It explores the dynamic evolution of the pattern of population and economic imbalance in the representative Wumeng Mountain Area of the western region from 2000 to 2020. Additionally, geographical detectors and spatiotemporal geographically weighted models are utilized to investigate the influencing factors. The aim is to provide a reference basis for the formulation of relevant policies to consolidate poverty alleviation efforts and achieve effective rural integration in the Wumeng Mountain Area, and even throughout the western mountainous regions.

2 Study area, research methods and data sources

2.1 Overview of the study area

The Wumeng Mountain in China constitute the largest mountain range in the eastern Yunnan Plateau, situated at the junction of Yunnan, Guizhou, and Sichuan provinces. Stretching in a northeast-southwest direction, this region is crisscrossed by numerous rivers, spanning both the Yangtze and Pearl River basins. Characterized by widespread karst rocky desertification, this area exhibits poor ecological system stability, and there are prominent conflicts between human activities and the environment. It was once among the 11 concentrated contiguous areas of extreme poverty in China. As a crucial ecological security barrier in the upper reaches of the Yangtze River and a vital channel opening to the southwest, this region holds a significant position in the ecological conservation and high-quality development in the western areas. It is one of the key areas in the new era where the nation has been consolidating and expanding the achievements of poverty alleviation, effectively connecting with rural revitalization. Focusing on this particular natural region and based on the boundary of mountainous natural features, this study takes into account the continuity of socioeconomic conditions and county-level administrative units. The research area is delineated to include a total of 50 counties, cities, and districts across seven prefecture-level cities in Yunnan, Guizhou, and Sichuan (Fig. 1). The total area is approximately 1.253×105 km2, with a population of 27.32 million and a gross domestic product (GDP) of 1.0054 trillion yuan in 2020.

2.2 Research methods

2.2.1 Measurement methods of the population and economic spatial distributions

(1) Geographic concentration
This method is typically used as an indicator for measuring the distribution of a particular industry or economic activity within a specific geographic area. It is commonly employed to analyze the trends and characteristics of economic development in a given region (Wu et al., 2022). This study quantitatively measured the population and economic agglomeration status through population geographic concentration and economic geographic concentration. This approach accounts for factors such as total population, total economic output, and administrative area. The calculation formulas are as follows:
${{R}_{{{P}_{it}}}}=\frac{{{P}_{it}}/{{P}_{t}}}{{{S}_{it}}/{{S}_{t}}},\ {{R}_{{{G}_{it}}}}=\frac{{{G}_{it}}/{{G}_{t}}}{{{S}_{it}}/{{S}_{t}}}$
In Equation (1), ${{R}_{{{P}_{it}}}}$ and ${{R}_{{{G}_{it}}}}$ represent the population geographic concentration and economic geographic concentration of region i in year t, respectively. Pit denotes the end-of-year resident population of region i in year t. Git signifies the regional gross domestic product (GDP) of region i in year t. Sit stands for the land area of region i in year t. Pt denotes the end-of-year resident population of all regions in year t. Gt signifies the regional gross domestic product (GDP) of all regions in year t. St stands for the land area of all regions in year t.
(2) Centroid model
This model reflects the local distribution pattern and the evolutionary trends of the population and the economy through the relationship between the geometric center and the population centroid, as well as the economic centroid (Liang et al., 2021). This study takes the counties within the Wumeng Mountain Area as the research units, using the population figures and total economic output for each period as centroid indicators. The calculation formulas are as follows:
$X=\frac{\sum\limits_{i=1}^{n}{{{M}_{i}}}{{X}_{i}}}{\sum\limits_{i=1}^{n}{{{M}_{i}}}},\ Y=\frac{\sum\limits_{i=1}^{n}{{{M}_{i}}}{{Y}_{i}}}{\sum\limits_{i=1}^{n}{{{M}_{i}}}}$
In Equation (2), X and Y represent the longitudes and latitudes of the population and economic centroids of the Wumeng Mountain Area, respectively. Xi and Yi are the longitude and latitude of the geometric center of each county. Mi represents the attribute value of the spatial phenomenon. n is the total number of counties in the Wumeng Mountain Area.

2.2.2 Methods for measuring the imbalance pattern between population and economy

(1) The imbalance index
This method is used to measure the disparities in various aspects, such as economy, society, and population, among different regions within a certain area. It helps in analyzing the phenomenon of imbalanced development within a specific region. In this study, this index was used to comprehensively analyze the temporal changes in the imbalance pattern between population and economy in the Wumeng Mountain Area (Peng and Cao, 2022). The calculation formula is as follows:
$E=\sqrt{\frac{\underset{i=1}{\overset{n}{\mathop \sum }}\,\left[ \frac{\sqrt{2}}{2}{{({{W}_{i}}-{{Z}_{i}})}^{2}} \right]}{n}}$
In Equation (3), Wi represents the proportion of the total GDP of region I; Zi represents the proportion of the total population of region I; E represents the imbalance index. A larger value of E indicates a more uneven spatial distribution between population and economy in the Wumeng Mountain Area, and vice versa.
(2) Inconsistency index
To provide a localized perspective on the degree of coordination in the spatial distributions of the population and the economy within the research area and drawing upon the published literature, the inconsistency index was introduced. This was done to accurately measure the extent of imbalance between population and economy at the local level (Yan et al., 2019). The calculation formula is as follows:
${{W}_{it}}=\frac{{{P}_{it}}/{{P}_{t}}}{{{G}_{it}}/{{G}_{t}}}$
In Equation (4), Pit denotes the end-of-year resident population of region i in year t. Git signifies the regional gross domestic product (GDP) of region i in year t. Wit represents the inconsistency index of population and economy in region i in year t. Pt denotes the end-of-year resident population of all regions in year t. Gt signifies the regional gross domestic product (GDP) of all regions in year t. Wt represents the inconsistency index of population and economy in all regions in year t. The values of Wit can be categorized into five levels (Table 1).
Table 1 Inconsistency index classification
Inconsistency index range Inconsistency index type Meaning
Wit<0.5 Economic polarized type Economic agglomeration is much greater than population agglomeration
0.5≤Wit<1.0 Economic leading type Economic agglomeration is greater than population agglomeration
1.0≤Wit<1.5 Population and economic coordination type Economic agglomeration is roughly equal to population agglomeration
1.5≤Wit<2.0 Population leading type Population agglomeration is greater than economic agglomeration
2.0≤Wit Population polarized type Population agglomeration is much greater than economic agglomeration

2.2.3 Methods for measuring the factors affecting the pattern of imbalance between population and economy

(1) Indicator selection
New Economic Geography argues that the widening disparities in regional development stem from the self-reinforcing dynamics of factors such as population and capital mobility. For example, economic agglomeration leads to population concentration, although population changes exhibit a certain lag effect compared to other economic elements. This leads to disparities between the population and economic distributions (Li et al., 2016). Based on this theory, this study analyzed the impacts of differences in factor mobility on the pattern of imbalance between population and economy from the perspective of resource allocation. Market mechanisms and government regulation are two dominant forces in resource allocation. The market, through pricing and competitive mechanisms, drives factors of production to continuously aggregate in developed areas with higher capital returns. However, allowing this development to proceed unchecked would lead to the widening of regional disparities, resulting in the “Matthew effect” (Merton, 1968), which is detrimental to sustainable regional development. Therefore, the government, acting as a “visible hand,” intervenes in factor mobility through policy adjustments and differentiated investments to promote balanced regional development.
Taking into consideration the existing research and data availability, this study used the inconsistency index between population and economic distribution as the dependent variable. Human capital, reflecting market factors, was selected as an independent variable. The proportion of the employed population to the total population was chosen to reflect the regional differences in employment development levels. Government intervention variables were represented by medical infrastructure and government regulatory capacity. The number of hospital beds per thousand people was chosen to indicate the disparity in medical infrastructure services at the county level. Per capita local government expenditure was used to reflect the government’s regulatory capacity over counties. Furthermore, considering that urban development directly contributes to regional disparities, we introduced per capita GDP to represent regional economic power. The per capita retail sales of consumer goods was used to indicate the level of social consumption. The proportion of the secondary and tertiary industries represents the industrial structure, while the urbanization rate reflects differences in urban development levels. These factors were incorporated to study the influence of economic development disparities among counties on the pattern of imbalance between population and economy.
(2) Geographic Detector
Geographic Detector is a method based on spatial statistics and geographical analysis, and it is used to investigate the relationships between geographic phenomena, spatial patterns, and influencing factors. Its aim is to identify and quantify the spatial heterogeneity of geographic phenomena and the extent of influence of the different factors. It has found wide application in fields such as geography, environmental science, and urban planning. In this study, Factor Detector was employed to identify the explanatory powers of the different influencing factors on the inconsistencies between the population and economic distributions in the Wumeng Mountain Area. The measurement was conducted using the q-value (Wang and Xu, 2017), calculated as follows:
$q=1-\frac{1}{n{{\sigma }^{2}}}\sum\limits_{h=1}^{L}{{{n}_{h}}}\sigma _{h}^{2}$
In Equation (5), h=1,2,…, L represents the stratification of factors or variables. nh and n represent the totals for the h-th layer and the entire region, respectively. $\sigma _{n}^{2}$ and $\sigma _{{}}^{2}$ represent the sample variances for the h-th layer and the entire region, respectively. The q-value represents the degree to which the influencing factors explain the inconsistency between the population and economic distributions in the study area. The range for the q-value is [0,1]. A value closer to 1 indicates that the factor has a greater impact on the inconsistency between the population and economic distributions. Conversely, a value closer to 0 suggests that the factor has a smaller impact.
(3) Spatiotemporal geographically weighted model
Although traditional geographically weighted regression models can investigate the impacts of various factors on different regions from a local perspective, they fail to capture changes over time. The spatiotemporal geographically weighted model is an extension of the geographically weighted regression method that is used for analyzing spatiotemporal data. Its core lies in incorporating time factors into the spatial geographically weighted regression model. By integrating the characteristics of spatiotemporal data, this model accounts for the spatial heterogeneity and temporal variability of influencing factors across both geographical and temporal dimensions. This enhancement leads to an improved accuracy of the model, making it well suited for analyzing the factors influencing the inconsistency between the population and economic distributions. This study, based on ArcGIS software, utilized the Huang model (Huang et al., 2010) to construct the following formula:
${{U}_{i}}={{\beta }_{0}}({{X}_{i}},{{Y}_{i}},{{T}_{i}})+\sum\limits_{k=1}^{N}{{{\beta }_{k}}}({{X}_{i}},{{Y}_{i}},{{T}_{i}}){{X}_{ik}}+{{\delta }_{i}}$
In Equation (6), Ui represents the dependent variable of county i, and Xi represents the independent variable of county i. Yi represents the longitude coordinate of county i, and Ti represents the time coordinate of county i. N represents the number of explanatory variables. (Xi, Yi, Ti) represents the spatiotemporal dimension coordinates of county i, β0(Xi, Yi, Ti) represents the regression constant for county i, which is the constant term in this model, βk(Xi, Yi, Ti) represents the regression coefficient of the k-th explanatory variable in county i, Xik represents the k-th independent variable of county i, and ${{\delta }_{i}}$ represents the model residuals.

2.3 Data sources

The main sources of data for this study were the “Yunnan Statistical Yearbook (2001-2021)”, “Guizhou Statistical Yearbook (2001-2021)”, “Sichuan Statistical Yearbook (2001-2021)”, and “China County Statistical Yearbook (2001-2021)”. The population data for the years 2000, 2010, and 2020 were derived from the Fifth, Sixth, and Seventh National Population Census data. The vector data for the counties in the Wumeng Mountain Area were obtained by cropping the “Vector Map of Administrative Divisions of China”.

3 The evolution of imbalanced patterns between population and economy, and its influencing factors in the Wumeng Mountain Area

3.1 Spatiotemporal evolution of population and economic patterns

The population geographical concentration and economic geographical concentration for each county in the Wumeng Mountain Area from 2000 to 2020 were calculated according to Formula (1). Then, using the natural breakpoint method, the population and economic geographical concentrations were classified into five levels.

3.1.1 Spatiotemporal evolution of the population geographic concentration patterns

From the perspective of population geographical concentration spatial patterns, there is an overall pattern of “higher in the northeast and lower in the southwest” (Fig. 2). The areas in the northeastern part of the mountainous region within the Yangtze River Basin generally exhibit a higher population geographical concentration, indicating greater population density. In the southern part of the mountainous region, within the Pearl River Basin, the population geographical concentration is moderate, indicating an intermediate level of population density. In the western part of the mountainous region, within the Yalong River Basin, the population geographical concentration is relatively lower, indicating a sparser population. From the perspective of county-level differences, the central urban areas of various cities and prefectures, such as Zhongshan District, Qixingguan District, Zhaoyang District, and Qilin District, exhibit the highest population geographical concentrations. These areas have a strong population attraction. In the western part of the mountainous region, especially in Wuding and Luquan counties, the population geographical concentrations are the lowest. This is due to the harsh local geographical conditions that are unfavorable for human habitation.
Fig. 2 Spatial evolution of the population geographic concentration in the Wumeng Mountain Area
From a temporal perspective, the population geographical concentration in Zhongshan District increased from 3.885 in 2000 to 6.399 in 2020. On the other hand, in Wuding County, which had the lowest concentration, it decreased from 0.405 in 2000 to 0.372 in 2020. This indicates that there are significant differences in the population agglomeration dynamics among the different counties and districts. The trends of population agglomeration patterns in each county and district can be categorized into two types. The first type is the trend of population agglomeration, and the population geographical concentration increased in 18 counties and districts. This is primarily observed in the urban districts and surrounding areas of various cities and prefectures. The second type is the trend of population outflow, and the population geographical concentration decreased in 32 counties and districts. This indicates that with the development of socioeconomic conditions, the urbanization process was accelerating, and there was a noticeable trend of population outflow from the mountainous areas.

3.1.2 Spatiotemporal evolution of economic geographic concentration patterns

From the perspective of economic geographical concentration spatial patterns, economic agglomeration in the Wumeng Mountain Area is primarily centered around the central urban areas, with water systems acting as natural boundaries. The economic geographical concentration is generally higher in the southern part of the mountainous region within the Pearl River Basin. Next is the northeastern part of the mountainous region within the Yangtze River Basin, where the economic geographical concentration is at an intermediate level. Last, in the western part of the mountainous region, within the Yalong River Basin, the economic geographical concentration is relatively lower (Fig. 3).
Fig. 3 The spatial evolution of economic concentration in the Wumeng Mountain Area
From a temporal perspective, Zhongshan District is the area with the highest economic geographical concentration in the Wumeng Mountain Area. However, in contrast to the trend of increasing population geographical concentration, the economic geographical concentration in Zhongshan District has gradually decreased over time. Specifically, the economic geographical concentration in Zhongshan District was 11.475 in 2000, which then decreased to 7.980 in 2020. On the other hand, in Ziyun County, which had the lowest economic geographical concentration, it increased from 0.327 in 2000 to 0.440 in 2020. Based on the economic geographical concentration results, the economic evolutionary trends in the Wumeng Mountain Area region can be categorized into two types. The first type is economic diffusion. The economic concentrations in 19 counties and districts showed decreasing trends. Among them, Zhongshan, Qilin, Zhaoyang, and other regional central urban areas experienced a weakening polarization effect and an enhanced diffusion effect after reaching a certain level of economic development. This led to a decline in economic geographical concentration. In contrast, due to constraints such as the natural environment and factor mobility, other high- county areas such as Huize and Luquan, had poor economic development, resulting in a decrease in their economic geographical concentration. The second type is economic agglomeration, and the economic geographical concentrations of 31 counties and districts showed an increasing trend. This type of region is mainly located on the periphery of the central urban area, benefiting from their advantageous geographical positions. Leveraging the diffusion effect of the central urban area drove economic growth, thereby increasing the economic geographical concentration. This, in turn, contributed to the reduction of regional economic development disparities and altered the overall pattern.

3.2 The evolution of the population and economic centers

According to the gravity model calculation, the geometric centroid, population centroid, and economic centroid of the Wumeng Mountain Area, as well as their migration process, were obtained (Fig. 4). Note that the population centroid and economic centroid are both far from the geometric centroid (104°52′62″E, 26°38′14″N). The population center is located northeast of the geometric center, while the economic center is located directly south of the geometric center. This phenomenon has occurred because areas such as Qixingguan and Zhenxiong in the northeastern part of the mountainous region belong to the provincial border area, with frequent population movements. Additionally, the large historical population base has attracted a significant number of ordinary laborers from the surrounding regions, leading to population concentration. In the southern part of the mountainous area, central cities such as Qilin, Panzhou, and Xingren have achieved high levels of development in terms of socioeconomics. In particular, the secondary and tertiary industries are thriving. These cities have also attracted other developmental factors from the surrounding regions, further driving economic aggregation in the area. Therefore, it was inevitable for both the population and economic centers in the mountainous region to deviate from the geometric center of the basin.
Fig. 4 The migrations of the population centroid and the economic centroid in the Wumeng Mountain Area from 2000 to 2020
In terms of the migration distance of the population center, it shifted 4.65 km in the northwest direction from 2000 to 2009. From 2009 to 2010, it moved 3.60 km in the southwest direction. From 2010 to 2012, it shifted 5.84 km in the northeast direction. In the fluctuation from 2012 to 2020, it moved 7.37 km in the southwest direction. Overall, the population center has moved southward. The economic center has also undergone significant shifts. It migrated southward by 7.39 km from 2000 to 2006, shifted northeast by 18.91 km from 2006 to 2018, and rapidly moved northwest by 12.64 km from 2018 to 2020. The deviations between the population and economic centers indicate spatial imbalance, while the differences in dynamic evolutionary trends reflect varying degrees of promotion or restraint that have been influenced by multiple factors. Due to various constraints in the Wumeng Mountain Area, such as obstacles posed by the household registration system, employment disparities, and different living environments, the movements of the population center were relatively small. Meanwhile, the economic and social development in the mountainous areas was rapid, with the emergence of tertiary industry, gradually breaking free from traditional production methods and overcoming challenges such as inadequate transportation and an imperfect market. As a result, the shift in the economic center has been greater than the shift in the population center.

3.3 Temporal and spatial evolution of the population and economic disparities

3.3.1 Temporal changes

By utilizing county-level data, we calculated the population and economic imbalance indices for the Wumeng Mountain Area. This information can reflect the spatial evolution of the population and economic balance in the study area. As shown in Fig. 5, the overall imbalance index shows a decreasing trend from 2000 to 2020. This indicates that against the backdrop of rapid urbanization and the new type of industrialization in China, along with policies such as the Western Development and Poverty Alleviation Campaign, the spatial patterns of population and economy in the Wumeng Mountain Area are gradually moving toward a more balanced state. During the period from 2000 to 2005, the imbalance index declined from 0.0135 to 0.0126. This indicates that with the implementation of the Western Development Strategy and the targeted poverty alleviation efforts in the early 21st century, the counties and districts have significantly increased their attractiveness to the population during this period. This, in turn, has boosted the alignment between the population distribution and economic spatial patterns. The imbalance index showed a significant increase from 2005 to 2006, indicating a reduction in the level of alignment between the population and economic spatial distributions during that period. From 2006 to 2011, the imbalance index continued to decline from 0.0135 to 0.0116. During this period, the implementation of the “Volunteer Service in the West of China” program for college students attracted young talent, further improving the alignment between the population and economic spatial distributions. From 2011 to 2020, the imbalance index gradually decreased from 0.0123 to 0.0108, which can be attributed to progress in the poverty alleviation efforts during this period. As one of the 11 major areas targeted for poverty alleviation nationwide, the Wumeng Mountain Area has witnessed concurrent economic development and population aggregation across its counties and districts. The disparity in the levels of economic and population aggregation has gradually diminished, leading to an increasing alignment between regional development and demographic patterns. Examining the trend lines, the imbalance index between population and economy in the Wumeng Mountain Area shows a declining trend (Fig. 5), indicating a reduction in the imbalance. How- ever, some regional development disparities still persist.
Fig. 5 Index of population and economic imbalance from 2000 to 2020

3.3.2 Spatial evolution

The inconsistency index characterizes the coordinated relationship between the local spatial distributions of population and economy, and is used to study the spatial pattern of the inconsistency index between population and economy in the Wumeng Mountain Area. As shown in Fig. 6, there is a notable spatial disparity in the inconsistency index values throughout the Wumeng Mountain Area, displaying a pattern of higher inconsistency in the north and lower inconsistency in the south, with the Pearl River system acting as the dividing line. North of the rivers, the inconsistency index is high, indicating that the economic aggregation capability in the southern part of the mountainous region is significantly higher than the population aggregation capability. In contrast, although the northern part of the mountainous region has a higher total population, it experiences relatively lagging economic development, leading to a generally higher inconsistency index in this area.
Fig. 6 Spatial evolution of the population and economic imbalance patterns in the Wumeng Mountain Area from 2000 to 2020
In terms of spatial evolution, the counties and districts with an economic polarization pattern are mainly concentrated in a few areas, such as Qilin and Zhongshan. These areas exhibit higher levels of both population and economic concentration compared to other counties and districts, although the values of the former are significantly lower than the latter. The counties and districts with an economic dominance pattern have the broadest distribution, with an increasing number and area. They are mainly located in the southern and peripheral areas of the mountainous region. The counties and districts with balanced development between population and economy show an increasing trend in quantity initially followed by a decrease, and they are mainly distributed in the southern part of the Yalong River Basin and the eastern part of the Yangtze River Basin. The number of counties and districts with a population-dominant pattern decreased initially and then increased, indicating a transition from a dispersed distribution to a concentrated distribution in the northern part of the mountainous region. The counties and districts with a population polarization pattern saw an initial increase in both quantity and area, followed by a decrease. In 2020, they were sparsely distributed, with only Zhenxiong and Yanjin in the northern part of the mountainous region having sporadic occurrences.
Examining the reasons behind the declining trend in the inconsistency index, the counties and districts experiencing this trend primarily include Luquan, Dongchuan, Xingren, and Yongshan. These areas are in close proximity to the economic core zone. The economic radiation and diffusion effects from the core zone contributed to an elevation in their economic levels, but they did not attract concurrent population aggregation. On the other hand, there are two categories of counties and districts exhibiting an increasing trend in the inconsistency index. The first category includes counties such as Zhaoyang and Zhongshan, where economic development has expanded outward, and the continuous population influx has narrowed the difference between the population and economic aggregation levels. The second category comprises counties such as Weining and Daguang, where both population and economy have been developing, but the increase in population aggregation was more pronounced relative to the economic aggregation level.

3.4 Factors influencing the pattern of population and economy imbalance in the Wumeng Mountain Area

3.4.1 Overall analysis of influencing factors

The relevant variables were introduced into the geographical detector model. Using factor detection, the impacts of the various influencing factors on the imbalance pattern between population and economy from 2000 to 2020 were calculated. Finally, the influencing factors that passed the significance test at the 0.05 level and had q values > 0.1 were retained. The results (Table 2) indicate that over the past 20 years, the economic strength, social consumption level, industrial structure, urban development level, government regulatory capacity, and medical infrastructure in the Wumeng Mountain Area have significantly influenced the pattern of imbalance between population and economy. However, human capital does not show a statistically significant impact based on the tests conducted. The explanatory power of economic strength on the level of inconsistency between population and economy in the Wumeng Mountain Area is greater than 0.78. During the process of economic development, the population and economy grew simultaneously, promoting a reduction in the level of inconsistency between the population and economy. The social consumption level has high explanatory power, since the foundation of social consumption is the aggregation of population and economy. The increase in social consumption level is accompanied by better resource allocation, driving economic development and population influx, thereby reducing the level of inconsistency between population and economy. The explanatory power of government regulatory capacity is gradually increasing. In recent years, government support policies such as the Western Development Strategy and targeted poverty alleviation aimed at the research area have promoted the balanced development in both population and economy. The industrial structure provides a certain explanatory power for the inconsistency between population and economy in the Wumeng Mountain Area. The increase in the proportion of the secondary and tertiary industries has driven economic growth in the mountainous area, attracting external populations and reducing the imbalance between population and economy. The improvement of urban development levels will reduce the inconsistency between population and economy in the Wumeng Mountain Area. As the urbanization rate rises, it stimulates the simultaneous aggregation of the regional population and economy, promoting a more balanced development between the two. Medical infrastructure also has explanatory power for the inconsistency between population and economy in the Wumeng Mountain Area, so enhancing the construction of medical infrastructure and improving the residents’ health levels, will facilitate the sustainable development of both population and economy.
Table 2 Geographic detector results of the factors influencing the population and economic imbalance patterns
Influencing factors 2000 2010 2020
q-value P value q-value ranking q-value P value q-value ranking q-value P value q-value ranking
Economic strength 0.828 <0.001 1 0.783 <0.001 1 0.808 <0.001 1
Social consumption level 0.480 <0.001 3 0.543 <0.001 3 0.388 0.002 2
Government regulatory capacity 0.177 0.042 6 0.286 0.007 6 0.314 0.044 5
Industrial structure 0.665 <0.001 2 0.493 0.001 4 0.260 0.084 6
Urban development level 0.378 0.008 4 0.601 <0.001 2 0.331 0.014 3
Human capital - 0.114 - - 0.542 - - 0.215 -
Healthcare infrastructure 0.314 0.034 5 0.430 0.003 5 0.322 0.019 4

3.4.2 Spatial heterogeneity analysis of influencing factors

The Geodetector model provides a global perspective on the impacts of the various factors, but it may not reveal the spatiotemporal heterogeneity of these influencing factors. Using GeoDa software to calculate the global Moran’s I coefficient for the population and economic inconsistency index in the Wumeng Mountain Area, the Moran’s I index values for the inconsistency index were found to be greater than 0 for all years between 2000 and 2020. Additionally, the normality statistic Z value, which passed the 99% significance level test, confirmed that the inconsistency index exhibits significant positive spatial autocorrelation. To delve deeper into the spatial heterogeneities of the various influencing factors on the inconsistency index of each county, it was necessary to employ a spatiotemporal geographically weighted model for this investigation. First, a collinearity diagnosis was conducted on the explanatory variables selected through the geographic detector model. All variance inflation factors (VIF) were below 10, ensuring that the model was set up appropriately. Second, the remaining six factors were subjected to GTWR spatial modeling using the fixed distance method. The AIC method was employed for computation. Additionally, the model’s goodness of fit (R2) and adjusted R2 were both greater than 0.88, indicating the superiority of the results in this study. Finally, the GTWR model regression coefficient means were visualized and analyzed using the natural breaks method (Fig. 7), which clearly illustrated the differential effects of each factor on the local spatial level.
Fig. 7 The spatial patterns of the mean values of the spatial-temporal geographically weighted regression coefficients of the six factors
(1) In the Wumeng Mountain Area, the economic strength of all counties and districts has a negative impact on the inconsistency between the population and economic distributions (Fig. 7a). This indicates that the influence of county-level economic strength is more pronounced in driving economic development rather than contributing to the inconsistency between the population and economic distributions. The mean value of the economic strength regression coefficient is -2.440, and its impact spatially diminishes progressively from northeast to southwest. This inhibitory effect is most pronounced in the northeastern counties and districts such as Gulin, Xuyong, and Jinsha. The reason behind this is that these areas had a relatively strong existing economic foundation, with a relatively concentrated population. As a result, the economic development in these counties and districts was more likely to attract population inflow, leading to the observed inhibitory effect.
(2) In the majority of counties and districts in the Wumeng Mountain Area, the social consumption level has a negative impact on the inconsistency between the population and economic distributions (Fig. 7b). This suggests that the county-level social consumption level has a greater influence on attracting the population rather than contributing to the inconsistency between the population and economic distributions. The average regression coefficient for the level of social consumption is -0.026, indicating significant spatial heterogeneity. In counties such as Zhanyi, Malong, and Qilin, which are located in the south and southwest of the mountainous region, the level of social consumption has a positive impact on the disparity between the population and economic distributions. This is mainly because these counties have a relatively sound material foundation, thereby attracting population migration from areas with higher consumption capacity in pursuit of a better quality of life and broader consumption options. However, due to constraints such as low household income levels and insufficient purchasing power, regional consumption and economic development have not progressed synchronously. In counties such as Suijiang, Yongshan, and Xingwen, which are located in the north and northeast of the mountainous region, the social consumption level exhibits a more pronounced driving effect on the economy. This is primarily attributed to the well-matched local economic development and residents’ consumption demands, resulting in notable economic benefits derived from the residents’ consumption patterns.
(3) In all counties and districts of the Wumeng Mountain Area, the industrial structure has a negative impact on the inconsistency between the population and economic distributions (Fig. 7c). This suggests that the influence of industrial structure is more significant in promoting economic development than in driving population aggregation. The average regression coefficient for industrial structure is -0.374. Among the regression coefficients of the various counties, the industrial structure has the most negative impact on central economic hub counties in the middle of the mountainous areas, such as Weining, Xuanwei, Zhongshan and some western counties such as Zhaoyang and Ludian. These counties have well-developed foundations in their secondary and tertiary industries, as well as relatively complete transportation and public infrastructure. These features attract investments in nonagricultural and even high value-added industries, promoting faster economic growth. This, in turn, alleviates the degree of mismatch between the population and economic distributions.
(4) The urban development level has a negative impact on the mismatch between the population and economic distributions in most counties in the Wumeng Mountain Area (Fig. 7d). This indicates that the influence of the urban development level is more evident in economic development. This effect is most pronounced in counties located in the southern part of the mountainous region, such as Luliang, Shizong, and Luoping. These counties have low levels of urbanization, which hinders their ability to effectively promote regional industrial and economic concentration. As a result, the pace of economic development has been slow and market value remains low, leading to a lag in the regional economy. This has caused the degree of economic concentration to be much lower than the population concentration. However, the level of urban development has a positive impact on population concentration in counties such as Yiliang, Zhaoyang, and Suijiang in the northwestern part of the mountainous region. These counties have benefited from their geographic proximity and lenient household registration system, which has attracted rural populations to gather in the urban areas. As a result, an increase in urban development in these areas has led to higher levels of population concentration, causing a rise in the level of inconsistency between the population and economic distributions.
(5) In most counties of the Wumeng Mountain Area, the government regulatory capacity negatively affects the level of inconsistency between the population and economic distributions (Fig. 7e). This suggests that the impact of government regulatory capacity is mainly reflected in its ability to attract economic agglomeration. The average regression coefficient for government regulatory capacity is -0.476, and the impact gradually weakens from the southwest to the northeast. Counties in the southwest of the mountainous areas, such as Xundian, Luoping, and Wuding, exhibit a significant inhibitory effect of government regulatory capacity on the inconsistency index. This is because the financial focus of the government is inclined toward these counties, and substantial funds have been invested by the government in local economic development, thereby driving regional economic growth. This contribution to economic development has been significantly higher than the contribution to population agglomeration. However, the improvement of government regulatory capacity had a more pronounced effect on driving population agglomeration in counties in the northeast of the mountainous areas, such as Qixingguan, Gulin, and Xingwen. These counties have complex natural geographical environments and lack the radiating effect of central cities, so they rely heavily on government regulatory mechanisms. This passive economic development approach has made it difficult to achieve a state of coordinated development between population agglomeration and economic agglomeration.
(6) In most counties and districts of the Wumeng Mountain Area, healthcare infrastructure has a negative impact on the inconsistency between the population and economic distributions (Fig. 7f). This indicates that the impact of healthcare infrastructure construction on the inconsistency level is primarily reflected in attracting the aggregation of economic activities rather than driving population aggregation. Counties in the western part, such as Qiaojia, Dongchuan, and Huize, have a stronger impact of healthcare infrastructure on economic development compared to their effect on attracting population. However, in the “Yanjin-Shizong” line of counties, although the direction of the impact of healthcare infrastructure on the disparity index aligns with the former, the effect is much lower. This indicates that the counties along this line have smaller and less efficiently utilized health care infrastructure, resulting in a lower economic spillover effect compared to the western counties. The relatively comprehensive healthcare infrastructure construction in the western mountainous counties has led to a more pronounced economic agglomeration effect. Counties where healthcare infrastructure has a positive impact on the disparity in the population and economic distributions are mainly concentrated in the eastern mountainous regions. In these counties, healthcare infrastructure primarily serves to improve living conditions, making them more attractive for population influx. more attractive for population influx.

4 Conclusions and recommendations

4.1 Conclusions

In the context of changing development environments and shifts in regional development patterns, this study examined the evolution of the imbalance of population and economic distributions in the Wumeng Mountain Area from 2000 to 2020. Through a quantitative analysis of influencing factors, four main conclusions are drawn. 1) There are spatial variations in the geographic concentrations of both population and economics in the Wumeng Mountain Area. The population geographic concentration exhibits a spatial pattern of “higher in the northeast and lower in the southwest”, while the economic geographic concentration exhibits a spatial pattern of “higher in the south and lower in the north”. Overall, from 2000 to 2020, the fundamental patterns of population and economic aggregation in the Wumeng Mountain Area did not undergo significant changes. 2) Both the population center and economic center shifted toward the northeastern and southern directions from the geometric center of the mountainous area. Additionally, the magnitude of economic center displacement was greater than that of the population center. 3) The degree of population and economic imbalance showed a general decreasing trend. This indicates that the spatial patterns of population and economy in the Wumeng Mountain Area are gradually moving toward a more balanced state. However, regional disparities in development still exist. The spatial distribution of the disparity index between the population and economic distributions in the study area shows significant differences, following a pattern of “higher values in the north and lower values in the south”. 4) The overall impact analysis of various factors showed that economic strength, social consumption level, industrial structure, urban development level, government regulatory capacity, and health care infrastructure are the main factors influencing the inconsistency in the distributions of population and economy in the Wumeng Mountain Area. In comparison, human capital has a relatively smaller explanatory power. The spatial heterogeneity study on the influences of various factors reveals that all six factors primarily contribute to driving economic development. Among them, economic strength and industrial structure have impacts on all counties and districts in the area. Compared to the other influencing factors, the spatial variations in the impact of the social consumption level and medical infrastructure are the most pronounced.

4.2 Recommendations

(1) Leverage the diffusion effect of regional central cities
Areas with an inconsistency index greater than 1 are primarily located in the southern part of the mountainous region and the administrative districts of prefecture-level cities, and they should harness their geographical advantages and resource strengths. These counties and districts should develop into economic growth centers, fostering not only their own economic development but also driving the coordinated development of the surrounding areas. This can be achieved by providing educational and training resources, improving the quality and skill levels of the population, creating employment opportunities, and thereby attracting greater population concentration in conjunction with economic growth. The counties and districts with inconsistency index values exceeding 1.5 exhibit economic concentration levels that are lower than their population concentration, and these areas are located primarily in the northern part of the mountainous region. Due to constraints imposed by the natural environment and the slow mobility of production factors, the government can encourage enterprises in these areas to undergo industrial transformation and upgrading. This effort can enhance their competitiveness and profitability. Additionally, investing in infrastructure such as roads, bridges, railways, airports, power, and communication facilities can be increased to promote economic activities within the region, ensuring that economic concentration aligns synchronously with population concentration.
(2) Seize development opportunities
Efforts should be made to combine poverty alleviation with rural revitalization, cultivate advantageous industries, and enhance economic strength. The Wumeng Mountain Area faces relative weaknesses in endogenous development capacity, with insufficient local development factors such as talent, capital, and technology. It is crucial to leverage the policy dividends resulting from the national emphasis on poverty alleviation and rural revitalization in the region. Adhering to the principle of adapting strategies to local conditions, there is a need to foster and develop industries with competitive advantages while preserving the local ecology. Extending the agricultural industry chain and value chain, promoting the integrated development of the primary, secondary, and tertiary industries, and effectively enhancing the comprehensive benefits of industrial development can be achieved. By strengthening economic capabilities through industrial development, the region can leverage its economic functions to facilitate population aggregation.
(3) Harness government leadership
Interregional government collaboration should be strengthened to achieve integrated resource formation for comparative development advantages. The Wumeng Mountain Area is situated at the junction of three provinces, falling under different administrative units. Despite this, interregional collaboration under government guidance is essential for formulating regional development plans. Clear development directions should be outlined, seeking national policy and financial support. By optimizing and complementing resources, projects, funds, technology, talent, and infrastructure, cooperation can be promoted. Collaboration can particularly focus on developing strengths in sectors such as distinctive tourism and specialty agriculture, fostering mutual benefits and a gradual advancement toward spatial balance between population and economy in the mountainous region.

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