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Article

Dynamics between Population Growth and Construction Land Expansion: Evidence from the Yangtze River Economic Belt of China

by
Yuzhu Zang
1,
Junjun Zhu
2,
Xu Han
2 and
Ligang Lv
2,*
1
School of Public Administration, China University of Geosciences, Wuhan 430074, China
2
School of Public Administration, Nanjing University of Finance and Economics, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Land 2023, 12(7), 1288; https://doi.org/10.3390/land12071288
Submission received: 12 May 2023 / Revised: 16 June 2023 / Accepted: 23 June 2023 / Published: 26 June 2023
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Abstract

:
Population growth and construction land expansion’s link to sustainable development has gained attention. This study investigated the urban–rural divergence in the population–construction land relationship in China’s Yangtze River Economic Belt (YREB) from 2000 to 2020 using census and land-use data. This study utilized an integrated urban–rural framework to discuss reasons for the disparity. The findings suggested the following: (1) A spatial mismatch formed between population distribution and construction land allocation in the YREB from 2000 to 2020. The mismatch gap in rural areas was larger than in urban areas. (2) The urban areas maintained double growth rates in the population and construction land, while rural areas experienced constant population loss accompanied by construction land expansion. (3) An expansive negative decoupling relationship dominated the urban population–land system, while a strong negative decoupling relationship dominated the rural population–land system. (4) Institutional factors, such as land financialization and urban–rural dualism, were major triggers for the mismatch between population and construction land. Policy responses such as a new type of urbanization and rural revitalization strategies can shape the population–land relationship’s evolution. Our comparative analysis of urban and rural areas highlights the population–land relationship’s complexity, promoting sustainable land-use planning in urban–rural spaces.

1. Introduction

Land use and cover change (LUCC) is a critical force that alters the surface of our planet. In this context, the expansion of construction land1 has raised concerns because it has a substantial impact on the environment [1,2]. Population growth is considered a crucial factor in the expansion of construction land [3,4]. Additionally, the form and development of construction land also influence the spatial distribution and features of the population [5]. Therefore, understanding the nexus between population growth and construction land expansion is essential for land-use planning and the sustainable development of human settlements [6,7].
The development of remote sensing technology and widespread census statistics provide useful instruments for exploring the relationship between population and construction land. Well-designed, open, and free Earth observation data are now available to map the spatial patterns and temporal changes in population–land relationships [8]. Indicators have been developed to characterize the dynamics between population growth and construction land expansion based on multi-epoch and multi-source data. Examples of these indicators include urban population density (e.g., Xu et al. [9]), the coupling index (e.g., Zhao et al. [10]), and land-use efficiency (e.g., Koroso et al. [11]). Global comparative analyses have identified spatial heterogeneity in urban land expansion trends. As reported in previous studies, the largest urban land expansion occurred in Europe and North America from 1970 to 2000, while the highest urban land expansion rates have been observed in China, India, and Africa [12,13].
The rapid urban land expansion in China has received much attention from academia. Many studies have aimed to identify urban sprawl features (e.g., Feng et al. [14]; Liu et al. [15]), measure urban land-use efficiency (e.g., Zhou et al. [16]), and examine the potential correlations between urban land expansion and population concentration (e.g., Li et al. [17]). This substantial research has updated our perceptions of urbanization and uncovered a dispersed urbanization mode in China. Low land-use efficiency and declining population density have been identified as common features of rising large cities [18]. Furthermore, inefficient land use has been observed not only in cities but also in rural areas [19,20]. The arbitrary expansion of rural construction land and the constant loss of rural population present another challenge to sustainable land use in China [21].
The uncoordinated population–land relationship was presented in the table of rural sustainability in China in the beginning of the 21st century [22]. Scholars found that many villages in the agricultural areas of China were confronted with the confusing paradox in which people migrated to cities for work, while rural construction land did not cease to expand. This special phenomenon is referred to as “rural hollowing” [23]. Researchers defined rural hollowing in three dimensions, namely, population, land, and industry [24]. A typical hollowing village is generally featured by a declining population, industrial depression, and the abandonment of rural houses [25]. Urban-biased development policy, revenue disparity between agricultural and industrial products, and rural migration were identified as the main triggers for rural hollowing [26,27]. The subsequent consequences of rural hollowing challenged the sustainable development of rural areas because they deprived the economic vitality of the rural department, shrunk the number of public services in rural communities, and impaired the well-being of rural residents [28]. As the most significant feature of rural hollowing, the abandonment of rural houses has attracted much attention from academia [29]. Paralleling house abandonment, rural construction land was expanded to build new houses because rural migrants found it hard to settle in cities and hoped to return home once they lost their urban jobs [23]. These discussions on rural hollowing have shifted academic attention from urban built-up land expansion to rural construction land sprawl. Multiple models have been applied to examine the coupling relationships between rural population growth and construction land expansion as well as the potential driving mechanism of this phenomenon [30,31,32].
Scholars have devoted great effort to analyzing the changing population–land relationship with the pursuit of sustainability in human settlements, providing notable implications for building a sustainable human habitat. However, most existing studies have been conducted separately in urban and rural areas. Hence, comprehensive research into urban–rural spaces remains scant. Land conversion and population migration are driven by urban–rural interactions, and a comparative analysis between urban and rural areas is necessary to understand the holistic features of the population–land relationship. Moreover, policymakers need to determine the urban–rural divergences in population growth and construction land expansion to balance the land demands between urban and rural areas. The current study aims to fill this gap by conducting an evidence–based investigation to reveal the urban–rural divergences in the population–land relationship and delve into the roots of this relationship with an integrated urban–rural framework.
The remainder of this paper is organized as follows: Section 2 introduces the study area, data collection, and methods. Section 3 describes the spatial distribution and temporal changes in population and construction land and analyzes their coupling relationships. Section 4 discusses the reasons for and policy responses to the changing population–land relationship. Section 5 reports the conclusions.

2. Materials and Methods

2.1. Study Area

Our research was conducted in the Yangtze River Economic Belt (YREB) of China, which encompasses an area of 2.05 million km2 and comprises nine provinces and two municipalities (Figure 1). The YREB represents approximately 21.4% of the land in China, and in 2020, it accounted for 42.9% of the population and 46.6% of the country’s gross domestic product (GDP). Due to its vast territory, the YREB presents significant differences in natural conditions. From west to east, the YREB spans the Yunnan–Guizhou Plateau, Sichuan Basin, Southeast Hills, and Middle–Lower Yangtze Plains, with elevations ranging from −143 m to 6648 m (Figure 1). Socioeconomic outcomes also vary across the YREB (Table 1). The lower reaches, with only 17.1% land, are the most developed subregion of the YREB, accounting for 38.84% of the population and 51.89% of the GDP. The upper reaches, covering 55.4% of the land, are the largest subregion of the YREB, accounting for 33.26% of the population and 24.59% of the GDP. The middle reaches have the lowest population and GDP, accounting for 27.9% and 23.52%, respectively, although they cover 27.5% of the land.
The YREB was selected as the study area for three reasons. First, the YREB is an essential part of China’s development agenda, and its development is one of the top three national strategies of China (alongside the “One Belt, One Road” and “Beijing–Tianjin–Hebei Coordinated Development” strategies). Second, the YREB demonstrates significant spatial heterogeneity in natural and socioeconomic conditions, which may impact land use changes and population distribution. Finally, the YREB has three urban agglomerations (i.e., the Yangtze River Delta urban agglomeration, the middle reaches of the Yangtze River urban agglomeration, and the Chengdu–Chongqing urban agglomeration), where urban–rural divergences in construction land expansion and population growth may be observed.

2.2. Data Source

Two types of data were used in this research: population and construction land data. Population data were collected from the census data of China for 2000, 2010, and 2020. The census data have two types of statistical coverage: registered population and long-stay population. The registered population refers to people born in a particular city but who did not necessarily live in the city, while the long-stay population refers to people who have been living in a specific city for more than 6 months. To reflect the status quo of the population–land relationship, we used long-stay data in this study.
The census data that distinguished urban population from rural population were only available at the prefecture city level. Therefore, we used prefecture cities as analysis samples, including two municipalities given their importance in the development of the YREB. To make the data comparable between different years, we needed to use the statistics of the population within the same jurisdiction. Therefore, we modified the census data manually based on the official information about adjustments to cities’ jurisdictions in 2000–2020 and the Census Statistics Bulletin of each city. (The details of the sample cities are documented in Appendix B).
Construction land data was collected from the LUCC dataset published by the Data Center for Resources and Environment Sciences, Chinese Academy of Sciences. (The details of the LUCC dataset are provided in Appendix C). This dataset has been widely used in earlier studies and has been proven to be reliable [33,34]. The LUCC dataset used in our research was composed of raster data with a resolution of 100 m. Based on previous research, we counted the area of built-up, industrial, and transportation land in the total volume of urban construction land and regarded residential land in rural areas as rural construction land [17,32]. The raster land data were aggregated according to the jurisdiction boundaries of the prefecture cities in 2020. The DEM data in Figure 1 were also from the Data Center for Resources and Environment Sciences, Chinese Academy of Sciences.

2.3. Methods

2.3.1. Lorenz Curve and Gini Coefficient

Economists typically employ the Lorenz curve to illustrate wealth inequality in a society. Here, we applied the Lorenz curve to map the spatial mismatch between population distribution and construction land allocation in the YREB. The Lorenz curve was plotted according to the population and construction land data in the YREB.
Ideally, the cumulative percentage of the population and construction land should be proportional, achieving a spatial equilibrium. The Lorenz curve represents the extent to which this spatial equilibrium is reached. In the graph of the Lorenz curve in this study, the percentile of the population was plotted along the horizontal axis according to the area of construction land, and the cumulative area of construction land was plotted along the vertical axis. Therefore, an X-value of 30 and a Y-value of 17 indicated that the bottom 30% of the population occupied 17% of the total construction land.
The straight diagonal line with a slope of 1 in the graph of the Lorenz curve indicated perfect equality in construction land occupation among the population. However, the Lorenz curve was beneath the perfect equality line, and the area between the Lorenz curve and the perfect equality line represented the extent of inequality in a system. This inequality was expressed by the Gini coefficient. The Gini coefficient ranges from 0% to 100%. A higher Gini coefficient indicates a greater degree of inequality in a system. Referring to the calculation method of the Gini coefficient in former literature [35], the calculation formula adopted in this study is as follows:
G = 1 − ∑ i = 1 n ( X i − X i − 1 ) ( Y i + Y i − 1 )
The calculation may pertain to either urban or rural areas, where n is the number of prefecture-level cities, X0 = Y0 = 0, and Xn = Yn = 1. Starting from i = 2, Xi−1 is the cumulative percentage of the urban (or rural) population in class i – 1; Xi is the cumulative percentage of the urban (or rural) population in class i; Yi−1 is the cumulative percentage of urban (or rural) construction land in class i – 1; Yi is the cumulative percentage of urban (or rural) construction land in class i; and G is the Gini coefficient.

2.3.2. Hot Spot Analysis

We used the Hot Spot Analysis tool to characterize the spatial concentration of the population and construction land. A prefecture city with a large population surrounded by cities with a large population would be identified as a statistically significant hot spot in terms of population distribution. Conversely, a city with a small population surrounded by cities with small populations would be classified as a cold spot. The same rule was observed for construction land. The Getis–Ord Gi* statistic was calculated to indicate a hot spot or cold spot. The Getis–Ord Gi* statistics were calculated as follows [36]:
G i * = ∑ j = 1 n w i , j x j − X ¯ ∑ j = 1 n w i , j S [ n ∑ j = 1 n w i , j 2 − ( ∑ j = 1 n w i , j ) 2 ] n − 1
X ¯ = ∑ j = 1 n x j n
S = ∑ j = 1 n x j 2 n − X ¯ 2
where x j is the value of the population or construction land for city j, w i , j is the spatial weight between cities i and j, and n is the total number of prefecture cities in the YREB.
The threshold of the Getis–Ord Gi* statistic that determines hot or cold spots is related to the p-value and confidence level (Table 2). In this study, the confidence level was set to 90%. A Getis–Ord Gi* statistic higher than 1.65 indicated a hot spot, whereas a Getis–Ord Gi* statistic lower than −1.65 indicated a cold spot.

2.3.3. Annual Change Rate of Population and Construction Land

Construction land expansion and population growth were quantified using the average annual change rate. The calculation was as follows:
R p = P t − P 0 P 0 × t
R l = L t − L 0 L 0 × t
where R p and R l are the average annual change rates of population and construction land, respectively; P t and P 0 are the size of the population in a city in the final and initial years, respectively; L t and L 0 are the total volume of construction land in a city in the final and initial years, respectively; and t is the time span between the final year and the initial year. t indicates a decade.

2.3.4. Elasticity Value of Tapio Model

We constructed an elasticity value to measure the relationship between population growth and construction land expansion. The elasticity value was calculated as follows:
E = ( L t − L 0 ) / L 0 ( P t − P 0 ) / P 0
where E is the elasticity value; P t and P 0 are the size of the population in a city in the final and initial years, respectively; and L t and L 0 are the total volume of construction land in a city in the final and initial years, respectively.
An elasticity value of 1 indicated an ideal coupling relationship between population growth and construction land expansion. However, in the real world, construction land expansion cannot always be synchronized with population growth. Therefore, we referred to Tapio’s definition of coupling to conduct a feasible and realistic analysis [37]. According to Tapio’s framework, an elasticity value ranging from 0.8 to 1.2 indicated that the two measured systems in this study were coupled. Moreover, decoupling and negative decoupling relationships were observed between the two measured systems. According to the changing trends of the two measured systems, we divided their relationship into eight possible categories under Tapio’s framework (Table 3).

3. Results

3.1. Spatial Pattern of Population Distribution and Construction Land Allocation

The Lorenz curves of the population distribution and construction land allocation of the YREB are plotted in Figure 2. As presented in the graph, the Lorenz curves of the urban and rural population–land systems were below the equality line, indicating that the spatial distributions of population and construction land did not reach an equilibrium state in the YREB. The Gini coefficient of the urban population–land system was lower than that of the rural population–land system, indicating a more balanced population–land configuration in urban areas than in rural areas. Additionally, the Gini coefficients in both the urban and rural systems showed a downward trend from 2000 to 2020, revealing a narrowing gap between population distribution and construction land allocation in both areas.
Although the entire YREB approached an increasingly matched spatial pattern between population distribution and construction land allocation, the pace of change in urban areas was faster than that in rural areas. For the urban areas, the Gini coefficient decreased sharply from 0.269 in 2000 to 0.160 in 2020 (Figure 2a–c). For the rural areas, the Gini coefficient decreased only slightly from 0.575 in 2000 to 0.554 in 2020 (Figure 2d–f). This urban–rural disparity in the changes in the Gini coefficients indicated that the urban population–land system experienced a more rapid transition than the rural population–land system.
Urban–rural disparities were also evident in population concentration and construction land agglomeration. In the rural population–land system, hot spots of the population were concentrated in the junction area of the upper and middle reaches of the YREB, while high values of construction land were agglomerated in the lower reaches (Figure 3). Moreover, cold spots of rural construction land overlapped with the hot spots of the rural population in parts of Chongqing, Sichuan, and Guizhou. This mismatched spatial pattern remained constant from 2000 to 2020.
In contrast to rural areas, a relatively matched spatial pattern was observed in the urban population–land system. In the past two decades, hot spots of urban populations have been concentrated in the Yangtze River Delta urban agglomeration, overlaid by hot spots of urban construction land (Figure 4). From 2000 to 2020, the hot spots of urban construction land generally expanded and covered almost the entire territory of Jiangsu and Zhejiang. Meanwhile, the hot spots of the urban population changed slightly and were much smaller than those of urban construction land, indicating that the pace of population urbanization lagged behind that of land urbanization in the YREB from 2000 to 2020.

3.2. Characteristics of Population Growth and Construction Land Expansion

In the YREB, the area of urban construction land increased by 9.01% per year from 2000 to 2010, along with an annual growth in urban population of 5.44% (Table 4). The expansion of urban construction land intensified from 2010 to 2020. During this period, the annual growth rate of urban construction land rose to 14.69%, whereas the urban population grew only at 3.94% per year.
In contrast to the population and construction land in the urban areas that underwent double growth, the rural areas experienced population loss accompanied by construction land expansion in the past two decades (Figure 5). From 2000 to 2020, the area of rural construction land increased by 1.27% per year in the first decade and by 1.80% per year in the next decade, while the rural population decreased by 1.58% and 2.43% per year in the first and second decades, respectively (Table 4). The constant expansion of rural construction land in the YREB challenged this plausible assumption that rural population loss would shrink the expansion of rural construction land and was worth exploring further.
Heterogeneity in population growth and construction land expansion was also observed in the subregions of the YREB (Table 4 and Figure 5). From 2000 to 2020, the upper reaches of the YREB led the other two subregions in terms of the growth of the population and construction land of the urban areas. Furthermore, the fastest expansion of rural construction land emerged in the upper reaches of the YREB. The spatial pattern of population loss changed over time. From 2000 to 2010, the lower reaches of the YREB experienced the most rapid rural population loss, followed by the middle reaches. However, this pattern was reversed in the next decade, during which the middle reaches of the YREB surpassed the lower reaches and rose to the top in terms of rural population loss.

3.3. Relationships between Population Growth and Construction Land Expansion

According to Tapio’s framework, we classified the relationships between population growth and construction land expansion in the YREB into eight categories (Figure 6). Regarding the rural population–land system, most cities in the YREB exhibited a strong negative decoupling relationship from 2000 to 2020, which corresponds to the general trend of population loss and construction land expansion in the rural YREB. In about a dozen cities, a weak negative decoupling relationship was observed from 2000 to 2020, indicating that rural population loss was faster than construction land shrinkage in these cities. Recessive coupling and recessive decoupling also indicated the simultaneous decline in population and construction land. However, recessive decoupling implied that the shrinkage of construction land was faster than population loss. Both types of relationships were sparsely distributed in the rural YREB from 2000 to 2020.
Instead of showing a decreasing population, Shanghai presented a different scenario as it showed simultaneous growth in its rural population and rural construction land from 2000 to 2020, leading to an expansive negative decoupling relationship between population growth and construction land expansion (Figure 6). This phenomenon could be attributed to the long-distance commuting pattern in the metropolitan area. As the most developed metropolis in China, Shanghai has excessively high housing prices in its urban areas, and numerous commuters live in the suburban countryside and work downtown. As census data were recorded based on residential addresses, these urban commuters were counted in the rural population. Therefore, constant growth in the rural population was observed in Shanghai, despite its extremely high urbanization rate.
Unlike the consistent spatial pattern of the rural population–land relationship, the relationship between urban population growth and construction land expansion differed significantly between the two research periods. In the first decade (2000–2010), nearly half of the cities in the YREB experienced double growth in urban population and urban construction land, and the expansion of urban construction land was faster than urban population growth (Figure 6). However, during the next decade (2010–2020), this growth pattern, expressed by an expansive negative decoupling relationship, dominated the entire YREB. The rising number of cities with an expansive negative decoupling relationship indicated that urbanization accelerated in the YREB from 2010 to 2020, and the accelerated urbanization occurred at the cost of low land-use efficiency.
With the increasing momentum of the expansive negative decoupling relationship, the weak decoupling and expansive coupling relationships lost momentum in the urban YREB from 2000 to 2020. A weak decoupling relationship indicated that construction land expansion was slower than population growth, while an expansive coupling relationship indicated that population growth was almost synchronized with construction land expansion. From 2000–2010, more than a quarter of the cities in the YREB were marked with a weak decoupling relationship, and nearly two dozen cities were marked with an expansive coupling relationship. However, over the next decade (2010–2020), the number of cities with these two types of relationships declined to six. The transition of the population–land system from coupling/decoupling to negative decoupling further revealed that the urban YREB developed a highly extensive land use pattern from 2000 to 2020.

4. Discussion

4.1. Evidence of the Mismatch between Population and Construction Land

Based on the analysis of the census and LUCC data in the YREB from 2000 to 2020, we found urban–rural divergences in the relationship between population and construction land. In urban areas, population distribution and construction land allocation approached a more matched spatial pattern from 2000 to 2020. However, urban population growth still lagged far behind the construction land expansion and brought about an expansive negative decoupling relationship. In rural areas, a more significant spatial mismatch was found between population distribution and construction land allocation. This gap narrowed slightly from 2000 to 2020. Furthermore, population loss and construction land expansion remained constant in rural areas between 2000 and 2020, resulting in a strong negative decoupling relationship.
The negative decoupling relationship between population growth and construction land expansion was observed not only in the YREB but also in other regions, even in the entirety of China. For example, Qu et al. [38] found that the rural population decline is accompanied by construction land expansion in the Shandong province. Shi and Wang [30] revealed that population growth is negatively decoupled from residential land expansion in the rural Yellow River Basin. Song and Liu [31] and Zhang et al. [39] depicted negative decoupling between population growth and settlement land expansion in rural China. Tan et al. [18] and Xu et al. [9] characterized excessive urban land expansion in China by measuring the declining urban population density in large cities.
The mismatch between population and construction land that was prevalent in urban and rural China could be explained by certain factors. To delve into the roots of the negative decoupling of the population–land relationship, we needed a comprehensive framework to incorporate influencing factors into the feedback loops of the population–land system. In the next section, we present such a framework for structuring our analysis.

4.2. Reasons for the Mismatch between Population and Construction Land

In this section, we established an integrated urban–rural framework to analyze the reasons for the mismatch between population and construction land (Figure 7). Our framework identifies urban–rural dynamics as being driven by various factors, with rural-to-urban migration being the most active form. As explained in the dual sector model proposed by Lewis, the wage rate gap between the capitalist and the subsistence sector drives the transition of surplus labor from the traditional agricultural sector to the modern industrial sector. As industrialization tends to occur in urban areas, labor migration from rural areas has become a significant force driving urban population growth. With the influx of migrant labor and the endogenous growth of the urban population, more land is required for housing, as well as industrial development. These multiple land requirements have accelerated the expansion of urban construction land. Meanwhile, rural population loss is supposed to bring about the shrinkage of rural construction land or at least the stagnation of rural construction land expansion.
However, this plausible assumption derived from the economic model rarely occurs in the real world. Some factors are not always considered within model assumptions (e.g., culture, institution, and governance), meaning that a model can never fully represent the real world. China’s political regime, in particular, differs from that of Western countries; therefore, political and cultural factors (e.g., government regulation, institutional reforms, and planned economy legacy) must be considered when interpreting land and population issues.
In the urban discourse, land financialization led by local governments is regarded as an important trigger for the mismatch between population growth and construction land expansion [40,41]. Since the reform of the tax distribution system at the end of the 20th century, the revenue share possessed by local governments has been reduced by the central government, leading to a financial crisis in the budget of local governments. To obtain adequate financing to support public services and facilitate economic development, local governments resorted to land development to reap fiscal capital. The land is treated as a financial asset to increase the revenue for funding development projects, and the accelerated land financialization brings about a land-centered urbanization mode in China [42].
With the stimulus of land financialization, vast amounts of agricultural land have been developed into industrial parks and residential communities [43]. However, rapid land development has surpassed the demands of socioeconomic development in urban areas, resulting in many industrial parks with low land-use efficiency [44], and housing vacancies have emerged in residential communities [45]. Land-centered urbanization contributes to excessive urban land expansion, causing farmland loss as well as ecological degradation in urban areas [46,47].
Similarly to the causal links in urban areas, institutional factors also play an important role in generating a mismatch between rural population and rural construction land. The urban–rural dualism, formed during the planning economy, is considered an essential reason for the constant expansion of rural construction land [31,38,48]. In China, housing and public services are closely related to the household registration system, in which rural and urban population are registered separately. Due to barriers to the household registration system, many rural migrants cannot enjoy the same public services as urban residents (e.g., education and medical care) and can barely settle in cities. Without permanent residences in urban areas, rural migrants tend to maintain their settlement land in rural villages [23]. To embody a decent life brought about by their urban experience, many rural migrants want to build larger houses once they earn money in cities. Therefore, rural construction land continues to grow, and hollowing villages emerge, especially in traditional agricultural areas [49,50].
Another institutional constraint on the transition of rural construction land is the urban–rural duality in the land market [51]. According to China’s constitution, rural construction land is owned by rural collective organizations and cannot be transferred outside the collective scope. Even if rural residents are willing to sell their rural settlements and move to cities, they cannot easily obtain a fair return; thus, the redundancy in rural construction land cannot be consumed by market entities. Moreover, numerous rural residents are strongly attached to their settlement land and are inclined to retain their family houses despite having settled in cities [23]. The old houses left by residents are also obstacles to rural construction land transition.
According to the integrated urban–rural framework, formal institutions (e.g., land financialization and urban–rural dualism) and informal institutions (e.g., preferences for large houses and attachments to settlement land) are identified as major triggers for the negatively decoupled population–land relationship in China. As population migration and land conversion induced by urbanization and industrialization are quite prevalent in the Global South, the framework we propose here also provides implications for the analysis of the population–land relationship in other developing countries. Institutional factors need to be supplemented in the framework when applied to other countries owing to local context differences. In China, land financialization and urban–rural dualism mainly account for the mismatch between population and construction land [52], while these two institutional arrangements may not exist in other countries.

4.3. Policy Responses to the Mismatch between Population and Construction Land

The excessive expansion of construction land is a prominent topic not only in academic research but also among policymakers concerned regarding the sustainability of land use and outcomes of urbanization. Farmland loss is one of the most significant issues in urban land expansion [53]. To decelerate farmland loss caused by urban encroachment, the central government has formulated a series of policies to find a balance between farmland protection and urban expansion. One component of this policy package is the “increase vs. decrease policy,” which links the increase in urban construction land to the decrease in rural construction land [54]. The “increase vs. decrease policy” aims to offset the loss of farmland caused by urban expansion via the reclamation of rural construction land. With the implementation of this policy, rapid farmland loss has been curbed. However, the expansion of urban construction land has accelerated to some extent because this policy transfers the land consumption quota from rural to urban areas [55].
The growth of construction land provides sufficient space for industrial development, and revenues from land financialization fund a batch of municipal projects. However, the disorder of urban sprawl has also created a chaotic urban landscape, such as urban villages inside cities and impaired local ecosystem functions [56]. To mitigate the undesirable outcomes of land-centered urbanization, the central government proposed a new type of urbanization strategy in 2014. This new urbanization mode changes from land-centered urbanization to human-oriented urbanization, focusing more on the improvement of human well-being and a high level of socioeconomic development in urban areas. Owing to the hysteresis effects of the policy, this new strategy did not immediately reverse the rapid urban expansion (Figure 5b), but it may have an impact in the future.
As for addressing the mismatch between the rural population and rural construction land, the core of policy responses is to advance rural vitality and urban–rural equity. In 2017, the central government enacted the rural revitalization strategy to resolve rural decline issues in China. This national strategy designs a comprehensive blueprint for the revitalization of industries, skilled workforces, culture, ecology, and organizations in rural areas and for the furtherance of urban–rural integration development. A law on the promotion of the revitalization of rural areas was also issued and came into force in 2021 to guarantee the execution of the rural revitalization strategy.
Along with the long-term institutional design, detailed policies have been implemented to adjust the population–land relationship in rural areas. For example, land consolidation has been widely implemented to improve rural land-use efficiency, restructure rural spaces, and boost the rural economy [57]. Pilot projects of rural land reforms2 have also been conducted in several provinces to explore feasible schemes and accumulate experience for nationwide practice. With a top-down design and bottom-up innovations, a hierarchical policy system has been established to promote the sustainable development of rural population–land systems.

5. Conclusions

Using census data and an LUCC dataset for 2000, 2010, and 2020, we systematically analyzed the relationships between population and construction land in the urban and rural areas of the YREB. Our results showed that the population distribution and construction land allocation in the YREB did not reach an equilibrium state in 2000–2020, during which the urban population–land configuration was better than that in rural areas. Urban population growth and construction land expansion occurred simultaneously in the YREB from 2000 to 2020. However, land expansion was generally faster than population growth, resulting in an expansive decoupling relationship. Rural construction land expansion was accompanied by population loss, resulting in a strong negative decoupling relationship.
Our findings in the YREB aligned with those of studies conducted in other regions and even at the national scale. Thus, we established an integrated urban–rural framework to analyze the occurrence mechanism. According to our framework, land financialization and urban–rural dualism are the major reasons for the mismatch between population growth and construction land expansion. Top-down designs, such as new urbanization modes and rural revitalization strategies, have been proposed, and policy instruments, such as the “increasing vs. decreasing policy” and land consolidation, have been implemented by the central government to coordinate the population–land relationship in urban and rural areas. Given the commonality of urban–rural interactions, the integrated urban–rural framework can be adapted to explore the population–land relationship in other developing countries. Meanwhile, policy responses in China can provide implications for other countries to address complex population–land issues.

Author Contributions

Conceptualization: Y.Z.; methodology: Y.Z. and J.Z.; formal analysis: Y.Z., J.Z., X.H., and L.L.; investigation: Y.Z.; resources: Y.Z.; data curation: Y.Z.; writing—original draft preparation and review and editing: Y.Z. and L.L.; visualization: Y.Z., X.H., and L.L.; project administration: L.L.; funding acquisition: L.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China, grant numbers 42201223, 42171272, and 42271271; the MOE (Ministry of Education in China) Project of Humanities and Social Sciences, grant number 18YJCZH120; and the Jiangsu Government Scholarship for Overseas Studies.

Data Availability Statement

The authors have not obtained permission to publish the data. Therefore, the data can be obtained from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

The cities and districts that constitute the three urban agglomerations in the YREB are as follows:
(1) The Yangtze River Delta urban agglomeration (YRDUA) covers 26 cities, including Shanghai, Nanjing, Wuxi, Changzhou, Suzhou, Nantong, Yancheng, Yangzhou, Zhenjiang, and Taizhou in Jiangsu; Hangzhou, Ningbo, Jiaxing, Huzhou, Shaoxing, Jinhua, Zhoushan, and Taizhou in Zhejiang; and Hefei, Wuhu, Ma’anshan, Tongling, Anqing, Chuzhou, Chizhou, and Xuancheng in Anhui.
(2) The middle reaches of the Yangtze River urban agglomeration (MYRUA) covers 31 cities, including Wuhan, Huangshi, Ezhou, Huanggang, Xiaogan, Xianning, Xiantao, Qianjiang, Tianmen, Xiangyang, Yichang, Jingzhou, and Jingmen in Hubei; Changsha, Zhuzhou, Xiangtan, Yueyang, Yiyang, Changde, Hengyang, and Loudi in Hunan; and Nanchang, Jiujiang, Jingdezhen, Yingtan, Xinyu, Yichun, Pingxiang, Shangrao, and parts of Fuzhou, Ji’an in Jiangxi.
(3) The Chengdu–Chongqing urban agglomeration (CCUA) includes 27 districts (counties) of Chongqing: Yuzhong, Wanzhou, Qianjiang, Fuling, Dadukou, Jiangbei, Shapingba, Jiulongpo, Nan’an, Beibei, Qijiang, Dazu, Yubei, Banan, Changshou, Jiangjin, Hechuan, Yongchuan, Nanchuan, Tongnan, Tongliang, Rongchang, Bishan, Liangping, Fengdu, Dianjiang, Zhongxian, as well as parts of Kaizhou and Yunyang. It also covers 15 cities of Sichuan: Chengdu, Zigong, Luzhou, Deyang, Mianyang (except for Beichuan and Pingwu), Suining, Neijiang, Leshan, Nanchong, Meishan, Yibin, Guang’an, Dazhou (except for Wanyuan), Ya’an (except for Tianquan and Baoxing), and Ziyang.
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Figure A1. Pictures of the rural areas in the YREB (photographed by the authors).
Figure A1. Pictures of the rural areas in the YREB (photographed by the authors).
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Figure A2. Urban landscape of the typical counties in the YREB (Wushan is located in the Chengdu–Chongqing urban agglomeration, Xiantao county is located in the middle reaches of the Yangtze River urban agglomeration, and Jiangyin county is located in the Yangtze River Delta urban agglomeration).
Figure A2. Urban landscape of the typical counties in the YREB (Wushan is located in the Chengdu–Chongqing urban agglomeration, Xiantao county is located in the middle reaches of the Yangtze River urban agglomeration, and Jiangyin county is located in the Yangtze River Delta urban agglomeration).
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Appendix B

Details of the prefecture-level cities in the YREB.
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Figure A3. Location of the prefecture-level cities.
Figure A3. Location of the prefecture-level cities.
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Table
Table A1. Names of the prefecture-level cities.
Table A1. Names of the prefecture-level cities.
Province/MunicipalityPrefecture-Level City
Sichuan1. Chengdu, 2. Zigong, 3. Panzhihua, 4. Luzhou, 5. Deyang, 6. Mianyang, 7. Guangyuan, 8. Suining, 9. Neijiang, 10. Leshan, 11. Nanchong, 12. Meishan, 13. Yibin, 14. Guang’an, 15. Dazhou, 16. Ya’an, 17. Bazhong, 18. Ziyang, 19. Aba, 20. Ganzi, and 21. Liangshan.
Yunnan22. Kunming, 23. Qujing, 24. Yuxi, 25. Baoshan, 26. Zhaotong, 27. Lijiang, 28. Pu’er, 29. Lincang, 30. Chuxiong, 31. Honghe, 32. Wenshan, 33. Xishuangbanna, 34. Dali, 35. Dehong, 36. Nujiang, and 37. Diqing.
Chongqing38. Chongqing.
Guizhou39. Guiyang, 40. Liupanshui, 41. Zunyi, 42. Anshun, 43. Bijie, 44. Tongren, 45. Qianxinan, 46. Qiandongnan, 47. Qiannan.
Hubei48. Wuhan, 49. Huangshi, 50. Shiyan, 51. Yichang, 52. Xiangyang, 53. Ezhou, 54. Jingmen, 55. Xiaogan, 56. Jingzhou, 57. Huanggang, 58. Xianning, 59. Suizhou, 60. Enshi, 61. Xiantao, 62. Qianjiang, 63. Tianmen, and 64. Shennongjia.
Hunan65. Changsha, 66. Zhuzhou, 67. Xiangtan, 68. Hengyang, 69. Shaoyang, 70. Yueyang, 71. Changde, 72. Zhangjiajie, 73. Yiyang, 74. Chenzhou, 75. Yongzhou, 76. Huaihua, 77. Loudi, and 78. Xiangxi.
Jiangxi79. Nanchang, 80. Jingdezhen, 81. Pingxiang, 82. Jiujiang, 83. Xinyu, 84. Yingtan, 85. Ganzhou, 86. Ji’an, 87. Yichun, 88. Fuzhou, and 89. Shangrao.
Anhui90. Hefei, 91. Wuhu, 92. Bengbu, 93. Huainan, 94. Ma’anshan, 95. Huaibei, 96. Tongling, 97. Anqing, 98. Huangshan, 99. Chuzhou, 100. Fuyang, 101. Suzhou, 102. Lu’an, 103. Bozhou, 104. Chizhou, and 105. Xuancheng.
Jiangsu106. Nanjing, 107. Wuxi, 108. Xuzhou, 109. Changzhou, 110. Suzhou, 111. Nantong, 112. Lianyungang, 113. Huai’an, 114. Yancheng, 115. Yangzhou, 116. Zhenjiang, 117. Taizhou, and 118. Suqian.
Shanghai119. Shanghai.
Zhejiang120. Hangzhou, 121. Ningbo, 122. Wenzhou, 123. Jiaxing, 124. Huzhou, 125. Shaoxing, 126. Jinhua, 127. Quzhou, 128. Zhoushan, 129. Taizhou, and 130. Lishui.

Appendix C

The multiple-epoch Land Use/Cover (LUCC) Dataset of China was interpreted from remote sensing images using a visual identification method based on human–computer interaction. The remote sensing data were captured by the US Landsat MSS, TM/ETM, and Landsat 8 satellites. The LUCC dataset of 2000 and 2010 was mainly interpreted from the Landsat-TM/ETM remote sensing data. The LUCC dataset of 2020 was mainly updated on the basis of the Landsat 8 remote sensing data.
The classification system of land use types in LUCC dataset is shown in Table A2.
Table
Table A2. Schema for reclassifying land use/cover classes.
Table A2. Schema for reclassifying land use/cover classes.
Reclassified CategoryOriginal classificationLand use/cover description, cited from Liu et al. [33] and Zhou and Lv [53]
FarmlandPaddy landCropland that has enough water supply and irrigation facilities for planting paddy rice, lotus, etc., including rotation land for paddy rice and dry farming crops.
Dry landCropland for cultivation without water supply and irrigating facilities; cropland that has water supply and irrigation facilities and planting dry farming crops; cropland planting vegetables; fallow land.
Industrial, mining, and transportation landBuilt-up land (others)Lands used for factories, quarries, mining, and oil-field slattern outside cities and lands for special uses such as transportation and airport.
Rural settlementRural settlementsLands used for settlements in villages.
Urban built-up landUrban built-upLands used for urban construction.
Sparsely/barely vegetated landWoodsLands covered by trees with canopy cover between 10–30%.
Woodland (others)Lands such as tea gardens, orchards, groves and nurseries.
Sparse grassGrassland with canopy cover between 5% and 20%.
Permanent ice and snowLands covered by perennial snowfields and glaciers.
Sandy landSandy land covered with less than 5% vegetation cover.
GobiGravel covered land with less than 5% vegetation cover.
SalinaLands with salina accumulation and sparse vegetation.
Bare soilBare exposed soil with less than 5% vegetation cover.
Bare rockBare exposed rock with less than 5% vegetation cover.
Unused land (others)Other lands such as alpine desert and tundra.
Densely/moderately vegetated landForestNatural or planted forests with canopy cover greater than 30%.
ShrubLands covered by trees less than 2 m high and canopy cover >40%.
Dense grassGrassland with canopy coverage greater than 50%.
Moderate grassGrassland with canopy coverage between 20% and 50%.
Water bodyStream and riversLands covered by rivers including canals.
LakesLands covered by lakes.
Reservoir and pondsMan-made facilities for water reservation.
Beach and shoreLands between high tide level and low tide level.
BottomlandLands between normal water level and flood level.
SwamplandLands with a permanent mixture of water and herbaceous or woody vegetation that cover extensive areas.

Notes

1
Construction land refers to the impervious surface of the planet on which buildings and structures are constructed, including land for urban and rural housing and public facilities; land for industrial and mining purposes; land for energy, transportation, water conservancy, communication, and other infrastructure facilities; land for tourism; land for military purposes; etc.
2
Rural land reform refers to reforming rural land acquisition systems, the entry of rural collectively owned commercial construction land into the market, and rural housing land systems.

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Figure 1. Location of the Yangtze River Economic Belt (YREB). The three urban agglomerations from left to right are as follows: the Chengdu–Chongqing urban agglomeration, the middle reaches of the Yangtze River urban agglomeration, and the Yangtze River Delta urban agglomeration. The details of the three urban agglomerations are documented in Appendix A.
Figure 1. Location of the Yangtze River Economic Belt (YREB). The three urban agglomerations from left to right are as follows: the Chengdu–Chongqing urban agglomeration, the middle reaches of the Yangtze River urban agglomeration, and the Yangtze River Delta urban agglomeration. The details of the three urban agglomerations are documented in Appendix A.
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Figure 2. Lorenz curves of population distribution and construction land allocation.
Figure 2. Lorenz curves of population distribution and construction land allocation.
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Figure 3. Hot spots of rural population and construction land in the YREB.
Figure 3. Hot spots of rural population and construction land in the YREB.
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Figure 4. Hot spots of urban population and construction land in the YREB.
Figure 4. Hot spots of urban population and construction land in the YREB.
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Figure 5. Population growth and construction land expansion in the YREB from 2000 to 2020 at the individual city scale.
Figure 5. Population growth and construction land expansion in the YREB from 2000 to 2020 at the individual city scale.
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Figure 6. Relationships between population growth and construction land expansion in the YREB.
Figure 6. Relationships between population growth and construction land expansion in the YREB.
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Figure 7. Integrated urban–rural framework for understanding the mismatch between population and construction land.
Figure 7. Integrated urban–rural framework for understanding the mismatch between population and construction land.
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Table
Table 1. Spatial heterogeneity of population and economic development in the Yangtze River Economic Belt (YREB).
Table 1. Spatial heterogeneity of population and economic development in the Yangtze River Economic Belt (YREB).
SubregionsSpatial ExtentTerritorial Area
(104 km2)		Total Population
(106 People)		Total GDP
(1010 Yuan)
Upper Reaches of
the YREB		Sichuan, Chongqing, Guizhou, and Yunnan113.74 (55.4%) 201.6 (33.26%)1159.50 (24.59%)
Middle Reaches of
the YREB		Jiangxi, Hubei, and Hunan56.46 (27.5%) 169.09 (27.90%)1109.16 (23.52%)
Lower Reaches of
the YREB		Shanghai, Jiangsu, Zhejiang, and Anhui35.03 (17.1%) 235.38 (38.84%)2447.14 (51.89%)
Note: The numbers in brackets are the corresponding proportions of the subregions to the YREB. Data source: China Statistical Yearbook 2021.
Table
Table 2. Getis–Ord Gi* statistics and p-values for different confidence levels.
Table 2. Getis–Ord Gi* statistics and p-values for different confidence levels.
Getis–Ord Gi* Statisticp-Value (Probability)Confidence Level
<−1.65 or >+1.65<0.1090%
<−1.96 or >+1.96<0.0595%
<−2.58 or >+2.58<0.0199%
Table
Table 3. Definitions and connotations of the eight possible relationships.
Table 3. Definitions and connotations of the eight possible relationships.
Category of Possible RelationshipsConnotationsRange of Elasticity Value
DecouplingStrong decoupling△land < 0, △population > 0E < 0
Weak decoupling△population > △land > 00 < E < 0.8
Recessive decoupling△land < △population < 0E > 1.2
CouplingExpansive coupling△land > 0, △population > 00.8 ≤ E ≤ 1.2
Recessive coupling△land < 0, △population < 00.8 ≤ E ≤ 1.2
Negative decouplingExpansive negative decoupling△land > △population > 0E > 1.2
Strong negative decoupling△land > 0, △population < 0E < 0
Weak negative decoupling△population < △land < 00 < E < 0.8
△land is the percentage change in construction land area from the initial to the final year. △population is the percentage change in population from the initial to the final year. E is the elasticity value.
Table
Table 4. Population growth and construction land expansion in the YREB from 2000 to 2020 at the subregional scale.
Table 4. Population growth and construction land expansion in the YREB from 2000 to 2020 at the subregional scale.
Subregions of the YREBGRUCL 2000–2010GRUP 2000–2010GRRCL 2000–2010GRRP 2000–2010GRUCL 2010–2020GRUP 2010–2020GRRCL 2010–2020GRRP 2010–2020
Whole territory of the YREB9.01%5.44%1.27%−1.58%14.69%3.94%1.80%−2.43%
Upper reaches of
the YREB		9.85%5.89%1.50%−1.31%19.74%4.93%3.16%−2.27%
Middle reaches of the YREB7.89%4.97%0.74%−1.65%13.45%3.33%0.41%−2.64%
Lower reaches of
the YREB		9.08%5.37%1.49%−1.82%10.04%3.38%1.52%−2.42%
GRUCL: Annual average growth rate of urban construction land; GRUP: annual average growth rate of urban population; GRRCL: annual average growth rate of rural construction land; GRRP: annual average growth rate of rural population.
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MDPI and ACS Style

Zang, Y.; Zhu, J.; Han, X.; Lv, L. Dynamics between Population Growth and Construction Land Expansion: Evidence from the Yangtze River Economic Belt of China. Land 2023, 12, 1288. https://doi.org/10.3390/land12071288

AMA Style

Zang Y, Zhu J, Han X, Lv L. Dynamics between Population Growth and Construction Land Expansion: Evidence from the Yangtze River Economic Belt of China. Land. 2023; 12(7):1288. https://doi.org/10.3390/land12071288

Chicago/Turabian Style

Zang, Yuzhu, Junjun Zhu, Xu Han, and Ligang Lv. 2023. "Dynamics between Population Growth and Construction Land Expansion: Evidence from the Yangtze River Economic Belt of China" Land 12, no. 7: 1288. https://doi.org/10.3390/land12071288

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