Spatial Differentiation and Driving Factor Analysis of Urban Construction Land Change in County-Level City of Guangxi, China

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Article Spatial Differentiation and Driving Factor Analysis of Urban Construction Land Change in County-Level City of Guangxi, China

Dong Ouyang 1,2,*, Xigang Zhu 1, Xingguang Liu 2, Renfei He 2 and Qian Wan 2

1 School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China; [email protected] 2 Hualan Design and Consulting Group Company Ltd., Nanning 530011, China; [email protected] (X.L.); [email protected] (R.H.); [email protected] (Q.W.) * Correspondence: [email protected]

Abstract: The change of urban construction land is most obvious and intuitive in the change of global land use in the new era. The supply and allocation of construction land is an important policy tool for the government to carry out macro-control and spatial governance, which has received widespread attention from political circles, academia, and the public. An empirical study on the change of construction land and its driving factors in 70 county-level cities in Guangxi, China based on the GeoDetector method reveals the driving mechanism of the construction land change in county-level cities and provides more detailed information and a more accurate basis for county-level city policy makers and decision makers. The study shows a significant heterogeneity in the action intensity and interaction between construction land change and its driving factors in county-level cities, where population and GDP size, transportation, and industrial structure are determining factors. Besides, Citation: Ouyang, D.; Zhu, X.; Liu, the factors of fiscal revenue, social consumption, utility investment, and real economy have a very X.; He, R.; Wan, Q. Spatial weak action force individually, but they can achieve significant synergistic enhancement effects when Differentiation and Driving Factor coupled with other factors. In the end, urban construction land change at different scales and their Analysis of Urban Construction Land driving mechanisms are somewhat different, and it is recommended to design differentiated and Change in County-Level City of Guangxi, China. Land 2021, 10, 691. precise construction land control and spatial governance policies according to local conditions. https://doi.org/10.3390/land10070691 Keywords: urban construction land; land use change; spatial planning; driving mechanism; China Academic Editors: Tomasz Noszczyk and Abreham Berta Aneseyee

Received: 15 May 2021 1. Introduction Accepted: 24 June 2021 1.1. Background Published: 30 June 2021 The study on land use/cover change (LUCC) and its driving mechanisms is an advanced field of intense scholarly debate in global change research today. Urban land use Publisher’s Note: MDPI stays neutral change is the most significant and intuitive manifestation of global land use change in the with regard to jurisdictional claims in urban era, and how to put urban land use change into the same framework or model with published maps and institutional affil- economy, society, environment, policy. and politics for an integrated study has attracted the iations. attention of scholars and decision makers [1,2]. According to the UN World Urbanization Prospects, the number of global urban residents surpassed the global rural population in 2009 and the world entered the urban age [3]. In the urban era, the urban construction land change has become a dominant player in global land use change, especially in countries Copyright: © 2021 by the authors. with rapid urbanization and industrialization, where urban construction land change has Licensee MDPI, Basel, Switzerland. become a core element of land planning and spatial governance, receiving wide attention This article is an open access article from government departments, academic researchers, and the public [4,5]. distributed under the terms and According to China Statistical Yearbooks 2019, China’s urbanization rate exceeded conditions of the Creative Commons 50% in 2011, marking the transition from “rural China” to a new era of “urban China”. Attribution (CC BY) license (https:// According to China Statistical Yearbooks 2020, China’s urbanization rate reached 60.6% by creativecommons.org/licenses/by/ 4.0/). 2019, and the “14th Five-Year Plan” proposes to reach 65% by 2025, with an average annual

Land 2021, 10, 691. https://doi.org/10.3390/land10070691 https://www.mdpi.com/journal/land Land 2021, 10, 691 2 of 21

growth of 0.88%. China is still at a rapid pace of urbanization. In the context of “land finance” (where some city governments rely on revenues from the sale of land use rights to support local fiscal expenditures), the change of urban construction land has become the central feature of land use change in the rapid process of urbanization in China, and it has become a key research topic for many subjects such as urban geography, planning, management, economics, and land science to discuss the change of urban construction land quantity and quality [6–9]. Changes in urban construction land are the result of many factors. To discover the main driving factors with the study of the interactive relationships among them is not only the difficulty and focus of academic research, but also the basis for making urban spatial planning and development policies. In summary, the study of the spatial-temporal change characteristics of urban construction land and its driving mechanism helps to reveal the mechanism of urban land use change and spatial evolution law, and it is of great significance to the improvement of urban land use, the control of construction land scale and its change, the optimization of urban spatial structure, and the achievement of sustainable development of urban society and economy.

1.2. Literature Review Current studies related to urban construction land and land use changes focus on four areas, that is, monitoring and evaluation, impact analysis, dynamics mechanisms, and planning and control. They can be further divided into eight research directions of simulation analysis of land use changes, suitability evaluation, economic and social impacts, arable land and landscape ecological impacts, driving factors, coupling relationships, planning methods, and planning evaluation. Besides, correlation studies such as comprehensive evaluation models for urban construction land quality and drivers of rural construction land structure evolution have emerged. For example, Cui constructed a model for comprehensive evaluation of urban construction land quality, involving dimensions such as economic quality, social quality, and ecological quality [10]; Huang analyzed the driving factors of the evolution of rural construction land structure during urbanization in China [11]. The detailed analysis is as follows. First, it focuses on the monitoring and evaluation of land use and its changes. In the field of change simulation analysis, it includes Noszczyk [12] and Cegielska [13], who modeled and statistically analyzed the land use changes in Poland and Hungary, respectively; Li performed simulation analysis of the spatio-temporal changes of construction land in the Chang-Zhu-Tan urban agglomeration, i.e., the movement of the center of gravity [14]; Yao established the classification and regression tree-cellular automata model for urban construction land expansion simulation [15]; Aliani [16] and Karimi [17] built the cellular automata–Markov model for land use monitoring and prediction and conducted a case study; Xiong carried out a simulation study on urban construction land supply and demand [18]. In the field of suitability evaluation, it includes Huang, who offered a comprehensive evaluation of the suitability of urban construction land changes in Nanchang during rapid urbanization [19]; using the Beihu New District of Jining City, China, as a case study, Yan introduced a novel research approach for comprehensive suitability evaluation based on vertical-horizontal processes [20]; Cao proposed a suitability evaluation method for urban construction land in mountainous areas based on the example of Baoxing County, Sichuan, China [21]; Xu took Hangzhou as an example and put forward the evaluation method of urban construction land suitability based on geographical environment factors [22]; Bozdag [23], Ustaoglu [24], and Zhang [25] evaluated and analyzed the suitability of urban construction land in Cihanbeyli and Pendik District of Turkey and Hefei of China using GIS tools, respectively. Second, the analysis focuses on the impact of land use and its changes. In the field of economic and social impact research, Xie [26] believes that urban construction land expansion has a positive effect on China’s regional economic development; Wang points out that decreased urban construction land will have a negative impact on the regional Land 2021, 10, 691 3 of 21

labor market [27]. In the field of Arable land and landscape ecological impact research, Guo argues that urban construction land expansion leads to the reduction of cultivated land, and has a greater impact on landscape pattern [28]; Aneseyee [29–31] and Sadat [32] hold that land use change has a significant ecological effect, affecting the value of ecosystem services, especially land-use intensity, population density, elevation, and slope, which are significantly correlated with the distribution of habitat quality; Li believes that the change of urban construction land has a great impact on carbon emissions [33]. Third, the analysis focuses on the dynamic mechanism of land use and its change. In the field of driving factor research, it includes Jin, who believes that non-agricultural industry development, land resource scarcity, population growth, economic extroversion, and domestic capital have different effects on NCL at different stages of development [34]; Yu pointed out that the social economy, science and technology, and the management system are major driving factors of green use efficiency of urban construction land [35]; Wang argues that the level of indebtedness, especially new debt, positively drives the efficiency of urban construction land use [36]; Liu, based on the analysis of panel data, holds that capital, labor, and land investment all contributed to the non-agricultural GDP growth have an important impact on China’s construction land allocation efficiency [37]; Ye found that several site factors, including landscape types and the distances to roads, coastlines, or city centers, had significant impacts on the expansion of construction land, influencing the direction, scale, and intensity of the expansion [38]. In the field of coupling relationship research, Li found there is a long-term bidirectional causal relationship between urban land expansion and economic and population growth [39]; Zhou revealed the coupling relationship between urban construction land expansion and PM2.5 [40]; Cai [41] and Li [42] analyzed the coupling relationship between construction land and spatial-temporal changes of mobile population and economic development level during the rapid urbanization in China; Yang studied the coupling relationship between urban construction land expansion and changes of the urban heat island effect [43]; He analyzed collaborative optimization of rural residential land consolidation and urban construction land expansion [44]. Finally, the analysis focuses on land use planning and control. In the field of planning method research, Zhu put forward an ecological planning method for urban construction land based on comprehensive benefits [45]; Zhu analyzed the conflicts and compromises of governments at different levels in construction land planning decisions [46]; Tang analyzed the countermeasures for benefit distribution in the coordination of construction land changes [47]. In the field of planning evaluation research, Zhou evaluated the effectiveness of annual land use plan and land use master planning in controlling the growth of urban construction land [48,49]. Although a wealth of research results on urban construction land change has now been achieved, there are still three shortcomings. Our thoughts and comments are as follows. First of all, in terms of research methods, emphasis is placed on the analysis of the influencing factors and the strength of their forces, while there is a lack of research on the interaction effect of factors. Existing papers mainly rely on socio-economic and land use change data to carry out correlation, regression, and simulation analyses with the help of remote sensing and GIS tools [50,51]. In most papers, regression analysis, hierarchical analysis, cluster analysis, the conversion of land use and its effects at small regional extent model, the two-regime spatial Durbin model, and the cellular automata model are employed to analyze the change of construction land and its influencing factors. The change of construction land is influenced by many factors in a complex way, and the joint force of the factors is not equal to the sum of forces of individual factors. However, there is still a lack of research on their interaction effect in existing papers. In addition, on the research scale, research mainly focuses on internationalized large cities or urban agglomerations, while paying little attention to small and medium-sized cities or less-developed cities. Small and medium-sized cities are numerous and occupy an important place in the regional town system; however, the present studies mainly focus on large cities [52] (such as Shanghai [53], Ningbo [54], Chongqing [55], etc.), cities Land 2021, 10, 691 4 of 21

in developed coastal areas [56,57], and city clusters [58,59], and the research on small and medium-sized cities at the county and town level, especially in the underdeveloped western region, is very insufficient. The spatial heterogeneity of large-medium-small-sized and prefecture-county-town-level cities and their driving mechanisms differ significantly under the influence of scale effect. To improve the applicability and accuracy of the present research findings, a multi-scale empirical study is urgently needed. Last but not least, in terms of research perspectives, most papers are case studies of individual cities or city clusters, and there are fewer on comparative analysis of differences between cities and studies on spatial-temporal coupling. There are significant differences in the scale of construction land and its changes in different cities, which characterize the spatial differentiation of the region under the comprehensive action of multiple factors such as economy, society, politics, and ecology in the region. Analysis based on time series data is often hard to explain the effects of spatial heterogeneity on land use change. Studies dedicated to exploring the spatial heterogeneity of urban construction land and its driving mechanisms from a spatial perspective have just emerged at present, and only Li [60,61], Huang [62], Xie [63], and Zhu [64] have conducted exploratory studies on the spatial and temporal differences of urban construction land and their driving factors in prefecture-level cities and poor counties as well as special geographical areas such as the Loess Plateau.

1.3. Aim and Question The different resource endowments, planning, and positioning lead to a significant difference in the urban construction land scale and its changes. It is helpful to gain full knowledge of the urban spatial change law and provide an important basis for the development of land use planning and spatial governance policies by clarifying the difference and further analyzing its influencing factors and driving mechanisms from a spatial perspective. This study is dedicated to exploring the following questions: What are the differences in the scale of construction land and its changes in county-level cities? What are the dynamic evolution characteristics of construction land change against the background of the new urbanization policy and the strategy of “big county” (Guangxi provincial government increasing investment in county-level cities to promote the rapid expansion of their population and land area)? What are their driving forces? Answers to the above questions have a profound theoretical and practical significance for finding out the spatial distribution and scale changes of construction land in county-level cities, promoting the precise supply of land and its effective distribution in different cities, and speeding up the urbanization and even modernization of counties. Therefore, based on the case study of 70 counties in Guangxi, this paper conducted an empirical study on the characteristics and driving forces of construction land change from 2015 to 2019 by the GeoDetector and GIS analysis methods, providing a basis for policy makers and decision makers to know the complexity of dynamic changes in construction land and to establish a precise land supply mode and a spatial distribution pattern that meet the high-quality development requirements.

2. Research Design 2.1. Study Area: Guangxi In recent years, Chinese governments at all levels have attached great importance to the development of county-level cities, taking them as a critical space carrier for new approach to urbanization. Therefore, they have promulgated and implemented a series of plans and policies to support the development of county economy and the construction of county seats. The central government of China promulgated the “National New-type Urbanization Plan (2014)” and “Some Opinions on Further Promoting the Construction of New Urbanization (2016)”, clearly proposing to accelerate the development of county-led small and medium-sized cities. With the “Several Opinions on Innovation-Driven Develop- ment of the County” plan implemented in 2017, the policies have been increasingly focused and specialized. At the local government level, provincial and municipal governments have made their own special policies for county development such as the “Guidelines of Sichuan Land 2021, 10, x FOR PEER REVIEW 5 of 21

plans and policies to support the development of county economy and the construction of county seats. The central government of China promulgated the “National New-type Ur- banization Plan (2014)” and “Some Opinions on Further Promoting the Construction of New Urbanization (2016)”, clearly proposing to accelerate the development of county-led small and medium-sized cities. With the “Several Opinions on Innovation-Driven Develop- ment of the County” plan implemented in 2017, the policies have been increasingly focused and specialized. At the local government level, provincial and municipal governments have made their own special policies for county development such as the “Guidelines of Sichuan Province on Promoting High-Quality Development of County Economy”, the “Opinions of Hunan Province on the Development of County Economy and Strong Counties”, and the “Opinions of Qinghai Province on Further Supporting the Development of County Econ- omy”, besides introducing local plans for new-type urbanization in succession. Land 2021, 10, 691In recent years, local governments at all levels in Guangxi have attached great5 of 21im- portance to the development of county-level cities and implemented a series of policies to support the construction of counties and the development of county economy on the basis of the national strategicProvince deployment, on Promoting High-Quality which are Development very typical of County and representative. Economy”, the “Opinions In 2014, of Hunan Province on the Development of County Economy and Strong Counties”, and the “Guangxi New-typethe “Opinions Urbanization of Qinghai ProvincePlan” was on Further promulgated Supporting and the Developmentimplemented, of County clearly proposing to “implementEconomy”, the besides strategy introducing of building local plans large for new-type counties”. urbanization The Guangxi in succession. Provincial Government then promulgatedIn recent years, and local implement governmentsed at allspecial levels inpolicies Guangxi such have attachedas “Opinions great im- on portance to the development of county-level cities and implemented a series of policies Implementing theto Strategy support the of construction Building Larg of countiese Counties and the developmentto Improve of the county County economy Urbaniza- on the tion Development”,basis of“Decision the national on strategic Accele deployment,rating whichCounty are veryEconomy typical and Development representative. In in Guangxi”, “Opinions2014, theon “Guangxi Strengthening New-type Urbanizationthe Classification Plan” was and promulgated Assessment and implemented, of County clearly proposing to “implement the strategy of building large counties”. The Guangxi Economy Development”,Provincial and Government the “13th then Five-Y promulgatedear Plan” and implemented for county special economy policies suchdevelopment as “Opin- in Guangxi, and establishedions on Implementing detailed the plans Strategy for of implementation Building Large Counties of key to Improve tasks in the six County areas, that is, characteristicUrbanization agricultural Development”, demonstration “Decision zones, on Accelerating industrial County parks, Economy tourism, Development charac- in Guangxi”, “Opinions on Strengthening the Classification and Assessment of County teristic small towns,Economy financial Development”, support, and and the fiscal “13th Five-Year system Plan” reform. for county economy development This paper studiesin Guangxi, 70 county-level and established detailedcities of plans Guangxi, for implementation located ofin key the tasks western in six areas, region that of China (see Figure is,1). characteristic County-level agricultural cities demonstrationare the basic zones, units industrial on which parks, Guangxi tourism, characteristic is based for small towns, financial support, and fiscal system reform. economic and social developmentThis paper studies and 70 county-level town cons citiestruction, of Guangxi, covering located inan the area western of 76% region of of the total land of GuangxiChina with (see Figure more1). than County-level 65% of cities the are local the basic population units on which and Guangxi more isthan based 40% for of the local gross domesticeconomic product and social (GDP). development In 2015, andtown urban construction, construction covering land an area in Guangxi of 76% of the cov- total land2 of Guangxi with more than2 65% of the local population and more than 40% of the 2 ered an area of 1844local km gross, including domestic product 683 (GDP).km in In county-level 2015, urban construction cities, while land in Guangxiit was 2178 covered km in 2019, includingan 809 area km of 18442 in kmcounty-level2, including 683 cities. km2 inFrom county-level 2015 to cities, 2019, while urban it was construction 2178 km2 in land in Guangxi increased2019, including by 809 335 km km2 in2 county-level in area, including cities. From 2015126 to km 2019,2 in urban county-level construction landcities. in Guangxi increased by 335 km2 in area, including 126 km2 in county-level cities. County- County-level citieslevel maintained cities maintained about about 37% 37% of of Guangxi’sGuangxi’s total total in the in current the valuecurrent and changevalue ofand change of constructionconstruction land landarea area from from 2015 2015 toto 2019, 2019, playing playing an important an important role in the role urbanization in the urbanization constructionconstruction and and the the county county economy development development in Guangxi. in Guangxi.

Figure 1. Study Area. Figure 1. Study Area. Land 2021, 10, 691 6 of 21

The studies on land use and cover change in Guangxi and their impacts have attracted the attention of scholars, including the evaluation of land use and cover change in Guangxi [65], the construction land pattern in the Xijiang River basin [66], and the interaction between land use and karst [67]. However, there is still a lack of research on the spatial differentiation of urban construction land in Guangxi and its changes, and the size of the driving forces and their interactions have not been effectively identiﬁed and quantiﬁed for characterization.

2.2. Research Methods The GeoDetector, created by Professor Wang Jinfeng in 2010 and constantly improving, is an emerging statistical method to detect spatial heterogeneity and reveal its influencing factors [62]. With the characteristics of clear principle, defined meaning, broad application conditions, and nonlinear mechanism, this method has been widely used in many natural and humanities disciplines including geography, sociology, economics, ecology, environmental science, landscape science, planning science, and even medicine [68]. There have been papers based on the GeoDetector method at present in the field of land use and cover research [62,69–71] to explain the spatial divergence and spatial-temporal evolution characteristics of land use and their driving factors. The GeoDetector offers four functions of factor detection, interaction detection, risk detection, and ecology detection Land 2021, 10, x FOR PEER REVIEW(http://www.geodetector.cn , accessed on 7 April 2021), and in this paper the first two7 of 21 Land 2021, 10, x FOR PEER REVIEW 7 of 21 Land 2021, 10, x FOR PEER REVIEWfunctions are used to study the size of the force of influencing factors and their interaction7 of 21 Land 2021, 10, x FOR PEEReffects REVIEW on land use changes in county-level cities. That is, with the urban construction 7 of 21

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The distance is estimated by the measure- 201520152015 and and and 2019; 2019; 2019; the the the independent independent independent variables variables variables 𝑋 𝑋𝑋 are are are 16 16 16 indexes indexes indexes taken taken taken from from from five five five drivers: drivers: drivers: pop- pop- pop- ment tool of Baidu Map. The evaluation of traffic conditions comprehensively considers ulation,ulation,ulation, economy, economy, economy, investment, investment, investment, industry, industry, industry, and and and soci soci societyetyety (see (see (see Table Table Table 2). 2). 2). 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𝑋 is a ItIt Itis is isworth worth worth noting noting noting that that that 𝑋 𝑋𝑋 is is issignificantly significantly significantly different different different from from from other other other indicators. indicators. indicators. 𝑋 𝑋𝑋 is is isa a a comprehensivecomprehensivecomprehensive index, index, index, and and and the the the value value value of of of access access accessibilityibilityibility is is isdetermined determined determined based based based on on on the the the compre- compre- compre- hensivehensivehensive evaluation evaluation evaluation of of of urban urban urban location location location and and and traf traf trafficficfic conditions. conditions. conditions. Location Location Location conditions conditions conditions are are are deter- deter- deter- minedminedmined based based based on on on the the the distance, distance, distance, including including including the the the di di distancestancestance from from from the the the county county county government government government to to to the the the centralcentralcentral city city city government, government, government, coastline, coastline, coastline, and and and border border border. .The .The The distance distance distance is is isestimated estimated estimated by by by the the the measure- measure- measure- mentmentment tool tool tool of of of Baidu Baidu Baidu Map. Map. Map. The The The evaluation evaluation evaluation of of of traffic traffic traffic conditions conditions conditions comprehensively comprehensively comprehensively considers considers considers Land 2021, 10, 691 7 of 21

data comes from statistical yearbooks, statistical bulletins, and government work reports of provinces and cities.

2.3. Index Selection The study was conducted between 2015 and 2019. The latest data were available in 2019, while in 2015 the data were mainly affected by the implementation time of the development planning and supporting policies of counties in China and Guangxi. In 2014, the central government promulgated the national new urbanization plan, explicitly increasing investment in county seat construction. In the same year, the “Guangxi New- type Urbanization Plan” took effect, clearly proposing to “implement the strategy of building large counties”. In 2015, the starting year to implement the “13th Five-Year Plan”, the national and Guangxi governments introduced a series of new policies to support the development of the counties during the implementation of the “13th Five-Year Plan”. Based on comprehensive consideration of the time of policy promulgation, performance lag, and data availability and comparability, the study base period was set to 2015. Urban construction land change is the result of a combination of economic, social, and investment factors, and it is highly complex and uncertain. Identifying the main influencing factors and quantifying the interaction effects between the measured factors are keys to revealing the driving mechanisms of urban construction land change. According to the studies by Wu [73], Qu [74], Zhao [75], and Liu [76], economic and social development, population and investment growth, industrial structure adjustment, policy planning and guidance, and ecological and environmental constraints are the dominant factors affecting the change of urban construction land, and appropriate indexes must be selected from them as independent variables for analysis. According to the review of literature, the common indicators as independent variables include population and GDP size, tertiary industrial structure, urbanization rate, fixed asset investment, residents’ income level, transportation accessibility, GDP per capita, import and export trade volume, actual utilization of foreign capital, fiscal revenue and support, employed population, R&D, and policies and institutions; common indicators as dependent variables are total urban construction land area, population carried per unit area, GDP or tax revenue output per unit area, and jobs created per unit area. By further statistical description and qualitative and quantitative analysis, we can find the relationship between these independent and dependent variables. In this paper, the dependent variable Yi is the construction land area of each county-level city in Guangxi in 2015 and 2019; the independent variables Xi are 16 indexes taken from five drivers: population, economy, investment, industry, and society (see Table2). It should be noted that due to a great lack of environmental data and the difficulty in quantifying policy data, such indexes were dropped during the selection of independent variables to ensure the objectivity and accuracy of the analysis results. It is worth noting that X17 is significantly different from other indicators. X17 is a comprehensive index, and the value of accessibility is determined based on the comprehensive evaluation of urban location and traffic conditions. Location conditions are determined based on the distance, including the distance from the county government to the central city government, coastline, and border. The distance is estimated by the measurement tool of Baidu Map. The evaluation of traffic conditions comprehensively considers high-speed railways, railways, expressways, national highways, and provincial highways. Including opening time, mileage, and site construction. The 2015 and 2019 “Guangxi Traffic Map” and “Guangxi Comprehensive Transportation Development Plan” are the most important evidence for evaluation. Land 2021, 10, 691 8 of 21

Table 2. Model variable description.

Variable Index Code Driving Force Data Sources

Urban construction land area in 2019 Y1 Dependent —— Y variable i Urban construction land area in 2015 Y2 Guangxi Construction Yearbook Resident population in county area X1 Population

Resident population in county town X2 driving force

Gross domestic product (GDP) X3 Economic Government revenue X4 Guangxi Statistical driving force Yearbook and County Total retail sales of consumer soods X 5 Statistical Bulletin Public ﬁnance expenditure X6 Fixed asset investment in urban municipal X public facilities 7 Investment driving force Guangxi Construction Road length X8 Yearbook Independent Road area X variable Xi 9 Primary industry X10

Secondary industry X11 Industrial structure X Tertiary industry 12 driving force Guangxi Statistical Number of standard-sized enterprises X13 Yearbook and County Per-Capita disposable income of X Statistical Bulletin urban residents 14 Social driving Per-Capita disposable income of rural residents X15 force Urbanization rate X16

Accessibility X17 Guangxi Trafﬁc Map

2.4. Research Steps There are four steps and nine key points mainly involved in the research (see Figure2 ), which are detailed as follows. The first step is “Raw Data and Preprocessing”. 1 Prepare a complete table of raw data with the data released by the relevant statistical websites, and for missing data, acquire them by consulting the departments concerned by telephone. If there are too many missing data on the indicator, choose a substitute indicator or abandon the indicator. For example, we replaced the road area with the road length during data processing and gave up the initially selected indicator of temporary population. 2 Discrete the continuous data of the independent variables using Python and divide the data of 70 county-level cities into 2–15 categories by natural breaks to eliminate artificial influence. The second step is “Data Processing”. 3 Calculate the maximum, minimum, average, standard deviation, coefficient of variation (low accuracy), and Gini index (high accuracy) of the dependent variables and their changes with Excel to measure the spatial heterogeneity of construction land. 4 Conduct spatial clustering analysis and kernel density analysis of dependent variables and their changes using GIS. 5 Import the raw data of the dependent variable and the discretized data of the independent variable into the GeoDetector to calculate the analysis results. The third step is “Data Review”. 6 Compare and select discretization schemes for independent variables based on qi and pi in the factor detection results. At the same level of significance, the scheme with the largest qi is taken as final. The significance test pi in this paper is based on 0.01, and a more stringent significance test is measured, such as 0.001, is adopted in preference where reasonable. The 4th step is “Data Analysis”. 7 Determine the explanatory power of the independent variables by ranking them according to the value of qi. 8 Analyze the interaction Land 2021, 10, x FOR PEER REVIEW 9 of 21

Land 2021, 10, 691 9 of 21 The 4th step is “Data Analysis”. ⑦ Determine the explanatory power of the independent variables by ranking them according to the value of 𝑞. ⑧ Analyze the interac- tioneffect effect of the of driving the driving factors factors based ba onsed interaction on interaction detection detection results. results. 9 Calculate ⑨ Calculate the average the averageof the independent of the independent variables variablesqi that have 𝑞 thatpassed have the passed signiﬁcance the significance test as the test representation as the rep- resentationof the intensity of the of intensity the ﬁve drivingof the five forces driving to further forces revealto further the reveal driving the mechanisms driving mecha- and nismspolicy and insights policy of insights construction of construction land change land incounty-level change in county-level cities. cities.

Figure 2. Research framework and steps. Figure 2. Research framework and steps.

3. Results Results 3.1. Differentiation Differentiation Analysis Due to the different development stages, resource endowment, and location conditions, the area of construction land and its changes in county-level cities of Guangxi show significantsignificant heterogeneity. Table Table 33 showsshows thatthat inin 2015,2015, HepuHepu CountyCounty hadhad thethe largestlargest areaarea 2 2 of urban urban construction construction land land (29.39 (29.39 km km2) and) and Jinxiu Jinxiu County County had had the smallest the smallest (1.08 (1.08 km2), km with), awith difference a difference of about of about 28 times. 28 times. In 2019, In 2019,the la thergest largest county county in urban in urban construction construction land landarea 2 2 wasarea Guiping was Guiping (37.38 (37.38 km2) kmand )the and smallest the smallest was Jinxiu was Jinxiu (1.42 km (1.422), kmwith), a with difference a difference of about of 26about times. 26 times.According According to statistics, to statistics, the coefficient the coefficient of variation of variation indicates indicates the degree the degree of varia- of tionvariation in the instudy the studysample sample and shows and the shows level the of levelsample of heterogeneity. sample heterogeneity. According According to the study to ofthe Guan study [77], of GuanZhang [ 77[78],], Zhang Ruan [79], [78], Liu Ruan [80], [79 Mi],yamoto Liu [80 ],[81], Miyamoto and She [[82]81], et and al., She the [heteroge-82] et al., neitythe heterogeneity is divided into is dividedcategories into of categoriesweak, medium, of weak, and medium, strong by and the strong coefficient by the of coefficient variation. Thatof variation. is, when Thatthe coefficient is, when theof variation coefficient is 0–0. of variation15, the heterogeneity is 0–0.15, the is heterogeneity weak, when it is is weak, 0.16– 0.35,when the it isheterogeneity 0.16–0.35, the is medium, heterogeneity and when is medium, it is greater and than when 0.36, it is the greater heterogeneity than 0.36, is thestrong. het- Theerogeneity coefficient is strong. of variation The coefficient of urban construction of variation land of urban area in constructioncreased from land 0.69 area in 2015 increased to 0.74 from 0.69 in 2015 to 0.74 in 2019, much larger than 0.36, showing high heterogeneity. in 2019, much larger than 0.36, showing high heterogeneity. Land 2021, 10, 691 10 of 21

Land 2021, 10, x FOR PEER REVIEW 10 of 21 Table 3. Differentiation analysis of urban construction land and its change.

Index 2015 2019 Change There wasMax a large spatial heterogeneity 29.93 in the change 37.38of industrial land in 16.48 county- level cities of GuangxiMin from 2015 to 2019. 1.08 The largest area growth 1.42 happened in Hengxian−2.45 County (16.48 km2), while the largest area contraction happened in Yongfu County (−2.45 km2), with Max–Mina high coefficient of variation 28.85 of 1.30, showing high 35.96 heterogeneity. It18.93 is worth noting thatMax/Min Lingchuan County, Guanyang 27.71 County, Zhaoping 26.32 County, and Yongfu County – experiencedAverage a negative growth, but it was 9.76 minor in size, which 11.74 may be the result 1.98 of urbanization and the transformation of poverty alleviation policies. Standard Deviation 6.77 8.44 2.58 The quantile-based spatial clustering analysis in GIS shows significant spatial differ- entiationCoefficient in construction of Variation (CV)land and its changes 0.69 in county-level 0.74cities of Guangxi (see 1.30 Figure 3). By comparing the results of clustering analysis in 2015 and 2019, it was found that the spatialThere distribution was a large of construction spatial heterogeneity land in county-level in the change cities of of industrial Guangxi land was ingenerally county-level in citiesa stable of Guangxi pattern in from 2015 2015 and to2019, 2019. with The “High” largest counties area growth mostly happened distributed in in Hengxian the southeast County (16.48region km of 2Guangxi,), while the“Medium” largest areacounties contraction concentrated happened and contiguous in Yongfu in County the central (− 2.45region, km 2), withand a“Low” high coefficientcounties mainly of variation found in of the 1.30, northwest showing and high northeast heterogeneity. regions. It Moreover, is worth notingthe spatial pattern of construction land changes in county-level cities of Guangxi from 2015 to that Lingchuan County, Guanyang County, Zhaoping County, and Yongfu County experi- 2019 also showed similar regularity, and the spatial distribution was more concentrated. enced a negative growth, but it was minor in size, which may be the result of urbanization Tableand 3. theDifferentiation transformation analysis of of poverty urban construction alleviation land policies. and its change. The quantile-based spatial clustering analysis in GIS shows significant spatial differen- Index tiation in construction2015 land and its changes in2019 county-level cities of GuangxiChange (see Figure3 ). Max By comparing the results29.93 of clustering analysis37.38 in 2015 and 2019, it was16.48 found that the Min spatial distribution of1.08 construction land in county-level1.42 cities of Guangxi−2.45 was generally in Max–Min a stable pattern in 201528.85 and 2019, with “High”35.96 counties mostly distributed18.93 in the southeast Max/Min region of Guangxi, “Medium”27.71 counties concentrated26.32 and contiguous in the-- central region, Average and “Low” counties mainly9.76 found in the northwest11.74 and northeast regions.1.98 Moreover, the Standard Deviationspatial pattern of construction 6.77 land changes in 8.44 county-level cities of Guangxi 2.58 from 2015 to Coefficient of Variation2019 (CV) also showed similar 0.69 regularity, and the spatial 0.74 distribution was more 1.30 concentrated.

(2015) (2019) (Change)

FigureFigure 3. Cluster3. Cluster analysis analysis of of urban urban construction construction land land andand itsits changechange in Guangxi. LeftLeft: :2015, 2015, MiddleMiddle: 2019,: 2019, RightRight: Change: Change.

3.2.3.2. Factor Factor AnalysisAnalysis In 2015, the driving factors were ranked by intensity in the order of X > X > In 2015, the driving factors were ranked by intensity in the order of 𝑋 𝑋2 𝑋＞12 X >X > X > X > X > X > X > X > X > X > X > X > X > X > X ; 𝑋3 𝑋9 𝑋8 𝑋1 𝑋6𝑋4𝑋 10𝑋 𝑋5 𝑋11 𝑋13 𝑋7 𝑋15 𝑋17 ; the 16driv- 14 theing driving forces were forces ranked were by ranked intensity by in intensity the order inof thePopulation order of > Economic Population > Investment > Economic > > InvestmentIndustrial Structure > Industrial > Social. Structure In 2019, > Social.the driving In 2019, factors the were driving ranked factors by intensity were ranked in the by > > > > > > > > > > intensityorder of in𝑋 the𝑋 order 𝑋 of𝑋X8 𝑋X9 𝑋X2𝑋X3𝑋X13𝑋X𝑋1 𝑋X12 𝑋X11 𝑋X 10𝑋X6 X𝑋5>𝑋X16 𝑋> X4; the> Xdriving17 > X forces15 > Xwere7 > rankedX14; the by driving intensity forces in the were order ranked of Population by intensity > Industrial Structure > Investment > Economic > Social (see Figures 4 and 5). The average Land 2021, 10, x FOR PEER REVIEW 11 of 21 Land 2021, 10, 691 11 of 21

Land 2021, 10, x FOR PEER REVIEW 11 of 21 force of driving factors was 0.64 in 2015 and 2019, with 11 and 10 factors exceeding the inmean, the order respectively. of Population The changes > Industrial in the force Structure ranking > Investment of driving factors > Economic were complex > Social (seefrom Figures2015 to4 2019,and5). which The average can be forceclassified of driving into th factorsree categories was 0.64 inaccording 2015 and to 2019, the withdirection 11 and and 10amount factorsforce of of exceedingdriving change, factors that the was mean,is, factors0.64 respectively. in 2015significantly and 2019, The improved, changeswith 11 and in factors the10 factors force remaining rankingexceeding ofstable, the driving and mean, respectively. The changes in the force ranking of driving factors were complex from factorsfactors weresignificantly complex decreased. from 2015 Fact to 2019,ors significantly which can improved be classified include into three𝑋 , 𝑋 categories, 𝑋 , 𝑋 , 2015 to 2019, which can be classified into three categories according to the direction and accordingand 𝑋 ; the to the factors direction remaining and amount stable include of change, 𝑋 , that𝑋 , is,𝑋 , factors 𝑋 , 𝑋 significantly, and 𝑋 ; and improved, the fac- amount of change, that is, factors significantly improved, factors remaining stable, and factorstors significantly remaining stable,decreased and factorsinclude significantly 𝑋, 𝑋, 𝑋, decreased. 𝑋, 𝑋, and Factors 𝑋. significantly improved factors significantly decreased. Factors significantly improved include 𝑋, 𝑋, 𝑋, 𝑋, include X8, X9, X11, X13, and X16; the factors remaining stable include X1, X2, X3, X10, X14, and 𝑋; the factors remaining stable include 𝑋, 𝑋, 𝑋, 𝑋, 𝑋, and 𝑋; and the fac- and X ; and the factors significantly decreased include X , X5, X6, X7, X , and X . tors17 significantly decreased include 𝑋, 𝑋, 𝑋, 𝑋, 𝑋, and4 𝑋. 12 15

FigureFigureFigure 4. 4.Analysis Analysis of of factor factor detector. detector. detector.

Figure 5. Analysis of driving force. FigureFigure3.3. 5.Interaction 5.Analysis Analysis Analysis of of driving driving force. force. 3.3. InteractionThe drive Analysis factors were all bifactorially enhanced with each other, and there was no 3.3.independent, Interaction asymptotic,Analysis or non-linear enhancement relationship. The value of the inter- actionTheThe force drivedrive was factorsfactors 0–1, and were a larger all bifactorially bifactorially value indicated enha enhanced a strongernced with with force. each each There other, other, was and aand great there there dif- was was no no independent, asymptotic, or non-linear enhancement relationship. The value of the independent,ference in the asymptotic, interacting force or non-linear between factor enhancement pairs, with relationship. a stable Gini coefficientThe value of of 0.52 the inter- interaction force was 0–1, and a larger value indicated a stronger force. There was a great action(see Table force 4). was It should 0–1, and be noted a larger that value the interacting indicated forces a stronger of 𝑋 ∩ force.𝑋, 𝑋 There ∩ 𝑋 ,was 𝑋 ∩a 𝑋great, dif- difference in the interacting force between factor pairs, with a stable Gini coefﬁcient of ference𝑋 ∩ 𝑋in the in 2015interacting and 𝑋 force∩ 𝑋 , between𝑋 ∩ 𝑋, factor𝑋 ∩ 𝑋 pa irs, in 2019 with reached a stable 0.98 Gini and coefficient above. The of 0.52 0.52driving (see Tableforce4 and). It interaction should be force noted intensity that theof urban interacting construction forces land of Xchange∩ X factors, X ∩ X , (see Table 4). It should be noted that the interacting forces of 𝑋 ∩ 𝑋 , 4𝑋 ∩ 𝑋9 , 4𝑋 ∩ 12𝑋 , X ∩ X , X ∩ X in 2015 and X ∩ X , X ∩ X , X ∩ X in 2019 reached 0.98 and 𝑋4 ∩ 𝑋8 11 in 201512 and 𝑋 ∩ 𝑋 , 𝑋1 ∩ 𝑋13, 𝑋2 ∩ 𝑋 9 in8 2019 10reached 0.98 and above. The above. The driving force and interaction force intensity of urban construction land change driving force and interaction force intensity of urban construction land change factors factors changed, and so did the composition of leading factors, showing that the driving mechanism is becoming more and more complex. The mean factor interactions for X2, X4, X8, X9, X11, X12 in 2015 and X2, X3, X8, X9, X10, X13 in 2018 were all greater than 0.9, Land 2021, 10, 691 12 of 21

and the three factors X2, X8, X9 exerted a strong interaction effect for a long time. The Land 2021, 10,interaction x FOR PEER REVIEW forces of 136 factor pairs were classified into three categories of high, medium, 12 of 21 and low by criteria of 0.85 and 0.95 (see Figure6). In 2015, there were 25 high-factor pairs, accounting for about 18.38%; 56 medium-factor pairs, accounting for about 41.18%; and 55 low-factor pairs,changed, accounting and so fordid aboutthe composition 40.44%. In of 2019, leading there factors, were showing 24 high-factor that the driving pairs, mecha- accounting for about 17.65%; 66 medium-factor pairs, accounting for about 48.53%; and nism is becoming more and more complex. The mean factor interactions for 𝑋, 𝑋, 𝑋, 46 low-factor pairs,𝑋, accounting𝑋, 𝑋 in 2015 for aboutand 𝑋 33.82%., 𝑋, 𝑋, In𝑋 summary,, 𝑋, 𝑋 in the 2018 number were all of greater counties than in 0.9, and the high categorythe remained three factors basically 𝑋, 𝑋 stable,, 𝑋 exerted while a those strong in interaction the low category effect for experienced a long time. aThe inter- decline and thoseaction in the forces medium of 136 category factor pairs enjoyed were classified a significant into three increase, categories showing of high, that medium, the and interaction effectlow is getting by criteria better of 0.85 on theand whole,0.95 (see and Figure the synergy6). In 2015, among there were the influencing 25 high-factor pairs, factors is growing.accounting for about 18.38%; 56 medium-factor pairs, accounting for about 41.18%; and 55 low-factor pairs, accounting for about 40.44%. In 2019, there were 24 high-factor pairs, Table 4. Differentiationaccounting analysis for of about interaction 17.65%; and 66 enhancement medium-factor effect. pairs, accounting for about 48.53%; and 46 low-factor pairs, accounting for about 33.82%. In summary, the number of counties in the high category remained2015 basically stable, while those in 2019 the low category experienced a declineInteraction and those in theEnhancement medium category Interactionenjoyed a significantEnhancement increase, showing that the interactionDetector effect is gettingEffect better on the whole,Detector and the synergy amongEffect the influencing factors is growing. MaxIt is 0.99worth noting that the 0.39 mean, median, plural, 0.98 maximum, and 0.64 minimum values Minwere considered 0.09 when determining 0.01 the classification 0.09 thresholds, with 0.01 reference to the balance and coordination of the sample size in each category. The thresholds in this paper Median 0.87 0.12 0.89 0.12 were set mainly for the following reasons: first, the mean interaction forces for all factor Modepairs in 2015 0.94 and 2019 were 0.84 0.12 and 0.85, the median 0.93 values were 0.87 0.10 and 0.89, the mode Max–Minvalues were 0.90 0.94 and 0.93, the 0.38 maximum values 0.89were 0.99 and 0.98, 0.63respectively, and the minimum values were both 0.09. Second, the number of samples of each type should be Max/Min 10.96 39.11 10.91 64.33 kept balanced and coordinated to some extent after classification. The aforementioned Averagetwo conditions 0.84 can be satisfied 0.13 at the same time by 0.85 criteria of 0.85 and 0.17 0.95. The thresholds Gini Coefficientwere also 0.54 moderately fine-tuned 0.68 when there were 0.54 changes in the study 0.71 sample size, indicator system, analysis content, and results.

Figure 6. Analysis of interactionFigure detector. 6. Analysis of interaction detector.

It is worth noting that the mean, median, plural, maximum, and minimum values were considered when determining the classiﬁcation thresholds, with reference to the Land 2021, 10, x FOR PEER REVIEW 13 of 21

There were large differences between the driving factors in the enhancement effect, with the Gini coefficient increasing from 0.68 in 2015 to 0.71 in 2019, the difference between the maximum and minimum values expanding from 0.38 to 0.63, and the ratio between the maximum and minimum values broadening from about 30 to about 65 times Land 2021, 10, 691 (see Table 4). The enhancement effect of the factor pairs was gradually improved13 of 21 after interaction, with the mean value of the enhancement effect increased from 0.13 in 2015 to 0.17 in 2019. The number of factor pairs with enhancement greater than 0.5 increased from 0balance in 2015 and to coordination7 in 2019, including of the sample 𝑋 ∩ size 𝑋 in, 𝑋 each ∩ category.𝑋, 𝑋 ∩ The𝑋 thresholds, 𝑋 ∩ 𝑋 in, this𝑋 ∩ paper 𝑋, and 𝑋 ∩were 𝑋 set. The mainly mean, for median, the following and mode reasons: values first, theof th meane post-interaction interaction forces enhancement for all factor effect of allpairs factors in 2015 in and 2015 2019 and were 2019 0.84 ranged and 0.85, from the 0.10 median to 0.17. values With were comprehensive 0.87 and 0.89, the consideration mode ofvalues their were maximum 0.94 and and 0.93, minimum the maximum values values and were the level 0.99 and of heterogeneity, 0.98, respectively, this and paper the classi- fiesminimum the enhancement values were botheffects 0.09. after Second, factor the intera numberction of into samples three of categories each type of should high, be medium, kept balanced and coordinated to some extent after classification. The aforementioned two and low by criteria of 0.15 and 0.3 (see Figure 7). In 2015, there were 10 high-factor pairs, conditions can be satisfied at the same time by criteria of 0.85 and 0.95. The thresholds were accountingalso moderately for about fine-tuned 7.35%; when 46 medium-factor there were changes pairs, in the accounting study sample for about size, indicator 33.82%; and 80 low-factorsystem, analysis pairs, content, accounting and results. for about 58.82%. In 2019, there were 22 high-factor pairs, accountingThere were for about large differences 16.18%; 35 between medium-factor the driving pairs, factors accounting in the enhancement for about effect, 25.74%; and 79with low-factor the Gini coefficient pairs, accounting increasing fromfor about 0.68 in 201558.09%. to 0.71 𝑋 in, 𝑋 2019,, 𝑋 the, 𝑋 difference, 𝑋, and between 𝑋 in 2015 andthe maximum 𝑋, 𝑋, 𝑋 and, 𝑋 minimum, 𝑋, 𝑋 values, 𝑋 expanding, and 𝑋 from in 2018 0.38 to experienced 0.63, and the a ratio large between enhancement the af- maximum and minimum values broadening from about 30 to about 65 times (see Table4 ). ter interaction, and the five factors of 𝑋, 𝑋, 𝑋, 𝑋, and 𝑋 exerted a strong syner- gisticThe enhancement effect for a effectlong time. of the factor pairs was gradually improved after interaction, with the mean value of the enhancement effect increased from 0.13 in 2015 to 0.17 in 2019. The number of factor pairs with enhancement greater than 0.5 increased from 0 in 2015 to 7 in Table 4. Differentiation analysis of interaction and enhancement effect. 2019, including X7 ∩ X8, X7 ∩ X9, X7 ∩ X10, X7 ∩ X11, X7 ∩ X12, and X7 ∩ X13. The mean, median, and mode2015 values of the post-interaction enhancement effect2019 of all factors in 2015

Interactionand 2019 Detecto rangedr fromEnhancement 0.10 to 0.17. WithEffect comprehensive Interaction consideration Detector of theirEnhancement maximum Effect Max and0.99 minimum values and the 0.39 level of heterogeneity, this 0.98 paper classiﬁes the enhancement 0.64 effects after factor interaction into three categories of high, medium, and low by criteria Min 0.09 0.01 0.09 0.01 of 0.15 and 0.3 (see Figure7). In 2015, there were 10 high-factor pairs, accounting for Median 0.87 0.12 0.89 0.12 about 7.35%; 46 medium-factor pairs, accounting for about 33.82%; and 80 low-factor pairs, Mode accounting0.94 for about 58.82%. 0.12In 2019, there were 22 high-factor 0.93 pairs, accounting for 0.10 about Max–Min 16.18%;0.90 35 medium-factor pairs, 0.38 accounting for about 0.89 25.74%; and 79 low-factor 0.63 pairs, Max/Min accounting10.96 for about 58.09%.39.11X4, X8, X9, X10, X11, and10.91X12 in 2015 and X7, X8, X64.339, X10 , Average X110.84, X 12, X13, and X16 in 2018 0.13 experienced a large enhancement 0.85 after interaction, and 0.17 the Gini Coefficient ﬁve 0.54 factors of X8, X9, X10, X11 0.68, and X12 exerted a strong 0.54 synergistic effect for a long 0.71 time.

Figure 7.7.Comparative Comparative analysis analysis of enhancementof enhancement effect. effect. Land 2021, 10, 691 14 of 21 Land 2021, 10, x FOR PEER REVIEW 14 of 21

4. Discussion Discussion 4.1. Driving Mechanism 4.1. Driving Mechanism The driving mechanism of urban construction land change is constructed based on The driving mechanism of urban construction land change is constructed based on the classification and grading of the driving forces and their interaction relationships, the classification and grading of the driving forces and their interaction relationships, while taking into account the influence of development stages (see Figure8). Based on the while taking into account the influence of development stages (see Figure 8). Based on the classification and grading criteria of TOP5 and average, the driving factors are classified classification and grading criteria of TOP5 and average, the driving factors are classified into three categories: “Key factors”, “Important factors” and “Auxiliary factors”. For the into three categories: “Key factors”, “Important factors” and “Auxiliary factors”. For the “Key factors”, the direct effect is dominant, with the interaction between factors taken into “Key factors”, the direct effect is dominant, with the interaction between factors taken into account. As the direct and interaction forces of “Important factors” are small, the indirect account.effects should As the be direct dominant, and interaction with the forces interaction of “Important between factors” factors isare taken small, into the account.indirect effectsAs the should direct forcesbe dominant, of “Auxiliary with the factors” interactio aren very between weak, factors and the is taken interaction into account. should As be thedominant, direct forces with specialof “Auxiliary attention factors” to the are special veryrole weak, of super-interactionand the interaction factors. should be dominant, with special attention to the special role of super-interaction factors.

Figure 8. Driving mechanism of urban construction land change in Guangxi. Land 2021, 10, 691 15 of 21

The “Population Driving Force” has long played a key role in the expansion of urban construction land, especially the resident population in county town. During the rapid urbanization, a large number of rural residents gathered in county seats, creating a huge demand in housing, transportation, public service facilities construction, and other fields, and directly driving the expansion of county construction land. “Economic Driving Force” and “Investment Driving Force” used to be very important, but there is a clear downward trend in their positions, with only GDP and road area and length maintaining a strong driving role, reflecting that developmentalism and infrastructure orientation are still the mainstream ideas. The “Industrial Structure Driving Force” is rapidly rising in status; especially the “Secondary Industry” and “Standard-Sized Enterprises”, which are playing an increasingly prominent role, reflecting that the implementation of policies to strengthen the real economy and adjust the industrial structure has achieved significant results. The “Social Driving Force” has been weak for a long time, and the rise of urbanization rate and residents’ income has little impact on the scale of construction land, which may be in connection with the policy orientation of intensive land use and stock development. Some conclusions of this paper lend support to some viewpoints of existing papers, but some are contrary. These new findings are of great value to supplement and improve the driving mechanism and evolution law of urban construction land change. For example, Cai [83], Wang [84,85], Liu [86], Jin [87], and Cao [88], et al., based on the study of large cities, believe that GDP, population, value added of secondary industry, transportation conditions, and industrial structure are important drivers of construction land expansion, which is in agreement with the viewpoint of this paper. They think that the urbanization rate (Cai, Jin, Cao), residents’ income, living standard (Liu), and other factors are also the determinants of urban construction land change, which, contrary to the viewpoint in this paper, may be affected by the scale effect. This paper discusses county-level cities, which are on a micro scale. Their research focuses on prefecture-level cities, provinces, and even regions on a macro scale.

4.2. Policy Implications: Precision and Differentiation The policy on construction land allocation is the most important macro-economic control policy adopted by the Chinese central government [89,90], and the most critical policy instrument for local governments to control urban expansion and sprawl [91]. There are many driving factors for urban construction land change, and their forces and interactions are characterized by heterogeneity, complexity, and dynamics. Therefore, this paper suggests a precise, refined, and differentiated policy design for urban construction land planning and control as well as spatial governance based on the urban construction land change driving mechanism constructed in the previous part and the findings of Li [92], Zhou [93], Ye [94], and others. The governing policy for urban construction land supply and allocation should be designed around key factors such as population size, infrastructure conditions, economic development level, and industrial structure to implement the development orientation of “tightening up increment, revitalizing the stock, and improving the quality”. A series of special supporting policies should be designed in response to the orientation of expanding domestic demand and optimizing the development of the real economy and be based on the interactive effects of factors such as fiscal revenue, social consumption, fixed asset investment, industry, and enterprises. In addition, the differentiated management of urban construction land is a significant manifestation of the modernization of the land spatial governance of a country. It is suggested to design differentiated performance evaluation standards according to local conditions, develop a new system of comprehensive urban construction land control and spatial development governance policies, and change the policy design and implementation from a “one-size-fits-all approach” to a “differentiation approach”. From the population perspective, the population size of the county seat and county region has a long-term impact on urban construction land above 0.75, and population has become a key driver of urban construction land change as the fundamental to development. Land 2021, 10, 691 16 of 21

In recent years, the “war for talent attraction” has become increasingly intense in China’s major cities, especially in the first- and second-tier cities. That is, cities across China have introduced a series of preferential policies at high frequencies to attract talents and residents, in turn resulting in a competition for human resources. Third- and fourth-tier small and medium-sized cities and counties are the hardest hit by population drainage, for example, about one-fourth of Guangxi county-level cities experienced population outflow from 2015 to 2019, especially Luzhai County, with the largest population outflow, experiencing a population decrease of 32,000, accounting for one-third of its total in 2019. Counties have a natural disadvantage in attracting immigrants, and in the context of the growing competition for human resources, the design of population policy has changed from a “war for talent attraction” to a “war for talent retention”. In other words, the core task of county-level cities is to ensure that their own population does not flow out, and the priority is to protect the population of the county seat from loss. How to make use of the new policies given to the county development in the new era by the state and autonomous regions and to retain the population in county seats and county regions should be the entry point for the design of the population policy for county-level cities, and also the point to leverage the change of construction land in county-level cities. In terms of infrastructure conditions, the area and length of intra-city roads and the level of public and public service facilities become key influencing factors, and there is no significant effect of inter-city accessibility on urban construction land. Guangxi, benefiting from the implemented strategies of the ASEAN-oriented international corridor, the new strategic pivot point for open development in the southwest and south-central regions, and organic convergence of important gateways through the “Belt and Road”, has increased investment in intercity railroads and highways, especially high-speed railways (HSRs) and expressways since 2015, establishing a fast, efficient, and smooth comprehensive transportation network. However, intercity transportation facility improvements have little impact on the construction land change in county cities, largely due to the current deviation in the direction of intercity transportation construction. Guangxi county-level city governments and citizens are enthusiastic about high-speed railway construction, and the railroad investment is mainly put in high-speed railways. Since 2015, HSR stations have been built or are under construction in more than 35% of counties, yet there are very few general rail lines or train stations. The HSR, which is mainly passenger oriented, accelerates the outflow of local population, instead of bringing immigrants as expected by the government. In contrast, the general railway mostly focuses on freight transportation and serves industrial development. Therefore, the lack of general railways has led to a scarcity of investment in county-level cities, further resulting in difficulties in expanding urban construction land. The complex topography of Guangxi and the high cost of highway construction have led to high freight costs and, thus, the highway plays a limited role in the development of counties. As Aristotle said, people come to the city to live, and stay in the city to live a good life. Urbanites and those preparing to settle in the city are increasingly focused on quality of life while considering their own career development. Convenience of transportation in the city, the tertiary industry, especially the atmosphere of commercial consumption, education, and medical facilities, as well as the potential and vitality of the city’s development, have become an important “soil” for outside population to “come” and inside population to “stay”. For county-level cities, the entry point in the new infrastructure plan should be changed from the convenience of inter-city transportation to the comfort of intra-city services and facilities. From the perspective of economic development, total GDP, industrial structure optimization, and real economy development are key driving forces for the change of construction land in county-level cities, while the driving force of finance is weakening as “well-governed prefectures and counties will ensure a stable society”; the sound governance of counties has always played an important role in the national and regional spatial governance system of China. Since the establishment of the system of prefectures and counties in the Qin Dynasty, counties have long been the most stable and important spatial Land 2021, 10, 691 17 of 21

units in the administrative division system of China, and county-level cities have become an important strategic stronghold for China to integrate urban and rural economic development and social governance in the new era. The driving force of GDP, secondary industry, and especially industrial enterprises above the scale on the change of construction land in county-level cities from 2015 to 2019 has been rapidly increasing. The influence of the tertiary and primary industries has decreased but remains high, with a driving force of more than 0.66. County-level cities, in general, should focus on promoting the simultaneous development of new industrialization, new urbanization, and agricultural modernization in economic development, accelerating the construction of industrial parks, trying to introduce more industrial manufacturing enterprises, agricultural product-processing enterprises, and logistics and tourism enterprises, as well as attracting all types of economic resources, industrial elements, and entrepreneurial individuals to the county, thus further driving the expansion of urban construction land. It should be noted that the influence of financial revenues on the county’ urban construction land decreases quickly, from 0.74 in 2015 to about 0.5 in 2019. Therefore, county-level city governments should further implement the “Guidance on Innovative Government Allocation of Resources” and the “Opinions on Building a More Perfect System and Mechanism of Market Allocation of Factors” issued by the central government. They should promote market-oriented reforms in breadth and depth, significantly reduce the direct allocation of resources by the government, expand the scope of market-oriented allocation of factors, perfect the factor market system, promote the construction of the factor market system, innovate the allocation of government financial resources by classification and sector, introduce more market mechanisms and market-oriented means, enhance the attractiveness and agglomeration of the county seats for population and resources, and further expand the total amount of urban construction land to revitalize its stock and improve its quality.

5. Conclusions The study on the spatial-temporal differences of urban construction land change at different scales and its driving mechanism is helpful to comprehensively master and reveal the spatial laws of land use and its evolution and provides more detailed and accurate information for urban policy makers and decision makers. It is of great theoretical significance and practical value to provide a scientific basis for urban governments at all levels to optimize the construction land supply mode and allocation plan, and to develop differentiated and precise construction land planning and spatial governance policies. The spatial-temporal evolution of construction land is correlated with its driving mechanism in cities of different scales and grades, but small-sized cities have special laws that are different or even contrary to those of large-sized cities due to the influence of scale effects, management systems, and operation mechanisms. The micro-level empirical study based on county-level cities in this paper fills the gap of the study on “small-sized cities”, and it contributes to the overall “map” of cross-scale research in combination with the research results of “large-sized cities” at the macro level. Since small and medium-sized cities have the largest share in the world urban system, it is of the most extensive theoretical and practical value to integrate economic and social factors and conduct a comprehensive study on their development mechanism. It not only provides guidance for the development and planning of small cities in China, but also offers a certain reference value for more countries such as Vietnam, Thailand, Myanmar, Indonesia, Malaysia, and Laos, with great case study value. Our study also has some shortcomings, which may have some impact on the accuracy and applicability of the conclusions. For example, although the indexes of driving forces employed in this study involve a wide range, there is a lack of indexes concerning policy regimes and natural environment in this paper due to the constraints on data acquisition. Besides, the economic geographical phenomenon and their development trends and mechanisms may not be the same in different spatial scales. There are no detailed and systematic comparative studies at multiple scales or across scales, such as county, prefecture, provincial, regional, and Land 2021, 10, 691 18 of 21

national levels. Further in-depth research is needed in the future on how to incorporate policy regimes, natural environments, and scale effects into the research framework to reach a more precise conclusion.

Author Contributions: Conceptualization, X.Z. and D.O.; methodology, D.O.; software, D.O. and X.L.; validation, D.O. and R.H.; formal analysis, Q.W. and X.L.; investigation, D.O. and R.H.; resources, X.Z.; data curation, D.O. and R.H.; writing—original draft preparation, D.O. and X.L.; writing— review and editing, X.Z.; visualization, D.O. and R.H.; supervision, D.O.; project administration, X.Z. and D.O.; funding acquisition, X.Z. and D.O. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Major Research Project of Guangxi in 2021: Countermeasures of Urbanization Construction in counties of Guangxi (Project No. KTZB-2021-07) and Guangxi Philosophy and Social Sciences Planning Research Project in 2017: Study on the Coupling Mechanism of Local Urbanization and Targeted Poverty Alleviation in the Process of New Urbanization in Guangxi (Project No. 17FJL004). Informed Consent Statement: Not applicable. Data Availability Statement: The data mainly comes from Guangxi Urban Construction Yearbook and Guangxi Statistical Yearbook, and select missing data comes from statistical yearbooks, statis-tical bulletins, and government work reports of provinces and cities. Interested readers and researchers can find data from http://www.gxcic.net/zj/showlist.aspx?oneid=268 (accessed on 6 June 2021) and http://tjj.gxzf.gov.cn/tjsj/ (accessed on 6 June 2021). Acknowledgments: We provided assistance to Sidong Zhao from the School of Architecture of Southeast University in the process of method and conception. Conflicts of Interest: The authors declare no conflict of interest.

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