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Article

Spatiotemporal Analysis of High-Quality Development and Coordination in Cities Along the Lower Yellow River

by
Ge Zhai
1,2,
Maoxin Zhang
2,
Tingting He
2 and
Peng Ren
1,*
1
Yellow River Engineering Consulting Co., Ltd., Zhengzhou 450003, China
2
School of Public Affairs, Zhejiang University, Hangzhou 310027, China
*
Author to whom correspondence should be addressed.
Land 2024, 13(11), 1863; https://doi.org/10.3390/land13111863
Submission received: 29 September 2024 / Revised: 2 November 2024 / Accepted: 5 November 2024 / Published: 7 November 2024
(This article belongs to the Special Issue Big Data in Urban Land Use Planning)
Browse Figures
Versions Notes

Abstract

:
The current urban development in cities along the Lower Yellow River is in tension regarding human–land relations. To achieve the goals of ecological protection and high-quality development (HQD), it is urgent to scientifically measure and analyse the region’s function development and development coordination (DC). This study focuses on cities along the Lower Yellow River, constructs a three-dimensional HQD assessment framework based on urban functions through multiple remote sensing data, and evaluates DCs by feature classification. The results show the following: (1) The HQD of the study area shows a trend of decreasing and then increasing during 2000–2020 and reaches its highest level at the end. HQD shows a spatial trend of decreasing from south to north and from east to west. (2) The overall agricultural function of the study area declined slightly; the ecological function declined first and then increased, with the highest value occurring in 2000; and the urban function increased steadily and improved significantly after 2015. (3) DCs under different administrative levels are polarised, with high-level DCs exhibiting a spatial leader effect. (4) Urban development preferences in the study area are divergent, and the functional type with the highest share under different administrative scales is agro-ecological, which is mainly influenced by differences in natural base. This study reveals the characteristics of HQD and functional changes in cities along the Lower Yellow River, combined with a hierarchical classification of DCs and the types of development preferences, providing a reference for the formulation of spatial governance strategies.

1. Introduction

As an important ecological barrier and economic zone in China, the Yellow River Basin has a crucial position in socio-economic development and environmental security [1]. In October 2021, China’s State Council issued the Outline of the Plan for Ecological Protection and High-Quality Development of the Yellow River Basin, which sets out clear targets and requirements for ecological protection, use of cropland resources, and construction of towns and cities in the Yellow River Basin. With the acceleration of urbanisation and the rise in population, the ecological and environmental problems of the Yellow River Basin have become a current focus [2,3]. The downstream of the Yellow River has been a flood-prone area since ancient times, threatening the livelihoods and production of coastal residents and leading to further ecological degradation due to the large amount of silt accumulation [4]. The region, which includes the provinces of Henan and Shandong, is one of China’s most critical grain-producing areas. The two provinces account for 46.8% and 52.2% of the population and economy of the Yellow River Basin, respectively [5]. Restricted by limited natural resources, the contradiction between urban development and agriculture and ecology in the region is prominent [6], and the economic growth between regions shows a severe imbalance [7]. Therefore, assessing the HQDs of the cities along the Lower Yellow River is a scientific basis for formulating governance policies.
Currently, there are two main types of studies on ecological protection and high-quality development in the downstream Yellow River Basin. The first is the single-element study type, such as population and economic development matching [8]; soil and water conservation and vegetation construction [9,10]; changes in the number of rural settlements [11]; land use and land cover [12]; ecosystem services [13]; water resources [14]; and nature reserves [15]. The second type is the integrated study type with multiple elements, spatial types, and subsystems as research objects. For example, Liu (2021) explored land use change and ecosystem service value responses under the spatial perspective of “production–life–ecology” [16]; Wei (2022) examined the coupling relationship between ecosystem service functions and urbanisation in urban clusters along the Yellow River, portrayed the coupling coordination degree and sequential lag relationship, and categorized regions according to ecological management [17]. Some scholars have also measured the composite subsystems in the downstream of the Yellow River from the perspective of socio-ecological composite systems [18]. Ding (2022) employed the grey prediction GM (1,1) model and Tobit model to measure the degree of economic–population–environmental coupling coordination and influencing factors of the downstream Yellow River urban agglomeration from 2010 to 2019 [19]. He and Zhang (2024) used a BP neural network model to map subsystems to system vulnerability, analysing the spatiotemporal distribution patterns and barrier factors of the composite system’s vulnerability in the downstream Yellow River from 2001 to 2020 [19]. Although the ecological protection of the downstream Yellow River Basin and high-quality development have received attention, the following shortcomings still exist. On the one hand, scholars have not yet reached a consensus regarding the ecological protection priorities and strategies for high-quality development in the downstream Yellow River Basin, and constructions of measurement index systems are very diverse. Previous studies mostly used statistical data, which inevitably caused errors and could not accurately portray spatial heterogeneity. On the other hand, the study area is rich in agricultural resources and is an essential grain-producing area in China. Balancing the development coordination of multiple main functions, including agriculture, has become the focus of current attention.
With the development of urbanisation, the function of land has gradually shifted from single- to multi-functional, and this change has slowly evolved from “conflict of land use functions” to “conflict of urban function space” [20]. Along with this transformation, the rapid shift in the function of limited land resources has generated a series of problems, such as development constraints between production and living spaces [21], conflicts between economic development and ecological protection [22], and conflicts between urban expansion and cropland protection [23]. Recognising the multidimensional functional space of cities is an important way to solve the above problems, helping to coordinate social and economic development and promote efficient and sustainable land use [24]. The shift from economic-first development to coordinating production–living–ecology (PLE) space has resulted in a unique and practical analytical framework for this perspective [25]. Current research on PLE covers the construction of PLE indicator systems [26], quantification of functions [27], and spatial representation [28]. However, such comprehensive indicators usually rely on statistical datasets, such as statistical yearbooks at the county level or other administrative units [29]. These data cannot provide sufficient detail at the scale of fine management. Land use changes lead to significant spatial heterogeneity in the multidimensional functional space of cities, and monitoring units at small scales can reflect hidden attributes [30]. Therefore, quantifying PLE functional space at smaller management scales can effectively reflect its spatial heterogeneity characteristics. This applies particularly to areas with extreme resource scarcity, intense functional conflicts, and complex natural conditions [25].
In recent years, with the development of remote sensing and machine learning algorithms, multivariate remote sensing data characterised by long time series, high accuracy, and extensive range have been widely adopted in urban research and quantitative measurement [31]. Applying multi-source remote sensing data compensates for the insufficiency of statistical data in the study of ecology, agriculture, urbanisation, and other regional functions. At present, the urban spatial types divided by the major function orientations has become an essential basis for spatial governance [32]. Such an integrated spatial governance model is centred on the rational allocation of various functional land use types. It considers the needs of economic development and urban construction, as well as the objectives of ecological protection and environmental governance. Therefore, this study measured the coordinated dynamic relationship between the HQD and DC of cities and towns in the study area from 2000 to 2020 from the functional and spatial perspectives of land use based on the Google Earth Engine (GEE) platform, selecting multi-source data. The precise portrayal of the functional development preference and coordination of the cities along the Lower Yellow River provides a scientific basis for the government to formulate targeted governance strategies.

2. Methodology and Data Resource

2.1. Study Area

The Lower Yellow River flows through the two provinces of Henan and Shandong, starting from Zhengzhou of Henan in the west and ending at the estuary of Dongying of Shandong in the east. There are 16 cities and 149 counties and districts along the downstream, covering a distance of 786 km, with a total area of 194,700 km2 [33]. The topography of the study area is complex. The Yellow River forms the famous “Hanging River on the Ground” in Henan Province due to the large amount of sediment it carries [34,35], and then enters the sea in the Dongying delta via the northern side of the Luzhong Mountains in Shandong Province (Figure 1). The Yellow River Basin serves as China’s main food production base, and the region is densely urbanised and populated, but with a relatively weak economic base. In the past two decades, rapid urbanisation has promoted the expansion of urban construction land and the agglomeration of non-agricultural population [36]. However, irrational human development activities have led to a decline in cropland quality, land salinisation, and wetland destruction, further weakening the stability of the ecosystem. The contradiction between human beings and nature is becoming more and more prominent [37].

2.2. Data Sources

In this study, the newly released China Land Cover Dataset (CLCD) [38] from Wuhan University was used, and the land use types included nine types of cropland, forest, shrub, grassland, water, snow and ice, barren, impervious, and wetland. The agricultural functional area was studied using cropland in the dataset, including permanent basic and general cropland. We used the cumulative NDVI value of effective temperature to further calculate cropland production [39]. The ecological protection functional area usually refers to the ecological protection red line range and general ecological space, corresponding to the range of forests, shrubs, grasslands, barrens, and wetlands in the dataset. Therefore, the Remote Sensing Ecological Index (RSEI) [40,41], widely used in environmental quality assessment, was used to measure ecological quality. The urbanisation development space includes part of the built-up land inside and outside the town development boundary, corresponding to the impervious surfaces in the dataset as the research object. Meanwhile, to further reflect the complexity of human activities, this study used population (POP), point of interest (POI), Nighttime Light (NTL), and Building Height Index (BHI) to comprehensively measure the urbanisation development level of the study area. Among them, POI can accurately depict the urban spatial pattern and function [42,43]. At the same time, NTL can quickly monitor human social activities and reflect the regional development level [44]. The above indices are important indicators of the regional urban development level, and details of the data are shown in Table 1.

2.3. Methodology

2.3.1. HQD Measurement from Multidimensional Functional Perspective

To assess the level of HQD in the study area, we introduce the PLE analysis framework. Since the development of urbanisation, the town space has evolved from a purely residential function to a composite set of functions, including residence, commercial activities, and industrial development. The existence of permanent basic cropland in the study area, as China’s most important grain-producing area, has led to strict control of its industrial development [45]. Therefore, to better reflect the current land use situation, we reclassify the spatial functions of the study area into “Agro–Urban–Eco” spaces according to the land use categories and measure them separately.
Eco-spatial quality:
The GEE platform was used to acquire Landsat Collection2 Level2 data released by the United States Geological Survey (USGS). Clouds and their shadows were removed using the image quality band QA_PIXEL, and further NDVI, NDBSI, WET, LST spatial and temporal metrics were generated for the years 2000, 2005, 2010, 2015, and 2020, and normalised to the extremes [46]. The 1st principal component, PC1, was derived by principal component analysis to obtain the initial RSEI0, and then the improved RSEI of the study area was obtained by the polar normalisation method.
R S E I = R S E I 0 − R S E I 0 min R S E I 0 max − R S E I 0 min
where the RSEI value ranges from 0 to 1, with values closer to 1 indicating a better ecological environment, and values closer to 0 indicating a worse one. RSEI0max and RSEI0min represent the maximum and minimum values of the RSEI0 index, respectively.
Urban spatial development:
U L = α P O P + β B U I + γ P O I + δ N T L
where UL is the urbanisation development level, POP is the number of people per unit of construction land, BUI is the volume of buildings per unit, POI is the hotspot density per unit of construction land, and NTL is the intensity of lighting per unit of construction land. α, β, γ, and δ are the weights, which are normalised, respectively. In this study, all four indicators reflect the socio-economic level of towns and cities, and an equally important value is assigned as 0.25.
Cropland utilisation efficiency:
A L = ε A R E A + ζ ∑ i = S D T 10   ° C E D T 10   ° C N D V I / A c r o p l a n d
where AL is the utilisation efficiency of cropland, the first term is the proportion of cropland area within the unit, and the second term is the cumulative NDVI value of cropland used to characterise cropland yield [47]. Acropland refers to the cropland area within the unit, and SDT10 °C and EDT10 °C denote the start date and the end date of the 5-day moving average air temperature T ≥ 10 °C. The calculation unit was a 1 km × 1 km grid, counted at the county and city administrative scales, and normalised separately. ζ and ε are the weighting coefficients, which we determined using the Delphi method [48]. During the Delphi process, we cooperated with experts from the Yellow River Water Conservancy Commission and government agencies in the study area. In this study, field visits were made to governments, institutions, and farmers, and an anonymous questionnaire survey (Supplementary Materials) was conducted. Cropland area and production were finally determined as the assessment indexes of cropland use efficiency using Principal Component Analysis. Meanwhile, the experts reached a consensus on the cropland utilisation efficiency weights through multiple scoring rounds. ζ is 0.74, and ε is 0.26. Cropland area reflects the dependence of agricultural functions on basic resources of cropland, while the higher weight of yield reflects the decisive role of cropland quality on agricultural functions.
HQD measurement:
The key to high-quality urban development depends on achieving a balance of multidimensional functions [49]. Therefore, we further measured the HQD level of each administrative unit by evaluating the “Agro–Urban–Eco” spaces and considered each of its functions equally important. This setup reduces the influence of subjective factors and improves the objectivity and fairness of the evaluation without knowing the true status of development. It also helps to make policy recommendations for improvement in the diagnosis of functional development preferences. HQD is calculated as follows:
H Q D = R S E I s t × A L s t × U L s t
where RSEIst, ALst, and ULst represent the normalised values of RSEI, AL, and UL for a given county.

2.3.2. “Agro–Urban–Eco” Functional Preference for DC

In regional development, the contradiction between coordinated and uncoordinated development always exists [50]. An imbalanced “Agro–Urban–Eco” functional space is not conducive to regional optimisation, upgrading, and sustainable development. However, as the contradiction deepens, the regional system will autonomously generate the evolution from a low level to high level. In this study, the developmental coordination of the three functions is reflected by the differences in the contributions of the indicators, drawing on the ternary contour method to show the developmental preferences of different units [51]. The three-dimensional indicators are first normalised separately:
V A = R A R A + R U + R E
V U = R U R A + R U + R E
V E = R E R A + R U + R E
RA, RU, and RE are the “Agro–Urban–Eco” rankings of a given county among all counties in the study area (from lowest to highest). VA, VU, and VE denote the normalised values of the three variables, each set to contribute to the corresponding axis plotted in the ternary contour line. This ternary contour provides a visual representation of the difference in contribution between the indicators. Figure 2 explains the ternary diagram using Anyang as an example. Figure 2a shows that Anyang ranks 45%, 31%, and 24% in the agricultural, ecological, and urban dimensions. If the degree of contribution of the three variables is equal, the values of these three variables are relatively close to each other, characterising a county as being located near the centre point of the triangle. On the contrary, if the contribution degree of the three indicators of a county differs significantly, the point representing the county will be located near the edge of the triangle. At the same time, the ternary diagram can help us identify which factors in a region contribute more to the integrated ecological–agricultural–urban development. Based on the distance of the drop point from the centre, DC is classified into four classes: H1, H2, H3, and H4, i.e., coordinated, more coordinated, less coordinated, and uncoordinated (Figure 2b). From the diagram, we can see that the DC of Anyang city is H2 but the counties within its jurisdiction are distributed unequally in H2, H3, and H4. In order to better identify the development preferences of each county, we divide the ternary map into six equal parts, corresponding to each part representing the first two dominant functional preferences of the county units located in the region. As shown in Figure 2c, we divided the ternary map into the following six categories based on functional preferences: Agro-Eco T1 (agriculture > ecology > urban), Agro-Urban T2 (agriculture > urban > ecology), Eco-Agro T3 (ecology > agriculture > urban), Eco-Urban T4 (ecology > urban > agriculture), Urban-Eco T5 (urban > ecology > agriculture), and Urban-Agro T6 (urban > agriculture > ecology).

3. Results

3.1. Spatiotemporal Evolutionary Characteristics of HQD in the Study Area

The assessment of HQD in the cities along the Lower Yellow River reveals that the study area shows a decreasing and then increasing trend, with the lowest value occurring in 2010, followed by a significant increase, reaching the maximum value in 2020. Provincially, the HQD of Henan Province is higher than that of Shandong Province. From the HQD ranking of each city, Shangqiu’s HQD is significantly higher than the others, but its HQD in 2020 is still not higher than that in 2000. Binzhou, Zibo, and Dongying’s HQD levels have always ranked lower during the study period, but they have shown a steady improvement. Zhengzhou, Hebi, Jinan, Xinxiang, and Dongying have shown large increases in HQD, especially Zhengzhou, which has risen from fifth place in 2000 to second place in 2020. Kaifeng, Heze, Puyang, and Shangqiu experienced a significant decline in HQD. Kaifeng’s HQD continues to decline across the study period and falls to its lowest value in 2020. From the changes in the cities, we find that although the HQD of the cities in Shandong Province was lower during the study period, all of them except Heze showed a steady growth trend. All the municipalities in Henan Province except Zhengzhou show significant fluctuation or a slight decline trend.
To demonstrate the spatial distribution pattern, we used the natural breakpoint method to classify the HQD of the counties into five classes, from low to high: I, II, III, IV, and V. In 2000, there were 27, 40, 45, 23, and 14 counties at levels I, II, III, IV, and V. Level I was mainly distributed in LiJin County in Dongying, Linzi District in Zibo, Wucheng County in Dezhou, and Xintai City in Tai’an. Level V was distributed primarily in Yucheng and Liangyuan counties in Shangqiu City and scattered in the Jinshui District of Zhengzhou and the surrounding area of Yutai County of Jining. From 2000 to 2010, the HQD of counties declined significantly, with V-level counties shrinking from 14 in 2000 to 9 in 2005, and only 4 counties reaching the V-level in 2010. It is noteworthy that Yongcheng, Xiayi, and Suoyang in Shangqiu City were always at level V during the study period. After 2015, the improvement of HQD in counties within the study area showed a trend of spreading from south to north and west to east. By 2020, there were 24, 24, 53, 35, and 13 counties in levels I, II, III, IV, and V, respectively (Figure 3).

3.2. Measurement of “Agro–Urban–Eco” Development

The development level of the cities along the Lower Yellow River urban agglomeration is quantified from three dimensions: agriculture, urban, and ecology, respectively (Figure 4). Overall, the value of agriculture in the five periods of 2000, 2005, 2010, 2015, and 2020 was 0.7312–0.7546, showing a fluctuating and slightly decreasing trend. The values of ecological function were 0.7044, 0.6412, 0.6217, 0.6889, and 0.6464, showing an initial decline followed by an increase. The urban function values were 0.2269, 0.2230, 0.2120, 0.2910, and 0.3785, rising rapidly after bottoming out in 2010. At the municipal level, Shangqiu, Kaifeng, Heze, Liaocheng, and Puyang have better agricultural functions, while Dongying, Zibo, and Jinan have worse agricultural functions. Among these, Zhengzhou, Jining, and Dongying exhibited a more pronounced decline in agricultural function. The ecological function of all cities except Dezhou declined. In 2020, Shangqiu, Xinxiang, and Jiaozuo had the highest ecological functions, and the lowest values were distributed in Jinan, Dongying, and Binzhou. Regarding urban functions, Zhengzhou experienced the most significant increase during the study period and reached the maximum value in the study area in 2020. In contrast, the urban functions of Dezhou and Binzhou showed little improvement over the study period and are well below the average.
In order to better demonstrate the functional distribution and change trends of counties, we used the natural breakpoint method to classify them (Figure 5). In terms of agricultural functions, there were 3, 15, 35, 50, and 46 counties in 2000 from the lowest to the highest rank. Among them, the high grades were mainly distributed in Yongcheng, Xiayi, and Lankao in the south. In contrast, the low grades were primarily distributed near the estuary of the Yellow River, such as Wudi and Kenli. Over time, the agricultural functions in the study area showed a significant quality decline, with a significant increase in A-I and a sharp decrease in the intermediate grades III and IV. Spatially, the area showed a decline in agricultural function in the south and an increase in agricultural function in the north. Notably, Licheng District in Jinan and Zichuan District in Zibo are located in grades A-V, but their surrounding counties show a cascading and spreading decline in agricultural function. As of 2020, the agricultural function classes ranked from lowest to highest included 11, 19, 31, 40, and 48, respectively.
In terms of urban functions, 63, 47, 29, 8, and 2 counties were in the five classes from low to high in 2000. U-I is primarily located in Kenli District in Dongying and Zhanhua District in Binzhou, while U-IV is located in Yongcheng City and the surrounding counties in Shangqiu. U-V is distributed in the main urban areas of two provincial capital cities, such as Lixia District in Jinan and Jinshui District in Zhengzhou. The number of counties with IV and V ratings in the study area increased significantly as time progressed. Spatially, this demonstrates a spreading development around the original counties of U-V, particularly in the years before and after 2015, indicating a significant leading effect. By 2020, there were 4, 36, 62, 28, and 19 counties with urban functional classes from low to high, respectively. Only Zhanhua District in Binzhou, Xazin County in Texas, and Plain County in Heartland remain in the lowest rank.
In 2000, 5, 20, 39, 59 and 46 counties were in the five grades of ecological function from low to high. The lowest grade, E-I, is mainly distributed in Zhengzhou’s Erqi District, Kaifeng’s Gulou District, and Zibo’s Zhangdian District. The highest grade, E-V, is primarily distributed in Yongcheng City of Shangqiu, Linzhou District of Anyang, Dongping County of Tai’an, and Hekou District of Dongying. It is worth noting that the ecological function along the Yellow River estuary showed a significant decline during the study period. The ecological function level of Cao County in Heze City and Puyang County in Puyang City in the south of the study area also decreased from E-IV to E-III. The study area showed a significant increase in E-I and a significant decrease in E-IV. There were 17, 24, 48, 41, and 19 ecological functions from low to high by 2020, respectively.

3.3. DC for Three-Dimensional Functions

Through the presentation of ternary diagrams (Figure 6), we identified their development function preferences and DC degrees at the city and county scales, respectively. At the municipal level, the number of cities with a DC rating of H1-H4 in 2000 was 0, 9, 7, and 1, respectively; the number of cities in the four levels of H1-H4 in 2020 was 1, 6, 6, and 4. The rank shows a significant divergence in the DCs of the cities, with significant polarisation. In 2000, there was only Zibo in H4, but by 2020, H4 had increased to four, including Jinan, Dongying, Kaifeng, and Zhengzhou. Of these, Zibo’s DC grew steadily after entering H3 in 2005, while Jinan’s and Kaifeng’s DC continued to decline and ultimately stayed at H4. It is worth noting that in 2000, Hebi, Xinxiang, and Liaocheng were all close to H1, but ultimately only Xinxiang entered the H1 range. All other cities hovered between H2 and H3, with no dramatic changes in DC rankings.
At the county scale, the number of counties with H3 and H4 in 2000, 2005, 2010, 2015, and 2020 accounted for 56.37%, 60.40%, 61.07%, 66.44%, and 61.07 per cent, respectively. In 2020, the number of counties in the four levels of H1-H4 was 5, 53, 49, and 42, respectively. In terms of spatial distribution, H1 is mainly distributed in Zouping City, Yongping City, Liangyuan District, Suiyang District, and Puyang District, while H4 is distributed in Dongying District, Kenli District, Xazin County, Jinan’s Lixia District, Jinshui District, Qingfeng County, and Qixian County. Compared with 2000, a total of 40 grades of DC in counties in 2020 decreased, 80 maintained the original level, and 29 grades increased.

4. Discussion

4.1. Analysis of Factors Leading to Changes in HQD

The overall HQD in the cities along the Lower Yellow River experienced a decrease followed by an increase, reaching its lowest point in 2010. There are several main reasons for this phenomenon. Firstly, rapid urbanisation has led to the destruction of the ecological environment. Around 2008, China accelerated the process of urbanisation, with a dramatic expansion of land for construction, encroaching on a large amount of cropland and natural ecological space [52]. This brash land use has led to declining agricultural and ecological functions in the HQD. In addition, urbanisation has greatly contributed to the construction of infrastructure and secure housing projects, providing a strong impetus for enhancing urban functions [53].
Spatially, HQD scores varied significantly across regions, revealing a geographical clustering. From a municipal perspective, Shangqiu, Zhengzhou, and Hebi are the cities with the highest level of comprehensive development, benefiting from superior resource environments, location advantages, and solid industrial foundations [54,55]. Such land use has put enormous pressure on agricultural production and the ecological environment, leading to a decline in agricultural and ecological functions in the HQD. In addition, urbanisation has greatly contributed to the construction of infrastructure and secure housing projects, injecting a strong impetus to the enhancement of urban functions. In contrast, Binzhou, Zibo, and Dongying are the three cities with the lowest HQD ratings. Due to topographic constraints, soil salinization, and erosion, Binzhou and Dongying, especially Hekou District, are unsuitable for cropland production and urban construction, making ecological protection efforts particularly challenging [56]. Zibo’s development is restricted by its location in the mountainous area of central Shandong, with many mountains and hills [57]. Additionally, some counties in Jinan, Tai’an, and Anyang are affected by natural factors and have a low level of food security. With population growth and urbanisation, the human–land relationship in the Lower Yellow River has become increasingly tense, resulting in a significant decline in the cropland areas in Zhengzhou [58] and Jinan [59], as well as a deterioration in the ecological quality of the Yellow River Delta region [60]. However, Xinxiang, Hebi, and Jiaozuo are the core regions of the Central Plains urban agglomeration due to their favourable locations [61]. Hebi, in particular, experienced the fastest growth during the study period thanks to its favourable geographical location, rich resources, good industrial base, and policy support. In contrast, the development of Heze and Kaifeng was relatively slow, limited mainly by factors such as resource endowment, poor infrastructure, and insufficient industrial support [62].
With the implementation of sustainable development policies and the strengthening of environmental governance, the government has emphasised the construction of an ecological civilisation and promoted the upgrading of industrial structures [63]. At the same time, it has increased financial investment in ecological environmental protection and implemented a series of ecological restoration projects, such as returning farmland to forests, wetland restoration, and water pollution prevention [64]. These initiatives have effectively eased environmental pressure and promoted the restoration and upgrading of agricultural and ecological functions, leading to a gradual rebound in HQD after 2010.

4.2. Trends and Reasons for the Evolution of Functional Types of Cities in the Study Area

Clustering the trends in the evolution of urban functional types in the study area, we find that more than 70% of the urban functional types during the study period are concentrated in T1 (Agro-Eco), T2 (Agro-Urban), and T3 (Eco-Agro). This indicates that urban development along the Lower Yellow River is still dependent on the expansion of agricultural functional space, with relatively slow progress in urbanisation and ecological protection. Similar trends have been observed in other agriculture-dominated regions. The interactive development of ecological and agricultural functions has become an important direction for current urban development transformation [65], which further illustrates the resource base and future green development goals of the study area as a traditional grain-dominated region in China [66]. In terms of the evolution of functional categories, Zibo, Jinan, Jiaozuo and Kaifeng have not changed their urban functional types during the study period, with the former two and the latter two always located in T1 (Agro-Eco) or T5 (Urban-Agro), respectively. Although all four cities are traditional agricultural production areas, there are differences in policy orientation and resource endowment between the two provinces. Zibo and Jinan have well-developed industrial infrastructure in addition to agriculture, and the severe ecological damage caused by early industrial development has led to environmental protection becoming the government’s priority for economic development in the future [67,68]. Although Jinan is the capital city of the province, the strict ecological functional zoning policy limits the occupation of agricultural space by urban expansion, resulting in the slow progress of urbanisation and continued maintenance of the T1 functional type in both [69]. On the contrary, although Jiaozuo and Kaifeng are both agriculture-led urban development types, their ecological quality continues to deteriorate due to overexploitation of mineral resources [70] and saline land problems [70], respectively.
Some cities in the study area have experienced significant changes in their functional types. For example, Heze evolved from T1 to T2, reflecting the further enhancement of the city’s agricultural function and the acceleration of the urbanisation process during the study period. The adjustment of the industrial structure of Heze through the vigorous development of agricultural product processing and the construction of agricultural industrial parks has led to the gradual fusion of the agricultural function with the urban function [71], making Heze show an “agricultural city” preference, while the ecological function has relatively weakened. Zhengzhou, on the other hand, has transformed from T3 to T5. As a national centre city, Zhengzhou has gradually shifted from agriculture to a modern industrial structure of manufacturing and service industries, which has further weakened its agricultural function [72]. Meanwhile, rapid population growth and the release of the Yellow River High Quality Development Programme have prompted Zhengzhou to make a large financial investment in ecological protection, which not only improves the ecological quality of the Yellow River wetlands but also maintains the liveability and attractiveness of the city [73]. It is worth noting that there is also some convergence of function types in the study area. For example, Dongying and Xinxiang start from different functional types but eventually converge to T1, while Anyang and Shangqiu evolve from T6 to T5. This functional convergence phenomenon reflects the strategic adjustment of the cities along the Yellow River in ecological protection and sustainable development. It also reflects the balancing efforts of local governments in responding to the dual demands of environmental protection and economic development.

4.3. Diagnosis of Multidimensional Functional Preferences and Policy Recommendations for Study Area

Through overlaying the HQD and functional DC of the cities, it can be observed that cities with higher DC usually have higher HQD ratings (Figure 7a), implying the barrel effect and the necessity of identifying development shortcomings [51]. As the smallest level of governmental institutions, the identification of development preferences and shortcomings of counties is an important scientific basis for the formulation of grassroots governance policies [74].
According to the development preferences of counties (Figure 7b), there were 60, 17, 3, 16, 42, and 11 counties of types T1-T6, respectively, in 2020, reflecting an obvious development imbalance in the study area. T1 and T2 counties, as agricultural function-dominated counties, accounted for 51.68% of the study area, which is mainly distributed in the southern part of the area around Shangqiu and Dezhou. These areas have a low level of urban functions, and under the current premise of a gradual slowdown in urbanisation, they need to take the initiative to transform towards ecological and green agriculture, and to fully leverage their basic agricultural resources. These include the development of high-value-added green agriculture, the construction of high-standard agricultural demonstration zones, and other measures to improve agricultural production efficiency while balancing the goal of ecological protection. Meanwhile, the government and social capital are encouraged to provide ecological compensation or special financial support for green agriculture [75].
Types T3 and T4 are ecologically function-dominated counties, which are fewer in number. They are mainly concentrated in Dongping County in Tai’an City and around Yiyuan County in Zibo City. These counties are mostly mountainous and hilly, and villagers are encouraged engage in tertiary services to increase their income through eco-tourism scenic spots and agro-tourism parks. At the same time, they rely on topographical advantages to plant mountain cash crops, create influential, branded green agricultural products, and ensure the sustainability of tourism and agriculture while protecting ecological quality [76].
Types T5 and T6 are urban function-dominated counties, accounting for 35.57% of the study area, mainly concentrated in Zhengzhou, Jinan, Xinxiang, Zibo, Dongying, and other medium and large cities. These regions have population advantages and a good foundation for urbanisation. For counties with a high level of development and high coordination, such as the area around the Jinshui District of Zhengzhou, which has location and natural resource advantages, it is necessary to leverage economic advantages, strengthen urban–rural integration and county linkage, and expand the role of radiation [77]. For counties with a lower level of development and lower coordination, such as Zibo, Dongying, and other traditional industrial cities, it is necessary to vigorously promote the county economy to accelerate the shift from a single-factor-driven path to an innovation-driven one, to clarify the leading industries and breakthrough directions, and to expand the advantageous production capacity. Meanwhile, relying on the potential of urban–rural economic construction, the economy drives agricultural development and promotes ecological protection and restoration [78].

5. Conclusions

The results showed that the HQD in the study area generally decreased and then increased between 2000 and 2020, ultimately reaching its highest level. Spatially, it exhibits a decreasing pattern from south to north and from east to west. The overall agricultural function of the study area declined slightly; the ecological function declined initially and then increased, with the highest value occurring in 2000; while the urban function increased steadily and improved significantly after 2015. Unlike previous studies that focused solely on a single natural element, this study integrates spatiotemporal analyses and synergies of multidimensional urban functions. Meanwhile, we found that DCs at different administrative levels exhibit polarisation, with high-level DCs demonstrating a clear spatial leading effect. Urban development preferences are differentiated in the study area, with the highest proportion of agro-ecological functional types primarily influenced by the differences in natural foundations. Through identifying the types of urban functional preferences of the municipalities in the study area, this study targeted policy recommendations based on their own developmental foundations, and provides a scientific basis for the formulation of effective regional governance. Future studies should further integrate additional socio-economic factors and deepen the exploration of regional development coordination mechanisms to comprehensively promote the high-quality development of the cities along the Lower Yellow River.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land13111863/s1, Questionnaire on perception of agricultural functions.

Author Contributions

Conceptualization, P.R.; methodology, T.H. and G.Z.; software, G.Z.; validation, M.Z. and T.H.; formal analysis, G.Z.; investigation, P.R.; data curation, G.Z. and T.H.; writing—original draft preparation, G.Z. and M.Z.; writing—review and editing, G.Z.; visualization, G.Z. and M.Z.; supervision, P.R.; project administration, P.R.; funding acquisition, T.H. All authors have read and agreed to the published version of the manuscript.

Funding

Zhejiang Natural Science Foundation: Q22D018818; Philosophy and social sciences planning in Zhejiang Province: 24JCXK04YB.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author(s).

Conflicts of Interest

Author Ge Zhai and Peng Ren were employed by the company Yellow River Engineering Consulting Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Chen, Y.; Fu, B.; Zhao, Y.; Wang, K.; Zhao, M.M.; Ma, J.; Wu, J.-H.; Xu, C.; Liu, W.; Wang, H. Sustainable Development in the Yellow River Basin: Issues and Strategies. J. Clean. Prod. 2020, 263, 121223. [Google Scholar] [CrossRef]
  2. Qiu, M.; Yang, Z.; Zuo, Q.; Wu, Q.; Jiang, L.; Zhang, Z.; Zhang, J. Evaluation on the Relevance of Regional Urbanization and Ecological Security in the Nine Provinces along the Yellow River, China. Ecol. Indic. 2021, 132, 108346. [Google Scholar] [CrossRef]
  3. Quan, J.; Xu, Y.; Ma, T.; Wilson, J.P.; Zhao, N.; Ni, Y. Improving Surface Water Quality of the Yellow River Basin Due to Anthropogenic Changes. Sci. Total Environ. 2022, 836, 155607. [Google Scholar] [CrossRef] [PubMed]
  4. Chen, Y.; Syvitski, J.P.M.; Gao, S.; Overeem, I.; Kettner, A.J. Socio-Economic Impacts on Flooding: A 4000-Year History of the Yellow River, China. AMBIO 2012, 41, 682–698. [Google Scholar] [CrossRef] [PubMed]
  5. China Statistical Yearbook 2022. Available online: https://www.stats.gov.cn/sj/ndsj/2022/indexch.htm (accessed on 20 April 2024).
  6. Ren, Y.; Bai, Y.; Liu, Y.; Wang, J.; Zhang, F.; Wang, Z. Conflict or Coordination? Analysis of Spatio-Temporal Coupling Relationship between Urbanization and Eco-Efficiency: A Case Study of Urban Agglomerations in the Yellow River Basin, China. Land 2022, 11, 882. [Google Scholar] [CrossRef]
  7. Wang, S.-Y.; Liu, J.-S.; Yang, C.-J. Eco-Environmental Vulnerability Evaluation in the Yellow River Basin, China1. Pedosphere 2008, 18, 171–182. [Google Scholar] [CrossRef]
  8. Zhou, Y.; Li, W.; Li, H.; Wang, Z.; Zhang, B.; Zhong, K. Impact of Water and Land Resources Matching on Agricultural Sustainable Economic Growth: Empirical Analysis with Spatial Spillover Effects from Yellow River Basin, China. Sustainability 2022, 14, 2742. [Google Scholar] [CrossRef]
  9. Wang, K.; Zhou, J.; Tan, M.L.; Lu, P.; Xue, Z.; Liu, M.; Wang, X. Impacts of Vegetation Restoration on Soil Erosion in the Yellow River Basin, China. CATENA 2024, 234, 107547. [Google Scholar] [CrossRef]
  10. Jia, G.; Hu, W.; Zhang, B.; Li, G.; Shen, S.; Gao, Z.; Li, Y. Assessing Impacts of the Ecological Retreat Project on Water Conservation in the Yellow River Basin. Sci. Total Environ. 2022, 828, 154483. [Google Scholar] [CrossRef]
  11. Zhang, B.; Zhai, J.; Zhai, B.; Qu, Y. Understanding the “Conflict-Coordination” Theoretical Model of Regional Land Use Transitions: Empirical Evidence from the Interconversion between Cropland and Rural Settlements in the Lower Yellow River, China. Habitat Int. 2024, 148, 103072. [Google Scholar] [CrossRef]
  12. Wang, S.-Y.; Liu, J.-S.; Ma, T.-B. Dynamics and Changes in Spatial Patterns of Land Use in Yellow River Basin, China. Land Use Policy 2010, 27, 313–323. [Google Scholar] [CrossRef]
  13. Zhang, Y.; Lu, X.; Liu, B.; Wu, D.; Fu, G.; Zhao, Y.; Sun, P. Spatial Relationships between Ecosystem Services and Socioecological Drivers across a Large-Scale Region: A Case Study in the Yellow River Basin. Sci. Total Environ. 2021, 766, 142480. [Google Scholar] [CrossRef] [PubMed]
  14. Xu, Z.X.; Takeuchi, K.; Ishidaira, H.; Zhang, X.W. Sustainability Analysis for Yellow River Water Resources Using the System Dynamics Approach. Water Resour. Manag. 2002, 16, 239–261. [Google Scholar] [CrossRef]
  15. Zhang, P.; Li, X.; Yu, Y. Relationship between Ecosystem Services and Farmers’ Well-Being in the Yellow River Wetland Nature Reserve of China. Ecol. Indic. 2023, 146, 109810. [Google Scholar] [CrossRef]
  16. Liu, Y.; Geng, W.; Shao, J.; Zhou, Z.; Zhang, P. Land Use Change and Ecosystem Service Value Response from the Perspective of “Ecological-Production-Living Spaces”. Areal Res. Dev. 2021, 40, 129–135. [Google Scholar]
  17. Wei, H.; Xue, D.; Huang, J.; Liu, M.; Li, L. Identification of Coupling Relationship between Ecosystem Services and Urbanization for Supporting Ecological Management: A Case Study on Areas along the Yellow River of Henan Province. Remote Sens. 2022, 14, 2277. [Google Scholar] [CrossRef]
  18. Liu, J.; Huang, L.; Zuo, Q. Evaluation of Harmonious Development of Economy-Population-Resource-Environment in the Lower Reaches of the Yellow River. Resour. Sci 2021, 43, 412–422. [Google Scholar] [CrossRef]
  19. He, H.; Zhang, H. Study on Vulnerability Assessment and Obstacle Degree of Economic-Social-Ecological Complex System in the Lower Yellow River Regions. Ecol. Econ. 2023, 39, 174–181. [Google Scholar]
  20. Zhou, D.; Xu, J.; Lin, Z. Conflict or Coordination? Assessing Land Use Multi-Functionalization Using Production-Living-Ecology Analysis. Sci. Total Environ. 2017, 577, 136–147. [Google Scholar] [CrossRef]
  21. Wang, Q.; Zhang, X.; Wu, Y.; Skitmore, M. Collective Land System in China: Congenital Flaw or Acquired Irrational Weakness? Habitat Int. 2015, 50, 226–233. [Google Scholar] [CrossRef]
  22. Adam, Y.O.; Pretzsch, J.; Darr, D. Land Use Conflicts in Central Sudan: Perception and Local Coping Mechanisms. Land Use Policy 2015, 42, 1–6. [Google Scholar] [CrossRef]
  23. Liu, F.; Zhang, Z.; Zhao, X.; Wang, X.; Zuo, L.; Wen, Q.; Yi, L.; Xu, J.; Hu, S.; Liu, B. Chinese Cropland Losses Due to Urban Expansion in the Past Four Decades. Sci. Total Environ. 2019, 650, 847–857. [Google Scholar] [CrossRef] [PubMed]
  24. Steinhäußer, R.; Siebert, R.; Steinführer, A.; Hellmich, M. National and Regional Land-Use Conflicts in Germany from the Perspective of Stakeholders. Land Use Policy 2015, 49, 183–194. [Google Scholar] [CrossRef]
  25. Li, J.; Zhang, H.; Xu, E. Quantifying Production-Living-Ecology Functions with Spatial Detail Using Big Data Fusion and Mining Approaches: A Case Study of a Typical Karst Region in Southwest China. Ecol. Indic. 2022, 142, 109210. [Google Scholar] [CrossRef]
  26. Yu, Z.; Chen, L.; Zhang, T.; Li, L.; Yuan, L.; Teng, G.; Xiao, J.; Shi, S.; Chen, L. Land Pressure Evaluation in the Yangtze River Delta Region: A Perspective from Production-Living-Ecology. Land Degrad. Dev. 2023, 34, 5312–5327. [Google Scholar] [CrossRef]
  27. Kong, L.; Xu, X.; Wang, W.; Wu, J.; Zhang, M. Comprehensive Evaluation and Quantitative Research on the Living Protection of Traditional Villages from the Perspective of “Production–Living–Ecology. ” Land 2021, 10, 570. [Google Scholar] [CrossRef]
  28. Tao, J.; Lu, Y.; Ge, D.; Dong, P.; Gong, X.; Ma, X. The Spatial Pattern of Agricultural Ecosystem Services from the Production-Living-Ecology Perspective: A Case Study of the Huaihai Economic Zone, China. Land Use Policy 2022, 122, 106355. [Google Scholar] [CrossRef]
  29. Fang, C.; Jia, K.; Li, G.; Wang, Y. Theoretical Analysis of the Index System and Calculation Model of Carrying Capacity of Land Ecological-Production-Living Spaces from County Scale. Acta Ecol. Sin. 2017, 37, 5198–5209. [Google Scholar]
  30. Qin, Y.; Li, Y.; Xu, R.; Hou, C.; Armstrong, A.; Bach, E.; Wang, Y.; Fu, B. Impacts of 319 Wind Farms on Surface Temperature and Vegetation in the United States. Environ. Res. Lett. 2022, 17, 024026. [Google Scholar] [CrossRef]
  31. Li, F.; Yigitcanlar, T.; Nepal, M.; Nguyen, K.; Dur, F. Machine Learning and Remote Sensing Integration for Leveraging Urban Sustainability: A Review and Framework. Sustain. Cities Soc. 2023, 96, 104653. [Google Scholar] [CrossRef]
  32. Wang, W.; Wang, W.; Xie, P.; Zhao, D. Spatial and Temporal Disparities of Carbon Emissions and Interregional Carbon Compensation in Major Function-Oriented Zones: A Case Study of Guangdong Province. J. Clean. Prod. 2020, 245, 118873. [Google Scholar] [CrossRef]
  33. Shen, H.; Cao, B.; Wu, H.; Qiao, W. Parameters Variation Law with Distance in the Discharge-Sediment Relation Model of the Lower Yellow River. Acta Geogr. Sin. 2022, 77, 635–649. [Google Scholar]
  34. Lu, J.; Wu, M.; An, C.; Liang, Y.; Chen, C. Sediment Disposal Scheme for Maintaining the Stability of the Lower Reaches of the Yellow River. Yellow River 2023, 45, 30–35. [Google Scholar]
  35. Wang, S.; Yan, Y.; Li, Y. Spatial and Temporal Variations of Suspended Sediment Deposition in the Alluvial Reach of the Upper Yellow River from 1952 to 2007. CATENA 2012, 92, 30–37. [Google Scholar] [CrossRef]
  36. Zhang, J. Reconstruction and Ecological Management of the Lower Yellow River Floodplain. Yellow River 2017, 39, 24–27. [Google Scholar] [CrossRef]
  37. Feng, Y.; Li, C.; Li, Y.; Ma, J.; Feng, J. Characteristics of Urban Land Expansion and Its Mechanism of Cities and Towns around the Floodplain of the Lower Yellow River. Geogr. Res. 2023, 42, 955–976. [Google Scholar]
  38. Yang, J.; Huang, X. The 30 m Annual Land Cover Dataset and Its Dynamics in China from 1990 to 2019. Earth Syst. Sci. Data 2021, 13, 3907–3925. [Google Scholar] [CrossRef]
  39. Barnes, A.D.; Jochum, M.; Mumme, S.; Haneda, N.F.; Farajallah, A.; Widarto, T.H.; Brose, U. Consequences of Tropical Land Use for Multitrophic Biodiversity and Ecosystem Functioning. Nat Commun 2014, 5, 5351. [Google Scholar] [CrossRef]
  40. Yuan, B.; Fu, L.; Zou, Y.; Zhang, S.; Chen, X.; Li, F.; Deng, Z.; Xie, Y. Spatiotemporal Change Detection of Ecological Quality and the Associated Affecting Factors in Dongting Lake Basin, Based on RSEI. J. Clean. Prod. 2021, 302, 126995. [Google Scholar] [CrossRef]
  41. Gong, C.; Lyu, F.; Wang, Y. Spatiotemporal Change and Drivers of Ecosystem Quality in the Loess Plateau Based on RSEI: A Case Study of Shanxi, China. Ecol. Indic. 2023, 155, 111060. [Google Scholar] [CrossRef]
  42. Zhang, X.; Du, S.; Zheng, Z. Heuristic Sample Learning for Complex Urban Scenes: Application to Urban Functional-Zone Mapping with VHR Images and POI Data. ISPRS J. Photogramm. Remote Sens. 2020, 161, 1–12. [Google Scholar] [CrossRef]
  43. Liu, K.; Yin, L.; Lu, F.; Mou, N. Visualizing and Exploring POI Configurations of Urban Regions on POI-Type Semantic Space. Cities 2020, 99, 102610. [Google Scholar] [CrossRef]
  44. Bennett, M.M.; Smith, L.C. Advances in Using Multitemporal Night-Time Lights Satellite Imagery to Detect, Estimate, and Monitor Socioeconomic Dynamics. Remote Sens. Environ. 2017, 192, 176–197. [Google Scholar] [CrossRef]
  45. Yang, R.; Zhong, C.; Yang, Z.; Yang, S.; Ji, G.; Yu, J.; Cao, L.; Yang, J.; Luo, L.; Shen, Y.; et al. Study on China’s Farmland–Grain Resource Curse: Empirical Testing Based on 31 Provinces and 2843 Counties. Land Use Policy 2024, 144, 107241. [Google Scholar] [CrossRef]
  46. Xu, H.; Wang, M.; Shi, T.; Guan, H.; Fang, C.; Lin, Z. Prediction of Ecological Effects of Potential Population and Impervious Surface Increases Using a Remote Sensing Based Ecological Index (RSEI). Ecol. Indic. 2018, 93, 730–740. [Google Scholar] [CrossRef]
  47. Liu, X.; Liu, Y.; Liu, Z.; Chen, Z. Impacts of Climatic Warming on Cropping System Borders of China and Potential Adaptation Strategies for Regional Agriculture Development. Sci. Total Environ. 2021, 755, 142415. [Google Scholar] [CrossRef]
  48. Curtis, I.A. Valuing Ecosystem Goods and Services: A New Approach Using a Surrogate Market and the Combination of a Multiple Criteria Analysis and a Delphi Panel to Assign Weights to the Attributes. Ecol. Econ. 2004, 50, 163–194. [Google Scholar] [CrossRef]
  49. Bruns-Berentelg, J.; Noring, L.; Grydehøj, A. Developing Urban Growth and Urban Quality: Entrepreneurial Governance and Urban Redevelopment Projects in Copenhagen and Hamburg. Urban Stud. 2022, 59, 161–177. [Google Scholar] [CrossRef]
  50. Liu, Y.; Zhang, X.; Pan, X.; Ma, X.; Tang, M. The Spatial Integration and Coordinated Industrial Development of Urban Agglomerations in the Yangtze River Economic Belt, China. Cities 2020, 104, 102801. [Google Scholar] [CrossRef]
  51. He, T.; Lu, Y.; Yue, W.; Xiao, W.; Shen, X.; Shan, Z. A New Approach to Peri-Urban Area Land Use Efficiency Identification Using Multi-Source Datasets: A Case Study in 36 Chinese Metropolitan Areas. Appl. Geogr. 2023, 150, 102826. [Google Scholar] [CrossRef]
  52. Liu, T.; Liu, H.; Qi, Y. Construction Land Expansion and Cultivated Land Protection in Urbanizing China: Insights from National Land Surveys, 1996–2006. Habitat Int. 2015, 46, 13–22. [Google Scholar] [CrossRef]
  53. Lang, W.; Chen, T.; Li, X. A New Style of Urbanization in China: Transformation of Urban Rural Communities. Habitat Int. 2016, 55, 1–9. [Google Scholar] [CrossRef]
  54. Song, X.; Wu, S.; Xu, X. An Overview of China’s Regional Development and Overall Pattern. In The Great Change in the Regional Economy of China Under the New Normal; Song, X., Wu, S., Xu, X., Eds.; Springer: Singapore, 2019; pp. 1–90. ISBN 978-981-329-475-2. [Google Scholar]
  55. Dong, X.; Fu, W.; Yang, Y.; Liu, C.; Xue, G. Study on the Evaluation of Green Technology Innovation Efficiency and Its Influencing Factors in the Central Plains City Cluster of China. Sustainability 2022, 14, 11012. [Google Scholar] [CrossRef]
  56. Yanbo, Q.; Guanghui, J.; Yuting, Y.; Qiuyue, Z.; Yuling, L.; Wenqiu, M. Multi-Scale Analysis on Spatial Morphology Differentiation and Formation Mechanism of Rural Residential Land: A Case Study in Shandong Province, China. Habitat Int. 2018, 71, 135–146. [Google Scholar] [CrossRef]
  57. Zhao, X.; Wang, P.; Gao, S.; Yasir, M.; Islam, Q.U. Combining LSTM and PLUS Models to Predict Future Urban Land Use and Land Cover Change: A Case in Dongying City, China. Remote Sens. 2023, 15, 2370. [Google Scholar] [CrossRef]
  58. Mu, B.; Mayer, A.L.; He, R.; Tian, G. Land Use Dynamics and Policy Implications in Central China: A Case Study of Zhengzhou. Cities 2016, 58, 39–49. [Google Scholar] [CrossRef]
  59. Wang, C.; Wang, Q.; Liu, N.; Sun, Y.; Guo, H.; Song, X. The Impact of LUCC on the Spatial Pattern of Ecological Network during Urbanization: A Case Study of Jinan City. Ecol. Indic. 2023, 155, 111004. [Google Scholar] [CrossRef]
  60. Shi, H.; Lu, J.; Zheng, W.; Sun, J.; Li, J.; Guo, Z.; Huang, J.; Yu, S.; Yin, L.; Wang, Y.; et al. Evaluation System of Coastal Wetland Ecological Vulnerability under the Synergetic Influence of Land and Sea: A Case Study in the Yellow River Delta, China. Mar. Pollut. Bull. 2020, 161, 111735. [Google Scholar] [CrossRef]
  61. He, L.; Tao, J.; Meng, P.; Chen, D.; Yan, M.; Vasa, L. Analysis of Socio-Economic Spatial Structure of Urban Agglomeration in China Based on Spatial Gradient and Clustering. Oeconomia Copernic. 2021, 12, 789–819. [Google Scholar] [CrossRef]
  62. Liu, K.; Qiao, Y.; Shi, T.; Zhou, Q. Study on Coupling Coordination and Spatiotemporal Heterogeneity between Economic Development and Ecological Environment of Cities along the Yellow River Basin. Env. Sci. Pollut. Res. 2021, 28, 6898–6912. [Google Scholar] [CrossRef]
  63. Meng, F.; Guo, J.; Guo, Z.; Lee, J.C.K.; Liu, G.; Wang, N. Urban Ecological Transition: The Practice of Ecological Civilization Construction in China. Sci. Total Environ. 2021, 755, 142633. [Google Scholar] [CrossRef] [PubMed]
  64. Yurui, L.; Xuanchang, Z.; Zhi, C.; Zhengjia, L.; Zhi, L.; Yansui, L. Towards the Progress of Ecological Restoration and Economic Development in China’s Loess Plateau and Strategy for More Sustainable Development. Sci. Total Environ. 2021, 756, 143676. [Google Scholar] [CrossRef] [PubMed]
  65. Verburg, P.H.; Dearing, J.A.; Dyke, J.G.; van der Leeuw, S.; Seitzinger, S.; Steffen, W.; Syvitski, J. Methods and Approaches to Modelling the Anthropocene. Glob. Environ. Change 2016, 39, 328–340. [Google Scholar] [CrossRef]
  66. Zhou, J.; Hu, T.; Wei, Z.; Ji, D. Evaluation of High-Quality Development Level of Regional Economy and Exploration of Index Obstacle Degree: A Case Study of Henan Province. J Knowl Econ 2024. [Google Scholar] [CrossRef]
  67. Wang, S.; Liu, J.; Ren, L.; Zhang, K.; Wang, R. The Development and Practices of Strategic Environmental Assessment in Shandong Province, China. Environ. Impact Assess. Rev. 2009, 29, 408–420. [Google Scholar] [CrossRef]
  68. Zheng, B.; Guo, Q.; Wei, Y.; Deng, H.; Ma, K.; Liu, J.; Zhao, J.; Zhang, X.; Zhao, Y. Water Source Protection and Industrial Development in the Shandong Peninsula, China from 1995 to 2004: A Case Study. Resour. Conserv. Recycl. 2008, 52, 1065–1076. [Google Scholar] [CrossRef]
  69. Zhang, X.; Han, H. Characteristics and Factors Influencing the Expansion of Urban Construction Land in China. Sci Rep 2024, 14, 16040. [Google Scholar] [CrossRef]
  70. Li, G.; Zhang, J.; Zhu, L.; Tian, H.; Shi, J.; Ren, X. Spatial Variation and Driving Mechanism of Soil Organic Carbon Components in the Alluvial/Sedimentary Zone of the Yellow River. J. Geogr. Sci. 2021, 31, 535–550. [Google Scholar] [CrossRef]
  71. Qu, Y.; Zhang, Y.; Wang, S.; Shang, R.; Zong, H.; Zhan, L. Coordinated Development of Land Multi-Function Space: An Analytical Framework for Matching the Supply of Resources and Environment with the Use of Land Space for Ecological Protection, Agricultural Production and Urban Construction. J. Geogr. Sci. 2023, 33, 311–339. [Google Scholar] [CrossRef]
  72. Wang, X.; Tomaney, J. Zhengzhou–Political Economy of an Emerging Chinese Megacity. Cities 2019, 84, 104–111. [Google Scholar] [CrossRef]
  73. Jiang, X.; Zhai, S.; Liu, H.; Chen, J.; Zhu, Y.; Wang, Z. Multi-Scenario Simulation of Production-Living-Ecological Space and Ecological Effects Based on Shared Socioeconomic Pathways in Zhengzhou, China. Ecol. Indic. 2022, 137, 108750. [Google Scholar] [CrossRef]
  74. Tan, X.; Wang, Z.; An, Y.; Wang, W. Types and Optimization Paths Between Poverty Alleviation Effectiveness and Rural Revitalization: A Case Study of Hunan Province, China. Chin. Geogr. Sci. 2023, 33, 966–982. [Google Scholar] [CrossRef]
  75. Gao, Y.; Liu, B.; Yu, L.; Yang, H.; Yin, S. Social Capital, Land Tenure and the Adoption of Green Control Techniques by Family Farms: Evidence from Shandong and Henan Provinces of China. Land Use Policy 2019, 89, 104250. [Google Scholar] [CrossRef]
  76. Dhakal, B.; Kattel, R.R. Effects of Global Changes on Ecosystems Services of Multiple Natural Resources in Mountain Agricultural Landscapes. Sci. Total Environ. 2019, 676, 665–682. [Google Scholar] [CrossRef] [PubMed]
  77. Khalaf, C.; Michaud, G.; Jolley, G.J. Toward a New Rural Typology: Mapping Resources, Opportunities, and Challenges. Econ. Dev. Q. 2022, 36, 276–293. [Google Scholar] [CrossRef]
  78. Zeng, L.; Wang, J.; Zhang, J.; Lv, J.; Cui, W. New Urbanization Paths in Mineral Resource Abundant Regions in China: A Three-Dimensional Cube Framework. Resour. Policy 2020, 68, 101709. [Google Scholar] [CrossRef]
Land 13 01863 g001
Figure 1. The location of the study area (a) and cities along the riverbank (b).
Figure 1. The location of the study area (a) and cities along the riverbank (b).
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Figure 2. Interpretations of (a) the ternary diagram, (b) the determination of the degree of “Agro–Urban–Eco” development coordination, and (c) the six different development preference categories in the case of Anyang.
Figure 2. Interpretations of (a) the ternary diagram, (b) the determination of the degree of “Agro–Urban–Eco” development coordination, and (c) the six different development preference categories in the case of Anyang.
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Figure 3. Distribution of integrated development effectiveness levels of towns and cities in lower reaches of Yellow River in different periods.
Figure 3. Distribution of integrated development effectiveness levels of towns and cities in lower reaches of Yellow River in different periods.
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Figure 4. Levels of ecological–cropland–urban development in cities of Lower Yellow River in different periods.
Figure 4. Levels of ecological–cropland–urban development in cities of Lower Yellow River in different periods.
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Figure 5. Levels of “Agro–Urban–Eco” development at different times.
Figure 5. Levels of “Agro–Urban–Eco” development at different times.
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Figure 6. Ranks of DC at municipal (a) and county (b) scales and their spatial distribution (c) in study area.
Figure 6. Ranks of DC at municipal (a) and county (b) scales and their spatial distribution (c) in study area.
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Figure 7. HQD and DC levels for each city in 2020 (a), and bar charts showing the number of different functional types within county-level (b).
Figure 7. HQD and DC levels for each city in 2020 (a), and bar charts showing the number of different functional types within county-level (b).
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Table 1. Composition of indicator system and data sources.
Table 1. Composition of indicator system and data sources.
Data TypeIndicatorResolutionData Resource
Ecological qualityNDVI30 mLandsat TM/ETM+/OLI
https://espa.cr.usgs.gov/ accessed on 6 November 2024
NDBSI30 m
WET30 m
LST30 m
Cropland utilisation efficiencyCropland area30 m
Crop productivity
(Cumulative NDVI)		30 mhttp://doi.org/10.5281/zenodo.4417810
accessed on 6 November 2024
UrbanisationPOP100 mhttps://hub.worldpop.org/doi/10.5258/SOTON/WP00645
accessed on 6 November 2024
BHI10 mhttps://zenodo.org/record/7064268#.YxtVAuxBz0p
accessed on 6 November 2024
POI-https://map.baidu.com/
accessed on 6 November 2024
NTL500 mhttp://www.ngdc.noaa.gov
accessed on 6 November 2024
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Zhai, G.; Zhang, M.; He, T.; Ren, P. Spatiotemporal Analysis of High-Quality Development and Coordination in Cities Along the Lower Yellow River. Land 2024, 13, 1863. https://doi.org/10.3390/land13111863

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Zhai G, Zhang M, He T, Ren P. Spatiotemporal Analysis of High-Quality Development and Coordination in Cities Along the Lower Yellow River. Land. 2024; 13(11):1863. https://doi.org/10.3390/land13111863

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Zhai, Ge, Maoxin Zhang, Tingting He, and Peng Ren. 2024. "Spatiotemporal Analysis of High-Quality Development and Coordination in Cities Along the Lower Yellow River" Land 13, no. 11: 1863. https://doi.org/10.3390/land13111863

APA Style

Zhai, G., Zhang, M., He, T., & Ren, P. (2024). Spatiotemporal Analysis of High-Quality Development and Coordination in Cities Along the Lower Yellow River. Land, 13(11), 1863. https://doi.org/10.3390/land13111863

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