Interactions and Conflicts between Urbanization and Greenness: A Case Study from Nanjing, China

Abstract

Urbanization is rapidly occupying green spaces, making it crucial to understand implicit conflicts between urbanization and greenness. This study proposes an ecological greenness index (EGI) and a comprehensive urbanization index (CUI) and selects Nanjing, a megacity in China, as the study area to research the spatial and temporal evolutionary trends of the EGI and CUI in the context of land use/land cover (LULC) changes from 2000 to 2020. Meanwhile, the conflicts and complex interaction characteristics of the EGI and CUI are discussed from both static and dynamic perspectives, and their driving mechanisms are investigated by combining specific indicators. The results demonstrate that over the past 20 years, LULC in Nanjing was dominated by cultivated land, forest land, and artificial surfaces. The encroachment of artificial surfaces on green space was strengthened, resulting in a decrease in the proportion of cultivated land from 70.09% in 2000 to 58.00% in 2020. The CUI increased at a change rate of 0.6%/year, while the EGI showed significant browning (change rate: −0.23%/year), mainly concentrated within the main urban boundaries. The relationship between the CUI and EGI made the leap from “primary coordination” to “moderate coordination”, but there remains a risk of further deterioration of the decoupling relationship between the CUI and ecological pressures. The multi-year average contribution of the CUI to the EGI was 49.45%. Urbanization activities that dominate changes in greenness have changed over time, reflecting the timing of urban conflict management. The results provide important insights for urban ecological health monitoring and management.

1. Introduction

Green spaces, primarily comprising permeable “soft” surfaces of soil, grass, shrubs, and trees, play a pivotal role in safeguarding biodiversity, retaining precipitation, enhancing microclimates, and improving human health [1,2,3]. With cities serving as hubs of socio-economic activity, it is anticipated that nearly 70% of the population will inhabit urban areas by mid-century [4,5]. The push for urbanization often hinges on reclaiming natural land, transforming it from “soft” to “hard” surfaces [6]. Rapid socio-economic development has significantly increased population densities and activity intensities in urban areas, exerting immense pressure on the health of urban ecosystems [7].

Greenness is an important indicator of the resilience of urban ecosystems [8,9]. Key indicators such as the fractional vegetation cover (FVC) [10], leaf area index (LAI) [11], and vegetation productivity (e.g., net primary productivity (NPP)) [12] play a crucial role in identifying regional greenness gain and damage. Evidently, if singularly taken, there are differences in the performance benefits and sensitivity of the FVC, LAI, and NPP [13,14,15]. Urbanization equally involves multiple dimensions such as demographic urbanization, economic urbanization, and spatial urbanization [16]. The interactive effects of urbanization on vegetation greenness are similarly integrated and complicated [17,18,19]. Previous studies focused mainly on examining the relationship between vegetation greenness and urbanization from a single dimension/indicator, lacking comparisons between the whole and the local and between the long-term and the short-term [20,21]. At the same time, there is a certain lack of multidimensional and three-dimensional assessments of the greenness performance of urban ecological construction processes, which may lead to assessment bias [22,23]. That is to say, the current research on the dynamic process of coupling and interaction between urbanization construction and vegetation greenness is relatively insufficient. This is particularly unfavorable to the future sustainable development of urban ecology.

Conflict and coordination between urbanization and urban greenness usually go hand in hand, which forms the basis for their interaction [24,25]. Conflict and coordination in ecosystems were considered to coexist in equilibrium for a long period of time, similar to the coexistence of conflict and cooperation in transboundary waters in the context of political interactions [26,27]. Cities are typical compounds, where conflict and coordination are the main themes in their development process, which reflects the complex relationships of trade-offs (conflicts) and synergies between the urban system and other systems and subsystems [28,29,30]. Identifying and quantifying conflicts and coordination is a hot direction for conducting research on the interaction mechanisms between urbanization and the ecosystem [31,32,33]. Fang and Wang [34] revealed the basic laws and stage characteristics of the interactive coercion between urbanization and the ecological environment and proposed urban management strategies based on the optimal dynamic equilibrium point. Vegetation greenness, as a functional indicator of urban ecosystems, can effectively reflect external stresses such as ecological pressures (EPs) [35].

Symbiosis is a higher level of coordination [36]. Accurately identifying and properly handling the contradiction or conflict between urbanization and greenness is a necessary path to achieve the symbiosis between humans and nature [37]. The construction of comprehensive indices and the use of multidimensional measures provide powerful tools for revealing the implicit coordination and conflict states/relationships between urbanization and greenness. The static coupling coordination degree (CCD) model and the dynamic Tapio decoupling model have been widely used in the fields of urban development [38,39], water resources and energy [40,41], and economy and ecology [42,43]. The joint use of these two models can complement each other [44], which is conducive to fully exploring the data information and capturing the subtle relationships within systems. Therefore, this paper aims to construct a novel ecological greenness index (EGI) based on the FVC, LAI, and NPP and a comprehensive urbanization index (CUI) grounded in demographic, economic, and spatial dimensions. Additionally, this study endeavors to comprehensively characterize the potential conflicts and interactions between urbanization and greenness through the application of the CCD and Tapio decoupling models.

Following reforms and opening up, China has achieved significant urbanization progress but at the same time faces emerging challenges in preventing and restoring ecological degradation [45,46,47]. Several studies have shown that urbanization has adversely affected the vegetative ecology of most Chinese cities [48,49,50,51]. For example, irrational urban planning has led to biodiversity destruction [52], and rapid urbanization has caused substantial loss of highly productive agricultural land [53]. Nanjing, the capital of Jiangsu Province, is one of the fastest growing megacities in China. Rapid urbanization has posed a huge ecological challenge for the region [54,55], testing the ecological and environmental governance capacity of local government departments. At present, significant progress has been made in research on various aspects of urban ecological development in Nanjing, including urban growth patterns [56], the heat island effect [57], ecosystem services [58,59], water and soil resources [60,61], and the system carrying capacity [62,63]. Unfortunately, there remains a relative lack of understanding regarding the coupling and coordination relationship between urbanization and greenness. This cognitive gap greatly hinders a clear answer to the questions of whether Nanjing has achieved a high degree of coordinated development between urbanization and vegetation greenness and whether its urbanization is decoupled from the EP.

Therefore, taking Nanjing as the study area, this paper aims to achieve the following specific objectives: (1) to analyze the evolution of land use/land cover (LULC) and expansion patterns in Nanjing from 2000 to 2020, revealing the dynamic game between ecological land use (e.g., cropland, forest land) and artificial surfaces; (2) to construct the CUI and EGI of Nanjing, unveiling their spatiotemporal trends over the past two decades; (3) to explore the coupling coordination relationships between the CUI and EGI, as well as the decoupling state of the CUI and EP, identifying coordination and conflict signals between urbanization and greenness at the regional scale; (4) to quantify the contribution of urbanization activities to greenness by combining specific indicators, analyzing the analysis of the interaction-driven mechanism among indices. This study will shed light on the efficient and coordinated economic and ecological development of megacities around the world.

2. Materials

2.1. Study Area

Nanjing (31°14″~32°37″N, 118°22″~119°14″E) is located in the southwestern part of Jiangsu Province along the coast of China, covering about 6583 km² (Figure 1). This city is influenced by the East Asian monsoon and has a humid subtropical climate. Nanjing stands as the only city in Jiangsu Province to have developed across a river, with its water area constituting 11.4% of the total area. There are 11 municipal districts in Nanjing, of which Xuanwu, Qinhuai, Jianye, Gulou, Qixia, and Yuhuatai Districts are known as the main urban region (MUR), accounting for 12% of the total area [64]. In recent years, the main urban boundary (MUB) of Nanjing has experienced outward expansion, marked by an urbanization rate notably surpassing the provincial average [65]. This rapid urbanization has placed considerable focus on the effectiveness of ecological and environmental governance in Nanjing [66].

2.2. Data Sources

This study collected various data, including administrative district data, NPP data, population density (PD) spatial data, gross domestic product density (GDPD) data, nighttime lighting density (NTLD) data, and MUB, FVC, LAI, and LULC data (

Table 1. A detailed description of the study data.

Name	Spatial Resolution	Time Resolution	Time Frame	Data Sources
The administrative division data	1:1 million	Annual	2020	National Catalogue Service Geographic Information (https://www.webmap.cn/, accessed on 8 February 2022)
NPP	500 m	Annual	2000–2020	MOD17A2HGF (https://appeears.earthdatacloud.nasa.gov/, accessed on 16 November 2023)
PD	100 m	Annual	2000–2020	WorldPop Global Project Population Data (https://www.worldpop.org/, accessed on 13 January 2024)
GDPD	1000 m	Annual	2000–2020	Paper [67]
NTLD	500 m	Annual	2000–2020	Paper [68]
MUB	30 m	Annual	2018	Paper [69]
FVC	250 m	Months	2000–2020	China regional 250 m fractional vegetation cover data set (2000–2022) (https://data.tpdc.ac.cn/zh-hans/data/f3bae344-9d4b-4df6-82a0-81499c0f90f7, accessed on 5 January 2024)
LAI	500 m	8-Days	2000–2020	Paper [70]
LULC	30 m	Annual	2000, 2010, 2020	National Geomatics Center of China [71]

Note: NPP, net primary productivity; PD, population density; GDPD, gross domestic product density; NTLD, nighttime lighting density; MUB, main urban boundary; FVC, fractional vegetation cover; LAI, leaf area index; LULC, land use/land cover.

). This study used ArcGIS 10.8 and R language for image batch cropping, projection, resampling, maximum value synthesis, and vacancy filling. Data statistical analysis and visualization were performed using MATLAB R2023a, Excel 2021, and Origin 2021. The vacancies of CUI and EGI were filled by the average of the pixels in their neighboring domains, and all annual raster data were resampled to the same spatial resolution (1 km) as GDPD by the nearest-neighbor method.

3. Methods

The main workflow of this study consists of the following parts (Figure 2): (1) proposing a basic hypothesis, i.e., the conflict and coordination coexistence (CCC) hypothesis (Figure 2a); (2) constructing indices, including data standardization of specific indicators and the construction of the CUI and EGI based on PCA (Figure 2b); (3) revealing and discussing the historical evolution patterns and spatial patterns of LULC changes in our study area (Nanjing) in 2000, 2010, and 2020 (Figure 2c); (4) analyzing the temporal and spatial change trends of the CUI and EGI in Nanjing from 2000 to 2020 (Figure 2d,e); (5) performing empirical validation of the proposed hypothesis, for which CCD and decoupling models were selected to analyze the CUI and EGI at different time scales and indicate decoupling hotspots as well as ecologically weak areas (Figure 2f,g); (6) analyzing interactions and driving mechanisms (Figure 2h,i).

3.1. Basic Hypothesis

Based on the understanding of the coercive relationship between urbanization and urban greenness, we consider coexistence as the cornerstone of symbiosis. Therefore, we propose the CCC hypothesis between urbanization and urban greenness, suggesting that the relationship between urbanization and vegetation greenness in the process of urban development exhibits a positive interaction, but there is also a potential risk of conflict or degradation. For example, urbanization and vegetation greenness are predominantly in coordinated coupling during a certain period but may be in antagonism or conflict during another period. Due to spatiotemporal heterogeneity, conflict and coordination may coexist in the same period [17]. This means that some single indicator may reveal positive aspects of the ecological situation, but this is not representative of the system in its entirety or as a whole. Multiple games or trade-offs together drive urban ecological civilization forward. A full understanding of this dynamic relationship can help to achieve the highly coupled synergy between urban development and ecosystem sustainability.

3.2. Index Construction

3.2.1. Construction of the CUI

We utilized PD and GDPD to gauge the population urbanization index (PUI) and economic urbanization index (EUI), respectively, and measured the spatial urbanization index (SUI) by NTLD [72]. Firstly, PD, NTLD, and GDPD data were normalized to obtain the PUI, SUI, and EUI, respectively [73]; see Equation (1). Then, PC₁, PC₂, PC₃, and their respective eigenvalue percentages r_i were obtained using PCA [74,75] (Table S1), as shown in Equation (2). Finally, the CUI was calculated according to Equation (3). Note that a higher urbanization index indicates a higher urbanization degree, and vice versa.

$${\mathrm{X}}_{\mathrm{i}}=\frac{\mathrm{x}-\mathrm{m}\mathrm{i}\mathrm{n}}{\mathrm{m}\mathrm{a}\mathrm{x}-\mathrm{m}\mathrm{i}\mathrm{n}}$$

(1)

$${\mathrm{P}\mathrm{C}}_{\mathrm{i}}=\mathrm{P}\mathrm{C}\mathrm{A}(\mathrm{P}\mathrm{U}\mathrm{I},\mathrm{E}\mathrm{U}\mathrm{I},\mathrm{S}\mathrm{U}\mathrm{I})$$

(2)

$$\mathrm{C}\mathrm{U}\mathrm{I}={\mathrm{r}}_1{\mathrm{P}\mathrm{C}}_1+{\mathrm{r}}_2{\mathrm{P}\mathrm{C}}_2+\cdots +{\mathrm{r}}_{\mathrm{i}}{\mathrm{P}\mathrm{C}}_{\mathrm{i}}$$

(3)

where r_i is the contribution ratio of the principal component (PC), and i is the quantity of the PC.

3.2.2. Construction of the EGI

The proposed EGI integrates comprehensive ecological information including vegetation cover, canopy structure, and vegetation productivity by combining the FVC, LAI, and NPP. Specifically, the respective normalized indices FVC_n, LAI_n, and NPP_n were computed as Equation (1); then, the PCA of FVC_n, LAI_n, and NPP_n was completed; see Equation (4). Following the acquisition of PC₁, PC₂, PC₃, and their respective eigenvalue percentages t_i (Table S2), the EGI of Nanjing from 2000 to 2020 was calculated according to Equation (5):

$${\mathrm{P}\mathrm{C}}_{\mathrm{i}}=\mathrm{P}\mathrm{C}\mathrm{A}({\mathrm{F}\mathrm{V}\mathrm{C}}_{\mathrm{n}},{\mathrm{L}\mathrm{A}\mathrm{I}}_{\mathrm{n}},{\mathrm{N}\mathrm{P}\mathrm{P}}_{\mathrm{n}})$$

(4)

$$\mathrm{E}\mathrm{G}\mathrm{I}={\mathrm{t}}_1{\mathrm{P}\mathrm{C}}_1+{\mathrm{t}}_2{\mathrm{P}\mathrm{C}}_2+\cdots +{\mathrm{t}}_{\mathrm{i}}{\mathrm{P}\mathrm{C}}_{\mathrm{i}}$$

(5)

where t_i is the contribution ratio of the PC, and i is the quantity of the PC. A larger EGI value means a higher degree of greenness as well as stronger ecological resilience of the city, and vice versa.

3.3. Trend Analysis

The Theil–Sen Median trend analysis, a robust nonparametric statistical method, can effectively address minor outliers and missing value noise [76,77]. It has demonstrated efficacy in detecting changing trends and quantifying alterations within time series data [78]. In this paper, we performed the Theil–Sen Median trend analysis to assess the dynamic changes in the CUI and EGI in Nanjing from 2000 to 2020, as follows:

$$\mathsf{\beta }=\mathrm{M}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{a}\mathrm{n}\left(\frac{{\mathrm{x}}_{\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}}}{\mathrm{j}-\mathrm{i}}\right),\mathrm{j}>\mathrm{i}$$

(6)

where β is the median value of the slope of all data pairs, and its positive or negative value indicates the trend direction of the indicator in the time series. When β > 0, it reflects the upward trend of the indicator; when β < 0, it reveals the downward trend of the indicator. The absolute value of β indicates the average rate of change. Median () is the function of taking the median. x_j and x_i denote the j^th and i^th terms in the time series, respectively.

The Mann–Kendall test, is a nonparametric method used to assess trends in time series data [79,80]. The Mann–Kendall test is not dependent on the assumption of normal distribution in measurements and shows an ability to be resilient against the effects of missing values and outliers [81]. Its calculation process is as follows:

$$\mathrm{S}={\sum }_{\mathrm{i}=1}^{\mathrm{n}-1}{\sum }_{\mathrm{j}=\mathrm{i}+1}^{\mathrm{n}}\mathrm{s}\mathrm{g}\mathrm{n}({\mathrm{x}}_{\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}})$$

(7)

$$\mathrm{s}\mathrm{g}\mathrm{n}\left({\mathrm{x}}_{\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}}\right)=\left\{\begin{array}{c}+1, {\mathrm{x}}_{\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}}>0\\ 0, {\mathrm{x}}_{\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}}=0\\ -1, {\mathrm{x}}_{\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}}<0\end{array}\right.$$

(8)

The selection of the significance test statistic Z varies from the time series length n. In this study, in which n ≥ 10, the value of Z was calculated by the following formula:

$$\mathrm{Z}=\left\{\begin{array}{c}\frac{\mathrm{S}-1}{\sqrt{\mathrm{V}\mathrm{a}\mathrm{r}\left(\mathrm{S}\right)}}, \mathrm{if} \mathrm{S}>0\\ 0, \mathrm{if} \mathrm{S}=0\\ \frac{\mathrm{S}+1}{\sqrt{\mathrm{V}\mathrm{a}\mathrm{r}\left(\mathrm{S}\right)}}, \mathrm{if} \mathrm{S}<0\end{array}\right.$$

(9)

$$\mathrm{V}\mathrm{a}\mathrm{r}\left(\mathrm{S}\right)=\frac{\mathrm{n}\left(\mathrm{n}-1\right)\left(2\mathrm{n}+5\right)-\sum _{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{t}}_{\mathrm{i}}({\mathrm{t}}_{\mathrm{i}}-1)(2{\mathrm{t}}_{\mathrm{i}}+5)}{18}$$

(10)

where n denotes the length of the time series; m is the number of knots in the series; and t_i is the width of the knots. A bilateral trend test was used to find the critical value Z_1−α/2 according to the normal distribution table when given a significance level α. If |Z| ≤ Z_1−α/2, the original hypothesis is accepted, i.e., that the trend is not significant; if |Z| > Z_1−α/2, the original hypothesis is rejected, and the trend is considered significant. If the significance level α = 0.05 is given, the critical value Z_1−α/2 = ±1.96. When the absolute value of Z is greater than 1.65, 1.96, and 2.58, it means that the trend passes the significance test with 90%, 95%, and 99% confidence levels, respectively.

3.4. The CCD and Tapio Decoupling Models

3.4.1. The CCD Model

The CCD model is usually employed to describe the degree of interaction between multiple systems [82]. In this study, we used the CCD model, which was introduced to assess the static relationship between the CUI and EGI in Nanjing. Its calculation formula is as follows:

$$\mathrm{C}=2\times \sqrt{(\mathrm{E}\mathrm{G}\mathrm{I}\times \mathrm{C}\mathrm{U}\mathrm{I})/{\left(\mathrm{E}\mathrm{G}\mathrm{I}+\mathrm{C}\mathrm{U}\mathrm{I}\right)}^2}$$

(11)

$$\mathrm{T}=\mathrm{a}\times \mathrm{E}\mathrm{G}\mathrm{I}+\mathrm{b}\times \mathrm{C}\mathrm{U}\mathrm{I}$$

(12)

$$\mathrm{C}\mathrm{C}\mathrm{D}={(\mathrm{C}\times \mathrm{T})}^{1/2}$$

(13)

where C denotes the coupling degree between the CUI and EGI. T represents the comprehensive evaluation index of the EGI and CUI. In this study, the CUI and EGI are regarded as equally important, so they are given the same weight, i.e., a = b = 0.5. The CCD represents the coupling coordination degree between the CUI and EGI, which can be classified into six categories [83], representing different stages of the coordination process between the CUI and EGI: 0 ≤ CCD < 0.2 (extreme disorder, ED), 0.2 ≤ CCD < 0.4 (moderate disorder, MD), 0.4 ≤ CCD < 0.5 (slight disorder, SD), 0.5 ≤ CCD < 0.6 (primary coordination, PC), 0.6 ≤ CCD < 0.8 (moderate coordination, MC), 0.8 ≤ CCD < 1.0 (high coordination, HC). The closer the CCD is to 1, the more harmonized the CUI and EGI are.

3.4.2. The Tapio Decoupling Model

The Tapio decoupling model was introduced to reveal the dynamic linkage between the CUI and EP [84,85]. We assumed a benign coupling between sustainable urbanization and ecological greenness. The EP indicator will decrease when the ecological greenness increases. In this study, we took the inverse of the EGI as the EP for better understanding, as follows:

$$\mathrm{E}\mathrm{P}=\frac{1}{\mathrm{E}\mathrm{G}\mathrm{I}}$$

(14)

$${\mathrm{D}\mathrm{I}}_{\mathrm{t}}=\frac{\mathsf{\Delta }\mathrm{E}\mathrm{P}}{\mathsf{\Delta }\mathrm{C}\mathrm{U}\mathrm{I}}=\frac{({\mathrm{E}\mathrm{P}}_{\mathrm{t}}-{\mathrm{E}\mathrm{P}}_{\mathrm{t}-1})/{\mathrm{E}\mathrm{P}}_{\mathrm{t}-1}}{({\mathrm{C}\mathrm{U}\mathrm{I}}_{\mathrm{t}}-{\mathrm{C}\mathrm{U}\mathrm{I}}_{\mathrm{t}-1})/{\mathrm{C}\mathrm{U}\mathrm{I}}_{\mathrm{t}-1}}$$

(15)

where DI_t is the decoupling index in year t; EP_t and EP_t−1 are the EP evaluation indices in year t and year t − 1, respectively; CUI_t and CUI_t−1 denote the CUI evaluation indices in year t and year t − 1, respectively. ΔEP means the annual rate of change in the EP, and ΔCUI means the annual rate of change in the CUI. The model takes 0, 0.8, and 1.2 as the criteria for the division of the decoupling state, and divides the decoupling index into three categories of negative decoupling, decoupling, and coupling, and then subdivided into eight different states based on the size of the specific values [84] (

Table 2. Categorization criteria for the decoupling states between the CUI and EP.

Types	Decoupling States	Connotation	$\mathsf{\Delta }\mathbf{E}\mathbf{P}$	$\mathsf{\Delta }\mathbf{C}\mathbf{U}\mathbf{I}$	Decoupling Index
Decoupling	Strong decoupling	The level of urbanization rises while EP falls (ecological greenness rises). It shows that EP has been effectively controlled during urbanization.	<0	>0	<0
	Weak decoupling	The rate of increase in EP is less than the rate of increase in urbanization.	>0	>0	0–0.8
	Declining decoupling	The decrease in EP accompanies a decrease in the level of urbanization, and the change in EP is (negative) less than the level of urbanization.	<0	<0	>1.2
Coupling	Expansive coupling	The rate of increase in EP basically equals the rate of urbanization progress.	>0	>0	0.8–1.2
	Declining coupling	The rate of EP reduction is almost equal to the rate of reduction in the level of urbanization.	<0	<0	0.8–1.2
Negative decoupling	Expansive negative decoupling	EP is characterized by increases with the level of CUI, and the rate of increase in EP is markedly greater than the rate of urbanization advance.	>0	>0	>1.2
	Weak negative decoupling	EP decreases significantly less than the rate of CUI change.	<0	<0	0–0.8
	Strong negative decoupling	EP increases (i.e., ecological greenness browning) and urbanization levels regress.	>0	<0	<0

Note: $\mathsf{\Delta }$EP and $\mathsf{\Delta }$CUI denote the change rate of ecological pressures (EPs) and the comprehensive urbanization index (CUI) for the respective time periods.

).

3.5. Identification of Correlations and Interactions

3.5.1. Correlation Analysis

The measurement of the correlation between the CUI and EGI in Nanjing from 2000 to 2020 was achieved by the pixel-wise Pearson’s correlation coefficient (PCC) to enhance the understanding of the role of urbanization on vegetation greenness and its spatial relationship over time. As for column X_a in matrix X and column Y_b in matrix Y, their mean values are ${\overline{\mathrm{X}}}_{\mathrm{a}}=\sum _{\mathrm{i}=1}^{\mathrm{n}}\left({\mathrm{X}}_{\mathrm{a},\mathrm{i}}\right)/\mathrm{n}$ and ${\overline{\mathrm{Y}}}_{\mathrm{b}}=\sum _{\mathrm{j}=1}^{\mathrm{n}}\left({\mathrm{Y}}_{\mathrm{b},\mathrm{j}}\right)/\mathrm{n}$. PCC rho (a, b) is defined as follows:

$$\mathrm{r}\mathrm{h}\mathrm{o}(\mathrm{a},\mathrm{b})={\sum }_{\mathrm{i}=1}^{\mathrm{n}}({\mathrm{X}}_{\mathrm{a},\mathrm{i}}-{\overline{\mathrm{X}}}_{\mathrm{a}})({\mathrm{Y}}_{\mathrm{b},\mathrm{j}}-{\overline{\mathrm{Y}}}_{\mathrm{b}})/\sqrt{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{({\mathrm{X}}_{\mathrm{a},\mathrm{i}}-{\overline{\mathrm{X}}}_{\mathrm{a}})}^2{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{({\mathrm{Y}}_{\mathrm{b},\mathrm{j}}-{\overline{\mathrm{Y}}}_{\mathrm{b}})}^2}$$

(16)

where n is the length of each column. PCC values range from −1 to 1. PCC = −1 indicates a complete negative correlation, suggesting that urbanization may be hindering the development of ecological greenness; PCC = 1 represents a complete positive correlation, suggesting that urbanization and ecological greenness may be in a state of healthy interaction. PCC = 0 denotes no significant correlation relationship between urbanization and ecological greenness.

3.5.2. Spatially Stratified Heterogeneity

The GeoDetector (http://geodetector.cn/index.html, accessed on 28 January 2024) is an advanced statistical method that can be used to detect spatial changes and reveal the driving mechanism [86,87]. We used the factor detector, interaction detector, and ecological detector of GeoDetector to realize the quantification of the domain-wide contributions, interactions, and spatial differentiation-driven relationships. Firstly, all independent variables (CUI, PUI, EUI, and SUI) were reclassified into new types through the natural breakpoint method based on ArcGIS 10.8 (Figure S1). After that, the type values and greenness index (EGI, FVC_n, LAI_n, and NPP_n) values of the respective variables were obtained through the created centroids of the fishing nets in the study area (9123 points). Among these, 7724 valid values were retained by removing the invalid values in the water areas and other outliers. At last, the values were entered into GeoDetector’s interactive interface in Excel to execute the operations.

(1) Factor detector: to detect the spatial variability of vegetation greenness, and to measure the extent to which urbanization factors explain the spatial variability of vegetation greenness. Using q value measure, the formula is as follows:

$$\mathrm{q}=1-\frac{\sum _{\mathrm{h}=1}^{\mathrm{L}}{\mathrm{N}}_{\mathrm{h}}{\mathsf{\sigma }}_{\mathrm{h}}^2}{\mathrm{N}{\mathsf{\sigma }}^2}=1-\mathrm{S}\mathrm{S}\mathrm{W}/\mathrm{S}\mathrm{S}\mathrm{T}$$

(17)

where L is the strata of variable Y or factor X; N_h and N are the number of units in strata h and the whole region, respectively; σ_h² and σ² are the variances of the Y values in strata h and the whole region, respectively. SSW and SST are the sum of the intra-strata variance and the total variance in the whole region, respectively. q is in the domain of [0, 1], and the larger the value, the more pronounced the spatial dissimilarity of Y. In this study, q indicates that the independent variable explains $100\times \mathrm{q}\mathrm{\%}$ of the dependent variable. The larger the value, the greater the contribution of the independent variable to the dependent variable [86].

(2) Ecological detector: to compare whether the effects of any two urbanization activities on the spatial distribution of vegetation greenness are significantly different. The formula is expressed as an F statistic as follows:

$$\mathrm{F}=\frac{{\mathrm{N}}_{\mathrm{X}1}\left({\mathrm{N}}_{\mathrm{X}2}-1\right){\mathrm{S}\mathrm{S}\mathrm{W}}_{\mathrm{X}1}}{{\mathrm{N}}_{\mathrm{X}2}\left({\mathrm{N}}_{\mathrm{X}1}-1\right){\mathrm{S}\mathrm{S}\mathrm{W}}_{\mathrm{X}2}}$$

(18)

where N_X1 and N_X2 denote the sample sizes of the two factors; SSW_X1 and SSW_X2 are the sums of the intra-stratum variances of the stratification formed by independent variables X1 and X2, respectively. The null hypothesis is H₀: SSW_X1 = SSW_X2. If H₀ is rejected at the significance level of α, it suggests that there is a significant difference between the effects of the two independent variables on the spatial distribution of the dependent variable.

(3) Interaction detector: to identify whether the combined effect of any two urbanization activities increases or decreases the degree of contribution to greenness or whether the effects of these two urbanization activities on greenness are independent of each other. Specifically, the q-values of the two independent variables were calculated first, and then these two independent variables were superimposed, and their q-values were obtained. The relationship between these two factors can be categorized into five types by comparing the size of the q-values [86] (Table S3).

4. Results

4.1. Historical Changes and Spatial Patterns in LULC

Nanjing was dominated by cultivated land, artificial surfaces, and forest land during 2000–2020 (Figure 3). The artificial surfaces experienced a “doubling” growth, increasing from 10.06% in 2000 to 23.39% in 2020. Its expansion area amounted to 977.37 km², indicating a noticeable trend of “expansion from the MUR to the surrounding area” and “expansion along both sides of the Yangtze River”. The expansion rate was markedly faster on the south bank of the Yangtze River compared to the north bank. Conversely, the area of cultivated land exhibited a consistent decline from 70.09% in 2000 to 65.38% in 2010 and eventually to 58.00% in 2020. Notably, a substantial area of transferred cultivated land (906.99 km²) and forest land (50.36 km²) was converted to artificial surfaces (Figure 4 and Table S4).

Between 2000 and 2010, the largest area transferred out belonged to cultivated land at 489.64 km², followed by wetlands (120.56 km²) and forest land (106.20 km²). The largest area transferred in was water bodies (334.66 km²), followed by artificial surfaces (219.56 km²) and cultivated land (180.06 km²) (Figure 4 and Table S5). A significant two-way transfer occurred between artificial surfaces and cultivated land, with the area of cultivated land transferred to artificial surfaces (208.81 km²) far surpassing the area of artificial surfaces transferred to cultivated land (50.94 km²). During 2010–2020, the contraction of cultivated land persisted, with the area transferred out remaining substantially larger than the area transferred in. Similarly, the area transferred out of water bodies and forest land exceeded the area transferred in, while the expansion of artificial surfaces was most pronounced (Figure 4 and Table S6).

4.2. Evolutionary Trends in the CUI and EGI

In Nanjing, the CUI increased steadily throughout the study period (rate of change: 0.6%/year), with high values predominantly concentrated in the MUR and neighboring areas (Figure 5a and Figure S3a). Simultaneously, the EGI exhibited a consistent decrease (rate of change: −0.23%/year), gradually diminishing as urbanization intensified (Figure 5d and Figure S3b). Areas with higher EGI values were primarily located in Luhe District, the northwest of Pukou District, the central area of Jiangning District, and the eastern region of Lishui District. In terms of the change trend of the CUI, the overwhelming increase prevailed, encompassing 97.58% of the area (comprising very significant, moderately significant, and slightly significant increases at 95.39%, 1.52%, and 0.67%, respectively), while a minor portion, 0.13% of the CUI, experienced significant decreases (slightly significant decrease, moderately significant decrease, and very significant decrease), primarily concentrated in Gulou District of the MUR (Figure 5b,c and

Table 3. Characteristics of the trend of the EGI and CUI in Nanjing City during 2000–2020.

$\mathit{\beta }$	Z	Trend Features	Percentage of Total Study Area (Excluding Water Areas)
			CUI	EGI
$\beta$ > 0	2.58 < |Z|	Very significant increase/greening	95.39%	6.25%
	1.96 < |Z| ≤ 2.58	Moderately significant increase/greening	1.52%	6.84%
	1.65 < |Z| ≤ 1.96	Slightly significant increase/greening	0.67%	3.49%
	|Z| ≤ 1.65	No significant change	1.59%	23.48%
$\beta$ < 0			0.70%	26.30%
	1.65 < |Z| ≤ 1.96	Slightly significant decrease/browning	0.02%	5.27%
	1.96 < |Z| ≤ 2.58	Moderately significant decrease/browning	0.10%	10.11%
	2.58 < |Z|	Very significant decrease/browning	0.01%	18.26%
$\beta$ = 0	No change

Note: β refers to the direction of index changes, and Z denotes the confidence level at which these changes pass the test of significance.

). In addition, the change in the CUI in Nanjing’s emerging urban areas (areas outside of MUR) was more significant than the change in the CUI in MUR areas, which can almost be regarded as a sign of the rise of urbanization in emerging urban areas. However, the rate of increase in the CUI in the emerging urban areas was considerably lower than that of the MUR and their surrounding areas, reflecting a gradient of the CUI rate “from inside to outside”, indicative of the radiation-driven effect of the central city of Nanjing on its surrounding urban areas.

Furthermore, during 2000–2020, in the area of significant browning, the EGI (33.64%) far exceeded that of significant greening (16.58%) in Nanjing (Figure 5e,f and Table 3). Specifically, the EGI comprised 18.26% of very significant browning, 10.11% of moderately significant browning, and 5.27% of slightly significant browning; meanwhile, the EGI demonstrated 6.25% of very significant greening, 6.84% of moderately significant greening, and 3.49% of slightly significant greening. Remarkably, the EGI in different areas showed distinct characteristics, such as “patchy greening” in Luhe District in the northern suburbs and the central part of the study area, “greening and browning” in the central-eastern part, and a predominantly browning pattern in the Gaochun District, reflecting the varying impact of human activities and urbanization intensity among these regions.

4.3. The CCD and Decoupling States

The relationship between the CUI and EGI in Nanjing was dominated by MC, followed by PC (Table S7). The CCD in 2018 was the largest at 0.651, which still remained far from HC. The CCD of the units at the township scale in 2000 displayed a gradient distribution, with most units in the near and far suburbs in the stage of SD, and units in the central area mainly in the PC and MC stages (Figure 6). The MUR were found to be in PC and MC states, with a small incidence of HC. In 2010, the number of units in MC (86%) greatly surpassed those in PC (14%). By 2020, over 95% of the units were in MC, with a relative lag in the coordinated advancement of the CUI and EGI observed in the southwestern Gaochun District, where PC dominated the area.

The EP in Nanjing exhibited an overall increasing trend, coupled with “strong decoupling” of the CUI and EP occurring seven times, “weak decoupling” seven times, and “negative decoupling” six times (with strong negative decoupling four times, weak negative decoupling one time, and expansive negative decoupling one time) (Table S7). At the regional scale, all districts in Nanjing were in a “weak decoupling” state (Figure 7a). However, the CUI and EP showed an “expansive coupling” state in Qinhuai District from 2000 to 2010, while other districts were mainly in a decoupling state (Figure 7b). From 2010 to 2020, the “strong decoupling” state was mainly observed in MUR, including Qinhuai District, Yuhuatai District, Jianye District, and Xuanwu District; five districts were in a “weak decoupling” state, with Luhe, Pukou, and Lishui Districts transformed from a “strong decoupling” state (Figure 7c), while Gulou and Gaochun Districts were in “weak negative decoupling” and “expansive coupling” states, respectively. This indicated a weakening of the decoupling relationship between the CUI and EP in the study area.

4.4. Interactive Driving Mechanisms of Urbanization and Greenness

Through pixel-by-pixel PCC analysis, a predominantly significant negative correlation was found between the CUI and EGI in Nanjing (p < 0.05), with 46.21% and 53.79% of the area showing positive and negative correlations, respectively (Figure 8a). The negative correlation reflects the adverse effect of the CUI on the EGI, with over half of the area (53.3%) distributed in the MUB and its surrounding areas. Conversely, the positive correlation signifies the constructive impact of the CUI on the EGI, predominantly in suburban areas (e.g., Luhe District), higher elevation forest parks (e.g., Laoshan National Forest Park), and other woodlands. This was a reflection of the enhanced protection and maintenance of ecological reserves and vegetation greenery in Nanjing during the urbanization process as well as the limitations imposed by elevation and slope on urban sprawl.

The vegetation greenness changes were particularly sensitive to the response of urbanization. The factor detector showed that, in 2000, the average contribution (AC) of the PUI to greenness indices in Nanjing was the largest at 48.46%, followed by 47.87% for the EUI, 44.94% for the CUI, and 40.60% for the SUI (p < 0.05) (Figure 8b). In 2010, the AC of the urbanization indices to all greenness indices was as follows: CUI (46.22%) > SUI (45.72%) > EUI (44.22%) > PUI (31.62%) (Figure 8b). In 2020, the AC of the urbanization indices to all greenness indices was ordered as follows: SUI (39.37%) > CUI (38.75%) > EUI (36.58%) > PUI (24.59%) (Figure 8b). It is evident that, from 2000 to 2010, the AC of the CUI and SUI on greenness indices increased, while the AC of the EUI and PUI on greenness indices decreased. In 2010, the contribution of each urbanization index to NPP_n decreased, with the largest decrease in the contribution of the PUI to NPP_n—declining from 98.00% in 2000 to 23.96% in 2010. Furthermore, the contribution of urbanization indices to each greenness index decreased from 2010 to 2020. The magnitude of the multi-year AC of urbanization indices on the EGI was as follows: CUI (49.45%) > SUI (48.63%) > EUI (46.86%) > PUI (33.77%), reflecting the absolute influence of the CUI on vegetation greenness in Nanjing.

The interaction detector found that CUI∩EUI, CUI∩SUI, CUI∩PUI, EUI∩SUI, EUI∩PUI, and PUI∩SUI exhibited an “enhance, bi-” interaction type in 2000, 2010, and 2020, suggesting that the spatiotemporal divergence of greenness in the study area resulted from the combined effect of multiple urbanization activities (Figure S2). The ecological detector identified significant differences in the effects of certain urbanization indices on the greenness indices. For example, in 2020, a significant difference emerged between the spatial differentiation effects of the EUI and SUI on the EGI, FVC_n, and LAI_n (Figure S2). It follows that identifying the best attributes or ranges of differentiated features in the interaction mechanism between urbanization and greenness indices benefits the development of personalized strategies in the study area.

5. Discussion

5.1. Empirical Analysis of Hypothesis

Systematic thinking is an important guideline for the CCC hypothesis. In the past, “to exert control after development” and “governance while developing” were prevalent, after which “grasping great protection and not engaging in great development” took the stage of history. Changes in the concept of ecological governance illustrate that contradictions are everywhere, which provides scenarios for the application of the CCC hypothesis. The key empirical evidence underlying the CCC hypothesis lies in the choice of analytical perspectives and measurement methods. Different settings of weights α and β in the CCD model may introduce errors [38]. In this paper, the weights were all set to 0.5, which is in line with the connotation of the hypothesis, as urbanization and greenness are seen as keys to revealing the conflict. On the other hand, the vegetation greenness in Nanjing showed a decreasing trend from 2000 to 2020 (Figure S3b and Figure 8c). This was consistent with the decline of regional ecological quality [66,88]. It showed that the development of Nanjing’s urbanization has put obvious pressure on the ecological greenness of most areas. Even at the MC stage of the CUI and EGI, the complete decoupling of the CUI and EP was not realized in Nanjing, and the decoupling relationship between the CUI and EP may further deteriorate. This demonstrated an implicit conflict between urbanization and greenness in Nanjing. The difference analysis between CCD and decoupling states serves as empirical evidence for the CCC hypothesis. The differential distribution of correlations between the CUI and EGI also supports the hypothesis (Figure 8).

5.2. Analysis of Conflict Hotspots

The changing dynamics of LULC in Nanjing signified that a pattern of “expanding artificial surfaces while diminishing cultivated land” prevailed throughout the study period (Figure 3). This corresponds to changes in specific indicators of urbanization (Figure 8d). In this paper, the EUI and PUI were regarded as implicit internal drivers of LULC change, while the SUI embodied its explicit spatial differentiation. This synergistic amplification effect was quantitatively supported in this study (Figure S2). The driving force of urbanization activities on the evolution of ecosystem functions in Nanjing significantly outweighs that of climate change [88]. Specifically, the increase in artificial surfaces not only reduces soil water storage capacity but also alters surface temperature, leading to massive loss of vegetation and carbon [65,89]. For instance, the deterioration of the decoupling relationship between the CUI and EP in Gaochun District, Nanjing, was mainly caused by the degradation of its ecological lands (forest land, grassland, waters, etc.) [90]. While the Luhe District in the northern part of the study area was dominated by cultivated land, the Lishui, Pukou, and Jiangning Districts had a more concentrated distribution of forests [91]. The high vegetation greenness enabled them to maintain a relatively stable level of coupling and coordination with the CUI. Moreover, the MUR is the most economically prosperous and densely populated core of Nanjing, with the highest degree of urbanization, but it tends to be subjected to the greatest ecological governance, and thus the decoupling relationship between the CUI and EP in this region appears to be more stable. Areas with a negative correlation between the CUI and EGI, as well as areas with very significant EGI browning and poor decoupling, were identified as conflict hotspots. The study calls for focusing on these conflict hotspots and comprehensively scrutinizing urbanization activities during the historical period when the CUI and EP were in poor decoupling status.

5.3. Key Insights for Healthy Urban Development

The cultivated land occupation and compensation balance policy can effectively mitigate the decline of the total cultivated land area in urban areas [92]. However, it may lead to the loss of high-quality cultivated land due to the issue of “superior occupation and low compensation” [93]. Furthermore, areas with excellent ecological environments are often regarded as ideal sites for urban expansion, but urbanization is accompanied by reshaping and destroying vegetation greenness patterns [94]. This puts greater demands on the city’s dynamic conflict management capabilities. In order to promote the harmonious co-occurrence of urbanization and greenness, this study combines the vulnerability of the coupling and coordination between urbanization and greenness in Nanjing and the potential conflicts it faces. We recommend that Nanjing (1) reinforces the spatial constraints on land occupation, strictly abides by the red line of food security, highlights the functional position of ecological green space allocation in the whole process of urbanization promotion, and integrates natural greenery into urban landscapes; (2) enhances the protection of existing ecological protection zones and continuously improves the quality of new vegetation communities, which can be completed specifically by optimizing the morphology and structure layout of the vegetation to improve the quality of greenery; (3) establishes and improves the environmental monitoring system, strengthens the environmental protection laws and regulations, and actively carries out the assessment of multidimensional indicators such as urban vegetation coverage, productivity, biodiversity, and soil and water conservation.

Overall, the comprehensive scrutiny of ecological conflicts in the process of urbanization, taking into account demographic, economic, and spatial dimensions and at different time scales, and the flexible use of synergies or antagonisms between subsystems to promote the forward development of urban ecosystems is essential.

5.4. Uncertainty

The study chose PCA methodology to integrate information from multidimensional indicators, which helps to explore the subtle interactions between urbanization and greenness. A comparison of the specific indicators with the CUI and EGI showed that the CUI and EGI obtained a more considerable amount of change (Figure S3 and Figure 8c,d). But a limited number of indicators can only integrate a limited amount of information. In addition, uncertainties may be introduced by data processing at different spatial resolutions and statistics of regional averages, etc., but this has very little effect on the conclusions of this paper. However, expanding the empirical validation based on higher precision data and more advanced and multivariate analysis methods should be encouraged.

6. Conclusions

The healthy dynamic interaction relationship between urbanization and greenness reflects the rationalization of urban planning and governance and is an important aspect of urban civilization. Combined with an understanding of the dynamic relationship between urbanization and greening, we proposed the CCC hypothesis. This hypothesis to some extent embodies the hidden and unstable nature of the relationship between urbanization and urban greenness. In particular, this study constructed the CUI and EGI based on six specific indicators in order to achieve the validation of the basic hypotheses. Then, we revealed the spatial and temporal evolution dynamics of the CUI and EGI in Nanjing in the context of LULC changes and investigated their coupling coordination and decoupling hotspots at different time scales. Finally, the intrinsic driving mechanisms of the interactions were analyzed. The findings are as follows: (1) Nanjing’s LULC was dominated by cultivated land, artificial surfaces, and forest land. Artificial surfaces notably expanded rapidly from the MUR to the surrounding areas, encroaching upon cultivated and forested lands. A very significant increase in the CUI covered 95% of the total area. The area of very significant browning of the EGI accounted for 18% of the total area, about three times the area of very significant greening. (2) There were 12 MCs and 9 PCs between the CUI and EGI in Nanjing, exhibiting mainly “strong decoupling” and “weak decoupling” from 2000 to 2010, with an increase in “negative decoupling” after 2010. While the spatial performance of the CUI and EP improved from 2000 to 2020, the decoupling relationship tended to deteriorate in the subperiods. (3) The CUI and EGI in Nanjing exhibited a mainly negative correlation, with negative correlation areas primarily concentrated in the MUB. The AC of each urbanization index on each greenness index was above 41% in 2000 and decreased in 2020. The urbanization activities that dominated the fluctuations in Nanjing’s greenness varied across time, which provides a window to promote a harmonious symbiosis between urbanization and greenness in due course. This study calls for a more holistic view (a more robust system of indicators) of the impacts of urbanization.