The Construction of Resilience in Aging-Friendly Cities Driven by Land Adaptive Management: An Empirical Analysis of 269 Chinese Cities Based on the Theory of Social Ecosystems

Abstract

In the context of accelerated urbanization and ageing in China, the inefficient use of land resources and ecological space extrusion pose significant challenges to the development of age-friendly cities. Existing studies predominantly adopt a single-disciplinary perspective, and there is a paucity of systematic exploration of regional heterogeneity and the multidimensional influencing mechanisms of the effects of adaptive land management policies. Drawing upon the theoretical framework of social-ecological system resilience in environmental sociology, this study integrates the concept of adaptive land management as a factor in the resilience of old-age-friendly cities. Empirical investigation is conducted to examine the impact of adaptive land management on the resilience of old-age-friendly cities and its spatial differentiation. The entropy weight method and the panel regression model are utilized to analyze the panel data of 269 prefectural-level cities in China from 2010 to 2022. The study posits that effective land management is instrumental in enhancing the resilience of age-friendly cities through optimal spatial allocation and ecological restoration. The promotion effect is more pronounced in the central and western regions and in small and medium-sized cities. The internal dimensions of resilience demonstrate heterogeneity, with economic and social resilience showing significant improvement, and ecological resilience being constrained by natural thresholds with a limited effect. The study also puts forward policy recommendations for further building the resilience of age-friendly cities.

1. Introduction

The People’s Republic of China has witnessed the world’s largest and most rapid urbanization process. By 2022, it is projected that China’s urbanization rate will have surpassed 65 per cent, signifying that cities will become the primary catalyst for economic growth and a pivotal conduit for social transformation. However, the rapid process of urbanization has also exposed numerous contradictions between human populations and the environment. These include the inefficient use of land resources, the loss of arable land, the encroachment on ecological space, and the imbalance between urban and rural development. These issues are becoming increasingly prominent and pose a serious challenge to urban resilience. In this context, environmental sociology has become a key path to deconstruct the bottleneck of urban resilience with its unique perspective covering both environmental and social fields [1]. In the domain of environmental sociology in China, two predominant research strands can be identified. The first, termed the qualitative research line, is exemplified by the contributions of Chen Ajiang, Chen Tao, and Luo Vajuan. In contrast, the second strand, the quantitative research line, is represented by the works of Hong Dayong, Lu Spring, and Gong Wenjuan. It is evident that environmental pollution is the most salient manifestation of environmental problems. Over time, land pollution has gradually evolved into a social problem that poses a threat to human health and quality of life [2]. Land adaptive management (LM) can be defined as the systematic utilization of policy instruments and technical methodologies to integrate and optimize land resources in a comprehensive and multi-dimensional manner. This encompasses activities such as land remediation, ecological restoration, spatial planning, tenure adjustment and institutional innovation, with the overarching objective being the achievement of comprehensive goals including the enhancement of land use efficiency, the restoration of ecological function, the reconstruction of urban–rural spatial structures and the promotion of coordinated socio-economic development [3,4].

Whilst the significance of land management for urban development is broadly acknowledged among academics, there are still several key questions that have not been sufficiently addressed. Primarily, there is a need to ascertain whether adaptive land management truly possesses the systemic efficacy required to promote the resilience of age-friendly cities. Secondly, the investigation will explore the existence of spatial heterogeneity in the policy effects of adaptive land management in the context of significant regional differences in China. The question that needs to be addressed is how the effectiveness of the strategy fits dynamically with the stage of urban development, resource endowment, and governance capacity. Thirdly, what factors interact with age-friendly urban resilience building under the existing policy framework? In order to address the aforementioned questions, this paper integrates adaptive land management and age-friendly urban resilience development within a comprehensive framework and analyzes their relationship. Initially, the paper constructs a systematic framework and a comprehensive system of assessment indices, which are measured using the entropy method. Subsequently, the paper empirically estimates the direct and indirect impacts between the two using panel data of Chinese prefecture-level cities from 2010 to 2022. The primary contributions of this paper are as follows: (a) The paper enhances the theoretical underpinnings of urban resilience research and puts forward a framework for analyzing urban resilience using socio-ecological system resilience theory from the standpoint of environmental sociology. (b) The present paper establishes a correlation between adaptive land management and resilience, thus providing a valuable addition to the existing research on the factors influencing urban resilience from the perspective of land resource allocation. (c) It investigates and discusses the impact of adaptive land management on the regional heterogeneity of urban resilience.

2. Literature Review

2.1. Research on Urban Resilience

In recent years, the field of urban resilience research has seen a significant advancement in its theoretical underpinnings, with a notable deepening of research across three core dimensions: evaluation methods, theoretical frameworks, and influencing factors. In the field of evaluation methods, scholars have developed a variety of assessment systems. Some scholars have addressed the issue of indicator assignment through the integrated model of intuitionistic fuzzy sets and TOPSIS [5], while others have further dealt with the uncertainty of expert assessment by using the Z-DEMATEL model [6]. In 2022, further research combined entropy-weighted TOPSIS and fsQCA to reveal multifactor coupling effects [7], while Jiao et al. (2024) implemented a dynamic toughness measure based on the DPSIR-DEA framework [8]. In terms of theoretical expansion, some scholars have demonstrated through bibliometric studies that the research paradigm is shifting from engineering resilience to socio-ecological resilience [9,10], and highlighted the urgency of climate change adaptation, and that the optimization of land policy plays a key role in improving urban resilience [11]. In the context of the factors that influence resilience, scholars have posited that the socio-economic foundation serves as the fundamental element in the development of resilience [12]. Furthermore, research has been conducted that has substantiated the moderating effect of Multiple factors on the formulation of urban resilience strategies [13].

2.2. Research on Land Adaptive Management and Urban Resilience

The role of land adaptive management in supporting urban resilience offers a multifaceted avenue for innovation. In the domain of planning methodology, Kelm et al. (2021) utilized the FFP framework [14] to reconstruct the land management process. Luo et al. (2022) constructed an evaluation system that quantifies the intensity and coordination degree of land development in the Yangtze River Delta [15]. Zhang et al. (2022) proposed a multi-stage planning framework for drainage systems that adapt to changes in land use [16]. In Ashwini et al. (2024), machine learning was utilized to elucidate the mechanism by which heat wave dynamics are linked to land cover [17]. With respect to the ecological resilience optimization dimension, Duo et al. (2022) quantified the three-dimensional characteristics of the resistance-adaptation-vitality of the Nanchang ecosystem in China [18]. In Li et al. (2022), the marginal contribution of green infrastructure protection to resilience was verified through scenario simulation [19]. In 2023, Villavicencio-Valdez et al. revealed the enhancing effect of agroecological practices on food system resilience in Mexico City [20] and on the socio-technical synergy pathway. Mensah et al. emphasized in 2021, that there is a necessity for strengthened regulatory enforcement and institutional synergies in Ghanaian cities [21]. Furthermore, Wang et al. (2023) developed a performance-oriented resilience assessment model based on high-precision flood risk mapping [22]. In addition, Hussain et al. (2024) demonstrated that social media-driven public engagement enhances resilience in green space management [23].

2.3. Research Review

The concept of urban resilience has emerged as a pivotal area of research in the context of urban governance in China. As was demonstrated in the preceding section, the extant research in the domain of land management has formed three primary research areas: indicator construction, theory optimization, and influencing factors. However, the majority of the extant results are confined to the single-disciplinary frameworks of environmental science or economics, thus failing to effectively integrate the critical perspective of sociology on spatial justice and the power structure analysis of political science. This study introduces the analytical framework of ‘social-ecological system resilience theory’ in environmental sociology, thus innovatively transcending the conventional unidimensional research logic.

3. Methodology and Analysis Section

3.1. Theoretical Perspective and Research Hypotheses

3.1.1. Urban Resilience in Socio-Ecological System Resilience Theory

Urban resilience research has gradually evolved into a multifaceted, interdisciplinary field of study within the broader context of social-ecological system resilience theory. From the perspective of cultural systems, scholars have analyzed the important role of local cultural systems in the shaping of climate adaptation strategies, providing a cultural perspective for understanding urban resilience [24]. Some scholars have also proposed the idea of dynamically incorporating urban ecological services into the governance system from the perspective of the integration of ecological services and governance in order to enhance resilience, expanding the governance dimension of urban resilience research [25]. Pioneering scholars in the field of spatial morphology have integrated spatial configuration analysis and resilience science, thereby establishing the foundations for research on the spatial dimension of urban resilience [26]. This pioneering work has been further advanced by the construction of a social-physical composite network model, which has deepened the research connotation of the spatial dimension [27]. Furthermore, a significant number of scholars have conducted research in the practice dimension. On the one hand, they have argued for the double gain effect of local management practices on social-ecological resilience, emphasizing the importance of community participation [28]. On the other hand, they have verified the path-dependent characteristics of historical memory on resilience strategies, considering the case of Mexico City wetland [29], thereby enriching the research results in the community dimension. In the field of system dynamics, scholars have revealed the threshold effect of adaptive management based on hydrological governance, thereby providing a theoretical basis for the dynamic management of urban resilience research [30]. In addition, scholars have proposed a transformation path for technocratic and grassroots knowledge synergy in the Chinese context, thus providing localized ideas for urban resilience research from the perspective of dynamic management [31]. A synthesis of extant studies indicates that urban resilience is fundamentally a coupled process of institutional resilience, spatial resilience, community resilience, and ecological resilience. In light of the homogeneity exhibited by extant research perspectives, this paper puts forward a novel framework for the analysis of urban resilience, which integrates the theory of social-ecological system resilience (Figure 1).

3.1.2. Impact of LM on Urban Resilience

Effective land management can optimize the spatial layout of cities through scientific planning and rational use of land resources, build ecological corridors and green space systems, and enhance the buffer capacity of cities to cope with natural disasters. Simultaneously, land management can regulate the intensity of land use, avoid ecological problems caused by over-development, promote the stability of the urban socio-economic system, and promote the development of the community and the construction of infrastructure. This enhances the resilience of cities to cope with risks, and to recover and adapt to changes in multiple dimensions, including social, economic, and ecological. Based on this, this paper puts forward the following hypotheses:

H1. 

LM can promote the degree of urban resilience.

3.1.3. Heterogeneous Effects of LM on Urban Resilience

The heterogeneous impact of land management on urban resilience is evident in the fact that, due to variations in the level of economic development, institutional environment, resource endowment, and disaster risk in different regions, land management policies have a differentiated effect on urban resilience through mechanisms such as spatial resource allocation, optimization of the functional structure, and risk buffer capacity. In economically developed eastern coastal areas, strict land use control and intensive use policies enhance the economic resilience of cities by suppressing disorderly expansion. However, these policies can potentially compromise ecological resilience due to excessive development intensity. Conversely, in ecologically fragile central and western regions, land ecological restoration and low-intensity development policies can enhance environmental resilience. Nevertheless, insufficient land market allocation may constrain economic adaptive capacity. A number of scholars have identified substantial variations between distinct geographical areas in relation to the advancement of China’s digital economy [32]. Furthermore, research conducted by several scholars has revealed that the advantages associated with environmental policies exhibit considerable heterogeneity across China’s eastern, central, and western cities [33]. The present study thus puts forward the following hypothesis:

H2. 

The impact of LM on urban resilience is characterized by regional heterogeneity.

3.1.4. Mechanisms for Green Technology Innovation

The economic resilience (ER), social resilience (SR), environmental resilience (ENR), and governance resilience (GR) dimensions of urban resilience are not uniform and are based on the theory of socio-ecological system resilience and the framework of complex adaptive systems. The concept of economic resilience is predicated on the short-term reallocation of resources, market efficiency, and technological innovation [34]. Social resilience, by contrast, is rooted in the medium-term adaptation of social capital and institutional synergy [35]. Ecological resilience is constrained by the nonlinear long-run interactions between natural thresholds and human activities. Finally, governance resilience is the ‘synergistic-antagonistic’ dynamic equilibrium of the coupled multiple subsystems. The concept of dynamic equilibrium can be defined as a non-additive result. The discrepancy may be attributed to hierarchical adaptive cycles and cross-scale coordination mechanisms. In light of the aforementioned analyses, the following hypotheses are put forward in this paper:

H3. 

There are differences within urban resilience indicators.

3.2. Model Design and Variable Selection

3.2.1. Model Design

In order to assess the impact of CLM on USD, this paper refers to Li, Tan [36,37] and Baron and Kenny [38] to construct the following panel regression and mechanism testing models:

$${\mathrm{U}\mathrm{R}}_{\mathrm{i}\mathrm{t}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{L}\mathrm{M}}_{\mathrm{i}\mathrm{t}}+\sum {\mathsf{\alpha }}_2{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}+{\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(1)

$${\mathrm{U}\mathrm{R}}_{\mathrm{i}\mathrm{t}}={\mathrm{b}}_0+{\mathrm{b}}_1{\mathrm{L}\mathrm{M}}_{\mathrm{i}\mathrm{t}}+{\mathrm{b}}_2{\mathrm{M}}_{\mathrm{i}\mathrm{t}}+\sum {\mathrm{b}}_3{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{t}}_{\mathrm{i}\mathrm{t}}+{\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(2)

In this study, the variables are defined as follows: I represents the city, T denotes the year, UR indicates the urban resilience, LM is land management, and control is a set of control variables affecting USD. The year variable denotes a time fixed effect, μ represents a city fixed effect, and ε is a random perturbation term.

3.2.2. Variable Selection

Explained Variable

The explanatory variable of this study is urban resilience (UR). In the theoretical framework of social-ecological system resilience, the concept of urban resilience is predicated on the system’s absorptive, adaptive, and transformational capacities in response to external perturbations. Therefore, from the perspective of the synergistic effects of three subsystems (namely, the economic, social, and ecological spheres), the present paper selects four first-level indicators of the economy, society, the environment, and governance, and 16 specific indicators. The urban resilience indicators are synthesized under the sociological perspective by means of the entropy weighting method (

Table 1. Resilience of elderly-friendly cities indicator system.

Primary Index	Specific Index	Unit of Measurement	Attributes
Economic resilience (ER)	Consumption of social retail goods/GDP	%	+
	Import/export/GDP	%	+
	Share of tertiary sector	%	+
	Per capita disposable income of urban residents	¥	+
	Enrolment in higher education	P	+
Social resilience (SR)	Total number of patents granted	%	+
	Number of registered unemployed persons at the end of the year	P	+
	Total consumption of social goods	t/104 ¥	+
	Industrial wastewater discharge/industrial output	t/104 ¥	−
Environmental resilience (ENR)	Industrial sulfur dioxide emissions/industrial output	t/104 ¥	−
	Industrial fume (dust) emissions/industrial output value	t/104 ¥	−
	Industrial NOx emissions per capita	%	−
	Non-hazardous domestic waste disposal rate	%	+
Governance resilience	Greening coverage in built-up areas	%	+
(GR)	Comprehensive industrial solid waste utilization rate	%	+
	Parkland area per capita municipal districts	ha/104 p	+

+ means positive, − means negative.

).

Explanatory Variable

From the perspective of environmental sociology, integrated land management is defined as the physical management of natural resources, as well as the adjustment and reconstruction of the interaction between social systems and ecosystems. It is able to reflect the effectiveness of integrated land management from the dimensions of equitable distribution of resources, social-ecological metabolic balance, and spatial justice [39,40]. Consequently, this paper elects to utilize the per capita built-up area as a metric for evaluating land adaptive management practices.

Control Variables

In this paper, the characteristics that may have an impact on urban resilience are employed as control variables.

Level of urbanization (urban): High urbanization is frequently accompanied by population concentration and infrastructure expansion, which may enhance economic resilience through scale effects. However, this process may also exacerbate ecological resource overload and pressure on social services [41]. In this paper, the ratio of the urban population to the resident population of each prefecture-level city in China is utilized as a metric to ascertain the urbanization level.

The degree of government intervention (Gov) is a factor that can directly shape urban resilience through financial inputs and administrative means [42]. In this paper, the degree of government intervention is indicated by the general government fiscal expenditure/gross regional product ratio.

Population density (PD): Dense regions have been demonstrated to promote economic resilience through knowledge spillovers. However, it has been hypothesized that this may be accompanied by a reduction in ecological and social resilience due to resource competition [43]. In this paper, population density is expressed by the use of the natural log of the total population at the conclusion of the year.

Economic development level (EDL): It is evident that economically developed regions generally possess a superior technological reserve and financial capacity to manage adversity. However, their industrial structure lock-in may impede the transition towards a more environmentally sustainable economic model [44]. Therefore, the present study employs logarithmic GDP per capita as a metric to express the level of urban economic development.

Foreign investment level (FIL): The impact of foreign investment on economic resilience is multifaceted, with the potential to both enhance resilience through technology transfer and, concomitantly, exacerbate ecological loads due to the ‘pollution shelter’ effect [45]. In this paper, the FIL is expressed as the annual real utilization of foreign capital relative to the gross regional product.

3.3. Modeling Evaluation

As demonstrated in

Table 2. Correlation analysis.

Variable	UR	ER	SR	ENR	GR	LM	Urban	Gov	PD	EDL	FIL
UR	1.000
ER	0.713 ***	1.000
SR	0.891 ***	0.318 ***	1.000
ENR	0.015	0.002	0.012	1.000
GR	0.203 ***	0.219 ***	0.124 ***	−0.005	1.000
LM	0.264 ***	0.123 ***	0.277 ***	0.003	0.075 ***	1.000
Urban	0.552 ***	0.551 ***	0.389 ***	−0.036 **	0.299 ***	0.187 ***	1.000
Gov	−0.236 ***	−0.036 **	−0.296 ***	0.023	−0.059 ***	−0.107 ***	−0.260 ***	1.000
PD	0.280 ***	−0.036 **	0.401 ***	0.059 ***	0.046 ***	0.165 ***	−0.217 ***	−0.208 ***	1.000
EDL	0.569 ***	0.582 ***	0.391 ***	−0.049 ***	0.409 ***	0.173 ***	0.760 ***	−0.367 ***	−0.068 ***	1.000
FIL	0.218 ***	0.090 ***	0.236 ***	0.023	0.104 ***	0.116 ***	0.192 ***	−0.226 ***	0.073 ***	0.191 ***	1.000

Standard errors in parentheses, ** p < 0.05, *** p < 0.01.

, the relationship between LM and UR exhibits a correlation coefficient of 0.264, which is significant at the 1% level, thereby initially validating the role of CIL in fostering UR. Concurrently, the Variance Inflation Factor (VIF) test (see

Table 3. VIF test results.

Variable	VIF
Urban	2.61
Gov	1.25
PD	1.23
EDL	2.56
FIL	1.09
CLM	1.17
Mean	1.65

) reveals that the VIF values of all variables are less than 3, with a mean value of 1.65, which is considerably lower than the critical value of 5. This finding suggests that the model does not exhibit a significant multicollinearity issue and that the regression results are reliable.

3.4. Data Sources

In order to guarantee the scientific rigor of the data, the research sample was designed to exclude Hong Kong, Macau, and Taiwan. This paper employs data from 269 Chinese cities spanning the period from 2010 to 2022, which represents a sample with good continuity. The data pertinent to this study were primarily sourced from the China Urban Statistical Yearbook, the China Environmental Statistical Yearbook, and publicly accessible data from municipal statistical bureaus from previous years. To address the gaps in the data for 2022, an interpolation method was employed.

Table 4. Descriptive statistics.

Variable	N	Mean	SD	Min	Max
USD	4309	0.055	0.044	0.009	0.422
ES	4309	0.104	0.074	0.007	0.790
SS	4309	0.029	0.047	0.000	0.441
ENS	4309	0.108	0.014	0.015	1.000
GS	4309	0.285	0.029	0.151	0.586
CLM	4309	0.014	0.038	0.000	0.998
Urban	4309	0.539	0.159	0.115	1.001
Gov	4309	0.185	0.093	0.043	1.485
PD	4309	5.876	0.663	2.898	7.350
EDL	4309	10.583	0.671	4.595	13.056
FIL	4309	0.018	0.019	0.000	0.229
ISO	4309	0.409	0.099	0.114	0.805
PG	4309	304.675	895.969	0.000	15,410.000

presents the descriptive statistics of the data.

4. Results

4.1. Basic Regression Analysis

Table 5. Basic regression results.

Variable	(1)	(2)	(3)	(4)
	UR	UR	UR	UR
LM	0.420 ***	0.154 ***	0.028 ***	0.025 ***
	(0.017)	(0.013)	(0.010)	(0.010)
Urban		0.110 ***		−0.042 ***
		(0.006)		(0.008)
Gov		0.064 ***		0.033 ***
		(0.007)		(0.008)
PD		0.026 ***		0.043 ***
		(0.001)		(0.006)
EDL		0.029 ***		0.008 ***
		(0.002)		(0.002)
FIL		0.091 ***		−0.079 ***
		(0.029)		(0.026)
_cons	0.047 ***	−0.487 ***	0.042 ***	−0.274 ***
	(0.001)	(0.017)	(0.001)	(0.042)
City	NO	NO	Yes	Yes
Year	NO	NO	Yes	Yes
N	3497.000	3497.000	3497.000	3497.000
R2	0.150	0.543	0.239	0.268

Standard errors in parentheses, *** p < 0.01.

presents the regression results of Equation (1), which illustrate the impact of LM on UR. To guarantee the reliability of the model, the paper takes steps to eliminate the potential for double fixation of UR and time, while also introducing additional control variables incrementally. In particular, column (1) illustrates the impact of LM on UR in the absence of supplementary controls. At the 1% level of significance, the regression coefficients yield a positive result, indicating that LM is an effective means of promoting UR. Column (2) presents the results of the regression analysis after the addition of control variables. The regression coefficients consistently demonstrate a significant positive correlation with UR at the 1% level, indicating a robust positive correlation between LM and UR. Furthermore, columns (3) and (4) demonstrate a markedly positive outcome for the net regression of UR and the regression incorporating the control variables on a fixed city and year basis, respectively. The initial hypothesis is thus validated, demonstrating that LM can exert a beneficial influence on UR.

As illustrated in

Table 6. Multi-dimensional regression results.

Variable	(1)	(2)	(3)	(4)
	ER	SR	ENR	GR
LM	0.026	0.026 **	0.000	0.017 *
	(0.020)	(0.011)	(0.001)	(0.010)
Urban	−0.080 ***	−0.028 ***	−0.001	0.009
	(0.016)	(0.009)	(0.001)	(0.008)
Gov	0.070 ***	0.018 *	0.003 ***	0.029 ***
	(0.017)	(0.009)	(0.001)	(0.009)
PD	0.107 ***	0.019 ***	0.001	−0.035 ***
	(0.012)	(0.006)	(0.001)	(0.006)
EDL	0.006	0.009 ***	−0.000	0.012 ***
	(0.004)	(0.002)	(0.000)	(0.002)
FIL	−0.089	−0.078 ***	0.009 **	0.024
	(0.055)	−0.164 ***	(0.004)	(0.027)
_cons	−0.592 ***	(0.048)	0.000	0.000
	(0.089)	0.026 **	0.103 ***	0.349 ***
City	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
N	3497.000	3497.000	3497.000	3497.000
R2	0.545	0.132	0.021	0.148

Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

, the regression findings of LM on the four dimensions of UR are presented. The findings of this study demonstrate that LM exerts a significant positive effect on ER and SR, yet no significant effect on ENR and ER. Government intervention (Gov) has been demonstrated to have a significant impact on ER, SR, and GR, while population density (PD) has been shown to have the strongest positive effect on ER (0.107). The impact paths of the resilience dimensions demonstrate significant heterogeneity, necessitating a synergistic enhancement through the implementation of policy combinations that are tailored to specific contexts. Therefore, H3 is verified.

4.2. Robustness Analysis

Given the possibility of lagged effects in the benchmark analysis, this study re-evaluates the empirical regressions by introducing a time lag for the explanatory variables. The results of this analysis are presented in column (1) of

Table 7. Robustness analysis results.

Variable	(1)	(2)
	UR	UR
Time lag	0.020 **
	(0.010)
Delete year		0.038 ***
		(0.011)
Urban	−0.038 ***	−0.040 ***
	(0.008)	(0.008)
Gov	0.061 ***	0.032 ***
	(0.011)	(0.009)
PD	0.040 ***	0.043 ***
	(0.006)	(0.006)
EDL	0.011 ***	0.008 ***
	(0.002)	(0.002)
FIL	−0.066 **	−0.082 ***
	(0.029)	(0.027)
_cons	−0.298 ***	−0.271 ***
	(0.049)	(0.044)
City	Yes	Yes
Year	Yes	Yes
N	3187.000	3225.000
R2	0.232	0.269

Standard errors in parentheses, ** p < 0.05, *** p < 0.01.

and show that the regression coefficients remain positively significant at the 1% level. In addition, the study also excludes certain years and reruns the regression, with the results shown in column (2). These two robustness checks aim to support the credibility of the benchmark regression results reported in this study.

4.3. Heterogeneity Analysis

4.3.1. Regional Heterogeneity

The preceding empirical results demonstrate that comprehensive land management exerts a favorable influence on urban resilience. Nevertheless, it is evident that there are discernible spatial discrepancies in the economic development levels between China’s eastern, central, and western regions, both with regard to the efficacy of policy implementation and due to the substantial land area of China.

In this paper, the sample was divided into eastern, central, and western cities according to geographical location, and the regression was re-run in subsamples. The results are shown in

Table 8. Regression results for heterogeneity in urban areas.

Variable	Region
	Eastern City	Central City	Western City
LM	0.005	0.017 *	0.215 ***
	(0.018)	(0.010)	(0.032)
Urban	−0.055 ***	−0.040 ***	0.027 *
	(0.013)	(0.010)	(0.016)
Gov	0.006	0.034 ***	0.034 **
	(0.014)	(0.013)	(0.014)
PD	0.165 ***	−0.013 **	−0.056 ***
	(0.013)	(0.006)	(0.012)
EDL	0.017 ***	0.011 ***	−0.001
	(0.004)	(0.003)	(0.005)
FIL	−0.044	0.007	−0.001
	(0.052)	(0.029)	(0.070)
_cons	−1.088 ***	0.014	0.336 ***
	(0.091)	(0.050)	(0.086)
City	Yes	Yes	Yes
Year	Yes	Yes	Yes
N	1224.000	1375.000	872.000
R2	0.464	0.247	0.257

Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

, which indicates that the promotion effect of urban resilience has a gradually increasing trend from east to west. This may be due to the following factors: first, the ecological environment level. The ecological environment problems faced by the central and western regions are more complicated and severe, such as land sanding and desertification, and these problems directly threaten the urban resilience and the quality of life of residents. Therefore, integrated land management in the central and western regions has a more significant effect on improving the ecological environment and enhancing the urban resilience. Secondly, economic development. In comparison with the eastern region, the central and western regions have experienced comparatively slower economic development and less advanced urban infrastructure. Integrated land management has been shown to enhance urban infrastructure, thereby providing substantial support for urban resilience. Concurrently, improvements in infrastructure have been demonstrated to enhance a city’s disaster resilience and promote urban resilience. Thirdly, the level of government support is a crucial factor. In recent years, China has been increasing its support for the central and western regions and has introduced a series of policies and measures to promote coordinated regional development (in 2024, the transfer of industries from coastal areas to the central and western regions was proposed). These policies have provided strong policy support and financial guarantee for integrated and comprehensive land management. Consequently, the implementation of integrated and comprehensive land management in the central and western regions will also be more effective.

4.3.2. Scale Heterogeneity

In this paper, the sample is divided into small and medium-sized cities and large cities according to population size (as outlined in the Chinese Government’s Circular on the Adjustment of the Criteria for the Classification of the Size of Cities): urban areas with a population exceeding one million are designated as large cities, while those with a population of less than one million are categorized as small or medium-sized cities); the regression results are presented in

Table 9. Analysis of urban size heterogeneity.

Variable	Size
	Small and Medium-Sized City	Big City
LM	0.114 ***	0.026 **
	(0.022)	(0.012)
Urban	−0.020 **	−0.053 ***
	(0.009)	(0.012)
Gov	0.048 ***	0.015
	(0.010)	(0.012)
PD	0.050 ***	0.005
	(0.007)	(0.013)
EDL	0.012 ***	0.010 ***
	(0.002)	(0.004)
FIL	0.018	−0.163 ***
	(0.033)	(0.039)
_cons	−0.353 ***	−0.057
	(0.046)	(0.099)
City	Yes	Yes
Year	Yes	Yes
N	1722.000	1775.000
R2	0.350	0.260

Standard errors in parentheses, ** p < 0.05, *** p < 0.01.

. The results show that both small and medium-sized cities and large cities pass the significance test, but the effect of small and medium-sized cities is superior to that of large cities. The following reasons may be postulated: first, the initial use types of land resources are different. In comparison with large cities, small and medium-sized cities exhibit a higher proportion of agricultural land use, and in contrast to villages, a higher proportion of construction land use. Concurrently, small and medium-sized cities are characterized by the presence of urban enterprises, collective township enterprises, private enterprises, and other types of land-use subjects, which is evident in the complexity of the tenure and diverse land relations. In contrast, the land use type of big cities is relatively unitary, dominated by construction land. In comparison, small and medium-sized cities exhibit a more pronounced distinction. Secondly, the rate of land use change varies. The rate of land use change in large cities is relatively slow, due to the stability of their land use and the number of policies and planning constraints they face. In contrast, small and medium-sized cities experience rapid land use changes, but through comprehensive land management, they can be adjusted in a timely manner to optimize the land use structure and improve the efficiency of land resources. This, in turn, enhances the urban resilience. Thirdly, city scale and population density are different. The lower population density and urban scale of small and medium-sized cities relative to large cities facilitates more efficient resource allocation and a rational spatial layout during comprehensive land management.

5. Discussion

Urban resilience, as a concept in the theory of socio-ecological system resilience, is increasingly recognized as a multifaceted structure that integrates ecological, social and governance dimensions. A close analysis of case studies of New Orleans, Cape Town, and Phoenix has been undertaken to illustrate how cities function as part of a wider ‘urban system’. This analysis demonstrates that urban resilience cannot be understood in isolation but is interconnected with other urban systems [46]. This perspective aligns with the concept that urban resilience is contingent on cross-scale interactions and thresholds that influence how cities respond to environmental and social challenges. The necessity for planning and administrative functions to be recognized as integral components of the social-ecological system has also been acknowledged [47]. A considerable body of research has been conducted on the key factors influencing social-ecological resilience. These factors include inclusive decision-making and regulatory incentives, as well as governance structures and community participation [48]. This research is in alignment with the construction of indicators in this paper. The role of digital economic systems in enhancing urban ecological resilience has been examined by other scholars. This analysis, which was based on data from 278 cities in China, highlighted the role of technology [49].

The present article is confined in its analysis to data relating to 269 prefecture-level cities in China from 2010 to 2022, and the sample is not fully utilized with regard to years, with Hong Kong, Macao, and Taiwan being excluded, which represents a certain limitation in terms of sample selection. It is evident that the article in question has failed to take into full consideration the potential interfering effects that may be exerted by other related policies. It is recommended that future research endeavors focus on expanding the sample size and extending the time span. Additionally, there is a need to enhance the research on policy interference.

6. Recommendations

In accordance with the theory of the resilience of social and ecological systems, the resilience of age-friendly cities must overcome the conventional ‘unidimensional policy intervention’ model and transition to a multidimensional and complex governance framework. The following recommendations are thus proposed by this study:

Firstly, the State has established a land adaptation management mechanism based on ‘flexible planning and dynamic assessment’. At the level of territorial spatial planning, it has raised the mandatory standard for the proportion of land used for elderly care in newly built urban areas and has established a negative list for the ageing-adapted renovation of old communities, requiring communities more than 15 years old to be equipped with day-care centers and fall-prevention flooring systems. It is argued that land use control should overcome the conventional rigid constraints and initiate a novel, malleable substitution mechanism between medical land and land designated for senior care facilities. This would establish an innovative spatial carrier for a ‘medical and nursing service complex’ within the central urban area. In addition, a dynamic monitoring platform for land suitability for the elderly should be developed, integrating the seventh population census grid data and the distribution of POI facilities. This platform should automatically trigger the land use adjustment process when the average annual growth rate of the aging rate in the region exceeds 2 per cent, ensuring that the supply of facilities is dynamically matched with demand. This mechanism has the capacity to systematically resolve the structural contradiction that exists in 43 per cent of cities, namely, that the allocation of facilities lags behind the pace of population ageing.

Secondly, the Government has established a cross-domain collaborative response platform for ‘spatial governance and health services’. A committee on spatial governance for the elderly has been established at the municipal level, with the objective of integrating land-use approval data from the natural resources department, heat maps of chronic disease distribution from the health department, and information on the operation of elderly care facilities from the civil affairs department. The overarching aim of this initiative is to construct a database that will facilitate the linkage of ‘health and land space for the elderly’. An intelligent decision support system was developed, and the Spatial Dubin Model was utilized to identify blind spots in the services provided by elderly facilities. In communities where the elderly population density exceeds 150 people per hectare, yet the coverage of healthcare facilities extends to less than 60 per cent of the area within a 500-m radius, it becomes imperative to allocate land for the establishment of an embedded nursing station. The following innovations are proposed for consideration: firstly, an alteration to the land supply model of ‘senior care facility packages’; secondly, the creation of senior-friendly transportation systems; and thirdly, the introduction of pre-built interfaces for emergency call devices, which would be a prerequisite for land transfer. In addition, incentives are suggested to encourage developers to build community intergenerational integration centers, through plot ratio incentives.

Thirdly, the Government must construct a sophisticated resilience enhancement system incorporating ‘digital twins and intergenerational integration’. The government has the capacity to incorporate a ‘silver-haired resilience’ thematic module into the urban information modeling platform. This would facilitate the integration of data on the movement trajectory of the elderly population, the response heat of emergency resources, and the evaluation of age-friendly environments. Furthermore, it would enable the construction of a model for dynamic optimization of the 15-min emergency service circle. The establishment of a digital sand table system that is conducive to ageing is imperative for the development of land. This system must be utilized to conduct extreme ageing stress tests, which are crucial for evaluating key indicators, such as walking accessibility and lift waiting time. These tests are particularly pertinent for newly planned communities, as they simulate the scenario in which 30% of the elderly population is considered. The primary objective of these stress tests is to ensure that the spatial design of a community meets the criteria for ageing-friendly resilience certification prior to the issuance of a construction permit. The development of the ‘silver-haired digital twin’ technology represents a significant innovation in the field. This technology utilizes smart bracelets to collect real-time behavioral data, including the frequency of falls and the duration of outdoor activities, of elderly individuals. The collected data are then processed through machine learning algorithms to optimize the layout of community service centers in real time.

7. Conclusions

The present study focuses on the panel data of 269 Chinese cities from 2010 to 2022, with a view to optimizing the evaluation criterion system of urban resilience and conducting in-depth research into the impact of LM on UR. The findings of the study demonstrate that:

(a)

LM exerts a substantial contribution to UR within the theoretical framework of environmental sociology, a conclusion that remains consistent following rigorous robustness tests.

(b)

Heterogeneity analyses reveal that the efficacy of urban resilience is more pronounced in the central and western regions with respect to geographic location, and in terms of city size, it is more evident in small- and medium-sized cities.

(c)

The study’s results indicate that the effectiveness of urban resilience is more pronounced in the central and western regions.