Assessment of Socio-Economic Adaptability to Ageing in Resource-Based Cities and Its Obstacle Factor

Abstract

The resource-based city is a type of city with pronounced ageing problems. Correctly grasping the socio-economic adaptability to ageing in resource-based cities can help actively guide the direction of urban development and build a new socio-economic order for the elderly. This paper first selects 125 resource-based cities in China from 2000 to 2020 for characteristic analysis, and finds that resource-based cities are characterized by fast growth rate and a large proportion of ageing population, deep ageing, large regional differences and uncoordinated ageing development with regional socio-economic development levels. The research objective of this article is to explore the temporal evolution characteristics, spatial patterns, agglomeration characteristics, and factors hindering the socio-economic adaptability to ageing in resource-based cities in China from 2000 to 2020. Therefore, an indicator system for assessing the socio-economic adaptability to ageing was constructed, and the improved entropy-weighted TOPSIS model was used to measure the socio-economic adaptability to ageing in 113 resource-based cities in China from 2000 to 2020. The spatio-temporal variation characteristics of the socio-economic adaptability to ageing in resource-based cities were analyzed by descriptive analysis and Moran’s index, and the main obstacle dimensions and obstacle indicators were clarified by the obstacle factor model. The findings can be summarized as follows: Temporal Analysis: Over the timeframe assessed, the socio-economic adaptability of ageing in resource cities demonstrates a consistent year-on-year improvement. The spatial clustering pattern exhibits a noteworthy trend of “significant-significant-insignificant”. Spatial Pattern: Examining the spatial arrangement from 2000 to 2020, areas with medium-low and low adaptability are prominently concentrated in Eastern China and Northeastern China, while regions with medium-high and high adaptability are focal in Northern China and Eastern China. Hebei Province and its neighboring provinces consistently display H-H clustering, contrasting with the Southwestern regions that persistently exhibit L-L clustering. Obstacle Degree Analysis: Across the 2000 to 2020 period, dimensions related to economic development, social participation, and social security consistently emerge as the primary obstacles. Notably, the top 12 average annual obstacle indicators are selected, and within economic development dimension all 4 indicators predominate as the key obstacles. Within the social participation dimension, tertiary sector value added and total retail sales of consumer goods per capita feature as primary obstacles. Regarding social security, impediments are primarily associated with the ratio of Medicare coverage, the ratio of pension coverage, alongside the development level of the social security sector. In the domain of service provision, key obstacle indicators include park area per capita, number of books in public libraries per 100 inhabitants, and public trams and buses per 10,000 inhabitants.

1. Introduction

The issue of population has long been a fundamental and strategic concern for China, with significant implications for the country’s socio-economic development [1]. As such, it is a critical factor in shaping China’s growth and progress. Data from the fifth and seventh China population censuses indicate that between 2000 and 2020, the proportion of China’s population aged 60 and over increased from 10.46% to 18.73%, while the proportion of those aged 65 and over rose from 7.1% to 13.52%. These figures demonstrate that China is experiencing rapid population ageing, with a large and growing elderly population, and that the issue of population ageing is becoming increasingly pressing. To address the issue of population ageing, China has implemented a series of policy measures aimed at promoting socio-economic adaptability to the ageing process and establishing a new, age-friendly socio-economic order.

Numerous empirical studies have investigated the spatio-temporal evolution of ageing and its impact on the socio-economy in China [2,3,4,5]. However, the complexity of the socio-economic system and the potential for long-term effects of ageing to manifest necessitate a comprehensive time-series assessment. Such an assessment is essential for ensuring that current socio-economic development is properly adapted to the ageing process [6] and for anticipating potential future challenges [7]. Despite the importance of regional socio-economic adaptability to population ageing, this issue has received relatively little attention in the literature. Globally, existing research has employed indicator assessments to measure national or regional adaptability to ageing [6,8,9], with a focus on intergenerational harmony, resource equity, cohesion, and social participation. In the Chinese context, related studies have adopted various disciplinary perspectives to examine public services and urban development, assessing the level of ageing, the equitable distribution and development of public facilities, and the level of healthy ageing [10,11,12,13]. In summary, while the majority of extant research employs cross-sectional data to assess the ability of cities to adapt or cope with population ageing, relatively few studies have undertaken longitudinal analyses of the adaptability process. In terms of the object of study, the majority of existing research evaluations focus on global, national, or specific administrative units [8,9,13], with relatively little research examining the adaptability of particular types of cities to population ageing at the meso and macro levels. In the construction of indicator systems, the prevailing adaptability paradigm posits that studies of adaptability suggest considering the question of “adapt to what” [14]. Nevertheless, extant research on population ageing has neglected to incorporate the ageing disturbance intensity into their evaluative frameworks.

As a city type characterized by an ageing population, resource-based cities have received extensive attention regarding demographic issues [15]. The pronounced ageing of the population, combined with the unique social background and resource endowment of resource-based cities, influences their sustainability [16], leading to poor sustainable development [17,18]. Since its accession to the World Trade Organization in 2001, China has experienced rapid economic growth. However, resource-based cities facing resource depletion have exhibited insufficient economic development momentum and sluggish growth [15]. The phenomenon of ageing before achieving affluence is prominent, and thus, the adaptability of resource-based cities to an ageing population warrants further attention. In light of these observations, this study selects 125 resource-based cities from the National Sustainable Development Plan for Resource-Based Cities (2013–2020) for an analysis of ageing characteristics. We assess the socio-economic adaptability to ageing in 113 of these cities over the period 2000–2020, utilizing available data. The aim is to address the following inquiries: (i) what are the characteristics of the temporal evolution of socio-economic adaptability to ageing in resource cities from 2000 to 2020? (ii) what are the spatial patterns of socio-economic adaptability to ageing in resource-based cities and their clustering characteristics from 2000 to 2020? (iii) what are the factors that impede the enhancement of socio-economic adaptability to ageing in resource-based cities from 2000 to 2020? This study attempts to make the following marginal contributions: theoretical enrichment and improvement of the index system, modeling methods, and conceptual paradigm for assessing socio-economic adaptability to ageing; an attempt to provide valuable insights for optimizing national support policies.

2. Materials and Methods

2.1. Methodology

This paper aims to select 21 indicators from five aspects, namely, ageing disturbance intensity, economic development, service provision, social participation, and social security, to construct an evaluation index system for the socio-economic adaptability to ageing in resource-based cities (see

Table 1. Socio-economic adaptability to ageing evaluation index system in resource-based cities ¹.

Target Layer	Guideline Layer	Weight	Indicator Layer	Weight
Socio-economic adaptability to ageing	Ageing disturbance intensity y1	0.0542	The proportion of the population over 60 (−) x1	0.0192
			The number of the population over 60 (−) x2	0.0089
			The ratio of the aged and the young (−) x3	0.0086
			Elderly dependency rate (−) x4	0.0175
	Economic development y2	0.2883	General public budget expenditure per capita (+) x5	0.0845
			GDP per capita (+) x6	0.0724
			The rationalization of industrial structure index (+) x7 2	0.0705
			Number of college students per 10,000 inhabitants (+) x8	0.0610
	Service provision y3	0.1696	The number of hospital beds per 1000 inhabitants (+) x9	0.0322
			Park area per capita (+) x10	0.0661
			Public trams and buses per 10,000 inhabitants (+) x11	0.0368
			Number of books in public libraries per 100 inhabitants (+) x12	0.0344
	Social participation y4	0.1996	Urban unemployment rate (−) x13	0.0063
			Total retail sales of consumer goods per capita (+) x14 3	0.0766
			Internet access rates (+) x15	0.0283
			Tertiary sector value added (+) x16	0.0708
			The proportion of tertiary sector value added to GDP x17	0.0176
	Social security y5	0.2884	The ratio of minimum living security benefits to per capita disposable income (+) x18	0.0276
			The ratio of Medicare coverage (+) x19	0.1182
			The ratio of pension coverage (+) x20	0.1081
			Development level of the social security sector (+) x21 4	0.0344

¹ (+) means the indicator is positive (−) means it is negative. The data in the table are mainly obtained from the provincial statistical yearbooks, China Urban Statistical Yearbook, China Civil Affairs Statistical Yearbook, China Urban Construction Statistical Yearbook, population census and public government data; ² Reference [19] for the calculation of rationalization of industrial structure index; ³ Urban unemployment rate: Urban registered unemployed population/(Population employed in various occupations + Urban registered unemployed population); ⁴ Development level of the social security sector: the number of employees in health care, social security and welfare institutions, social organizations and public administration per 10,000 inhabitants.

). A total of 113 resource-based cities were selected for evaluation based on data accessibility. The socio-economic adaptability to ageing assessment at medium and macro scales for special-development type cities under time series is conducted using methods such as the improved entropy—weighted TOPSIS model, Moran’s Index, and the obstacle factors.

2.2. Study Methods

2.2.1. Evaluation Index System

Existing studies suggest that adaptation studies should consider the question “adapt to what” in order to scientifically evaluate the heterogeneity of perturbations to system shocks [14,20]. The existing studies on socio-economic adaptability to ageing mainly evaluate the adaptation of urban ageing by constructing indicator systems from the perspectives of social participation, social security, economic development, urban construction, social environment, and resource equity [6,8,13,21]. However, they have not yet incorporated ageing-related indicators into the rating index system, failing to consider the heterogeneity of ageing differences in the intensity of shocks to socio-economic development. In this study, we draw upon existing research and first incorporate two dimensions, namely, ageing disturbance intensity and economic development, into the evaluation framework. Zaidi et al. [21] and Hess et al. [9] conducted research on the Active Ageing Index (AAI) and concluded that the measurement of active ageing should encompass four aspects: employment, participation in society, independent, healthy and secure living, capacity and enabling environment for active and healthy ageing. However, the AAI heavily relies on employment and social support. Building upon this, Chen et al. [8] and Goldman et al. [6] proposed the Societal Ageing Index, which focuses on five dimensions: productivity and engagement, well-being, equity, cohesion, and security. Yang et al. combined the Chinese development context and summarized various ageing evaluation indices, including AAI, Age-Friendly Cities and Communities, and Wellbeing [22]. Based on this, they proposed the “China Urban Healthy Ageing Assessment System” [13,22], which focuses on five aspects of cities: healthcare services, living environment, transportation, social equity and participation, social security and finance. Building on existing research on social ageing evaluation and adopting an urban development perspective, we include three dimensions: service provision, social participation, and social security, into the evaluation framework [6,8,9,13,21,22]. The indicators of the evaluation index system are constructed by considering five main aspects:

1.

Ageing disturbance intensity

Because different resource-based cities are affected differently by the ageing process, they will exhibit distinct adaptations in dealing with it. This paper examines the ageing disturbance intensity across four key aspects: size, extent, growth rate [23], and burden level of ageing. These aspects are characterized by specific indicators, including the proportion of the population over 60, the number of individuals aged 60 and above, the ratio of the aged to the young population, and the elderly dependency rate.

2.

Economic development

Economic development is the foundational macro aspect that underpins the adaptability of urban populations to the challenges posed by ageing. This connection is twofold. Firstly, cities at varying stages of economic development exhibit diverse adaptive traits in response to the ageing phenomenon. Secondly, robust urban innovation capacities and well-structured industrial frameworks amplify cities’ development potential, thereby augmenting the government’s efficacy in discharging public responsibilities. A judiciously structured industrial landscape holds particular significance in fostering the sustained growth of China’s economy over the medium- to long term [19]. Therefore, this paper assesses the level of regional economic development by considering four aspects: the capacity for government public functions, the level of economic development, the industrial structure [19], and the foundation of urban innovation [24]. These aspects are characterized by indicators such as general public budget expenditure per capita, GDP per capita, rationalization of industrial structure index, and the number of college students per 10,000 inhabitants.

3.

Service provision

Influenced by the construction concept of “production before life”, resource-based cities primarily prioritize enterprise production, often resulting in inadequate urban service functions [25]. Addressing the gaps in urban services is beneficial for promoting urban socio-economic development in alignment with the ageing process [26]. Therefore, this paper assesses the level of urban service provision from four perspectives: medical service provision, public space provision, traffic and transportation, and cultural facilities. These aspects are characterized by indicators such as the number of hospital beds per 1000 inhabitants, park area per capita, public trams and buses per 10,000 inhabitants, and the number of books in public libraries per 100 inhabitants.

4.

Social participation

The active engagement of older individuals in social and productive activities not only promotes their physical and mental well-being but also enhances socio-economic adaptability to ageing [27]. Enhancing the consumption capacity of residents, addressing the prevailing employment challenges and “digital divide” in resource-based cities are crucial factors in promoting the social participation of older individuals [28,29]. Existing studies have reached the following conclusions:

• Cities with higher-quality employment patterns have a greater capacity to accommodate the working population [30]. Additionally, urban employability is reflected in lower urban unemployment rates;
• The advancement of digital infrastructure facilitates the integration of older individuals into modern society and promotes the development of a silver-haired economy [28];
• Greater purchasing power contributes to a more fulfilling and satisfying life for older adults [31].

All three aspects contribute to enhancing the social participation of the elderly population in urban areas. Thus, this paper assesses urban social participation across four aspects, drawing on existing studies. These aspects include employment absorption capacity [30], urban employability, social integration, and consumption capacity. These aspects are characterized by indicators such as the urban unemployment rate, total retail sales of consumer goods per capita, Internet access rates, tertiary sector value added and the proportion of tertiary sector value added to GDP.

5.

Social security

The quality development of social security plays a vital role in regulating intergenerational and intragenerational relations to promote socio-economic adaptability to ageing [8,32]. Pensions and Medicare hold paramount importance as fundamental pillars of social security in China. These programs possess inherent attributes of universality and mandatory participation. Their extensive implementation holds the potential to enhance economic and health safeguards for the elderly population. Furthermore, the mandatory and all-encompassing nature of these social security measures contributes to bridging the economic and health protection disparities that may exist across various regions. Simultaneously, bolstering the capacity of social assistance and fostering the growth of the social security industry can play a pivotal role in fortifying urban social security capabilities within the context of population ageing [8]. Accordingly, this paper assesses the level of urban social security in four aspects: economic security coverage, medical security coverage, the level of social security development and welfare capacity. These aspects are characterized by indicators such as the ratio of pension coverage, the ratio of Medicare coverage, development level of the social security sector and the ratio of minimum living security benefits to per capita disposable income.

2.2.2. The Improve Entropy-Weighted TOPSIS

The entropy-weighted TOPSIS model effectively eliminates human factors and provides a more objective assessment of the significance of each rating index within the overall rating index system [33]. Additionally, it enables the ranking of the distance between the index value and the positive and negative ideal values for each evaluated object [34]. To address potential correlation interference among indicator dimensions and to ensure independence from dimensionality [35] this paper employs the Mahalanobis distance in the calculation, replacing the conventional Euclidean distance. The calculation procedure is as follows:

• Constructing an evaluation matrix

The original evaluation data matrix Y was constructed to assess the socio-economic adaptability of the ageing in resource-based cities.

$$\mathrm{Y}=\left[\begin{array}{cc}\begin{array}{cc}{\mathrm{x}}_{11}& {\mathrm{x}}_{12}\\ {\mathrm{x}}_{21}& {\mathrm{x}}_{22}\end{array}& \begin{array}{cc}\cdots & {\mathrm{x}}_{1\mathrm{n}}\\ \cdots & {\mathrm{x}}_{2\mathrm{n}}\end{array}\\ \begin{array}{cc}\cdots & \cdots \\ {\mathrm{x}}_{\mathrm{m}1}& {\mathrm{x}}_{\mathrm{m}2}\end{array}& \begin{array}{cc}\ddots & \cdots \\ \cdots & {\mathrm{x}}_{\mathrm{mn}}\end{array}\end{array}\right]$$

(1)

In this context, ${\mathrm{x}}_{\mathrm{ij}}$ (i = 1, 2, ..., m, j = 1, 2, ..., n) denotes the observed value of the i-th indicator in the j-th sample. Here, n signifies the total number of evaluation indicators, and m represents the total number of evaluation objects.

2.

Dimensionless processing

Given the varying magnitudes of the original evaluation data, normalization was performed using the extreme difference normalization method. Equations (2) and (3) were employed as the processing methods for the positive and negative indicators, respectively, to derive the standardized rating matrix ${\mathrm{Y}}^{’}$.

$${\mathrm{r}}_{\mathrm{ij}}=\frac{{\mathrm{x}}_{\mathrm{ij}}-{\mathrm{MIN}\{\mathrm{x}}_{\mathrm{j}}\}}{\mathrm{MAX}\left\{{\mathrm{x}}_{\mathrm{j}}\right\}-{\mathrm{MIN}\{\mathrm{x}}_{\mathrm{j}}\}}$$

(2)

$${\mathrm{r}}_{\mathrm{ij} }=\frac{\mathrm{MAX}\left\{{\mathrm{x}}_{\mathrm{j}}\right\}-{\mathrm{x}}_{\mathrm{ij}}}{\mathrm{MAX}\left\{{\mathrm{x}}_{\mathrm{j}}\right\}-\mathrm{MIN}\left\{{\mathrm{x}}_{\mathrm{j}}\right\}}$$

(3)

Here, ${\mathrm{r}}_{\mathrm{ij}}$ represents the value of the i-th indicator’s observation on the j-th sample after dimensionless processing, and ${\mathrm{x}}_{\mathrm{j}}$ represents the observation of the j-th sample within the same indicator series.

3.

Determining indicator weights

Determine the weight for the i-th evaluation indicator:

$${\mathsf{\omega }}_{\mathrm{i}}=\frac{1-{\mathrm{e}}_{\mathrm{i}}}{{\sum }_{\mathrm{i}=1}^{\mathrm{m}}\left(1-{\mathrm{e}}_{\mathrm{i}}\right)}$$

(4)

The indicator information entropy, ${\mathrm{e}}_{\mathrm{i}}$, is defined as $-\frac{{\sum }_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{p}}_{\mathrm{i}}{\mathrm{lnp}}_{\mathrm{i}}}{\mathrm{lnn}}$, where ${\mathrm{p}}_{\mathrm{i}}=\frac{{\mathrm{r}}_{\mathrm{ij}}}{{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{r}}_{\mathrm{ij}}}$, and n represents the total number of evaluation indicators.

4.

Constructing the Entropy-Weighted TOPSIS evaluation matrix

Construct the weighted standardized assessment matrix A for the socio-economic adaptability to ageing, based on the indicator weights ${\mathsf{\omega }}_{\mathrm{i}}$:

$$\mathrm{A}=\left[\begin{array}{cccc}{\mathrm{a}}_{11}& {\mathrm{a}}_{12}& \cdots & {\mathrm{a}}_{1\mathrm{n}}\\ {\mathrm{a}}_{21}& {\mathrm{a}}_{22}& \cdots & {\mathrm{a}}_{11}\\ ⋮& ⋮& \ddots & ⋮\\ {\mathrm{a}}_{\mathrm{m}1}& {\mathrm{a}}_{\mathrm{m}2}& \cdots & {\mathrm{a}}_{\mathrm{mn}}\end{array}\right]=\left[\begin{array}{cccc}{\mathrm{r}}_{11}·{\mathrm{\omega }}_1& {\mathrm{r}}_{12}·{\mathrm{\omega }}_2& \cdots & {\mathrm{r}}_{1\mathrm{n}}·{\mathrm{\omega }}_{\mathrm{n}}\\ {\mathrm{r}}_{21}·{\mathrm{\omega }}_1& {\mathrm{r}}_{22}·{\mathrm{\omega }}_2& \cdots & {\mathrm{r}}_{2\mathrm{n}}·{\mathrm{\omega }}_{\mathrm{n}}\\ ⋮& ⋮& \ddots & ⋮\\ {\mathrm{r}}_{\mathrm{m}1}·{\mathrm{\omega }}_1& {\mathrm{r}}_{\mathrm{m}2}·{\mathrm{\omega }}_2& \cdots & {\mathrm{r}}_{\mathrm{mn}}·{\mathrm{\omega }}_{\mathrm{n}}\end{array}\right]$$

(5)

5.

Identifying ideal indicator values

${\mathrm{A}}^{+}$ represents the maximum value of indicator i among all samples, referred to as the “ideal value” for positive indicators; ${\mathrm{A}}^{-}$ represents the minimum value of indicator i among all samples, referred to as the “ideal value” for negative indicators. ${\mathrm{a}}_{\mathrm{j}}^{+}$ represents the ideal value of indicator j in sample i. The calculation method is described as follows:

$${\mathrm{A}}^{+}=\left\{\underset{0\le \mathrm{j}\le \mathrm{n}}{\max }{\mathrm{a}}_{\mathrm{ij}}\mid \mathrm{j}=1,2,\dots ,\mathrm{n}\right\}=\left\{{\mathrm{a}}_1^{+}{,\mathrm{a}}_2^{+}{,\dots ,\mathrm{a}}_{\mathrm{m}}^{+}\right\}$$

(6)

$${\mathrm{A}}^{-}=\left\{\underset{0\le \mathrm{j}\le \mathrm{n}}{\max }{\mathrm{a}}_{\mathrm{ij}}\mid \mathrm{j}=1,2,\dots ,\mathrm{n}\right\}=\left\{{\mathrm{a}}_1^{-}{,\mathrm{a}}_2^{-}{,\dots ,\mathrm{a}}_{\mathrm{m}}^{-}\right\}$$

(7)

6.

Quantifying Closeness: evaluation index value and ideal index value

Let ${\mathrm{d}}_{\mathrm{j}}^{+}$ denote the distance from the observation of the i-th indicator in the j-th sample to ${\mathrm{a}}_{\mathrm{j}}^{+}$ and let ${\mathrm{d}}_{\mathrm{j}}^{-}$ denote the distance from the observation of the i-th indicator in the j-th sample to ${\mathrm{a}}_{\mathrm{j}}^{-}$, The distances can be computed using the following formula:

$${\mathrm{d}}_{\mathrm{j}}^{+}=\sqrt{{\left({\mathrm{a}}_{\mathrm{ij}}-{\mathrm{a}}_{\mathrm{j}}^{+}\right)}^{\mathrm{T}}{\mathrm{W}}^{\mathrm{T}}{\mathsf{\Sigma }}^{-1}\mathrm{W}\left({\mathrm{a}}_{\mathrm{ij}}-{\mathrm{a}}_{\mathrm{j}}^{+}\right)}$$

(8)

$${\mathrm{d}}_{\mathrm{j}}^{-}=\sqrt{{\left({\mathrm{a}}_{\mathrm{ij}}-{\mathrm{a}}_{\mathrm{j}}^{-}\right)}^{\mathrm{T}}{\mathrm{W}}^{\mathrm{T}}{\mathsf{\Sigma }}^{-1}\mathrm{W}\left({\mathrm{a}}_{\mathrm{ij}}-{\mathrm{a}}_{\mathrm{j}}^{-}\right)}$$

(9)

where $\mathrm{W}=\mathrm{diag}(\sqrt{{\mathsf{\omega }}_1},\sqrt{{\mathsf{\omega }}_2},\dots ,\sqrt{{\mathsf{\omega }}_{\mathrm{m}}})$ and ${\mathsf{\Sigma }}^{-1}$ represent the inverse of the covariance matrix.

Let ${\mathrm{T}}_{\mathrm{j}}$ represent the closeness, which indicates the degree of proximity between sample j’s socio-economic adaptability to ageing and the ideal value. The higher the closeness value, the better the socio-economic adaptability to ageing, while a lower closeness value indicates poorer socio-economic adaptability to ageing. The closeness values range from 0 to 1. The formula for calculating the closeness is as follows:

$${\mathrm{T}}_{\mathrm{j}}=\frac{{\mathrm{d}}_{\mathrm{j}}^{-}}{{\mathrm{d}}_{\mathrm{j}}^{+}{+\mathrm{d}}_{\mathrm{j}}^{-}}$$

(10)

2.2.3. Moran’s Index

Moran’s index is utilized to assess the level of spatial dependence or spatial correlation between the socio-economic adaptability to ageing in resource-based cities and the geographical location of each city. Moran’s Index is employed to assess the degree of spatial dependence or spatial correlation between the socio-economic adaptability to ageing in resource-based cities and the geographical location of each city. Moran’s index consists of two components: the global Moran’s index and Anselin local Moran’s index. The global Moran’s index is used to examine whether there is a clustering distribution of socio-economic adaptability to ageing across the cities. The specific formula for the global Moran’s index is as follows:

$$\mathrm{I}=\frac{\mathrm{n}{\sum }_{\mathrm{a}=1}^{\mathrm{n}}{\sum }_{\mathrm{b}=1}^{\mathrm{n}}{\mathsf{\omega }}_{\mathrm{ab}}\left({\mathrm{x}}_{\mathrm{a}}- \overline{\mathrm{x}}\right)\left({\mathrm{x}}_{\mathrm{b}}- \overline{\mathrm{x}}\right)}{{\sum }_{\mathrm{a}=1}^{\mathrm{n}}{\sum }_{\mathrm{b}=1}^{\mathrm{n}}{\mathsf{\omega }}_{\mathrm{ab}}{\left({\mathrm{x}}_{\mathrm{a}}- \overline{\mathrm{x}}\right)}^2}$$

(11)

where n represents the total number of resource-based towns in the study area; ${\mathrm{x}}_{\mathrm{a}}$ and ${\mathrm{x}}_{\mathrm{b}}$ denote the socio-economic adaptability to ageing of each town; $\overline{\mathrm{x}}$ represents the mean value; ${\mathsf{\omega }}_{\mathrm{ab}}$ represents the spatial weight matrix; and Moran’s index belongs to the range of [–1, 1].

Anselin local Moran’s index identifies the locations where spatial agglomeration occurs. The local agglomeration characteristics of socio-economic adaptability to ageing in resource-based cities can be classified into four types: high-high agglomeration (H-H), high-low agglomeration (H-L), low-high agglomeration (L-H), and low-low agglomeration (L-L).

2.2.4. The Obstacle Factors

The obstacle factors are employed to determine the degree of obstacle posed by each indicator in assessing the socio-economic adaptability to ageing in each resource-based city. This approach helps clarify the level of influence exerted by each indicator on socio-economic adaptability to ageing, facilitating a precise analysis of the primary obstructive factors that limit the enhancement of socio-economic adaptability to ageing. The formula for calculating the obstacle factors is as follows:

$${\mathrm{h}}_{\mathrm{ij}}=\frac{\left(1-{\mathrm{r}}_{\mathrm{ij}}\right)·{\mathsf{\omega }}_{\mathrm{i}}}{{\sum }_{\mathrm{i}=1}^{\mathrm{m}}\left(1-{\mathrm{r}}_{\mathrm{ij}}\right)·{\mathsf{\omega }}_{\mathrm{i}}}$$

(12)

In the formula, ${\mathrm{h}}_{\mathrm{ij}}$ represents the degree of impairment of the i-th indicator for the socio-economic adaptability to ageing of the j-th sample. ${\mathrm{r}}_{\mathrm{ij}}$ represents the normalized value of a single indicator. ${\mathsf{\omega }}_{\mathrm{i}}$ denotes the weight assigned to the i-th indicator. ${\mathrm{H}}_{\mathrm{kj}}$ represents the obstacle degree of the k-th criterion layer for the j-th sample city.

2.3. Ageing Characteristics of Study Area

Resource-based cities are among the cities in China that face significant challenges with population ageing. These cities are characterized by a rapid growth rate and a large population of ageing individuals. They also experience deep ageing, substantial regional disparities, and a lack of alignment between ageing trends and the levels of socio-economic development within their respective regions.

2.3.1. Rapid Ageing Population Growth and Large Size

Based on data from the fifth, sixth and seventh China population censuses, the elderly population in resource-based cities has experienced rapid growth from 2000 to 2020. Specifically, the population aged 60 and above increased from 37.449 million to 76.71 million, while the population aged 65 and above increased from 24.98 million to 55.37 million. The growth rates for the 60+ and 65+ age groups were 9.88% and 7.66%, respectively. In comparison, the national growth rates for the same age groups were 7.64% and 5.88%, respectively, indicating that the ageing population in resource-based cities grew at a significantly higher rate than the national average. By 2020, the proportion of the 60+ and 65+ population to the total population within the same age group in resource-based cities reached 29.06% and 29.04%, respectively, surpassing the national average of 27.91%.

2.3.2. Deep Ageing and Regional Disparities

From 2000 to 2020, the proportion of the population aged 60 and 65 or older in resource-based cities increased from 9.62% to 19.50% and from 6.41% to 14.07%, respectively. At the national level, the percentages of people aged 60 and 65 or older increased from 10.46% to 18.10% and from 7.10% to 12.99%, respectively. As a result, the degree of population ageing in resource-based cities has transitioned from being below the national level to being above the national level.

Based on the criteria provided by the World Health Organization and the United Nations for determining the stages of an ageing society [36], the degree of population ageing in resource-based cities is classified into four types:

• Non-ageing society: The proportion of the population aged 60 years or older is equal to or less than 10%, and the proportion of the population aged 65 years or older is equal to or less than 7%.
• Mild ageing society: The proportion of the population aged 60 years or older is between 10% and 20%, or the proportion of the population aged 65 years or older is between 7% and 14%.
• Moderate ageing society: The proportion of the population aged 60 years or older is between 20% and 30%, or the proportion of the population aged 65 years or older is between 14% and 21%.
• Severe ageing society: The proportion of the population aged 60 years or older is greater than 30%, and the proportion of the population aged 65 years or older is greater than 21%.

The percentage of the population aged 60 and 65 or older in resource-based cities will increase from 9.62% and 6.41% to 19.50% and 14.07% from 2000 to 2020 and the national percentages of people age 60 and 65 or older increased from 10.46% and 7.10% to 18.10% and 12.99%. The degree of population ageing in resource-based cities has changed from below the national level to above the national level. There are considerable variations in the degree of ageing among different resource-based cities.

In the fifth China population census period (Figure 1a), around 64% of China’s 125 resource-based cities were classified as non-ageing societies. The remaining cities fell into the category of mildly ageing societies, with a significant concentration in eastern and northern China, and a few scattered instances in northeastern and southwestern China.

In the sixth China population census period (Figure 1b), the number of resource-based cities in China experiencing mild population ageing increased significantly, with 118 cities accounting for 93.65% falling into this category. Northeast, north, east, and southwest China exhibited the highest concentration of mildly ageing resource-based cities, while the remaining seven cities that had not entered the ageing society were distributed in the northwest and northern regions.

In the seventh China population census period (Figure 1c), the trend of population ageing in resource-based cities continued, with 63 cities in Mild ageing society having entered the moderate and severe ageing society. This accounted for 50.4% of the total number of resource-based cities, with cities such as Zigong and Fushun being classified as severe ageing societies. Across different regions, all resource-based cities in Northeast China have entered the moderate or severe ageing society, while significant growth in the moderate ageing society has been observed in northern and eastern China. Only Haixi Mongolian Tibetan Autonomous Prefecture in the northwest region has yet to enter an ageing society.

2.3.3. Uncoordinated between Ageing and Regional Socio-Economic Development

Comparing the level of ageing and GDP per capita of resource-based cities with China and typical developed countries, we find (

Table 2. Ageing and GDP per capita in resource-based cities in China in 2020 compared to major countries ¹.

Countries and Major Cities in China	2020
	Percentage of Population over 65 (%)	GDP per Capita (USD)
Australia	16.23	51,720.37
United States of America	16.22	63,530.63
South Korea	15.83	31,721.30
Singapore	13.15	60,729.45
China	12.60	10,408.67
Resource-based Cities	14.07	8102.39

¹ Data sources: The main data sources are the World Bank database, China Urban Statistical Yearbook.

) that resource-based cities have out-paced the national average in terms of ageing, close to the ageing levels of developed countries such as South Korea, Singapore, the United States and Australia. However, the GDP per capita is below the national average of USD 2306.28, far below the level of economic development of developed countries. The level of ageing in resource-based cities is extremely uncoordinated with economic and social development, showing a situation of “getting old before getting rich”; thus, ageing will pose a serious challenge to the city’s socio-economic development.

3. Results

The normalized raw data were first assigned appropriate weights to each variable (Table 1) and assessed the development of each dimension of resource-based cities. The adaptability of overall cities is presented in

Table 3. The degree of socio-economic adaptability to ageing in resource-based cities.

Time	Degree
2000	0.4436
2010	0.4653
2020	0.5166

and Figure 2.

3.1. The Temporal Evolution of Socio-Economic Adaptability to Ageing

Between 2000 and 2020, the socio-economic adaptability to ageing within resource-based cities exhibited a notable upward trajectory, gradually transitioning from a state of low adaptability to a more advanced level. This positive shift can be attributed to several factors.

From 2000 to 2010, resource-based cities experienced sustained socio-economic development alongside the expansion of ageing policies. Noteworthy enactments such as the Decision of the CPC Central Committee and the State Council on Strengthening the Work of the Elderly, as well as the Eleventh Five-Year Plan for the Development of China’s Elderly Career, were sequentially promulgated. Although these policies were at the exploratory stage, and the overall socio-economic development level remained relatively modest [22], significant progress in ageing-related efforts was achieved during this period. Notably, the scope of pensions and Medicare was expanded, and the standard of social assistance was elevated. Additionally, resource-based cities had a lower ageing level compared to the national average in 2000. By 2010, they had just entered a mildly ageing society, with the ageing level still relatively moderate.

From 2010 to 2020, China further intensified its commitment to addressing the ageing challenge. This was exemplified through amendments to the Law of the People’s Republic of China on the Protection of the Rights and Interests of the Elderly and the issuance of key documents such as the Twelfth Five-Year Plan of the Development of China’s Ageing Cause, the National Ageing Career Development Plan, and the National Ageing Strategy. The 19th National Congress reported that China’s economy had shifted from high-speed growth to a high-quality development stage. In 2019, in response to the increasing ageing trend and the challenge of adjusting to deepening urban ageing, China elevated ageing to a national strategy. This led to further improvements in social security provision and public services during the period of 2010–2020, concurrently enhancing the engagement of the elderly in society. As a result, the resource-based cities’ socio-economic adaptability to ageing was notably enhanced, particularly in the face of heightened ageing disturbance intensity.

3.2. Spatial Characteristics of Socio-Economic Adaptability to Ageing

3.2.1. Spatial Distribution of Socio-Economic Adaptability to Ageing

The spatial distribution of socio-economic adaptability to ageing in resource-based cities from 2000 to 2020 exhibits significant unevenness. Based on the data characteristics for different years, the socio-economic adaptability to ageing is categorized into five grades using the natural breakpoint method: low, medium-low, medium, medium-high, and high.

In 2000, there were 43 cities in resource-based areas with a medium to high level or above of socio-economic adaptability to ageing, with 65.12% of these cities located in Northern, Central and Eastern China. On the other hand, 41 cities had a medium to low level or below of adaptability, with 53.66% of these cities situated in the less economically developed regions of Northeastern, and Eastern China.

By 2010, the overall socio-economic adaptability to ageing in resource-based cities experienced a slight upward. A total of 40 cities maintained a medium to high level or above, with 65.00% of these cities located in Northern, Northeastern and Eastern China. 40 cities had a medium-low to low level of adaptability, with 65% of these cities situated in the regions of Northern, Northeastern and Eastern China.

As of 2020, a total of 38 resource-based cities showcased a notable level of socio-economic adaptability to ageing, achieving the medium-high level or above. Among these cities, approximately 57.89% were concentrated in the regions of Eastern, Northeastern, and Northern China. Additionally, 38 cities were classified at the low and medium-low level, with approximately 73.68% of them situated in the regions of Northeastern, Southwestern, Northwestern, and Eastern China.

3.2.2. Spatial Relevance of Socio-Economic Adaptability to Ageing

In this study, Moran’s index is chosen as the analytical tool to examine both global and local spatial correlations. A geographical distance matrix is employed to assess the global and local characteristics of socio-economic adaptability to ageing in resource-based cities, providing a comprehensive understanding of the spatial correlation patterns within these cities.

The results of the global Moran’s index are presented in

Table 4. Global Moran’s index of socio-economic adaptability to ageing in resource-based cities.

Year	2000	2010	2020
Moran’s index	0.070343 *	0.098426 **	0.050788
p value	0.071832	0.014849	0.174845
Z score	1.800183	2.436044	1.356801

Note: p values are in parentheses. ** p < 0.05, * p < 0.10.

. Firstly, it is observed that the global Moran’s index of socio-economic adaptability to ageing in resource-based cities for both 2000 and 2010 is greater than zero and passes the significance test at the 10% level. This suggests a positive spatial autocorrelation between socio-economic adaptability to ageing in resource-based cities. High-adaptability cities tend to be spatially adjacent to each other, as do low-adaptability cities.

Secondly, the global Moran’s index demonstrates a general trend of decreasing over the study period, with varying levels of significance: “significant—insignificant”. Based on the global Moran’s index, the spatial autocorrelation of socio-economic adaptability to ageing decreased from 0.070343 in 2000 to 0.050788 in 2020, indicating a gradual diminish in the spatial autocorrelation of socio-economic adaptability to ageing from 2000 to 2020.

Through the application of Anselin local Moran’s index, the results reveal specific clusters in the socio-economic adaptability to ageing among resource-based cities (Figure 3). In 2000, the global Moran’s index exhibits significance, and Anselin local Moran’s index also indicates the existence of localized agglomeration. Notable H-H (high-high) agglomeration effects were observed in certain resource-based cities within Hubei, Southern Shanxi and northwestern Hebei provinces. Conversely, an L-L (low-low) agglomeration effect was evident in resource-based cities in Eastern Sichuan and Central Hunan.

In 2010, the phenomenon of spatial clustering in socio-economic adaptability to ageing within resource-based cities became notably significant. Specifically, in Northern Yunnan, Southern Sichuan, Eastern Hebei and Western Liaoning, a H-H clustering pattern emerged. Conversely, resource-based cities in regions like Gansu, Anhui, Southwestern Shaanxi, Northeastern Sichuan and Southern Hunan, exhibited an L-L clustering effect.

By 2020, H-H clustering became more concentrated in northwestern Hebei and Shandong, while L-L clustering remained predominant in Eastern Sichuan, Eastern Heilongjiang and Northern Shaanxi.

3.2.3. Extent Analysis of Obstacles to Socio-Economic Adaptability to Ageing

The impediments associated with each indicator of resource-based cities have exhibited variability from 2000 to 2020. Considering individual dimensions, the obstacle degree for the economic development dimension, service provision dimension, and social participation exhibited an upward trajectory between 2000 and 2020. Conversely, the obstacle degree concerning social security experienced a decline. From a temporal perspective, spanning each time cross-section from 2000 to 2020, economic development, social security, and social participation consistently held positions within the top three obstacle dimensions. Their respective annual average obstacle degrees were 32.26 percent, 28.53 percent and 20.34 percent. While the economic development and social participation dimensions demonstrated a rising trend, the social security dimension indicated a gradual reduction in obstacle degree. This observation underscores the significance of economic development and social participation as pivotal dimensions impeding the socio-economic adaptability to ageing. An intriguing point to note is that the social security dimension witnessed an average annual obstacle degree of 28.53 percent during the period from 2000 to 2020. Notably, this obstacle degree declined from 31.82 percent to 23.14 percent, causing the ranking of this obstacle dimension to shift from first to second place. The rationale underlying this trend is attributable to the expanding coverage of social security measures and the consequent enhancement of protection levels within the social security realm.

For statistical analysis, the annual average obstacle degrees of the top 12 obstacle factors for each indicator were extracted from the years 2000 to 2020 (see

Table 5. Obstacle degree to socio-economic adaptability to ageing in resource-based cities by dimension.

Guideline Layer	2020		2010		2000
	Obstacle Degree	Rank	Obstacle Degree	Rank	Obstacle Degree	Rank
y1	0.031291	5	0.028811	5	0.03557	5
y2	0.342794	1	0.316182	1	0.308721	2
y3	0.208093	4	0.191616	4	0.183186	4
y4	0.223835	3	0.196085	3	0.190335	3
y5	0.23141	2	0.306196	2	0.318156	1

and

Table 6. Obstacle degree to socio-economic adaptability to ageing in resource-based cities by indicator.

Factor	2020		2010		2000
	Obstacle Degree	Rank	Obstacle Degree	Rank	Obstacle Degree	Rank
x1	0.012799	16	0.012111	16	0.015473	16
x2	0.003795	19	0.00348	20	0.003495	20
x3	0.003578	20	0.003916	19	0.004976	19
x4	0.011119	18	0.009305	18	0.011627	17
x5	0.100389	1	0.097313	3	0.1048	3
x6	0.086927	4	0.085552	4	0.087698	4
x7	0.07854	8	0.068198	8	0.055805	9
x8	0.076938	9	0.065118	9	0.060418	8
x9	0.030201	14	0.02521	15	0.025695	14
x10	0.082132	5	0.080086	6	0.08265	5
x11	0.045592	11	0.042298	11	0.035963	12
x12	0.050168	10	0.044023	10	0.038877	10
x13	0.002093	21	0.001561	21	0.001878	21
x14	0.079492	6	0.074926	7	0.070058	7
x15	0.04048	12	0.028333	13	0.0283	13
x16	0.089113	3	0.081517	5	0.080412	6
x17	0.012657	17	0.009748	17	0.009686	18
x18	0.023722	15	0.026233	14	0.017722	15
x19	0.079423	7	0.123602	1	0.139271	1
x20	0.090841	2	0.11747	2	0.125195	2
x21	0.037424	13	0.03889	12	0.035968	11

). Considering each indicator, the 12 most prominent obstacle factors, ranked by their annual average obstacle degrees, are as follows: the ratio of Medicare coverage (11.41%) > the ratio of pension coverage (11.12%) > general public budget expenditure per capita (10.08%) > GDP per capita (8.67%) > tertiary sector value added (8.37%) > park area per capita (8.16%) > total retail sales of consumer goods per capita (7.48%) > the rationalization of industrial structure index (6.751%) > number of college students per 10,000 inhabitants (6.749%) > number of books in public libraries per 100 inhabitants(4.44%) > public trams and buses per 10,000 inhabitants (4.13%) > the development level of the social security sector (3.74%).

• The ratio of pension coverage and Medicare coverage exhibit the highest and second-highest obstacle degrees, respectively, underscoring the substantial influence of social security on the socio-economic adaptability to ageing within resource-based cities. Notably, the Medicare and pension coverage rates in resource-based cities escalated from 7.3% and 7.8% in 2000 to surpass 95% and 69.9% in 2020. Although the obstacle degree for the social security dimension thus diminishes to 23.14%, enhancing the caliber and efficiency of social security systems remains a pivotal avenue to augment the socio-economic adaptability to ageing. Consequently, further enhancements are warranted in the realms of pension coverage and social assistance, coupled with the elevation of Medicare quality and efficiency. Concurrently, fostering the robust and high-quality evolution of the social security industry is crucial, ensuring the elderly are endowed with ample “support and care in their golden years”. Throughout the period 2000–2020, the obstacle degree of development level of the social security sector maintained a sub-4 percent standing, yielding a comparatively modest influence on the socio-economic adaptability to ageing.
• The tertiary sector value added and the total retail sales of consumer goods per capita rank as the second and seventh factors, respectively. This ranking is attributed to the relatively subdued development of the tertiary sector, owing to the strong reliance advancement pattern on industry in resource-based cities and the “production first, life later” mindset. Consequently, there exists a need to expand the trajectory of tertiary sector development. Achieving this involves enlarging the market scope of the tertiary sector and enhancing socio-economic adaptability to ageing through the exploration of local cultural attributes and an amplified support for distinctive business models. While the total per capita retail sales of consumer goods in resource-based cities surged from RMB 2099.95 to RMB 19,330.2, its obstacle degree exhibited a consistent upward trajectory. This trend underscores a scenario where resource-based cities experience ageing before achieving significant prosperity. The lower consumption level hampers the socio-economic adaptability to the ageing process in these cities. Therefore, to unlock the consumption potential of the elderly, it becomes essential to not only foster industries linked to the silver economy but also skillfully adapt traditional sectors such as payment, travel and Internet accessibility to provide elderly assistance. By mitigating consumption barriers, the overall obstacle degree can be reduced.
• The most influential factor impeding the enhancement of socio-economic adaptability to ageing in resource-based cities pertains to the dimension of economic development. This dimension showcases an annual average obstacle degree of 32.26%. Specifically, the four indicators contributing to this factor include general public budget expenditure per capita, GDP per capita, the rationalization of industrial structure index, and number of college students per 10,000 inhabitants, with corresponding obstacle degrees of 10.08%, 8.67%, 6.751% and 6.749%. Notably, all of these indicators’ obstacle degrees exceeding 6.7% indicates a high degree of impediment to the socio-economic adaptability to ageing. Consequently, resource-based cities need to adopt a multi-faceted approach. Internally, they should explore novel local distinctive industries and leverage external relative advantages to attract emerging industries and business models. This approach accelerates industrial transformation and facilitates robust economic development. Simultaneously, resource-based cities should fortify the foundation of urban innovation to encourage the growth of the industrial structure and to adapt emerging regional industries. This strategy, in turn, generates favorable conditions for public revenue and expenditure, ultimately enhancing the government’s capacity to effectively execute public functions.
• The obstacle degrees for park area per capita, number of books in public libraries per 100 inhabitants and public trams and buses per 10,000 inhabitants are ranked 6th, 10th and 11th, respectively. These indicators exhibit obstacle degrees over 4% and show an upward trend. This situation is attributed to inadequate planning of service facilities within resource-based cities and the challenge of existing service establishments to keep pace with the accelerated growth of the elderly population. Despite improvements in the level of relevant service facilities, there remains a significant obstruction in addressing the challenges posed by population ageing.

4. Discussion

This study conducted the inaugural assessment of “socio-economic adaptability to ageing” within resource-based cities in China. It addresses the three inquiries posed in this article:

• The analysis of temporal evolution reveals an increase in the socio-economic adaptability to ageing within resource-based cities from 2000 to 2020, attributed to China’s policy endorsements for elderly rights and urban ageing adjustment. Notably, growth between 2000 and 2010 lagged behind that of 2010 and 2020. This phenomenon in 2000–2010 can be attributed to the aftermath of the global financial crisis, whereby nascent ageing policies exhibited limited efficacy in augmenting socio-economic adaptability to ageing within resource-based cities characterized by sluggish economic development. In 2010–2020, China’s support for ageing underwent a decade-long exploratory phase, fostering the maturation and further deepening of the ageing policy framework. Concurrently, China’s economy experienced a phase of quality-conscious development, culminating in a marked enhancement of socio-economic adaptability to ageing across resource-based cities.
• The examination of spatial patterns reveals a concentration of Chinese cities with adaptability less than medium-low between 2000 and 2020 in the northeastern and eastern regions. In contrast, cities with adaptability exceeding medium-high levels are primarily clustered in the northern and eastern regions of China. Northeastern China serves as the largest old industrial base in the country, facing significant challenges in industrial transformation and a limited potential for economic development, thereby consistently maintaining low adaptability. Furthermore, the eastern part of China witnesses a notable prevalence of both medium-high and high and medium-low and low adaptability cities. This pattern can be attributed to the concentration of China’s largest population in the eastern provinces. This demographic distribution also results in a larger ageing population in the eastern region, while simultaneously maintaining a substantial internal wealth disparity compared to other regions. The per capita disposable income of resource-based cities in East China in 2000 ranged from RMB 8684 to RMB 4494, increasing to RMB 61,743 and RMB 32,015 by 2020. This significant disparity in urban development levels has contributed to the observed heterogeneity in cities’ abilities to manage ageing challenges. Further insights into micro-level agglomeration effects can be gained through the local Moran index analysis. Between 2000 and 2020, a consistent H-H (High-High) agglomeration effect was observed in the region of Northern China. This phenomenon might be attributed to the emulation of policies among cities and effective collaboration within the region. In 2000, a L-L (Low-Low) agglomeration effect was prevalent in the southwest and central regions, which subsequently shifted to the northwest and southwest areas in 2010. This pattern was sporadically distributed in Hubei and Anhui provinces during the same period. By 2020, an intensified L-L agglomeration effect was prominent in the eastern region of Heilongjiang Province. The reasoning behind this trend can be traced to specific factors. In 2000, the cities in the eastern part of Sichuan faced challenges such as rugged terrain and inadequate transportation infrastructure, leading to higher urban development costs. During the construction of the third line, industrial transfer was limited, impeding urban development. Historically, the transportation route of Chongqing–Sichuan mainly passed through the southern part of Sichuan, favoring investment attraction in that region and hampering the resource-based cities in the eastern part of Sichuan. Subsequently, post-China’s WTO accession, the northwest region experienced a significant population outflow, contributing to an elevated level of urban ageing. After 2010, as emerging business formats shifted westward, the northwest region witnessed increased development capacity and potential. By 2020, L-L agglomeration was more prevalent in the eastern part of Heilongjiang Province, which was characterized by sluggish economic development, limited economic foundation and severe resource depletion. These regions lacked strong collaboration and development capabilities, resulting in limited potential for highly adaptive agglomeration areas.
• This study employs obstacle degree analysis to pinpoint the development dimensions and pivotal factors impeding the socio-economic adaptability to ageing. Among these factors, the top three dimensions contributing to the obstacle degree encompass economic development, social security and social participation. Furthermore, within the upper 60 percent (the top 12) of obstacle factors, critical influences comprise the ratio of Medicare coverage, the ratio of pension coverage, general public budget expenditure per capita, GDP per capita, tertiary sector value added, park area per capita, total retail sales of consumer goods per capita, the rationalization of industrial structure index, number of college students per 10,000 inhabitants, number of books in public libraries per 100 inhabitants, public trams and buses per 10,000 inhabitants and the development level of the social security sector.

The ensuing policy recommendations stem from the comprehensive analyses conducted above. Regarding temporal analysis, this paper contends that China’s resource-based cities’ economic adaptability to ageing is poised for further enhancement in the future. However, this progress hinges on the government’s adept identification and mitigation of potential economic and financial crisis risks. Additionally, the imperative lies in the continued pursuit of safeguarding elderly rights and interests and bolstering the urban ageing infrastructure. For instance, the augmentation of investment in urban ageing-associated service facilities within the public budget should be coupled with the reduction in barriers for enterprises, groups or individuals seeking entry into relevant sectors. This can be facilitated through industrial promotion subsidies and the facilitation of elderly workforce integration. Such a multi-faceted approach is imperative to fortify the potential for augmenting the socio-economic adaptability to ageing within resource-based cities. Spatial pattern analysis underscores the need for tailored policies by the central government to bolster the socio-economic adaptability to ageing in resource-based cities. In the northeastern region, deliberate measures are warranted to amplify path creation effort and mitigating development path dependency. Simultaneously, this region should enhance resource utilization efficiency, refine the business environment, and invigorate the regional innovation effort to accommodate emerging industries. Conversely, Eastern China should harness the coastal region’s geographical advantages to bridge socioeconomic disparities, thereby fostering intra-regional harmonization.

With respect to the obstacle degree analysis, this paper contends that:

• Social Security: While Medicare boasts a coverage rate exceeding 95%, pension insurance coverage requires augmentation. Furthermore, the government should encompass elevating the quality and efficiency of both pension and health insurance systems. Strengthening social assistance mechanisms is imperative, encompassing heightened elderly social assistance through increased payment levels or indirect economic support mechanisms such as community canteens and drop-in centers.
• Social Participation: Local governments and private capital should harness inherent local natural and cultural attributes to invigorate tourism and related sectors, capitalizing on policy support, product innovation and cultural advocacy. This approach will not only enrich employment prospects for the elderly but also necessitates ageing-appropriate services and products, particularly addressing Internet entry barriers. Family members and service industry professionals should provide guidance and services, ameliorating the growing “digital gap” engendered by technological advancement. Such endeavors bolster elderly societal participation, concurrently elevating consumption propensity, stimulating economic activity and ameliorating elderly livelihoods.
• Economic Development: Prominence should be accorded to enhancing urban innovation capacity and introducing fitting talent policies, thereby fortifying the bedrock of urban progress. Industrial structural optimization is pivotal to sound economic growth, concurrent with heightened public revenue and augmented socio-economic adaptability to ageing.
• Service Provision: Augmentation of urban public space allocation and construction of cultural amenities and transport infrastructure catering to elderly needs are paramount. This measure, facilitating activity space and upgraded transportation, stands to expedite elderly population adaptability to society.

The study’s limitations predominantly arise from the scarcity of more current demo-graphic data. Notably, China’s population census is conducted on ten-yearly frequency. This study relies on the fifth, sixth and seventh censuses conducted in 2000, 2010 and 2020 to probe the socio-economic adaptability to ageing within resource-based cities. Acquiring more recent data for consecutive time frames could potentially facilitate an enhanced exploration of the evolving trend in socio-economic adaptability to ageing and offer timelier insights into resource-based cities’ responses to the ageing phenomenon. Furthermore, accessibility constraints pertain to data applicability. Notably, the indicators utilized by researchers from other nations in their investigations of global contexts [6,8] and 27 European countries [9] prove infeasible for direct adoption within China. Drawing from the scope of ageing adaptability research for China [13,22], this study diligently selects pertinent indicators for assessing China’s socio-economic adaptability to ageing, aligning with the research’s thematic emphasis. Nonetheless, gauging socio-economic adaptability to ageing across countries through these indicators presents challenges. Therefore, attaining indicators of the same statistical caliber with those of other nations could potentially pave the way for a comparative assessment of socio-economic adaptability to ageing in a global context.

5. Conclusions

Over the period spanning 2000 to 2020, the socio-economic adaptability to ageing in resource-based cities exhibits a positive trajectory. In terms of spatial correlation, the global Moran’s index’s significance unveils a pattern characterized by “insignificant-significant-significant”.

The spatial pattern analysis reveals that during the period 2000–2020, areas with medium-low and low adaptability are predominantly concentrated in the eastern and northeastern regions of China, whereas regions with medium-high and high adaptability are primarily clustered in the northern and eastern parts of the country. Anselin local Moran’s index shows that: In 2000, H-H agglomeration effects were evident in re-source-based cities located in Southern Shanxi and northwestern Hebei, while L-L agglomeration effects emerged in cities of Hunan and eastern Sichuan. By 2010, H-H agglomeration effects persisted in resource-based cities within northern Yunnan, southern Sichuan, eastern Hebei and western Liaoning. Notably, a L-L clustering effect characterized resource-based cities in Gansu, Anhui, southwestern Shaanxi, Hunan and northeastern Sichuan. By 2020, resource-based cities in Shandong and northwestern Henan demonstrated H-H clustering effects, whereas Sichuan, East Heilongjiang and Shaanxi exhibited an L-L clustering effect. Between 2000 to 2020, persistent H-H clustering consistently characterizes Hebei Province and its neighboring regions, while sustained L-L clustering is noticeable in the southwest areas.

On the obstacle degree diagnostic front, the dimensions of economic development, social participation and social security emerge as the foremost impediments influencing the socio-economic adaptability to ageing. Among the key obstacle factors are metrics encompassing the ratio of Medicare coverage, the ratio of pension coverage, general public budget expenditure per capita, GDP per capita, tertiary sector value added, park area per capita, total retail sales of consumer goods per capita, the rationalization of industrial structure index and number of college students per 10,000 inhabitants, number of books in public libraries per 100 inhabitants, public trams and buses per 10,000 inhabitants and the development level of the social security sector.