Coupling and Coordination of Art Intervention and Community Resilience in Urban Villages: Evidence from Three Cases in Beijing

Abstract

Art intervention has emerged as an innovative pathway for community regeneration, significantly enhancing physical and socio-economic conditions, yet its specific impacts on community resilience remain underexplored. This study proposes an evaluation framework that integrates the BRIC community resilience model with key dimensions of art intervention. Taking three typical art villages in suburban Beijing (Feijia, Xiaopu, and Xinzhuang) as cases, 452 questionnaires were conducted. The coupling and coordination model was used to analyze interactions between subsystems, and the obstacle factor model was employed to identify barriers to their synergistic development. The results show that: (1) There is a significant positive correlation between the degree of art intervention and community resilience. (2) The coupling and coordination degree exhibits distinct stage differentiation, with art intervention directly affecting its level. Xiaopu Village has the highest coupling and coordination degree (0.8004), followed by Xinzhuang Village (0.6914) and Feijia Village (0.6400). (3) Key obstacles include participation in art activities (9.2%), influence of interactions (9.0%), cultural literacy (8.5%), use of art spaces (7.2%), and industrial influence (6.3%). This study establishes a novel theoretical framework for the synergy between art intervention and community resilience, offering practical strategies for sustainable urban village revitalization.

1. Introduction

The UNESCO Creative Cities Network (UCCN) was established in 2004 to foster collaboration with cities that recognize creativity as a strategic factor for sustainable urban development. As urbanization progresses, cities have gradually entered an era of urban esthetics, where esthetic practices increasingly enhance urban value. Art, as a process that expresses human emotions and consciousness, has begun to engage in community building and drive urban–rural development [1], giving rise to the concept of art intervention.

Florida’s theory of the creative class proposes that artists and other related groups interact with urban spaces in economic, social, and cultural dimensions to build creative cities. However, the benefits generated by this theory are more economic in nature, with insufficient attention paid to social and cultural impacts [2]. Arnold Berling, a leading figure in the theory of “art intervention”, proposed the concept of “intervention” from an esthetic perspective in his work “Art and Intervention”, affirming the dual attributes of art’s autonomy and social nature, and extending the concept of intervention into the ideological and social domains of art [3]; broadly speaking, art intervention refers to the act of using artistic forms such as artists, artworks, art phenomena, and art activities to intervene in social spaces and daily life, thereby influencing local residents, governments, and other entities [4]. Liu Yuhan analyzes art intervention from the perspective of community development, viewing it as a community development tool mediated by art, emphasizing its public attributes rather than individual artistic expression [1]. Li DZ argues that art intervention uses “intangible” artistic forms to bridge the urban–rural divide through government policies, industrial development, citizen participation, and community art practices, thereby achieving high-quality development [5].

Due to urban center policies and spatial constraints, artists face limited creative space. Urban villages, however, have gained increasing favor among artists for their abundant spatial resources and relatively harmonious social environment, giving rise to “artist-cluster villages” [6]. Against the backdrop of growing demand for spiritual and cultural needs and government-driven art-driven rural development policies, the number of art villages in China has been steadily increasing. Artists engage with communities in urban villages through three primary modes: landscape rejuvenation, cultural preservation, and the re-establishment of community identity [1,7]. Landscape rejuvenation effectively integrates artistic elements into the environment, renovating and beautifying the physical space of the community, upgrading infrastructure, and enhancing living comfort and esthetic appeal to meet residents’ social, recreational, and cultural needs. Cultural preservation involves uncovering local cultural heritage, preserving community history and customs through artistic forms, and combining these with traditional local resources to transform indigenous elements into artistic products, thereby developing distinctive cultural tourism industries and reinforcing local identity and collective memory. Subjectivity reshaping empowers villagers, enhances their artistic literacy and participation, strengthens community members’ cultural identity and sense of belonging to the community, helps repair strained social relationships, and promotes intergenerational exchange and cultural inheritance. These approaches collectively drive community cultural revitalization and economic development.

Community resilience refers to the ability of a community to cope with perturbations brought about by social, political, and environmental changes [8], and the construction of resilient communities is related to the overall level of risk management and governance capacity of cities. Adger argues that environmental change and socio-economic upheaval create external pressures on communities, and that diversified socio-economic systems can enhance resilience [8]; Aslani et al. reveal that identity enhances cohesion by reinforcing a sense of belonging, and that collective memory provides crisis coping strategies through shared experiences [9]; and Poortinga’s findings suggest that political effectiveness enhances the efficiency of resource coordination, and that public trust facilitates social capital accumulation, both of which constitute key mechanisms of community resilience [10]; Cui P et al. identified that the direct influences on community resilience in the post epidemic era include health insurance, resident participation and infrastructure, while demographics and community management play an indirect role [11].

Resilient community camping is related to the overall risk governance level and governance capacity of the city, and community resilience assessment helps to understand the current resilience status of the community, which is a prerequisite for community development prognosis and subsequent decision-making. Various organizations and scholars have attempted to measure this from both subjective and objective perspectives using multiple methodologies [12,13,14,15,16,17,18,19,20,21]. The PEOPLES community resilience assessment framework evaluates community resilience from seven perspectives: demographic, ecological, governmental services, infrastructure, lifestyle and community competitiveness, economic development, and socio-cultural capital [12]; the ARC-D model highlights the community’s response to disaster risk, and assesses community resilience from the perspectives of education, economy, environment, policy and governance, health, infrastructure, society and culture, and disaster risk management from eight perspectives to measure the level of resilience [13]; the CCRAM model analyzes the level of community resilience from the perspective of residents’ perceptions, and the assessment framework contains five dimensions: leadership, collective efficacy, preparedness, local attachment, and social trust [14]. To comprehensively assess urban resilience, UN-Habitat developed the CRPT model by evaluating and analyzing the impacts, pressures, and challenges faced by cities, as well as internal urban factors (buildings, supply chains and logistics, infrastructure, mobility, government public services, social inclusion and protection, economy, ecology) [21]. The BRIC framework developed by Cutter et al. establishes baseline conditions for measuring community resilience, assessing regional resilience levels from social, economic, infrastructure, institutional, and community capital perspectives. The model encompasses both objective evaluations reflecting changes in community resilience and assessments of community residents’ subjective agency. Compared to other models, BRIC is highly comprehensive, emphasizes long-term adaptive capacity, and has been empirically validated in both urban and rural settings, making it applicable to both urban and rural areas with greater applicability [22].

Initially, the focus of community resilience was on coping with natural disasters and other shocks [23,24], and research on the impact of socio-economic changes brought about by human factors on the level of resilience is gradually unfolding [25,26]. The impact of art spaces on urban resilience emphasizes that art venues enhance the ability of cities to cope with crises through three core paths: strengthening community bonds, reconfiguring spatial adaptability and activating cultural identity [27]. Urban villages are sensitive areas for socio-economic development and are susceptible to pressures and disturbances brought about by man-made activities. Existing studies have mostly focused on the impact of art intervention on social relations and spatial landscape of urban villages [28,29,30,31,32,33,34,35,36,37,38], lacking comprehensive analysis and exploration of two-way action mechanisms. This study constructs an evaluation system for art intervention and community resilience, focusing on typical cases influenced by art intervention in Beijing, and comprehensively measures the degree of art intervention and the level of community resilience. The coupling and coordination model is used to analyze the interactions between subsystems, and the obstacle factor model is used to identify the obstacle factors constraining the coupling of art intervention and community resilience, and to explore the main factors affecting the coupling and coordination of the two. Based on the obstacle factors, we propose a planning response strategy for the refined construction and governance of art communities. The study provides localized, quantifiable implementation pathways, offering planners breakthrough diagnostic tools that elevate research from descriptive analysis to actionable practical insights.

2. Materials and Methods

2.1. Study Area

Beijing is the capital and cultural and artistic center of China. The earliest artist village in China emerged here. Currently, there are numerous types of artist villages, covering almost all kinds. Taking into account factors such as the artistic development levels of the research subjects and the differences in their socio-spatial structures, this study selected Feijia Village in Chaoyang District, Xiaopu Village in Tongzhou District, and Xinzhuang Village in Changping District as case studies (Figure 1). They present a panoramic view of the development of art villages: the start-up phase (Xinzhuang), the mature cluster (Xiaopu), and the transitioning market (Feijia). Their diversity in spatial integration, governance models, and industrial types enhances the internal validity of the research (

Table 1. Basic Characteristics of the Three Art Villages.

Characteristics	Feijia Village	Xiaopu Village	Xinzhuang Village
Location (straight-line distance from the nearest fourth ring road)	5.8 km	19.2 km	26.6 km
Operating Methods	Natural villages, investment in rehabilitation	Natural villages, spontaneous formation	Natural villages, spontaneous formation
Spatial Arrangement	Separate: art district and residential living area do not interfere with ach other	Mixed: artists’ houses scattered in village settlements, some artists concentrated outside the residents’ residential areas	Intertwined: artists and art fairs within the village’s residential living area
Size (Area)	318,000 square meters	824,000 square meters	195,800 square meters
Types of Art Industry	Digital industry	Traditional studio + digital industry	Art Bazaar + Studio

).

The Feijia Village art district in Chaoyang District was formed in 1999, initially developed slowly by individual artists, and then influenced by the 798 Art District, where a large number of artists moved in. Feijia Village’s overall art scale is medium, and the art industry is dominated by the form of digital media. In terms of spatial layout, the art district and the residents’ living area present a split state in spatial layout (Figure 2a).

Xiaopu Village in Tongzhou District is the core area of Songzhuang Art District, which started in 1993, and was first formed by artists, residents and village organizations by chance, and later developed into a cultural center reconstruction model jointly built by the three. The art industry is mainly based on traditional studios and digital media, with a large scale and a mixed layout of art areas and residential living areas, and the development of the art cluster is now stabilizing [39] (Figure 2b).

The art development of Xinzhuang Village in Changping District was influenced by the Xiayuan Art District and formed later. The natural environment of mountains and water and the entrepreneurial environment created by the village committee have attracted a number of art creators to develop here. The art industry in Xinzhuang Village is mainly based on art bazaars, which are small in scale. In terms of spatial layout, the art district and the residents’ living area present a half-integrated and half-separated state (Figure 2c).

2.2. Construction of the Indicator System

The BRIC community resilience assessment model evaluates regional resilience levels from the perspectives of population, economy, infrastructure, institutions, and community capital. This study removed outdated indicators such as mobile phone ownership rates and overly targeted indicators such as disaster reduction plan coverage rates from the original model. It also adjusted the indicators based on the actual conditions of urban villages and summarized the factors influencing communities through art to construct an art intervention and community resilience evaluation system (

Table 2. Art Intervention and Community Resilience Evaluation Indicator System.

Target Level	Standardized Level	Factor Level
Art intervention	A1 Social impact	A11 Impact of interactions
		A12 Neighborhood impact
	A2 Economic impact	A21 Industry impact
		A22 Income impact
	A3 Space environment impact	A31 Environmental impact
		A32 Space impact
	A4 Cultural impact	A41 Cultural environment
		A42 Cultural literacy
		A43 Arts Activity Participation
		A44 Art spaces use
Community resilience	B1 Population subsystem	B11 Educational attainment of community residents
		B12 Population of working age
		B13 Health status of community residents
	B2 Economic subsystem	B21 Income of the population
		B22 Income stability
		B23 Community Business
	B3 Institutional subsystem	B31 Crisis Response Plan
		B32 Organizational Leadership
		B33 Participation in decision-making
	B4 Infrastructure subsystem	B41 Public Transportation Accessibility
		B42 Educational Facility Accessibility
		B43 Medical Facility Accessibility
		B44 Commercial Facility Accessibility
		B45 Open Space
	B5 Social capital subsystem	B51 Social equity
		B52 Social trust
		B53 Social Networking
		B54 Community Belonging

and Appendix A).

2.3. Data Sources

In this paper, data were collected using a variety of methods such as in-depth interviews, questionnaires, participatory observation, and Gaode Map POI data. The questionnaire and interview design were refined through field research and preliminary interviews. In-depth interviews were conducted with local residents, community managers, and artists to collect basic information and interview data. The basic data is derived from the industrial income and demographic information provided by the Gaode Map POI data facility sites and the community. Data related to the institutional and social capital subsystems in the evaluation of art intervention and community resilience were obtained through questionnaires. The questionnaire design is based on the evaluation indicator system in Table 2. The sample size was calculated using the finite population correction formula with a 95% confidence level (Z = 1.96), an error margin of ±5% (e = 0.05), and an expected proportion p = 0.5 (maximum variance):

$$n={\displaystyle \frac{N\times Z^2\times p(1-p)}{e^2(N-1)+Z^2\times p(1-p)}}$$

(1)

Infrastructure accessibility is calculated using the two-step floating catchment area method. The formula is as follows:

$$A_i^F={\displaystyle \sum _{j\in (d_{ij}\le d_0)}\frac{S_j}{{\displaystyle \sum _{k\in (d_{ij}\le d_0)}{P}_j}}}$$

(2)

The calculated infrastructure accessibility is shown in

Table 3. Infrastructure accessibility.

Infrastructure Type	Feijia Village	Xiaopu Village	Xinzhuang Village
Public Transportation	4.2	2.7	3.9
Educational Facility	3.1	3.3	3.2
Medical Facility	3.4	4.5	4.1
Commercial Facility	4.2	4.0	3.9
Open Space	2.8	3.9	3.4

.

The research team conducted a six-month formal investigation in three case study areas starting in October 2024, interviewing over 40 residents, artists, and community managers. For these three groups, 498 paper questionnaires were distributed offline, with 452 valid responses collected—a response rate of 90.76%.

Using SPSS 26.0, reliability analysis was conducted on the scale-related data in the questionnaire. Results showed Cronbach’s α at 0.881 and Cronbach’s α based on standardized items at 0.877, indicating good internal consistency. In validity analysis, the KMO value was 0.864, demonstrating high external validity. With a significance level of 0.00, the structural validity was sound, making the data suitable for subsequent analysis. The questionnaire design was based on a systematic review of existing resilience frameworks and underwent localization adjustments for the urban village context through preliminary field interviews with residents, artists, and community managers. Additionally, five experts in relevant fields were invited to review the indicator system (Table 2). The initial expert agreement rate was 85%. After revising the detailed question formats for indicators such as “Cultural Literacy” and “Use of Art Spaces,” the agreement rate increased to 92%, ensuring high alignment between the indicators and the research objectives.

2.4. Methods

2.4.1. Calculation of Indicator Weights

Based on the evaluation index system of art intervention and community resilience constructed above, in order to avoid the assessment results being too subjective, this paper uses the entropy weight method to determine the weights of each index. The evaluation data are normalized using the extreme value standardization method to construct the decision matrix; secondly, the entropy value of the indicators is calculated, and finally the entropy weight results are derived [40].

$$r_{ij}={\displaystyle \frac{\max (x_i)-x_{ij}}{\max (x_i)-\min (x_i)}}$$

(3)

$$H_j=-{\displaystyle \frac{1}{\ln n}}({\displaystyle {\sum }_{i=1}^n}{p}_{ij}\ln p_{ij})$$

(4)

$$p_{ij}={\displaystyle \frac{r_{ij}}{{\displaystyle {\sum }_{j=1}^n{r}_{ij}}}}$$

(5)

$$w_j={\displaystyle \frac{1-H}{{\displaystyle {\sum }_{j=1}^n(1-H_j)}}}$$

(6)

Art Intervention Composite Indicator Index:

$$T_1={\displaystyle \sum _{j=1}^{10}{r}_{ij}\times w_j}$$

(7)

Community Resilience Composite Indicator Index:

$$T_2={\displaystyle \sum _{j=1}^{18}{r}_{ij}^{\prime }}\times w_j^{\prime }$$

(8)

2.4.2. Coupling and Coordination Analysis

Coupling coordination models are widely applied in regional synergy analysis to quantify the interaction level between two subsystems. This paper draws upon coupling degree model to reflect the degree of interconnection between art intervention and community resilience.

$$C={\displaystyle \frac{2\sqrt{T_1\times T_2}}{T_1+T_2}}$$

(9)

In the above formula, $C$ is the coupling degree, $T_1$ is the art intervention evaluation score, and $T_2$ is the community resilience evaluation score. The value range of the coupling degree is [0, 1], and the more the value tends to 1, the higher the degree of coupling between art intervention and community resilience [41].

The degree of coupled coordination is used to describe the extent to which art intervention and community resilience interact and synergize.

$$D=\sqrt{C\times Q}$$

(10)

$$Q=\alpha T_1+\beta T_2$$

(11)

In the above formula, $D$ denotes the degree of coupling and coordination, $Q$ denotes the integrated synergy effect of the two systems, $\alpha$ and $\beta$ denote the importance of the two systems, respectively, and based on the reference to the existing research, it is considered that the two are equally important, and both are taken to be 0.5. The range of values for the degree of coupling and coordination, $D$, is [0, 1], and the closer the value of $D$ is to 1, the closer it is to 1, indicating that the level of development of the coupling and coordination of the art intervention and community resilience [42]. The coupling and coordination level is shown in

Table 4. State distribution of coupling and coordination degree.

Range of Values	States
0 ≤ D < 0.1	Extreme disorder state
0.1 ≤ D < 0.2	Severe disorder state
0.1 ≤ D < 0.2	Moderate disorder state
0.3 ≤ D < 0.4	Mild disorder state
0.4 ≤ D < 0.5	On the verge of disorder state
0.5 ≤ D < 0.6	Barely coordinated state
0.6 ≤ D < 0.7	Primary coordinated state
0.7 ≤ D < 0.8	Intermediate coordinated state
0.8 ≤ D < 0.9	Good coordinated state
0.9 ≤ D ≤ 1	Excellent coordinated state

.

2.4.3. Obstacle Analysis

$$O_j={\displaystyle \frac{F_j{I}_j}{{\displaystyle {\sum }_{j=1}^{28}{I}_j{W}_j}}}$$

(12)

$$F_j=W_j\times X_{ij},I_j=1-Y_j$$

(13)

The barrier model facilitates the identification of key constraints to system coordination, providing a basis for precise policy recommendations. The factor contribution degree $F_j$, indicator deviation degree $I_j$, and barrier degree $O_j$ were used to identify and analyze the barrier factors affecting the coupled and coordinated relationship between art intervention and community resilience, so as to propose more targeted optimization suggestions [43].

3. Results

3.1. Art Intervention and Community Resilience System Evaluation Results

From the perspective of overall development level, the comprehensive index of art intervention is 0.45, and the comprehensive index of community resilience is 0.58; art intervention has not yet fully activated the overall promotion potential of the community resilience system (Figure 3a). From the overall level, art intervention and community resilience present multi-dimensional interactive characteristics and differentiated development situations, and there is non-equilibrium between the two, with the overall performance of art intervention lagging behind the level of community resilience. In terms of case differences, Xiaopu Village has the highest scores for both art intervention and community resilience, and Feijia Village has the lowest score. The evaluation results of the three case districts show a stepwise divergence, with a positive correlation between the depth of art intervention and the level of community resilience, but with significant differences in the synergistic effect between the subsystems.

3.1.1. Results of the Evaluation of the Art Intervention Subsystem

The results of the evaluation of art intervention in the three art villages are shown in Figure 3b,c. The three art villages show significant differences in the four dimensions of social impact, economic impact, space environment impact and cultural impact: Xiaopu Village is outstanding in social impact (0.1403) and space environment impact (0.1263), with a social impact value almost 2.8 times higher than that of Xinzhuang Village (0.0511), and a space environment impact 3.2 times higher than that of Feijia Village (0.0393); Feijia Village is ahead in economic impact (0.1059), significantly higher than that of Xiaopu Village (0.0491); Xinzhuang Village is ahead in cultural impact (0.1955), and Feijia Village is ahead in economic impact (0.1059). Feijia Village leads in economic impact (0.1059), which is significantly higher than that of Xiaopu Village (0.0491); this aligns with the findings of Li et al. [5], who concluded that “art villages driven by digital media generate higher short-term economic benefits due to their high technological added value”; Xinzhuang Village is far ahead of the other two villages in cultural impact (0.1955), with a value close to three times that of Feijia Village (0.0661). The differences reflect the differences in the focus of art intervention in the villages; Xiaopu is strong in social and environmental transformation, Feijia focuses on economic benefits, and Xinzhuang is strong in cultural impact. Despite the differences, the three villages show clear commonalities. Cultural impact is the strongest overall (mean of 0.1375 across the three villages), especially in Xinzhuang and Xiaopu (0.1955 and 0.1508), with art intervention generally prioritizing the enhancement of the cultural sphere; economic impact is consistently relatively weak across the three villages (mean only 0.0794), with the smallest inter-village variation (range 0.0568), suggesting that economic benefits are a common challenge.

3.1.2. Results of the Evaluation of the Community Resilience Subsystem

Combining the weights of the existing community resilience indicators and their scores, and drawing on relevant studies by scholars, the weight-score model was constructed with the weights as the vertical axis and the scores as the horizontal axis, and the weight value of 0.20 and the overall average value of the first-level resilience indicators of 0.12 were selected as the critical values to categorize the assessment results into four quadrants. Quadrant 1: High weight-high score; this type of indicator is the basic guarantee of the current community resilience, and should continue to maintain the basis of optimization. Quadrant 2: High weight-low score; this type of indicator is of high importance but the level of resilience is low, and should be the key areas of improvement of the existing community priorities. Quadrant 3: Low weight-low score; this type of indicator is important but low level of resilience, and should be the key areas of improvement of established communities. Quadrant 4: Low Weight-High Score, which could be maintained but not prioritized for community resilience improvement. The results of the weight-score model for the resilience indicators at the guideline level and the assessment of the resilience indicators at the element level are analyzed for the three case areas (Figure 3d,e).

The infrastructure subsystem and the social capital subsystem of the three art villages are in the high weight-high subregion, and most of the remaining coordinate points are in the low weight-low score region, except for the economic subsystem of Xiaopu village, which is in the low weight-high subregion.

In the data comparison of the three art villages, Feijia Village excels in demographic and socio-demographic indicators, such as educational attainment (0.017), working-age population (0.058), and health status (0.029), as well as accessibility to public transportation facilities (0.046), which reflects its strong demographic structure and accessibility to transportation, while Xiaopu Village has significant strengths in the community economy and social capital dimensions, such as community business richness (0.083), decision-making participation (0.075), social equity (0.079), and social trust (0.049), as well as healthcare facility accessibility (0.065) and park facility accessibility (0.067), which shows its leadership in economic vitality and community cohesion; Xinzhuang Village has an intermediate overall performance, but a relatively high social network (0.033) indicator that reflects its stronger social connections. Differences between indicators are also striking: in the overall data, social dimension indicators such as social equity (average 0.069), participation in decision-making (average 0.056), community business richness (average 0.046), and social trust (average 0.043) are generally high, while educational attainment (average 0.013), crisis response preparedness (average 0.016), and organizational leadership ability (average 0.016) indicators of educational and organizational preparedness were generally low, suggesting that community resilience is more dependent on social and economic factors than on educational and crisis management infrastructure.

3.2. Results of the Coupled Harmonization of Art Intervention and Community Resilience

The results of the coupling degree of art intervention and community resilience in the three case districts (

Table 5. Results of the evaluation of the coupled coordination of art intervention and community resilience in the case area.

Case Area	Coupling	Degree of Coupling and Coordination	Coupling Level	Coupling and Coordination Level
Feijia Village	0.9813	0.6400	High-level coupling stage	Primary coordination
Xiaopu Village	0.9993	0.8004	High-level coupling stage	Good coordinated
Xinzhuang Village	0.9880	0.6914	High-level coupling stage	Primary coordination
Synthesize	0.9895	0.7106	High-level coupling stage	Intermediate coordination

) show that the comprehensive coupling degree is 0.9895, with a high degree of coupling between art intervention and community resilience; the coupling and coordination degree is 0.7106, with an intermediate level of coupling and coordination. The reason for maintaining the intermediate level of coordination is that the local art intervention does not have a large impact on the local community resilience and does not threaten the stable state of the resilience system.

Comparison of different art intervention stage villages shows that the Xiaopu village art intervention and community resilience system coupling degree is 0.9993; the art intervention and community resilience coupling degree is high. The coupling and coordination degree of Xiaopu Village is 0.8004, which is in a good coordination state. The coupling degree of Xinzhuang Village with a medium level of art intervention is 0.9880, with a coupling and coordination degree of 0.6914, and the Feijia Village with a weaker level of art intervention has a current coupling degree of 0.9813, which is still at a high level of coupling, but with a coordination degree of only 0.6400, which is in a state of primary coordination.

3.3. Screening for Impairment Factors

The identification of key constraints to the coupled and coordinated development of art intervention and community resilience systems through the Barrier Degree Model (

Table 6. Case Area Art Intervention Coupled with Community Resilience: Main Barriers to Coordination Factors.

Case Area	Event	Obstacle Factor 1	Obstacle Factor 2	Obstacle Factor 3	Obstacle Factor 4	Obstacle Factor 5
Feijia village	Obstacle Factor	Impact of interactions	Cultural literacy	Arts Activity Participation	Space Impact	Art spaces use
	Degree of Obstruction(%)	11.1	10.5	10.2	8.6	8.0
Xiaopu village	Obstacle Factor	Industry Impact	Arts Activity Participation	Population of working age	Impact of interactions	Art spaces use
	Degree of Obstruction(%)	12.2	9.7	8.8	8.2	7.5
Xinzhuang village	Obstacle Factor	Cultural literacy	Impact of interactions	Arts Activity Participation	Art spaces use	Space Impact
	Degree of Obstruction(%)	7.8	7.7	7.5	6.1	5.8
Synthesize	Obstacle Factor	Arts Activity Participation	Impact of interactions	Cultural literacy	Art spaces use	Industry Impact
	Degree of Obstruction(%)	9.2	9.0	8.5	7.2	6.3

) showed that the top five barrier factors in descending order were participation in arts activities (9.2%) > influence of interactions (9.0%) > cultural literacy (8.5%) > use of arts space (7.2%) > industrial influence (6.3%).

In cross-case comparisons, the top five ranking barrier factors for Feijia Village were, in descending order, Interaction Influence (11.1%) > Cultural Literacy (10.5%) > Arts Activity Participation (10.2%) > Spatial Influence (8.6%) > Arts Space Use (8.0%). The top five ranking barrier factors in Xiaopu Village are, in descending order, industrial influence (12.2%) > arts activity participation (9.7%) > working age population (8.8%) > interaction influence (8.2%) > arts space use (7.5%). The top five ranking barrier factors in Xinzhuang Village are, in descending order, cultural literacy (7.8%) > interaction impact (7.7%) > arts activity participation (7.5%) > arts space use (6.1%) > space impact (5.8%).

4. Discussion

Arts activity participation, interaction impact and arts space use are the core barrier factors common to the two villages, while spatial impact, cultural literacy, industrial impact and working age population show differential characteristics between the case areas. By analyzing the barrier factors of the case districts (

Table 7. Analysis of obstacles.

Case Area	Obstacle Factor	Causes	Mode of Action
Feijia Village/ Xiaopu Village/ Xinzhuang Village	Arts Activity Participation	Feijia Village: activities are infrequent and far away from residential areas; Xiaopu Village: low participation rate of senior aborigines; Xinzhuang Village: residents’ art activities are geared towards tourists	Weakening cultural capital accumulation, social network reconfiguration and knowledge spillovers
	Impact of interactions	Feijia Village: spatial fragmentation leading to low interaction Xiaopu Village: superficial social interaction Xinzhuang Village: superficial social interaction	Limiting knowledge spillovers and constraining the resilience of social capital subsystems
	Art spaces use	Feijia Village: poor accessibility and busy crowds; Xiaopu Village: homogenization of users; Xinzhuang Village: coexistence of spaces but differentiated community activities	Monofunctionalization of space reduces the actual efficacy of physical space modifications for resilience
Feijia Village/ Xinzhuang Village	Space Impact	Feijia Village: art district and living area are separated, forming a functional island Xinzhuang Village: spatial-functional segregation between the art district and the living district	Low utilization of space resources and reduced emergency response capacity
	Cultural literacy	art intervention Fails to Trigger Deep Cultural Identity	Broken cultural identity makes it difficult to transform arts capital into community cohesion
Xiaopu Village	Industry Impact	Rigid rent as a percentage of total community revenue is low	Economic structural homogenization, constraining sustainable synergies in resilient systems
	Population of working age	High rate of Aboriginal labor outflow and intergenerational disconnect between foreign artists and Aboriginal people	Heterogeneous populations impede the interface between innovative ideas and traditional modes of governance

), the main constraints can be consolidated into the following three aspects based on the causes and modes of action of the barrier factors:

(1) Imbalance in the mechanism of arts participation weakens the accumulation of cultural capital

Insufficient participation in artistic activities is the primary factor hindering system coupling and coordination (overall obstacle rate of 9.2%). In addition to promoting economic gains, the hosting of arts events also plays an important role in developing various forms of human capital and enhancing local leadership, organizational, and management skills [44,45,46]. Feijia Village, due to its digital art forms and low frequency of artistic activities, has resulted in 59% of residents never having participated in artistic activities; although 93% of its residents have participated in activities, Xiaopu Village has insufficient participation rates among elderly indigenous residents, leading to a mismatch in intergenerational needs; Xinzhuang Village has a competitive relationship between indigenous residents and artists, resulting in little or no participation in art activities.

“There’s little interaction between artists and residents—they don’t know each other. Most people here are renters, many working in nearby Wangjing, plus numerous delivery drivers and couriers. They leave early and return late, with little time to participate in those art events.”—Feijia Village Community Manager

“There are quite a few artists, but I rarely go to their spaces. Most of the kids work outside the village, leaving just us elderly folks here. I’m over 70, just had surgery recently, and my health isn’t great, so I don’t bother joining the commotion.”—Xiaopu Village Resident

“We don’t go to that art market. They charge us 5% rent just to sell things there. Back when they [the artists] weren’t here, we never paid anything like that.”—Xinzhuang Village Resident

The one-way output model of art activities has severed the deep connection between residents and art, hindering the penetration of the implicit knowledge and innovative concepts inherent in art into the community. This makes it difficult for the community to internalize art resources as cultural strategies to address crises, while also weakening the potential role of art in building collective identity and strengthening community cohesion. Artistic capital cannot be effectively converted into endogenous momentum to enhance community resilience, hindering the synergistic enhancement of art’s cultural influence and social capital resilience.

(2) Social Network Disconnection Constrains Knowledge Spillover Effects

The weak connection characteristics of social interaction significantly affect system coordination (overall obstruction rate of 9.0%). Community art promotes social connections by fostering social capital, which constitutes important, actual, or potential collective resources embedded in social networks [47,48,49]. Xiaopu Village and Xinzhuang Village, which host frequent artistic events, exhibit higher levels of social capital than some other urban villages in Beijing [26], yet certain challenges persist.

The fragmented spatial layout of Feijia Village has led to extremely low interaction frequency between artists and indigenous residents, forming parallel social networks; in Xiaopu Village, despite the mixed-residence model, many residents have never entered the artists’ workspaces, with interactions remaining superficial; in Xinzhuang Village, the competitive relationship between residents and artists has reduced interaction between the two groups.

“The village entrance leads straight into the art district. We work right here in the district and go home directly after work, so we don’t interact much with the residents. They have their own main access roads and rarely pass through our district. Plus, the environment inside the district is quite pleasant, so we have no need to go into the residential area.”—Feijia Village Artist

“They [the artists] have their own circle and their own clientele. We generally don’t go in. We just exchange simple greetings when we meet, never forming deeper connections.”—Xiaobao Village Resident

“We’re not allowed to sell on that road; we can only set up here at the alley entrance. Property management keeps watch, forbidding us from crossing building boundaries. If we do, they come to shoo us away. They can set up stalls on the road, but us locals can’t. That’s not fair.”—Xinzhuang Village Resident

This weak or even broken social network severely limits knowledge flow and creative collision across groups. Artists’ cutting-edge thinking and practical experience cannot smoothly permeate and integrate into the community’s collective wisdom, while the local knowledge and needs of the indigenous population also struggle to effectively feed back into and influence artistic practice. The effects of art intervention are confined to the level of material space transformation, failing to trigger deeper-level social capital restructuring (such as strengthening trust networks or enhancing collective action capacity), resulting in the lack of necessary social foundations and social learning processes for the enhancement of social capital and institutional resilience.

(3) Difficulties in spatial functional adaptation reduce the effectiveness of resource transformation

The functional specialization of spatial systems constitutes a key constraint (overall obstacle rate of 7.2%). Spatial structure has a significant impact on a community’s disaster resilience. A well-designed spatial structure can increase pedestrian traffic, improve accessibility, and enhance spatial efficiency [50,51,52]. In Feijia Village, 20% of the art-related floor area is concentrated in an independent zone, but residents’ weekly usage rates are low, and the fragmented layout creates “Spatial segregation”. In Xiaopu Village, while spatial mixed-use has been achieved, the homogenization of user demographics leads to diminished interactive efficiency. In Xinzhuang Village, the linear layout of art spaces fails to integrate art more deeply into residential areas (Figure 4 and Figure 5).

“I’ve never spoken to them [the artists] or even met them. They’re all in their own compound with security guards at the gate, so I never really dared to go in. I did try once, but they all had their doors shut, working inside. It didn’t seem like they were open to the public, so I never went back. The art square next to the compound is too far from where we live, so I rarely go there for a stroll either.”—Fei Village Resident

“We rent a room to those artists. We just collect rent year-round and rarely chat with them. Young people like going to those places [art venues] and visit often. Us older folks usually just sit by the door chatting or take our grandchildren to play at the square. There are lots of parents with kids there too, and the children have fun playing together.”—Xiaopu Village Resident

“The two art markets and this maker street are the liveliest spots in our village. Visitors can head straight there. We’ve also allocated most of our management and promotional resources [property management, volunteers, etc.] to these three areas. Actually, there are a few smaller art venues scattered around the village, but they’re not centralized and have limited reach, so they’re generally not visited.”—Xinzhuang Village Community Manager

The fragmentation of spatial layout or functional specialization hinders the organic integration and functional synergy between artistic spaces and community daily life. This not only reduces the utilization efficiency and vitality of spatial resources under normal conditions (e.g., failing to effectively stimulate economic diversity) but also weakens their emergency response potential under abnormal conditions (e.g., lacking multifunctional resilient spaces). Physical space renovations have failed to effectively translate into tangible momentum for enhancing community resilience, and the spatial benefits of art intervention have been significantly diminished. Despite the homogenization of public space usage in Xiaopu Village, its mixed spatial layout validates the assertion by Van der Waart et al. [38] that “the mixed residential layout of art villages can promote social interaction and enhance trust.”

By analyzing the impact of art intervention on community resilience and the obstacle factors to the coupling and coordination of art intervention and community resilience, we put forward suggestions to enhance the community resilience of arts villages and help the sustainable development of art intervention and community resilience:

(1)

Optimize the participation mechanism of art activities. Art villages such as Zhuan Tang Village and Ge Jia Village in Zhejiang Province, Zhong He Village in Yunnan Province, and Gamcheon Culture Village in South Korea have rooted art in rural life by inviting artists to reside there and establishing mechanisms that enable residents to participate, benefit, and have a voice [53]. Drawing from the above cases, to address the imbalance in art participation mechanisms, Feijia Village implemented a “Community Art Points Bank,” adding “Art Points Redemption Points” in supermarkets and pharmacies. Participants earn points by engaging in art activities, which can be exchanged for daily necessities. For Xiaopu Village’s elderly residents, a generational co-creation project was designed, regularly inviting artists into homes to guide seniors in crafting. A dual-reward system for both artists and seniors expanded participation across all age groups: refine rental income policies in Xinzhuang Village and optimize stall allocation between residents and artists, and increase daily activities and exchange meetings to foster interaction between residents and artists. Artists and the village committee jointly fund an “Art Neighbors Fund” to support skill training programs for residents in arts-related services, promoting collaborative artistic activities between residents and artists.

(2)

Promote spatial renewal to achieve multifunctional integration. Drawing on case studies such as South Korea’s Heyri Art Village and Japan’s Echigo-Tsumari Art Triennale, we will enhance cultural value, economic returns, and community well-being through flexible, multifunctional spatial design, thereby realizing truly efficient and sustainable land use [54]. Feijia Village breaks down barriers between the art district and residential areas, extending artistic activities into daily living spaces. Closed art spaces are opened for residents and tourists to visit, while a community activity center is added within the art district. The integration of “art + community services” enhances spatial interactivity. Xiaopu Village deepens the “courtyard alley economy” model by transforming idle courtyards into “art + community service points.” These spaces offer art training services while developing emerging sectors like art trading and digital art, extending the “art+” industrial chain. Xinzhuang Village vertically connects Maker Street with art spaces deep within residential areas through wall murals and artistic flower bed installations. Leveraging Xinzhuang Village’s strawberry economy, idle spaces within the village are revitalized to establish a “Strawberry Plus” creative hub. Artists provide guidance to develop income-generating pathways such as “strawberry-infused cultural and creative products.”

(3)

Establish a collaborative mechanism between artists and residents. Projects like the Abbotsford Artists’ Studios and the “Artistic Rural Development” initiative in Songyang County, Zhejiang Province, not only beautify village environments through spatial renovations but also foster deep interactions by creating institutionalized collaboration mechanisms [55]. These mechanisms enable artists and residents to build emotional connections and social networks through joint creative endeavors. Based on these cases, to address the fragmented social networks in the three villages, an “Artist-Village Collaboration Committee” could be established in each village. Each committee would comprise representatives from all age groups of residents, artists, and community workers. Monthly coordination meetings would be held to jointly discuss the preparation and organization of village art activities, space allocation, and usage. Artists would be responsible for artistic transformation and technical implementation, while residents and community workers provide feedback from perspectives of practicality, cultural identity, and safety, thereby achieving collaborative governance.

5. Conclusions

This study pioneers the integration of community resilience assessment models with art intervention, establishing a unified “art-resilience” coupling framework that aligns with international urban resilience and cultural policy frameworks. Employing coupling and coordination degree and barrier degree models, it empirically analyzes the synergistic mechanisms between art intervention and community resilience in Beijing’s Feijia Village, Xiaopu Village, and Xinzhuang Village.

The results show that:

(1)

There is a clear positive correlation between the degree of arts involvement and the level of community resilience. Based on the empirical analysis of three art villages in Beijing, it is found that Xiaopu Village, which has the deepest degree of art intervention, has the highest level of community resilience and the coupling degree of art intervention-community resilience system (0.8004); Xinzhuang Village, which has a medium degree of intervention, has the second highest level of community resilience (coupling degree of 0.6914); and Feijia Village, which has the shallowest degree of intervention, shows the lowest level of resilience and the lowest degree of coupling degree of resilience (0.6400). This clearly confirms the rule that the higher the depth of art intervention, the higher the level of community resilience.

(2)

Coupling and coordination exhibit distinct phased differentiation characteristics. Xiaopu Village’s art district and residential area feature functional integration, achieving a coordination level of 0.8004 (good coordination). Feijia Village maintains complete spatial separation between its art district and residential areas, with the art district serving solely artistic functions and achieving only 0.6400 coordination (primary coordination). Xinzhuang Village’s strip-shaped art district placement corresponds to a primary coordination level (0.6914).

(3)

Barrier factors reveal the key bottlenecks of system synergy. It is found that the imbalance of the arts participation mechanism, the fracture of the social network, and the dilemma of spatial functionality are the main obstacles. The imbalance of the arts participation mechanism weakens the accumulation of cultural capital, making it difficult for art intervention to enhance the community’s crisis response ability through tacit knowledge dissemination, and weakening the support of cultural identity for community cohesion. The break in the social network restricts the knowledge spillover effect, and the innovative thinking of artists cannot be transformed into the collective wisdom of the community, failing to trigger the resilience reconstruction of the social capital subsystem, resulting in the lack of a social basis for the enhancement of institutional resilience. The dilemma of spatial functionality reduces the effectiveness of resource transformation. It is difficult for art intervention to stimulate economic vitality through functional superposition, and the transformation of physical space fails to be transformed into the actual kinetic energy of resilience enhancement.

Research Limitations and Future Directions: (1) The case studies are geographically limited, failing to encompass diverse types of art communities across different cultural contexts. The objectives of artistic development in villages and urban districts differ. These distinct purposes and governance models may exert varying influences on outcomes. Future research will adopt cross-regional comparative studies, using a three-dimensional comparison of “art intervention intensity-resilience level-regional characteristics” to identify how regional differences affect coupling mechanisms. (2) Data collection was concentrated in 2024–2025, lacking monitoring of dynamic effects. Future work will conduct five-year longitudinal tracking of the three case villages, annually collecting questionnaire and interview data. A dynamic panel model will be constructed to analyze the long-term causal relationship between art intervention and resilience, identifying changes in coupling characteristics at key time points (e.g., the maturation phase of the arts industry). Simultaneously, employing “in-depth interviews + participant observation,” we will conduct long-term tracking of selected representative households. Through narrative analysis, we will reveal the micro-level processes by which cultural capital transforms into social capital and subsequently enhances resilience, thereby supplementing the limitations of existing macro-level analysis.