Introduction

Urbanization and rural development are pivotal forces in the modernization journey of nations and are core components in achieving the United Nations Sustainable Development Goals (SDGs). The integration of urban and rural areas (URI) facilitates the flow of resources towards rural regions, enhances infrastructure, and gradually improves public services, thereby establishing a robust material foundation for rural resilience (RR) construction1. In turn, the development of RR strengthens the capacity of rural areas to cope with risks, solidifying the foundation for advancing URI. China’s southwestern region, characterized by its multi-ethnic population, ecological vulnerability, and imbalanced urban–rural development, is at a critical juncture in URI and RR construction. It bears the national strategic missions of the “Western Development” and “Rural Revitalization” initiatives. Despite challenges such as complex terrain, significant differences in resource endowments, and persistent institutional barriers, this region strives to chart a unique and effective path to sustainable development. The development practices in China’s southwestern region serve as a microcosm of the country’s progress in urban–rural integration and offer localized experiences for addressing the “imbalanced development” dilemma in global underdeveloped areas2. A deep-going exploration of the coupled and coordinated evolution of URI and RR in this region, and driving mechanisms.

URI represents the integration and coordinated development of urban and rural areas across multiple dimensions, emphasizing the equal status of urban and rural areas. It aims to achieve common development through the two-way flow and effective allocation of resources and production factors. Specifically, URI includes economic integration, which promotes the Synergistic development of urban and rural industries, narrows the income gap between urban and rural residents, and optimizes economic resource allocation. Social integration focuses on the equalization of urban and rural public services, including education, healthcare, and social security, as well as the exchange and integration of urban and rural cultures and values. Spatial integration emphasizes rational planning of urban and rural spatial layouts, promotes interconnectivity of urban and rural infrastructure, and achieves coordinated development of urban and rural spaces. However, the implementation of URI faces numerous challenges, such as institutional barriers to the urban–rural dual structure, uneven resource distribution, and lagging infrastructure construction. In Europe, research and practice on URI focus on balanced development and policy support. European experiences indicate that policy tools, such as the European Agricultural Fund for Rural Development (EAFRD), can effectively promote URI. In North America, URI research tends to rely on market mechanisms and local government policies. The URI model there emphasizes the fundamental role of the market in resource allocation. In Asia, initiatives like South Korea’s “New Village Movement” and Japan’s “Integrated Village Construction Demonstration Project” have become powerful explorations for coordinating urban–rural development under globalization and urbanization. The evolution of urban–rural relations in Western developed countries generally shows a three-stage development pattern, progressing from urban–rural mixing and competition to urban–rural integration. URI is an important characteristic of a country entering a high-level urbanization stage. China’s urban–rural relations have gone through stages of urban–rural separation, urban–rural confrontation, urban–rural integration, and urban–rural integration. The 19th National Congress of the Communist Party of China formally proposed the general goal of “following a path of urban–rural integrated development.” URI is regarded as the main avenue for China to achieve its rural revitalization strategy and is also a necessary path for Chinese-style modernization. The overall development of URI in China reveals a gradient transition from the southeastern coast to the northwest. URI research topics include basic theories, multidimensional practices, multiscale characteristics, multiple development models, and systemic institutions and policies3. URI is an inevitable requirement for Chinese-style modernization and is closely related to economic system reform and high-quality development. It aligns with the SDGs4. For instance, URI contributes to poverty reduction, economic growth, and promoting sustainable and community development5. URI provides essential support for achieving these goals by facilitating resource flow and functional coordination between urban and rural areas4.

RR emphasizes the ability of rural areas to adapt to and recover from stresses such as natural disasters and socio-economic changes. RR involves multiple dimensions: ecological resilience refers to the ability of rural ecosystems to restore their ecological functions and balance after being damaged by natural disasters or human activities; social resilience focuses on the ability of rural residents to maintain social stability and enhance social cohesion in the face of social change and crises; economic resilience refers to the ability of rural economies to recover quickly and achieve sustainable development after experiencing economic fluctuations or external shocks; cultural resilience is reflected in the ability of rural traditional cultures to be inherited and innovated during modernization; and institutional resilience is demonstrated in the innovation of rural governance mechanisms and policy response efficiency. Domestic and international research on RR suggests that studies should focus on how rural systems respond to internal and external shocks and how these responses reshape the future of rural systems6,7. Research topics on RR include the response of rural systems to sudden and external shocks, the multiscale interconnected effects of RR, and the spatiotemporal evolution processes and mechanisms of RR under urban–rural integration8. Existing research mainly focuses on discussions of concepts and connotations, the composition and characteristics of RR, quantitative measurement, influencing factors, formation mechanisms, and resilience governance and construction paths9,10,11,12. Quantitative measurements tend to use indicator systems to calculate the comprehensive development index of RR, with indicators covering multiple dimensions such as nature, economy, and society. In identifying influencing factors, qualitative methods and various quantitative techniques, including composite index methods, gray relational analysis, TOPSIS models, and geographic detectors, are employed to explore the determinants of RR13,14. Research primarily concentrates on the scale of villages and rural settlements, with insufficient attention to macro-scale issues. Enhancing RR contributes to economic recovery and sustainable livelihoods in rural areas, which is directly related to poverty reduction and development efforts8,15,16. Unlike the “external interactivity” of URI, RR emphasizes the “internal robustness” of rural systems. However, URI and RR form a symbiotic relationship through factor mobility and functional complementarity. URI provides resource inputs for RR construction, while RR constitutes the foundational support for the sustainability of URI.

We identify that relevant research primarily concentrates on several key areas by exploring the intrinsic connections, impact processes, and mechanisms between URI and RR. First, the impact of URI on RR is emphasized. URI is regarded as a crucial driver for enhancing RR. Empirical studies in China’s southwestern region have found that URI strengthens rural communities’ disaster resistance and recovery capabilities by promoting resource and information flows, improving infrastructure, and enhancing public service levels17,18. URI also boosts the economic resilience of rural communities by promoting non-agricultural employment and economic growth in rural areas19.

Additionally, cultural integration and knowledge dissemination during the URI process significantly enhance rural communities’ social resilience and environmental adaptability20. Second, RR influences URI. As an integral part of URI, RR affects the quality and progress of the integration process. Communities with higher resilience are better equipped to adapt to and absorb changes by URI, thereby achieving sustainable development21. Enhancing RR helps narrow the urban–rural gap and promotes URI22. At the same time, enhancing RR provides a stable social foundation and environmental security for URI23. Third, the mutual relationship between URI and RR is of great interest. Scholars argue that URI and RR interact with and promote each other; however, this relationship is controversial. Some researchers suggest that URI may jeopardize rural communities’ traditional culture and ecological integrity, thereby affecting their resilience24. These studies aim to reveal the driving mechanisms, causal relationships, and levels of coordinated development between URI and RR25. The findings show that the overall degree of coupling and coordination between the two systems gradually improves; however, this relationship exhibits significant spatial differences13.

Furthermore, the coupled and coordinated development of URI and RR is influenced by many factors. Policy support, economic development level, infrastructure construction, educational resources, cultural heritage, and social participation significantly impact the coordinated development of URI and RR26. The protection of the ecological environment and adaptation to climate change are also crucial factors influencing URI and RR27.

Existing literature on urban–rural integration and rural resilience in China has made important progress, but research on southwest China still has three significant shortcomings. First, most existing conceptual frameworks are based on the experience of the eastern developed regions and the interpretation of the connotations of urban–rural integration and resilience in southwest China, which has a complex geographical pattern, multi-ethnic population structure, and dual constraints of ecological protection and development, is not precise enough28,29. Second, although some studies have focused on the interactive relationship between urban–rural integration and rural resilience25,30, the unique coupling path of “policy-driven” urban–rural integration and “ecologically constrained” rural resilience in southwest China has not been deeply analyzed. Third, there is a significant development gradient within southwest China, with substantial differences in the level of urban–rural integration and the foundation of rural resilience. However, existing studies mostly remain at the statistical description level of national and eastern coastal provincial scales, lacking a comparative analysis of the differentiation mechanisms within southwest China. Fourth, despite growing academic interest in the close relationship between urban–rural integration and rural resilience—recognizing rural resilience as a vital support for urban–rural integration and urban–rural integration as a material foundation for enhancing rural resilience—their spatiotemporal characteristics, coupled coordination relationships, and influencing mechanisms have yet to be effectively verified.

In light of these gaps, this study focuses on southwest China to provide new empirical evidence and theoretical insights into the coupling and coordinated development of URI and RR. By constructing an evaluation system that includes region-specific indicators for URI and RR, this study reveals the spatiotemporal evolution patterns of their coupling and coordination and examines the driving mechanisms of multidimensional factors such as the geographical environment, economic basis, and institutional policies. Specifically, this study explores the following research questions: How do URI and RR interact and achieve coupling and coordination in southwest China? What are the key factors influencing their coupling and coordination, and what are the underlying mechanisms? What spatial patterns and evolution trends does this coupling and coordination relationship exhibit? Based on these explorations, this study proposes a theoretical framework (see Fig. 1). First, the study refines the dimensional definitions of URI and RR by incorporating region-specific characteristics and constructs an evaluation system that includes indicators such as ethnic culture and ecological vulnerability. Second, the entropy weight method and the coupling coordination degree model are employed to quantify the development levels and coordination of URI and RR. Finally, geographical detectors and spatial autocorrelation analyses are used to identify the key factors influencing the coupling and coordination and to reveal their spatiotemporal heterogeneity. The findings of this study not only offer references for regional policy formulation but highlight the unique experiences and academic value of southwest China in global urban–rural transformation research.

Fig. 1
figure 1

The theoretical mechanism and logical framework proposed in this research. (Note The map was generated using ArcMap 10.8 software, which can be obtained from https://desktop.arcgis.com, without any modifications to the boundaries of the base map.)

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Research area and data sources

Research area

As a geographical category, the Southwest region of China can be divided in various ways. This study adopts the national administrative division approach, encompassing five provinces and municipalities: Sichuan, Guizhou, Yunnan, Chongqing, and Tibet (see Fig. 2). This area is characterized by its complex terrain, spanning China’s three-step topography, including the Bashu Basin and surrounding mountainous regions, the mid-high mountain hilly areas of the Yungui Plateau, and the high mountain areas of the Tibetan Plateau. Since the beginning of the twenty-first century, the Southwest region has received significant attention as an integral part of China’s “Western Development Strategy.”

Fig. 2
figure 2

Study area. (Note The map was generated using ArcMap 10.8 software, which can be obtained from https://desktop.arcgis.com, without any modifications to the boundaries of the base map.)

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Research on urban–rural integration and rural resilience in the Southwest region is typical and representative. Firstly, the diversity of the region’s topography and landforms provides unique natural conditions for the models and pathways of urban–rural integration and the formation and evolution of rural resilience. Secondly, as one of the regions with the highest concentration of ethnic minorities in China, the Southwest’s multicultural background offers typical cases for studying cultural integration, social cohesion, and the cultural foundations of rural resilience during the urban–rural integration process. Furthermore, there are considerable differences in the levels of urban–rural integration development and rural revitalization progress among the provinces and municipalities in this region; compared to the central and eastern parts of the country, the overall pace of development is relatively slow. This unique economic development context makes the Southwest region a representative area for investigating the coupling coordination and evolutionary dynamics of urban–rural integration and rural resilience, along with their driving mechanisms.

Lastly, as a key area under the “Western Development Strategy,” urban–rural integration and rural resilience in the Southwest region benefit from national policy support, making it exemplary. Studying urban–rural integration and rural resilience in this region provides theoretical support for sustainable development in Southwest China and offers valuable insights for urban–rural integration development in other countries and regions.

Data sources and indicator selection

Data sources

Considering the accuracy and availability of data, this study employs provincial-level panel data from the Southwest region, using 2010 as the base year and covering the period from 2012 to 2022. The data has been compiled and organized from various sources, including the China Statistical Yearbook, China Rural Statistical Yearbook, China Urban–Rural Construction Statistical Yearbook, China Agricultural Statistical Yearbook, China Water Resources Statistical Yearbook, China Environmental Statistical Yearbook, and China Population and Employment Statistical Yearbook, as well as statistical yearbooks from the provinces of Sichuan, Chongqing, Yunnan, Guizhou, and Tibet. For any missing data, interpolation methods will be utilized to ensure completeness and reliability.

Construction of the indicator system

Considering that rural resilience and urban–rural integration are two mutually independent yet interrelated systems, this study integrates the mechanisms of rural resilience and urban–rural integration. Based on the realities of urbanization, rural revitalization, and urban–rural integration development in Southwest China28,29,31,32, this research draws on existing relevant studies to provide a comprehensive analysis. An evaluation indicator system for urban–rural integration (URI) and rural resilience (UR) has been established. Considering the representativeness, objectivity, and availability of data for the indicators, the rural resilience evaluation indicator system encompasses 24 indicators organized into five dimensions: ecological resilience, social resilience, economic resilience, cultural resilience, and institutional resilience. Meanwhile, the urban–rural integration evaluation indicator system consists of 13 indicators organized into four dimensions: economic integration, social integration, spatial integration, and ecological integration (Table 1).

Table 1 Rural resilience indicator system.
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The rural resilience indicator system encompasses five dimensions: ecological resilience, social resilience, economic resilience, cultural resilience, and institutional resilience, comprising 24 specific indicators. These indicators reflect various aspects of rural resilience from different perspectives. Ecological resilience includes metrics such as water resource availability, air emissions, wastewater discharge, afforestation status, and fertilizer application intensity, aimed at assessing the health of the ecological environment and the sustainable development capacity of rural areas. Social resilience indicators include per capita agricultural machinery power, per capita electricity consumption, per capita grain ownership, number of hospital beds per thousand people, road density, the proportion of employed individuals in rural areas, and percentage of villages with broadband access, which evaluate the level of social development within rural communities and the quality of life of residents. Economic resilience consists of indicators like per capita regional GDP, disposable income of rural residents, per capita retail sales of consumer goods, per capita general public budget revenue, household savings, and growth in the output value of agriculture, forestry, animal husbandry, and fishery, reflecting the economic vitality of rural regions and the economic well-being of their residents. Cultural resilience is measured through the number of agricultural technicians per thousand people, educational training potential, and the average number of cultural, educational, broadcasting, and postal systems per thousand villages, assessing the level of cultural development and education in rural areas. Finally, institutional resilience includes indicators such as the number of supply and marketing cooperatives and the proportion of social security and employment expenditures relative to fiscal expenditure, evaluating the institutional development and social security levels in rural regions.

The indicator system for urban–rural integration development includes four dimensions: economic integration, social integration, spatial integration, and ecological integration, encompassing 13 specific indicators to measure the depth and breadth of urban–rural integration in the western region. Economic integration involves indicators such as the urban–rural income gap, the ratio of urban to rural Engel coefficients, per capita grain output, and the level of industrial synchronization, aiming to assess the balance of economic levels between urban and rural areas and the status of coordinated industrial development. Social integration includes indicators such as the ratio of security standards, education levels, the number of doctors per ten thousand people in urban and rural areas, and the number of hospital beds per ten thousand people in urban and rural areas, designed to gauge the degree of integration in social services and welfare protection between urban and rural regions. Spatial integration is reflected through indicators such as the growth rate of urban built-up areas and the number of cars per hundred households, illustrating the integration of spatial development and living conditions for residents in urban and rural areas. Ecological integration includes indicators like the number of public toilets per ten thousand people, per capita fertilizer usage measured by regional GDP, and the forest coverage rate in urban and rural areas, assessing the extent of integration in ecological environmental protection and the utilization of natural resources. Compared with previous studies that focused on single dimensions, the indicator system outlined in this paper comprehensively covers key areas of urban–rural integration and rural resilience, providing a solid data foundation and theoretical support for in-depth analysis and assessment of the coupling and coordinated evolution of urban–rural integration and rural resilience in Southwest China. Through quantitative analysis of these indicators, we can more accurately grasp the dynamic developments of urban–rural integration and rural resilience, providing a scientific basis for formulating relevant policies and measures. Additionally, the research employs range standardization processing and principal component factor analysis to eliminate the impacts of magnitude and dimensionality, and the entropy method is used for weighting the indicators (see Table 2).

Table 2 Indicator system for integrated urban–rural development.
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Research methods

Entropy method

Using the entropy method to objectively assign weights to the indicators in the evaluation system for urban–rural integration and rural resilience in the Southwest region can reduce errors associated with subjective weighting. First, the data undergoes range normalization. Standardization is necessary since the different types of indicator data are not comparable due to dimensional differences. For positive indicators, larger values are better calculated using formula (1); for negative indicators, smaller values are preferable, as shown in Formula (2). In addition, we adopt practices from relevant literature to prevent zero and negative values during the range transformation of the indicator values33,34. Add 0.01 uniformly in the formulas.

$$S_{ij} = (x_{ij} - x_{{_{j} }}^{\min } )/(x_{j}^{\max } - x_{j}^{\min } ) + 0.001$$
(1)
$$S_{ij} = (x_{j}^{\max } - x_{ij} )/(x_{j}^{\max } - x_{j}^{\min } ) + 0.001$$
(2)

where i = 1,2,…,m represents the years, j = 1,2,…,n represents indicators, \(x_{ij}\) represents the initial value of indicator j in year i, and \(x_{j}^{\max }\) and \(x_{j}^{\min }\) represent the maximum and minimum values of the j indicator, respectively. \(R_{ij}\) is the standardized value of \(x_{ij}\) after processing.

Based on the principles of normalization, first, regarding the standardization of indicator values, we calculate the proportion of indicator j in the total for the first indicator i with weight \(M_{ij}\) Secondly, we calculate the information value \(N_{j}\) of indicator j; next, we recalculate the differential coefficient \(A_{j}\) of indicator j; finally, we obtain the weight \(W_{j}\) of the calculation indicator, as seen in formulas (3) to (6).

$$M_{ij} = S_{ij} /\sum\limits_{i - 1}^{m} {S_{ij} }$$
(3)
$$N_{j} = - k\sum\limits_{i = 1}^{m} {M_{ij} \ln M_{ij} (k = 1/\ln m)}$$
(4)
$$A_{j} = 1 - N_{j}$$
(5)
$$W_{j} = A_{j} /\sum\limits_{j = 1}^{n} {A_{j} }$$
(6)

Comprehensive evaluation model

The ordinal parameters of the urban–rural integration and rural resilience subsystems reflect the “total contribution” of the indicators to the comprehensive measurement of the subsystems, which can be obtained through a comprehensive evaluation method. Using the determined weights, the evaluation index for the level of urban–rural integration development Ti​ and the level of rural resilience Ui​ in the Southwest region for year i is generally calculated using the weighted average method35. As shown in formulas (7) and (8).

$$T_{i} = \sum\limits_{j = 1}^{4} {w_{j} } \cdot \sum\limits_{j = 1}^{n} {w_{ij} S_{ij} }$$
(7)
$$U_{i} = \sum\limits_{j = 1}^{5} {w_{j} } \cdot \sum\limits_{j = 1}^{n} {w_{ij} S_{ij} }$$
(8)

In the formulas, Ti represents the comprehensive evaluation index for the urban–rural integration development level, and Ui represents the comprehensive evaluation index for the rural resilience development level. W is the weight of the evaluation factor layer for the evaluation index, Wij is the weight of the evaluation index layer for each indicator, \(\sum\limits_{j = 1}^{n} {w_{ij} = 1,i = 1,2,3}\), Sij is the standardized value of each evaluation index, and n corresponds to the number of indicators included in the system.

Coupling coordination model

Due to the close relationship between urban–rural integration and rural resilience, we draw on the concept of capacity coupling from physics and the capacity coupling coefficient model36,37,38. Coupling the two systems at their corresponding time series points allows us to measure the coupling degree of urban–rural integration and rural resilience using the coupling degree model, as shown in formula (9).

$$C = 2\sqrt {(T_{i} \times U_{i} )/[(T_{i} + U_{i} )^{2} }$$
(9)

where Ti and Ui represent the comprehensive evaluation indices for urban–rural integration and rural resilience levels. However, the coupling degree indicator has limitations; when the development levels of both subsystems are relatively low, the coupling degree may still appear high. This high coupling degree at low development levels does not accurately reflect the overall effectiveness and synergistic effects of urban–rural integration and rural resilience. Therefore, this paper introduces the coordination degree model39, to objectively reflect the coordinated development level of the two subsystems.

$$D = \left( {C \times F} \right)^{1/2}$$
(10)
$$F = \alpha T_{i} + \beta U_{i}$$
(11)

where D represents the coupling coordination degree, and F is the comprehensive development index of urban–rural integration and rural resilience, reflecting the contribution of the overall development level of urban–rural integration and rural resilience to the coordination degree. \(\alpha\) and \(\beta\) are undetermined coefficients. Given the mutual promotion and influence between urban–rural integration and rural resilience, and considering the situation in the Southwest region, we refer to the studies of relevant scholars40,41 This paper sets the values of both to 0.5. A higher coupling coordination degree indicates a greater compatibility between the two systems, suggesting that the systems tend toward a more orderly development direction. To intuitively explain the temporal and spatial conditions of the coupling and coordinated development of urban–rural integration and rural resilience, this paper classifies the coupling coordination degree into different levels based on previous research findings and actual development conditions37. As shown in Table 3, when \(T_{i} \ge U_{i}\), it represents a lagging urbanization development type; when \(T_{i} \le U_{i}\), it represents a lagging tourism industry development.

Table 3 Coupling coordination degree level classification criteria.
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Results analysis

Analysis of the comprehensive development level of rural resilience and urban–rural integration

Using the indicator system mentioned above, we calculated the comprehensive rural resilience index (URI) and the comprehensive urban–rural integration development index (UR) for the five provinces in Southwest China from 2012 to 2022, as illustrated in Fig. 3. Throughout this period, the rural resilience index and the urban–rural integration development index exhibited a continuous upward trend with fluctuations. Notably, the rural resilience index consistently outpaced the urban–rural integration index in each province.

Fig. 3
figure 3

(a) Urban–rural integration development level from 2012 to 2022, (b) rural resilience level from 2012 to 2022.

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At the end of 2018, apart from Chongqing, the indexes of other provinces experienced a short-lived and significant decline. This can be attributed to the Rural Revitalization Strategy proposed in 2017. In 2018, the first five-year plan (2018–2022) was launched. Although there was a temporary slowdown in RR levels during the policy transition period, a growth peak emerged by the end of the year.

When examining the rural resilience development levels, Yunnan and Chongqing generally ranked at the forefront, with the provincial rankings before 2018 being Yunnan, Chongqing, Tibet, Sichuan, and Guizhou. After 2018, both Chongqing and Sichuan experienced rapid growth, surpassing Yunnan and Tibet to secure the first and third positions, respectively, while Guizhou consistently maintained the lowest overall level of rural resilience throughout the study period. The upward trajectory of rural resilience levels in Southwest China from 2012 to 2022 indicates an enhancement in the region’s adaptability and recovery capacity when facing various challenges. This trend can be linked to several policies to improve rural infrastructure, foster agricultural technology innovation, and reinforce ecological and environmental protection measures.

During this decade, the growth rate of rural resilience accelerated in certain years, potentially due to the introduction of specific policies or an increase in external investments. For instance, notable growth peaks were observed in 2015 and 2018, coinciding with the implementation of national poverty alleviation policies and the rural revitalization strategy.

In terms of urban–rural integration development indices, all provinces in Southwest China displayed an overall upward trend, with Yunnan leading in urban–rural integration, followed by Sichuan, Guizhou, Tibet, and Chongqing. Despite having the lowest level of urban–rural integration, Chongqing showed a consistent upward trend. Specifically, the advancement of strategies such as urbanization, industrialization, and new urbanization effectively promoted the urban–rural integration index in the Western region. From 2013 to 2018, all provinces experienced rapid increases in urban–rural integration, with Guizhou demonstrating the highest growth rate, while Chongqing maintained a steady upward trajectory. Throughout the study period, the continual improvement in urban–rural integration development levels across provinces indicated a gradual narrowing of the development gap between regions, signifying substantial progress in economic, social, and spatial integration within Southwest China.

Analyzing the development situation in the Southwest region over two years from a temporal perspective, we utilized ArcGIS software to present the evolution of rural resilience and urban–rural integration for 2012, 2014, 2016, 2018, 2020, and 2022. The results are illustrated in Figs. 4 and 5.

Fig. 4
figure 4

Spatial–temporal evaluation of tourism economy development level in Southwest China in 2012, 2014, 2016, 2018, 2020, and 2022. (Note The map was generated using ArcMap 10.8 software, which can be obtained from https://desktop.arcgis.com, without any modifications to the boundaries of the base map.)

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Fig. 5
figure 5

Spatial–temporal evaluation of urbanization development level in Southwest China in 2012, 2014, 2016, 2018, 2020 and 2022. (Note The map was generated using ArcMap 10.8 software, which can be obtained from https://desktop.arcgis.com, without any modifications to the boundaries of the base map.)

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Figure 4 shows that rural resilience in the five provinces of Southwest China is undergoing dynamic changes with considerable regional disparities. Yunnan and Chongqing consistently ranked at the top regarding rural resilience, while Guizhou showed minimal changes and remained nearly constant. Notably, Tibet experienced significant growth in rural resilience in 2014 but saw a decline after 2018. Following the introduction of the rural revitalization strategy proposed in the report of the 19th National Congress at the end of 201, there was a marked improvement in rural resilience across the Southwest region. Furthermore, the rural resilience levels in all provinces remained consistent in 2018, 2020, and 2022, with a relatively stable distribution.

According to Fig. 5, the level of urban–rural integration in the Southwest region exhibits an overall trend of dynamic growth. Yunnan and Sichuan maintain higher levels of urban–rural integration, leading to a gradual reduction in the development gap between urban and rural areas within the region.

Analysis of the spatiotemporal evolution of coupling coordination between rural resilience and urban–rural integration

Analysis of the temporal evolution of coupling coordination degree

The changes in the coupling coordination degree of rural resilience and urban–rural integration in the Southwest region and its provinces are illustrated in Fig. 6. From a temporal evolution perspective, from 2012 to 2021, the coupling coordination degree across the provinces in the Southwest region exhibited a continuous fluctuating upward trend, with values ranging between 0.12 and 0.52. Based on the quantitative analysis of the coupling coordination degree over the years and considering the actual developmental conditions of each province, we categorized them into five levels of coupling coordination types: highly coordinated type [0.5,1), moderately coordinated type [0.45,0.5), weakly coordinated type [0.4,0.45), mildly disordered type [0.35,0.4), and moderately disordered type [0,0.35).

Fig. 6
figure 6

The CCD evolution characteristics of five Provinces (cities) in southwest China from 2012 to 2022.

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At the regional level, the coupling coordination degree in the southwest region has experienced two stages of mild imbalance and low coordination, and the overall coordination has gradually improved. In 2012, only Tibet was in the low disorder phase, while Chongqing and Guizhou were in the low coordination phase. By 2013, Tibet’s degree of coupling coordination had improved, attaining the low coordination phase. By 2021, all provinces within the Southwest region attained the moderate coordination phase.

During the research period, Yunnan consistently ranked first in the URI-RR coupling coordination degree. It progressed from moderate coordination in 2012 to high coordination in 2021. In contrast, Yunnan, Sichuan, and Chongqing had relatively high URI-RR coupling coordination levels, exceeding the regional average, while Guizhou followed and Tibet had the lowest. Overall, Southwestern China’s URI-RR coupling coordination degree showed a clear upward trend, yet significant provincial differences existed. Yunnan and Sichuan recorded the most substantial increases, whereas Tibet had the lowest growth rate, indicating generally low coordination levels.

This study further applied the Granger causality test to establish the causal relationship between URI and RR. The results indicated that URI improvement significantly promoted RR enhancement at a 5% significance level, and RR growth also propelled URI development. This suggests an abidirection causal relationship between them. Specifically, by promoting resource and information flows, improving infrastructure, and boosting public services, URI advancements strengthened rural communities’ disaster resistance and recovery capabilities, thereby driving RR up. Conversely, enhanced RR provided a stable social and environmental foundation for URI, fostering coordinated urban–rural development.

Moreover, policy factors played a vital role in the URI-RR coupling coordination process. For instance, Yunnan issued the “Yunnan New Urbanization Plan (2014–2020)” in 2015, accelerating urban–rural integration by strengthening rural infrastructure and promoting rural industrial development, enhancing RR and URI. Sichuan, in 2014, released the “Sichuan Rural Reform Experiment Scheme,” which explored new rural reform paths, promoted equal urban–rural factor exchange, balanced public resource allocation, and boosted rural vitality. These policy implementations provided robust support for URI-RR coordinated development.

In conclusion, from 2012 to 2021, the URI-RR coupling coordination degree in Southwestern China generally rose, with a significant causal relationship between the two. This reveals the intrinsic development patterns of URI and RR in the region and offers a scientific basis for tailored regional development policies.

Analysis of the spatial evolution of coupling coordination degree

From the perspective of the average level of coupling coordination degree, the rural resilience and urban–rural integration coordination degrees for the years 2012, 2014, 2016, 2018, 2020, and 2022 were 0.373,0.380,0.393,0.396,0.403 and 0.414, respectively. Although the overall increase is modest, there has been a steady rise in the overall development level, indicating that significant progress has been made in the coordinated development of rural resilience and urban–rural integration. This suggests that the region’s rural resilience and urban–rural integration development have essentially entered a stage of coordinated development.

From a spatial-difference perspective, between 2012 and 2022, the coupling-coordination degree of the five southwestern provinces showed a “high in the middle and low around the edges” spatial pattern (see Fig. 7). The central region, represented by Yunnan, Sichuan, and Chongqing, has consistently led in coupling–coordination degree, with values between 0.45 and 0.55. At different evolutionary stages, these provinces, from low–low-coordination to medium-coordination and finally high–high-coordination, outperformed Guizhou in the east and Tibet in the west.

Fig. 7
figure 7

Spatial and temporal distribution of rural resilience and coordination of urban–rural integrated development in 2012, 2014, 2016, 2018, 2020, and 2022. (Note The map was generated using ArcMap 10.8 software, which can be obtained from https://desktop.arcgis.com, without any modifications to the boundaries of the base map.)

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Yunnan and Sichuan have small differences in RR and URI levels, indicating balanced development. However, Chongqing has a significantly higher RR than URI, reflecting a lagging in URI development. Meanwhile, Tibet has relatively low coupling-coordination degrees, mostly in low-coordination status, except in 2012. Its URI level has consistently lagged behind RR, with both below the regional average, indicating slow development.

Complex factors drive the spatial disparities behind this phenomenon. Yunnan and Sichuan provinces exhibit more prominent coupling coordination degrees between Urban–Rural Integration (URI) and Rural Resilience (RR), which can be attributed to multi-faceted policy support and regional advantages. For instance, in 2015, Yunnan introduced the New Urbanization Plan for Yunnan Province (2014–2020), explicitly aiming to accelerate urban–rural integration. Through measures such as strengthening rural infrastructure construction and promoting rural industrial development, the plan enhances RR while fostering URI. Sichuan, in 2014, released the Implementation Plan for Rural Reform Pilot Zones in Sichuan Province, actively exploring new paths for rural reform to facilitate equal exchange of urban–rural elements and balanced allocation of public resources. These efforts have invigorated rural development, thereby improving the coupling coordination between URI and RR.

Additionally, Yunnan and Sichuan benefit from favorable geographical locations and resource endowments. Situated in the Yangtze River Economic Belt, continuous improvements in transportation infrastructure enable them to undertake industrial transfer from the eastern region better, boosting rural economic development and enhancing rural economic resilience. Furthermore, abundant natural resources and diverse ethnic cultures have promoted the growth of ecotourism and cultural industries, providing diversified support for RR construction and further strengthening the coordination of URI.

In contrast, Tibet and Guizhou exhibit relatively low coupling coordination degrees, which are closely related to their policy environments and regional characteristics. Tibet faces harsh natural conditions, fragile ecosystems, and difficult infrastructure construction. Although the Tibet Autonomous Region Action Plan for Rural Human Settlement Environment Improvement was introduced in 2016 to enhance rural living environments and RR, its effects have been slow to materialize. Despite the 2012 State Council Guidance on Supporting Guizhou to Accelerate the Construction of a National Ecological Civilization Pilot Demonstration Zone providing policy opportunities for ecological civilization construction and URI, Guizhou is constrained by its extensive karst topography and severe stone desertification. These factors hinder rural infrastructure development and industrial growth, leading to relatively slow progress in the coupling coordination between URI and RR.

To further enhance the coupling coordination of URI and RR in Southwest China, policymakers should fully account for regional differences and tailor development strategies to local conditions. For Yunnan and Sichuan, efforts should focus on building upon existing advantages, deepening URI and RR initiatives, and exploring more coordinated and sustainable development models. For Tibet and Guizhou, increased policy support, resource investment, infrastructure construction, ecological environmental improvement, and cultivation of characteristic industries are necessary to narrow the gap with developed regions and achieve synergistic development of URI and RR.

Analysis of spatial effects and influencing factors

Analysis of spatial effects

Through the results of local bivariate spatial autocorrelation analysis, it is shown in Fig. 8 that during the periods of 2012, 2014, 2016, 2018, 2020, and 2022, the primary relationship types between rural resilience and urban–rural integration were HL type (low rural resilience level and high urban–rural integration level) and HH type (high rural resilience level and high urban–rural integration level). These relationship types exhibited dynamic evolutionary characteristics across different regions and periods.

Fig. 8
figure 8

LISA clusters of coupling coordination degree in Southwest China in 2012, 2014, 2016, 2018, 2020, and 2022. (Note The map was generated using ArcMap 10.8 software, which can be obtained from https://desktop.arcgis.com, without any modifications to the boundaries of the base map.)

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Specifically, the HL type was primarily found in Chongqing, Sichuan, and Tibet. In particular, Chongqing maintained an HL type until 2018 but showed no significant clustering after 2020. Tibet displayed an HL type in 2014 and 2016, transitioning to an HH type in 2020. Additionally, Yunnan and Guizhou showed no significant clustering for HL or HH types in 2012 and 2022, while Sichuan exhibited an HL type only in 2022, with no significant clustering in other years.

The bivariate spatial autocorrelation analysis indicates that the coordination between rural resilience and urban–rural integration levels in the provinces of Southwest China requires further adjustment and optimization. This is essential for jointly promoting deep urban–rural integration and high-quality, sustainable development in the region.

Overall, there are significant spatial disparities in rural resilience and urban–rural integration levels across the Southwest region during the study period. These disparities may be attributed to differences in economic development levels, policy implementation intensity, and social investments among the provinces. Consequently, policymakers and implementers must emphasize regional balanced development in their future work, particularly regarding rural resilience and urban–rural integration, to achieve high-quality and sustainable regional development.

Analysis of driving mechanisms

The coupled and coordinated development of rural resilience and urban–rural integration is influenced by multiple factors, and its spatial differentiation characteristics are also affected by a complex interplay of social, economic, cultural, and ecological factors. Drawing on existing research findings33,42 this research explores the decisive factors influencing the coupling and coordinated development of rural resilience and urban–rural integration from six aspects: urban–rural income disparity, infrastructure level, upgrading of industrial structure, economic development level, government capacity, and resource endowment (see Table 2). Using impact factors from 2012, 2014, 2016, 2018, 2020, and 2022 as baseline data, we applied a five-level natural breakpoint method for classification. The classified results were then matched with the coupling coordination degree of the rural resilience and urban–rural integration systems. We utilized geographic detectors to examine the driving forces behind their coupling coordination. The specific variables selected include the ratio of per capita disposable income of urban residents to that of rural residents to represent urban–rural income disparity, road network density as an indicator of infrastructure level, the proportion of value added in the tertiary industry to that in the secondary industry to reflect the upgrading of industrial structure, economic growth rate for economic development level, the proportion of social security and employment expenditure to total fiscal expenditure for government capacity, and the average arable land area per rural household to indicate resource endowment (see Table 4).

Table 4 Indicator system of factors influencing the coordination of rural resilience and urban–rural integrated development.
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The analysis of the geographic detector results indicates that all six influencing factors are significant (q > 0). Based on the multi-year average results, the q values in 2012, ranked from highest to lowest, were: X2 > X4 > X6 > X1 > X3 > X5. Among these, the factors with q values greater than 0.4 are the first two, indicating that infrastructure and economic development levels are the primary factors affecting coupling coordination. In 2016, the ranking of q values from highest to lowest was: X4 > X2 > X6 > X3 > X1 > X5. Here, only the economic development level factor had a q value exceeding 0.4, making it the main influence on coordination. In 2020, the q values ranked as follows: X6 > X2 > X4 > X1 > X3 > X5. After four years of development, resource endowment (0.395) emerged as a major factor influencing the CCD. In 2022, the ranking of q values from highest to lowest was: X4 > X6 > X2 > X5 > X3 > X1. Only the economic development level and resource endowment factors had q values greater than 0.4. This indicates that infrastructure level, economic development level, and resource endowment are the key drivers of the coupling and coordinated development of rural resilience and urban–rural integration.

Specifically, (1) The convenience of infrastructure impacts the two-way flow of production factors like people, capital, materials, culture, and information between Urban–Rural Integration (URI), influencing rural residents’ quality of life, economic growth, and disaster—resilience capacity. (2) Economic development is the driving force and foundation of URI. A strong economy can enhance rural risk—resistance and provide momentum for high-quality URI and Rural Resilience (RR). (3) High-quality URI needs to address resource endowment differences. Building RR requires leveraging resource endowments, as the average farmland area per rural household directly determines agricultural productivity and resource abundance, thereby influencing the coordination degree of URI and RR (see Fig. 9).

Fig. 9
figure 9

Radar map of impact factor measurements.

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Additionally, regarding the trends in influence, the impact of economic development level, government capacity, and resource endowment continues to strengthen. In contrast, the influence of urban–rural income disparity, infrastructure level, and upgrading of industrial structure has relatively declined. Notably, the proportion of social security and employment expenditure to total fiscal expenditure reflects the differences in material support for promoting social equity as determined by a region’s macroeconomic regulation. This indicates that government capacity, to some extent, influences the economic development level and progress in the southwest region, affecting the coordinated development of rural resilience and urban–rural integration.

Furthermore, while road network density is a primary factor affecting transportation accessibility, its driving effect has decreased compared to 2012. This decline may be attributed to the orderly implementation of strategies aimed at integrating urban and rural development through new-type urbanization and comprehensive rural revitalization in China. As a result, the disparities in resource endowment among provinces in the southwest region have gradually diminished, increasing rural resilience in economically lagging areas. This change facilitates more harmonious development between rural resilience and urban–rural integration in Southwest China, thereby reducing the overall influence of the infrastructure level.

In order to improve the scientific, theoretical, and reliability of the discussion, we further explore the influencing factors between different provinces. The provinces significantly differ in the pathways of driving factor roles. First, regarding infrastructure level, the focus has shifted from accessibility-driven to quality—upgrade-oriented. In Tibet and Guizhou, road network density remains a key bottleneck. In 2022, Tibet’s road network density was only 0.21 km/km2 (1/6 of Sichuan’s), restricting resource inflow. Guizhou has achieved hardened roads for natural villages with 30 or more households through its “Group–Group—Access” road program. But karst mountain highway maintenance is costly. Thus, these regions need to switch to digital infrastructure like 5G networks to break geographical limits. Second, in terms of economic development level, the focus has shifted from total—volume-driven to structure-optimization-oriented. In Yunnan, the q–q-value of economic growth rate (X4) rose from 0.42 in 2012 to 0.48 in 2022. This is due to the outward-oriented economy of border trade and the green energy of hydropower and photovoltaics, which have a spillover effect on rural employment. Guizhou has seen an increase in economic output but has a low q-value of 0.35. As its industry is mainly resource-based, it has a limited spillover effect on rural resilience and needs to develop “big data + agriculture” to enhance connectivity. Third, in terms of resource endowments, the focus has shifted from natural resource dependence to innovation-conversion-oriented. In Sichuan and Yunnan, the q-value of the average farmland area per rural household (X6) has remained above 0.38. The Chengdu Plain and the Dianzhong Plain have improved agricultural resilience through large-scale land management. For example, Sichuan’s land transfer rate has reached 45%. Combined with characteristic farming like Chuanxiong and Panax notoginseng, these regions have formed a “resource endowment → industrial cluster → deepening integration” model. Although Tibet’s average farmland area per household is higher than the southwestern average, its cold climate limits crop diversity. It relies on single industries like barley and yaks and needs to boost resource use efficiency through biotechnology and ecological compensation.

Discussion

This study analyzes the mechanisms of coupling-coordination evolution between URI and RR in Southwest China, their interactions, and the far-reaching influence on regional sustainable development. Previous research often viewed URI as enhancing RR through factor flow and resource reallocation4,43, but the unique mechanisms in Southwest China’s multi-dimensional constraints were rarely explored.

The study found that the coupling -coordination degree of URI and RR in Southwest China shows an “east high, west low; south high, north low” pattern. Unlike the “market-driven” URI in the eastern coastal regions, Southwest China’s URI is characterized by “policy-driven + ecological constraints”29,44. For instance, Yunnan and Sichuan have relied on the Belt and Road Initiative and the Chengdu-Chongqing Dual-City Economic Circle policy to improve infrastructure. Each 1% increase in road network density raises the coupling-coordination degree by 0.23%. This promotes factor reflux and confirms the key role of policy intervention in breaking geographical isolation. It also aligns with the “institutional resilience” theory proposed by Guo et al.12. In contrast, Tibet and Guizhou, limited by fragile plateau ecosystems and karst topography, show a “low URI-low RR” lock-in effect. This highlights the fundamental constraints of natural endowments on urban–rural interaction and supplements Darnhofer et al.’s6 theoretical framework on “resilience spatial heterogeneity.”

In terms of driving factors, the study identifies the core roles of infrastructure, economic development level, and resource endowment. This aligns with Zhan et al.’s4 multi-dimensional analysis of URI in China. However, it also reveals that the influence of resource endowment in Southwest China has increased over time. This reflects the unique value of specialty agriculture (such as Yunnan’s plateau agriculture and Sichuan’s lovage cultivation) and ecological resources (like Tibet’s yak industry and Guizhou’s carbon trading pilots) in building RR. This expands the “resource dependence theory” application scenarios in underdeveloped regions. Additionally, although the impact of governance capacity is weaker than in the East, transfer payments have significantly enhanced social resilience in Tibet and other regions. This confirms the direct role of policy tools in shaping “institutional resilience”9.

The multi-ethnic settlement characteristics and ecological fragility of Southwest China form the core context of the study. The research shows that cultural resilience, such as the integration of intangible cultural heritage transmission and rural tourism, plays an implicit role in the coupling-coordination of URI and RR in Guizhou and Yunnan. This empirically supports Moya & Goenechea’s10 view on “cultural capital empowering rural resilience”. For example, the “intangible cultural heritage + tourism” model in Qiannan and Qiandongnan has increased the rural social resilience index. This shows that national cultural resources can be transformed into buffering capital to cope with economic fluctuations. However, Tibet’s “high ecological protection-low development” paradox shows that when ecological constraints exceed economic carrying capacity, the positive severally effect of URI on RR is significantly weakened. This engages with Su et al.’s27 “ecological threshold theory” and emphasizes the need for a “protection-development” balance mechanism in fragile ecosystems.

The phased nature of policy-driven initiatives also deserves attention. After the rural revitalization strategy was implemented in 2018, the degree of coupling coordination in Chongqing and Sichuan increased by 25%. This confirms the short-term activation effect of “external policy shocks” on system coordination17. However, the delayed response in Tibet and Guizhou indicates that policy effectiveness is limited by geographical accessibility and industrial foundations. It is necessary to design “policy transmission intermediaries” such as industrial belts along the Sichuan-Tibet Railway and Guizhou’s big data agricultural platforms. This has reference value for understanding “policy-driven” urban–rural transformation in developing countries3,13,45,46.

However, there are limitations to this study: on the one hand, while the indicator system for measuring rural resilience and urban–rural integration development includes many calculation indicators, the lack of readily available primary data affects the scientific rigor of the measurements. On the other hand, the analysis primarily adopts a macro perspective at the provincial level, lacking insights from a micro perspective at the county or city level43,47,48. Therefore, future research should optimize data acquisition methods and further explore the coupling and coordinated relationship, evolutionary patterns, and characteristic mechanisms of rural resilience and urban–rural integration development across multiple regions, scales, and cases.

Conclusion

This study constructed an evaluation index system of urban–rural integration and rural resilience and used the entropy weight method, comprehensive evaluation model, coordination degree model, and detector model to systematically analyze the coupled and coordinated development of urban–rural integration and rural resilience in Southwest China from 2012 to 2022. The main conclusions are as follows: (1) From the perspective of time evolution characteristics, the coupled coordination degree of urban–rural integration and rural resilience in Southwest China in 2012–2022 shows an overall upward trend, indicating that the interactions and coordinated development between urban–rural integration and rural resilience have achieved a certain degree of effectiveness. However, there are significant differences in the level of development between provinces, with Yunnan and Sichuan having higher coupling coordination degrees, while Tibet and Guizhou are relatively lower. (2) In terms of spatial distribution, the coupling and coordination between urban–rural integration and rural resilience in Southwest China show a spatial pattern of ‘high in the middle and low on both sides,’ i.e., the coupling and coordination degrees of Yunnan, Sichuan, and Chongqing are higher, while those of Guizhou and Tibet are lower. (3) From the analysis of influencing factors, the level of infrastructure, economic development, and resource endowment are the main factors affecting the coupling and coordination between urban–rural integration and rural resilience. These factors influence the coordinated development of urban–rural integration and rural resilience through different ways and mechanisms.

Based on the above conclusions, this study puts forward the following recommendations: (1) Regional differentiated development strategies should be implemented to overcome spatial imbalance bottlenecks. Given the significant differences in coupling coordination degrees among provinces, policies should be tailored accordingly: High coordination regions like Yunnan and Sichuan should leverage their locational advantages. Yunnan, as a Belt and Road Initiative (BRI) hub, and Sichuan, within the Chengdu-Chongqing dual-city economic circle, should focus on enhancing Urban–Rural Integration (URI) quality. A two-way flow mechanism of production factors between the Dianzhong city cluster and surrounding rural areas should be established. The “cross-border e-commerce + rural revitalization” model should be piloted to promote the return of technical talent. A “metropolitan rural resilience demonstration zone” should be built on the Chengdu Plain, with a cold chain logistics network improved and characteristic small towns developed. The eco-product value realization mechanisms of Sichuan’s Giant Panda National Park and Yunnan’s plateau peculiarity agriculture should be promoted to strengthen ecological and economic resilience. Medium coordination regions like Chongqing, where rural resilience (RR) exceeds URI, should implement a “URI short-board-supplementing program”. Investments in transportation infrastructure in the northeastern and southeastern mountainous areas of Chongqing should be increased. The “flying ge economy” model between the main urban area and ecological conservation areas should be promoted to boost non-agricultural employment. A rural ecological RR monitoring system in the Three Gorges Reservoir Area should be established, and natural disaster early-warning capabilities should be enhanced. Low coordination regions like Tibet and Guizhou should overcome geographical and ecological constraints to stimulate internal driving forces. Tibet, relying on the “National Ecological Civilization Highlands” construction, should advance the connection of infrastructure along the Sichuan-Tibet Railway and support characteristic industries like barley and yak farming as well as the integration of intangible cultural heritage and tourism. Guizhou should use “big data + agriculture” technology to improve production efficiency and implement “ecology-economy” coupling projects in karst desertification areas to increase farmers’ income. (2) The synergy between infrastructure and resource endowments should be strengthened to solidify the foundation for coordinated development. Focusing on key driving factors, a “hard infrastructure + soft services” urban–rural connection network should be built. In Yunnan and Sichuan, a backbone cold chain logistics network should be developed. In Tibet and Guizhou, 5G network coverage in administrative villages should be achieved. Digital platforms like “Shu Nong Tong” and “Gui Nong Wang” should be set up to promote accurate resource allocation. Resource endowment advantages should be developed in a location-specific manner. Sichuan and Yunnan should develop characteristic industrial clusters, while Tibet and Guizhou should explore “ecological carbon trading” mechanisms. A regional resource endowment database should also be established to support policy-making dynamically. (3) Cross-departmental collaborative governance and differential support should be carried out to form a long-term mutually reinforcing mechanism. A “Southwest Urban–Rural Integration and Rural Resilience Joint Laboratory” should be set up to integrate multi-departmental data and promote “resilience-oriented” urban–rural planning standards. Central transfer payments to Tibet and Guizhou should be increased. Social capital participation should be guided, and “rural resilience insurance” products should be developed. In ethnic regions, “bilingual education + cultural heritage preservation” and characteristic governance models should be strengthened to enhance cultural identity and community self-organization capabilities. (4) The precision and effectiveness of policies should be emphasized. When formulating policies related to URI and RR, the actual local conditions and characteristics should be fully considered to enhance policy precision and effectiveness. For instance, in rural infrastructure construction, projects should be planned and laid out according to the geographical conditions and actual needs of different regions to ensure policies are effectively implemented and provide a strong guarantee for the coordinated development of rural resilience and urban–rural integration. These measures will further promote the coordinated development of URI and RR in the southwestern region, support regional sustainable development, and help achieve the United Nations Sustainable Development Goals.