China has achieved a comprehensive victory in poverty alleviation in 2021, with all districts (counties) across the country successfully lifted out of poverty, thereby providing a valuable model for rural poverty reduction worldwide. Understanding China's experience in rural development holds significant implications for promoting poverty reduction efforts in other countries globally. This paper constructs a theoretical framework to analyze rural resilience based on the distinctive concept of production-living-ecology that characterizes rural construction in China. Furthermore, it examines 26 mountainous counties within Chinese provinces exhibiting minimal disparities between urban and rural wealth as case studies. The vertical and horizontal Scatter Degree method is employed to assess the level of rural resilience within these counties, while spatial autocorrelation and obstacle degree models are utilized to identify spatial heterogeneity and key influencing factors affecting their resilience. The findings are as follows: firstly, the overall resilience level of the villages in the case area is higher. Secondly, there exists a spatial differentiation pattern of "higher in the northern region and lower in the southern region" regarding rural resilience among 26 counties in the case area, with a significant spatial agglomeration effect. Thirdly, the main obstacles influencing rural resilience level in the case area include proportion of non-agricultural employment, scale of agricultural land, mobile phone penetration rate, and residents' living standards.
Research Article
Spatial-temporal characteristics and implications of rural resilience in mountainous counties in China with the lowest wealth gap under the guidance of the concept of "production-living-ecology": An empirical study based on 26 counties
https://doi.org/10.21203/rs.3.rs-3952637/v1
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Rural resilience
Production-living-ecological perspective
Spatial differentiation
Influencing factors
26 counties in Zhejiang mountainous region
Rural poverty reduction has emerged as a pressing challenge confronted by numerous nations (Li 2023). The development and construction of rural areas at the county level play a pivotal role in addressing the issue of rural poverty reduction and have long been a focal point for diverse stakeholders (Torre, Wallet, and Huang 2023; Harriss 2023). In 2015, China successfully implemented an extensive targeted poverty alleviation campaign, achieving comprehensive triumph in eradicating poverty by 2021. This accomplishment serves as an exemplary model for global rural poverty reduction, with all regions and counties nationwide effectively lifting themselves out of destitution. Consequently, China initiated measures to prevent relapse into poverty through robust prevention and monitoring strategies that have now become integral components of contemporary rural development endeavors. Enhancing the resilience level of county-level rural areas assumes paramount importance in promoting overall rural development while mitigating the risk associated with persistent impoverishment(Li 2023; Cui et al. 2023).
In fact, numerous scholars have extensively explored the field of "rural resilience"(Asmamaw, Mereta, and Ambelu 2019; Schouten et al. 2012; Glover 2012; H. Li et al. 2022). However, the majority of their research has primarily focused on disaster risk(Ristino 2019;Cox and Hamlen 2015༛Singh-Peterson and Underhill 2017༛Kapucu, Hawkins, and Rivera 2013). Nevertheless, rural poverty reduction targets rural areas at the county level as the fundamental governance unit. It emphasizes the overall poverty situation and sustainable development capacity of rural areas considering comprehensive factors. This approach also represents a significant experience in China's efforts towards reducing rural poverty by introducing the concept of "production-living-ecology" to guide rural development.
The concept of "production-living-ecology" embodies a coordinated and sustainable approach to development in the realms of production, living conditions, and ecology. It was initially proposed by the World Commission on Environment and Development in their 1987 report "Our Common Future". The primary objective is to establish a model of urban-rural development that promotes intensive utilization of production space, high production efficiency, livable living conditions, and green ecological development. Ultimately, the aim is to accomplish sustainable, ecological, low-carbon, and green rural development. In the context of rural construction in China, the intertwined concepts of "production-living-ecology "and "rural resilience" have long been prevalent. Therefore, adopting a perspective centered around "production-living-ecology", with districts or counties as fundamental research units for investigating rural resilience issues in representative regions of China will facilitate a better understanding of the underlying logic behind poverty reduction efforts in Chinese villages while also enabling us to summarize experiences gained from these endeavors.
The main innovations and contributions of this study are as follows: Firstly, it offers a novel perspective for researching rural resilience, specifically the concept of "production-living-ecology". This perspective aligns with the future direction of rural construction and development in both developed and developing countries. Secondly, by assessing the resilience level of rural areas at the county level, it addresses the existing research gap on meso-level resilience in rural regions. Thirdly, this paper aims to select a province in China that has achieved remarkable economic development at the county level while minimizing urban-rural wealth disparity. Identifying the characteristics of resilience levels in its subordinate districts and counties can serve as an excellent benchmark and learning sample for rural construction worldwide.
2.1 Concept of ''production-living-ecology''
The concept of production-living-ecology integration, proposed by the Chinese government, aims to achieve coordinated development in urban and rural areas by effectively dividing land functional space(Pérez-Soba et al. 2008). It serves as a model for harmonious development across three key domains: production, living, and ecology. Notably, classical theories on urban land functional space research such as the 'belt-shaped city,' 'garden city,' and 'organic dispersion' from the late 19th century laid a solid foundation for the formation of this concept encompassing three essential spatial dimensions. Moreover, subsequent concepts like the 'urban growth boundary' and 'green belt around cities' bear striking resemblance to the connotation of the integrated spatial concept of 'production-living-ecology'(Kates et al. 2001; Foley et al. 2005; Tang et al. 2018; Van Der Valk 2002; Priemus 2004). Furthermore, emerging theories such as the 'compact city' and 'smart growth' further emphasize that comprehensive utilization should be prioritized in urban-rural spatial development across multiple dimensions including ecology, living conditions, and production while emphasizing their organic coordination(Hruška 2012).
The concept of "production-living-ecology" is frequently explored in the research conducted by Chinese scholars. They focus on various areas, such as urban-rural spatial planning (Y. Yang, Bao, and Liu 2020; Duan et al. 2021; Chen, Su, and Wang 2022) and tourism planning (Yu et al. 2020), guided by the concept of "production-living-ecology". However, limited attention has been given to integrating this concept with "rural resilience". This suggests that our research in this field is still at the forefront and holds significant innovative value. Nevertheless, it also implies that a more scientifically rigorous analysis and the development of corresponding theoretical analytical frameworks are required to effectively analyze and measure the level of rural resilience within an integrated framework of "production-living-ecology".
2.2Measurement of rural resilience level
Existing research has primarily focused on micro-level resilience of rural households' livelihoods, resilience of rural communities, and provincial-level resilience at the macro level (Ellis 1998; Lindenberg 2002; Anthopoulou, Kaberis, and Petrou 2017; Y. Li, Song, and Huang 2021). However, there is a relative dearth of studies that measure rural resilience at the county level and investigate its spatiotemporal pattern evolution (Ma et al. 2023).
Regarding quantitative measurement methods, there exist well-established analysis frameworks and evaluation models(Asmamaw, Mereta, and Ambelu 2019;Su et al. 2022 ;Rudiarto et al. 2019; H. Li et al. 2022;Payne et al. 2021). These can be categorized into three groups: descriptive models that focus on constructing an indicator system for rural resilience from various components (elements), determining the weights of sub-indicators in each dimension using techniques like principal component analysis and value method, and assessing the level of rural resilience through weighted measures (Schouten et al. 2012; H. Li et al. 2022; Y. Li, Song, and Huang 2021; F. Sun et al. 2023). This approach enables researchers to gain a fundamental understanding of the level and characteristics of rural resilience, making it widely employed in comparative studies examining variations in rural resilience across different regions. The second approach, known as Causal models, builds upon descriptive model analysis and further explores and explains the causal relationship between rural resilience and other factors using regression analysis(Jurjonas and Seekamp 2018; Steiner and Atterton 2015; Singh, Kumar, and Dey 2023; Glover 2012). However, this method still has certain limitations as it solely investigates the direct causality between them, providing a static explanation. The essence of rural resilience lies in its capacity to recover or adapt to a new reasonable state after being disrupted by external factors. The dynamic nature of this process is crucial for generating rural resilience. To enhance research in this aspect, scholars have proposed analytical models that analyze mechanisms related to elasticity formation and recovery(Y. Sun et al. 2022;E. Li, Deng, and Zhou 2022). Overall, there are already well-established research methods supporting analyses on different levels of rural resilience depth which can provide robust methodological support for the development of this study.
3.1 Study Area Overview
The research focuses on a case study area comprising 26 counties located in the mountainous region of Zhejiang Province, China (Fig. 1). Zhejiang is renowned for its robust economic development along the coastal areas and has successfully achieved poverty alleviation goals ahead of other provinces. Moreover, it boasts the smallest income disparity between urban and rural areas in China. In order to address rural poverty, particular attention has been given to the development of these 26 mountainous counties which cover approximately 43% of the province's land area and are considered crucial targets for poverty reduction efforts. Furthermore, rural development in Zhejiang Province exhibits notable diversification and regional heterogeneity characteristics. By selecting these specific mountainous counties as our research subjects, this paper aims to provide valuable insights into China's experiences with regards to rural poverty reduction.
3.2 Research Methodology
3.2.1 Theoretical Framework
Rural resilience refers to the capacity of rural areas as dynamic social-ecological systems to continuously adapt and regulate changing internal and external environments, while maintaining satisfactory levels of production and living standards. It emphasizes that rural systems possess durability, adaptability, and transformative capacity (M. Yang, Jiao, and Zhang 2022; Kim, Kang, and Lee 2020). 'Rural resilience' is a comprehensive concept that encompasses the unique natural resource base, long-standing human activities, as well as the production and living systems generated by these activities. Therefore, rural resilience can be described in terms of three dimensions: production resilience, living resilience, and ecological resilience. Rural areas achieve relative equilibrium through continuous adjustments and adaptations within these three subsystems to realize sustainable development.
Production resilience primarily manifests through intensive utilization of production resources with localized strategies for optimizing internal spatial adjustments within county-level rural areas while fostering external spatial connections and cooperation. Living resilience is mainly reflected in regulating regional living conditions reasonably by enhancing residents' livelihood capital, improving basic public service facilities, emphasizing rural cultural construction, and enriching the spiritual lives of rural residents. Ecological resilience primarily pertains to population carrying capacity, natural coverage rate, degree of ecological conservation, and green low-carbon development. Enhancing rural resilience not only contributes to improving the quality of the ecological environment but also promotes economic development and social progress in rural areas. The logical analysis between these three subsystems can be illustrated using Fig. 2.
3.2.2 Indicator System
In this study, the construction of the indicator system adheres to the principles of scientific rigor, operational feasibility, and comprehensiveness. Drawing upon three-dimensional space theories, resilience theory, and existing research findings, we have developed a rural resilience evaluation indicator system for 26 counties in Zhejiang mountainous areas across three dimensions: production, living, and ecology. Rural production resilience serves as a crucial indicator reflecting the overall level of industrial development and structure in rural areas(Wang et al. 2019), particularly with regard to productivity levels and attractiveness. To achieve efficient county-level rural productivity development, a series of measures need to be implemented including enhancing regional agricultural mechanization levels, optimizing industrial structures, and promoting industrialization(Batool, Ain, and Rehman 2023). Rural living resilience encompasses driving forces for social development, capacity to withstand risks, and ensuring living environments; these aspects can be measured by indicators such as education levels, healthcare service quality, and social security. Rural ecological resilience primarily reflects natural environmental protection levels and ecological conservation measures with key evaluation indicators being vegetation coverage rate, population density, and agricultural fertilizer usage. Based on these considerations, the present study constructs a comprehensive evaluation indicator system for rural resilience (Table 1).
|
Target layer |
Target layer |
Target layer |
Target layer |
Target layer |
Target layer |
Target layer |
|---|---|---|---|---|---|---|
|
Rural resilience |
Rural production resilience |
agricultural machanization x1 |
forward direction |
Total dynamic force / cultivated land area of agricultural machinery |
0.0643 |
Kw/hm2 |
|
Agricultural production efficiency x2 |
forward direction |
Agricultural output value of agriculture, forestry, animal husbandry and fishery/GDP |
0.0543 |
% |
||
|
Non-farm payrolls x3 |
forward direction |
Non-farm workers / agricultural employment personnel |
0.0393 |
% |
||
|
The size of the rural labor force x4 |
forward direction |
The proportion of the rural employed population in the total population |
0.0794 |
% |
||
|
food production x5 |
forward direction |
Grain output per unit of arable land area |
0.0552 |
(t/ha) |
||
|
Farmland scale x6 |
forward direction |
Per capita arable land area |
0.0113 |
hm²/thousands of people |
||
|
Rural life resilience |
Mobile phone penetration rate x7 |
forward direction |
Mobile phones per 10,000 people |
0.0488 |
Individual |
|
|
health care x8 |
forward direction |
Number of beds in hospitals and hospital centers per capita |
0.0321 |
Bed / person |
||
|
educational resources x9 |
forward direction |
Educational undertaking expenses / general public budget |
0.0637 |
% |
||
|
social learning x10 |
forward direction |
Libraries per 10,000 people |
0.0571 |
This / ten thousand people |
||
|
Living standards for residents x11 |
forward direction |
The year-end balance of savings deposits for urban and rural residents |
0.0483 |
ten thousand yuan |
||
|
social security x12 |
forward direction |
Participation rate of basic medical insurance for both urban and rural residents |
0.0711 |
% |
||
|
Rural ecological resilience |
density of population x13 |
negative direction |
Population quantity and the pressure on the ecological environment |
0.0997 |
human being/hm² |
|
|
topographic relief x14 |
negative direction |
Calculate the relief degree based on the terrain relief formula |
0.0674 |
m |
||
|
Vegetation coverage x15 |
forward direction |
NDVI |
0.1080 |
% |
||
|
Amount of agricultural chemical fertilizer application x16 |
negative direction |
Average chemical fertilizer application amount |
0.1002 |
t/hm² |
||
| Note: "+" indicates that the indicator is positively correlated with system resilience; "-" indicates that the indicator is negatively associated with system resilience. | ||||||
3.2.3 Research Methods
(1) The vertical and horizontal Scatter Degree method
The vertical and horizontal Scatter Degree method is based on the mining of observational data, making it suitable for "three-dimensional" panel data analysis. This approach enables a comprehensive evaluation of the differences between evaluated objects to their maximum extent, effectively compensating for the limitations of evaluation methods such as Analytic Hierarchy Process (AHP) and Entropy Method in dynamic panel data assessment(W. Zhang et al. 2022).
In Eq. (1), the variable\({ x}_{ij}\left({t}_{k}\right)\) denotes the jth indicator value of the ith object under evaluation at time tk, where i = 1,2,...,n; j = 1,2,...,m; k = 1,2,...,T. The weight of the jth indicator is denoted by ωj and \({y}_{i}\left({t}_{k}\right)\) represents the comprehensive score of the ith evaluation object at time tk.
(2)Spatial autocorrelation method
Spatial autocorrelation is an analytical technique that employs statistical methods to quantify the extent of spatial correlation between variables in a research study. It facilitates comprehension of the relationships and spatial distribution characteristics among specific geographic units and their surrounding counterparts. The global autocorrelation coefficient serves as an indicator for validating spatial patterns and assessing attribute value distribution across the entire study area, enabling determination of the level of spatial correlation and directionality exhibited by research variables throughout the region. Commonly used global autocorrelation coefficients include Moran's I coefficient and Geary's C coefficient. The local spatial association index (Moran's I) functions as an indicator for revealing similarity or correlation in attribute values between geographical units and their neighboring counterparts, thereby identifying hotspots within a region, testing data heterogeneity, as well as pinpointing clusters on a map where geographical units exhibit similar attribute values.
①Global spatial autocorrelation:
The Eq. (2) represents the relationship between spatial units in the region, where n denotes the number of spatial units, xi and xj represent attribute observation values for regions i and j respectively. \(\overline{x}\) refers to the average value of attributes, while ωij signifies the weight of influence from region i to region j in the spatial weight matrix, indicating their spatial adjacency relationship.
② Local spatial autocorrelation
In Eq. (3), the sign of Ii is determined by the resilience level of the ith county and its average resilience level, as well as the relative heights of neighboring counties. A positive value of I indicates a spatial clustering effect within local sub-regions, characterized by high-high or low-low values. Conversely, a negative value of I suggests a dispersed state in sub-regions, displaying clustering patterns of high-low or low-high.
(3)Obstacle Degree Model
The obstacle degree model is a methodology employed for calculating the obstacle degree of diverse evaluation indicators in comprehensive assessments. Its fundamental concept involves multiplying the weights assigned to each evaluation indicator by their corresponding normalized values, followed by aggregating the resulting products. This approach effectively captures the extent to which individual evaluation indicators impede progress in various domains. The utilization of the obstacle degree model facilitates decision-makers in gaining enhanced insights into issues and identifying pivotal factors.
In Eq. (4), Itij represents the barrier level of the jth indicator in province i in year t. vtij is the deviation, indicating the disparity between the standardized value of the jth individual indicator in province i in year t and its optimal value of 100%, expressed as 1-x'tij, where x'tij denotes the standardized value of each indicator. wj signifies factor contribution, denoted by weights assigned to individual indicators.
3.2.4 Data Sources
This study focuses on 26 counties located in the mountainous regions of Zhejiang province. The research year selected for this investigation is 2021, and the data utilized in this study were obtained from the 'Zhejiang Population Census Yearbook' and 'Zhejiang Statistical Yearbook' for 2022, along with local statistical yearbooks and bulletins from each county (city, district). In cases where data was missing, trend extrapolation methods were employed for supplementation.
4.1 Overall Level Analysis of Rural Resilience
Currently, further research is still required to determine the classification of resilience levels in mountainous county-level rural areas. Drawing upon relevant studies(Xiao et al. 2020; Y. Zhang et al. 2020), and taking into account the specific conditions of the study area, a five-tiered resilience index has been established: R ≤ 0.15 is classified as lower resilience, 0.15 < R ≤ 0.30 as low resilience, 0.30 < R ≤ 0.45 as moderate resilience, 0.45 < R ≤ 0.60 as high resilience, and R > 0.6 as higher resilience.
According to the classification criteria, the analysis results (Fig. 3) demonstrate that rural areas in 26 mountainous counties of Zhejiang Province exhibit a relatively high overall resilience level, all of which are categorized as moderate resilience level or above. Among them, 21 counties showcase a high level of resilience in their rural areas. This observation suggests that regions that have successfully overcome poverty generally display elevated levels of rural resilience.
Furthermore, the analysis reveals notable disparities in the levels of resilience among rural areas across 26 counties situated in the mountainous regions of Zhejiang Province, with certain districts exhibiting substantial room for improvement. Amongst the six cities encompassing these mountainous counties, Hangzhou, Jinhua, Taizhou, and Lishui exhibit higher levels of rural resilience compared to Wenzhou and Quzhou. Overall, Taizhou demonstrates a relatively elevated level of rural resilience amongst all six cities. Within the 26 counties examined, Sanmen County showcases the highest level of rural resilience while Qujiang District exhibits the lowest. Notably, Quzhou's ranking in terms of rural resilience is comparatively lower than that observed within all six cities.
4.2 Dimensional Analysis of Rural Resilience Levels
The rural production resilience index in Zhejiang Province exhibits a relatively high overall level, with six regions demonstrating a notable degree of resilience in rural production. These regions are primarily concentrated in the central part of the research area, namely Changshan County, Wuyi County, Suichang County, Songyang County, Yunhe County, and Sanmen County. Only two regions within the research area exhibit a comparatively lower level of rural production resilience: Liandu District and Kecheng District.
The general level of rural living resilience in Zhejiang Province is relatively low; however, Liandu District and Cangnan County stand out as having a relatively higher level. The study area exhibits a concentration of low rural resilience, primarily in the western region, particularly in Quzhou City. This highlights the need for improving residents' quality of life and enhancing their living conditions in these areas. The relatively low level of resilience development in rural regions can pose challenges to maintaining normal living order and restrict opportunities for rural residents' quality of life and development.
Zhejiang Province, with its unique geographical location, sparse population, and abundant natural resources, generally demonstrates a favorable state of rural ecological resilience. Notably, Chun'an County, Pan'an County, and Sanmen County exhibit relatively high degrees of rural ecological resilience at 0.33 each. However, the rural ecological resilience in Qujiang District, Suichang County, and Pingyang County is relatively low within the research area. The relevant departments in this region must enhance their focus on the ecological environment and ensure adequate protection of the existing ecology while continuously promoting environmental quality improvement. Conversely, other regions exhibit higher values for rural ecological resilience, indicating their consistent commitment to environmental preservation throughout their rural development process.
5.1 Global spatial autocorrelation analysis
The spatial correlation among the 26 counties in the mountainous area of Zhejiang was explored by constructing a spatial adjacency weight matrix using Stata and Geoda. The rural resilience was assessed through calculation of global Moran's I value, which yielded values of I, P, Z, and variance as 0.241869, 0.015099, 2.429995, and 0.013455 respectively. These findings indicate a robust positive spatial correlation among the counties in the Zhejiang mountainous area, suggesting a significant clustering pattern in terms of the spatial distribution of rural resilience.
5.2 Local spatial autocorrelation analysis
The spatial distribution of rural resilience in the 26 counties of mountainous areas in Zhejiang Province is shown in Fig. 4, presenting four types of spatial differentiation patterns: high-high agglomeration, low-high agglomeration, low-low agglomeration, and high-low agglomeration. The main types are 'high-high agglomeration,' 'low-low agglomeration,' and 'high-low agglomeration.' The low-low agglomerations are mainly concentrated in Kecheng District, Qujiang District, Suichang County, and Longyou County of Quzhou City. In this region, there is little spatial difference between counties in terms of rural resilience levels, all counties exhibit a low level of resilience. The prevalence of high-high agglomeration types in counties is relatively low. In Xianju County, Taizhou City, there are no significant variations in rural resilience among the counties in this region, indicating an overall pattern of high-level agglomeration. The counties with high-low agglomeration are primarily concentrated in Changshan County and Jiangshan City of Quzhou City, where the spatial heterogeneity of rural resilience is more pronounced compared to surrounding counties.
The obstacles to resilient development in the mountainous areas of Zhejiang Province's 26 counties play a pivotal role in promoting sustainable rural development. Figure 5 illustrates the distribution of contributions from different Counties to various dimensions of obstacle levels. The ranking of obstacle levels is as follows: rural living resilience > rural production resilience > rural ecological resilience. Rural living resilience emerges as the primary inhibiting factor for each County, exhibiting significantly higher obstacle levels compared to other dimensions.
Comparing the disparities in resilience between mountainous areas, it is evident that Chun'an County, Pan'an County, and Sanmen County encounter greater challenges in rural production resilience, with respective values of 0.1432, 0.1337, and 0.1252. These difficulties primarily stem from their distinctive geographical location and topographical conditions, as well as their exceptional ecological environment which has fostered abundant tourism resources within these regions. Consequently, economic development predominantly relies on the tourism industry while the potential for economic growth in mountainous areas remains insufficient. As a result, residents experience lower living standards and limited opportunities for rural living resilience.
Yunhe County and Kaihua County encounter greater challenges in rural production resilience compared to other mountainous counties, with obstacle indexes of 0.1337 and 0.1201 respectively. On one hand, this is attributed to the constraints imposed by their geographical location, resulting in inadequate circulation of economic, informational, and transportation factors that impede the economic development of these mountainous counties. On the other hand, the relatively slower pace of agricultural development and technological innovation also restricts the enhancement of rural production resilience. Pingyang County and Cangnan County exhibit higher obstacle indexes (0.1104 and 0.1379) concerning rural ecological resilience due to agriculture's predominant role in their rural economies, leading to regional ecological pollution caused by excessive utilization of agricultural fertilizers.
According to Fig. 6, it is evident that the variations in economic level, characteristic industries, development positioning and goals, as well as ecological environmental quality among the 26 mountainous counties vilead to diverse performance of resilience barrier indicators. Specifically:
(1) In counties with higher levels of economic development and stronger scientific and technological innovation capabilities such as Pingyang County, Qujiang District, Suichang County, prominent barrier factors are associated with ecology. These encompass population density, terrain fluctuations, vegetation coverage rate, and agricultural fertilizer usage. This implies that while mountainous counties have made progress in terms of economic development, they have also incurred environmental pollution costs.
(2) In county-level areas with a higher level of rural ecological resilience, such as Chun'an County, Sanmen County, and Pan'an County, which are mountainous counties with favorable natural environments, the primary challenges include low agricultural productivity efficiency, limited labor force scale, inadequate grain production capacity, insufficient agricultural land size. Additionally, these areas face obstacles in terms of low mobile phone penetration rate and relatively underdeveloped education, healthcare and social security systems. This suggests that these types of mountainous counties primarily rely on tourism development due to their superior natural environment and strong ecological restoration capabilities. However, they encounter significant difficulties in achieving both living improvement and production resilience. The high-quality development of villages in these mountainous counties is confronted with numerous challenges.
(3) In mountainous counties at an intermediate stage of resilience development, the obstacles to resilient development in rural areas are diverse. Some face constraints due to insufficient production resilience, such as Yongjia County, Pingyang County, Cangnan County, Wencheng County, Taishun County, Kecheng District, Tiantai County, Xianju County, Liandu District, Qingtian County and Jinyun County. Others are hindered by inadequate living resilience like Wuyi County, Changshan county, Kaihua county, Longyou county, Jiangshan city, Suichang city, Songyang county, Yunhe county, Qingyuan county Jingning Yao Autonomous Country and Longquan City.
The obstacle degree model was employed in this study to evaluate the obstacles faced by 26 counties located in the mountainous regions of Zhejiang Province, with a focus on their resilient development. A set of 16 indicators (Table 2) served as the basis for assessment. By calculating the average obstacle degree using these high-quality development evaluation indicators, the top five indicators were identified as the primary obstacles each year (Table 2).
Specifically, key factors hindering progress in these counties encompassed agricultural mechanization, agricultural production efficiency, non-agricultural employment ratio, rural labor force scale, grain production, agricultural land scale, mobile phone penetration rate, availability of healthcare services and sanitation facilities, accessibility to education resources, opportunities for social learning, residents' living standards, coverage of social security measures, population density, terrain fluctuations, vegetation coverage rate and criteria for application of agricultural fertilizers.
Among them, the primary obstacle in Wuyi County, Longyou County, Sanmen County, Tiantai County, Xianju County, and Jinyun County is the scale of agricultural land (x6). In Suichang County, Yunhe County, Qingyuan County, Jingning County, and Longquan county, the primary obstacle is the standard of living for residents (x11). The scale of agricultural land (x6) has the highest degree of obstruction among the top five obstacles in 26 mountainous counties in Zhejiang Province. This situation arises from rapid urbanization and extensive land development near developed cities in these 26 counties resulting in a sharp decrease in agricultural land area.
One of the top five obstacles in 17 mountainous counties is the proportion of non-agricultural employment (x3), as regional economic development and urban expansion projects have led to fewer people engaged in agriculture while relatively more people are employed outside agriculture sectors. In the top five obstacles to residents' living standards among the 15 mountainous counties, one major factor is that some rural residents in these counties are affected by their economic income, leading to a large number of young and middle-aged people moving or settling in surrounding developed cities. As a result, the remaining residents in these mountainous counties have insufficient productivity and lower living standards. Another obstacle to the popularity of mobile phones is due to the outflow of a large number of highly skilled talents and labor force, leaving behind mostly elderly or children in rural areas who have less demand for using mobile phones. Overall, there are similar obstacles such as non-agricultural employment ratio, agricultural land scale, mobile phone penetration rate, and residents' living standards among various mountainous counties.
| Area | Sort | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|---|
| Chunan | Disorder factors | x5 | x3 | x6 | x11 | x9 |
| Disorder degree | 0.143 | 0.122 | 0.114 | 0.104 | 0.102 | |
| Yongjia | Disorder factors | x5 | x6 | x8 | x10 | x2 |
| Disorder degree | 0.113 | 0.106 | 0.098 | 0.095 | 0.090 | |
| Pingyang | Disorder factors | x13 | x5 | x6 | x10 | x2 |
| Disorder degree | 0.11 | 0.102 | 0.097 | 0.084 | 0.08 | |
| Cangnan | Disorder factors | x13 | x5 | x6 | x8 | x7 |
| Disorder degree | 0.137 | 0.126 | 0.116 | 0.095 | 0.087 | |
| Wencheng | Disorder factors | x5 | x7 | x8 | x6 | x1 |
| Disorder degree | 0.114 | 0.114 | 0.096 | 0.093 | 0.087 | |
| Taishun | Disorder factors | x7 | x6 | x1 | x8 | x11 |
| Disorder degree | 0.102 | 0.101 | 0.101 | 0.1 | 0.099 | |
| Wuyi | Disorder factors | x6 | x10 | x12 | x2 | x9 |
| Disorder degree | 0.119 | 0.107 | 0.104 | 0.089 | 0.087 | |
| Panan | Disorder factors | x5 | x11 | x6 | x3 | x8 |
| Disorder degree | 0.133 | 0.122 | 0.109 | 0.097 | 0.09 | |
| Kecheng | Disorder factors | x3 | x5 | x2 | x4 | x12 |
| Disorder degree | 0.116 | 0.102 | 0.098 | 0.098 | 0.098 | |
| Qvjiang | Disorder factors | x15 | x3 | x7 | x4 | x11 |
| Disorder degree | 0.1 | 0.1 | 0.085 | 0.083 | 0.08 | |
| Changshan | Disorder factors | x9 | x7 | x11 | x6 | x8 |
| Disorder degree | 0.113 | 0.109 | 0.106 | 0.103 | 0.087 | |
| Kaihua | Disorder factors | x9 | x3 | x7 | x11 | x6 |
| Disorder degree | 0.12 | 0.102 | 0.099 | 0.099 | 0.096 | |
| Longyou | Disorder factors | x6 | x3 | x1 | x10 | x11 |
| Disorder degree | 0.094 | 0.092 | 0.084 | 0.081 | 0.08 | |
| Jiangshan | Disorder factors | x3 | x10 | x9 | x6 | x7 |
| Disorder degree | 0.102 | 0.098 | 0.096 | 0.096 | 0.096 | |
| Sanmen | Disorder factors | x6 | x8 | x11 | x7 | x5 |
| Disorder degree | 0.125 | 0.122 | 0.11 | 0.103 | 0.1 | |
| Tiantai | Disorder factors | x6 | x7 | x3 | x8 | x2 |
| Disorder degree | 0.113 | 0.094 | 0.094 | 0.093 | 0.085 | |
| Xianju | Disorder factors | x6 | x3 | x8 | x7 | x5 |
| Disorder degree | 0.11 | 0.093 | 0.091 | 0.09 | 0.081 | |
| Liandu | Disorder factors | x3 | x5 | x4 | x6 | x12 |
| Disorder degree | 0.123 | 0.111 | 0.11 | 0.108 | 0.098 | |
| Qingtian | Disorder factors | x7 | x10 | x8 | x3 | x6 |
| Disorder degree | 0.115 | 0.109 | 0.101 | 0.098 | 0.098 | |
| Jinyun | Disorder factors | x6 | x1 | x3 | x2 | x11 |
| Disorder degree | 0.102 | 0.095 | 0.084 | 0.081 | 0.08 | |
| Suichang | Disorder factors | x11 | x16 | x6 | x14 | x3 |
| Disorder degree | 0.106 | 0.103 | 0.101 | 0.095 | 0.094 | |
| Songyang | Disorder factors | x1 | x11 | x3 | x7 | x10 |
| Disorder degree | 0.132 | 0.114 | 0.114 | 0.094 | 0.088 | |
| Yunhe | Disorder factors | x11 | x6 | x8 | x9 | x14 |
| Disorder degree | 0.133 | 0.112 | 0.086 | 0.085 | 0.083 | |
| Qinyuan | Disorder factors | x11 | x3 | x14 | x7 | x8 |
| Disorder degree | 0.112 | 0.102 | 0.094 | 0.092 | 0.091 | |
| Jingning | Disorder factors | x11 | x3 | x9 | x14 | x6 |
| Disorder degree | 0.119 | 0.108 | 0.105 | 0.1 | 0.093 | |
| Longquan | Disorder factors | x11 | x3 | x14 | x10 | x6 |
| Disorder degree | 0.106 | 0.096 | 0.093 | 0.091 | 0.09 |
7.1 Conclusion
From the perspective of production-living-ecological resilience, this article constructs an evaluation index system for rural resilience that includes three dimensions: production, living, and ecology. The evaluation results of rural resilience in 26 counties in Zhejiang mountainous areas are calculated using the vertical-horizontal grade method, and ArcGIS is used to visually represent the levels of rural resilience in these counties. Furthermore, spatial autocorrelation analysis is conducted to explore the spatial clustering characteristics of rural resilience levels across these 26 counties. Finally, obstacle identification models are utilized to identify factors hindering rural resilience at the county level. The main conclusions are as follows:
(1) From a spatial distribution perspective, there is significant differentiation in the resilience levels of rural areas across 26 counties, exhibiting an overall spatial pattern of "higher resilience in the northern regions and lower resilience in the southern regions". In terms of subsystems, there is a strong homogeneity in the spatial distribution of rural production resilience, with high values concentrated in the central part of the study area. Kecheng District and Liandu District are identified as having the lowest rural production resilience index within this study area. The spatial pattern of rural living resilience shows more heterogeneity, with higher indices mainly observed in the eastern part of the study area while western regions exhibit relatively lower indices. Areas with higher rural ecological resilience are primarily concentrated in northern parts of the study area, presenting a spatial distribution pattern characterized by "higher ecological resilience observed in western and northern regions and weaker resilience observed in central parts".
(2) From the perspective of spatial correlation in rural resilience, a significant agglomeration effect is observed in 26 counties located in the mountainous areas of Zhejiang Province. In terms of global correlation, each county exhibits a positive Moran's I index for rural resilience, displaying a "convex" pattern. Regarding local correlation, Tiantai County situated in the northern part of the study area demonstrates high-high agglomeration with scattered distribution. Conversely, low-low agglomeration is observed in western counties such as Kecheng District, Qujiang District, Longyou County, and Suichang County. Additionally, Changshan County and Jiangshan City to the west of Quzhou City exhibit high-low agglomeration with an extremely unstable distribution.
(3) From the perspective of obstacles, the main obstacles in the 26 counties of Zhejiang mountainous areas are quite similar, including non-agricultural employment ratio, agricultural land scale, mobile phone penetration rate, and residents' living standards.
7.2 Discussion
Although the research on sustainable rural resilience at the county level, from the perspective of "production-living-ecology," demonstrates some degree of innovation, this innovation is primarily evident in the construction of indicator systems and representative selection of case study areas. However, there remains a dearth of academic contribution concerning research methods and approaches. While referencing the resilience analysis framework for rural sustainable development, this paper merely combines and measures certain indicators from three different dimensions during the process of constructing an indicator system in a simplistic manner. Hence, it raises uncertainty regarding whether these proposed indicators and their combination truly embody the concept of "production-living-ecology" or define an ideal state for "production-living-ecology.
In fact, for further research on subsequent papers, it is crucial to explore the inherent connection between the theory of "production-living-ecology" and the resilience level of rural areas. Subsequently, precise evaluation indicators should be identified based on this connection, and the weights of indicators in three dimensions - "ecology", "living", and "production" - should be allocated reasonably. It is not merely a matter of separately finding some indicators from these three dimensions and mechanically integrating them into a framework to measure the resilience level of rural areas. Undoubtedly, this approach represents an ideal research methodology that necessitates innovation in both research methods and content.
The primary aim of this paper is to provide effective policy recommendations for enhancing governance in rural areas at the county level. However, there are certain limitations in terms of offering these efficacious policy suggestions based on existing research findings. This corresponds with the "Kaplan gap" theory and is a prevalent issue observed in analogous studies(Kaplan and Thomas 2017). Therefore, investigating how to bridge the chasm between academic research and the "last mile" of public policy represents an important avenue for further exploration in this paper.
The study area is situated in Zhejiang Province, renowned for its exemplary rural development in China. With a high level of urban-rural integration, the overall resilience of the countryside is significantly enhanced. Moreover, owing to its mountainous terrain, the case area exhibits abundant vegetation coverage and boasts a thriving natural ecology. However, challenges impeding the resilience level of rural areas within this region are primarily concentrated in two dimensions: living and production. Consequently, we propose three policy optimization suggestions to effectively govern and enhance the resilience of rural areas under county-level jurisdictions within the case area.
Firstly, it is recommended to enhance rural production capacity while protecting the existing ecological environment. Key areas for strengthening digital infrastructure in agriculture include gradually transforming traditional agricultural production methods and increasing investment in agricultural infrastructure, which will actively promote modern agriculture. Secondly, it is crucial to enhance financial investment in rural development through the implementation of inclusive financial policies tailored for rural areas. These policies should aim to facilitate convenient access to financing options for agricultural production among farmers. Moreover, there should be a specific emphasis on formulating rural entrepreneurship insurance policies that offer essential policy support for fostering innovative entrepreneurial activities within the farming community. Additionally, expediting non-agricultural employment opportunities and augmenting investments in vocational training will contribute towards diversifying employment prospects for farmers. Lastly, with the acceleration of population aging and youth migration from rural areas coupled with declining population density in these regions, enhancing the efficiency of public service facility utilization becomes challenging. Therefore, optimizing the spatial arrangement of rural living spaces and appropriately increasing spatial aggregation levels are recommended.
Funding
The authors would like to express sincere gratitude for the invaluable support received from the Humanities and Social Sciences Youth Fund Project of the Ministry of Education, under grant number 21YJCZH005, which greatly facilitated the research on quantifying and investigating the driving impacts of "Nongchuangke" in rural revitalization. The authors would also like to express gratitude for the support from the National Natural Science Foundation of China under grant number 42071159, for the project titled 'Recognition and Targeted Policies Revelation of Rural Community Resilience in the Yangtze River Delta from the Perspective of Translocal Livelihoods.
Author Contribution
Yirong Cao is responsible for the overall writing and polishing of the manuscript; Fei Su is in charge of proposing and guiding the viewpoints; Xueyong Zheng and Lei Tong are responsible for data analysis and processing; Yi Zou and Yuanyuan Zhang are responsible for data collection.All authors reviewed the manuscript.
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Competing interest reported. none