Spatio-Temporal Analysis and Influencing Factors of Rural Resilience from the Perspective of Sustainable Rural Development

Yang, Mei; Jiao, Mengyun; Zhang, Jinyu

doi:10.3390/ijerph191912294

Cited by 14 publications

(11 citation statements)

References 53 publications

(78 reference statements)

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“…The smaller the information entropy reflected by the evaluation index, the more information it provides, and the greater the weight. When the values of various evaluation indexes are determined, the entropy weight can reflect the information contained in each index of the community and solve the problem of index quantification [37][38][39]. Specific steps are as follows:…”

Section: Research Methods 231 Entropy Weight Methodsmentioning

confidence: 99%

Community Resilience Assessment and Identification of Barriers in the Context of Population Aging: A Case Study of Changchun City, China

Zhao

Wang

et al. 2023

Sustainability

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As a susceptible demographic, elderly individuals are more prone to risks during sudden disasters. With the exacerbation of aging, new challenges arise for urban disaster reduction and prevention. To address this, the key is to establish a community-scale resilience assessment framework based on the aging background and to summarize factors that influence the resilience level of communities. This approach is a crucial step towards seeking urban disaster prevention and reduction from the bottom up, and serves as an important link to enhance the capacity of urban disaster reduction. This paper explores community resilience evaluation indicators under the background of aging, builds a community resilience evaluation index system based on the Pressure–State–Response, uses the entropy weight method to weigh the indicators, and carries out a resilience evaluation of 507 communities in the main urban area of Changchun. The empirical results indicate significant spatial differentiation of community resilience in the main urban area of Changchun. Moreover, the regional development is unbalanced, showing a spatial distribution pattern of weakness in the middle and strength in the periphery. The ring road network highlights the difference between the new and old urban areas. The high contribution indexes of community resilience in the main urban area of Changchun were concentrated on disaster relief materials input, community self-rescue ability, and disaster cognition ability. Finally, strategies to improve community resilience are proposed from the perspectives of stress, state, and response, emphasizing community residents’ participation, conducting disaster prevention and reduction training, and improving community response-ability.

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Section: Research Methods 231 Entropy Weight Methodsmentioning

confidence: 99%

Community Resilience Assessment and Identification of Barriers in the Context of Population Aging: A Case Study of Changchun City, China

Zhao

Wang

et al. 2023

Sustainability

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“…Such research has good theoretical and Land 2023, 12, 1122 2 of 19 practical significance for rural sustainable development and rural revitalization. In the context of climate change and food security concerns, the sustainable development of rural areas has received increasing attention, giving rise to research on sustainable livelihood, community resilience, vulnerability, eco-economy, and bio-economy [7][8][9][10]. At the same time, against the background of rural revitalization, there is consensus among researchers on the need to strengthen research on the evolution theory of rural settlement, change trends in spatial structure, the transformation of professional villages, the improvement and optimization of settlements, and the landscape of settlements [9].…”

Section: Introductionmentioning

confidence: 99%

Spatial Morphology Evolution of Rural Settlements in the Lower Yellow River Plain: The Case of Menggang Town in Changyuan City, China

Liu

Song

Wang

2023

Land

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This study investigated the spatial pattern evolution of the rural settlement system in the town of Menggang, China, based on settlement patches extracted from remote sensing data for four time points between 1990 and 2018. Five typical villages were selected to study their spatial textures from point to surface. The reasons for the evolution of the rural settlements were examined as well. The results indicated that the number of settlement patches in Menggang decreased, the area of a single patch expanded, and the scale of the rural settlements continued to expand. Settlements were distributed randomly, but an agglomeration trend began to appear. The plain terrain had little restriction on settlement site selection. Cofferdams and embankments were the limiting factors for settlement expansion, which led to the compact development of the settlements. Economic development, population growth, and family miniaturization were positive factors for the expansion of the rural settlements. In the typical villages, cluster settlements and cluster settlements with a banded tendency had higher spatial utilization efficiency. Several high-density building centers were formed in the settlements, and the trend of residential buildings was mainly northeast–southwest. The plain terrain had little effect on the orientation of buildings, single-building areas, or the public spaces of the settlements. Traditional culture and traffic routes affected the orientation of the main buildings of folk houses. This study has theoretical and reference value for the structure optimization and sustainable development of rural settlements in the lower Yellow River plain.

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“…Ma et al quantitatively analyzed the urbanrural transition in Gansu Province by constructing a comprehensive evaluation index system for county population and land industrial systems and a quantitative model of the degree of urban-rural transition and explored its spatiotemporal changes and driving forces through the use of hotspot identification and a geographic detector model [43]. Yang et al used the entropy weight-TOPSIS method to measure the rural resilience level in 31 regions in China and analyzed the configuration of influencing factors using a fuzzy-set qualitative comparative analysis (fsQCA) [44]. Yuan et al used a spatial regression model to determine the core influencing factors and main driving mechanisms extracted at different stages [45].…”

Section: Introductionmentioning

confidence: 99%

Spatial–Temporal Evolution Characteristics and Driving Factors of Rural Development in Northeast China

Zhang

et al. 2023

Land

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An assessment of rural development and its driving factors can effectively reflect the characteristics and transformation of rural areas and provide important information for the formulation and implementation of rural development strategies. Taking Northeast China as study area, a rural development index framework was constructed from three dimensions, i.e., basic rural conditions, the state of agricultural development, and farmers’ living standards, based on which the rural development level of each city in Northeast China for the years 2000, 2005, 2010, 2015 and 2020 was assessed. Then, an exploratory spatial data analysis was used to explore the spatial and temporal variations in the rural development level in Northeast China during the period 2000–2020. The driving factors were also analyzed using a geographically and temporally weighted regression model. The results showed that the rural development level showed an increasing trend overall, with a spatial pattern of “high in the central, low in the east and west” in most periods. The degree of spatial agglomeration of the rural development level also showed a strengthening trend overall. The hots spots of rural development were mainly distributed in the Southern and Northern regions, while the cold spots were mostly concentrated in the central, eastern and western regions. Urbanization processes, elevation, annual precipitation and other natural factors have weakened the level of rural development to a certain extent, while agricultural production upgrading, an increase in the general public budget expenditure per capita and the sound financial situation of the government can promote rural development in Northeast China. The effects of the natural environment and local economic conditions on rural development were different in different regions. To improve rural development in the future, we should scientifically grasp the basic conditions, such as rural resource endowment, location conditions, agricultural technology, policies, investment and other external conditions, and formulate regional rural development strategies according to local conditions and in light of local rural characteristics.

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Spatio-Temporal Analysis and Influencing Factors of Rural Resilience from the Perspective of Sustainable Rural Development

Cited by 14 publications

References 53 publications

Community Resilience Assessment and Identification of Barriers in the Context of Population Aging: A Case Study of Changchun City, China

Community Resilience Assessment and Identification of Barriers in the Context of Population Aging: A Case Study of Changchun City, China

Spatial Morphology Evolution of Rural Settlements in the Lower Yellow River Plain: The Case of Menggang Town in Changyuan City, China

Spatial–Temporal Evolution Characteristics and Driving Factors of Rural Development in Northeast China

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