Spatio-Temporal Evolution and Driving Factors of Rural Settlements in Low Hilly Region-A Case Study of 17 Cities in Hubei Province, China

The transition characteristics and driving mechanisms of rural residential land in metropolitan areas-a case study of Tianjin, China

Rural revitalization is the core decision for eradicating global poverty and achieving shared prosperity, and one of the most important works is to optimize and manage rural land space. A theoretical framework based on urbanization theory was built to reveal the rural residential land transition in the Tianjin metropolitan area, China from 1990 to 2020. The transition features are identified by computing the land-use conversion matrix and the rural residential land expansion index (RRLEI) and the influencing factors and mechanisms through a multiple linear regression model. The spatial distribution of rural residential land shows that it expands from the inner suburbs to the outer suburbs, then diminishes in the outer suburbs, and extends to the Binhai New Area. Low-level conflicts between rural residential land and urban construction land occurred during the rapid urbanization period, resulting in disorganized and wasteful growth. The inner suburbs have edge-expansion, dispersion, and urban encroachment patterns; the outer suburbs have edge-expansion, infilling, and dispersion patterns, with little urban encroachment; and Binhai New Area has just an edge-expansion pattern. During decelerated urbanization stage, a high-level conflict occurred between rural residential land and arable land, forestland, grassland, water, and urban construction land. Dispersion grew as urban encroachment reduced in the inner suburbs; dispersion increased while urban encroachment declined in the outer suburbs; dispersion, infilling expansion, and urban encroachment increased in the Binhai New Area. During the saturation stage of urbanization, rural residential land evolved in tandem with other forms of land use, with more efficient land use and diverse uses. The main pattern of rural residential land in a suburban region is still edge-expansion, dispersion has expanded in the Binhai New Area, and urban encroachment is the way of urban development in the inner suburbs. Economic factors and economic location strongly impact the dispersion pattern. Edge-expansion and infilling patterns are impacted by comparable variables, including geographical location, topography, population resources, and economic location. Furthermore, the amount of economic growth influences the edge-expansion pattern. It might be influenced by land policy, and the eight elements have no substantial relationship with urban occupancy. Based on resource endowment and pattern features, certain optimization techniques are given.

Spatial Distribution Characteristics and Influencing Factors of Rural Governance Demonstration Villages in China

Rural governance plays a significant role in constructing national governance systems and promoting rural development. An accurate understanding of the spatial distribution characteristics and influencing factors of rural governance demonstration villages is conducive to giving full play to their leading, demonstration and radiating roles and further promoting the modernization of rural governance systems and governance capacity. Therefore, this study uses Moran's I analysis, local correlation analysis, kernel density analysis and a geographic concentration index to analyze the spatial distribution characteristics of rural governance demonstration villages. Moreover, this study proposes a conceptual framework to construct the cognition of rural governance and uses Geodetector and vector data buffer analysis methods to explore the internal influence mechanism of their spatial distribution. The results show the following: (1) The spatial distribution of rural governance demonstration villages in China is unbalanced. The distribution difference between the two sides of the "Hu line" is significant. The peak appears at 30° N and 118° E. (2) The rural governance demonstration villages in China are clustered, which forms a high-density core area, a sub-high density belt, two sub-high-density centers and several single core concentration areas. Additionally, the hot spots of rural governance demonstration villages in China are mostly located on the eastern coast, tending to cluster in places with superior natural conditions, convenient transportation, and excellent economic development. (3) Based on the distribution characteristics of Chinese rural governance demonstration villages, this study proposes a "one core, three axes and multiple centers" spatial structure to optimize the distribution of rural governance demonstration villages. (4) A rural governance framework system consists of a governance subject subsystem and influencing factor subsystem. The results of Geodetector show that under the mutual leading role of the three governance subjects, the distribution of rural governance demonstration villages in China is the result of multiple factors. Among them, nature is the basic factor, economy is the key factor, politics is the dominant factor, and demographic is the important factor. The interaction network formed by general public budget expenditure and total power of agricultural machinery affects the spatial distribution pattern of the rural governance demonstration villages in China.

Spatial Coupling Pattern and Driving Forces of Rural Settlements and Arable Land in Alpine Canyon Region of the Maoxian County, China

For mountainous areas in different regions, the study of the spatial coupling relationship between rural settlements and arable land resources is a key aspect of coordinated rural development. In this study, a spatial coupling relationship model and a Geodetector are introduced to explore the spatial coupling relationship and driving factors of rural settlements and arable land in the alpine canyon region. The nearest neighbor index, Voronoi diagram, and landscape pattern index system based on the geographic grid are used to analyze the spatial differentiation characteristics of rural settlements in the alpine canyon region, and the spatial coupling relationship model is introduced to explore the spatial coupling relationship between rural settlements and arable land. Finally, the driving factors of the coupling relationship are detected based on Geodetector. The results show that (1) the spatial distribution of rural settlements in the study area is "T-shaped" with a relatively regular settlement shape; (2) the population in the alpine canyon region is relatively small, and the conflict between people and land is not prominent in most areas, so the overall coupling situation between rural settlements and farming land is dominated by fewer people and more land; and (3) the spatial coupling between rural settlements and arable land in the alpine canyon region is mainly affected by four types of factors: terrain topography, meteorology, soil and population, and economy. The interaction between the factors has a synergistic enhancement effect. The results of the study provide theoretical support for the development of rural settlements in the alpine canyon region.

Spatial and temporal distribution of rural settlements and influencing mechanisms in Inner Mongolia, China

Revealing the patterns and influencing mechanisms of spatial and temporal distribution of rural settlements is crucial for rural revitalization and sustainable development. However, our limited understanding of the rural settlements in China’s ethnic minority border areas has hindered the process of their agricultural and rural modernization. Based on data on rural settlements in Inner Mongolia, China in four periods between 1990 and 2020, this study reveals its spatial and temporal distribution characteristics and describes the dynamic transformation process of settlement. Using a geographical detector approach, 17 factors are explored to identify the influencing mechanisms of each factor on the distribution of rural settlements in different regions. The results show obvious regional differences in the spatial distribution of rural settlements in Inner Mongolia, with the largest kernel density values in the west (Hetao irrigation area) and higher kernel densities in the central (Hohhot) and the east (Chifeng and Tongliao). While rural population decreases, rural settlements expand into cultivated land, grassland, and unused land resources. Its spatial distribution is significantly influenced by the factors of distance to cultivated land, distance to towns, and population density. The east of the study area is mainly controlled by temperature, while vegetation type and vegetation coverage have a greater impact in the west. The interactions between two influencing factors possess bilinear or nonlinear enhancement relationships. This study enriches the understanding of the rural settlements in ethnic minority border areas, which provide reference for the improvement of rural human settlement environment in Inner Mongolia.

The spatial spillover effect and nonlinear relationship analysis between land resource misallocation and environmental pollution: Evidence from China

Compared with other countries, China's local governments often adopt the land supply strategy of "low price and sufficient supply" for industrial land and "high price and limited supply" for commercial land in the allocation of land resources. The allocation of land resources is an important means to promote the rapid development of China's economy, and the impacts of land resource misallocation (LRM) on environmental pollution are increasingly apparent. This paper uses panel data from 30 provinces in China from 2009 to 2018 to discuss the relationship between LRM and environmental pollution. The ratio of the average price of commercial land to the average price of industrial land is used to measure the degree of LRM. The Ordinary Least Squares (OLS), spatial Durbin model (SDM), threshold model, and mediation effect model are used to study the direct effect, spatial spillover effect, nonlinear relationship, and conduction mechanism of LRM on environmental pollution. The results show that LRM significantly aggravated environmental pollution. This conclusion still holds after robustness tests including the substitution of dependent variables and IV estimates. The LRM aggravates environmental pollution through industrial structure and technological progress. Interestingly, the impact of LRM on environmental pollution also has a significant positive spatial spillover effect in adjacent regions. In addition, there is also evidence that the adverse effect of LRM on environmental pollution is nonlinear at different levels of industrial structure and technological progress. The threshold model shows that with the optimization of the industrial structure, the impact of LRM on environmental pollution shows a weakening trend of "inverted V-shaped", and with the advancement of technology, the impact of LRM on environmental pollution presents an "S-shaped" changing trend of "strong-weak-strong".

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

Rural resilience is not only a comprehensive reflection of "thriving businesses, pleasant living environments, social etiquette and civility, effective governance, and prosperity". It is also the unity of resilience in industry, ecology, culture, organization and livelihood. This paper uses the entropy weight-TOPSIS method to measure the rural resilience level in 31 regions in China and analyzes the configuration of influencing factors with the Fuzzy-set qualitative comparative analysis (fsQCA). The results of the study are as follows: (1) The level of rural resilience in China showed a stable increase from 2010 to 2019, but the overall level was low, with large regional disparities, showing a significant positive spatial correlation. (2) In the high-level rural resilience explanatory path, labor-driven, cultural-driven and market-labor-technology linkage-driven play a core role, while administrative force is not playing a significant role. In the explanation path of non-high level rural resilience, the market-labor absent, administrative-market absent and cultural absent hinder the improvement of rural resilience. In summary, we put forward the following suggestions. Policy renovation and support should be strengthened. Adaption to local conditions should be considered in order to achieve sustainable and differentiated development. Development should be coordinated and balanced in different regions so as to achieve an overall resilience level in rural areas.

Theoretical Development Model for Rural Settlements against Rural Shrinkage: An Empirical Study on Pingyin County, China

China’s rural areas face population loss and expanded residential land in the context of rapid urbanization. The increasing hollowing of villages leads to extensive land use, making it necessary to optimize and consolidate rural settlements immediately. Therefore, how to choose an appropriate development model for rural settlements is at issue. This article builds a theoretical development model for rural settlements based on their primary development conditions and spatial patterns. It determines the development model according to the classification matrix formed by interweaving different elements in each dimension. Finally, it demonstrates the model through a case study on Pingyin County, China. The empirical findings for Pingyin County are as follows. (1) The scale of rural settlements increased from 2010 to 2020, and the agglomeration and spatial correlation intensified, but they tended to be irregular. (2) The various elements of the development foundation showed apparent spatial differentiation, and the spatial distribution of rural settlements at different levels presented a core–edge structure. (3) The development of rural settlements was reduced to six models: in-situ urbanization, satellite urbanization, competitive-industry-driven, characteristic-tourism-driven, modern-agriculture-driven, and village relocation. Finally, the article proposes different development paths for different development models.

How do varying socio-economic factors affect the scale of land transfer? Evidence from 287 cities in China

With the rapid development of China's social economy, the scale of land transfer has also increased, which has led to a new pattern of urban land space. This article uses global regression of ordinary least squares (OLS), spatial lag model (SLM), spatial error regression model (SEM) and local regression of geographically weighted regression model (GWR), and multi-scale geographically weighted regression model (MGWR) to explore the influence of socio-economic factors on the scale of land transfer. The relationship between these factors and the scale of land transfer varies greatly from region to region. The local model (MGWR) can express the non-stationary relationship between variables, and the regression estimation results are more robust. The results show that total investment in fixed assets (TIFA) and the non-agricultural population (NAP) had significant effects on the scale of land transfer in 2005, with regression coefficients of 0.964 and -0.247, respectively. In 2010, per capita GDP (PCG), population density (PD), proportion of tertiary industry in GDP (PTIG), and TIFA had significant impacts on the scale of land transfer, and the corresponding impact coefficients were 0.413, -0.085, -0.081, and 0.322. In 2015, the variable of PCG had significant impact on land transfer, with the coefficient of 0.048. The influencing factors of the scale of land transfer are changing at different points in time, and the formulation of land transfer policies should be treated differently according to the different socio-economic conditions in each period.

Geospatial characterization of rural settlements and potential targets for revitalization by geoinformation technology

To better implement the Strategy of Rural Revitalization, it is essential to characterize the rural settlements and understand their roles in the socio-environmental interactive system. This paper is hence aimed at achieving such a study using different spatial analysis such as kernel density and spatial autocorrelation (SA) and modeling approaches, e.g., simple and multiple linear regression analyses taking Jiangxi, a province in China as an example. Remote sensing, topographic and socioeconomic data were employed for this purpose. Through these analyses, it is found that the rural settlements in the study area appear to have a spatial distribution pattern of "dense north and sparse south" as an "F" type, and are quantitatively characterized as low elevations, flat terrain, high river and road densities, rich cultivated land resources and susceptible to the impact of urban radiation with a R² of 0.520-0.748. Based on this understanding, a new inequality evaluation indicator of rural development, i.e., socio-environmental evaluation index (SEI), was developed. Areas with SEI lower than 0.40 should be given a priority to implement the revitalization strategy in the province. This index can also be extended to study of the imbalance of rural development in other regions and countries.

Spatial Patterns and Driving Factors of Rural Population Loss under Urban–Rural Integration Development: A Micro-Scale Study on the Village Level in a Hilly Region

Under the background of urban–rural integration, balanced development between urban and rural areas has been increasingly advocated. Rural population loss (RPL) is not only due to the laws of social and economic development but also the comprehensive action of natural, social, and economic factors. Taking 774 administrative villages in Laiyang County, which is in a hilly region, as our research area, we comprehensively used spatial analysis and geographic detectors to explore the spatial characteristics and driving factors of RPL, which was significantly correlated with rural planning. The research demonstrated that: (1) The rural population in Laiyang County generally had a low level of RPL (1.9%), but each village varied greatly. The village with the greatest RPL had a rate of 56%. The RPL between urban and rural areas, towns and streets, and villages and villages were unbalanced, and rural population flow mainly occurred between urban and rural areas. (2) RPL in Laiyang County was generally low in the central urban area and high in the northern and southern areas. Population loss presents agglomeration globally and high–low agglomeration locally. (3) The distance from village to county, elevation, cultivated land quantity, collective economic income, village area, and ecological service value were the key factors influencing RPL in Laiyang County. When comparing the dominant factors, the interaction between collective income and elevation was the strongest. Exploring the spatial characteristics and influencing factors of RPL provided us with ideas for the classified promotion of rural revitalization, preparation of rural development planning, and promotion of the integrated development of urban and rural areas.

Research on the Spatio-Temporal Impacts of Environmental Factors on the Fresh Agricultural Product Supply Chain and the Spatial Differentiation Issue-An Empirical Research on 31 Chinese Provinces

Environmental factors in time and space play a critical role in advancing the sustainable development of the fresh agricultural product supply chain. This paper, availing the panel data of 31 Chinese provinces from 2008 to 2019, constructs a system of indicators assessing the development of the fresh agricultural product supply chain, and obtains the comprehensive development level in the Entropy Weight Method (EWM). Furthermore, it establishes a comparison between optimal solutions generated by the Instrumental Variables Method (IVM) and the Generalized Method of Moments (GMM) over the endogeneity issue of variables, creates the comparison between the weighted regression methods of Geographically Weighted Regression (GWR) and Multi-scale Geographic Weighted Regression (MGWR), and obtains the relationship among the 14 environmental factors in their spatio-temporal impacts on the development of the fresh agricultural product supply chain. The results indicate that: (1) the environmental influencing factors in this paper have significant endogenous problems and various environmental factors impact on the fresh agricultural product supply chain in different trends and to different degrees. (2) With different bandwidths, the environmental factors could impact the fresh agricultural product supply chain to greatly varied degrees, demonstrating a strong attribute of regional correlation.