Does Rural Production–Living–Ecological Spaces Have a Preference for Regional Endowments? A Case of Beijing-Tianjin-Hebei, China

Abstract

Production–Living–Ecological Space (PLES) is the functional projection of sustainable development in territory spatial planning. Its rational layout has become the most important task for developing countries to enhance ecological awareness and achieve sustainable goals. This study took the rural areas of Beijing-Tianjin-Hebei (BTH) as an example to analyze the relationship by means of quantitative cumulation between regional endowments (natural factors, location and facilities) and PLES to figure out the preference mechanism. The Boosted Regression Tree model (BRT) was used to obtain the contribution rate of factors and the internal marginal effect between 1980~2018. Our conclusions are as follows: Living space (LS) enjoyed the highest advantage of regional endowment level, followed by production space (PS). Except for the distance to water, other indicators were significantly different in the PLES, and the suitable range of various types was expanded from LS to PS and ecological space (ES). During the transfer, elevation had a universal effect. The process of increasing naturalness was affected by the distance of high-level urban areas, which verified the continuous effect of Chinese ecological civilization. This study clarified the selectivity of regional endowments to PLES, which will greatly guide the direction of regional territory spatial planning and the next step of regional sustainable development.

A Novel Integrated Spatiotemporal-Variable Model of Landscape Changes in Traditional Villages in the Jinshaan Gorge, Yellow River Basin

“…These include evolution of the overall natural land use and landscape patterns, as well as transformations of socio-economic production levels involving population, economy, technology, and culture [16]. Furthermore, human history contributes to the evolution of architecture and human spaces [17,30,31]. These evolutionary processes manifest varying positive or negative characteristics over different periods and under diverse socio-economic contexts [24].…”

Spatial Evolution and Multi-Scenario Simulation of Rural “Production–Ecological–Living” Space: A Case Study for Beijing, China

“…Zhao [27] and others expanded the application of the emerging machine learning algorithm in the identification of the dominant function of the "production life ecological" space in the city by using the random forest model and the quadrat proportion method. The optimization of PEL functional space is based on the extension of the connotation of PEL spatial theory and the identification method of functional space [28], and the process of constantly maximizing the comprehensive benefits of different land spatial configurations through models and other methods, so as to build an adjustment scheme for the comprehensive optimization of the regional system of human land relations [29]. In terms of research methodology, Xie [30] and others think that the existing spatial optimization methods are mainly 3S space technology, quantitative statistical models, and index model analysis methods in ecology, but it is difficult to achieve effective unification of land quantitative structure and spatial structure using these methods.…”

Spatial pattern optimization of rural production-living-ecological function based on coupling coordination degree in shallow mountainous areas of Quyang County, Hebei Province, China

“…The contradiction among PLES in shallow mountainous areas is more prominent, leading to a series of problems known as "rural diseases" such as land pollution, poor environmental quality, etc. Optimizing the pattern of PLES is an essential measure for the healthy development of rural areas (Duan et al, 2021;Zeng et al, 2021), which is significant to urban-rural coordination development and the construction of rural revitalization in shallow mountainous areas.…”

“…In terms of research scales, previous studies have mostly taken the national, watershed (Li et al, 2021;, urban agglomerations (Wu, 2019;Yang et al, 2020;Zeng et al, 2021) and other macro-scales, and the mesoscales are such as cities, counties and districts (Niu et al, 2021;Yin et al, 2021), The micro-scale, such as the central city (Tian et al, 2020; and other administrative units as the research scale, the village-scale research are less (Liu et al, 2019a,b;Duan et al, 2021). The main reason is that as the evaluation unit shrinks, it becomes more difficult to obtain data, which leads to the lack of in-depth research on PLES at the village scale.…”

“…From the northwest to the southeast in Quyang County, there are low mountains, hills and plains, which are typical shallow mountain areas and counties in Hebei Province. At present, some scholars have carried out on the pattern of rural PLES in the shallow mountainous areas of Hebei Province and even the BTH region (Yang et al, 2020;Duan et al, 2021;Zeng et al, 2021). For example, Zeng used the lifting regression tree model to explore the driving force between the regional endowment of the BTH region and the PLES (Zeng et al, 2021).…”