Measurement and identification of relative poverty level of pastoral areas: an analysis based on spatial layout

Abstract

Pastoral areas are the key difficulty in China’s pursuit of common prosperity and a key region for China to build the northern ecological safety barrier and to realize the Two Centenary Goals. It is of great significance to scientifically evaluate the quality of rural life (QRL), measure the relative poverty level (RPL), and identify the relatively poor areas, making it possible to dock poverty elimination with rural revitalization. Based on the socio-economic data of 18 pastoral areas in Inner Mongolia, this paper draws on spatial layout theory to evaluate QRL and measures RPL by the natural breakpoint method and then identifies the relatively poor areas in Inner Mongolia. The results show that (1) the QRLs of pastoral areas in Inner Mongolia were unbalanced and highly polarized. The mean score of QRLs was 0.2598. Eleven (61.11%) of the counties/banners had a QRL smaller than the mean score. On the spatial layout of QRLs, the western areas were stronger than the central areas. High QRL counties/banners are mainly concentrated in the western region. In the central region, the QRLs were very fragmented, falling onto all five levels. (2) The pastoral areas in Inner Mongolia differed significantly in RPL. The mean score of RPL stood at 0.3788. Nine counties/banners (50%) had an RPL greater than the mean. Contrary to the spatial layout features of QRLs, the central pastoral areas in Inner Mongolia had stronger RPLs than the eastern ones. High RPL counties/banners are mostly clustered in the central region. The spatial layout of RPLs is relatively reasonable in the central region: the RPLs decreased gradually from Dorbod Banner. (3) Nearly 45% of the pastoral areas in central and western Inner Mongolia face serious relative poverty and a high risk of returning to poverty. Eight counties/banners (45%) were identified as high composite relative poverty areas. From spatial layout, the composite relatively poor counties/banners clustered clearly, mainly in the western region. Finally, this paper establishes a warning mechanism against large-scale returning to poverty, aiming to lower composite RPL. The research results provide empirical reference and implementation path for consolidating the results of poverty eradication and facilitating rural revitalization.

Nonlinear spatial impacts of the digital economy on urban ecological welfare performance: evidence from China

“…From the viewpoint of the internal urban environment, the imbalance and inadequacy of the development of DE between urban and rural areas is an important reason for the digital divide (Lin et al, 2022). The urban DE is developing faster than the rural DE, resulting in a mismatch between digital technology and digital infrastructure in rural areas, creating a digital divide that is not conducive to the DE playing a positive role in the EWP of rural areas (Liu and Zhou, 2023).…”

What influence farmers’ relative poverty in China: A global analysis based on statistical and interpretable machine learning methods

“…China is a family-based society, and in addition to individual characteristics, family elements also affect poverty [ 39 ], such as per capita household income, proportion of agricultural income, and household consumption, and the external environment, including the individual's family environment, can also have an impact on poverty, such as regional financial support policies [ 40 ], and household capital perspectives such as house type [ 41 ] and the value of durable goods [ 42 ]. In addition to the above dimensions, scholars have also explored poverty status from the perspective of psychological conditions, such as confidence and stress [ 43 ]; geographic perspective, such as rain [ 44 ]and sunlight [ 45 ].…”

“…formal sector = 1, informal sector = 0 Cichello [ 127 ] migrant_worker Whether the respondent is currently working outside the home, yes = 1, no = 0 Peng [ 128 ] farm_forest Whether the respondent's family is engaged in agriculture, forestry, animal husbandry and by-fishing, yes = 1,no = 0 Ghimire [ 129 ] major_thing Whether there was a major incident in the household of the respondent in the previous year, yes = 1,no = 0 Healey [ 130 ] loan The per capita loan amount of the respondents' households Burgess [ 131 ] total_house_property Respondent's household total expenditure Bartoşová [ 132 ] food_cost Respondent's household food expenditure Bartoşová [ 132 ] medicel_cost Respondent's household medical expenditure Bartoşová [ 132 ] consume_cost Respondent's household consume expenditure Bartoşová [ 132 ] education_cost Respondent's household education expenditure Lin [ 133 ] Individual Characteristics Production_risk Respondent's family dependency ratio Allen [ 134 ] CFPS water Respondents' domestic water consumption Adetayo [ 135 ] fuel Respondent's living and cooking fuel ...…”

Construction and demonstration of early-warning evaluation index system of poverty return from the perspective of rural revitalization

“…Literature [12], in order to study the quantitative measurement of multidimensional poverty regression in Chinese rural households, combined the worker neural network and multidimensional measurement model to assess the dynamic evolution of multidimensional regression poverty in rural households in China, showed that the per capita annual net income is the main factor leading to the decline of the multidimensional poverty rate, and put forward relative recommendations. Literature [13] used socio-economic data from 18 pastoral areas in Inner Mongolia as the basis for measuring the ecological carrying capacity of pastoral areas using the natural breakpoint method, evaluating the ecological carrying capacity using the spatial layout theory, identifying the relatively impoverished areas in Inner Mongolia and establishing an early warning mechanism for large-scale return to poverty, with the aim of reducing the comprehensive RPL and realizing the revitalization of the countryside.…”