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Journal of Geographical Sciences    2018, Vol. 28 Issue (12) : 1810-1824     DOI: 10.1007/s11442-018-1580-z
Research Articles |
Geographical patterns and anti-poverty targeting post-2020 in China
GUO Yuanzhi1,2,3(),ZHOU Yang1,2,CAO Zhi1,2,*()
1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2. Center for Assessment and Research on Targeted Poverty Alleviation, CAS, Beijing 100101, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract  

Poverty has been a focus of Chinese government for a long time. It is therefore of great significance to investigate both the mechanisms and spatial patterns of regional impoverishment in order to adequately target Chinese anti-poverty goals. Based on the human-environment relationship and multidimensional poverty theory, this study initially develops a three-dimensional model encompassing human, society, and environmental factors to investigate the mechanisms of rural impoverishment as well as to construct an indicator system to evaluate the comprehensive poverty level (CPL) in rural areas. A back propagation neural network model was then applied to measure CPL, and standard deviation classification was used to identify counties that still require national policy-support (CRNPSs) subsequent to 2020. The results of this study suggest that CPL values conform to a decreasing trend from the southeast coast towards the inland northwest of China. Data also show that 716 CRNPSs will be present after 2020, mainly distributed in high-arid areas of the Tibetan Plateau, the transitional zones of the three-gradient terrain, as well as karst areas of southwest China. Furthermore, CRNPSs can be divided into four types, that is, key aiding counties restricted by multidimensional factors, aiding counties restricted by human development ability, aiding counties restricted by both natural resource endowment and socioeconomic development level, and aiding counties restricted by both human development ability and socioeconomic development level. We therefore propose that China should develop and adopt scientific and targeted strategies to relieve the relative poverty that still exist subsequent to 2020.
Keywords human-environment relationship      multidimensional poverty      comprehensive poverty level      geographical pattern      anti-poverty targeting      poverty geography      China     
Fund:National Key Research and Development Program of China, No.2017YFC0504701; National Natural Science Foundation of China, No.41871183, No.41471143
Corresponding Authors: CAO Zhi     E-mail: guoyz.16b@igsnrr.ac.cn;caoz.14b@igsnrr.ac.cn
Issue Date: 27 December 2018
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GUO Yuanzhi
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GUO Yuanzhi,ZHOU Yang,CAO Zhi. Geographical patterns and anti-poverty targeting post-2020 in China[J]. Journal of Geographical Sciences, 2018, 28(12): 1810-1824.
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http://www.geogsci.com/EN/10.1007/s11442-018-1580-z     OR     http://www.geogsci.com/EN/Y2018/V28/I12/1810
Figure 1  Rural poverty mechanisms founded on the man-land areal system
Dimension Indicator Description
HDA A1: Educational attainment Average educational attainment of people aged six and above
A2: Labor force Proportion of population aged 15-64 / Total population
A3: Minority nationality Proportion of the population of national minorities / Total population
SED B1: Per capita gross domestic product (GDP) GDP / Total population
B2: Per capita public budget revenue General public budget revenue / Total population
B3: Per capita disposable income of rural residents Income of rural households after initial distribution and redistribution
B4: Number of beds in health institutions Including individually-run clinics
B5: Child survival Number of live babies / Total live births
B6: Urbanization rate Urban population / Total population
B7: Per capita living space The average residential area owned by each person
B8: Road density Total road length / Total area
NRE C1: Altitude The average raster value in each county
C2: Slope Proportion of areas at more than 15 degrees of slope
C3: Degree of fragmentation Standard deviation (SD) of county raster value
C4: Annual rainfall Annual rainfall across counties in 2015
C5: Per capita arable land Including paddy fields and dryland
C6: Net primary productivity (NPP) Obtained using a GLO_PEM model (Prince and Goward, 1995)
C7: Farmland production potential (FPP) Calculated using a GAEZ model (Liu et al., 2015)
Table 1  An indicator system for comprehensive poverty level in rural China
Indicator Correlation Sig. Indicator Correlation Sig. Indicator Correlation Sig.
A1 -0.194 0.000 B4 -0.277 0.000 C2 0.261 0.000
A2 -0.173 0.006 B5 -0.158 0.000 C3 0.230 0.000
A3 0.227 0.000 B6 -0.345 0.000 C4 -0.059 0.150
B1 -0.362 0.000 B7 -0.226 0.000 C5 -0.124 0.003
B2 -0.075 0.069 B8 -0.149 0.000 C6 -0.131 0.001
B3 -0.644 0.000 C1 0.272 0.000 C7 -0.359 0.000
Table 2  Pearson correlation coefficient analysis of selected indicators and incidence of poverty
Figure 2  BP neural network model topology diagram
Figure 3  Maps showing the spatial pattern of CPL values in rural China
DBMA SGKR RDA BMA LPMA LXMA LLMA QBMA
HDI 8.96 11.8 3.52 7.78 9.95 8.24 13.02 11.68
REI 11.23 9.06 9.74 7.87 7.22 10.52 8.16 6.82
SEI 8.43 7.45 6.50 5.19 5.38 8.14 6.52 7.76
CPL 28.62 28.31 19.76 20.84 22.55 26.89 27.7 26.26
TAFP TPSX YTMA WMMA WLMA Tibet CPADs China
HDI 5.96 4.39 10.75 4.66 5.57 4.87 7.38 11.04
REI 4.00 5.76 7.76 6.66 9.72 3.78 7.39 9.56
SEI 6.85 3.83 6.83 5.04 7.84 5.18 6.48 10.07
CPL 16.81 13.98 25.33 16.36 23.13 13.83 21.24 30.68
Table 3  CPLs in contiguous poor areas with difficulties
Figure 4  Map showing the evolution of national key counties for poverty alleviation and development in China
Figure 5  Map showing the classification of CPLs in rural China
Figure 6  A spatial comparison of CRNPSs after 2020 and present-day poor counties
HDI REI SEI CPL
CRNPSs 6.37 7.17 6.62 20.06
CCPADs 7.38 7.39 6.48 21.24
NKCs 8.22 8.27 6.69 23.18
Table 4  Comparison of CPL values between CRNPSs after 2020 and present-day poor counties
Figure 7  The spatial distribution of different CRNPSs types subsequent to 2020
Type Number Main features
Type I 208 HDI, SEI, and REI values are 4.00, 5.00, and 4.41, respectively. They are deeply poverty-stricken counties distributed within high-arid and remote areas, including the Tibetan Plateau, TPSX, and WMMA. These areas are mainly ethnic enclaves and encapsulate numerous restrictive conditions and extremely fragile ecosystems.
Type II 87 HDI, SEI, and REI values are 2.48, 10.73, and 7.28, respectively. These counties are distributed in karst areas of southwest China. The development level of these areas is low due to low population quality caused by backward education and cultural differences of minorities.
Type III 142 HDI, SEI, and REI values are 10.16, 5.74, and 6.65, respectively. These countries are concentrated on the eastern side of the Tibetan Plateau as well as in the BMA and Taihang mountainous area. Educational attainment in these regions is relatively high, but they are nevertheless influenced by fragile ecosystems, complex terrain, and inadequate infrastructure. The development levels of these counties are therefore backward.
Type IV 279 HDI, SEI, and REI values are 7.42, 8.41, and 7.79, respectively. These counties are distributed in Xinjiang and southwest China, exhibiting shortcomings in personal abilities and social economy, but have better social and natural conditions, which create a foundation for development.
Table 5  Classification and characteristics of CRNPSs
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