Geographical patterns and anti-poverty targeting post-2020 in China

doi:10.1007/s11442-018-1580-z

Abstract

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.

Key words: human-environment relationship, multidimensional poverty, comprehensive poverty level, geographical pattern, anti-poverty targeting, poverty geography, China

Yuanzhi GUO, Yang ZHOU, Zhi CAO. Geographical patterns and anti-poverty targeting post-2020 in China[J].Journal of Geographical Sciences, 2018, 28(12): 1810-1824.

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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)

Indicator	Correlation	Sig.	Indicator	Correlation	Indicator	Correlation	Sig.
A1	-0.194	0.000	B4	-0.277	C2	0.261	0.000
A2	-0.173	0.006	B5	-0.158	C3	0.230	0.000
A3	0.227	0.000	B6	-0.345	C4	-0.059	0.150
B1	-0.362	0.000	B7	-0.226	C5	-0.124	0.003
B2	-0.075	0.069	B8	-0.149	C6	-0.131	0.001
B3	-0.644	0.000	C1	0.272	C7	-0.359	0.000

	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

	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

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.