Identification and alleviation pathways of multidimensional poverty and relative poverty in counties of China

doi:10.1007/s11442-021-1919-8

摘要/Abstract

Abstract:

To realize efficient and sustainable poverty alleviation, this study firstly investigated the identification of multidimensional poverty and relative poverty, and then explored relevant poverty alleviation pathways. Poverty levels in 31 provinces including the autonomous regions and municipalities of China were identified at the county level using the average nighttime light index (ANLI), county multidimensional development index (CMDI), and a method combining multidimensional poverty index and relative poverty standards. Poverty alleviation pathways for poverty-stricken counties were explored from the aspects of industry, education, tourism and agriculture. The results revealed that nearly 60% of counties in China were primarily under relative poverty, most of which were corresponded to light relative poverty. In terms of ANLI and CMDI, 63% and 79% of the national poverty-stricken counties, as of 2018, could be identified, suggesting that CMDI had a higher performance for identifying poverty at the county level. In terms of poverty alleviation pathways, 414, 172, 442, and 298 poverty-stricken counties were receptive to industry poverty alleviation, education poverty alleviation, tourism poverty alleviation, and agriculture poverty alleviation, and 61% of counties had more poverty-causing factors, implying that multidimensional poverty alleviation is suitable in most of the counties.

Key words: nighttime light index, multidimensional poverty, relative poverty, coupling coordination model, targeted poverty alleviation

. [J]. 地理学报(英文版), 2021, 31(12): 1715-1736.

XU Lidan, DENG Xiangzheng, JIANG Qun’ou, MA Fengkui. Identification and alleviation pathways of multidimensional poverty and relative poverty in counties of China[J]. Journal of Geographical Sciences, 2021, 31(12): 1715-1736.

图/表 14

参考文献 31

[1]	Chen Z G, Bi J Y, Wu G B et al., 2019. Post-2020 urban integrative poverty reduction strategy: Development status, evolution, new vision and key areas. Chinese Rural Economy, (1): 2-16. (in Chinese)
[2]	Ding J J, Wang Z, Liu Y H et al., 2020. Rural households’ livelihood responses to industry-based poverty alleviation as a sustainable route out of poverty. Regional Sustainability, 1(1): 68-81. doi: 10.1016/j.regsus.2020.07.002
[3]	Duclos J-Y, Tiberti L, 2016. Multidimensional poverty indices: A critical assessment. SSRN Electronic Journal, 16(1): 221-255.
[4]	Fan J, Zhou K, Wu J X, 2020. Typical study on sustainable development in relative poverty areas and policy outlook of China. Bulletin of Chinese Academy of Sciences, 35(10): 1249-1263. (in Chinese)
[5]	Feng Y X, Li G D, 2020. Interaction between urbanization and eco-environment in Tibetan Plateau. Acta Geographica Sinica, 75(7): 1386-1405. (in Chinese)
[6]	Feng Y Y, Pan J H, Yang L J, 2018. Analysis on spatial simulation of rural poverty at county level in China. Journal of Geo-information Science, 20(3): 321-331. (in Chinese)
[7]	Huang C W, 2020. Poverty alleviation highlights the CPC’s governance capabilities. Chinese Leadership Science, (3): 23-27. (in Chinese)
[8]	Ji X F, Li W, Hao J J, 2017. Spatial coupling model between accessibility and poverty intensity in poverty contiguous destitute areas. Journal of Transportation Systems Engineering and Information Technology, 17(4): 33-39. (in Chinese)
[9]	Jin G, Deng X Z, Dong Y et al., 2020. China’s multidimensional poverty measurement and its spatiotemporal interaction characteristics in the perspective of development geography. Acta Geographica Sinica, 75(8): 1633-1646. (in Chinese)
[10]	Li H R, Mao L L, Mei X R, et al., 2018. Analysis on influencing factors of grain production fluctuation during the last 30 years in China. Chinese Journal of Agricultural Resources and Regional Planning, 39(10): 1-10, 16. (in Chinese)
[11]	Li X, Qi W P, Tan C et al., 2019. Coupling between ecological environment vulnerability and multidimensional poverty: An empirical analysis from 1586 poverty-stricken villages in Hechi City, Guangxi, Southwest China. Chinese Jounal of Applied Ecology, 30(12): 4303-4312. (in Chinese)
[12]	Liu Y H, Xu Y, 2015. Geographical identification and classification of multi-dimensional poverty in rural China. Acta Geographica Sinica, 70(6): 993-1007. (in Chinese)
[13]	Liu Y H, Xu Y, 2016. A geographic identification of multidimendional poverty in rurual China under the framework of sustainable livelihoods analysis. Applied Geography, 73: 62-76.
[14]	Liu Y S, Li J T, 2017. Geographic detection and optimizing decision of the differentiation mechanism of rural poverty in China. Acta Geographica Sinica, 72(1): 161-173. (in Chinese)
[15]	Liu Y S, Zhou Y, Liu J L, 2016. Regional differentiation characteristics of rural poverty and targeted poverty alleviation strategies in China. Bulletin of Chinese Academy of Sciences, 31(3): 269-278. (in Chinese)
[16]	Luo Q, Fan X S, Gao G H et al., 2016. Spatial distribution of poverty village and influencing factors in Qinba Mountains. Economic Geography, 36(4): 126-132. (in Chinese)
[17]	Luo Y W, Ren Z P, Ge Y et al., 2020. Analysis on spatio-temporal patterns and drivers of poverty at village based on PCA-GWR. Journal of Geo-information Science, 22(2): 231-245. (in Chinese)
[18]	Ma T, Zhou C H, Tao P et al., 2014. Responses of Suomi-NPP VIIRS-derived nighttime lights to socioeconomic activity in China’s cities. Remote Sensing Letters, 5(2): 165-174. doi: 10.1080/2150704X.2014.890758
[19]	Okwi P O, Ndeng’E G, Kristjanson P et al., 2007. Spatial determinants of poverty in rural Kenya. Proceedings of the National Academy of Sciences of the United States of America, 104(43): 16769-16774.
[20]	Pan J H, Zhao H Y, Dong L L, 2018. Spatial identification of multi-dimensional poverty in rural China by using nighttime light and sustainable livelihoods. Acta Ecologica Sinica, 38(17): 6180-6193. (in Chinese)
[21]	Rong X B, Hu Z H, Xiong Xi, 2016. Regional differences in the coupling relationship between tourism development and ecological civilization construction. Economic Geography, 36(8): 189-194. (in Chinese) doi: 10.2307/141815
[22]	Sun J W, Xia T, 2019. China’s poverty alleviation strategy and the delineation of the relative poverty line after 2020: An analysis based on theory, policy and empirical data. Chinese Rural Economy, (10): 98-113. (in Chinese)
[23]	Wang S F, Zhou W, 2020. Research status and development trend of poverty alleviation in deeply impoverished areas. Rural Economy and Science, 31(7): 180-182. (in Chinese)
[24]	Wang X L, Feng H X, 2020. China’s multidimensional relative poverty standards in the Post-2020 Era: International experience and policy orientation. Chinese Rural Economy, (3): 2-21. (in Chinese)
[25]	Wu P, Li T S, Li W M, 2018. Spatial differentiation and influencing factors analysis of rural poverty at county scale: A case study of Shanyang county in Shaanxi Province, China. Geographical Research, 37(3): 593-606. (in Chinese)
[26]	Yang G T, Wang G J, 2005. Estimation and simulation of China’s rural poverty: 1995-2003. Resources and Environment, (6): 30-34. (in Chinese)
[27]	Yang Y Y, Bao W K, Liu Y, 2020. Coupling coordination analysis of rural production-living-ecological space in the Beijing-Tianjin-Hebei region. Ecological Indicators, 117(4): 106512.
[28]	Yu B L, Shi K F, Hu Y J et al., 2015. Poverty evaluation using NPP-VIIRS nighttime light composite data at the county level in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(3): 1217-1229. doi: 10.1109/JSTARS.4609443
[29]	Zeng Y M, Zhang G, 2011. Spatial simulating in regional rural poverty based on GIS and BP Neural Network: A new appraisement method on regional rural poverty. Geography and Geo-Information Science, 27(2): 70-75. (in Chinese)
[30]	Zhao X Z, Bai L Y, Liu Y et al., 2019. Estimation of poverty using random forest regression with multi-source data: A case study in Bangladesh. Remote Sensing, 11(4): 375-393. doi: 10.3390/rs11040375
[31]	Zhou Y, Guo Y Z, Liu Y S, 2018. Comprehensive measurement of county poverty and anti-poverty targeting after 2020 in China. Acta Geographica Sinica, 73(8): 1478-1493. (in Chinese)

Target layer	Index layer	Index attribute	Weight
Human	Population density	+	0.0633
	Number of middle and primary school students	+	0.0281
	Number of rural employees	+	0.0363
Material	Total power of machinery	+	0.0486
	Grain output	+	0.0503
	Proportion of road area	+	0.1277
Economy	GDP per capita	+	0.0678
	Net income of rural residents	+	0.0703
	Regional fiscal revenue	+	0.0809
	Savings deposit balance of residents	+	0.0504
	Total retail sales of social marketing	+	0.0529
Society	Urbanization rate	+	0.0476
	Number of beds in health institutions	+	0.0506
	Nighttime light data	+	0.0919
Nature	Multi-year mean precipitation	+	0.0230
	Mean elevation	-	0.0277
	NPP	+	0.0218
Environmental/background vulnerability	Proportion of area with gradient greater than 15 °	-	0.0075
	Terrain fragmentation	-	0.0533

	Coordinated development type	Imbalance type	Recession type.
Industry	≥0.57	[0.36,0.57)	<0.36
Education	≥0.59	[0.42,0.59)	<0.42
Tourism	≥0.56	[0.33,0.56)	<0.33
Agriculture	≥0.52	[0.35,0.52)	<0.35

Classification of coupling coordination types	Classification references	Classification of coupling coordinated difference types
Coordinated development type	\|u₁-u₂\|≤0.1	Common development type
	u₁-u₂<-0.1	County development lagging type
	u₁-u₂>0.1	Industry/Education/Tourism/Agriculture development lagging type
Imbalance type and recession type	\|u₁-u₂\|≤0.1	Common lagging type
	u₁-u₂<-0.1	County development lagging type
	u₁-u₂>0.1	Industry/Education/Tourism/Agriculture development lagging type

Classification	Types	Amount	Classification	Types	Amount
Industry	Coordinated development type	97	Tourism	Coordinated development type	102
	Imbalance type	103		Imbalance type	72
	Recession type	440		Recession type	466
Education	Coordinated development type	212	Agriculture	Coordinated development type	145
	Imbalance type	174		Imbalance type	141
	Recession type	254		Recession type	354

Coupling coordinated difference types	Selection basis	Poverty alleviation suggestions
Common development type	Industry/education/tourism/agriculture development and county development promote each other and develop simultaneously, which means corresponding methods for helping the poor need not to be adopted	Poverty alleviation through other pathways
Common lagging type	Industry/education/tourism/agriculture development and county development inhibit each other and have a small gap between them, which means it is necessary to weaken the inhibition effect by supporting the development of one party or accelerating the development of both parties, so as to achieve the purpose of accelerating county development	Combination of industry/education/tourism/agriculture poverty alleviation pathways with others
Industry/education/tourism/ agriculture development lagging type	The development of industry/education/tourism/agriculture in the county limits the development of the county, which means it is necessary to adopt the poverty alleviation mode of industry/education/tourism/agriculture to improve the development of industry/education/tourism/agriculture in the county, so as to alleviate the restrictions on the development of the county	Pay attention to industry/education/tourism/agriculture poverty alleviation pathways
County development lagging type	County development limits the development of industry/education/tourism/agriculture, which means it is necessary to adopt other pathways to alleviate county poverty	Poverty alleviation through other pathways