Comprehensive methods for measuring regional multidimensional development and their applications in China

doi:10.1007/s11442-018-1549-y

Abstract

Abstract:

National and international research on regional development has matured from the use of single elements and indicators to the application of comprehensive multi-element and multi-indicator measures. We selected 12 indicators from six dimensions for analysis in this study, including income, consumption, education, population urbanization, traffic, and indoor living facilities. We then proposed the polyhedron method to comprehensively measure levels of regional multidimensional development. We also enhanced the polygon and vector sum methods to render them more suitable for studying the status of regional multidimensional development. Finally, we measured levels of regional multidimensional development at county, city, and provincial scales across China and analyzed spatial differences using the three methods above and the weighted sum method applied widely. The results of this study reveal the presence of remarkable regional differences at the county scale across China in terms of single and multidimensional levels of regional development. Analyses show that values of the regional multidimensional development index (RMDI) are high in eastern coastal areas, intermediate in the midlands and in northern border regions, and low in the southwest and in western border regions. Districts characterized by enhanced and the highest levels of this index are distributed in eastern coastal areas, including cities in central and western regions, as well as areas characterized by the development of energy and mineral resources. The regional distribution of reduced and the lowest levels of this index is consistent with concentrations of areas that have always been impoverished. Correlation analyses of the results generated by the four methods at provincial, city, and county scales show that all are equivalent in practical application and can be used to generate satisfactory measures for regional multidimensional development. Additional correlation analyses between RMDI values calculated using the polyhedron method and per capita gross domestic product (GDP) demonstrate that the latter is not a meaningful proxy for the level of regional multidimensional development.

Key words: regional multidimensional development, comprehensive methods, polyhedron method, polygon method, vector sum method, weighted sum method, China

Yong XU, Jian DUAN, Xiaoren XU. Comprehensive methods for measuring regional multidimensional development and their applications in China[J].Journal of Geographical Sciences, 2018, 28(8): 1182-1196.

Figures/Tables 10

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Element	Feature indicator/ Dimension	Attribute indicator	Equation
Economy	Level of residential income (I)	Per capita disposable income of urban households (I_C) Per capita net income of rural households (I_R) Per capita local financial budget revenue (I_F)	I=I_C×B+I_R×(1-B)+I_i
Economy	Level of residential consumption (C)	Per capita total retail sales of urban and rural consumer goods
Society	Level of labor-force education (E)	Average number of education years for population of school age
Society	Level of population urbanization (U)	Rate of population urbanization (β)
Human settlement	Allocation level of indoor living facilities (F)	Allocation rate for indoor tap water (F_W) Allocation rate for indoor kitchens (F_K) Allocation rate for indoor toilets (F_T) Allocation rate for indoor bathrooms(F_B)	F=1/4×(F_W+F_K+F_T+F_B) Values for indicators have been normalized
Human settlement	Level of traffic development (T)	Proportion of traffic land to total land (T_P) Traffic Dominance (T_D) (Jin et al., 2008)	T=1/2×(T_P+T_D) Values for indicators have been normalized

Indicator/dimension	Rank	Range	Number of counties	Average value
Level of residential income	Highest	0.39-1	103	0.49
	Higher	0.25-0.39	305	0.3
	Medium	0.14-0.25	993	0.18
	Lower	0.1-0.14	578	0.12
	Lowest	0-0.1	397	0.08
Level of residential consumption	Highest	0.15-1	153	0.229
	Higher	0.08-0.15	405	0.106
	Medium	0.035-0.08	1,006	0.053
	Lower	0.018-0.035	563	0.027
	Lowest	0-0.018	249	0.013
Level of labor-force education	Highest	0.75-1	259	0.81
	Higher	0.68-0.75	515	0.71
	Medium	0.6-0.68	1,114	0.64
	Lower	0.4-0.6	415	0.54
	Lowest	0-0.4	73	0.32
Level of population urbanization	Highest	0.75-1	212	0.88
	Higher	0.5-0.75	318	0.60
	Medium	0.3-0.5	1,018	0.38
	Lower	0.2-0.3	562	0.26
	Lowest	0-0.2	266	0.14
Allocation level of indoor living facilities	Highest	0.8-1	406	0.87
	Higher	0.65-0.8	648	0.72
	Medium	0.45-0.65	849	0.56
	Lower	0.25-0.45	370	0.37
	Lowest	0-0.25	103	0.15
Level of traffic development	Highest	0.42-1	344	0.508
	Higher	0.323-0.42	610	0.346
	Medium	0.2-0.323	1,034	0.241
	Lower	0.102-0.2	280	0.110
	Lowest	0-0.102	108	0.101

Level	Method	Equation	R²
City	RMDI__A VS RMDI__M	RMDI__M = 0.3902ln(RMDI__A) + 1.5412	0.9322
	RMDI__A VS RMDI__W	RMDI__W = 0.1624ln(RMDI__A) + 0.5655	0.9462
	RMDI__M VS RMDI__W	RMDI__W = 0.3403(RMDI__M)^1.1574	0.9838
Provincial	RMDI__A VS RMDI__M	RMDI__M = 0.427ln(RMDI__A) + 1.567	0.9721
	RMDI__A VS RMDI__W	RMDI__W = 0.1821ln(RMDI__A) + 0.583	0.9664
	RMDI__M VS RMDI__W	RMDI__W = 0.1143e^1.0723(RMDI^_M⁾	0.9747

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