A study of provincial differences in China’s eco-compensation framework

doi:10.1007/s11442-017-1374-8

Abstract

Abstract:

In this study, we developed a theoretical framework to analyze the provincial differences in eco-compensation and selected appropriate measurement methods to investigate these differences in the operation of the eco-compensation framework. Via the use of the coefficient of variation, Atkinson index, and Gini coefficient, we investigated the overall differences in Chinese provincial eco-compensation time series data from 2004 to 2014 and studied the driving mechanism underlying these differences. The results showed that: (1) The provincial eco-compensation standard has geographical features. For example, the provinces crossed by the “HU Huanyong Line”, or located to its northwestern side, have obtained extensive eco-compensation. (2) There was a trend for differences in eco-compensation to increase over time, but with some fluctuations in 2006, 2009, and 2014 as shown by the coefficient of variation, in 2005, 2007, 2011, 2013, and 2014 as shown by the Gini coefficient, and in 2007, 2008, 2011, and 2012 as shown by the Atkinson index. (3) Time series curves indicated that while the signals from the three metrics (coefficient of variation, Atkinson index, and Gini coefficient) differ in a short-term analysis, they show the same tendency in the longer term. The results indicate that it is necessary to evaluate the differences in eco-compensation at the provincial level over a long period of time. (4) Via the calculation of the virtual Gini coefficient, we found that among the factors that influence provincial differences in eco-compensation, the economic value of eco-resources played the decisive role, explaining more than 73% of the difference. The cost of environmental pollution abatement was the second most important factor, accounting for more than 19% of the difference. The input to environmental pollution abatement had the least influence, accounting for less than 8% of the difference. The results agreed with those obtained from other studies, and could be used as a reference by policy makers.

Key words: provincial eco-compensation, difference, measure, China

Chunla LIU, Weidong LIU, Dadao LU, Mingxing CHEN, Mei XU. A study of provincial differences in China’s eco-compensation framework[J].Journal of Geographical Sciences, 2017, 27(2): 240-256.

Figures/Tables 9

Figure 1

Figure 2

Table 1

The eco-compensation standard as a proportion (%) of GDP for each province in China from 2004 to 2014"

Province	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014
Beijing	0.39	0.24	0.23	0.23	0.23	0.27	0.25	0.25	0.31	0.19	0.22
Tianjin	0.31	0.06	0.12	0.10	0.08	0.02	0.09	0.08	0.07	0.20	0.02
Hebei	0.57	0.64	0.51	0.48	0.46	0.41	0.33	0.36	0.32	0.39	0.50
Shanxi	0.99	0.96	1.17	1.12	1.11	0.94	0.61	0.54	0.55	0.65	0.45
Inner Mongolia	11.49	9.64	8.03	6.62	6.04	5.28	4.46	4.15	3.88	4.00	3.80
Liaoning	0.78	0.86	0.92	0.54	0.49	0.45	0.35	0.30	0.26	0.33	0.35
Jilin	2.02	1.90	1.61	1.48	1.47	1.27	1.06	0.97	0.91	0.83	0.81
Heilongjiang	2.91	2.74	2.49	2.44	2.52	2.49	2.04	1.87	1.72	1.87	1.82
Shanghai	1.34	1.02	1.04	0.83	0.81	0.71	0.71	0.57	0.37	0.21	0.17
Jiangsu	0.21	0.10	0.13	0.00	0.04	0.05	0.09	0.06	0.10	0.03	0.00
Zhejiang	0.09	0.15	0.14	0.11	0.09	0.12	0.05	0.07	0.05	0.11	0.12
Anhui	0.60	0.56	0.50	0.53	0.53	0.47	0.34	0.30	0.28	0.43	0.26
Fujian	0.94	1.10	0.76	0.59	0.61	0.51	0.45	0.39	0.40	0.48	0.43
Jiangxi	1.79	1.72	1.44	1.31	1.27	1.15	0.96	0.84	0.81	0.79	0.70
Shandong	0.17	0.26	0.20	0.19	0.21	0.11	0.05	0.08	0.12	0.14	0.22
Henan	0.30	0.33	0.30	0.32	0.25	0.20	0.14	0.17	0.10	0.18	0.20
Hubei	0.72	0.77	0.67	0.63	0.59	0.64	0.52	0.36	0.43	0.47	0.41
Hunan	1.01	1.07	0.99	0.82	0.83	0.75	0.62	0.54	0.60	0.56	0.49
Guangdong	0.04	0.02	0.04	0.00	0.02	0.00	0.01	0.04	0.02	0.02	0.02
Guangxi	2.03	1.99	1.68	1.61	1.53	1.53	1.24	1.16	1.07	1.13	0.95
Hainan	0.05	0.14	0.31	0.19	0.30	0.34	0.28	0.40	0.51	0.30	0.38
Chongqing	0.13	0.06	0.07	0.15	0.14	0.19	0.20	0.15	0.15	0.20	0.13
Sichuan	1.78	1.64	1.45	1.29	1.31	1.18	0.98	0.93	0.95	0.96	0.86
Guizhou	1.87	1.84	1.76	1.34	1.49	1.47	1.20	1.16	1.20	1.24	1.03
Yunnan	3.83	3.77	3.37	3.01	3.06	2.96	2.61	2.33	2.18	2.11	1.84
Tibet	147.62	140.27	122.96	112.86	117.39	103.26	92.37	86.18	78.54	75.70	65.81
Shaanxi	1.74	1.67	1.25	1.06	1.02	1.08	1.01	0.80	0.76	0.80	0.67
Gansu	4.77	4.56	4.23	3.87	3.79	3.75	3.21	2.81	2.74	2.46	2.13
Qinghai	33.65	31.28	26.66	23.34	22.01	20.45	16.68	15.10	14.02	16.20	14.89
Ningxia	3.09	2.09	2.17	1.83	2.03	1.47	1.18	1.05	1.08	1.32	1.46
Xinjiang	9.02	8.36	7.35	6.90	7.02	7.05	5.54	5.13	4.62	4.60	4.19

Table 1

Table 2

The per capita eco-compensation standard for each province in China from 2004 to 2014 (yuan)"

Province	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014
Beijing	-158.20	-110.27	-117.86	-133.00	-142.24	-179.19	-182.87	-199.46	-265.78	-179.53	-213.76
Tianjin	-93.07	-19.94	-50.70	-45.56	-47.79	-10.42	-61.20	-65.39	-60.74	-191.86	20.69
Hebei	71.39	94.67	85.45	93.49	105.75	100.10	94.80	121.93	116.62	152.27	200.63
Shanxi	106.27	120.75	168.53	199.68	238.70	200.79	157.44	168.19	185.00	227.01	157.11
Inner Mongolia	1459.67	1566.38	1643.48	1750.93	2100.86	2090.76	2108.05	2402.22	2472.26	2695.39	2692.56
Liaoning	123.78	163.52	199.84	139.16	155.85	157.29	146.58	152.45	148.16	202.41	225.64
Jilin	232.41	253.17	252.59	286.41	344.77	336.46	335.78	374.57	393.13	391.56	405.06
Heilongjiang	361.75	396.07	405.47	453.92	546.90	557.97	552.70	614.50	614.48	702.84	712.29
Shanghai	-543.32	-500.63	-560.69	-502.81	-529.61	-484.25	-526.53	-462.42	-313.08	-191.09	-167.17
Jiangsu	-42.71	-24.88	-37.66	-1.49	-17.29	-22.93	-46.16	-36.32	-70.40	19.48	1.13
Zhejiang	20.89	39.05	44.85	39.50	37.26	52.58	26.47	39.78	32.44	77.65	85.50
Anhui	46.53	49.28	50.47	63.26	76.97	77.30	71.51	77.75	80.62	136.45	87.95
Fujian	161.07	202.61	160.64	151.25	182.14	171.63	180.07	183.98	209.71	274.42	269.20
Jiangxi	146.20	161.96	160.08	173.37	201.59	198.78	204.03	219.92	232.88	251.61	242.95
Shandong	29.35	53.04	47.70	51.02	70.05	38.93	19.24	38.55	64.30	76.00	135.63
Henan	26.04	37.00	40.24	50.64	47.10	40.52	34.56	47.46	31.74	53.51	64.48
Hubei	71.15	88.53	90.04	102.86	116.56	145.28	144.45	122.46	164.38	199.16	194.08
Hunan	85.37	111.08	119.77	121.13	149.50	153.22	150.96	160.51	199.77	205.66	196.89
Guangdong	-8.10	-4.51	-12.14	-0.37	5.61	-0.41	-2.80	-20.63	-13.25	-12.49	12.45
Guangxi	142.65	173.71	171.60	197.02	222.36	244.38	257.07	292.03	299.19	306.83	312.17
Hainan	4.40	15.70	39.00	28.52	52.81	64.70	65.36	115.85	165.13	106.52	147.27
Chongqing	-13.00	-6.86	-9.79	25.64	28.39	44.28	54.97	52.00	58.74	84.54	56.47
Sichuan	140.62	147.86	154.25	168.01	203.59	204.13	209.72	243.49	279.53	310.87	302.16
Guizhou	80.48	98.87	111.25	106.17	147.61	162.68	159.36	190.98	235.80	283.05	272.47
Yunnan	267.59	293.87	300.98	318.54	382.85	400.13	409.28	447.31	482.06	527.07	500.30
Tibet	11784.78	12584.29	12555.44	13333.22	15873.63	15396.28	15624.33	17231.68	17877.36	19594.46	19055.43
Shaanxi	136.10	166.42	152.85	165.26	199.70	236.71	272.64	267.81	292.16	340.13	313.91
Gansu	316.77	346.88	378.29	409.97	470.56	497.10	516.09	549.26	600.26	597.50	560.77
Qinghai	2910.02	3129.65	3154.38	3371.92	4047.11	3968.94	4000.71	4441.20	4633.86	5890.33	5877.77
Ningxia	281.97	214.93	261.26	275.41	394.66	317.60	315.17	346.48	389.62	515.76	608.41
Xinjiang	1033.77	1097.51	1092.24	1159.62	1377.57	1397.13	1377.89	1535.90	1553.15	1699.85	1689.18

Table 2

Figure 3

Figure 4

Figure 5

Table 3

Figure 6

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Factor of influence	G* (virtual)	G (total)	S (factor contribution)
Eco-resources value	0.018	0.029	0.733
Economic input of pollution abatement	0.017		0.077
Cost of pollution abatement	0.018		0.190