基于Context-dependent DEA方法的碳排放减额分配策略

doi:10.18402/resci.2020.11.10

Abstract

Abstract:

Carbon emission control is an important part of the global response to climate change under the new situation of development. It is urgent to establish a set of carbon emission reduction distribution plans that takes both efficiency and fairness into account. Based on the efficiency evaluation results, this study adopted the context-dependent data envelopment analysis (DEA) method that is fair in nature and introduced the emission reduction capacity coefficient that can ensure fairness in constructing an improved central resource allocation model. It used the data of 30 provinces (municipalities, autonomous regions) of China’s mainland in 2016 as an example to carry out the carbon emission reduction allocation, and finally measured the fairness of the allocation scheme by Gini coefficient. The results show that: (1) The carbon emission efficiency of the eastern region is significantly better than that of the central and the western regions, and the amount of emission reduction required is also higher than that of the central and the western regions. (2) The actual carbon emissions of some large traditional energy provinces (autonomous regions) show a large gap from the target carbon emissions, which are in a state of serious over emission and low carbon emission efficiency, and it is necessary to accelerate the pace of economic transformation and implement emission policies strictly in these provinces (autonomous regions). (3) The carbon emission reduction distribution plan reasonably improves the carbon emission efficiency of the provinces (municipalities, autonomous regions) and reduces the Gini coefficient to a certain extent. The research results not only provides a new perspective on how to allocate emission reductions effectively and fairly, but also provides a reference for the decomposition of emission reduction targets at the national level and the formulation of emission reduction policies at the provincial level.

Key words: Context-dependent DEA, carbon emissions, resource allocation, Gini coefficient, China

ZHU Weiwei, MIAO Ziyang, GAN Guisheng. Carbon emission reduction allocation strategy based on the context-dependent DEA method[J].Resources Science, 2020, 42(11): 2170-2183.

Figures/Tables 10

Figure 1

Table 1

Evaluation index system of carbon emissions"

投入/产出	变量	单位	来源
投入	资本	亿元	《中国统计年鉴》^[24]
	人口	万人	《中国统计年鉴》^[25]
	能源消耗	万t标准煤	《中国能源统计年鉴》^[26]
期望产出	生产总值	亿元	《中国统计年鉴》^[25]
	就业人数	万人	《中国统计年鉴》^[24]
非期望产出	$C O 2$ 排放	百万t	《中国能源统计年鉴》^[27]

Table 1

Table 2

Table 3

Input and output data by region, 2016"

地区	投入			期望产出		非期望产出
地区	资本/亿元	人口/万人	能源消耗/万t标准煤	生产总值/亿元	就业人数/万人	CO₂排放/百万t
北京	10074.10	2173.00	7988.19	25669.13	2632.18	24.00
天津	10584.10	1562.00	12332.30	17885.39	527.34	31.32
河北	18636.50	7470.00	36948.44	32070.45	2468.72	152.01
山西	9464.30	3682.00	23021.09	13050.41	1391.66	183.10
内蒙古	12617.80	2520.00	28091.29	18128.10	1321.00	186.29
辽宁	9682.00	4378.00	31203.39	22246.90	2080.70	102.91
吉林	10152.90	2733.00	12653.96	14776.80	1547.07	51.72
黑龙江	9360.30	3799.00	16324.56	15386.09	911.59	77.04
上海	11321.20	2420.00	12947.30	28178.65	3003.11	47.00
江苏	33123.30	7999.00	37280.40	77388.28	7831.17	163.08
浙江	21393.80	5590.00	21555.90	47251.36	6452.08	90.86
安徽	12507.80	6196.00	15439.51	24407.62	2656.07	88.26
福建	16633.80	3874.00	15265.93	28810.58	2890.20	46.21
江西	9303.60	4592.00	9642.85	18499.00	2259.75	45.22
山东	34637.30	9947.00	48972.14	68024.49	5966.73	260.22
河南	29560.10	9532.00	29655.87	40471.79	2954.32	131.68
湖北	18786.30	5885.00	23878.90	32665.38	3936.08	74.48
湖南	16737.30	6822.00	22441.03	31551.37	1848.34	69.12
广东	34647.10	10999.00	36241.52	80854.91	9148.14	120.59
广西	12363.90	4838.00	12846.77	18317.64	1768.87	40.67
海南	2577.10	917.00	2461.92	4053.20	447.37	10.17
重庆	9551.60	3048.00	13256.38	17740.59	2664.37	39.70
四川	16165.30	8262.00	27679.14	32934.54	3535.48	71.56
贵州	8195.00	3555.00	13329.66	11776.73	1469.37	74.39
云南	13903.30	4771.00	14151.42	14788.42	1882.79	45.43
陕西	12907.70	3813.00	15232.12	19399.59	1303.39	105.95
甘肃	4875.10	2610.00	9032.14	7200.37	1044.62	36.76
青海	3565.50	593.00	5383.26	2572.49	205.45	13.75
宁夏	3827.90	675.00	6274.65	3168.59	384.00	45.14
新疆	8670.10	2398.00	19025.18	9649.70	841.68	107.65

Table 3

Figure 2

Table 4

Table 5

Figure 3

Table 6

Figure 4

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燃料	煤	汽油	煤油	柴油	燃料油	天然气
CCF	27.28	18.90	19.60	20.17	21.09	15.32
HE	192.140	448.000	447.500	433.300	401.900	0.384
COF/%	92.30	98.00	98.60	98.20	98.50	99.00

层次	地区	CO₂排放减额 /百万t	较2016年CO₂ 排放减少程度/%	2017年目标CO₂ 排放/百万t	2017年实际CO₂ 排放/百万t	实际与目标的差额/百万t
1	北京	0.00	0.00	24.00	22.98	-1.02
	天津	2.68	8.55	28.64	30.05	1.41
	上海	5.01	10.66	41.99	48.44	6.45
	江苏	15.19	9.31	147.89	161.44	13.55
	浙江	7.63	8.40	83.22	93.71	10.49
	广东	10.49	8.70	110.10	123.68	13.58
	海南	0.00	0.00	10.17	10.75	0.58
	青海	0.00	0.00	13.75	13.14	-0.61
	宁夏	3.53	7.81	41.62	56.58	14.96
2	内蒙古	17.30	9.28	168.99	196.17	27.18
	辽宁	10.72	10.42	92.18	107.00	14.82
	吉林	5.92	11.45	45.80	51.93	6.13
	福建	4.71	10.20	41.50	50.00	8.50
	江西	4.42	9.78	40.80	46.50	5.70
	山东	23.41	8.99	236.81	252.32	15.51
	湖北	8.52	11.45	65.95	75.45	9.50
	广西	0.22	0.53	40.45	41.41	0.96
	重庆	0.00	0.00	39.70	40.29	0.59
	四川	8.33	11.64	63.23	68.04	4.81
	甘肃	0.00	0.00	36.76	36.83	0.07
	新疆	11.81	10.97	95.84	114.41	18.57
3	河北	10.87	7.15	141.14	148.97	7.83
	黑龙江	6.24	8.10	70.80	79.65	8.85
	安徽	9.54	10.81	78.72	90.90	12.18
	河南	11.64	8.84	120.04	130.12	10.08
	湖南	7.35	10.64	61.77	73.64	11.87
	贵州	5.27	7.08	69.12	73.56	4.44
	云南	4.21	9.27	41.21	44.51	3.30
	陕西	13.99	13.20	91.97	108.09	16.12
4	山西	10.99	6.00	172.11	219.33	47.22

层次	地区	分层分配	全国分配	Yang等^[30]	Kong等^[31]
层次	地区	减排额/百万t	减排额/百万t	减排额/百万t	减排额/百万t
1	北京	0.00	0.00	-75.12	-74.37
	天津	2.68	2.68	-0.64	-61.81
	上海	5.01	5.01	-60.01	-51.69
	江苏	15.19	15.19	-122.84	28.92
	浙江	7.63	7.63	-99.14	-29.37
	广东	10.49	10.49	-164.25	-39.27
	海南	0.00	0.00	-3.08	-79.54
	青海	0.00	0.00	10.24	-73.32
	宁夏	3.53	0.00	36.90	-40.43
2	内蒙古	17.30	17.30	141.95	164.91
	辽宁	10.72	10.72	60.99	54.51
	吉林	5.92	5.92	-0.40	7.72
	福建	4.71	4.71	-66.60	-35.54
	江西	4.42	0.00	-6.40	-22.91
	山东	23.41	23.41	48.33	164.51
	湖北	8.52	8.52	-41.87	-11.51
	广西	0.22	0.00	-7.92	-32.46
	重庆	0.00	0.00	-24.87	-54.10
	四川	8.33	8.33	-4.21	-32.78
	甘肃	0.00	0.00	23.27	-7.42
	新疆	11.81	11.81	89.70	81.94
3	河北	10.87	10.87	92.13	78.25
	黑龙江	6.24	6.24	55.07	32.00
	安徽	9.54	9.54	26.15	20.04
	河南	11.64	11.64	34.83	27.29
	湖南	7.35	7.35	12.01	-23.23
	贵州	5.27	12.22	45.57	33.17
	云南	4.21	4.04	4.30	-17.46
	陕西	13.99	13.99	58.02	58.27
4	山西	10.99	12.38	157.87	155.68
公平性	基尼系数	0.179	0.173	0.260	0.060

Carbon emission reduction allocation strategy based on the context-dependent DEA method

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