碳交易背景下依托平台的协作配送双重低碳激励策略研究

doi:10.16381/j.cnki.issn1003-207x.2022.1434

Abstract

Abstract:

If collaborative distribution is optimized in a low-carbon model as opposed to a low-cost model， the cost and carbon emissions will be different. Distribution companies lack motivation to reduce emissions due to higher low carbon expenditure prices and difficulties with carbon data verification. Enterprise alliances seeking to maximize economic benefits typically opt for the low-cost model， so collaborative distribution model has not yet achieved the state of minimizing carbon emissions currently. How to precisely incentivize collaborative distribution companies to adopt the low-carbon model is crucial to achieving the dual-carbon goal.A dual low-carbon incentive strategy is created based on information collected from a government-led digital platform to encourage distribution companies to adopt the low-carbon model. Firstly， adopting incentive strategies requires a scientific estimate of the benefit loss and carbon emission of the alliance members. This problem is modelled as an integer programming which minimizes vehicle routing costs or carbon emissions for sub-alliances， and then costs and carbon emissions are calculated. The cost-sharing values and carbon emissions of each participant in the two models are determined using the Shapley value method and benefit loss models and carbon emission models are created using these data. Secondly， distribution companies are only encouraged to adopt the low-carbon model when the benefit loss is fully made up for. Based on this， a dual low-carbon incentive strategy is suggested considering government subsidies and revenue from carbon quota trading， a carbon quota mechanism is designed based on emission reduction contribution， and compares and the impact of a single incentive strategy and a dual incentive strategy on the best choice made by companies and the government is analyzed. Finally， numerical experiments are used to determine whether the model and strategy are valid and workable.Results indicate that compared to the conventional model， the collaborative distribution low-carbon model based on the digital platform reduces carbon emissions by 31% to 67%. Distribution companies may be more inclined to adopt a low-carbon model if carbon quota mechanisms are based on emission reduction contributions. The dual low-carbon incentive strategy can reduce government fiscal outlay by 15% to 31%， and the carbon emissions overrun penalty mechanism can penalize high-emitting enterprises. The government can use this study as a supplement to current theory and practice to distribute carbon quotas for distribution companies， implement low-carbon incentives， impose penalties for exceeding them， and assist in meeting the double carbon target on schedule.

Key words: collaborative distribution, digital platform, carbon quota allowance & trading, government subsidy, dual incentive strategy

CLC Number:

Weizhen Rao, Lichen Zhou, Xiangyu Ma, Qinghua Zhu. Dual Low-carbon Incentive Strategy for Collaborative Distribution Based on a Digital Platform under the Background of Carbon Trading[J]. Chinese Journal of Management Science, 2025, 33(4): 299-312.

Figures/Tables 15

模型	指标	企业1	企业2	企业3	企业4
历史排放法	τ_i	10.24	8.2	9.83	10.42
LCE-MOCVRP	$τ i 1$	10.32	9.54	9.38	9.45
LC-MOCVRP	$τ i 2$	10.58	8.05	9.72	10.34

目标	指标	企业1	企业2	企业3	企业4	合计
LCE	$σ i 1$	0.47	0.43	0.40	0.42	1.72
LC	$σ i 2$	0.31	0.21	0.26	0.27	1.05
Gain(i)		0.16	0.23	0.14	0.14	0.67

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j	B^j	S^j	LCE-MOCVRP				LC-MOCVRP
			cost	NC-CO₂	C-CO₂	CER	cost	NC-CO₂	C-CO₂	CER
			/元	/千克	/千克	/千克	/元	/千克	/千克	/千克
1	[0, 0, 0, 1]	{4}	44.48	3.96	3.96	0.00	42.36	10.42	10.42	0.00
2	[0, 0, 1, 0]	{3}	41.93	4.18	4.18	0.00	39.94	9.83	9.83	0.00
3	[0, 0, 1, 1]	{3, 4}	58.49	8.15	6.04	2.11	55.71	20.25	13.71	6.54
4	[0, 1, 0, 0]	{2}	34.99	3.76	3.76	0.00	33.32	8.20	8.20	0.00
5	[0, 1, 0, 1]	{2, 4}	59.95	7.72	6.11	1.62	57.09	18.62	14.05	4.57
6	[0, 1, 1, 0]	{2, 3}	56.48	7.94	6.05	1.89	53.79	18.03	13.24	4.79
7	[0, 1, 1, 1]	{2, 3, 4}	77.11	11.91	8.53	3.38	73.44	28.45	18.07	10.38
8	[1, 0, 0, 0]	{1}	43.68	4.12	4.12	0.00	41.60	10.24	10.24	0.00
9	[1, 0, 0, 1]	{1, 4}	60.31	8.09	6.32	1.76	57.44	20.66	14.14	6.53
10	[1, 0, 1, 0]	{1, 3}	59.15	8.31	6.17	2.13	56.33	20.07	13.86	6.20
11	[1, 0, 1, 1]	{1, 3, 4}	78.36	12.27	8.72	3.55	74.63	30.49	18.37	12.12
12	[1, 1, 0, 0]	{1, 2}	56.27	7.88	5.69	2.20	53.59	18.44	13.19	5.25
13	[1, 1, 0, 1]	{1, 2, 4}	74.83	11.85	8.01	3.84	71.26	28.86	17.54	11.32
14	[1, 1, 1, 0]	{1, 2, 3}	75.60	12.07	8.58	3.48	72.00	28.27	17.72	10.55
15	[1, 1, 1, 1]	{1, 2, 3, 4}	90.68	16.03	10.08	5.95	86.37	38.69	21.25	17.44

模型	指标	企业1	企业2	企业3	企业4
LCE	Save(i)₁	20.35	15.48	18.69	19.87
LCE	S_C₁%	47%	44%	45%	45%
LC	Save(i)₂	19.38	14.74	17.80	18.93
LC	S_C₂%	47%	44%	45%	45%
Lost(i)		0.97	0.74	0.89	0.94

信息类别	模型	指标	企业1	企业2	企业3	企业4	企业5	企业6
成本信息	LCE-MOCVRP	NC	15725.60	10496.01	15069.77	9309.72	5560.88	1562.18
		C	8797.43	5866.58	8705.44	5038.74	3826.83	1104.18
		Save(i)₁	6928.17	4629.43	6364.33	4270.98	1734.06	458.01
		S_C₁%	44%	44%	42%	46%	31%	29%
	LC-MOCVRP	NC	14976.77	9996.20	14352.16	8866.40	5296.08	1487.79
		C	8378.51	5587.22	8290.89	4798.80	3644.60	1051.60
		Save(i)₂	6598.26	4408.98	6061.27	4067.60	1651.48	436.20
		S_C₂%	44%	44%	42%	46%	31%	29%
碳排放信息	LCE-MOCVRP	NC	1532.28	1149.30	1553.80	841.67	799.42	182.66
		C	974.45	695.42	1027.60	514.79	518.55	147.32
		CER(i)₁	557.83	453.88	526.20	326.88	280.87	35.34
		S_CE₁%	36%	39%	34%	39%	35%	19%
	LC-MOCVRP	NC	3685.82	2460.08	3532.01	2182.01	1303.36	366.15
		C	2061.94	1375.03	2040.44	1181.01	896.94	258.80
		CER(i)₂	1623.88	1085.05	1491.57	1001.00	406.43	107.35
		S_CE₂%	44%	44%	42%	46%	31%	29%

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Dual Low-carbon Incentive Strategy for Collaborative Distribution Based on a Digital Platform under the Background of Carbon Trading

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指标	企业1	企业2	企业3	企业4
E_min	10.32	9.54	9.38	9.45
E_allow	10.42	9.64	9.47	9.54
E_i	10.32	9.58	9.42	10.12
penalty(i)	0	0	0	0.58χ

策略	指标	企业1	企业2	企业3	企业4	合计
Lost(i)		0.97	0.74	0.89	0.95	3.54
碳交易收益	Gain(i)	0.16	0.23	0.14	0.14	0.67
碳交易收益	百分比（%）	17	31	16	15	19
政府补贴	Excitation(i)	0.81	0.51	0.75	0.80	2.87
政府补贴	百分比（%）	83	69	84	85	81