碳配额与交易监管下基于区块链技术的平台供应链网络运营决策

doi:10.6040/j.issn.1671-9352.0.2023.049

摘要/Abstract

摘要：

在碳配额与交易监管下，基于Nash非合作博弈与变分不等式理论，考虑碳排放权初始分配环节中供应商存在谎报碳配额行为与绿色信息敏感型消费者对产品核实需求，构建应用与未应用区块链技术的2种平台供应链网络均衡模型，对比研究应用区块链技术前后各平台供应链成员的均衡决策及区块链应用阈值。利用修正收缩投影算法对模型进行求解，并通过数值算例可得：应用区块链技术可避免碳交易量不一致的情况，进一步减少资源的浪费并提高供应链利润；供应商谎报碳配额的行为在一定程度上能提高供应链整体利润，但抑制了供应链获得更高利润的能力；在一定的区块链运营成本阈值内，供应链成员才有采用区块链技术的动机。

关键词: 区块链技术, 碳配额与交易政策, 碳配额谎报行为, 绿色信息敏感, 平台供应链网络均衡

Abstract:

Under the cap-and-trade regulation, based on Nash non-cooperative game theory and variational inequality theory, considering supplier's misrepresentation of carbon caps during the initial allocation and the demand of green information sensitive consumers for product verification, two platform supply chain network equilibrium models with and without blockchain technology are constructed. Then this paper explores and compares the equilibrium conditions of supply chain members and blockchain technology application thresholds before and after the application of blockchain technology. The modified contraction projection algorithm is used to solve the problem and some numerical examples are given to show that: the application of blockchain technology can avoid the inconsistency of carbon emission trading volume, further reduce the waste of resources and improve the profit of the supply chain. The supplier's misrepresentation of the carbon cap can to some extent improve the overall profit of the supply chain, but it inhibits the ability of the supply chain to achieve higher profits. Within a certain threshold of blockchain technology operating costs, supply chain members have the motivation to adopt blockchain technology.

Key words: blockchain technology, cap-and-trade regulation, misrepresentation of carbon cap, green information sensitivity, platform supply chain network equilibrium

中图分类号:

F272.3

史聪,张桂涛,张萧,林帅成. 碳配额与交易监管下基于区块链技术的平台供应链网络运营决策[J]. 《山东大学学报(理学版)》, 2024, 59(1): 100-114, 123.

Cong SHI,Guitao ZHANG,Xiao ZHANG,Shuaicheng LIN. Platform supply chain network operation decision based on blockchain technology under cap-and-trade regulation[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(1): 100-114, 123.

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符号	含义
s	第s个供应商，s=1, 2, …, S
j	第j个高排放型制造商，j=1, 2, …, J
i	第i个低排放型制造商，i=1, 2, …, I
e	电商平台
c	“全碳链”服务中心
k	需求市场，k=1, 2, …, K
C_s^X	政府免费给供应商发放的碳配额
C_y	政府免费给2类制造商发放的碳配额，y=j, i
α_z1	生产单位原材料或新产品的碳排放量，z=s, j, i
α_y2	回收再制造环节单位产品的碳排放量，y=j, i
ε	供应商与2类制造商交易时“全碳链”服务中心收取的单位碳交易佣金
ω	单位碳交易价格
η	原材料转化率，η∈(0, 1)
μ	最低回收率，μ∈(0, 1)
β	理论再制造率，β∈(0, 1)
ϕ	供应商碳谎报失败的概率，ϕ∈(0, 1)
r	减排技术投资系数
δ	电商平台收取的佣金率，δ∈(0, 1)
t	检测信息花费的时间
s_c	服务水平的影响因子
h^X	检测出正品的概率，h^X=(0, 1]
$\vartheta^{X}$	单位碳交易价格的溢价程度，$\vartheta^{X}$∈[1, 2]
c^B	单位区块链运营成本；参考Yang等^[24]和Niu等^[25]的研究，本文认为区块链技术为供应商和制造商提供每件产品的信息追踪认证，并收取单位服务费(也指每件产品额外的区块链运营成本)，在本文中，供应链各方需支付各自的区块链运营费用
F	政府对企业单位碳配额谎报的惩罚成本
θ	消费者中对绿色产品、绿色信息感知敏感的比例，θ∈(0, 1)
τ	消费者对平台服务水平的偏好，τ∈(0, 1)
γ	消费者的低碳偏好，γ∈(0, 1)
g	消费者对检测时间的敏感程度，g∈(0, 1)
a	一个布尔变量，a=1(应用区块链技术)，a=0(未应用区块链技术)

符号	含义
q_sy^X	供应商与2类制造商原材料的交易量，$y=j, i, \boldsymbol{Q}_{1}^{1 X}=(q_{s j}^{X})_{S J \times 1} \in R_{+}^{S J}, \boldsymbol{Q}_{1}^{2}=(q_{s i})_{S \times 1} \in R_{+}^{S I}$
$q_{y k}^{v X}$	2类制造商利用原材料生产的产品数量，$y=j, i, \boldsymbol{q}_{1}^{\mathrm{vX}}=(q_{j \mathrm{k}}^{\mathrm{vX}})_{J K \times 1} \in R_{+}^{J K}, \boldsymbol{q}_{2}^{\mathrm{vX}}=(q_{i k}^{\mathrm{vX}})_{I K \times 1} \in R_{+}^{I K}$
$q_{y k}^{X}$	2类制造商销售给需求市场k的总产品供给量，$y=j, i, \boldsymbol{Q}_{2}^{1 X}=(q_{j k}^{X})_{J K \times 1} \in R_{+}^{J K}, \boldsymbol{Q}_{2}^{2 X}=(q_{i k}^{X})_{I K \times 1} \in R_{+}^{I K}$
$q_{k y}^{X}$	2类制造商从需求市场k回收的产品数量，$y=j, i, \boldsymbol{Q}_{3}^{1 X}=(q_{k j}^{X})_{J \times 1} \in R_{+}^{J K}, \boldsymbol{Q}_{3}^{2 X}=(q_{k i}^{X})_{I K \times 1} \in R_{+}^{I K}$
$t_{z}^{X}$	供应商与2类制造商的碳交易量，$z=s, j, i, \boldsymbol{t}_{1}^{X}=(t_{s}^{X})_{S \times 1} \in R_{+}^{S}, \boldsymbol{t}_{2}^{X}=(t_{j})_{J \times 1} \in R_{+}^{J}, \boldsymbol{t}_{3}^{X}=(t_{i})_{I \times 1} \in R_{+}^{I}$
$e_{i}^{X}$	低排放型制造商的碳减排量，$\hat{\boldsymbol{E}}^{X}=(e_{i}^{X})_{I \times 1} \in R_{+}^{I}$
$s_{\mathrm{e}}^{X}$	平台提供的服务水平，$\boldsymbol{S}^{X}=(s_{\mathrm{e}}^{X})_{E \times 1} \in R_{+}^{E}$
$p_{k}^{v X}$	消费者支付给两类制造商的单位产品价格，$y=j, i, \boldsymbol{P}_{1}^{X}=(p_{k}^{j X})_{K \times 1} \in R_{+}^{K}, \boldsymbol{P}_{2}^{X}=(p_{k}^{i X})_{K \times 1} \in R_{+}^{K}$

符号	含义
$c_{s}^{y}=c_{s}^{y}(q_{s y}^{X})$	供应商承担的与2类制造商的交易成本，$y=j, i$
$\hat{c}_{s}^{y}=\hat{c}_{s}^{y}(q_{s y}^{X})$	2类制造商承担的与供应商的交易成本，$y=j, i$
$c_{y k}=c_{y k}(q_{y k}^{X})$	2类制造商承担的与需求市场的交易成本，$y=j, i$
$\hat{c}_{y k}=\hat{c}_{y k}(q_{y k}^{X})$	需求市场承担的与2类制造商的交易成本，$y=j, i$
$f_{s}=f_{s}(q_{s y}^{X})$	供应商生产原材料的生产成本，$y=j, i$
$f_{y}=f_{y}(q_{y k}^{\mathrm{v} X})$	2类制造商生产新产品的生产成本，$y=j, i$
$c_{k}=c_{k}(q_{k y}^{X})$	2类制造商处理回收产品的处理成本，$y=j, i$
$f_{y}=f_{y}(q_{k y}^{N}, \beta)$	2类制造商再制造过程中的再制造成本，$y=j, i$
$c_{\mathrm{e}}=c_{\mathrm{e}}(s_{\mathrm{e}}^{X}, s_{\mathrm{c}})$	电商平台的基础服务投资成本
$w_{\mathrm{e}}=w_{\mathrm{e}}(q_{j k}^{X}, q_{i k}^{X}, s_{\mathrm{e}}^{X})$	电商平台的流量收益
$c_{z}^{t}=c_{z}^{t}(t_{z}^{X})$	由“全碳链”服务中心承担的与供应商与2类制造商的碳交易成本，$z=s, j, i$
$d_{k}^{y}(q_{y k}^{X})$	需求市场对2类制造商生产产品的需求量，$y=j, i$
$\rho_{s y}^{X}$	供应商与2类制造商之间的单位原材料价格，$y=j, i$
$\rho_{y k}^{X}$	2类制造商生产的新产品的单位零售价格，$y=j, i$
$\rho_{k y}^{X}$	2类制造商回收废旧产品的单位价格，$y=j, i$

参数	C_s^X	q_sj^X*	q_si^X*	q_jk^X*	q_ik^X*	q_jk^vX*	q_ik^vX*	q_kj^X*	q_ki^X*	t_s^X*	t_j^X*	t_i^X*
B模型	5.000 0	1.541 0	3.304 2	1.998 4	4.28 5	1.386 9	2.973 8	0.679 5	1.456 9	3.721 3	1.364 4	5.085 7
	6.000 0	1.277 2	3.108 3	1.656 3	4.030 9	1.149 5	2.797 4	0.563 2	1.370 5	1.893 9	0.754 2	5.285 9
	6.100 0	1.277 4	3.108 4	1.656 5	4.031 1	1.149 6	2.797 6	0.563 2	1.370 6	1.794 4	0.754 6	5.285 7
	6.200 0	1.277 5	3.108 6	1.656 8	4.031 3	1.149 8	2.797 7	0.563 3	1.370 6	1.695 0	0.755 0	5.285 5
N模型	6.300 0	1.277 7	3.108 7	1.657 0	4.031 5	1.149 9	2.797 9	0.563 4	1.370 7	1.595 6	0.755 4	5.285 3
	6.400 0	1.277 9	3.108 9	1.657 2	4.031 7	1.150 1	2.798 0	0.563 4	1.370 8	1.496 2	0.755 8	5.285 1
	6.500 0	1.278 0	3.109 1	1.657 4	4.031 9	1.150 2	2.798 2	0.563 5	1.370 9	1.396 8	0.756 2	5.284 9
	6.600 0	1.278 2	3.109 2	1.657 6	4.032 1	1.150 4	2.798 3	0.563 6	1.370 9	1.297 4	0.756 5	5.284 7

参数	C_s^X	p_k^jX*	p_k^iX*	s_e^X*	e_i^X*	π_s^X*	π_j^X*	π_i^X*	π_c^X*	π_e^X*	π^X*
B模型	5.000 0	28.782 0	38.941 0	10.000 0	2.396 9	25.587 6	33.103 8	165.993 0	8.156 5	102.443 0	335.254 0
	6.000 0	28.895 0	37.778 0	10.000 0	2.341 4	37.224 8	28.948 4	107.379 0	14.062 9	80.300 8	267.915 0
	6.100 0	28.895 0	37.777 4	10.000 0	2.341 4	37.460 3	28.953 1	107.386 0	13.871 8	80.307 0	267.978 0
	6.200 0	28.895 0	37.777 2	10.000 0	2.341 4	37.695 8	28.957 7	107.394 0	13.680 4	80.313 1	268.041 0
N模型	6.300 0	28.894 0	37.777 1	10.000 0	2.341 4	37.931 2	28.962 3	107.402 0	13.488 5	80.319 2	268.103 0
	6.400 0	28.894 0	37.769 0	10.000 0	2.341 4	38.166 7	28.966 9	107.410 0	13.296 3	80.325 3	268.165 0
	6.500 0	28.894 0	37.776 0	10.000 0	2.341 5	38.402 0	28.972 0	107.420 0	13.104 0	80.331 0	268.230 0
	6.600 0	28.894 0	37.776 7	10.000 0	2.341 5	38.637 7	28.976 1	107.426 0	12.910 6	80.337 5	268.287 0

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