MRV机制下碳数据质量监管多主体行为决策研究

doi:10.16381/j.cnki.issn1003-207x.2024.0953

Abstract

Abstract:

Carbon data quality is the lifeline of carbon trading and holds significant implications in realizing the “dual-carbon” strategy objectives. Consequently， in light of the existing risks associated with regulating carbon data quality under the MRV mechanism， the optimal behavior decision-making empowered by blockchain technology for multiple agents is explored. Initially， the enabling mechanism of blockchain technology on the behavior decisions of multiple agents in the context of carbon data quality supervision is dissected. Subsequently， an evolutionary game theory and method are utilized to construct a mixed-strategy game model involving government regulatory authorities， emission-capping enterprises， and third-party carbon verification agencies. By analyzing the stability of the strategies of the various agents and the equilibrium of the system， the effective conditions for collaborative carbon data quality management with the participation of multiple agents empowered by blockchain are solved. Furthermore， referring to policy documents and typical cases， the impact of key parameters on the behavioral evolution of participants is further explored using numerical simulation methods. The research findings indicate that：（1） The construction of an on-chain supervision system with multiple interactions of different nodes supported by blockchain can effectively alleviate the regulatory problems of carbon data quality. With the widespread adoption of blockchain technology and the strengthening of public environmental awareness， the implementation of blockchain regulatory models by the government is a prerequisite and trend for promoting joint governance by multiple parties. （2） Emission-capping enterprises have a subjective tendency to engage in collusion and speculation with the verification agencies. It is necessary to significantly increase the cost of fraud and the threshold for collusion on the basis of fiscal and tax incentives and on-chain subsidies， in order to effectively regulate the integrity of emission-capping enterprises in reducing emissions. （3） The focus of strategy selection for third-party carbon verification agencies is credit cost， but there exists a "failure interval" for credit cost parameters， which may lead to a game deadlock in system strategy evolution. A robust mechanism of multiple reviews and credit penalties is conducive to the verification institutions actively fulfilling their responsibilities and overcoming this situation. This research can help mitigate the risk of carbon data quality supervision under the MRV mechanism， and provide a valuable reference for the effective guidance of behavioral decision-making among carbon trading-related subjects and the regulation of data quality management within the carbon market.

Key words: blockchain technology enablement, carbon data quality supervision, tripartite evolutionary game, conspiracy to defraud

CLC Number:

F224.3

Xichen Lyu, Yinfeng Tian, Shihai Tian, Jiayuan Liu, Qingwei Kong. Research on Multi-Agent Decision-Making in Carbon Data Quality Supervision under MRV Mechanism: A Blockchain Technology Empowerment Perspective[J]. Chinese Journal of Management Science, 2025, 33(8): 355-368.

Figures/Tables 15

参数	说明
$T 1$ / $T 2$	政府区块链监管模式/普通监管模式时的潜在收益( $T 1 > T 2$ )
$S$	政府支付给第三方碳核查机构的服务成本
$C g$ / $C k$ / $C h$	政府进行区块链监管时付出平台搭建成本/控排企业链入平台付出成本/核查机构链入平台付出成本
$R 1$ / $R 2$	政府普通监管模式下控排企业虚假减排/诚信减排的收益( $R 1 > R 2$ )
$R 1'$ / $R 2'$	政府区块链监管模式下控排企业虚假减排/诚信减排的收益( $R 1' > R 2'$ )
$P$	控排企业虚假减排且不达成合谋关系时被政府监管到并予以处罚的损失
$Q$	控排企业与第三方碳核查机构合谋欺诈所获得的额外收益
$t$	控排企业与第三方碳核查机构合谋欺诈控排企业的利益分配系数
$h$	政府普通监管模式下控排企业虚假减排或机构不履责被政府监管到的概率
$η$	控排企业与第三方碳核查机构合谋欺诈时政府对控排企业的惩处力度
$θ$	控排企业与第三方碳核查机构合谋欺诈时政府对第三方碳核查机构的惩处力度
$C 1$ / $C 2$ / $C 3$	第三方碳核查机构合谋欺诈/不履责/依法履责的成本（ $C 3 > C 2$ ）
$α$	第三方碳核查机构引入区块链平台服务对履责成本的影响系数
$X$	第三方碳核查机构不履责且被政府监查到时损失的信用成本

政府监管部门

区块链监管模式

x

普通监管模式

1 - x

第三方碳核查机构

不履责

z

依法履责

1 - z

不履责

z

依法履责

1 - z

控排企业

虚假减排

y

合谋欺诈

$u$

$T 1 - S + η Q + θ Q - C g$ ，

$R 1' + t Q - η Q - C k$ ，

$S + (1 - t) Q - α C 1 - (θ Q + X) - C h$

$T 1 - S + P - C g$ ，

$R 1' - P - C k$ ，

$S - α C 3 - C h$

$T 2 - S + h η Q + h θ Q$ ，

$R 1 + t Q - h η Q$ ，

$S + (1 - t) Q - C 1 - h (θ Q + X)$

$T 2 - S + P$ ，

$R 1 - P$ ，

$S - C 3$

欺瞒上报

$1 - u$

$T 1 - S + P - C g$ ，

$R 1' - P - C k$ ，

$S - α C 2 - X - C h$

$T 1 - S + P - C g$ ，

$R 1' - P - C k$ ，

$S - α C 3 - C h$

$T 2 - S + h P$ ，

$R 1 - h P$ ，

$S - C 2 - h X$

$T 2 - S + P$ ，

$R 1 - P$ ，

$S - C 3$

诚信减排

1 - y

$T 1 - S - C g$ ，

$R 2' - C k$ ，

$S - α C 2 - X - C h$

$T 1 - S - C g$ ，

$R 2' - C k$ ，

$S - α C 3 - C h$

$T 2 - S$ ，

$R 2$ ，

$S - C 2 - h X$

$T 2 - S$ ，

$R 2$ ，

$S - C 3$

均衡点	$λ 1$	$λ 2$	$λ 3$
$E 1 = (0,0, 0)$	$T 1 - T 2 - C g$	$R 1 - R 2 - P < 0$	$C 3 - C 2 - h X$
$E 2 = (1,0, 0)$	$T 2 - T 1 + C g$	$R 1' - R 2' - P < 0$	$α (C 3 - C 2) - X$
$E 3 = (0,1, 0)$	$T 1 - T 2 - C g$	$R 2 - R 1 + P > 0$	$u [C 2 - C 1 + (1 - t) Q - h θ Q] + C 3 - C 2 - h X$
$E 4 = (0,0, 1)$	$T 1 - T 2 - C g$	$u (t Q - h η Q + P) + (1 - u) (1 - h) P + R 1 - R 2 - P$	$C 2 - C 3 + h X$
$E 5 = (1,1, 0)$	$T 2 - T 1 + C g$	$R 2' - R 1' + P > 0$	$u [α (C 2 - C 1) + (1 - t) Q - θ Q] + α (C 3 - C 2) - X$
$E 6 = (1,0, 1)$	$T 2 - T 1 + C g$	$u (t Q - η Q + P) + R 1' - R 2' - P$	$X - α (C 3 - C 2)$
$E 7 = (0,1, 1)$	$(1 - h) [P + u (η Q + θ Q - P)] + T 1 - T 2 - C g$	$- u (t Q - h η Q + P) - (1 - u) (1 - h) P - R 1 + R 2 + P$	$- u [C 2 - C 1 + (1 - t) Q - h θ Q] - C 3 + C 2 + h X$
$E 8 = (1,1, 1)$	$- (1 - h) [P + u (η Q + θ Q - P)] - T 1 + T 2 + C g$	$- u (t Q - η Q + P) - R 1' + R 2' + P$	$- u [α (C 2 - C 1) + (1 - t) Q - θ Q] - α (C 3 - C 2) + X$

$T 1$	$T 2$	$C g$	$R 1$	$R 2$	$R 1'$	$R 2'$	$P$	$Q$	$t$	$h$	$C 1$	$C 2$	$C 3$	$η$	$θ$	$α$	$X$	$u$
45	25	20	80	65	92	75	20	15	0.6	0.6	20	15	40	1	1	0.85	3	0.6

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Research on Multi-Agent Decision-Making in Carbon Data Quality Supervision under MRV Mechanism: A Blockchain Technology Empowerment Perspective

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