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Frontiers in Energy

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ISSN 2095-1698(Online)

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Front. Energy    2023, Vol. 17 Issue (2) : 266-285    https://doi.org/10.1007/s11708-021-0752-1
RESEARCH ARTICLE
Effects of herding behavior of tradable green certificate market players on market efficiency: Insights from heterogeneous agent model
Yi ZUO, Xingang ZHAO()
School of Economics and Management, North China Electric Power University, Beijing 102206, China; Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China
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Abstract

Tradable green certificate (TGC) scheme promotes the development of renewable energy industry which currently has a dual effect on economy and environment. TGC market efficiency is reflected in stimulating renewable energy investment, but may be reduced by the herding behavior of market players. This paper proposes and simulates an artificial TGC market model which contains heterogeneous agents, communication structure, and regulatory rules to explore the characteristics of herding behavior and its effects on market efficiency. The results show that the evolution of herding behavior reduces information asymmetry and improves market efficiency, especially when the borrowing is allowed. In addition, the fundamental strategy is diffused by herding evolution, but TGC market efficiency may be remarkably reduced by herding with borrowing mechanism. Moreover, the herding behavior may evolve to an equilibrium where the revenue of market players is comparable, thus the fairness in TGC market is improved.
Keywords tradable green certificate      herding behavior      evolution      heterogeneous agent model      complex network     
Corresponding Author(s): Xingang ZHAO   
Online First Date: 13 July 2021    Issue Date: 29 May 2023
 Cite this article:   
Yi ZUO,Xingang ZHAO. Effects of herding behavior of tradable green certificate market players on market efficiency: Insights from heterogeneous agent model[J]. Front. Energy, 2023, 17(2): 266-285.
 URL:  
https://academic.hep.com.cn/fie/EN/10.1007/s11708-021-0752-1
https://academic.hep.com.cn/fie/EN/Y2023/V17/I2/266
Fig.1  Rationale of ASM-TGC model.
Indices, parameters, and variables Meaning Initial value
DV Price deviation from value –
VP Price volatility –
Vt TGC value 0.3
t TGC trading period –
i The agent representing Disco –
Bt i‾ Benchmark transaction volume 1.558 × 108
Qbi Quota requirement of each Disco 1.87 × 1010
T Total trading period 120
Bjm,t−1 Last transaction decision of the neighbor –
Bti,BF,ti,BM,ti,BC,ti Final transaction decision of fundamentalist, momentum trader or contrarian –
P TGC price –
Bori,t Number of borrowed TGCs –
α, γ Sensitivity coefficient of fundamental strategy 2
β Sensitivity coefficient of trend strategy 3
CIi Coefficient of infection 0.5
mp Memory period 3
μ Change rate of CI 0.005
m Number of the neighbors 3
jm Neighbor of agent i –
ai,jm Contribution rate of the agent’s neighbor decision –
Nti Final decision of neighbors –
Trend Price trends over the past period –
L Observation period 3
Hti Amount of TGCs held by the agent after submitting the TGCs –
S j‾ Average volume of TGC sold in each transaction –
Cti Cost of agent –
f Fine 0.6
δ Random coefficient of RE production U(–0.1,0.1)
k Agent representing RE producer –
Stk Decision of RE producer –
λ Price adjustment coefficient 1
St Total supply of TGCs –
turn inti Amount of TGCs submitted to regulatory authority –
Dt Total demand of TGCs –
t_ma Moving average period 15
Tab.1  Nomenclature
Fig.2  Scale-free network in ASM-TGC model.
Information level Value estimation
Inf 1 (high grade) V
Inf 2 (medium grade) U(V×0.5, V×1.5)
Inf 3 (low grade) U(V×0.1, V×1.9)
Tab.2  Division of information level
Scenario Value estimation
1-0 V, U(V×0.1, V), U(V, V×1.9)
1-1 V, U(V×0.1, V), U(V, V×1.9)
1-2 V, U(V, V×1.5), U(V, V×1.9)
1-3 V, U(V×0.5, V), U(V×0.1, V)
Tab.3  Value estimation in scenarios
Fig.3  Output of a renewable energy power plant.
Fig.4  Simulation results in Scenario 1-1.
Fig.5  Simulation results in Scenario 1-2.
Fig.6  Simulation results in Scenario 1-3.
Market efficiency Scenario 1-0 Scenario 1-1 Scenario 1-2 Scenario 1-3
DV 2.41 × 10−3 1.15 × 10−4 5.77 × 10−4 3.22 × 10−4
VP 3.28 × 10−4 1.96 × 10−6 9.80 × 10−6 1.25 × 10−5
Tab.4  Market efficiency in information asymmetry scenarios
Fig.7  Interaction between TGC price and herding evolution.
Fig.8  Simulation results in Scenario 2-1 (1:1:1).
Fig.9  Simulation results in Scenario 2-2 (2:1:1).
Fig.10  Simulation results in Scenario 2-3 (1:2:1).
Fig.11  Simulation results in Scenario 2-4 (1:1:2).
Market efficiency 2-1 (1:1:1) 2-2 (2:1:1)
No herding Herding No herding Herding
DV 5.06 × 10−5 5.21 × 10−5 3.96 × 10−5 4.36 × 10−5
VP 1.43 × 10−6 1.54 × 10−6 1.66 × 10−6 1.77 × 10−6
Market efficiency 2-3 (1:2:1) 2-4 (1:1:2)
No herding Herding No herding Herding
DV 1.72 × 10−5 3.75 × 10−5 1.28 × 10−4 9.55 × 10−5
VP 1.94 × 10−6 2.71 × 10−6 2.24 × 10−6 1.63 × 10−6
Tab.5  Market efficiency in combined strategy preference scenarios
Fig.12  Impacts of regulatory rules in Scenario 1-1.
Fig.13  Impacts of regulatory rules in Scenario 1-2.
Fig.14  Impacts of regulatory rules in Scenario 1-3.
Market efficiency Scenario 1-1 Scenario 1-2 Scenario 1-3
Unlimited valid time and fine DV 1.15 × 10−4 5.77 × 10−4 3.22 × 10−4
VP 1.96 × 10−6 9.80 × 10−6 1.25 × 10−5
Limited valid time and fine DV 1.25 × 10−4 6.07 × 10−4 3.58 × 10−4
VP 1.87 × 10−6 9.68 × 10−6 1.98 × 10−5
Limited valid time, borrowing and fine DV 9.96 × 10−5 8.51 × 10−5 2.86 × 10−4
VP 7.80 × 10−7 6.22 × 10−6 1.55 × 10−5
Tab.6  Impacts of regulatory rules on market efficiency in information asymmetry scenarios
Fig.15  Impacts of regulatory rules in Scenario 2-1.
Fig.16  Impacts of regulatory rules in Scenario 2-2.
Fig.17  Impacts of regulatory rules in Scenario 2-3.
Fig.18  Impacts of regulatory rules in Scenario 2-4.
Market efficiency 2-1 (1:1:1) 2-2 (2:1:1) 2-3 (1:2:1) 2-4 (1:1:2)
Unlimited valid time and fine DV 5.21 × 10−5 4.36 × 10−5 3.75 × 10−5 9.55 × 10−5
VP 1.54 × 10−6 1.77 × 10−6 2.71 × 10−6 1.63 × 10−6
Limited valid time and fine DV 4.97 × 10−5 4.78 × 10−5 4.18 × 10−5 9.29 × 10−5
VP 1.29 × 10−6 1.93 × 10−6 2.24 × 10−5 2.03 × 10−6
Limited valid time, borrowing and fine DV 4.53 × 10−4 8.39 × 10−5 3.09 × 10−4 6.17 × 10−4
VP 1.37 × 10−5 1.58 × 10−5 4.65 × 10−5 4.91 × 10−5
Tab.7  Impacts of regulatory rules on market efficiency in strategy preference scenarios
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