Facing the challenge of global warming and the pressure of carbon emission reduction, the scheme of emission trading has been regarded as a flagship policy instrument. Numerous studies have been conducted on the design [1
] of this scheme. Additionally, the impact of the scheme has also been evaluated, including is impact on the macro economy [3
], economic performance of sectors or firms [6
], and financial performance of firms [8
]. However, several aspects of complexity have pushed traditional equilibrium-based models to their limit, including the dynamic aspect of market evolution, bounded rationality, heterogeneity, and incomplete information of the firms in the market. So, in our previous work [10
], we establish an agent-based framework for the simulation of the carbon market, considering the four aspects of complexity above. Within this framework, firms’ incomplete information is explicitly modelled as an attribute “Eyesight” of firms
, representing the limited number of other firms that a firm can observe when forming its forecast of the allowance price. Then, we analyse its impact on firms’ decision-making and consequent market results. We find that there is a statistically significant impact of firms’
on the market results: when
rises, the average allowance price rises, and firms’ total adoptions of low-carbon technologies decreases. Furthermore, we find that the social average abatement cost decreases with rising
, which indicates a higher efficiency of the carbon emission trading scheme. This is because with more information, more inefficient adoptions of low-carbon technologies are avoided.
Apart from the incompleteness of information, another aspect worthy of attention is the transmission mechanism, which is the main focus of this paper. It is depicted as an observation network among firms. The basis in reality is that the firms in the carbon market are usually from different industries or areas, and it is easier to observe the conditions of firms from the same industry or area. These conditions include production quantity, energy intensity, adoption of low-carbon technologies, etc. Neighbouring firms in the observation network correspond to firms from the same industry or area. As far as we know, no study has yet analysed the influence of firms’ observation network on the carbon market, neither theoretically nor empirically. However, in a broader view of network science, the influence of the observation or interaction network among agents has been a common issue in the studies of financial markets [11
] and technology diffusion processes [14
]. Correspondingly, the agent-based model is one of the major tools for a relevant analysis. Both of these strands of studies provide referential significance. In this paper, we attempt to simulate the influence of firms’ observation network in the carbon market based on an agent-based model.
In our model, the introduction of observation is related to the formation of firms’ forecasts of the allowance price. Firms form their forecast based on two aspects of information. First, a firm (e.g., firm i
) forms its fundamental estimate of the allowance price based on conditions of
neighbour firms that it can observe. By observing the other
firms’ condition, firm i
can calculate a fundamental estimate of the allowance price from the equilibrium perspective based on three aspects of information. The first is expected total emission of these
firms, which is calculated based on their historical emission, total production in the rest periods of the abatement phase, and current energy intensity. The second is current holdings of emission allowance of these
firms. The third is the marginal abatement cost curve (MACC) of these
firms, which is calculated based on their available low-carbon technologies, and the calculation method is introduced in Section 2.2
. The total allowance gap of these
firms can be calculated based on the first two aspects of information. Then, the fundamental estimate of the allowance price can be calculated with MACC combined. Second, firm i
calculates the moving average of the allowance price, which represents the technical aspect of information. Finally, firm i
’s forecast of the allowance price is a combination of both fundamental and technical information. This forecast serves as a uniform benchmark for firm i
to coordinate its multiple abatement-oriented decisions, to minimize its abatement cost and maximize its profit. As a result, the existence of an observation network among firms can influence firms’ forecasts of the allowance price through fundamental aspects of information, as well as their abatement-oriented decisions and the consequent market results.
Regarding the observation network, four scenarios are considered in this paper, including “no network”, “regular network”, “random network”, and “small-world network”. The reason for choosing these four kinds of network as scenarios is that their average can be controlled equal, which guarantees the comparability among different networks. The “no network” scenario serves as the base scenario in the analysis. Based on the simulation results, we find that the existence of an observation network has a significant influence on the market results, including the allowance price and trading volume in the carbon market, product price and production in the output market, and firms’ total adoption of low-carbon technologies. While there is no significant difference among the three scenarios with the observation network, above all, when the observation network exists, the social average abatement cost increases with firms’ average rising, which indicates an efficiency loss with increasing information. This is because the existence of an observation network among firms delays the transmission of information, which leads to higher revenue loss in the output market when the average of firms rises.
This paper is structured as follows: Section 2
presents a brief introduction of our model. A detailed introduction can be found in our previous work [10
]. Section 3
introduces settings of the simulation scenarios, and simulation results are shown and discussed in Section 4
. Finally, conclusions are given in Section 5
3. Simulation Settings
For the simulation, we assume the whole abatement phase includes 365 periods, and each period includes 240 ticks. There are 100 firms competing with each other in this virtual world, and they are ordered and numbered i
according to their scale. All the attributes of agents, parameters of market demand, and coefficients of low-carbon technologies are the same as the settings of the base scenario in our previous work [10
] (there is a complete introduction to all the attributes, parameters, and coefficients, as well as their value settings for simulation in our previous work [10
]; however, since most of them are not related to the discussion in this paper, they are omitted in this paper for simplicity). The only difference is the settings of the observation network among agents. As introduced in Section 2.3.1
, four kinds of networks are considered in this paper, including no network, regular network, random network, and small-world network. For each kind of network, the average
of agents ranges from 10 to 40 with an interval of five. For the “no network” scenario, each agent (e.g., agent i
) randomly observe
other agents in each period t
when forming its forecast of the allowance price. However, for the other three scenarios, the observation network among agents is set before the abatement phase. Each agent, say agent i
neighbour agents, and the observation relationship between two agents is mutual and constant. The “no network” scenario serves as the base scenario in the analysis.
Based on the agent-based model we established in our previous work [10
], we attempt to simulate the impact of the information transmission mechanism on the carbon emission trading scheme. The transmission mechanism is depicted as an observation network among firms. Four scenarios are considered, including no network, regular network, random network, and small-world network.
The simulation results are organized as three parts. First, the impact of different networks on the allowance price and firms’ adoption of low-carbon technologies are analysed. We find that with limited but global information, more inefficient adoptions of low-carbon technologies are avoided in the no network scenario than in the other three scenarios. This leads to lower total adoption of low-carbon technologies, and higher average allowance price. Additionally, when rises (which indicates more information for firms to make decisions), both firms’ total adoptions and average allowance price rise in all four scenarios. However, the no network scenario follows a different expansion path, and there is no obvious distinction among the other three scenarios. Second, the impact of different networks on the trading volume of allowance and firms’ total production are analysed. We find that the existence of the observation network delays the transmission of information, and also delays firms’ adjustments of production in the output market. This leads to lower total production of firms, as well as a higher revenue loss as the social cost of abatement. Third, following the second result, our model shows that the existence reverses the trend of the social average abatement cost with the increase of firms’ , since the increase of firms’ average leads to more abatement cost from revenue loss in the output market.
In conclusion, the existence of the observation network shows a significant impact on the carbon emission trading scheme in our model. The most important reason is that when the network exists, it delays the global transmission of fundamental information, which leads to more inefficient adoptions of low-carbon technology and more revenue loss in the output market. In other words, the existence of the observation network reduces the efficiency of the carbon emission trading scheme. In fact, the “no network” scenario we set does not exist in the real world. However, the results and discussions above implicate the necessity of establishing a public mechanism of information disclosure to offset the negative effect of a network that does exist in the real world. According to the results of our simulation, a dynamically updated list of firms’ adoption of low-carbon technologies would help to improve the efficiency of the carbon emission trading scheme.
Finally, several limitations of this model need to be clarified. First, the “firms” in the model only refers to firms with abatement targets in the carbon market, and the financial agents in the real carbon market are not yet included. Second, as a theoretical simulation model, the agent-based model foremost serves as a heuristic device for understanding the evolution of a complex system, scenario analysis, and policy evaluation. In our future work, more work will be done on calibration based on real data and empirical tests of the results of the model. Third, the impact of the carbon emission trading scheme also relies heavily on the allocation mechanism of the allowance. In this paper, the allowance is allocated based on a grandfathering mechanism, so firms’ allocations are not related to their real production. Thus, firms have stronger incentive to reduce their production for abatement, which shares the same view with Fischer and Fox [24
]. In our future work, more allocation mechanisms will be analysed.