Research on the Connection Market Trading Issues of Green Certificates and CCER Based on Contribution Degree and Social Welfare

Abstract

The connection between the green certificate market and the CCER market can further achieve the dual carbon goals, so it is imperative for the green certificate market, CCER market, and the connection market to coexist. it is necessary to convert green certificates into CCER in the connection market to achieve transactions between the two. The research is aimed at exploring the interface between green certificates and CCER, with the main objective of finding trading mode and trading method to address the interface between the two. This paper firstly proposes a concept of contribution degree that assists fair trading in the market, based on the different ways in which contribution degree is introduced in the trading of green certificates, CCER markets, and connecting markets, and establishes basic trading mode, unilateral contribution trading mode (UCM) and bilateral contribution trading mode. Then, the rolling matching trading model with the goal of maximizing total social welfare, the contribution degree model and the effect test model are established to realize the implementation of the above three modes through different combinations of the models. Finally, the quantitative results are obtained by simulating the trading situation of the three modes, and CCER offset ratio and contribution degree indicator weight are discussed. The results show that it is feasible to build a bridging market between green certificate and CCER by using the contribution degree, in which the UCM is the optimal solution to achieve the dual-carbon goal and market development. The setting of CCER offset ratio can be based on the difference of enterprise types, and the weight of indicators affecting the contribution level should be adjusted with the policy. The research in this paper has the following contributions: (1) a new criterion to satisfy fair trade—contribution degree is proposed to provide ideas for mutual recognition of products in the bridging market, and proposed a contribution degree calculation model for the green certificate and CCER markets, as well as the bridging market; (2) from the perspective of the contradiction between supply and demand in the market and fair trade, different schemes to build a bridging market are given in a fixed context and compared and analyzed; (3) from the market level, the quantitative analysis of the indicator settings affecting emission reduction, providing suggestions for the differentiated evaluation of warrants and the formulation of carbon reduction policies.

1. Introduction

Renewable energy power certificate (TGC) refers to the certification of renewable energy power generation, and is the only voucher that recognizes the production and consumption of renewable energy power. National Certified Voluntary Emission Reductions (CCER) are certificates that quantify the greenhouse gas emission reduction and mitigation effects of renewable energy, forestry carbon sinks, methane utilization and other projects in China. TGC and CCER are important tools for the development of renewable power generation in China’s power sector. Various types of certificates and related markets are the country’s key focus objects in the process of low-carbon energy transition [1]. The Chinese government proposes to focus on promoting the connection between markets and building a unified national market [2,3]. Currently, based on the situation in China, green certificates and CCER (after restart) will be traded separately in their respective markets. Due to insufficient supply, there should be no surplus after the CCER market is cleared. Due to policy reasons, there will be a surplus of green certificates after market clearance. Therefore, it is necessary to establish a connecting market and convert green certificates and CCER to achieve transactions between the two. So there will be three markets: (1) The green certificate market conducts green certificate trading, with the market entity being the green certificate seller and buyer. (2) CCER trading is conducted in the CCER market, with CCER sellers and CCER buyers as the market participants. (3) The connect market for the conversion of remaining green certificates into CCER transactions, with the market entity being the green certificate seller and CCER buyer. The opening of a connected market will bring additional trading opportunities to some market entities who still have trading needs after completing transactions in the green certificate market and CCER market, however, when buying and selling green certificates in the connecting market, how to ensure the interests of enterprises that contribute greatly to emissions reduction and social welfare cannot be ignored. Therefore, it is necessary to explore the issue of connecting market transactions.

The emergence of green certificates and CCER has brought important impacts on carbon emission reduction actions, so scholars at home and abroad have discussed them in depth. However, the CCER market closes in 2021 due to problems such as small transaction sizes and irregularities in individual projects, so research on the CCER market by domestic and foreign scholars has been relatively limited up to now.

Renewable energy quota system (RPS) refers to the system in which a country or region uses the form of law to mandate that renewable energy generation accounts for a certain percentage of total generation. The green certificate system, as a supporting system of RPS, has been widely applied and studied in many western countries. On the research of green certificate, foreign scholars have discussed its operation mechanism from the aspects of legal [4], energy investment [5], policy [6], social [7] and economic [8,9]. Based on the development paths and experiences of countries that have pioneered the green certificate system, most scholars in China have conducted in-depth research on the domestic green certificate market in terms of emission reduction effects, green certificate pricing and other issues [10,11,12,13,14,15]. However, the exploration of internal perspectives such as the operational efficiency, mechanism design and transactions evaluation of the green certificate market has insufficiently discussed. [10,16,17] In July 2023, a new policy on green certificates was introduced, and the green certificate system was further improved [2]. Since the CCER market is only open in China, the research on CCER is basically conducted by Chinese scholars. Previously, Chinese scholars have conducted basic research on the economic impacts [18], emission reduction efficiency [19], offset mechanism [20] of CCER. With the official launch of the CCER market in Beijing in January 2024, the relaunch of the CCER market has made the coexistence of multiple emission reduction markets a reality, especially the coexistence of the CCER and the green certificate market has triggered heated debates, so it is urgent to solve the problem of poor coordination between the two [21].

As important contributors to carbon emission reduction, numerous scholars have studied the importance of green certificates in conjunction with the carbon market. After the re-launch of the CCER market, some scholars have also explored the interface between CCER, green certificates and carbon markets.

Early on, some scholars used numerical analysis models, equilibrium models and optimization models to study the interaction between the two markets, and concluded that the joint trading of Tradable Emission Permits (TEP) and TGC has a positive impact on promoting new types of power investment, increasing profit margins and promoting the development of related technologies [22,23,24]. There is a “positive feedback” relationship between green certificates and carbon trading, and the combination of the two not only promotes further carbon reduction, but also reduces the uncertainty associated with the policy [25,26]. Taking Shanghai’s energy system as an example, by comparing the advantages and disadvantages of a single TGC or CET and a combination of the two, it is found that the combination of green certificates and CET will be the optimal cleaner production model for Shanghai’s energy system [15]. In China, the green certificate market and the carbon emissions trading market can not only expand the profit margin for renewable energy power generation enterprises to a certain extent, but some scholars have recently found that the conversion mechanism of TGC-Carbon Allowance-CCER has strengthened the close connection between electricity, carbon emissions trading and green certificates, and has also played an important role in broadening the trading space for market players and enhancing market competitiveness and liquidity [27]. In addition, CCER also play a linking role in the green power market and carbon market. Guiping Qing et al. designed a carbon credit mechanism relying on CCER to link electric vehicles with green power trading in order to encourage electric vehicles to consume green power [28]. Not only as a market link, CCER are also coupled with Cap-and-Trade (CaT), which together constitute and improve the carbon emissions trading scheme and enhance the role of CCER as a carbon offset mechanism [29]. In fact, some scholars have argued that the combination of green certificates and CET will not produce better economic and environmental results, and may even produce the opposite of the expected results [30].

Carbon market, green certificate market and electricity market are closely linked.

After explaining the interaction mechanism, Nan S. et al. (2023) proposed a linkage mechanism between the three markets in terms of market scope, price system, product system and governance system, which provides a reference idea for market construction [31]. At the same time, Nan S. et al. (2023) use “carbon emission reduction benefit” as the unified equivalent to realize mutual recognition of products under different market systems [32]. In addition to the above perspective of bridging market development and unified trading, some scholars have also discussed market bridging from the perspective of offsetting ratios, policy linkages and the characteristics of warrants [33,34,35]. In addition to the variety of research perspectives in the relevant studies, the research methodology also varies. Relevant scholars have explored the impact of the combination of green certificates and carbon emissions trading market on the sustainable development of electricity structure and market with equilibrium theory, scenario design theory combined with system dynamics [36]. Some scholars also used block-chain technology to establish a green certificates and carbon joint trading market model, which realizes the global allocation of green certificate and carbon resources through smart contracts, and to a certain extent realizes the convergence between the green certificate and carbon market [37].

To summarize, the emergence of green certificates and CCER has had a significant impact on carbon reduction actions, the carbon market, green certificate market, and electricity market are closely interconnected, the combination of carbon market and green certificate market plays an important role in promoting carbon reduction. The green certificate and CCER market have certain problems in mechanism design and market development. Through reading literature, it was found that: (1) the current research on the green certificates and carbon market is even more focused on the interaction and joint effect between the markets, although the importance of market articulation and the setting of articulation conditions have been intensely discussed from different perspectives and using different methods and theories, but the research involving the intrinsic bridging between the two markets is still insufficient. (2) from a comprehensive view of Chinese scholars’ research on the two markets, the discussion on the above markets in China focuses on the external role played by the bridging market, basically focusing on the economic and environmental aspects, and ignores the design of the internal mechanism for the construction of the bridging market, the principle of fairness in the operation of the market as well as the differences in the contribution of the participants in the reduction of carbon emissions.

On the basis of the above bridging market research results, from the perspective of market fairness, using contribution degree as the standard to measure fair trade in the market, the internal mechanism for the connecting market is designed. Establish a contribution degree model that can unify the trading standards of green certificates and CCER, and establish a connecting market trading model by introducing the contribution degree, and determine the trading mode with the best comprehensive effect. This paper not only explores the articulation mode of the green certificate and CCER market, but also discusses the current CCER offset ratio and the weighting of the comprehensive factor evaluation index of the CCER verification and issuance project, which provides a reference for improving the CCER market mechanism.

The article will be developed according to the following structure: in Section 2, the research methodology is introduced, the research idea of the article is briefly described, the definition of contribution degree is elaborated and the BM is selected, different trading modes are constructed and the model is built; in Section 3, numerical simulation is conducted based on the built modes, and the obtained results are analyzed; in Section 4, certain key factors are discussed, and the impacts of changes of the key factors on the market trading are analyzed; in Section 5, the summary of the obtained results and the discussion are made to draw conclusions.

2. Methods

2.1. Research Ideas

Based on the above background, the research of this paper will be carried out according to the following process, as shown in Figure 1. Firstly, the definition of contribution degree proposed in this paper will be explained, and the BM that is in line with the principle of fairness and suitable for the two markets and their bridging market will be selected. After that, different trading modes (Basic trading mode (BM), Unilateral contribution trading mode (UCM), Bilateral contribution trading mode (BCM)) are constructed based on the assumed conditions and the stage of the introduction of contribution degree. Then, three models (Rolling matching trading model, Contribution degree model, and Effect test model) are established, and the above three trading modes are realized through different combinations of the models. Setting the model parameters and providing basic data, numerical simulation of the three trading modes is carried out using MATLAB R2019b, and then the results obtained from the simulation are quantitatively analyzed and compared. Further, the key parameters of the optimal trading mode are adjusted, and the effects of the changes in the key parameters on the results are discussed. Finally, the simulation results as well as the discussion are summarized and conclusions are drawn.

2.2. Contribution Degree Explanation and Basic Trading Method Selection

2.2.1. Contribution Degree

This paper proposes that the contribution degree should be taken as the link and condition to establish the connection market, and help complete the connection transaction between the green certificate and CCER market, further strengthen the fair competition in the market, and thus stimulate the enthusiasm of market participants.

The meaning of the contribution mentioned above is the degree of effort made by buyers and sellers in the green certificate and CCER markets to fulfill the carbon quota tasks and enhance low-carbon production technologies. From the perspective of the power industry, the contribution of traditional power generation enterprises (buyers) in upgrading their generation technologies or reducing their generation capacity to reduce carbon emissions is reflected in the actual fulfillment of carbon quota tasks. On the other hand, the contribution degree of renewable energy power generation enterprises (sellers), as clean and low-carbon enterprises, is mainly reflected in the maturity and advancement of power generation technology. The more mature or advanced the technology, the lower the cost per unit of power generation, and the higher the possibility of feed-in tariffs. So in the study, the contribution degree of sellers will be represented by the unit cost of renewable energy power generation corresponding to the reduction of carbon emissions. By adopting the contribution degree for articulation trading, the market can accelerate the elimination of outdated technologies and improve the overall carbon emission reduction capacity of the market, ensuring that enterprises and organizations with advanced power generation technologies, strong emission reduction capacity and a high degree of completion of the quota task are given priority for development, and promoting high-quality and sustainable development in emission reduction. But simply according to the offer level for convergence trading is not conducive to encouraging renewable energy enterprises to actively develop low-carbon technologies, resulting in the market into a vicious price competition situation, disrupting the market order.

Based on the above conditions, before the establishment of the connecting market, it is necessary to choose a more appropriate basic transaction model from the perspective of the contribution degree, so as to make the contribution degree more compatible with the transaction mode.

2.2.2. Basic Trading Method Selection

Green certificate and CCER market have the same goal of energy conservation and emission reduction, but the operation status and supply and demand of the two markets are different. At this stage, the green certificate market is mainly listed online, and the market shows a state of supply greater than demand. The CCER market is dominated by offline bilateral transactions, and the market performance is in short supply. If the bridging market trades using either of the above two trading modes, we can see that: (1) The transparency of trading information under the offline bilateral trading mode is not high, which will make it difficult to obtain trading prices, which is not conducive to market participants to judge supply and demand, and will also cause difficulties in market supervision and risk identification. (2) The online listing transaction can reflect the sale price and sale volume in the practice process, but it cannot reflect the contribution difference and fair competition of market participants in the transaction process. Therefore, the above two mode should not become the basic trading methods in the connecting market.

When choosing a trading mode for connecting the market, it is necessary to consider not only the convenience of trading links, the transparency of trading information, and the economy of trading results, but also the contribution of market participants and the fairness of transactions. Rolling matching trading can effectively ensure the order and fairness of market transactions by matching both buyers and sellers with quotation. Meanwhile, this mode can automatically complete transactions through the program, and its transaction information is transparent and real. Therefore, this paper chooses rolling matching trading as the basic transaction mode of green certificate market, CCER market and their connection market. The combination of this mode and contribution degree can effectively meet the market’s trading needs and ensure fair competition in the market, so as to better connect the green certificate and CCER market.

2.3. Model Assumptions

The construction of trading modes and their models needs to be carried out under specific scenarios, for which the following assumptions are made in this paper:

Assumption 1. 

The carbon emission reduction suppliers in the market mainly consider renewable energy power generation enterprises, in which enterprises participating in the green certificate market follow the issuance criteria of “1 MWh corresponds to one green certificate”.

Assumption 2. 

To avoid double-counting problems, renewable energy generators can only choose between applying for green certificates or CCER.

Assumption 3. 

Only transactions in the spot market are considered, not the forward market and storage.

Assumption 4. 

The research process is set up so that the green certificate market is traded first, the CCER market is traded later, and the bridging market of the two is traded last.

Assumption 5. 

Green Certificates and CCER are only considered for one transaction and the liquidity is not considered.

2.4. Transaction Process of Different Modes

2.4.1. Basic Trading Model

In order to clearly reflect the impact of whether to establish a connecting market on market trading results and carbon reduction targets, it is necessary to establish a comparison mode, that is, the traditional trading market mode without connecting and introducing contribution degree. In this trading mode, the buyers and sellers of green certificate and CCER market respectively carry out rolling matching transactions in the quote-quote until the market clears.

In the context of carbon quota, with renewable energy power generation enterprises, market players providing CCER and emission control enterprises as market participants, the green certificate market and CCER markets successively conduct transactions in the rolling matching trading manner.

In the transaction process, the buyer is arranged in descending order according to the offer, the seller is arranged in ascending order according to the offer. Transactions can take place when the buyer’s offer is higher than the seller’s offer. The turnover is calculated according to the minimum bid quantity of both sides, the transaction price takes the average value of both sides. At the end of the transaction through the effect test model to calculate the transaction volume, the total cost of transactions and the average price, the total social welfare and carbon emission reduction.

2.4.2. Unilateral Contribution Trading Mode

In the green certificate market and CCER market before the opening of the connection market, the contribution degree is introduced, and the market evaluates the contribution degree of all participants, and the contribution degree level is constantly updated with the completion of the transaction. In the market of green certificate and CCER, the buyer and the seller will carry out rolling matching with the offer-contribution degree, and open the connecting market after the two markets are cleared. The two parties meeting the conditions can enter the connecting market, and the seller and the buyer will carry out rolling matching with the contribution degree -offer until the market is cleared.

In the transaction process of green certificate and CCER market, buyers are ranked in descending order of their offers, sellers are ranked in descending order of their contribution levels, and within the same rank, sellers are listed in ascending order of their bid prices. The transaction volume takes the minimum volume of both sides, and the transaction price takes the average value of the both sides under the premise that the buyer’s offer is higher than the seller’s offer. The buyer’s contribution level is updated continuously with the transaction. After the CCER market is cleared, the market determines the contribution level and the number of green certificates that can be converted into CCER according to the amount of carbon quota required by the state and the CCER offset ratio, so as to enable them to enter into the bridging market for trading. Emission-control enterprises that have not completed the carbon quota task enter the bridging market to become buyers. The buyers are listed in descending order according to the contribution level, and sellers are listed in ascending order according to the bid price. The transaction volume and transaction price are calculated in the same way as above. When the trading conditions are not met, the market will be cleared, and the trading effectiveness test is carried out at last.

2.4.3. Bilateral Contribution Trading Mode

After the rolling matching transactions in the green certificate and CCER markets respectively, the contribution degree is introduced to grade the participants with transaction balances, so as to screen the participants who can enter the connecting market. In the connecting market, the buyer and the seller make rolling matching according to the contribution degree-contribution degree until the clearance.

Green certificate market and CCER market transactions are consistent with the basic transaction mode, after the market participants complete the CCER market clearing, the contribution level of the buyers and sellers entering the bridging market is calculated according to the contribution degree model. The market determines the contribution level and the number of green certificates that can be converted according to the amount of carbon quota required and the CCER offset ratio, and then enters the bridging market. In the bridging market, buyers and sellers are ranked in descending order of their contribution levels, and the transaction is conducted at the average price on the premise that the seller’s offer is lower than the buyer’s offer, and finally the trading effectiveness is tested.

The basic way of the above three trading modes is rolling matching, so it is necessary to establish rolling matching trading model. Moreover, both unilateral contribution model and bilateral contribution model involve contribution degree evaluation, so it is necessary to establish contribution degree model. Finally, an effect test model is established to quantitatively analyze and compare the three models.

2.5. Modeling

2.5.1. Rolling Matching Trading Model

Rolling matching trading means matching the price of both sides according to the bid price of buyers and sellers, taking the average of the quotes of both parties as trading price. The participants with zero residuals among buyers and sellers are deleted after each round of trading and the queue is updated. The market implements rolling matching trading with the goal of maximizing social welfare. The corresponding mathematical model is as follow:

$$\begin{array}{l}\max F=\max (F_g+F_c+F_t)\\ =\max ({\displaystyle \sum _{j=1}^m{\displaystyle \sum _{i=1}^n(P_{x_j}-}}{P}_{s_i})\cdot \min \{x_j,s_i\}+{\displaystyle \sum _{j=1}^m{\displaystyle \sum _{i=1}^n(P_{y_j}-P_{w_i})}\cdot \min \{y_j},w_i\}+{\displaystyle \sum _{j=1}^m{\displaystyle \sum _{i=1}^n(P_{z_j}}-P_{u_i})\cdot \min \{z_j},u_i\})\end{array}$$

(1)

s.t.

$$P_{x_j}\ge P_{s_i}(\forall i,j)$$

(2)

$$P_{y_j}\ge P_{w_i}(\forall i,j)$$

(3)

$$P_{z_j}\ge P_{u_i}(\forall i,j)$$

(4)

$$\max {\displaystyle \sum _{j=1}^m{\displaystyle \sum _{i=1}^n\{G{g}_i,G{n}_j\}}}$$

(5)

As shown in Equations (1)–(5) above, there are $\mathrm{m}$ buyers and $\mathrm{n}$ sellers in the market. $F$ denotes the total social welfare, and $F\mathrm{g}$, $F\mathrm{c}$ and $F\mathrm{t}$ are the social welfare created by the green certificate market, the CCER market, and the bridging market, respectively. $P_{s_i},P_{x_j},P_{w_i},P_{y_j},P_{u_i},P_{z_j}$ denote the seller’s bid price and buyer’s bid price in the green certificate market, the seller’s bid price and buyer’s bid price in the CCER market, and the seller’s bid price and buyer’s bid price in the bridging market, respectively. $G{g}_i,G{n}_j$ denote the contribution degree of the seller and the buyer’s contribution degree respectively. Equation (5) represents the maximization of the contribution of the three markets of the green certificate, CCER and bridging market. There are three combinations of the above buyers and sellers: “green certificate seller-green certificate buyer”, “CCER seller-CCER buyer”, and “green certificate seller-CCER buyer”. Where Equations (4) and (5) are constraints required in transaction modes 2 and 3 and are not reflected in BM.

2.5.2. Contribution Degree Model

Contribution Degree Performance of Green Certificates

Every 1 MWh of green electricity generated by renewable energy corresponds to a certain amount of carbon emissions reduction, but the economic cost paid by different types of renewable energy per unit of carbon emissions reduction varies. In China’s green certificate market, there is a significant gap between the number of green certificates issued and the number of transactions for wind power and photovoltaic power generation. One of the reason is that the unit cost difference between wind power and photovoltaic power generation is obvious, resulting in different green certificate price for different types of energy [12]. This paper will assess the difference of different types of green certificates. The unit economic cost of carbon emission reduction will be used as one of the criteria for assessing the contribution degree of the seller of green certificates.

$$c_i={\displaystyle \frac{a_i}{r}}(i=1,2,\cdots ,n)$$

(6)

$$e_i={\displaystyle \frac{c_i}{{\displaystyle \sum _{i=1}^4{c}_i}}}(i=1,2,\cdots ,n)$$

(7)

Set the unit cost of different types of renewable energy generation as ${\mathrm{a}}_{\mathrm{i}}$ ($i$ = 1, 2, ..., n), and derive the carbon reduction unit cost ${\mathrm{c}}_{\mathrm{i}}$ according to Equation (6), where $r$ is the national average emission factor for electricity. After that, the carbon reduction unit cost is normalized according to Equation (7) to get $e_i$.

$$Gg=tT+(1-e)E+sS+hH$$

(8)

$$T+E+S+H=100$$

(9)

In Equation (8), $Gg$ is the contribution degree score of the corresponding type of renewable energy. $T$ denotes the score of technological factors, $E$ denotes the score of economic factors, $S$ denotes the score of resource factors, $H$ denotes the score of environmental factors, and $t$, $1-e$, $s$, $h$ are the weights of the corresponding factors. Where the relationship between the above four factors is shown in Equation (9). The contribution scores are divided into five levels, as shown in

Table 1. Contribution Level Classification.

Hierarchy	Contribution Score
Level 5	81–100
Level 4	61–80
Level 3	41–60
Level 2	21–40
Level 1	0–20

below.

Contribution Degree Performance of CCER

The price of CCER projects varies depending on the type of project and the methodology used. There are no clear and strict restrictions on the price of CCER projects during the application and approval process, so the contribution degree of different projects is not obvious enough. To address this problem, this paper proposes to use the unit cost of carbon emission reduction of CCER projects as one of the indicators for calculating the contribution degree.

$$c_j={\displaystyle \frac{b_j}{m_j}}(j=1,2,\cdots ,n)$$

(10)

$$e_j={\displaystyle \frac{c_j}{{\displaystyle \sum _{j=1}^5{c}_j}}}(j=1,2,\cdots ,n)$$

(11)

The total cost of different types of projects is set as $b_j$ ($j$ = 1, 2, ..., n), and the total amount of carbon emission reduction $m_j$ ($j$ = 1, 2, ...) of the CCER project is measured through project declaration and audit. The unit cost of carbon emission reduction $c_j$ ($j$ = 1, 2, ...) of each project is calculated according to equation (10). Then, the carbon emission reduction cost is normalized according to Equation (11) to get $e_j$ ($j$ = 1, 2, ..., n). The contribution scores of CCER projects are calculated by Equations (8) and (9), and finally the corresponding grades of different projects are obtained according to Table 1.

Contribution Performance of Carbon Emission Reduction Demanders

$$Gn=(1-{\displaystyle \frac{w-q}{q}})\times 100$$

(12)

The contribution of carbon emission reduction demanders will be based on the proportion of carbon quota tasks accomplished by the enterprises themselves. In Equation (12), $Gn$ represents the contribution score of emission reduction demanders, $q$ denotes the carbon emission limit allocated by the government to carbon emission reduction demanders, and $w$ denotes their actual carbon emissions. Combining the calculated contribution score and Table 1, the corresponding level of carbon emission reduction demanders is obtained. For active voluntary emission reducers without carbon quota tasks or enterprises that have completed carbon quota tasks ($Gn>100$), the market automatically categorizes them as the highest-level contributors. For enterprises with serious over-emission ($Gn<0$), the market automatically categorizes them as the lowest level contributors.

2.5.3. Effect Test Model

In order to compare the advantages and disadvantages of the three trading modes and to quantify the impact of establishing a bridging market in terms of contribution degree, a detailed analysis of the market results is needed. The trading volume reflects the active degree of the market, the total trading cost and the average price will reflect the development status of the market, the carbon emission reduction can reflect the environmental effect of the market, and the social welfare will show the social benefit of the market. Among them, social welfare has been reflected in Equation (1).

Transaction Volume

$$\begin{array}{l}J=\left[J_g,J_c,J_t\right]\\ =\left[{\displaystyle \sum _{j=1}^m{\displaystyle \sum _{i=1}^n\{s_i,x_j\}}},{\displaystyle \sum _{j=1}^m{\displaystyle \sum _{i=1}^n\{w_i,y_j\}}},{\displaystyle \sum _{j=1}^m{\displaystyle \sum _{i=1}^n\{u_i,z_j\}}}\right]\end{array}$$

(13)

As shown in Equation (13), $J$ represents the total market transaction volume, where $J_g$ represents the trading volume in the green certificate market, $J_c$ represents the trading volume in the CCER market, and $J_t$ represents the trading volume of green certificates converted into CCER in the bridging market. $s_i$, $w_i$, and $u_i$ represent the supply of the sellers in the green certificate market, CCER market, and the bridging market, respectively, and $x_j$, $y_j$, and $z_j$ represent the bid quantity of the buyers of the three markets in turn.

Total Transaction Cost and Average Transaction Price

$$\begin{array}{l}{C}_J=[C_g+C_t,C_c]\\ =[{\displaystyle \sum _{j=1}^m{\displaystyle \sum _{i=1}^n\frac{P_{s_i}+P_{x_{\mathrm{j}}}}{2}\cdot }}\min \left\{s_i\right.,\left.x_j\right\}+{\displaystyle \sum _{j=1}^m{\displaystyle \sum _{i=1}^n\frac{P_{u_i}+P_{z_j}}{2}\cdot }}\min \left\{u_i\right.,\left.z_j\right\},{\displaystyle \sum _{j=1}^m{\displaystyle \sum _{i=1}^n\frac{P_{w_i}+P_{y_j}}{2}\cdot }}\min \left\{w_i\right.,\left.y_j\right\}]\\ \end{array}$$

(14)

$$[c_{g\mathrm{t}},c_c]=[{\displaystyle \frac{C_g+C_t}{J_g+J_t}},{\displaystyle \frac{C_c}{J_c}}]$$

(15)

As shown in Equation (14), $C_J$ represents the total transaction cost, $C_g$, $C_c$, $C_t$ represent the green certificate transaction cost, CCER transaction cost and the green certificate transaction cost converted to CCER respectively. The buyer’s offer needs to be higher than or equal to the seller’s offer in order to carry out the transaction as shown in the constraints of Equations (2)–(4). As shown in Equation (15), $c_{g\mathrm{t}}$, $c_c$ represents the average transaction price of each of the green certificate and CCER.

Carbon Emission Reduction

$$D_a=D_c+D_g$$

(16)

$$D_c={\displaystyle \sum _{i=1}^n{w}_i}\times \delta$$

(17)

$$D_g={\displaystyle \sum _{i=1}^n{s}_i}\times r$$

(18)

As shown in Equations (16)–(18) above, the total carbon emission reduction ($D_a$) is equal to the CCER carbon emission reduction ($D_c$) plus the green certificate carbon emission reduction ($D_g$). The CCER carbon emission reduction is equal to the supply of CCER multiplied by the CCER carbon emission reduction coefficient $\delta$. The carbon emission reduction of the green certificates is equal to the supply of green certificates multiplied by the corresponding carbon emission reduction coefficient $r$.

3. Results

3.1. Parameter Setting and Data Modeling

It is assumed that there are 14 subjects participating in the green certificate and CCER markets respectively, and 7 subjects of buyers and 7 subjects of sellers in each market. The number of market transactions is set to 20, the national average emission factor for electricity r is set to 0.5839, the carbon offset coefficient δ of CCER is 1, the scores of technologies, economy, resources and environment account for 25, the corresponding coefficients of other indicators except for the economic indicator are set to 0.25, and the offset ratio of CCER market is 5%.

Using Function function, IF conditional statements, While and For loop statements, etc. in MATLAB software to create the code: (1) the use of Function, the normalization calculation method to build the contribution sub-function, the calculation of green certificates and CCER buyers and sellers contribution score, and the use of IF conditional statements for each participant’s contribution to the level of classification. (2) Use the Function function to build a summary transaction sub-function: first, the use of MAX and MIN functions on the buyer and seller quotes for rolling sorting and summarization, and the use of IF conditional statements for cyclic trading until the seller or seller queue zero or can not be summarized. Finally, the calculation of the respective remaining supply or demand for each market buyer and seller. (3) Use the Function function and the above summarized trading function to build three trading model sub-functions, and introduce the above contribution function in the unilateral and bilateral contribution model. According to the offer price, the remaining offer quantity and the contribution level, combine the While, For loop and If conditional statement to build the articulation market and summarize again in it. Finally, the trading volume, transaction cost, carbon emission reduction and social welfare of green certificates and CCER in each market are summed up. (4) Build the main function of the three trading modes: use the IF function to judge the trading results, thereby adjusting the next trading volume is very offer, through the For function to enter the 20 iterations of the cycle, and finally through the Figure function to draw a graph of the results of the 20 iterations. (5) Import the following data, each column of data represents a variable, the use of variables to retrieve the data after the simulation. Use the main code to connect the functions of the three trading modes as well as the data together to form a nested “main code (data, three trading mode main functions (three trading mode sub-functions (green certificates and CCER contribution function, summarized trading function)))”.

3.2. Data

Seven groups of buyer and seller bid prices are set with reference to the average price of transactions on the China Green Certificate Trading Platform from January to July 2023 and the published CCER transactions. The bid quantities of buyers and sellers are set according to the supply and demand situation of the two markets, as shown in

Table 2. Initial Data.

	Participating Entity	001	002	003	004	005	006	007
Offer (Yuan)	Green-Seller	37	48	49	50	39	45	30
	Green-Buyer	48	40	39	46	58	33	45
	C-Seller	68	52	42	58	57	70	46
	C-Buyer	60	59	57	61	76	53	61
Bid quantity (sheet or unit)	Green-Seller	175	192	298	199	230	146	256
	Green-Buyer	78	82	95	62	76	59	68
	C-Seller	58	96	75	77	69	86	59
	C-Buyer	111	94	148	86	73	152	88
Carbon allowances (tons)	Green-Buyer	472	357	381	289	486	251	342
	C-Buyer	389	361	482	395	297	432	364
Actual carbon emissions (tons)	Green-Buyer	500	420	433	314	530	274	376
	C-Buyer	2609	2241	3442	2115	1757	3472	2124
bridging market offer (Yuan)	Buyer	35	45	59	51	39	57	40

. The initial situations are in line with the reality of the imbalance between supply and demand of the two markets. Each buyer’s initial carbon quota is lower than its actual carbon emission, and the buyer’s bid quantity in the green certificate market is the difference between its actual emission and carbon quota, while each buyer’s bid quantity in the CCER market is 5% of its excess emissions. To prevent the price of green certificate in the bridging market from soaring, only buyers in the bridging market are allowed to make offers, while sellers make adjustments based on the difference in carbon emission reduction coefficients between the green certificate and CCER from the original offers. As the number of transactions changes, all trading entities in the market will randomly adjust the supply and demand of green certificate and CCER based on historical transaction situation. The total amount of carbon quotas in the two markets will be continuously reduced to ensure consistency with the dual carbon policy in the real world.

3.3. Analysis of Results

3.3.1. Analysis of Iterative Results

The MATLAB software was used to program the above model and the trading modes it formed, and the set parameters as well as the given initial data were inputted into the model to automatically iteratively calculate the results of the different trading modes.

Table 3. Table of results for the 20th iteration.

Trading Results	Trading Volume		Average Transaction Price		Carbon Emission Reduction	Social Welfare
	Green Certificate	CCER	Green Certificate	CCER
Mode 1	797	1191	43.31	54.32	29,520	11,890
Mode 2	6115	9909	39.74	50.87	46,430	225,200
Mode 3	889	2542	42.77	53.97	39,650	23,060
R1-Mode1	1542	1238	54.78	50.10	19,180	12,920
R1-Mode2	7741	6067	48.40	49.27	42,920	411,400
R1-Mode3	3637	4632	50.85	50.50	32,420	36,150
R2-Mode1	365	1557	55.16	54.73	24,240	13,460
R2-Mode2	2914	1031	51.96	50.99	64,680	181,300
R2-Mode3	476	3721	52.80	53.70	31,021	31,360
R3-Mode1	2540	3062	48.90	54.90	21,010	35,500
R3-Mode2	6639	1391	45.39	52.01	76,090	243,000
R3-Mode3	2789	5109	47.11	54.28	47,220	39,250

summarizes the results related to the three trading modes obtained after 20 iterations of calculations. From the data in the following table, it is easy to see that the trading results of introducing the contribution degree model to establish the bridging market are better than that without bridging market. Among them, the trading result of the unilateral contribution mode is obviously the best, but it is difficult to reflect the trend of the market by evaluating the three trading modes only on the basis of the final iteration results. In this regard, this paper will further analyze the trading results of the three modes to select the optimal mode.

In addition, in order to verify the validity of the model and the iterative results, further to the above data as a base to generate random arrays, random arrays are generated for each data multiplied by the range of [1−(2 × rand()−1)/10,1 + (2 × rand()−1)/10], of which “rand()” for the random numbers [0, 1] random number, and in order to prevent the random array of results and the original results of the gap is too large to affect the comparative analysis, it is also on the range of changes in the isotropic reduction of 10 times. Limited to space, this paper uses MATLAB software to generate three groups of random numbers (R1, R2, R3) and again for simulation, three groups of random numbers simulation results shown in Table 3. Observing the data, it can be seen that in R1 and R3, although the trading volume of CCER shows that it is not R3 > R1 > R2, which is different from the original data results, but the conclusion that the trading volume of green certificates, the average trading price, the amount of carbon emission reduction and the social welfare of mode 2 (unilateral contribution mode) is better than the other two modes still remains unchanged, so comparatively speaking, the unilateral contribution is still the optimal mode in the overall results.

3.3.2. Analysis of Transaction Volume Results

Compare the trading volume results of the three trading modes and analyze the reasons. Figure 2 and Figure 3 below show the comparison of the trading volume of green certificate and CCER market under the three trading modes, respectively. According to the figures, (1) The trading volume of green certificate in the BM slightly decreased, and the trading volume of CCER showed a trend of increasing and then decreasing; (2) At the beginning of the trading period, the trading volume of green certificate and CCER under the UCM was lower than that of the other two modes, but grown faster in the later period, and formed the fluctuating pattern of “stabilization-growth”; (3) The trading volume of green certificate under the BCM was basically stable, while the trading volume of CCER showed increased first and then stabilized, and its growth rate was higher than that of the BM. This is analyzed as follows:

With the cleaner transformation of the power sector, the number of buyers in the green certificate market will gradually decrease. However, the carbon quota mandate of the remaining buyers in the green certificate market will continue to increase. The establishment of the bridging market directly contributes to the increase in the trading volume of green certificates. The introduction of the early contribution degree enables high-quality sellers with high contribution level to complete the transaction to the maximum extent, which results in the green certificate sellers with high level of contribution increasing the generation of green power, and the green certificate sellers with low level of contribution withdrawing or shifting to the CCER market. The reduction of sellers in the green certificate market will further stimulate the demand for CCER and increase their trading volume. While the later introduction of contribution degree to establish a bridging market also stimulates green certificate and CCER trading to a certain extent, the limitation of the offset ratio makes it difficult for sellers with high contribution level to maximise the number of transactions, and the green certificate market still retains some sellers with low level, which is not able to adequately stimulate the green power feed-in. Meanwhile, under the carbon reduction policy, the improvement of the CCER review system will lead to the exit of CCER projects with low contribution level from the market, prompting their transactions to stabilise.

Figure 2. Comparison of Green Certificates Transaction Volume.

Figure 2. Comparison of Green Certificates Transaction Volume.

Figure 3. Comparison of CCER Trading Volume.

Figure 3. Comparison of CCER Trading Volume.

3.3.3. Analysis of Average Transaction Price

Total transaction volume and transaction price have a direct impact on the total trading cost, so the total trading cost of Green Certificates and CCER is consistent with their total trading volume. The total trading cost is difficult to reflect the whole picture of the market transaction, so the average price of the transaction is used for more in-depth analysis. The two graphs on the left side of Figure 4 represent the change of the total trading cost and the mean price of green certificate under the three modes, and the two graphs on the right side are the change of the total trading cost and average price of CCER. According to the graphs, (1) the average price of green certificate and CCER is the lowest in the UCM, and the average price of CCER is basically in the most stable state; (2) the average price of green certificate is lower than that of the BM in the BCM and the average price is stable, whereas the average price of CCER is the highest in the early stage, but the average price decreases at a fast rate, and then is lower than that of the BM in the late stage and reaches equilibrium.

In market transactions, participants with high cost contribution level have the advantage of low bid price due to lower cost, and thus receive preferential trading rights. However, as the UCM implements an offer-contribution matching method, it does not strictly control prices, and thus the trading price fluctuates with changes in the form of carbon reductions and supply and demand. In contrast, the entry condition of the second market in the BCM constrains the offers of participants in the first market, prompting participants who want to enter the articulated market to engage in a price game. As a result, the average price of its green certificates is more stable. Power generation enterprises with lower contribution level are difficult to trade in the oversupplied green certificate market, so they turn to the CCER market. The increase in the number of supplying entities in the CCER market makes the trading price fall. However, as the audit of CCER projects becomes stricter, some of the participants with low contribution level will leave the market. At the same time, the establishment of the bridging market between green certificate and CCER has a negative impact on the price of CCER, so its average price will be lower than the BM and reach equilibrium later.

3.3.4. Analysis of Carbon Emission Reduction

As shown in Figure 5, in the first five transactions, there is no obvious difference in the carbon emission reduction of the three modes. However, with the increase of the number of transactions, the growth rate of carbon emission reduction under the UCM accelerates. After the 13th transaction, the BCM also pulls away from the BM, and the growth rate of carbon emission reduction under the BCM is greater than that under the UCM. Combined with Figure 2 and Figure 3, it is analyzed that, firstly, the establishment of the bridging market has increased the trading volume of green certificate and CCER. The larger trading volume indicates that the larger the proportion of renewable energy power generation feed-in. The more successful the development of various low-carbon projects, and the better the overall carbon emission reduction effect. Secondly, the introduction of contribution degree also strengthens the fair competition in the market and promotes the continuous expansion of projects and enterprises with strong emission reduction ability. Finally, further analyzed in conjunction with Figure 4, the average price of CCER under the UCM is relatively stable, which is not conducive to the competitive development of the market and the development of new projects. However, the decrease in the average price of CCER under the BCM promotes the demand for CCER projects, which in turn drives the development of CCER projects. According to the comparison between the national average emission factor of electricity and the unit carbon emission reduction corresponding to CCER, one green certificate is currently equal to about 0.58 CCER. The growth of CCER is more conducive to improving the rate of carbon emission reduction.

3.3.5. Social Welfare Analysis

One of the aims of rolling matching transactions is to maximize social welfare, so this is how the three modes can be compared. As shown in Figure 6, in the first four transactions, the social welfare under the BM is the highest, while the UCM is the lowest. However, with the increase of the number of transactions, the social welfare under the BM shows a slow decline trend, the social welfare under the unilateral contribution degree mode grows rapidly, and the social welfare under the BCM stabilizes after a small increase. There are several reasons for the above result: (1) Unilateral and BCM established a bridging market, which increased the trading volume of green certificate and CCER, directly leading to the growth of social welfare; (2) The overall transaction volume increased significantly under the UCM, while the trading volume under the BCM grown slowly in the process; (3) Under the BM, with the trading process, the offer of buyer’s and seller’s will gradually approach the average price, making the social welfare of each group of transactions decrease, which in turn contributes to the overall social welfare decrease.

In summary, the UCM is the optimal solution among the three market trading modes in a comprehensive view. Its advantages are reflected in: (1) The total transaction volume of green certificate and CCER in the market is the largest, and the growth rate is relatively fast, and its average price is low; (2) The carbon emission reduction is the highest under the UCM, and the growth of carbon emission reduction is the most obvious; (3) The social welfare under the mode is the fastest growing, which is conducive to the realization of the social welfare maximization goal.

4. Discussion

To further determine the optimal market articulation scheme, this paper further adjusts the important parameters of the UCM. In terms of the process of market transactions, the key parameters affecting bridging market transactions are the offset ratio and the weight of indicators affecting the level of contribution of participants.

4.1. Offset Ratio

Explore the variation in results under the UCM by adjusting the offsetting ratio coefficients. It was assumed that the user’s initial offer remains unchanged and the influence of other policy factors was not considered. As shown in Figure 7. As the offset ratio increased, the overall trend of green certificate trading volume showed growth, but CCER trading volume fluctuated greatly. For the bridging market, the increase in the offset ratio was favorable to the conversion of green certificate to CCER, which promoted the consumption of green certificate while improving the motivation of market participants. However, too much conversion of green certificate will weaken the enthusiasm of CCER market suppliers and have a negative impact on the development of new CCER projects.

Observation of the folding line change in Figure 8 revealed that social welfare was highest at an offset ratio of 15%, lowest at 3%, and basically stable in the range of 6–8% and 11–14%. Therefore, an offset ratio of 6–8% or 11–14% was more conducive to improving social welfare and maintaining stable market development. From the bar chart changes in Figure 8, it was found that when the offset ratio was increased, the carbon emission reduction firstly showed a decreasing trend. This result supported the conclusion that the introduction and increase of the offset ratio will have an impact on the carbon price, thus weakening the carbon emission reduction effect [38,39]. However, previous studies have only considered the negative impacts of increasing offset ratios, ignoring their positive role in promoting renewable energy development. While the offset ratio raised carbon emissions due to the mutual recognition mechanism between TGC and CCER when it grown, the increased conversion of green certificates stimulates green power feed-in and green certificate consumption when it exceeds 11 per cent.

Currently, the offset ratios in China’s carbon pilot regions are not entirely consistent, basically ranging from 5 percent to 10 percent [40,41]. Combined with the curve changes of Figure 7 and Figure 8,carbon emission reductions fluctuated and grown when the offset ratio was between 5 and 10 per cent, while carbon emission reductions increased significantly when it exceeded 10 per cent. According to the relevant provisions of the Administrative Measures on Carbon Emission Trading issued by China’s Ministry of Ecology and Environment in 2021, the offset ratio of key emission entities shall not exceed 5% of the carbon emission quota to be cleared. Based on the above analysis, in order to achieve carbon peaking before 2030 and to ensure the market and industry order, the state should indeed strictly control the offset ratio of key emission control enterprises. So it is most appropriate to set the offset ratio at 5%. In addition, this paper argued that in order to reduce the pressure on the overall market development, the government can increase the offset ratio for non-key emission control enterprises as appropriate, but it should not exceed 10%.

4.2. Weighting of Indicators Affecting the Contribution Degree

The study initially set the weight of technology, economy, resources and environment at 0.25. However, the weight of the four indicators would not be exactly the same in practice due to the influence of policies. For example, in the study of differentiated allocation of renewable energy green certificate in the context of the new power system, Shuo Z. et al. (2022) obtained the weight of 22.40%, 16.32%, 28.08%, and 33.22% of the four indicators of technology, economy, resources, and environment, respectively, in the process of calculating the weight of the indicators and the ordering of the indicators [42]. In order to make the discussion more realistic, this paper reset the weights of the four relevant factors based on the sustainable development policy, dual-carbon policy, etc., so as to re-calculate and re-assess the contribution of emission reduction suppliers.

There was little literature that proposed and quantified the contribution of abatement suppliers, but there were more discussions on the factors affecting carbon emission reduction. Many scholars have analyzed the factors affecting carbon emission reduction in regions and cities using the logarithmic divisor index method (LDIM) and the generalized divisor index method (GDIM), and found that economic activity is an important indicator for carbon emission reduction through comparison [43,44,45]. Jiang X. et al. (2018) concluded that economic activity is the dominant factor affecting carbon emissions in China, population growth is the main driver of carbon emissions in the United States, and energy intensity has an impact on carbon emissions in both countries [46]. The degree of emphasis on emission reduction factors will directly affect the assessment of contribution. Nan S. et al. (2023) assessed the contribution of green certificate and CCER in the bridging market in terms of “carbon emission benefits” [32]. While Xin W. et al. (2023) calculated the contribution of emission reduction through the amount of fossil energy replaced by warrants [47]. The above scholars have comparatively analyzed the factors affecting carbon emissions at the macro level, and a few scholars have assessed the contribution of warrants in the bridging market. However, the comparison and ranking of indicators other than economic factors were not conducted in the study on factors affecting emission reduction and contribution assessment. Based on the above background, this paper innovatively compared the indicators of impact contribution assessment from the micro market level. First, under the background of fixed economic development, this paper readjusted the weight of all indicators except economy. The adjustments are shown in

Table 4. Changes of Indicator Weight.

Norm	A	B	C	D	E	F	G
T (technology)	0.25	−	−	+	+	0.25	0.25
S (resources)	0.25	0.25	+	−	0.25	−	+
H (Environment)	0.25	+	0.25	0.25	−	+	−

: A indicates the same weight for each indicator, B represents a decrease in technology weight and an increase in environmental weight, C represents a decrease in technology and an increase in resources, D is the opposite of C, E is the opposite of B, F represents a decrease in resources and an increase in the environment, and G is the opposite of F.

The 0 in Figure 9 represents the initial result with weight A. The 4 graphs in Figure 9 represent the result of gradually increasing or decreasing the weight of an indicator by 0.05. Observing the upper left and lower right figures, when the weight of resources was reduced to 0.15, an increase in either the environmental or technological weight contributed to an increase in the transaction volume of green certificate and social welfare. Further, increasing the weight of environment was more conducive to promoting green certificate trading and increasing social welfare than increasing the weight of technology. The greater the weight of the environment, that was, the higher the social emphasis on the environment, the more conducive to promoting the upgrading of new energy generation technology, thus promoting the development of new energy. Observing the upper-right figure, increasing the weight of environment or resources on the premise of reducing the weight of technology was conducive to the growth of CCER trading volume. Increasing the weight of environment more than that of resources can indeed stimulate the trading volume of CCER. However, when the weight of the two were changed drastically, for instance, when the weight of resources was 0.45 and that of environment 0.05, the volume of CCER trading volume increased sharply. Reducing the technology weight is tantamount to reducing the degree of control over the methodology of the applied CCER projects, increasing the supply of CCER and thus increasing their trading volume. Increased environmental weighting means increased demand for carbon reduction, which directly promotes trading in the CCER market. But due to the limitation of resources, the development of CCER is also limited, so its growth is relatively slow. However, if too much emphasis is placed on the use of resources without paying attention to the environment, it will lead to a dramatic increase in the development and trading of CCER projects in the beginning, but will also decline in the later stage due to the destruction of the environment. Observing the bottom left figure, there was not too much difference in the impact of each indicator on carbon emission reduction, but increasing the weight of technology was more conducive to promoting the carbon emission reduction target compared to environment and resources. Whereas, lowering the weight of resources compared to the environment was more likely to promote carbon reduction than lowering the weight of the environment. An increase in technology weighting could increase the carbon reduction capacity of the whole industry, however, overemphasizing technology can be counterproductive. In addition, the increase in environmental weighting was more conducive to the realization of energy conservation and emission reduction and the enhancement of social awareness of carbon reduction than the increase of resource weight.

Based on the above discussion results, the following analysis was made: (1) The increase of carbon offset ratio promoted the conversion of green certificates to CCER and further stimulated the supply of green certificates, but too much conversion of green certificates would have a negative impact on the supply of CCER, thus weakening the growth of carbon emission reduction. Therefore, in order to ensure the stable development of the market and the realization of emission reduction targets, the determination of carbon offset ratio needs to consider the game situation between green certificate and CCER in the connection market. It is recommended to implement 5% offset ratio for key control units, while non-key control enterprises can increase it as appropriate to reduce the pressure on the overall domestic market. (2) Since the weight of economic indicators is determined by the participants’ own situation, the study only considers the influence of the other three indicators. The increase of environmental weight has the greatest impact on market transactions and social welfare, and the increase of technology weight has the greatest impact on carbon emission reduction. Therefore, when establishing the contribution degree model of the connecting market, different green certificates or CCERs should be differentiated, and the environment and technology should be given a higher weight ratio, so as to ensure market fairness as much as possible and promote market development and the realization of the dual-carbon goal.

5. Conclusions and Suggestion

5.1. Conclusions

Aiming at the bridging problem between the green certificate market and the CCER market, the study established three different trading modes for comparative analysis, and used MATLAB to program and numerical simulate, and finally obtained the following conclusions:

(1)

Utilizing the contribution degree to build a bridging market for green certificate and CCER is conducive to promoting trading volume, lowering the average transaction price, reducing carbon emission and increasing social welfare. The introduction of the contribution degree will re-capture the market’s attention to fair competition;

(2)

After the analysis of the results of the three trading modes, it is found that the UCM is more conducive to promoting market articulation and stimulating the trading activity of the green certificate market while promoting the development of CCER projects;

(3)

The setting of the offset ratio needs to consider the influence of multiple factors and the game situation. In order to ensure the realization of the dual-carbon goal and the stable development of the market, the government should set the offset ratio of key emission control entities at 5% when establishing the unified carbon market, and raise the offset ratio of non-key emission control entities as appropriate, but it should not exceed 10%;

(4)

Increasing the weight of the two indicators of environment and technology in the setting of contribution level is more conducive to motivating market transaction, promoting carbon emission reduction and enhancing social welfare.

5.2. Obstacles and Suggestions

(1)

Data integration barriers and recommendations. Considering that the CCER market has just been fully re-launched, there is heterogeneity between green certificates and the CCER market in terms of operating markets, experience and scale of participation. Therefore, there are certain difficulties in data fusion and data transparency when establishing the bridging market. In this regard, the government should grasp the data similarities and differences, promote data openness and transparency, and strengthen data fusion when promoting the convergence of the two markets.

(2)

Articulation market data support platform challenges and recommendations. After the establishment of the articulation market, the number of trading entities in the market has increased dramatically, and considering that more rescheduling enterprises will join the market in the future, the government needs to establish a data support platform construction with stronger arithmetic power, set up a sharing mechanism for relevant data, and increase the reliability of sampling, rechecking, and cross-validation.

(3)

Market regulation challenges and recommendations. The increase in the number of market participants not only brings certain challenges to the data support platform, but also puts forward higher requirements for market supervision. In this regard, the government is required to raise the entry threshold of trading entities when establishing the articulation market, fully disclose the relevant data of the trading market in a timely manner, enhance the transparency of the market, continuously improve the market system according to the market problems, and promote the benign interaction between the operation of the market and the government’s policy design.

(4)

Challenges and Recommendations between Warrant Conversion and Growth. In this paper, the design process of the bridging market has weakened the game situation between green certificate conversion and CCER growth and the corresponding technical differences and updating problems. In the future articulation market, the government and the market should not only consider the cooperation between the two, but also the competition between the two in the process of conversion and trading of warrants in the future.

(5)

Articulation market development challenges and recommendations. In the future, the establishment of the articulation market will depend on policy guidance and economic support, so the development objectives of the articulation market should be consistent with policy guidance and economic development. Therefore the market should not only consider the contribution factor already mentioned in this paper in the process of establishment and development, but also take other factors into consideration.