Carbon Derivatives-Directed International Supervision Laws and Regulations and Carbon Market Mechanism

Abstract

With the global acknowledgment of the Kyoto Protocol came the carbon derivatives such as carbon futures, options, and swap contracts. The innovative carbon derivatives are complex in design and contain risks that are difficult to predict and avoid. The global Carbon Market should have higher requirements in the supervision laws and regulations. To this end, the financial system theories and the financial characteristics of carbon derivatives are expounded. The three-dimensional structural modeling technique of systems engineering is introduced to construct the Carbon Market framework. The proposed framework factors for the organization, product, and policy dimensions of the Carbon Market are also described. Additionally, this model explains the market organization, the instruments and media connecting market supply and demand, and government regulation measures. In particular, the supervision and management aspects of the policy dimension are introduced in detail. The Carbon Market and relevant law systems in the United States, the European Union, and India are mainly studied and compared. Based on the comparison results, the necessity of market supervision is explained. Finally, the Big Vector Autoregression model is used to study the relationship between the Carbon Market, energy market, and financial market. After the introduction of the National Carbon Market, the correlation between the energy market and the financial market has become relatively complex but also presents a certain degree of asymmetry. According to the above results, the paper proposes to use the “regulatory sandbox” mechanism to improve the regulation of the subject and object of the carbon financial and legal relationship and try to carry out regulatory innovation for the risks of the entire carbon market.

1. Introduction

Over the years, climate change has become a top concern globally, mainly due to human activities from the post-Industrial Revolution era. Humanity must reduce carbon emissions and carbon-intensive energy consumption to curb global warming and protect their homeland [1]. To this end, a cooperative mechanism was signed, the United Nations Framework Convention on Climate Change (UNFCCC), in 1992, being the world’s first international convention to control greenhouse gas emissions [2]. In 1997, the United Nations Climate Conference proposed the legally binding Kyoto Protocol. The Protocol implemented the objective of the UNFCCC to reduce the onset of global warming by reducing greenhouse gas concentrations in the atmosphere to “a level that would prevent dangerous anthropogenic interference with the climate system” [3]. Accelerating global warming and severe weather phenomena are demanding stricter policies for countries to fulfill and strengthen their emission reduction obligations. Moreover, the relevant legal and supervision systems for the Carbon derivatives financial Market (Carbon Market) must be formulated as soon as possible [4]. Kyoto Protocol clarifies the Carbon Peak and decomposition index of carbon emission reduction. It stipulates three market-based emission reduction mechanisms. They are Joint Implementation, stipulated in Article 6; Clean Development Mechanism (CDM), stipulated in Article 12; and International Emission Trading (IET) under Article 17 [5]. These mechanisms determine the general idea of the Carbon Market.

At present, the Carbon Market and its carbon derivatives have become a research hotspot. Yang and Luo (2020) reviewed international carbon financial derivatives transactions. They sorted out the relevant research on the connotation, concept, and current situation of the Carbon Market. Subsequently, seven aspects were comparatively analyzed to reveal the development status of the global Carbon Market. Their findings provided policy suggestions and theoretical references for countries with insufficient carbon finance research. They analyzed the impact of the emission source and the financial market regulation by the transnational climate policies on the European Union’s (EU) participation in the Carbon Market [6]. Yliheljo (2021) assessed the public–private nature of emission units and ownership. Ownership was understood as the legal status of emission unit holders, a collection of private law elements, climate law, and financial market regulation. As policy design choices and Carbon Market regulation evolve, a picture of legal status variables emerges across individual, temporal, and spatial dimensions. Ownership of emission units reflected an ongoing balance of different EU public policy objectives and differed from the economic theory underlying emissions trading. Regulatory intervention and risk had become inherent features of unit ownership due to the need for active management of programs. The impact of changes would vary across different market participants [7]. However, the research on the supervision mechanism affecting the Carbon Market is not thorough enough. There is a need to analyze the influencing factors of carbon emissions from the perspective of the market economy and policy. At the same time, few scholars have analyzed the Carbon Market from the perspective of international supervision laws and regulations. Carbon trading and carbon finance involve a wide range and a large transaction scale, so they need to be regulated by a sound legal system. The formation and development of carbon trading and carbon finance are relatively short, and the related legal system is in the process of constant adjustment and improvement. How to quickly establish an efficient and perfect carbon financial regulatory legal system and form a complete and sound legal system is of great significance for ensuring the trading of carbon financial products and their derivatives and ultimately achieving green, low-carbon, and sustainable development.

Thus, this work presents its innovation points. Firstly, it avoids the shortcomings of previous studies by more comprehensively understanding the Carbon Market as a complex system engineering. Secondly, a three-dimensional (3D) structure is introduced to model the Carbon Market from organizational, product, and policy dimensions. Finally, the supervision aspects of the policy dimension are presented in more detail. The results illustrate the necessity of market supervision and analyze the Carbon Market-oriented laws and regulations from three perspectives: supervisory entities, supervisory models, and international cooperation. Specifically, the Big Vector Autoregression (BigVAR) model is employed to study the relationship between Carbon Market, the energy market, and the financial market. The research finding ultimately has a reference role in improving the rights and obligations system of carbon emissions trading and the supervision of the Carbon Market.

2. Materials and Methods

2.1. Overview of the Financial Market System

The financial system refers to the set of markets and intermediaries used by households, companies, and governments to implement their financial decisions, including markets for stocks, bonds, and other securities, as well as various financial intermediaries such as banks and insurance companies [8]. Some believe a financial system is “the aggregate collection of markets and other institutions used to enter into financial contracts, exchange assets, and risk”. The financial system is a tool for capital flow and a complex of financial assets, market participants, intermediaries, and trading methods markets [9]. The financial market system refers to the composition of its sub-markets, including the money and capital markets. Its specific structure is shown in Figure 1:

In Figure 1, the main sub-markets in the financial market system have certain commonalities, such as risk or uncertainty. Additionally, because financial activities feature strong externalities and quasi-public goods, the government’s regulatory framework is indispensable to the financial system [10].

From the systems engineering perspective, the financial system refers to all entities engaged in financial activities in an economy, including financial institutions, enterprises, and individuals. These structures form an organic whole following certain principles. Therefore, the financial system is a complex system of financial instruments, intermediaries, financial markets, externalities, business relationships, and government supervision [11]. In addition to financial structure, it focuses more on the division of responsibilities and how to form an efficient ensemble by coordinating and promoting the relationships of the financial institutions and markets involved in financial activities. In simpler terms, the financial system is structured, operating as a dynamic and living organism [12]. Thus, the financial system is evaluated from its internal structure and the subject’s coordination.

2.2. Coase Theory and the Financial System of Carbon Derivatives

In recent years, the role of trade and exchanges has been augmented with the obvious trend of internalization of the over-the-counter (OTC) market. Carbon futures, options, and other carbon derivatives are developing rapidly. Several carbon derivatives trading markets have been formed internationally. Represented by Eurex and Energy Exchange, especially since 2017, the activity of carbon derivatives trading has increased dramatically. The notional value of carbon derivatives transactions soared from about USD 5 billion in the same period in 2017 to about USD 25 billion in the first quarter of 2018 [13]. On the one hand, carbon derivative finance drives the development of a low-carbon economy; on the other hand, many factors also drive carbon derivative finance. The specific situation of carbon derivative finance is displayed in Figure 2:

In Figure 2, carbon derivatives financial transactions are divided into four levels. The specific financial entities of carbon derivatives are denoted in Figure 3:

The Carbon Market is the product of combining the Coase theorem and carbon emission reduction needs. It is also a milestone for the Coase theorem to move toward practice. In the late 1950s, Ronald Harry Coase positively discussed the economic function of property rights: overcoming externalities and reducing social costs. He systematically guarantees the effectiveness of resource allocation [14]. Coase advocated establishing a complete property rights system to solve the external diseconomies of the environment, including property rights, emission rights, emission transfer rights, and use rights. Market transactions are an effective means of achieving optimal resource allocation [15]. Under clear and transferable property rights, carbon derivatives owners and users will naturally assess the cost and value in detail to achieve effective allocation.

2.3. Financial Market Analysis of Carbon Derivatives Based on a 3D Model

This section introduces the 3D conceptual model in systems engineering to simulate the Carbon Market to compare and analyze the current status of carbon derivatives finance in various regions. Systems engineering employs engineering methods, treats the system as the research object, and builds a suitable system model through analysis, reasoning, judgment, and synthesis to obtain optimal results. Arthur David Hall, a well-known system science researcher in the United States, first proposed the idea of the 3D structure of systems engineering called Hard System Methodology (HSM) [16]. It is mainly based on the 3D spatial structure formed by the time, logic, and knowledge dimensions to summarize and represent the various stages, steps, and knowledge aspects of systems engineering. Figure 4 portrays the 3D spatial structure of HSM:

In Figure 4, HSM divides the system engineering activities into seven stages that are closely linked before and after, including the planning stage, the formulation of the scheme, and the development stage. The seven steps include identifying problems, establishing a value system, and systematic analysis. The required professional knowledge includes engineering, medicine, law, and management. These methods or expertise provide a more unified way of thinking for solving complex system problems. HSM believes that engineering activities are mainly composed of coordinating procedures, human organizations, and tools, with the core being human organizations [17]. According to the HSM model, the organization dimension, product peacekeeping, and policy dimension are proposed to explain the composition of the Carbon Market. Specifically, the organizational dimension explains what the Carbon Market includes. The product dimension explains what the Carbon Market uses. The policy dimension is used to explain how to achieve a complete framework of the Carbon Market [18]. The 3D conceptual model of the specific Carbon Market is demonstrated in Figure 5:

In Figure 5, the organizational dimension is the core, summarizing financial institutions in the system according to the characteristics of various aspects of the Carbon Market. Its reasonable institutional composition will undoubtedly promote the financial system to complete its functions effectively. Here, the organizational dimension is subdivided into commercial banks, carbon derivatives exchange systems, institutional investors, intermediaries, carbon derivatives insurance, and other financial institutions [19]. Thereby, it can meet the needs of carbon derivatives financial activities and form a complete system.

The product dimension is a carbon derivative financial instrument launched by the Carbon Market’s financial institutions to meet the financial activities under the provisions of various national legal systems. The product dimension, the intermediary between financial institutions or other entities, is divided into three parts. Of these, the indirect financing products include green credit and project financing, while equity financing and financial bonds are direct financing products. Carbon derivatives trading includes carbon emissions trading and corresponding futures options. Lastly, consulting services can cover green wealth management, fund custody, carbon insurance, and other products [20].

The policy dimension is the coordinating factor in the Carbon Market. The financial market needs government regulation and support. Neoclassical economics believes that market failure is inevitable due to incomplete market competition, externality, public goods, and information asymmetry, etc. Intervention by introducing a government mechanism can partially or completely solve the problem of market failure and achieve the Pareto optimal state of resource allocation. Similarly, market failure in the carbon financial market requires government regulation to maintain the stable operation of the carbon financial market. Eventually, the Carbon Market management will fall under laws and regulations, policies, supervision and management, and internal management. The government regulation and strategy will help implement carbon derivatives financial activities and improve the Carbon Market. A proper legal system guarantees the benign and sustainable development of the Carbon Market. The 3D conceptual model explains the structure and basic content of the Carbon Market well and integrates the content, structure, and methods involved.

2.4. The Situation of Policy Dimension in the International Carbon Market

With the advancement of the United Nations Framework Convention on Climate Change and the efforts of various governments, the global Carbon Market is showing a geometric growth trend. The coronavirus disease 2019 (COVID-19) pandemic has hugely impacted the global market. Nevertheless, the global Carbon Market is growing against the trend, thanks to the continuous improvement of the international Carbon Market system [4]. Therefore, international supervision laws and regulations should be given more attention. Internationally, the trading of carbon derivatives is mainly distributed in four regions, namely the European Union, the United States, India, and some places in China. The United States, which is not a party to the Kyoto Protocol, currently does not have a unified federal carbon emission trading scheme but a carbon reduction system based on regions or states. The policy dimension in the US Carbon Market is mainly reflected in the legal system, support policies, supervision, and management. This work mainly analyzes the US Carbon Market’s legal system.

The US first implemented legal management of financial institutions in 1980. Typical examples are expressed in Figure 6: the Comprehensive Environmental Response, Compensation, and Liability Act, American Clean Energy Security Act, and Regional Green Gas Initiative:

In Figure 6, the Comprehensive Environmental Response, Compensation, and Liability Act requires companies to take responsibility for the environmental pollution they cause. The Clean Energy Security Act stipulates that companies in the US must be charged for pollution and emissions to promote carbon caps and carbon emissions trading programs. Regional Green Gas Initiative (RGGI) is a state-based regional climate change cooperation that adopts a cap-and-trade model and a market-driven trading system. The group requires it to take steps to limit emissions by power generators every other week within quota limits, and it also provides a more flexible mechanism for cutting emissions by projects.

In addition, the American Clean Energy Security Act, along with other legislation, quickly launched a federal carbon market. Chicago established the Carbon Emission Exchange, which is the first voluntary carbon emission reduction trading market platform in the world and the pioneer of the carbon emission futures trading mode.The focus of the US policy dimension is to regulate the government, enterprises, and banks’ behavior and the relationship between the three parties [21].

The EU is a pioneer in Energy Consumption and Emission Reduction (ECER) and has a set of mandatory emission reduction policies. The legal system in the policy dimension includes the carbon emission trading mechanism and the legal provisions on green investment and financing. The specific situation of EU’s ECER policies is exhibited in Figure 7:

Figure 7 outlines that the Kyoto Protocol stipulates the mandatory limits for carbon emission reductions in EU nations. The Copenhagen Accord provides legal support for implementing the EU’s mandatory ECER plan and the EU’s Emissions Trading System (ETS). The European Union Greenhouse Gas Emissions Trading Directive regulates the trading of derivatives, such as carbon emission rights, under the EU’s internal carbon emissions trading mechanism [22].

As a developing country, India does not need to undertake ECER obligations according to the Kyoto Protocol but follows a voluntary basis. However, since 2002, India’s carbon trading has shown a geometric growth trend, leading other developing countries in Carbon Market construction. The specific situation is presented in Figure 8:

In Figure 8, the National Action Plan on Climate Change reflects the country’s emphasis on climate change to control enterprise-emitted pollutants effectively. While the Renewable energy Trading system balances real economic growth and a low-carbon economy, it opens a huge Carbon Market. In terms of their legal systems, US laws are more mature in regulating Carbon Markets [23]. The EU member states have different laws and regulations. The supervision of green credit and transactions depends on the laws of each member state, whereas India’s Carbon Market-oriented laws and regulations are in their infancy.

2.5. Theory and Hypothesis

This work mainly applies the Vector Autoregression (VAR) model to conduct empirical analysis, which is a commonly used econometric model proposed by Christopher Sims. The core is to use all current variables in the model to carry out the regression of several lag variables. For this reason, the time series are treated as endogenous variables, with which comes the problem of a large degree of freedom loss caused by multiple dimensions of model variables. BigVAR model applies a sparse structure penalty function to the traditional VAR framework. Doing so reduces the parameter space and overcomes the over-parameterization challenge of the VAR model under high-dimensional and multi-delay series.

Based on the above theoretical analysis, the following hypotheses are proposed. First, after the introduction of the National Carbon Market, the steam coal, and asphalt futures show a significantly positive impact on the carbon-weighted price. Second, the overall effect of SHIBOR overnight on different types of futures presents asymmetry.

3. Empirical Results and Analysis Based on the BigVAR Model

This section uses the VAR model for empirical analysis. The BigVAR model includes the VARX-L (Structured regulation for BigVAR with exogenous variables) model and the Hierarchical VAR (HVAR) model [24].

The HVAR model structure is defined as:

$$y_t=v+{\displaystyle \sum }_{i=1}^p{\mathsf{\Phi }}^{\left(i\right)}{y}_{t-1}+{\displaystyle \sum }_{j=1}^k{\beta }^{\left(i\right)}{x}_{t-j}+u_t,t=1,\dots ,T$$

(1)

In Equation (1), $y$ demonstrates a $k$ × 1-dimensional intercept vector. $y_t$ stands for the $k\times 1$-dimensional time series as an endogenous variable, with lag order p. ${\mathsf{\Phi }}^{\left(i\right)}$ expresses the $k$×$k$ coefficient matrix of the endogenous variable. $x_t$ represents the m × 1-dimensional time series as the exogenous variable, with lag order s. ${\beta }^{\left(j\right)}$ denotes the $k$ × m coefficient matrix of the exogenous variable. $T$ means the selected sample time. $u_t$ indicates the $k$ × 1-dimensional white noise sequence, and $u_t$~N(0, ∑$u$). ∑$u$ is the nonsingular diagonal matrices. When there is no limitation of exogenous variable $x_t$, or m = s = 0, the model is a VAR model. The parameters of the VARX model are optimized by using the least squares estimation:

$$\underset{v,\mathsf{\Phi },\beta }{\arg min}{\displaystyle \sum }_{t=1}^T\parallel y_t-v-{\displaystyle \sum }_{l=1}^p{\mathsf{\Phi }}^{\left(l\right)}{y}_{t-1}-{\displaystyle \sum }_{j=1}^s{\beta }^{\left(j\right)}{x}_{t-j}{\parallel }_2^2$$

(2)

In Equation (2), $A_F=\sqrt{{\displaystyle \sum }_{i,j}^{}{A}_{\mathrm{ij}}^2}$ represents Frobenius norm of matrix A. $\mathsf{\Phi }=\left[\mathsf{\Phi }\left(1\right),\dots ,\mathsf{\Phi }\left(p\right)\right]$, and $\left[\beta \left(1\right),\dots ,\beta \left(s\right)\right]$. The number of parameters to be estimated is $k\left(1+kp+ms\right)$. In order to solve the problem of overparameterization in high-dimensional and multi-time delay, the VARX-L model introduces the concept of convex optimization based on the VARX model. Thereby, it reduces the parameter space with a structured sparse penalty function. The setting method is written in Equation (3):

$$\underset{v,\varphi ,\beta }{min}{\displaystyle \sum }_{t=1}^T\parallel y_t-v-{\displaystyle \sum }_{l=1}^p{\mathsf{\Phi }}^{\left(i\right)}{y}_{t-l}-{\displaystyle \sum }_{j=1}^s{\beta }^{\left(j\right)}{x}_{t-j}{\parallel }_2^2+\lambda \left(p_y\left(\mathsf{\Phi }\right)+p_x\left(\beta \right)\right),\lambda \ge 0$$

(3)

In Equation (3), $\lambda$ refers to the penalty parameter estimated by cross-validation. $p_y\left(\mathsf{\Phi }\right)$ denotes the penalty function of the coefficient matrix of the endogenous variable. $p_x\left(\beta \right)$ signifies the penalty function of the coefficient matrix of the exogenous variable. In the empirical analysis, all indexes are treated as endogenous variables. Therefore, when there is no exogenous variable $x_t$ or m = s = 0, VARX-L (with Lag-Group structure) model is defined as:

$$\underset{v,\mathsf{\Phi }}{min}{\displaystyle \sum }_{t=1}^T\parallel y_t-v-{\displaystyle \sum }_{l=1}^p{\mathsf{\Phi }}^{\left(l\right)}{y}_{t-l}{\parallel }_2^2+\lambda \left(p_y\left(\mathsf{\Phi }\right)\right),\lambda \ge 0$$

(4)

Then, the penalty function is set as Equation (5):

$$p_y\left(\Phi \right)=\sqrt{k^2}{\displaystyle \sum }_{l=1}^p\parallel {\Phi }^{\left(l\right)}\parallel {}_2$$

(5)

In terms of model testing, this section first uses Mean Square Forecast Error (MSFE) to measure the model performance under different settings. The smaller the MSFE is, the better the model performance is, and then the model form used to judge the relationship between variables can be selected. On this basis, the interaction between variables is classified and summarized according to the significance level and positive and negative values of parameter estimation. The specific expression of MSFE reads:

$$\mathrm{MSFE}\left({\lambda }_i\right)=\frac{1}{\left(T_2-T_1-1\right)}{{\displaystyle \sum }}_{t=T_1}^{T_2-1}\Vert {\widehat{y}}_{t+1}^{{\lambda }_t}-y_{t+1}{\Vert }_2^2$$

(6)

In Equation (6), $T_1$ is one third time point of the sampling period. $T_2$ stands for the two-third time point of the sampling period.

On the basis of Liu, Z et al. (2022) [24], this empirical study expanded the sample data size to more than one year, which improved the accuracy of the analysis after the launch of the National Carbon Market. On 16 July 2021, China’s Carbon Market began to operate, which had a structural impact on China’s economy. Considering the trading date of China’s crude oil futures, the sampling duration is divided into two sections: before and after the operation of the National Carbon Market. The research data are sampled after the National Carbon Market operation (16 July 2021 to 31 August 2022). In the empirical process, there is a one-to-one correspondence between the order of variables and the sparse graph of the coefficient estimation matrix in the following text Figure 9. In the empirical analysis after the operation of the National Carbon Market, the order of variables is Carbon Market volume weighted yield (turnover weighted yield), industry-weighted yield, CSI300, corporate debt index, steam coal continuous, crude oil recent, national debt index, asphalt recent, coke recent, fuel recent, dollar to RMB exchange rate by China Foreign Exchange Trade System (CFETS) and overnight Shanghai Interbank Offered Rate (SHIBOR). In addition, the penalty functions at Componentwise HVAR and Lag-Weighted Lasso are set according to Nicholson et al. (2017) [25]. Then, it determines the lag order of the model according to Akaike Information Criterion (AIC) and Schwarz Criterion (SC).

Table 1. MSFE of BigVAR estimation result.

	Carbon Market Trading Volume Weighted Yield	Carbon Market Turnover Volume Weighted Yield
Lag Group	0.0000221	0.00000503
Componentwise HVAR	0.000889679	0.000442365
Lag-Weighted Lasso	0.01001355	0.000152958

exhibits the model estimation effect of various penalty functions. After the release of the national carbon data, it is necessary to revise and demonstrate the results. From the above results, it can be seen that for the yields in both cases, Lag group performs relatively well, so the Lag group model is used to analyze the subsequent results.

As can be seen from the above six figures, in most cases, variables have a positive impact on the industry return rate, and each variable also influences each other. With the change of the model, the influence degree of each variable varies greatly. In the Lag-weighted Lasso model, most variables have a certain degree of mutual influence, but the influence of the variables is gradually weakened. In the volume-weighted results, each energy variable has a relatively obvious influence, while the enterprise debt index has a relatively strong influence on most variables, and the coke and the exchange rate of USD against RMB has a relatively significant influence. In the HVAR model, there are significant influences between asphalt and coke and most of the variables, and most of the influences are positive. From the above results, it can be seen that in most cases, HVAR has a better impact on the significance of various variables and has a better impact on the significance of various variables and is better reflected.

According to

Table 2. Summary of variable results affecting the Carbon Market.

Variables Affecting the Price of Carbon Emission Rights
Lag Group	Strong Influence	General Influence	Lag Group	Strong Influence	General Influence
Carbon Market trading volume Weighted yield	Steam coal continuous (+) Asphalt in the recent month (+) Coke in the recent month (-)	corporate debt index (+) Crude oil in the recent month (+) Fuel in the recent month (+)	Carbon Market turnover volume Weighted yield	Steam coal continuous * (+) Crude oil in the recent month (+) *
National carbon market yield	Steam coal continuous (-) National debt index (-) Asphalt in the recent month (+) Coke in the recent month (-)	CSI300 (+) corporate debt index (+) Crude oil in the recent month (+) Fuel in the recent month (+) USD to RMB (CFETS) (+)
variables affected by the carbon emission market
Carbon Market trading volume Weighted yield	corporate debt index (-) USD to RMB (CFETS) (+) SHIBOR Overnight (+)	National debt index (+) Asphalt in the recent month (+) Fuel in the recent month (+)	Carbon Market turnover volume Weighted yield	Asphalt in the recent month (+) Fuel in the recent month (+) USD to RMB (CFETS) (+)	SHIBOR Overnight (+)
National carbon market yield	corporate debt index (-) National debt index (-) Asphalt in the recent month (+) Coke in the recent month (-)	Industry weighted yield (+) CSI300 (+) Crude oil in the recent month (+) Fuel in the recent month (+)

Note: (+) and (-) represent the positive and negative influence, respectively. * indicates the consistent influence of the two models.

, when the Carbon Market turnover-weighted yield and the National Carbon Market yield are introduced into the model simultaneously. It can be seen from the above tables that, when carbon emission weighted yield rate and national carbon yield rate data are introduced at the same time, thermal coal continuous and asphalt recent months will have a positive impact on the result, which has a certain effect on volume weighting, and asphalt futures also have a positive impact on the national carbon yield rate. So, the energy market has an impact on both the industry and all carbon revenue data. Additionally, relatively speaking, most of the other energy sources will have a positive impact on either the volume-weighted yield or the turnover-weighted yield, as well as the national carbon data yield.

After the introduction of the national yield data, steam coal and asphalt futures will have a significant positive impact on the turnover weighted yield, coke will have a relatively strong negative impact. In the volume market, steam coal futures and crude oil futures have significant positive and negative influences on the distribution of results. The addition of USD to RMB in the carbon emission market has a significant positive impact, and the rest results are similar to the above results. In terms of carbon emission weight and national carbon yield data, the corporate debt index and coke futures all show negative intensity correlation, and the exchange rate between US dollar and RMB also shows corresponding positive correlation. It is similar in the carbon emission market, but the exchange rate variable of US dollar to RMB dollar has a significant impact the interaction between the energy and financial markets is complex, and the impact of steam coal futures and crude oil futures on SHIBOR overnight is negative. SHIBOR overnight has a negative impact on steam coal futures and coke futures and a positive impact on fuel futures and crude oil futures. The overall impact is asymmetric. In summary, after the introduction of the National Carbon Market, the correlation between the energy market and the financial market has become relatively complex but also presents a certain degree of asymmetry. Thus, the two hypotheses proposed above are verified.

4. Policy Recommendations

With the deepening of the reform of financial markets and the improvement of the degree of opening up, the linkage and risk contagion among financial markets will become more and more obvious [26]. Therefore, the government should constantly improve the financial market in terms of institutional construction, ensure the healthy and rapid development of the stock market, focus on avoiding and preventing inter-regional financial risk contagion, and improve the ability to resist financial crises and financial risks. In particular, in the process of maintaining the stable and orderly operation of the financial market, financial regulatory authorities should pay attention to strengthening legal supervision and pay attention to the linkage between markets and information transmission characteristics so as to improve regulatory efficiency and reduce systemic risks. Moreover, coordination and cooperation should be strengthened to implement a more systematic and effective risk control system.

Specifically, the concept of “regulatory sandbox” should be applied to encourage innovation in policy and regulatory mechanisms while adhering to financial security and carbon trading standards. The idea of a regulatory sandbox originated in the UK. The principle is to define the scope, take inclusive and prudent regulatory measures for the enterprises within the scope, and not spread the problem beyond the prescribed scope [27]. The regulatory sandbox can implement fault tolerance and error correction mechanisms within a controlled range. Regulators can supervise the entire operation process, effectively control risks and explore more quality and energy conservation supervision measures. The aim is to encourage safer innovation and find the best mix between protection and regulation. In the regulatory sandbox, attempts are made to improve the implementation of the responsibility system of relevant entities in the carbon financial market. At the same time, innovation in risk regulation should be carried out, including the establishment of a carbon finance risk prevention and disposal mechanism, mandatory information disclosure mechanism, credit punishment mechanism, green slanting mechanism, carbon finance crime prevention system, and judicial coordination communication mechanism.

Furthermore, in order to ensure the sound development of the carbon derivatives financial market, the legislative mechanism of each country should be unified, but there should also be regional differences. Subsequent international supervision should build a legislative path for an international unified carbon derivatives financial market system based on regional continuous pilot experience. However, it should also be encouraged and supplemented by local or regional special operational regulations.

5. Conclusions

In order to regulate the Carbon Market, such as carbon futures, options, and swap contracts, and to slow down climate change, it is necessary to construct and improve the supporting international supervisory laws and regulations. Following a review of the financial system theories and the Carbon Market’s characteristics, the 3D structural modeling technique in systems engineering was chosen to establish the Carbon Market framework. Next, the supervision and management aspects of the Carbon Market’s policy dimension were introduced. Then two hypotheses were proposed, and the BigVAR model was used for empirical analysis. The empirical results reveal that, after the introduction of the National Carbon Market, the steam coal, and asphalt futures have a significantly positive impact on the carbon-weighted price. However, the impact of the stock yield of the low-carbon transition industry on the weighted price of carbon emissions has regional and national differences, and the correlation between the financial market and the energy market has become relatively complex, showing a certain degree of asymmetry, which proves the correctness of the hypothesis. Finally, according to the idea of the regulatory sandbox, suggestions for improving the financial regulation of carbon derivatives are proposed. For example, to improve the responsibility system of relevant entities in the carbon financial market and innovation of risk supervision mechanisms. Countries differ in their development and should adapt their measures to local conditions. However, the analysis of the 3D structural model is not detailed enough. Future work will continue to focus on the operation mechanism of the Carbon Market and relevant international laws and regulations.