Carbon market efficiency evaluation method considering multi-dimensional view angle

Abstract

The invention discloses a carbon market efficiency evaluation method considering a multi-dimensional view angle, and relates to the technical field of carbon markets, and the method comprises the following steps: obtaining enterprises participating in a carbon credit transaction in a to-be-tested region, and obtaining carbon market efficiency influence data according to the enterprises; constructing a carbon market stability evaluation model based on a machine learning algorithm according to the carbon market efficiency influence data to obtain a carbon market stability state; obtaining carbon market efficiency according to the carbon market stability state; by establishing the carbon market stability evaluation model, the problem that the evaluation result is lack of timeliness and accuracy due to the fact that only few indexes are considered, the complexity of the market cannot be comprehensively reflected, and carbon emission technology progress factors are ignored is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon market, and more specifically, to a carbon market efficiency evaluation method considering multi-dimensional perspectives. Background Art

[0002] As the global climate change problem intensifies, the carbon market, as an important tool for reducing greenhouse gas emissions, has received widespread attention. However, traditional carbon market efficiency evaluation methods are often limited to a single dimension, focusing mainly on price signals and market liquidity. This one-sided analysis method cannot fully reflect the actual operation of the carbon market and its contribution to the realization of environmental policy goals. Therefore, it is particularly important to develop a carbon market efficiency evaluation method that considers a multi-dimensional perspective.

[0003] The evaluation method from a multidimensional perspective should not only consider economic factors, but also integrate social, environmental and technological aspects. First, in the economic dimension, the efficiency of the carbon market needs to be evaluated through price stability, trading volume and the diversity of market participants to ensure the rational allocation of resources. Secondly, in the social dimension, attention should be paid to the impact of the carbon market on different stakeholders and its fairness and acceptability should be evaluated. In addition, the environmental dimension emphasizes the contribution of the carbon market to the actual emission reduction effect to ensure that it can effectively promote green transformation and sustainable development.

[0004] The above disclosed technical solutions have at least the following technical problems: Traditional carbon market efficiency evaluation methods only consider a few indicators and cannot fully reflect the complexity of the market. They ignore policy transparency and other factors, and cannot timely reflect the impact of carbon emission technology progress and changes on market efficiency, resulting in inaccurate evaluation results. In addition, they do not fully consider the dynamic characteristics of time series data, resulting in a lack of timeliness and accuracy in the evaluation results. In view of the above problems, the present invention proposes a solution. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a carbon market efficiency evaluation method considering a multi-dimensional perspective. By constructing a carbon market stability evaluation model, it solves the problem that only considering a few indicators cannot fully reflect the complexity of the market, ignores the factor of carbon emission technology progress, and causes the evaluation results to lack timeliness and accuracy.

[0006] To achieve the above object, the present invention provides the following technical solutions: A carbon market efficiency evaluation method considering a multi-dimensional perspective includes obtaining enterprises participating in carbon credit trading in a test area, and obtaining carbon market efficiency impact data based on the enterprises; constructing a carbon market stability evaluation model based on the carbon market efficiency impact data based on a machine learning algorithm to obtain the carbon market stability status; and obtaining the carbon market efficiency based on the carbon market stability status.

[0007] In a preferred embodiment, the carbon market efficiency impact data includes a carbon credit impact index, and the specific steps for obtaining the carbon credit impact index are as follows: obtain the historical supply and demand data of carbon credits in the area to be tested, and clear the abnormal fluctuation data in the historical supply and demand data of carbon credits, and obtain the average value of supply and demand through the moving average method for the processed supply and demand data; obtain the historical carbon credit demand of the area to be tested, and obtain the carbon credit demand elasticity through a linear regression analysis method based on the historical carbon credit demand; obtain the historical supply of carbon credits in the area to be tested, and obtain the supply elasticity through a regression analysis method based on the historical supply; obtain the average value of supply and demand through the moving average method based on the supply and demand data, use the average value of supply and demand as a threshold, and calculate based on the supply, demand and the threshold of supply and demand based on a preset external influencing factor formula to obtain the external influencing factor; calculate based on the carbon credit demand, carbon credit supply, external influencing factor, carbon credit demand elasticity and carbon credit supply elasticity based on a preset carbon credit impact index formula to obtain the carbon credit impact index.

[0008] In a preferred embodiment, the carbon market efficiency impact data includes an emission reduction adjustment coefficient, and the specific steps for obtaining the emission reduction adjustment coefficient are as follows: obtain historical emission data of the area to be tested, identify and remove outliers and erroneous data in the data, and add up the historical emission data to calculate the average carbon emissions, and use the average carbon emissions as the baseline emissions; obtain carbon emission data for a period of time before and after the introduction of emission reduction technology, and calculate based on the carbon emission data for a period of time before and after the introduction of emission reduction technology to obtain the emission reduction ratio after the introduction of emission reduction technology; calculate based on the preset adjustment emission formula according to the baseline emissions and the emission reduction ratio caused by technological progress to obtain adjusted emissions; obtain historical carbon emission data in the area to be tested, capture time dependence and complex patterns through a time series model, and thus determine the impact of time factors; calculate based on the preset emission reduction adjustment coefficient formula according to the baseline emissions, actual emissions and the impact of time factors to obtain the emission reduction adjustment coefficient.

[0009] In a preferred embodiment, the carbon market efficiency impact data includes a price fluctuation state, and the specific steps for obtaining the price fluctuation state are as follows: obtain historical carbon price data of the area to be tested, process the obtained carbon price data, and remove outliers and missing values ​​therein; calculate based on the carbon price data to obtain the average value of the carbon price; set the carbon price data to several observation points, and obtain the carbon price data of each observation point; calculate based on the carbon price data of each observation point and the average value of the carbon price based on a preset price fluctuation state formula to obtain the price fluctuation state.

[0010] In a preferred embodiment, the carbon market efficiency is obtained according to the carbon market stability state, specifically: if the carbon market stability value is greater than 1, a stable signal is issued; if the carbon market stability value is less than 1, a danger signal is issued.

[0011] Technical effects and advantages of a carbon market efficiency evaluation method considering multi-dimensional perspectives of the present invention: 1. This invention takes into account the elasticity of demand and supply, so that the carbon price can better reflect the real dynamics of the market and enhance the applicability and accuracy of the model. Secondly, the introduction of external factors enables the model to capture the impact of technological progress on the market and improves its adaptability to changes in the market environment. By considering the differences in demand and supply, it allows for in-depth analysis of the price formation mechanism, which helps policymakers evaluate the effectiveness of different policy tools. The complex formula provides a more comprehensive perspective for academic research and practical applications, supports the goals of sustainable development and low-carbon economic transformation, and promotes a deep understanding and predictive ability of the carbon market.

[0012] 2. The present invention more accurately captures the impact of different driving factors on emission reduction by introducing carbon emission technology. Technological progress effectively reduces the carbon footprint of production and consumption, while economic factors affect the overall energy demand and emission levels. This comprehensive analysis reveals complex causal relationships, allowing the model to more accurately reflect the actual situation and provide decision-making support; the consideration of time factors effectively captures the lag in the effects of policy implementation and technological progress on emission reduction. Through dynamic modeling, the changing trend of emissions at different time nodes is identified, making the model more flexible and adaptable to the ever-changing environment. Such flexibility not only enables the model to respond to short-term emergencies, but also effectively supports long-term emission reduction strategies and planning; the dynamic nature of the model enables it to be used not only for short-term evaluation, but also to provide support for long-term environmental planning and emission reduction target setting, and to help achieve sustainable development goals. The implementation of this model requires the collection and analysis of relevant data, thereby promoting a data-driven decision-making process and improving the scientific nature of decision-making.

[0013] 3. The present invention directly reflects the combination of market economic incentives and environmental benefits through the carbon market stability model formula. High carbon credit impact index and emission reduction adjustment coefficient values ​​indicate that the market has strong economic incentives for emission reduction, which helps to promote the enthusiasm of market participants and thus enhance market stability. The carbon market stability model is flexible and can adjust parameters according to the specific conditions of different regions and markets. It is applicable to a variety of market environments and provides a basis for related market analysis and research. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1This is a structural schematic diagram of a carbon market efficiency evaluation method considering multi-dimensional perspectives according to the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Embodiment 1, Figure 1 The present invention provides a carbon market efficiency evaluation method considering multi-dimensional perspectives, comprising the following steps: S1, obtain the enterprises participating in carbon credit trading in the tested area, and obtain carbon market efficiency impact data based on the enterprises.

[0017] Carbon credit impact index refers to the price paid for emitting one unit of greenhouse gases such as carbon dioxide under the carbon trading or carbon pricing mechanism. It reflects the external cost of carbon emissions to the environment and society and is an important economic signal to encourage companies and individuals to reduce carbon emissions. By observing the carbon credit impact index, policymakers can encourage investment in low-carbon technologies and the use of renewable energy, thereby promoting greenhouse gas emissions reduction and supporting sustainable development goals.

[0018] The specific steps for obtaining the carbon credit impact index are as follows: Obtain the historical supply and demand data of carbon credits in the area to be tested, remove abnormal fluctuation data in the historical supply and demand data of carbon credits, and use the moving average method to obtain the average value of supply and demand; Obtaining historical carbon credit demand in the area to be tested, and calculating carbon credit demand elasticity through linear regression analysis method based on the historical carbon credit demand; Obtaining the historical supply of carbon credits in the region to be tested, and obtaining the supply elasticity through a regression analysis method based on the historical supply; According to the supply and demand data, the average value of the supply and demand is obtained by the moving average method, the average value of the supply and demand is used as a threshold, and the external influencing factor is obtained by calculating based on the supply, demand and the threshold of the supply and demand based on a preset external influencing factor formula; The carbon credit impact index is calculated based on a preset carbon credit impact index formula according to the carbon credit demand, carbon credit supply, external influencing factors, carbon credit demand elasticity and carbon credit supply elasticity to obtain a carbon credit impact index.

[0019] The specific calculation formula of the carbon credit impact index is as follows:

[0020] The specific calculation formula of the external influencing factor is as follows:

[0021] In the formula, is the Carbon Credit Impact Index, is the cost of the carbon credit demand curve, which depends on the quantity demanded and the elasticity of demand, is the cost of the carbon credit supply curve, which depends on the quantity supplied and the supply elasticity, is an external influencing factor, is the carbon credit demand, is the carbon credit supply, is the carbon credit demand elasticity, is the carbon credit supply elasticity, is the flat threshold of carbon credit supply, is the threshold for carbon credit demand.

[0022] It should be noted that the costs of demand and supply depend not only on quantity, but also on elasticity. Higher elasticity means that price changes have a greater impact on demand or supply. External influencing factors reflect possible influences in the market, including but not limited to carbon tax policies, fluctuations in the international carbon trading market, and advances in environmental protection technologies. The denominator introduces the difference between demand and supply and takes into account the impact of elasticity, which makes changes in carbon prices more flexible and dynamic.

[0023] Further, the advantages of analyzing the price elasticity of demand and the price elasticity of supply on the carbon credit impact index are as follows: By comprehensively considering the elasticity of demand and supply, the carbon price can better reflect the real dynamics of the market, enhancing the applicability and accuracy of the model. Secondly, the introduction of external factors enables the model to accurately capture the impact of technological progress on the market and improve its adaptability to changes in the market environment. By considering the differences in demand and supply, it allows for in-depth analysis of the price formation mechanism, which helps policymakers evaluate the effectiveness of different policy tools. The complex formula provides a more comprehensive perspective for academic research and practical applications, supports the goals of sustainable development and low-carbon economic transformation, and promotes a deep understanding and predictive ability of the carbon market.

[0024] Emission reduction adjustment factor, the emission reduction adjustment factor refers to the amount of greenhouse gas emissions reduced through various measures and policies. In environmental science and climate policy, the emission reduction adjustment factor is often used to evaluate the effectiveness of emission reduction measures and the impact of environmental policies. Specifically, the emission reduction adjustment factor is calculated by comparing baseline emissions with actual emissions.

[0025] The specific steps for obtaining the emission reduction adjustment coefficient are as follows: Obtain historical emission data of the area to be tested, identify and remove outliers and erroneous data in the data, and sum up the historical emission data to calculate the average carbon emissions, and use the average carbon emissions as the benchmark emissions; Obtain carbon emission data for a period of time before and after the introduction of the emission reduction technology, and calculate based on the carbon emission data for a period of time before and after the introduction of the emission reduction technology to obtain the emission reduction ratio after the introduction of the emission reduction technology; The adjusted emissions are calculated based on the baseline emissions and the emission reduction ratio caused by technological progress based on a preset adjusted emissions formula to obtain the adjusted emissions; Obtain historical carbon emission data in the area to be tested, and capture time dependencies and complex patterns through time series models to determine the impact of time factors; The emission reduction adjustment coefficient is calculated based on a preset emission reduction adjustment coefficient formula according to the baseline emission amount, actual emission amount and the influence of time factors to obtain the emission reduction adjustment coefficient.

[0026] The specific calculation formula of the emission reduction adjustment coefficient is as follows:

[0027] The specific calculation formula for the actual emissions is as follows:

[0028] In the formula, is the emission reduction adjustment factor, is the baseline emission, is the actual emission, It is the influence of time factor. It is the emission reduction effect caused by technological progress.

[0029] It should be noted that the calculation of the emission reduction adjustment coefficient not only takes into account static emission differences, but also reflects dynamic technological development factors, thereby providing policymakers with more accurate decision-making basis. This complex calculation method helps to more comprehensively understand and evaluate the effectiveness of emission reduction measures.

[0030] Furthermore, the advantages of obtaining the emission reduction adjustment coefficient by introducing the emission reduction effects caused by policy factors, the emission reduction effects caused by technological progress, and the emission reduction effects caused by economic factors are analyzed as follows: The introduction of technical factors can more accurately capture the impact of different driving factors on emission reduction, including the fact that technological progress effectively reduces the carbon footprint of production and consumption. This comprehensive analysis reveals complex causal relationships, allowing the model to more accurately reflect the actual situation and provide decision-making support; The consideration of time factors effectively captures the lag in the effects of policy implementation and technological progress on emission reduction. Through dynamic modeling, the changing trends of emissions at different time points can be identified, making the model more flexible and adaptable to the changing environment. Such flexibility not only enables the model to respond to short-term emergencies, but also effectively supports long-term emission reduction strategies and planning. The dynamic nature of the model allows it to be used not only for short-term assessments, but also to provide support for long-term environmental planning and emission reduction target setting, helping to achieve sustainable development goals. The implementation of this model requires the collection and analysis of relevant data, thereby promoting data-driven decision-making processes and improving the scientific nature of decision-making.

[0031] Price volatility status, price volatility status is an important indicator to measure the degree of price change, which is used to analyze market risks and uncertainties. In the financial field, price volatility status refers to the fluctuation range of prices within a specific time period, reflecting the instability of market prices.

[0032] The specific steps for obtaining the price fluctuation status are as follows: Obtain historical carbon price data for the area to be tested, process the acquired carbon price data, and remove outliers and missing values; Calculating according to the carbon price data to obtain an average carbon price; Setting the carbon price data to a plurality of observation points, and obtaining the carbon price data at each observation point; The price fluctuation state is obtained by calculating the carbon price data of each observation point and the average value of the carbon price based on a preset price fluctuation state formula.

[0033] The specific calculation formula for the price fluctuation state is as follows:

[0034] In the formula, is the price fluctuation state, is the total number of observed time points, is the carbon price at the i-th time point, is the average carbon price.

[0035] S2, constructing a carbon market stability assessment model based on the carbon market efficiency impact data and a machine learning algorithm to obtain the carbon market stability status.

[0036] In this embodiment, the carbon market stability assessment model is constructed based on the machine learning algorithm according to the carbon market efficiency impact data to obtain the carbon market stability status, specifically: According to the carbon credit impact index, emission reduction adjustment coefficient, price fluctuation status and policy transparency, a carbon market stability assessment model is constructed based on a machine learning algorithm to obtain the carbon market stability status.

[0037] The specific calculation formula of the carbon market stability assessment model is as follows:

[0038] In the formula, is the stability of the carbon market, is the Carbon Credit Impact Index, is the emission reduction adjustment factor, It is a price fluctuation state.

[0039] It should be noted that if the carbon market stability value is greater than 1, it means that the carbon market is relatively stable; if the carbon market stability is less than 1, the carbon market faces the risk of instability; the higher the price volatility, the more unstable the market, so the price volatility has a negative impact on stability; the carbon credit impact index and policy transparency are proportional to the stability of the carbon market. The higher the carbon credit impact index and policy transparency, the more stable the market. By monitoring these four parameters, market participants regularly evaluate the stability of the carbon market and adjust their strategies to cope with sudden market changes.

[0040] Furthermore, the advantages of the carbon market stability model are analyzed from four aspects: carbon credit impact index, emission reduction adjustment coefficient, price fluctuation status and policy transparency. The specific advantages are as follows: By multiplying the carbon credit transaction price with the emission reduction adjustment coefficient, the carbon market stability model formula directly reflects the combination of market economic incentives and environmental benefits. High carbon credit impact index and emission reduction adjustment coefficient values ​​indicate that the market has strong economic incentives for emission reduction, which helps to promote the enthusiasm of market participants and thus enhance market stability. Taking policy transparency into consideration, the impact of transparent policies on market confidence is emphasized. Higher transparency enables market participants to better predict future policy changes, reduce market shocks caused by policy uncertainty, and improve market stability. The carbon market stability model is flexible and can adjust parameters according to the specific conditions of different regions and markets. It is applicable to a variety of market environments and provides a basis for related market analysis and research.

[0041] S3, obtaining the carbon market efficiency according to the carbon market stability state.

[0042] In this embodiment, the carbon market efficiency is obtained according to the carbon market stability state, specifically: If the carbon market stability value is greater than 1, a stability signal is issued; If the carbon market stability value is less than 1, a danger signal is issued.

[0043] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0044] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0045] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0046] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0047] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0048] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A carbon market efficiency evaluation method considering multi-dimensional perspectives, characterized in that: include: Obtain enterprises participating in carbon credit trading in the area to be tested, and obtain carbon market efficiency impact data based on the enterprises; Constructing a carbon market stability assessment model based on the carbon market efficiency impact data and a machine learning algorithm to obtain the carbon market stability status; The carbon market efficiency is obtained according to the carbon market stability state.

2. A carbon market efficiency evaluation method considering multi-dimensional perspectives according to claim 1, characterized in that: The carbon market efficiency impact data includes a carbon credit impact index. The specific steps for obtaining the carbon credit impact index are as follows: Obtain the historical supply and demand data of carbon credits in the area to be tested, remove abnormal fluctuation data in the historical supply and demand data of carbon credits, and use the moving average method to obtain the average value of supply and demand; Obtaining historical carbon credit demand in the area to be tested, and calculating carbon credit demand elasticity through linear regression analysis method based on the historical carbon credit demand; Obtaining the historical supply of carbon credits in the region to be tested, and obtaining the supply elasticity through a regression analysis method based on the historical supply; According to the supply and demand data, the average value of the supply and demand is obtained by the moving average method, the average value of the supply and demand is used as a threshold, and the external influencing factor is obtained by calculating based on the supply, demand and the threshold of the supply and demand based on a preset external influencing factor formula; The carbon credit impact index is calculated based on a preset carbon credit impact index formula according to the carbon credit demand, carbon credit supply, external influencing factors, carbon credit demand elasticity and carbon credit supply elasticity to obtain a carbon credit impact index.

3. A carbon market efficiency evaluation method considering multi-dimensional perspectives according to claim 2, characterized in that: The carbon market efficiency impact data includes emission reductions, and the specific steps for obtaining the emission reduction adjustment coefficient are as follows: Obtain historical emission data of the area to be tested, identify and remove outliers and erroneous data in the data, and sum up the historical emission data to calculate the average carbon emissions, and use the average carbon emissions as the benchmark emissions; Obtain carbon emission data for a period of time before and after the introduction of the emission reduction technology, and calculate based on the carbon emission data for a period of time before and after the introduction of the emission reduction technology to obtain the emission reduction ratio after the introduction of the emission reduction technology; The adjusted emissions are calculated based on the baseline emissions and the emission reduction ratio caused by technological progress based on a preset adjusted emissions formula to obtain the adjusted emissions; Obtain historical carbon emission data in the area to be tested, and capture time dependencies and complex patterns through time series models to determine the impact of time factors; The emission reduction adjustment coefficient is calculated based on a preset emission reduction adjustment coefficient formula according to the baseline emission amount, actual emission amount and the influence of time factors to obtain the emission reduction adjustment coefficient.

4. A carbon market efficiency evaluation method considering multi-dimensional perspectives according to claim 3, characterized in that: The carbon market efficiency impact data includes price fluctuation status, and the specific steps for obtaining the price fluctuation status are as follows: Obtain historical carbon price data for the area to be tested, process the acquired carbon price data, and remove outliers and missing values; Calculating according to the carbon price data to obtain an average carbon price; Setting the carbon price data to a plurality of observation points, and obtaining the carbon price data at each observation point; The price fluctuation state is obtained by calculating the carbon price data of each observation point and the average value of the carbon price based on a preset price fluctuation state formula.

5. A carbon market efficiency evaluation method considering multi-dimensional perspectives according to claim 4, characterized in that: The carbon market efficiency is obtained according to the carbon market stability state, specifically: If the carbon market stability value is greater than 1, a stability signal is issued.

6. A carbon market efficiency evaluation method considering multi-dimensional perspectives according to claim 5, characterized in that: The specific calculation formula of the carbon credit impact index is as follows: The specific calculation formula of the external influencing factor is as follows: In the formula, is the Carbon Credit Impact Index, is the cost of the carbon credit demand curve, which depends on the quantity demanded and the elasticity of demand, is the cost of the carbon credit supply curve, which depends on the quantity supplied and the supply elasticity, is an external influencing factor, is the carbon credit demand, is the carbon credit supply, is the carbon credit demand elasticity, is the carbon credit supply elasticity, is the flat threshold of carbon credit supply, is the threshold for carbon credit demand.

7. A carbon market efficiency evaluation method considering multi-dimensional perspectives according to claim 6, characterized in that: The specific calculation formula of the emission reduction adjustment coefficient is as follows: The specific calculation formula for the actual emissions is as follows: In the formula, is the emission reduction adjustment factor, is the baseline emission, is the actual emission, It is the influence of time factor. is the proportion of emission reduction caused by technological progress.

8. A carbon market efficiency evaluation method considering multi-dimensional perspectives according to claim 7, characterized in that: The specific calculation formula for the price fluctuation state is as follows: In the formula, is the price fluctuation state, is the total number of observed time points, is the carbon price at the i-th time point, is the average carbon price.

9. A carbon market efficiency evaluation method considering multi-dimensional perspectives according to claim 8, characterized in that: The carbon market stability assessment model is constructed based on the machine learning algorithm according to the carbon market efficiency impact data to obtain the carbon market stability status, specifically: According to the carbon credit impact index, emission reduction adjustment coefficient and price fluctuation status, a carbon market stability assessment model is constructed based on a machine learning algorithm to obtain the carbon market stability status.

10. The specific calculation formula of the carbon market stability assessment model is as follows: In the formula, is the stability of the carbon market, is the Carbon Credit Impact Index, is the emission reduction adjustment factor, It is a price fluctuation state.

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