CN-122022168-A - Carbon emission trading method based on intelligent contract

CN122022168ACN 122022168 ACN122022168 ACN 122022168ACN-122022168-A

Abstract

The application discloses a carbon emission trading method based on an intelligent contract, which belongs to the technical field of information, and ensures the privacy of enterprises in the trading process by combining a blockchain technology and the intelligent contract with a zero knowledge proof technology, and improves the flexibility and compliance of market trading by a self-adaptive pricing engine and an automatic pay clearly mechanism. Specifically, the enterprise submits an order commitment in the transaction process, verifies the validity of the order through zero knowledge proof, and matches through a privacy protection algorithm to generate a transaction scheme. After the transaction is completed, the transaction is subjected to atomic exchange through a hash time lock contract, so that the safety and fairness of the transaction are ensured. Meanwhile, the cost price is dynamically adjusted based on market fluctuation, historical performance rate and enterprise emission data, and performance check is automatically triggered in pay clearly period, so that the enterprise is ensured to fulfill carbon emission responsibility. By the method, enterprises can flexibly and automatically trade the carbon emission rights on the premise of privacy protection, and the market efficiency and transparency are improved.

Inventors

TIAN ZEHAO
Zhang Maile
GAO JIANXIANG
Yue Benyong
WU XIAOFENG

Assignees

西安邮电大学
陕西交控绿色发展集团有限公司

Dates

Publication Date: 20260512
Application Date: 20260130

Claims (10)

1. A smart contract-based carbon emissions trading method, the method comprising: Creating a storage virtual account through a storage intelligent closing control enterprise according to a received registration request, and creating an enterprise attribute structure body bound with the storage virtual account, wherein the enterprise attribute structure body stores industry codes and historical performance rate; During a transaction period, the enterprise client submits an order promise comprising transaction quantity, transaction unit price and random salt value to a transaction intelligent contract according to the available certification quantity in the storage virtual account, and a first zero knowledge proof, wherein the first zero knowledge proof is used for verifying the validity of the order on the premise of not revealing specific numerical values; After the transaction period is finished, executing a verifiable privacy matching algorithm based on all verified order promises to generate a transaction scheme, and synchronously generating a second zero knowledge proof for verifying the execution correctness of the matching algorithm, wherein the transaction scheme comprises the transaction quantity and the transaction unit price of each order; Creating a hash time locking about example aiming at each matched transaction in the transaction scheme, and respectively locking a pass certificate stored in a virtual account of a seller and a digital asset of a purchaser; prior to pay clearly, the following operations are performed by the adaptive pricing pay clearly engine: Acquiring historical performance rate from the enterprise attribute structure body, and acquiring market fluctuation data and enterprise actual emission data from a market data predictor and a compliance data predictor respectively; dynamically calculating a discount ratio parameter through a state function based on the historical performance rate, market fluctuation data and a time attenuation factor of a distance pay clearly deadline; Carrying out pay clearly check on the pass in the storage virtual account based on the enterprise actual emission data, changing corresponding number of pass state identifiers into a performance freezing state if an emission gap exists, and generating a replenishment purchase request; wherein the result of pay clearly checks is used to update the historical performance rate in the enterprise attribute structure.
2. The smart contract-based carbon emission trading method as set forth in claim 1, wherein the enterprise attribute structure further stores risk levels, wherein the risk levels are recalculated and updated by a preset risk assessment model in accordance with the update results of the historical performance rates in combination with volatility indicators of recent trade behaviors of the enterprise, and wherein if the risk levels exceed a preset threshold, a trade limit is applied to the corresponding stored virtual account, wherein the trade limit includes limiting the number of individual transactions and increasing the trade guarantee requirements.
3. The smart contract-based carbon emissions trading method of claim 1, wherein the first zero-knowledge proof is specifically used to prove to the trading smart contract the following facts: The order promise is generated by effective transaction quantity, transaction unit price and random salt value, wherein the transaction quantity is a positive integer, the transaction unit price is in a preset effective price interval, if the order is sold, a submitter stores a virtual account, the year attribute is matched, the state mark is that the general balance of the available state is larger than the transaction quantity, and if the order is purchased, the digital asset value of the submitter transferred into the transaction intelligent contract is not smaller than the product of the transaction quantity and the transaction unit price.
4. The smart contract-based carbon emissions trading method of claim 1, wherein the verifiable privacy matching algorithm performs the following steps to generate a trading scheme: The method comprises the steps of determining a scribing unit price with the largest total traffic volume based on the traffic unit price corresponding to all order commitments, marking a selling order with the traffic unit price lower than the scribing unit price and a purchasing order with the traffic unit price lower than the scribing unit price as a traffic-capable order, respectively calculating the weighted average unit price of the traffic-capable purchasing order as a purchasing average price and the weighted average unit price of the traffic-capable selling order as a listing average price by taking the traffic quantity of each traffic-capable order as a weight, calculating a price ratio parameter according to the ratio of the purchasing average price to the listing average price, and determining the final traffic unit price of each traffic-capable order, wherein the traffic unit price of the selling order is the traffic unit price multiplied by the price ratio parameter, and the traffic unit price of the purchasing order is the traffic unit price divided by the price ratio parameter.
5. The smart contract-based carbon emissions trading method of claim 1, wherein creating the hashed time locking approximate instance, respectively, locks digital assets of the purchasers and the letters in the vendor's holding virtual account, specifically comprises: The method comprises the steps of when a hash time locking about example is deployed, receiving a hash value set by a seller and setting a public overtime block height, enabling the seller to transfer a pass of the transaction quantity into the example for locking, enabling a purchaser to transfer digital assets corresponding to the transaction value into the example for locking, automatically executing atomic exchange when the example receives a secret original image matched with the preset hash value, transferring the pass into the purchaser for storing a virtual account, transferring the digital assets into a designated address of the seller, and automatically returning the locked pass and the locked digital assets to original locking accounts of the seller and the purchaser if the correct secret original image is not received when the overtime block height is reached.
6. The smart contract-based carbon emissions trading method of claim 1, wherein the state function is: Cost performance parameters = benchmark coefficient x [1+ alpha x market volatility + beta x (1-average historical performance rate) -gamma x time decay factor ] The market fluctuation rate is provided by the market data predictive engine, the average historical performance rate is the average of the historical performance rates of all the diagonals in the current period, the time attenuation factor is calculated based on the time length from the current time to the pay clearly deadline day, alpha, beta and gamma are adjustable parameters, the generation of the replenishment purchase request comprises automatically taking the storage virtual account as a main body, generating an order for purchasing the gap quantity evidence according to the market price adjusted by the discount ratio parameter, and submitting the order to the next transaction period.
7. The smart contract-based carbon emissions trading method of claim 1, wherein the result of the pay clearly check is used to update historical performance rates in the enterprise attribute structure, in particular calculated by the following formula: Updated historical performance rate = λ x current pay clearly completion rate + (1- λ) x pre-update historical performance rate The current period pay clearly completion rate is the ratio of the total amount of the evidence number of pay clearly actually completed by the enterprise to the total amount of pay clearly quota in the current period pay clearly, and lambda is an attenuation coefficient between 0 and 1 and is used for adjusting the weight of recent performance in historical evaluation.
8. The carbon emission trading method based on intelligent contracts according to claim 1, wherein the market fluctuation rate is obtained by standardized processing of original fluctuation data provided by the market data predictor with reference to a preset time window, and the average historical performance rate is an arithmetic average of historical performance rates of all the enterprises in the current trading period; The time attenuation factor is calculated in the following way: (pay clearly deadline-current date)/total pay clearly cycle days So that the time decay factor drops to 0 at pay clearly cutoff days.
9. The smart contract-based carbon emissions trading method of claim 2, wherein the risk assessment model is: Risk level = w1× (1-history performance) +w2× transaction amount fluctuation rate + w3× hand-held concentration Wherein w1, w2 and w3 are preset weight coefficients, the fluctuation rate of the transaction amount is the statistical variance of the recent transaction amount of the enterprise, and the holding concentration is the proportion of the enterprise general evidence holding bin to the total quota of the industry to which the enterprise general evidence holding bin belongs.
10. The smart contract-based carbon emissions trading method of claim 1, wherein the verifiable privacy matching algorithm, when executed in the trade smart contract, all processing of order commitments occurs without decrypting trade volume and trade unit price specific values.

Description

Carbon emission trading method based on intelligent contract Technical Field The application relates to the technical field of information, in particular to a carbon emission trading method based on intelligent contracts. Background With the advancement of the worldwide carbon emission reduction goal, the carbon emission trading market has been widely used in various countries and regions as an important environmental protection tool. The carbon emission right trading system distributes carbon emission rights to different enterprises through a market mechanism, and the enterprises can adjust the carbon emission amount by purchasing and selling the emission rights. Conventional carbon transaction systems rely on centralized clearing and supervision, however, there are several technical issues: the privacy disclosure risk is that the enterprises must disclose key information such as quantity, price and the like when submitting trade orders, which not only can reveal the strategic intention of the enterprises, but also can lead to early information disclosure. The pricing mechanism is rigidified, and the pricing rule of the existing system usually adopts a fixed formula and cannot adapt to the dynamic market and the change of the performance capability of enterprises. The manual pay clearly has strong dependence, and the pay clearly process is usually manually interfered, so that the efficiency is low, and the performance violation and the execution deviation are easy to occur. In order to solve the above-mentioned problems, a new carbon emission right transaction system is needed, which can protect the privacy of both transaction parties, introduce a dynamic pricing mechanism, and realize automatic pay clearly and risk management. Disclosure of Invention Aiming at the technical problems in the background art, the invention provides the carbon emission trading method based on the intelligent contract, and the problems of privacy leakage, rigidification of the pricing mechanism, manual intervention pay clearly and the like in the prior art are solved through the intelligent contract, the zero knowledge proof, the dynamic pricing mechanism and the automatic pay clearly engine, so that a safer, flexible and automatic carbon emission trading method is provided. In order to solve the technical problems, the technical scheme of the invention is as follows: A smart contract-based carbon emissions trading method, the method comprising: Creating a storage virtual account through a storage intelligent closing control enterprise according to a received registration request, and creating an enterprise attribute structure body bound with the storage virtual account, wherein the enterprise attribute structure body stores industry codes and historical performance rate; During a transaction period, the enterprise client submits an order promise comprising transaction quantity, transaction unit price and random salt value to a transaction intelligent contract according to the available certification quantity in the storage virtual account, and a first zero knowledge proof, wherein the first zero knowledge proof is used for verifying the validity of the order on the premise of not revealing specific numerical values; After the transaction period is finished, executing a verifiable privacy matching algorithm based on all verified order promises to generate a transaction scheme, and synchronously generating a second zero knowledge proof for verifying the execution correctness of the matching algorithm, wherein the transaction scheme comprises the transaction quantity and the transaction unit price of each order; Creating a hash time locking about example aiming at each matched transaction in the transaction scheme, and respectively locking a pass certificate stored in a virtual account of a seller and a digital asset of a purchaser; prior to pay clearly, the following operations are performed by the adaptive pricing pay clearly engine: Acquiring historical performance rate from the enterprise attribute structure body, and acquiring market fluctuation data and enterprise actual emission data from a market data predictor and a compliance data predictor respectively; dynamically calculating a discount ratio parameter through a state function based on the historical performance rate, market fluctuation data and a time attenuation factor of a distance pay clearly deadline; Carrying out pay clearly check on the pass in the storage virtual account based on the enterprise actual emission data, changing corresponding number of pass state identifiers into a performance freezing state if an emission gap exists, and generating a replenishment purchase request; wherein the result of pay clearly checks is used to update the historical performance rate in the enterprise attribute structure. The enterprise attribute structure body further stores risk grades, calculates and updates the risk grades through a preset risk assessment model according