Dynamic Adjustment Method for Carbon Credit Evaluation and Allocation

Abstract

The present invention relates to the technical field of carbon emission management, and particularly to a dynamic adjustment method for carbon credit evaluation and allocation. The method includes the following steps: obtaining carbon emission data; setting a carbon emission baseline based on the carbon emission data to obtain carbon emission baseline data; calculating carbon emission performance based on the carbon emission baseline data to obtain carbon emission performance data; and adjusting the carbon credit allocation amount based on the carbon emission performance data to obtain carbon credit allocation amount data. The present invention adopts a mechanism in which carbon emission performance is linked to carbon credit allocation, forming a positive incentive effect, promoting enterprises to continuously optimize production processes, improve technologies, enhance energy efficiency, and move towards low-carbon and green transformation, achieving a win-win situation for economic and environmental benefits.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission management, and in particular to a dynamic adjustment method for carbon credit evaluation and allocation. Background Art

[0002] As global climate change becomes increasingly serious, greenhouse gases, especially carbon dioxide (CO 2 ) The control and management of emissions has become the focus of international attention. Governments of various countries have introduced carbon emission reduction policies and established carbon emission trading systems (ETS) to encourage enterprises to reduce carbon emissions through market mechanisms and achieve effective control of greenhouse gases. Carbon credit, as an important part of the carbon trading system, refers to the emission reduction of enterprises within their carbon emission quotas or the tradable emission rights obtained through specific emission reduction projects.

[0003] At present, carbon credit management mainly relies on fixed carbon emission baselines and emission standards, which makes it difficult to effectively respond to the volatility of corporate production activities and changes in market supply and demand. In addition, traditional carbon emission management systems mainly rely on static carbon emission data reported by enterprises. Data acquisition is not timely and information is incomplete, which easily leads to deviations in carbon emission calculations. With the complexity and changeability of corporate production activities and external environments, how to achieve dynamic carbon emission management and carbon credit allocation based on real-time data has become a technical problem that needs to be solved in the field of carbon emission management. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a dynamic adjustment method for carbon credit evaluation and allocation to solve at least one of the above technical problems.

[0005] The present application provides a method for dynamically adjusting carbon credit evaluation and allocation, the method comprising:

[0006] S1. Obtain carbon emission data;

[0007] S2. Setting a carbon emission baseline according to the carbon emission data to obtain the carbon emission baseline data;

[0008] S3. Calculate carbon emission performance based on carbon emission baseline data to obtain carbon emission performance data;

[0009] S4. Adjust the carbon credit allocation amount according to the carbon emission performance data to obtain the carbon credit allocation amount data.

[0010] In the present invention, through Internet of Things (IoT) devices, intelligent sensors and automated data acquisition systems, real-time acquisition of carbon emission data at all levels and in all aspects of an enterprise can be achieved. By means of a dynamic adjustment algorithm, the baseline emissions are updated in real time according to the volatility of the enterprise's production activities and policy changes, which can adapt to the changes in the actual situation of the enterprise, flexibly adjust the baseline, and avoid the problem of inaccurate baseline caused by production fluctuations or policy adjustments, thereby improving the flexibility and adaptability of carbon credit management. Based on accurate carbon emission baseline data, a multi-dimensional performance evaluation model is used to comprehensively analyze and evaluate the carbon emission performance of the enterprise. By comparing the actual emissions with the baseline emissions, the emission reduction effect and performance level of the enterprise are accurately measured. The carbon credit allocation amount is dynamically adjusted according to the carbon emission performance data of the enterprise to ensure that the carbon credit allocation matches the enterprise's emission reduction efforts. Enterprises with excellent emission reduction performance will receive more carbon credit incentives, while enterprises that do not meet the emission reduction requirements will have their carbon credit quotas reduced. This can effectively encourage enterprises to actively take emission reduction measures and promote the reduction of overall carbon emissions.

[0011] Optionally, S1 includes:

[0012] S11. Collect production energy consumption data and equipment loss data from the production equipment of the enterprise to obtain production energy consumption data and production equipment loss data;

[0013] S12. Collect energy system consumption data from the energy consumption system to obtain energy system consumption data;

[0014] S13. Collect transportation carbon emission data from transportation vehicles to obtain transportation carbon emission data;

[0015] S14. Cross-screen the energy system consumption data according to the production energy consumption data to obtain energy system consumption screening data;

[0016] S15. Calculate carbon emissions according to the production energy consumption data, production equipment loss data, transportation carbon emission data and energy system consumption screening data to obtain carbon emission data.

[0017] In the present invention, the production energy consumption data is directly collected from the enterprise production equipment, which can accurately record the actual energy consumption of each equipment, avoiding the problems of data distortion and omission. Through the data collection of energy systems (such as power, steam, cooling systems, etc.), the overall energy consumption of each energy system within the enterprise can be obtained, identifying high-energy-consuming links and potential energy-saving spaces. The collection of carbon emission data of transportation vehicles enables the enterprise to comprehensively grasp the carbon emissions in the logistics and transportation links. By cross-screening the production energy consumption data and the energy system consumption data, duplicate energy consumption data can be identified and eliminated, ensuring the accuracy of the data and avoiding the overestimation or underestimation of carbon emissions caused by double counting. Combining the production energy consumption, equipment loss, transportation carbon emissions, and the screened energy system consumption data, the overall carbon emission level of the enterprise can be calculated more comprehensively and accurately, avoiding omission or double counting, and ensuring the scientificity and accuracy of the calculation results.

[0018] Optionally, the equipment loss data collection includes:

[0019] Collecting operation data through sensors preset in the production equipment to obtain equipment operation status data;

[0020] Extracting equipment energy consumption characteristics based on the equipment operation status data to obtain equipment energy consumption characteristic data;

[0021] Evaluating the equipment aging and loss based on the equipment energy consumption characteristic data to obtain the production equipment loss data.

[0022] In the present invention, by deeply analyzing the equipment operation status data, the equipment energy consumption characteristic data can be extracted, such as indicators like instantaneous energy consumption, average energy consumption, energy efficiency ratio, peak energy consumption, etc. The extraction of energy consumption characteristic data helps to identify the energy consumption fluctuations of the equipment under different working conditions, such as the energy consumption changes under different states like startup, shutdown, full load, and low load. Through the long-term analysis of the equipment energy consumption characteristic data, the health status and aging degree of the equipment can be evaluated. As the equipment usage time increases, its energy efficiency will gradually decline. By evaluating the change trend of energy consumption characteristics, the aging process of the equipment can be quantitatively described. The equipment loss data, as an element of the carbon emission index, can effectively reflect the additional energy consumption and the corresponding carbon emissions caused by factors such as aging, failure, or reduced operation efficiency during the use of the equipment.

[0023] Optionally, the cross-screening includes:

[0024] Conducting energy consumption matching analysis based on the production energy consumption data and the energy system consumption data to obtain energy consumption matching data;

[0025] Calculating the feature similarity based on the energy consumption matching data to obtain energy consumption feature similarity data;

[0026] Perform energy consumption redundancy removal processing based on the energy consumption feature similarity data to obtain the energy system consumption screening data.

[0027] In the present invention, by performing matching analysis on the production energy consumption data and the energy system consumption data, the corresponding relationship of the sources of each energy consumption data can be accurately identified, such as the corresponding relationship between the energy consumption of production equipment and the energy consumption of the overall energy system. Energy consumption matching analysis can identify the overlapping parts between data from different sources. For example, the energy consumption of production equipment is simultaneously included in the energy consumption of the production line and the total energy consumption. Through matching analysis, these overlapping data can be eliminated to reduce data deviation. By calculating the similarity of energy consumption data, it is possible to effectively distinguish which data are redundant or highly similar, thereby providing a scientific basis for redundancy removal processing and avoiding errors in energy consumption estimation caused by repeated calculation of data with high similarity. Performing redundancy removal processing based on the feature similarity data can identify and eliminate highly similar energy consumption data, avoiding repeated calculation of the same or similar energy consumption data.

[0028] Optionally, the carbon emission calculation includes:

[0029] Extract carbon emission factors based on the production energy consumption data, production equipment loss data, transportation carbon emission data, and energy system consumption screening data to obtain carbon emission factor data;

[0030] Construct a carbon emission calculation model based on the carbon emission factor data to obtain a carbon emission calculation model;

[0031] Perform sub-item carbon emission calculation according to the carbon emission calculation model to obtain sub-item carbon emission data;

[0032] Calculate the carbon emission intensity based on the sub-item carbon emission data to obtain carbon emission data.

[0033] In the present invention, by analyzing various data sources (production energy consumption, equipment loss, transportation carbon emissions, energy system consumption), carbon emission factors of various types of energy are extracted, such as emission coefficients of different energies like coal, natural gas, electricity, etc. Extracting carbon emission factors from multi-source data comprehensively considers various emission sources in the production process and effectively integrates information from different data sources. The carbon emission calculation model can be adjusted and optimized according to the actual situation of different enterprises and is applicable to various types of production enterprises. The model can be adjusted according to different technological processes and energy usage situations, and has strong generality and flexibility. Through itemized calculation, carbon emissions can be accurately decomposed into specific production processes, equipment operations, and transportation links. Enterprises can clearly understand the carbon emission situation of each link and implement energy efficiency improvement and emission reduction measures accordingly. Carbon emission intensity data (such as carbon emissions per unit output value) can measure the carbon emissions corresponding to unit output during the production process of an enterprise and reflect the carbon emission efficiency of the enterprise.

[0034] Optionally, S2 includes:

[0035] S21. Obtain historical carbon emission data, and perform trend analysis based on the carbon emission data and the historical carbon emission data to obtain carbon emission trend data;

[0036] S22. Perform industry benchmark clustering processing based on the carbon emission trend data to obtain carbon emission benchmark clustering data;

[0037] S23. Calculate the industry benchmark deviation based on the carbon emission benchmark clustering data to obtain carbon emission benchmark deviation data;

[0038] S24. Calculate the carbon emission baseline based on the carbon emission benchmark deviation data and the carbon emission trend data to obtain preliminary carbon emission baseline data;

[0039] S25. Obtain the actual production activity data of the enterprise, and perform Bayesian optimization based on the actual production activity data of the enterprise and the preliminary carbon emission baseline data to obtain carbon emission baseline data.

[0040] In the present invention, through the joint analysis of historical carbon emission data and current carbon emission data, the long-term change trend of an enterprise's carbon emissions can be identified, including patterns such as rising, falling, or fluctuating. Through the cluster analysis of carbon emission trend data, an enterprise can be benchmarked against other enterprises in the industry with similar carbon emission characteristics to identify the best practices in the industry. Cluster analysis can classify an enterprise into a benchmark cluster similar to its own carbon emission characteristics, avoiding the simple average or general benchmark comparison method, providing a more accurate and segmented industry benchmark positioning, and providing reliable reference data for baseline setting. Benchmark deviation calculation can quantify the gap between an enterprise and the industry benchmark, helping the enterprise clarify the difference between its own carbon emissions and the industry average or best level. By considering the benchmark deviation data and carbon emission trend data, the initial carbon emission baseline of the enterprise can be set scientifically and reasonably. The set baseline can accurately reflect the actual emission capacity and industry position of the enterprise, avoiding the situation of too high or too low baseline. Bayesian optimization can dynamically respond to changes in production activities, such as production load, equipment operation status, energy usage, etc., and adjust the carbon emission baseline in real time to avoid the baseline inaccuracy caused by fluctuations in the actual situation.

[0041] Optionally, the carbon emission benchmark clustering data includes carbon emission enterprise type benchmark clustering data and carbon emission enterprise scale benchmark clustering data. The industry benchmark clustering process includes:

[0042] Obtain industry carbon emission benchmark data;

[0043] Perform enterprise type clustering calculation according to the industry carbon emission benchmark data and carbon emission trend data to obtain carbon emission enterprise type benchmark clustering data;

[0044] Perform enterprise scale clustering calculation according to the industry carbon emission benchmark data and carbon emission trend data to obtain carbon emission enterprise scale benchmark clustering data.

[0045] In the present invention, by collecting industry carbon emission benchmark data, a reliable benchmark based on extensive industry data can be established, ensuring a solid data foundation for subsequent cluster analysis and providing an accurate benchmark reference for the clustering calculation of enterprise types and scales. Through enterprise type clustering calculation, clustering analysis is performed on enterprises according to sub-industry types (such as manufacturing, service, chemical industry, energy industry, etc.), which can effectively avoid the influence of carbon emission differences between different industries on benchmark calculation and improve the accuracy of comparison. Enterprise scale has a significant impact on carbon emissions. The total carbon emissions of large enterprises are usually higher than those of small enterprises, but they may perform better in terms of carbon emission intensity. Through enterprise scale clustering, the influence brought by enterprise scale differences can be eliminated, making the benchmark comparison more fair and reasonable.

[0046] Optionally, S3 includes:

[0047] S31. Compare the carbon emissions based on the carbon emission baseline data and the carbon emission data to obtain carbon emission comparison data;

[0048] S32. Generate carbon emission performance indicators based on the carbon emission comparison data to obtain carbon emission performance indicator data;

[0049] S33. Conduct multi-criteria decision analysis based on the carbon emission performance indicator data to obtain carbon emission performance indicator selection data;

[0050] S34. Conduct multi-level performance evaluation on the carbon emission data according to the carbon emission performance indicator selection data to obtain carbon emission performance data.

[0051] In the present invention, the carbon emission comparison data can identify whether the enterprise's carbon emissions exceed the predetermined baseline or are lower than the baseline, helping the enterprise to timely discover the situation of excessive emissions or savings, take corresponding measures, and reduce the risk of carbon emission management. Through the analysis of the carbon emission comparison data, multi-dimensional performance indicators are generated, such as emission intensity, emission reduction rate, over-standard rate, compliance rate, etc., making the carbon emission performance evaluation more comprehensive and three-dimensional, and being able to better reflect the comprehensive performance of the enterprise's carbon emission management. Through multi-criteria decision analysis, the most critical indicators for the enterprise's carbon emission management are identified among numerous performance indicators, ensuring that the enterprise can focus on the most important areas of carbon emission management and improve management efficiency. The multi-level evaluation can help the enterprise accurately identify the bottlenecks and weak links in carbon emission management. For example, the carbon emission efficiency of a certain department or workshop is low, or the energy efficiency of a certain device is not high. The enterprise can formulate targeted improvement measures based on this to improve the overall performance level.

[0052] Optionally, S4 includes:

[0053] S41. Calculate the preliminary carbon credit allocation quantity based on the carbon emission performance data to obtain preliminary carbon credit allocation quantity data;

[0054] S42. Obtain the market carbon credit supply and demand data;

[0055] S43. Calculate the allocation coefficient based on the market carbon credit supply and demand data and the preliminary carbon credit allocation quantity data to obtain carbon credit allocation coefficient data;

[0056] S44. Adjust the carbon credit allocation quantity according to the carbon credit quota data and the carbon credit allocation coefficient data in the market carbon credit supply and demand data to obtain carbon credit allocation quantity data.

[0057] In the present invention, by quantifying carbon emission performance into preliminary carbon credit allocation amounts, enterprises can clearly understand the relationship between their emission reduction efforts and the benefits they obtain in the carbon market. This quantification method improves the transparency and fairness of carbon credit allocation. Obtaining market carbon credit supply and demand data can help enterprises understand the current supply and demand situation of carbon credits in the market, such as the total amount of carbon credits, market transaction prices, demands of buyers and sellers, etc., providing key market background information for enterprises' carbon credit allocation strategies. By combining market supply and demand data with enterprises' preliminary carbon credit allocation amount data, a carbon credit allocation coefficient is calculated, which can find a balance between enterprises' emission reduction performance and market supply and demand conditions, effectively avoiding unfair allocation caused by solely relying on emission reduction performance or market supply and demand. After considering market supply and demand conditions, enterprises' preliminary allocation amounts and allocation coefficients, the carbon credit allocation amounts are precisely adjusted to ensure that the carbon credit allocation amounts obtained by each enterprise can truly reflect its performance level and market supply and demand conditions, improving the accuracy of allocation.

[0058] The purpose of the present invention is to use Internet of Things sensors and intelligent data acquisition systems to obtain carbon emission data in real time from multiple sources such as production equipment, energy consumption systems, and transportation tools. By using enterprises' historical carbon emission data, industry benchmark data, and actual production activity data, combined with carbon emission trend analysis and industry benchmark clustering analysis, a carbon emission baseline is scientifically set. Through the Bayesian optimization method, the actual production activity data of enterprises is dynamically adjusted with the preliminary carbon emission baseline data, enabling the baseline to respond in real time to the volatility of enterprises' production activities and policy changes, improving the dynamic adaptability and accuracy of the carbon emission baseline. Multi-dimensional carbon emission performance indicators are generated through carbon emission comparison data, and multi-criteria decision analysis (MCDA) is used to comprehensively evaluate and rank different indicators to determine the most representative key performance indicators (KPIs). Carbon credit allocation is not only based on enterprises' carbon emission performance data but also considers the supply and demand situation of market carbon credits. By calculating the allocation coefficient through the combination of preliminary allocation amounts and market supply and demand data, the carbon credit allocation amounts of enterprises can more accurately reflect their relative positions in the market and carbon emission performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Other features, objects, and advantages of the present application will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0060] Figure 1 The flowchart of the steps of a dynamic adjustment method for carbon credit evaluation and allocation according to an embodiment is shown;

[0061] Figure 2 The flowchart of the steps of a carbon emission data acquisition method according to an embodiment is shown;

[0062] Figure 3The flowchart of the steps of a carbon emission baseline setting method according to an embodiment is shown;

[0063] Figure 4 The flowchart of the steps of a carbon emission performance calculation method according to an embodiment is shown;

[0064] Figure 5 The flowchart of the steps of a carbon credit allocation adjustment method according to an embodiment is shown;

[0065] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Specific Embodiments

[0066] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0067] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0068] It should be understood that although terms such as "first", "second", etc. may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed associated items.

[0069] Manufacturing enterprise A mainly produces mechanical parts and mainly emits carbon dioxide during the production process. The enterprise's historical carbon emission data and industry benchmarks are as follows:

[0070] Annual production volume: 10,000 tons of mechanical parts, carbon emission per unit of production (historical average): 0.8 tons of CO 2 / ton of mechanical parts, carbon emission baseline target: 0.7 tons of CO 2 / ton of mechanical parts, enterprise's historical annual total carbon emissions: 8,000 tons of CO 2 、industry benchmark annual carbon emissions (average of similar enterprises): 0.75 tons of CO 2 / ton of mechanical parts.

[0071] I. Enterprise Carbon Emissions

[0072] 1.1 Annual electricity consumption: 15,000,000 kWh, annual natural gas consumption: 500,000 cubic meters; Equipment aging factor: 2%, equipment failure rate: 1.5% (energy consumption loss caused by equipment failure in the past year was approximately 120,000 kWh); Transportation mileage: 300,000 kilometers, carbon emission factor of transportation vehicles: 0.25 kg CO 2 / km; Total energy consumption after cross - screening (removing redundant data): Electricity consumption: 14,880,000 kWh, natural gas consumption: 490,000 cubic meters.

[0073] 1.2 Carbon emission factors: Carbon emission factor of electricity: 0.5 kg CO 2 / kWh, carbon emission factor of natural gas: 1.9 kg CO 2 / cubic meter.

[0074] 1.3 Carbon emission calculation:

[0075] Carbon emissions from electricity: 14,880,000 kWh × 0.5 kg CO 2 / kWh = 7,440 tons of CO 2 ,

[0076] Carbon emissions from natural gas: 490,000 cubic meters × 1.9 kg CO 2 / cubic meter = 931 tons of CO 2 ,

[0077] Carbon emissions from transportation: 300,000 kilometers × 0.25 kg CO 2 / km = 75 tons of CO 2 ,

[0078] Carbon emissions from equipment loss: 120,000 kWh × 0.5 kg CO 2 / kWh = 60 tons of CO 2 ,

[0079] Total carbon emissions: 7,440 tons + 931 tons + 75 tons + 60 tons = 8,506 tons of CO 2 .

[0080] II. Setting of Carbon Emission Baseline

[0081] 2.1 Analysis of historical carbon emission trends

[0082] Carbon emissions in the past five years gradually decreased from 8,200 tons of CO 2 to 8,000 tons of CO 2 , showing an annual decline trend of 2.5%.

[0083] 2.2 Industry Field and Enterprise Scale

[0084] Manufacturing, mechanical parts industry, medium-sized enterprise (annual output of 10,000 tons).

[0085] 2.3 Calculation of Industry Benchmark Deviation

[0086] Current emission intensity of the enterprise: 8,506 tons of CO 2 / 10,000 tons = 0.8506 tons of CO 2 / ton,

[0087] Industry benchmark deviation: 0.8506 tons of CO 2 / ton - 0.75 tons of CO 2 / ton = 0.1006 tons of CO 2 / ton.

[0088] 2.4 Calculation of Basic Carbon Emission Baseline Data

[0089] Basic carbon emission baseline: 10,000 tons × 0.75 tons of CO 2 / ton = 7,500 tons of CO 2 .

[0090] 2.5 Bayesian Optimization of Carbon Emission Baseline

[0091] Considering factors such as equipment update and energy structure adjustment, the optimized carbon emission baseline is set as: 7,500 tons of CO 2 (Preliminary baseline) × 0.95 (optimization coefficient) = 7,125 tons of CO 2 .

[0092] III. Calculation of Carbon Emission Performance

[0093] 3.1 Carbon Emission Comparison

[0094] Actual carbon emissions: 8,506 tons of CO 2 ,

[0095] Carbon emission baseline: 7,125 tons of CO 2 ,

[0096] Carbon emission comparison difference: 8,506 tons - 7,125 tons = 1,381 tons of CO 2 .

[0097] 3.2 Carbon Emission Performance Index

[0098] Carbon emission intensity: 0.8506 tons of CO 2 / ton (actual), 0.7125 tons of CO 2 / ton (baseline),

[0099] Exceedance rate: 1,381 tons / 7,125 tons = 19.4%,

[0100] Emission reduction potential: 0.8506 tons of CO 2 / ton - 0.7125 tons of CO 2 / ton = 0.1381 tons of CO 2 / ton.

[0101] 3.3 Decision-making analysis based on key performance indicators

[0102] Key performance indicators: Emission intensity, exceedance rate, emission reduction potential.

[0103] 3.4 Multi-level performance evaluation

[0104] Overall performance score: Calculated through a multi-criteria decision-making model, the comprehensive performance score is: 75 (out of 100).

[0105] Hierarchical evaluation:

[0106] Equipment level: 85 points (significant effect of equipment upgrade),

[0107] Production link: 70 points (great potential for energy efficiency improvement),

[0108] Transportation link: 60 points (carbon emissions exceed the standard, great potential for transportation optimization).

[0109] IV. Adjustment of carbon credit allocation

[0110] 4.1 Calculation of basic carbon credit allocation

[0111] Initial market allocation rule: Reward for the part below the baseline, for every 1 ton of CO reduced 2 Reward 10 carbon credit quotas,

[0112] Reduction amount below the baseline: 7,125 tons - 7,500 tons = 375 tons,

[0113] Basic carbon credit allocation: 375 tons × 10 = 3,750 carbon credit quotas.

[0114] 4.3 Calculation of allocation coefficient

[0115] Total market carbon credit supply: 100,000 quotas,

[0116] Total market carbon credit demand: 90,000 quotas,

[0117] Market equilibrium price: 20 yuan / carbon credit.

[0118] 4.3 Calculation of allocation coefficient

[0119] Allocation coefficient: 90,000 / 100,000 = 0.9,

[0120] The calculated enterprise allocation coefficient is: 0.9 (market supply and demand) × 1.2 (enterprise performance coefficient) = 1.08.

[0121] 4.4 Adjustment of carbon credit allocation

[0122] The adjusted carbon credit allocation is: 3,750 × 1.08 = 4,050 carbon credit quotas.

[0123] Please refer to Figures 1 to 5 , this application provides a dynamic adjustment method for carbon credit evaluation and allocation, and the method includes:

[0124] S1. Obtain carbon emission data;

[0125] Specifically, collect relevant emission data from carbon emission sources such as factories, vehicles, and buildings, including but not limited to information such as fuel consumption, electricity consumption, and emission concentration.

[0126] S2. Set a carbon emission baseline based on the carbon emission data to obtain carbon emission baseline data;

[0127] Specifically, determine the baseline for evaluating carbon emission performance. For example, use the average carbon emissions in the past three years as the baseline. Consider the impact of policy changes, technological improvements, or other external factors on carbon emissions, and dynamically adjust the baseline data. Allocate the overall baseline to different carbon emission sources, taking into account factors such as the production capacity and emission types of different carbon emission sources. Finally, determine the carbon emission baseline data and confirm and archive it in the system.

[0128] S3. Calculate the carbon emission performance based on the carbon emission baseline data to obtain carbon emission performance data;

[0129] Specifically, calculate the total emissions of each emission source based on the actual carbon emission data. Compare the actual emissions with the baseline data to calculate the difference part. Calculate the carbon emission performance indicators of each emission source based on the difference part, such as emission reduction rate, excess amount, etc. Classify different emission sources according to the performance indicators, such as into different grades of excellent, qualified, and unqualified.

[0130] S4. Adjust the carbon credit allocation based on the carbon emission performance data to obtain carbon credit allocation data.

[0131] Specifically, according to the carbon emission baseline data and the performance grading results, the carbon credit volume is initially allocated. According to the real-time performance data of each emission source, the carbon credit allocation is dynamically adjusted. For example, the carbon credit of the emission source that exceeds the standard is reduced, and the carbon credit of the emission source with excellent performance is increased. The adjusted carbon credit allocation is compared with the actual usage to calculate the final carbon credit surplus or deficit. For the emission source with insufficient carbon credits, it is recommended to conduct carbon credit trading and purchase the remaining credits of other emission sources; for the emission source with a surplus, it can choose to sell its excess carbon credits.

[0132] In the present invention, through Internet of Things (IoT) devices, intelligent sensors and automated data acquisition systems, real-time acquisition of enterprise's carbon emission data in all aspects and at multiple levels can be achieved. Through the dynamic adjustment algorithm, the baseline emissions are updated in real time according to the volatility of enterprise production activities and policy changes, which can adapt to the changes in the actual situation of the enterprise, flexibly adjust the baseline, and avoid the problem of inaccurate baseline caused by production fluctuations or policy adjustments, thereby improving the flexibility and adaptability of carbon credit management. Based on accurate carbon emission baseline data, using a multi-dimensional performance evaluation model, a comprehensive analysis and evaluation of the enterprise's carbon emission performance is carried out. By comparing the actual emissions with the baseline emissions, the emission reduction effect and performance level of the enterprise are accurately measured. The carbon credit allocation is dynamically adjusted according to the enterprise's carbon emission performance data to ensure that the carbon credit allocation matches the enterprise's emission reduction efforts. Enterprises with excellent emission reduction performance will receive more carbon credit incentives, while enterprises that do not meet the emission reduction requirements will have their carbon credit quotas reduced. This can effectively encourage enterprises to actively take emission reduction measures and promote the reduction of the overall carbon emissions.

[0133] Optionally, S1 includes:

[0134] S11. Collect production energy consumption data and equipment loss data from the production equipment of the enterprise to obtain production energy consumption data and production equipment loss data;

[0135] Specifically, use the energy consumption monitoring sensors installed on the production equipment to collect the energy data consumed during the production process, such as electricity consumption, fuel consumption (coal, natural gas, oil, etc.). Use the equipment status monitoring system to collect data such as the wear condition and failure rate during the operation of the equipment to estimate the change in the energy consumption efficiency and additional loss of the equipment.

[0136] S12. Collect energy system consumption data from the energy consumption system to obtain energy system consumption data;

[0137] Specifically, the enterprise's internal energy management system (EMS) monitors and records the overall energy consumption of the enterprise, including the consumption of electricity, natural gas, steam and other energies in each department.

[0138] S13. Collect transportation carbon emission data from transportation vehicles to obtain transportation carbon emission data;

[0139] Specifically, use GPS and OBD (On-Board Diagnostic System) devices installed on vehicles to collect data such as vehicle mileage, fuel consumption, and carbon emissions in real time. For vehicles without automatic monitoring functions, the driver needs to manually record the vehicle mileage and fuel consumption after each transportation task.

[0140] S14. Cross-screen the energy system consumption data based on the production energy consumption data to obtain the screened energy system consumption data;

[0141] Specifically, compare the energy consumption data of production equipment with the overall consumption data of the energy system to screen out the differences between the two. Calculate the energy utilization rate of each production unit and eliminate abnormal energy consumption data caused by equipment losses or uneven energy distribution.

[0142] S15. Calculate carbon emissions based on the production energy consumption data, production equipment loss data, transportation carbon emission data, and screened energy system consumption data to obtain carbon emission data.

[0143] Specifically, calculate carbon emissions using different carbon emission coefficients (such as electricity, natural gas, fuel, etc.) based on various energy consumption data. Accumulate the carbon emissions of each department and transportation vehicle to obtain the total carbon emissions of the enterprise.

[0144] In the present invention, directly collecting production energy consumption data from enterprise production equipment can accurately record the actual energy consumption of each equipment, avoiding data distortion and omission problems. Through data collection of energy systems (such as electricity, steam, cooling systems, etc.), the overall energy consumption of each energy system within the enterprise can be obtained, identifying high-energy-consuming links and potential energy-saving spaces. The collection of transportation vehicle carbon emission data enables the enterprise to comprehensively grasp the carbon emissions in the logistics and transportation links. By cross-screening the production energy consumption data and the energy system consumption data, duplicate energy consumption data can be identified and eliminated, ensuring data accuracy and avoiding overestimation or underestimation of carbon emissions caused by double counting. Combining production energy consumption, equipment losses, transportation carbon emissions, and screened energy system consumption data can calculate the overall carbon emission level of the enterprise more comprehensively and accurately, avoiding omission or double counting, and ensuring the scientificity and accuracy of the calculation results.

[0145] Optionally, the collection of equipment loss data includes:

[0146] Collect operation data through sensors preset in production equipment to obtain equipment operation status data;

[0147] Specifically, set the data acquisition frequency. The power and temperature sensors can be set to once per minute, and the vibration and sound sensors can be set to once per second.

[0148] Extract the equipment energy consumption characteristics based on the equipment operation status data to obtain the equipment energy consumption characteristic data;

[0149] Specifically, extract the key energy consumption characteristics in the equipment operation status data to identify the energy consumption level and operation efficiency of the equipment. Calculate the average energy consumption level of the equipment over a certain period of time, such as the average power and average current. Calculate the fluctuation amplitude and frequency of the energy consumption to identify the energy consumption changes of the equipment under different working conditions. Calculate the energy efficiency ratio (output power / input power) of the equipment to evaluate the operation efficiency of the equipment. Judge the load rate (actual load / rated load) of the equipment based on the current and power changes.

[0150] Conduct equipment aging and loss assessment based on the equipment energy consumption characteristic data to obtain the production equipment loss data.

[0151] Specifically, set the energy consumption benchmark value and loss threshold of the equipment according to the design parameters and historical operation data of the equipment. For example, the designed energy efficiency ratio of a certain equipment is 0.9, and the average power is 5 kW. Compare the current energy consumption characteristic data with the equipment historical data, and analyze the change trends of indicators such as the energy efficiency ratio and energy consumption volatility. If the energy efficiency ratio continues to decline and the energy consumption volatility increases, it indicates that the equipment may have problems of aging and increasing loss.

[0152] Use the loss assessment model, input the energy consumption characteristic data into the model, and calculate the loss degree of the equipment, such as mechanical loss, electrical loss, etc. The loss assessment model can be calculated based on the equipment aging curve (P(t)=P 0 ×e -γt , where P(t) is the performance of the equipment at time t, P 0 is the initial performance, e is the natural exponential term, γ is the aging rate coefficient, and t is the usage time data), vibration analysis model, temperature change model, etc. Generate a loss report with the evaluated loss data, including information such as the aging degree, loss type, and failure risk of the equipment. Generate a production equipment loss data table, including information such as the aging degree, loss amount, loss type, and potential failure risk of the equipment.

[0153] Specifically, use a regression model to conduct equipment aging and loss assessment on the equipment energy consumption characteristic data to obtain the production equipment loss data, where the regression coefficients of the regression model are calculated through training with historical data.

[0154] In the present invention, by deeply analyzing the device operation status data, energy consumption characteristic data of the device can be extracted, such as indicators like instantaneous energy consumption, average energy consumption, energy efficiency ratio, peak energy consumption, etc. The extraction of energy consumption characteristic data helps to identify the energy consumption fluctuations of the device under different working conditions, such as the energy consumption changes under different states like startup, shutdown, full load, low load, etc. Through long-term analysis of the device energy consumption characteristic data, the health status and aging degree of the device can be evaluated. As the usage time of the device increases, its energy efficiency will gradually decline. By evaluating the change trend of the energy consumption characteristics, the aging process of the device can be quantitatively described. The device loss data, as part of the carbon emission index, can effectively reflect the additional energy consumption and corresponding carbon emissions caused by factors such as aging, faults, or decreased operation efficiency during the use of the device.

[0155] Optionally, the cross-screening includes:

[0156] Perform energy consumption matching analysis based on the production energy consumption data and the energy system consumption data to obtain energy consumption matching data;

[0157] Specifically, the specific energy consumption information from each production device, such as the electricity, natural gas, etc. consumed by a certain production device within a specific time period. Extract characteristic information from the matching data, such as: the energy consumption change trend of each device or system at different time periods. The fluctuation amplitude and frequency of energy consumption within a specific time period. The operation duration and shutdown time ratio of the device.

[0158] Perform characteristic similarity calculation based on the energy consumption matching data to obtain energy consumption characteristic similarity data;

[0159] Specifically, by calculating the similarity between the energy consumption characteristics of the production device and the overall energy system consumption characteristics, possible duplicate or abnormal energy consumption data can be identified. Standardize the energy consumption data with different units and magnitudes to ensure comparability between features during similarity calculation. Use distance measurement methods (such as Euclidean distance, cosine similarity, etc.) to calculate the similarity between the energy consumption characteristics of the production device and the energy system consumption characteristics. Assign a similarity score (0 - 1) to each device according to the similarity calculation result. The higher the similarity, the more similar the energy consumption characteristics of the device are to the overall system characteristics. The total energy consumption information at the enterprise or workshop level, including the total electricity consumption, gas consumption, etc. within each time period.

[0160] Perform energy consumption redundancy removal processing based on the energy consumption characteristic similarity data to obtain energy system consumption screening data.

[0161] Specifically, for energy consumption data with a similarity higher than a certain threshold (e.g., similarity > 0.90), it is considered that they are repetitive or redundant, and their energy consumption values are merged. For data with obvious differences from the overall characteristics (e.g., similarity < 0.3), it is considered as abnormal data and is marked or removed. The data after removing redundancy or abnormality is corrected, and the overall energy consumption distribution is recalculated. The result after removing redundancy is compared with the original data to ensure that the change in the total energy consumption is within a reasonable range.

[0162] In the present invention, by performing matching analysis on production energy consumption data and energy system consumption data, the corresponding relationship of the sources of each energy consumption data can be accurately identified, such as the corresponding relationship between the energy consumption of production equipment and the energy consumption of the overall energy system. Energy consumption matching analysis can identify the overlapping parts between data from different sources. For example, the energy consumption of production equipment is simultaneously included in the energy consumption of the production line and the total energy consumption. Through matching analysis, these overlapping data can be eliminated, reducing data deviation. By calculating the similarity of energy consumption data, it is possible to effectively distinguish which data are redundant or highly similar, thus providing a scientific basis for redundancy removal processing and avoiding errors in energy consumption estimation caused by repeated calculation of data with high similarity. Redundancy removal processing based on feature similarity data can identify and remove highly similar energy consumption data, avoiding repeated calculation of the same or similar energy consumption data.

[0163] Optionally, carbon emission calculation includes:

[0164] Extracting carbon emission factors from production energy consumption data, production equipment loss data, transportation carbon emission data, and energy system consumption screening data to obtain carbon emission factor data;

[0165] Specifically, the carbon emission factor refers to the emissions of greenhouse gases such as carbon dioxide corresponding to unit energy consumption or activity. The carbon emission factors of different energy types, equipment, transportation tools, etc. are different. According to the carbon emission factor of the local power grid (e.g., 0.5 kg CO 2 / kWh). Extracting the corresponding carbon emission factor according to the fuel type (such as coal, natural gas, petroleum, etc.) (e.g., 1.9 kg CO 2 / m 3 for natural gas). Estimating the carbon emission factor during the loss period through the energy efficiency change rate of the equipment. For example, the rated carbon emission factor of a certain equipment is 1 kg CO 2 / kWh, and if the efficiency is reduced by 10% due to loss, the carbon emission factor during the loss period is 1.1 kg CO 2 / kWh. Extracting the corresponding carbon emission factor according to the fuel type and efficiency of the transportation tool. For example, the carbon emission factor of a diesel truck is 2.68 kg CO 2 / liter. Electricity consumption factor extraction: 0.5 kg CO 2 / kWh (local grid data). Gas consumption factor extraction: 1.9 kg CO 2 / m 3 (natural gas standard factor). Transportation vehicle factor extraction: 2.68 kg CO 2 / liter (diesel truck standard factor).

[0166] Construct a carbon emission calculation model based on carbon emission factor data to obtain a carbon emission calculation model;

[0167] Specifically, establish a mapping relationship between carbon emission factors and specific activity levels (such as electricity consumption, fuel consumption, transportation mileage, etc.). Set the carbon emission calculation formulas for each link: Carbon emission during production process = Production energy consumption × Corresponding carbon emission factor. Carbon emission due to equipment loss = Equipment loss amount × Corresponding carbon emission factor. Carbon emission during transportation process = Fuel consumption × Fuel carbon emission factor. Calibrate the parameters in the model according to historical data and empirical values, such as the efficiency change rate and fuel consumption rate of different equipment.

[0168] Conduct sub-item calculations of carbon emissions according to the carbon emission calculation model to obtain sub-item carbon emission data;

[0169] Specifically, calculate the carbon emissions of each item separately according to different energy types, equipment, transportation vehicles, etc., for further analysis and management. Calculate the carbon emissions of each production workshop and equipment. Calculate separately according to different energy types (electricity, natural gas, coal, etc.). Calculate the additional carbon emissions caused by equipment loss. For example, the loss of a certain equipment leads to an increase in energy consumption of 200 kWh, and the corresponding carbon emission is 200 × 0.5 = 100 kg CO 2 . Calculate the carbon emissions of different transportation vehicles, including vehicles, ships, etc. Calculate separately according to transportation tasks. For example, a certain task consumes 100 liters of diesel, and the carbon emission is 100 × 2.68 = 268 kg CO 2 . Calculate the overall carbon emissions of the energy system and subtract the redundant carbon emissions after removing redundancy.

[0170] Conduct carbon emission intensity calculations based on the sub-item carbon emission data to obtain carbon emission data.

[0171] Specifically, carbon emission intensity refers to the carbon emissions generated per unit of product or output value. It is an important indicator for measuring the carbon emission efficiency of enterprises. By combining the total carbon emissions with the total production volume or output value of the enterprise, the carbon emission intensity is calculated to reflect the carbon emission efficiency of the enterprise. An appropriate output indicator is selected as the denominator of the carbon emission intensity, such as total output value, total production volume, product weight, etc. Carbon emission intensity = total carbon emissions / total output. Calculate the corresponding carbon emission intensity according to the carbon emissions of each item. For example: carbon emission intensity during the production process = carbon emissions during the production process / total production volume. The weighted average of the intensities of each item is obtained to get the overall carbon emission intensity of the enterprise.

[0172] In the present invention, by analyzing multiple data sources (production energy consumption, equipment loss, transportation carbon emissions, energy system consumption), carbon emission factors of various types of energy are extracted, such as emission coefficients of different energies like coal, natural gas, electricity, etc. Extracting carbon emission factors from multi-source data comprehensively considers various emission sources during the production process and effectively integrates the information of different data sources. The carbon emission calculation model can be parameter-adjusted and optimized according to the actual situation of different enterprises and is applicable to various types of production enterprises. The model can be adjusted according to different technological processes and energy usage situations, and has strong generality and flexibility. Through itemized calculation, carbon emissions can be accurately decomposed into specific production processes, equipment operations, and transportation links. Enterprises can clearly understand the carbon emissions of each link and implement energy efficiency improvement and emission reduction measures targeted. Carbon emission intensity data (such as carbon emissions per unit output value) can measure the carbon emissions corresponding to unit output during the production process of enterprises and reflect the carbon emission efficiency of enterprises.

[0173] Optionally, S2 includes:

[0174] S21. Obtain historical carbon emission data, and conduct trend analysis based on the carbon emission data and the historical carbon emission data to obtain carbon emission trend data;

[0175] Specifically, obtain historical carbon emission data of the past several years from the enterprise's carbon emission monitoring system. The data may include carbon emissions generated during the production process, carbon emissions during transportation, carbon emissions caused by energy consumption, etc. Collect real-time carbon emission data during the current production cycle, including production energy consumption data, equipment loss data, and transportation emission data, etc. By comparing historical data and current data, identify the change trend of the enterprise's carbon emissions and understand whether there is an upward, downward, or stable trend.

[0176] Arrange historical carbon emission data in chronological order, plot a time series graph, and observe the variation pattern of carbon emissions. Use methods such as linear regression, exponential smoothing, or moving average to construct a trend model to predict future carbon emission trends. Identify outliers in the data (such as sudden increases or decreases in emissions), analyze the reasons, and correct the data. Extract trend data from the prediction results of the model, such as carbon emission growth rate, decline rate, etc.

[0177] S22. Perform industry benchmark clustering processing on the carbon emission trend data to obtain carbon emission benchmark clustering data;

[0178] Specifically, obtain the carbon emission data of enterprises in the same industry from the industry database. The data includes information such as the carbon emissions, production scale, and energy consumption of similar enterprises. Compare the carbon emission trend data of the enterprise with the data of enterprises in the same industry, and assign the enterprise to different industry benchmark groups through cluster analysis for subsequent benchmark comparison and analysis. Standardize the carbon emission data of the enterprise and the industry data to eliminate the influence of dimension and scale. Extract key features such as carbon emission intensity, production scale, and energy structure as the input for cluster analysis. Use methods such as K-means and hierarchical clustering to perform cluster analysis on the enterprise and industry data, and assign enterprises with high similarity to the same cluster group.

[0179] S23. Calculate the industry benchmark deviation based on the carbon emission benchmark clustering data to obtain carbon emission benchmark deviation data;

[0180] Specifically, calculate the deviation of the enterprise from the industry benchmark within the cluster group to understand the gap between the enterprise's carbon emission level and the industry average level. Determine the industry benchmark data of the cluster group where the enterprise is located, such as the average value of carbon emission intensity, the average value of energy consumption structure, etc. Deviation calculation: Deviation = Enterprise carbon emission value - Industry benchmark value; Relative deviation = Deviation / Industry benchmark value.

[0181] S24. Calculate the carbon emission baseline based on the carbon emission benchmark deviation data and the carbon emission trend data to obtain preliminary carbon emission baseline data;

[0182] Specifically, the carbon emission baseline refers to the reference emission level of an enterprise under specific conditions, which is used to evaluate the effect of future carbon emission reduction measures. Combine the carbon emission trend data and the industry benchmark deviation data to determine the preliminary carbon emission baseline of the enterprise, providing a reference standard for subsequent carbon emission performance evaluation. Baseline = Industry benchmark value ± Deviation value × Trend coefficient.

[0183] S25. Obtain the actual production activity data of the enterprise, and perform Bayesian optimization based on the actual production activity data of the enterprise and the preliminary carbon emission baseline data to obtain carbon emission baseline data.

[0184] Specifically, it includes production-related data such as production plans, actual output, equipment utilization rates, and energy consumption records, as well as external factors such as changes in market demand and policy impacts. Using the Bayesian optimization method, the actual production activity data of the enterprise is combined with the preliminary carbon emission baseline to optimize the baseline data and make it closer to the actual situation of the enterprise.

[0185] Take the preliminary carbon emission baseline as the mean of the prior distribution, and set a certain distribution variance to characterize the uncertainty of the baseline. Use the actual production activity data of the enterprise as the observed value to continuously update the prior distribution and obtain the posterior distribution. The goal is to minimize the gap between the carbon emission baseline and the actual emission data, while considering the reasonable volatility of the baseline value. Continuously update the prior information and converge to the optimal carbon emission baseline value. Use the posterior mean after Bayesian optimization as the carbon emission baseline data to ensure that it can fully reflect the actual production level and carbon emission capacity of the enterprise. Preliminary baseline: 0.6799 kg CO 2 / yuan output value. The actual output increases by 10%. The energy consumption increases by 5%. Optimized baseline: 0.7 kg CO 2 / yuan output value (the optimized baseline value, reflecting the actual situation of output and energy consumption growth).

[0186] In the present invention, through the joint analysis of historical carbon emission data and current carbon emission data, the long-term change trends of enterprise carbon emissions can be identified, including patterns such as rising, falling, or fluctuating. Through the clustering analysis of carbon emission trend data, an enterprise can be benchmarked against other enterprises in the industry with similar carbon emission characteristics to identify the best practices in the industry. Clustering analysis can classify an enterprise into a benchmark cluster similar to its own carbon emission characteristics, avoiding the simple averaging or general benchmark comparison methods, providing a more accurate and detailed industry benchmark positioning, and providing reliable reference data for baseline setting. Benchmark deviation calculation can quantify the gap between an enterprise and the industry benchmark, helping the enterprise clarify the difference between its own carbon emissions and the industry average level or best level. By considering the benchmark deviation data and carbon emission trend data, the preliminary carbon emission baseline of the enterprise can be set scientifically and reasonably, and the set baseline can accurately reflect the actual emission capacity and industry position of the enterprise, avoiding the situation of too high or too low baseline. Bayesian optimization can dynamically respond to changes in production activities, such as production load, equipment operation status, energy usage, etc., and adjust the carbon emission baseline in real time to avoid baseline inaccuracy caused by fluctuations in the actual situation.

[0187] Optionally, the carbon emission benchmark clustering data includes carbon emission enterprise type benchmark clustering data and carbon emission enterprise scale benchmark clustering data. The industry benchmark clustering process includes:

[0188] Obtain industry carbon emission benchmark data;

[0189] Specifically, obtain the overall industry carbon emission data from the government, industry associations, and third-party data providers, including information such as carbon emissions, production scale, energy consumption, and product types of different industries. Obtain the carbon emission information of enterprises in the same industry from publicly available enterprise annual environmental reports, carbon emission reports, and other documents. Extract industry carbon emission benchmark values and reference standards, such as carbon emission intensity and energy structure, from industry analysis reports and market research reports.

[0190] Perform enterprise type clustering calculations based on the industry carbon emission benchmark data and carbon emission trend data to obtain the benchmark clustering data for enterprise types of carbon emissions;

[0191] Specifically, cluster and analyze enterprises according to their industry types (such as manufacturing, service, agriculture, etc.) to identify the carbon emission characteristics of different types of enterprises and form the benchmark clustering data for enterprise types. Select features related to enterprise types, such as carbon emission intensity (kg CO 2 / yuan output value), energy consumption ratio (electricity, gas, coal, etc.), main product types, production processes, etc. Standardize different feature data to ensure consistent weights of different features in the clustering analysis. Use algorithms such as K-means and Hierarchical Clustering to perform clustering calculations based on features related to enterprise types. According to the clustering results, classify enterprises into different types, such as high-carbon emission manufacturing and low-carbon emission service industries. The clustering results are represented by centroids, and each centroid represents the benchmark feature data of an enterprise type. Cluster 1: High-carbon emission manufacturing, with an average carbon emission intensity of 0.75 kg CO 2 / yuan output value, and the main energy structure being coal and natural gas, including 50 enterprises. Cluster 2: Low-carbon emission service industry, with an average carbon emission intensity of 0.2 kg CO 2 / yuan output value, and the main energy structure being electricity, including 30 enterprises.

[0192] Perform enterprise scale clustering calculations based on the industry carbon emission benchmark data and carbon emission trend data to obtain the benchmark clustering data for enterprise scales of carbon emissions.

[0193] Specifically, perform clustering analysis according to the scale of enterprises (such as large, medium, and small enterprises) to identify the carbon emission characteristics of enterprises of different scales and form the benchmark clustering data for enterprise scales. Select features related to enterprise scale, such as total emissions (tons of CO 2( / year), output value (yuan / year), number of employees, production capacity (annual output, etc.). Standardize the characteristics of enterprises of different scales to eliminate the influence caused by scale differences between different enterprises. Use algorithms such as K-means or density clustering (DBSCAN) to perform clustering calculations based on the characteristics related to enterprise scale. According to the clustering results, classify enterprises into different scale categories, such as large enterprises, medium-sized enterprises, and small enterprises. The clustering results are represented by clustering centers, and each clustering center represents the benchmark characteristic data of an enterprise scale type. Cluster A: Large enterprises, with an average emissions of 100,000 tons of CO 2 / year, an annual output value of 500 million yuan, 5,000 employees, including 20 enterprises. Cluster B: Medium-sized enterprises, with an average emissions of 30,000 tons of CO 2 / year, an annual output value of 100 million yuan, 1,000 employees, including 50 enterprises. Cluster C: Small enterprises, with an average emissions of 5,000 tons of CO 2 / year, an annual output value of 20 million yuan, 200 employees, including 100 enterprises.

[0194] In the present invention, by collecting the industry carbon emission baseline data, a reliable baseline based on extensive industry data can be established, ensuring a solid data foundation for subsequent clustering analysis and providing an accurate baseline reference for the clustering calculation of enterprise types and scales. Through the enterprise type clustering calculation, clustering analysis is performed on enterprises according to the sub-industry types (such as manufacturing, service, chemical industry, energy industry, etc.), which can effectively avoid the influence of carbon emission differences between different industries on the baseline calculation and improve the accuracy of comparison. Enterprise scale has a significant impact on carbon emissions. The total carbon emissions of large enterprises are usually higher than those of small enterprises, but they may perform better in terms of carbon emission intensity. Through enterprise scale clustering, the influence brought by enterprise scale differences can be eliminated, making the baseline comparison more fair and reasonable.

[0195] Optionally, S3 includes:

[0196] S31. Perform carbon emission comparison based on the carbon emission baseline data and the carbon emission data to obtain carbon emission comparison data;

[0197] Specifically, the carbon emission baseline data is the benchmark carbon emission data calculated previously, representing the reference carbon emission level of an enterprise under normal production conditions. The carbon emission data is the actual carbon emission data of an enterprise at present or during a specific period, including energy consumption and transportation emissions during the production process. Calculate the absolute difference and relative difference between the actual carbon emission data and the baseline data: Absolute difference = actual carbon emissions - baseline carbon emissions. Relative difference = absolute difference / baseline carbon emissions.

[0198] S32. Generate carbon emission performance indicators based on the carbon emission comparison data to obtain carbon emission performance indicator data;

[0199] Specifically, the carbon emission performance indicator is an important parameter for measuring the effect of an enterprise's carbon emission management. It can include aspects such as emission reduction effect, energy efficiency level, emission intensity, etc. Multiple performance indicators are generated based on the carbon emission comparison data to comprehensively evaluate the enterprise's performance in carbon emission management;

[0200] Select performance indicators according to the industry characteristics and the actual situation of the enterprise. For example: Carbon emission compliance rate: Whether the actual carbon emissions reach the baseline or target. Carbon emission intensity change rate: The change of carbon emission intensity relative to the baseline. Energy efficiency improvement rate: The change of carbon emissions generated per unit of energy consumption;

[0201] Calculate each selected performance indicator to generate specific indicator values: Carbon emission compliance rate = (Actual carbon emissions / Carbon emission baseline) × 100%. Carbon emission intensity change rate = (Actual carbon emission intensity - Baseline carbon emission intensity) / Baseline carbon emission intensity. Energy efficiency improvement rate = (Baseline energy efficiency - Actual energy efficiency) / Baseline energy efficiency.

[0202] S33. Conduct multi-criteria decision analysis based on the carbon emission performance indicator data to obtain carbon emission performance indicator selection data;

[0203] Specifically, since the carbon emission performance indicators are diverse and may be contradictory to each other, it is necessary to comprehensively weigh different indicators through multi-criteria decision analysis methods (such as AHP, TOPSIS, etc.) to select key performance indicators. According to the enterprise's strategic goals, policy requirements and actual situation, assign a weight to each performance indicator. For example, the weight of the carbon emission compliance rate is 0.5, the weight of the carbon emission intensity change rate is 0.3, and the weight of the energy efficiency improvement rate is 0.2. Standardize the indicator data with different dimensions so that it is between 0 and 1 for easy comparison and calculation. Use the weighted synthesis method or other multi-criteria decision methods to comprehensively score each performance indicator to obtain the comprehensive performance score of each enterprise or project. According to the comprehensive score and weight, select the key performance indicators that have the greatest influence on the enterprise's carbon emission management. Generate carbon emission performance indicator selection data, including the score, weight and comprehensive performance score of each indicator.

[0204] S34. Conduct multi-level performance evaluation on the carbon emission data according to the carbon emission performance indicator selection data to obtain carbon emission performance data.

[0205] Specifically, according to the selected key performance indicators, systematically evaluate the carbon emission performance at different levels (such as enterprise, department, project, etc.), and identify the weak links and excellent performances in carbon emission management. Set performance evaluation criteria at different levels, such as enterprise level, department level, project level, etc. According to the actual carbon emission data and the selected key performance indicator data at different levels, conduct performance evaluation layer by layer:

[0206] Enterprise level: whether overall carbon emissions meet the benchmark.

[0207] Sector level: How each sector’s carbon emissions compare to its baseline.

[0208] Project level: Carbon emission performance of specific projects, such as energy efficiency improvement rate of new equipment, energy-saving transformation effect, etc.

[0209] Set performance scoring standards, such as excellent, good, qualified, unqualified, etc., and quantify and grade the evaluation results of each level. Summarize and analyze the evaluation results of all levels, identify the levels with outstanding performance and the links that need improvement, and provide a basis for enterprises to formulate carbon emission management strategies. Generate multi-level carbon emission performance data, including the performance scores and grading results of the enterprise as a whole, each department and each project.

[0210] Multi-level performance evaluation of Enterprise A:

[0211] Enterprise level: Carbon emission compliance rate is unsatisfactory, with a total score of 70.

[0212] Department Level:

[0213] Production Department: Met the target, total score 85.

[0214] Administrative department: Did not meet the standard, total score 60.

[0215] Project level:

[0216] Project 1 (equipment transformation): Energy efficiency improvement, total score 90.

[0217] Project 2 (Application of energy-saving technology): The effect is average, with a total score of 75.

[0218] Score: Enterprise A's overall performance score is 75, and its performance level is "good".

[0219] In the present invention, the carbon emission comparison data can identify whether an enterprise's carbon emissions exceed a predetermined baseline or are lower than the baseline, helping the enterprise promptly discover situations of excessive emissions or savings, take corresponding measures, and reduce the risk of carbon emission management. Through the analysis of the carbon emission comparison data, multi-dimensional performance indicators are generated, such as emission intensity, emission reduction rate, over-standard rate, compliance rate, etc., making the carbon emission performance evaluation more comprehensive and three-dimensional, and being able to better reflect the comprehensive performance of the enterprise's carbon emission management. Through multi-criteria decision analysis, the most critical indicators for the enterprise's carbon emission management are identified among numerous performance indicators, ensuring that the enterprise can focus on the most important areas of carbon emission management and improve management efficiency. The multi-level assessment can help the enterprise accurately identify bottlenecks and weak links in carbon emission management. For example, the carbon emission efficiency of a certain department or workshop is low, or the energy efficiency of a certain device is not high. The enterprise can formulate targeted improvement measures based on this to enhance the overall performance level.

[0220] Optionally, S4 includes:

[0221] S41. Calculate the carbon credit allocation quantity based on the carbon emission performance data to obtain the preliminary carbon credit allocation quantity data;

[0222] Specifically, the carbon emission performance data of each enterprise, department, or project includes information such as actual emissions, performance scores, and compliance status. Set the initial quota or allocation standard of carbon credits, such as enterprise production volume, historical emissions, industry benchmarks, etc. Based on the enterprise's carbon emission performance, preliminarily determine the carbon credit allocation quantity for each enterprise or department. Set the initial carbon credit baseline quota according to industry standards, government policies, or enterprise internal regulations. For example, the baseline quota for a certain industry is 100 tons of CO 2 / ten thousand yuan of output value. The preliminary carbon credit allocation quantity = baseline credit quantity × (1 - performance deviation coefficient). The performance deviation coefficient = (actual emissions - baseline emissions) / baseline emissions. If the actual emissions are less than the baseline emissions, the deviation coefficient is negative. Calculate the preliminary carbon credit allocation quantity for each enterprise according to the formula, positively motivating low-emission enterprises and negatively motivating high-emission enterprises. Generate a preliminary carbon credit allocation quantity data table, including the carbon credit allocation quantity for each enterprise, department, or project.

[0223] S42. Obtain the market carbon credit supply and demand data;

[0224] Specifically, obtain the market carbon credit supply and demand data from carbon trading exchanges or government platforms, including information such as the total carbon credit supply volume, total demand volume, trading price, trading volume, etc. The carbon credit supply data includes the carbon credit volume owned by the enterprise, the free quota allocated by the government, the tradable surplus credit volume, etc. The carbon credit demand data includes the carbon credit volume that the enterprise needs to purchase for excessive emissions, the demand volume of potential buyers, etc.

[0225] S43, calculating the allocation coefficient according to the market carbon credit supply and demand data and the preliminary carbon credit allocation data to obtain carbon credit allocation coefficient data;

[0226] Specifically, according to the market supply and demand situation, adjust the initial carbon credit allocation, calculate the allocation coefficient, make the carbon credit allocation more reasonable, and balance the supply and demand relationship. Supply and demand ratio calculation: supply and demand ratio = market supply / market demand. For example, if the supply is 5 million tons and the demand is 5.5 million tons, the supply and demand ratio = 500 / 550≈0.91. Allocation coefficient = initial allocation × supply and demand adjustment coefficient. Supply and demand adjustment coefficient = 1±(1-supply and demand ratio)×adjustment factor. The adjustment factor is set according to market fluctuations, for example, set to 0.5. Based on the supply and demand ratio and the initial carbon credit allocation, calculate the carbon credit allocation coefficient for each enterprise. Generate a carbon credit allocation coefficient data table, including the allocation coefficient and adjusted credit allocation for each enterprise.

[0227] S44. Adjust the carbon credit allocation amount according to the carbon credit quota data and the carbon credit allocation coefficient data in the market carbon credit supply and demand data to obtain the carbon credit allocation amount data.

[0228] Specifically, the company's own carbon credits include the company's own carbon credit quotas (such as the surplus quotas of the company's historical energy conservation and emission reduction). The government-allocated carbon credits are carbon credit quotas allocated by the government free of charge or for a fee. The market-tradable carbon credits are the carbon credits that the company can buy or sell on the market. Combined with the company's own carbon credits and allocation coefficients, the company's carbon credit allocation is determined to avoid over-allocation or under-allocation. The total amount of corporate carbon credits = the company's own carbon credits + the adjusted allocation.

[0229] For companies that exceed the standards: If a company's carbon credit demand is greater than its allocation, it can purchase additional carbon credits through the market.

[0230] For energy-saving enterprises: If the enterprise has surplus carbon credits, the excess credits can be sold on the market to increase corporate profits.

[0231] Determine the final carbon credit allocation for each enterprise and generate an allocation data table. Generate a carbon credit allocation data table, including each enterprise's preliminary allocation, adjustment coefficient, final allocation and tradable quota.

[0232] In the present invention, by quantifying carbon emission performance into preliminary carbon credit allocation amounts, enterprises can clearly understand the relationship between their emission reduction efforts and the benefits they obtain in the carbon market. This quantification method improves the transparency and fairness of carbon credit allocation. Obtaining market carbon credit supply and demand data can help enterprises understand the current supply and demand situation of carbon credits in the market, such as the total amount of carbon credits, market transaction prices, demands of buyers and sellers, etc., providing key market background information for enterprises' carbon credit allocation strategies. By combining market supply and demand data with enterprises' preliminary carbon credit allocation amount data, a carbon credit allocation coefficient is calculated, which can find a balance between enterprises' emission reduction performance and market supply and demand conditions, effectively avoiding unfair allocation caused by solely relying on emission reduction performance or market supply and demand. After considering market supply and demand conditions, enterprises' preliminary allocation amounts and allocation coefficients, the carbon credit allocation amounts are precisely adjusted to ensure that the carbon credit allocation amounts obtained by each enterprise can truly reflect their performance levels and market supply and demand conditions, improving the accuracy of allocation.

[0233] The object of the present invention is to use Internet of Things sensors and intelligent data acquisition systems to obtain carbon emission data in real time from multiple sources such as production equipment, energy consumption systems, and transportation tools. By using enterprises' historical carbon emission data, industry benchmark data, and actual production activity data, combined with carbon emission trend analysis and industry benchmark clustering analysis, a carbon emission baseline is scientifically set. Through the Bayesian optimization method, the actual production activity data of enterprises is dynamically adjusted with the preliminary carbon emission baseline data, enabling the baseline to respond in real time to the volatility of enterprises' production activities and policy changes, improving the dynamic adaptability and accuracy of the carbon emission baseline. Multidimensional carbon emission performance indicators are generated through carbon emission comparison data, and multi-criteria decision analysis (MCDA) is used to comprehensively evaluate and rank different indicators to determine the most representative key performance indicators (KPIs). Carbon credit allocation is not only based on enterprises' carbon emission performance data but also takes into account the supply and demand situation of market carbon credits. By calculating the allocation coefficient through the combination of preliminary allocation amounts and market supply and demand data, enterprises' carbon credit allocation amounts can more accurately reflect their relative positions in the market and carbon emission performance.

[0234] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended application documents rather than the above description. Therefore, it is intended to encompass all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0235] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A dynamic adjustment method for carbon credit evaluation and allocation, characterized in that: The method comprises: S1. Obtain carbon emission data; S2. Setting a carbon emission baseline according to the carbon emission data to obtain the carbon emission baseline data; S3. Calculate carbon emission performance based on carbon emission baseline data to obtain carbon emission performance data; S4. Adjust the carbon credit allocation amount according to the carbon emission performance data to obtain carbon credit allocation amount data; S1 includes: Collect production energy consumption data and equipment loss data from the company's production equipment to obtain production energy consumption data and production equipment loss data; Collect energy system consumption data from the energy consumption system to obtain energy system consumption data; Collect transportation carbon emission data from transportation tools to obtain transportation carbon emission data; Cross-screen the energy system consumption data according to the production energy consumption data to obtain the energy system consumption screening data; Carbon emissions are calculated based on production energy consumption data, production equipment loss data, transportation carbon emission data, and energy system consumption screening data to obtain carbon emission data; Cross-screening includes: Perform energy consumption matching analysis based on production energy consumption data and energy system consumption data to obtain energy consumption matching data; Perform feature similarity calculation based on energy consumption matching data to obtain energy consumption feature similarity data; Perform energy consumption redundancy removal processing based on energy consumption feature similarity data to obtain energy system consumption screening data; S2 includes: Obtain historical carbon emission data, and perform trend analysis based on the carbon emission data and historical carbon emission data to obtain carbon emission trend data; Perform industry benchmark clustering processing based on carbon emission trend data to obtain carbon emission benchmark clustering data; Calculate the industry benchmark deviation based on the carbon emission benchmark clustering data to obtain the carbon emission benchmark deviation data; Calculate the carbon emission baseline based on the carbon emission baseline deviation data and carbon emission trend data to obtain preliminary carbon emission baseline data; Obtain the actual production activity data of the enterprise, and perform Bayesian optimization based on the actual production activity data of the enterprise and the preliminary carbon emission baseline data to obtain the carbon emission baseline data; The carbon emission benchmark clustering data includes the carbon emission enterprise type benchmark clustering data and the carbon emission enterprise scale benchmark clustering data. The industry benchmark clustering processing includes: Obtain industry carbon emissions benchmark data; Perform enterprise type clustering calculations based on industry carbon emission benchmark data and carbon emission trend data to obtain carbon emission enterprise type benchmark clustering data; Based on the industry carbon emission benchmark data and carbon emission trend data, enterprise size clustering calculations are performed to obtain carbon emission enterprise size benchmark clustering data.

2. The method according to claim 1, characterized in that Equipment loss data collection includes: The operation data is collected through sensors preset in the production equipment to obtain the equipment operation status data; Extract equipment energy consumption characteristics based on equipment operation status data to obtain equipment energy consumption characteristic data; Equipment aging and loss assessment is performed based on equipment energy consumption characteristic data to obtain production equipment loss data.

3. The method according to claim 1, characterized in that Carbon emissions calculations include: Extract carbon emission factors based on production energy consumption data, production equipment loss data, transportation carbon emission data, and energy system consumption screening data to obtain carbon emission factor data; A carbon emission calculation model is constructed according to the carbon emission factor data to obtain a carbon emission calculation model; Calculate carbon emissions item by item according to the carbon emissions calculation model to obtain carbon emissions item by item data; The carbon emission intensity is calculated based on the carbon emission sub-item data to obtain the carbon emission data.

4. The method according to claim 1, characterized in that S3 include: Compare carbon emissions based on carbon emission baseline data and carbon emission data to obtain carbon emission comparison data; Generate carbon emission performance indicators based on carbon emission comparison data to obtain carbon emission performance indicator data; Conduct multi-criteria decision analysis based on carbon emission performance indicator data to obtain carbon emission performance indicator selection data; According to the carbon emission performance indicator selection data, a multi-level performance evaluation is performed on the carbon emission data to obtain the carbon emission performance data.

5. The method according to claim 1, characterized in that S4 includes: Calculate the carbon credit allocation amount based on the carbon emission performance data to obtain preliminary carbon credit allocation amount data; Obtain market carbon credit supply and demand data; Calculate the allocation coefficient based on the market carbon credit supply and demand data and the preliminary carbon credit allocation data to obtain the carbon credit allocation coefficient data; The carbon credit allocation amount is adjusted according to the carbon credit quota data and the carbon credit allocation coefficient data in the market carbon credit supply and demand data to obtain the carbon credit allocation amount data.

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