Dynamic adjustment method for carbon credit evaluation and distribution

Abstract

The invention relates to the technical field of carbon emission management, in particular to a dynamic adjustment method for carbon credit evaluation and distribution. The method comprises the following steps: acquiring carbon emission data; performing carbon emission baseline setting according to the carbon emission data to obtain carbon emission baseline data; performing carbon emission performance calculation according to the carbon emission baseline data to obtain carbon emission performance data; and according to the carbon emission performance data, carrying out carbon credit distribution amount adjustment to obtain carbon credit distribution amount data. According to the invention, a mechanism of linking the carbon emission performance and the carbon credit distribution is adopted, a forward excitation effect is formed, an enterprise is promoted to continuously optimize the production process, improve the technology, improve the energy efficiency and go forward to low-carbon and green transformation, and a win-win situation of economic benefits and environmental benefits is realized.

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 the global climate change problem becomes increasingly serious, the control and management of greenhouse gas emissions, especially carbon dioxide (CO2), 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 the carbon emission quota 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 the Internet of Things (IoT) devices, smart sensors and automated data acquisition systems, it is possible to achieve real-time collection of carbon emission data of enterprises in all directions and at multiple levels. By dynamically adjusting the algorithm, the baseline emissions are updated in real time according to the volatility of the production activities of the enterprise and policy changes, which can adapt to the changes in the actual situation of the enterprise, flexibly adjust the baseline, and avoid the baseline misalignment 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 is dynamically adjusted according to the carbon emission performance data of the enterprise to ensure that the carbon credit allocation matches the emission reduction efforts of the enterprise. Enterprises with excellent emission reduction performance will receive more carbon credit incentives, while enterprises that fail to meet the emission reduction requirements will reduce their carbon credit quotas. This can effectively motivate enterprises to actively take emission reduction measures and promote the decline of overall carbon emissions.

[0011] Optionally, S1 includes:

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

[0013] S12, collecting energy system consumption data from the energy consumption system to obtain energy system consumption data;

[0014] S13, collecting transport carbon emission data from transport tools to obtain transport 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 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.

[0017] The present invention directly collects production energy consumption data from the enterprise's production equipment, which can accurately record the actual energy consumption of each device and avoid 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, and high-energy-consuming links and potential energy-saving spaces can be identified. The collection of carbon emission data of transportation tools enables enterprises to fully grasp the carbon emissions of logistics and transportation links. By cross-screening the production energy consumption data and the energy system consumption data, it is possible to identify and eliminate duplicate energy consumption data to ensure the accuracy of the data and avoid the problem of overestimation or underestimation of carbon emissions caused by repeated calculations. Combining production energy consumption, equipment loss, transportation carbon emissions and screened energy system consumption data, the overall carbon emission level of the enterprise can be calculated more comprehensively and accurately, avoiding omissions or repeated calculations, and ensuring the scientificity and accuracy of the calculation results.

[0018] Optionally, equipment loss data collection includes:

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

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

[0021] Equipment aging and loss assessment is performed based on equipment energy consumption characteristic data to obtain production equipment loss data.

[0022] In the present invention, by conducting an in-depth analysis of the equipment operation status data, the energy consumption characteristic data of the equipment can be extracted, such as instantaneous energy consumption, average energy consumption, energy efficiency ratio, peak energy consumption and other indicators. The extraction of energy consumption characteristic data helps to identify the energy consumption fluctuations of the equipment under different working conditions, such as changes in energy consumption under different states such as startup, shutdown, full load, and low load. Through 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 is used for a longer time, its energy efficiency will gradually decrease. By evaluating the changing trend of the energy consumption characteristics, the aging process of the equipment can be quantitatively described. As a part of the carbon emission indicators, the equipment loss data can effectively reflect the additional energy consumption and corresponding carbon emissions caused by factors such as aging, failure or reduced operating efficiency during the use of the equipment.

[0023] Optionally, cross-screening includes:

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

[0025] Perform feature similarity calculation based on energy consumption matching data to obtain energy consumption feature similarity data;

[0026] Energy consumption redundancy is removed based on the energy consumption feature similarity data to obtain energy system consumption screening data.

[0027] In the present invention, by matching and analyzing the production energy consumption data and the energy system consumption data, the corresponding relationship between the sources of each energy consumption data can be accurately identified, such as the corresponding relationship between the energy consumption of the 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, such as the energy consumption of the production equipment is simultaneously included in the production line energy consumption and the total energy consumption. These overlapping data can be eliminated through matching analysis 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 de-redundancy processing and avoiding energy consumption estimation errors caused by repeated calculation of data with high similarity. De-redundancy processing is performed based on feature similarity data, which can identify and eliminate highly similar energy consumption data and avoid repeated calculation of the same or similar energy consumption data.

[0028] Optionally, carbon emissions calculations include:

[0029] 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;

[0030] A carbon emission calculation model is constructed according to the carbon emission factor data to obtain a carbon emission calculation model;

[0031] Calculate carbon emissions item by item according to the carbon emissions calculation model to obtain carbon emissions item by item data;

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

[0033] In the present invention, carbon emission factors of various energy sources are extracted by analyzing multiple data sources (production energy consumption, equipment loss, transportation carbon emissions, energy system consumption), such as emission coefficients of different energy sources such as coal, natural gas, and electricity. Carbon emission factors are extracted from multi-source data, various emission sources in the production process are comprehensively considered, and information from different data sources is effectively integrated. The carbon emission calculation model can adjust and optimize parameters according to the actual situation of different enterprises, and is suitable for various types of production enterprises. The model can be adjusted for different process flows and energy usage, and has strong versatility and flexibility. Through item-by-item calculations, 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 in a targeted manner. Carbon emission intensity data (such as carbon emissions per unit of output value) can measure the carbon emissions corresponding to the unit output of an enterprise during the production process, reflecting 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 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 according to the carbon emission baseline 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 the carbon emission baseline data.

[0040] In the present invention, by jointly analyzing historical carbon emission data and current carbon emission data, the long-term trend of carbon emission of enterprises can be identified, including rising, falling or fluctuating patterns. Through cluster analysis of carbon emission trend data, enterprises can be compared with other enterprises in the industry with similar carbon emission characteristics to identify industry best practices. Cluster analysis can divide enterprises into benchmark clusters similar to their own carbon emission characteristics, avoiding simple average or general benchmark comparison methods, providing more accurate and segmented industry benchmark positioning, and providing reliable reference data for baseline setting. Benchmark deviation calculation can quantify the gap between enterprises and industry benchmarks, helping enterprises to clarify the difference between their own carbon emissions and the average or optimal level of the industry. By considering 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 where the baseline is too high or too low. Bayesian optimization can dynamically respond to changes in production activities, such as production load, equipment operating status, energy usage, etc., and adjust the carbon emission baseline in real time to avoid baseline inaccuracy caused by fluctuations in actual conditions.

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

[0042] Obtain industry carbon emissions benchmark data;

[0043] 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;

[0044] 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.

[0045] In the present invention, by collecting industry carbon emission benchmark data, a reliable benchmark based on industry-wide data can be established to ensure that subsequent cluster analysis has a solid data foundation, and provide an accurate benchmark reference for cluster calculations of enterprise types and scales. Through enterprise type cluster calculations, enterprises are clustered and analyzed according to subdivided industry types (such as manufacturing, service industry, chemical industry, energy industry, etc.), which can effectively avoid the impact of carbon emission differences between different industries on benchmark calculations 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 impact of differences in enterprise scale can be eliminated, making benchmark comparisons more fair and reasonable.

[0046] Optionally, S3 includes:

[0047] S31. Perform carbon emission comparison 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, performing multi-criteria decision analysis based on the carbon emission performance indicator data to obtain carbon emission performance indicator selection data;

[0050] S34. Perform a multi-level performance evaluation on the carbon emission data based on the selected data according to the carbon emission performance indicators to obtain the carbon emission performance data.

[0051] The carbon emission comparison data in the present invention can identify whether the carbon emissions of an enterprise exceed the predetermined baseline or are lower than the baseline, helping enterprises to promptly discover emission excesses or savings, take corresponding measures, and reduce carbon emission management risks. By analyzing the carbon emission comparison data, multi-dimensional performance indicators are generated, such as emission intensity, emission reduction rate, excess rate, compliance rate, etc., so that the carbon emission performance evaluation is more comprehensive and three-dimensional, and can better reflect the comprehensive performance of the enterprise's carbon emission management. Through multi-criteria decision analysis, the most critical indicators for the company's carbon emission management are identified among many performance indicators to ensure that the company can focus on the most important areas of carbon emission management and improve management efficiency. Multi-level evaluation can help companies accurately identify bottlenecks and weak links in carbon emission management, such as low carbon emission efficiency in a department or workshop, or low energy efficiency of a certain equipment. Companies can formulate targeted improvement measures based on this to improve overall performance.

[0052] Optionally, S4 includes:

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

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

[0055] 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;

[0056] 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.

[0057] In the present invention, by quantifying carbon emission performance into a preliminary carbon credit allocation, enterprises can clearly understand the relationship between their emission reduction efforts and the benefits they obtain in the carbon market, and the quantification method improves the transparency and fairness of carbon credit allocation. Obtaining market carbon credit supply and demand data can help enterprises understand the supply and demand of carbon credits in the current market, such as the total amount of carbon credits, market transaction prices, and the needs of buyers and sellers, etc., and provide key market background information for the company's carbon credit allocation strategy. By combining market supply and demand data with the company's preliminary carbon credit allocation data and calculating the carbon credit allocation coefficient, a balance can be found between the company's emission reduction performance and market supply and demand conditions, effectively avoiding the unfair distribution caused by simply based on emission reduction performance or market supply and demand. After considering the market supply and demand conditions, the company's preliminary allocation and the allocation coefficient, the carbon credit allocation is accurately adjusted to ensure that the carbon credit allocation obtained by each company can truly reflect its performance level and market supply and demand conditions, and improve the accuracy of the 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 from multiple sources such as production equipment, energy consumption systems, and transportation tools in real time. Using the company's historical carbon emission data, industry benchmark data, and actual production activity data, combined with carbon emission trend analysis and industry benchmark cluster analysis, the carbon emission baseline is scientifically set. Through the Bayesian optimization method, the actual production activity data of the enterprise and the preliminary carbon emission baseline data are dynamically adjusted, so that the baseline can respond to the volatility and policy changes of the company's production activities in real time, and improve 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 the company's carbon emission performance data, but also takes into account the supply and demand of market carbon credits. By combining the preliminary allocation amount and market supply and demand data to calculate the allocation coefficient, the company's carbon credit allocation can more accurately reflect its relative position 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 non-limiting implementations made with reference to the following drawings:

[0060] Figure 1 A flowchart showing a method for dynamically adjusting carbon credit evaluation and allocation according to an embodiment of the present invention is provided;

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

[0062] Figure 3A flowchart showing a method for setting a carbon emission baseline according to an embodiment is shown;

[0063] Figure 4 A flowchart showing a method for calculating carbon emission performance according to an embodiment of the present invention is provided;

[0064] Figure 5 A flowchart showing the steps of a method for adjusting carbon credit allocation according to an embodiment is shown;

[0065] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0066] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0067] In addition, the 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 network and / or processor methods and / or microcontroller methods.

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

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

[0070] Annual production volume: 10,000 tons of mechanical parts, carbon emissions per unit of output (historical average): 0.8 tons CO2 / ton of mechanical parts, carbon emissions baseline target: 0.7 tons CO2 / ton of mechanical parts, the company's historical annual total carbon emissions: 8,000 tons CO2, industry benchmark annual carbon emissions (average for similar companies): 0.75 tons CO2 / ton of mechanical parts.

[0071] 1. Corporate 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 loss caused by equipment failure in the past year was approximately 120,000 kWh); transportation mileage: 300,000 kilometers, transportation vehicle carbon emission factor: 0.25 kg CO2 / kilometer; 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 factor: Electricity carbon emission factor: 0.5kg CO2 / kWh, natural gas carbon emission factor: 1.9kg CO2 / cubic meter.

[0074] 1.3 Carbon emission calculation:

[0075] Electricity carbon emissions: 14,880,000kWh×0.5kg CO2 / kWh=7,440 tons CO2,

[0076] Natural gas carbon emissions: 490,000 cubic meters × 1.9 kg CO2 / cubic meter = 931 tons of CO2,

[0077] Transport carbon emissions: 300,000 km x 0.25 kg CO2 / km = 75 tons CO2,

[0078] Equipment loss carbon emissions: 120,000kWh×0.5kg CO2 / kWh=60 tons of CO2,

[0079] Total carbon emissions: 7,440 tons + 931 tons + 75 tons + 60 tons = 8,506 tons of CO2.

[0080] 2. Carbon Emissions Baseline Setting

[0081] 2.1 Analysis of historical carbon emission trends

[0082] Over the past five years, carbon emissions have gradually decreased from 8,200 tons of CO2 to 8,000 tons of CO2, showing an average annual downward trend of 2.5%.

[0083] 2.2 Industry fields and enterprise scale

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

[0085] 2.3 Calculation of Industry Benchmark Deviation

[0086] The company's current emission intensity: 8,506 tons of CO2 / 10,000 tons = 0.8506 tons of CO2 / ton,

[0087] Industry benchmark deviation: 0.8506 tons CO2 / ton - 0.75 tons CO2 / ton = 0.1006 tons CO2 / ton.

[0088] 2.4 Calculation of basic carbon emission baseline data

[0089] Basic carbon emissions baseline: 10,000 tons x 0.75 tons CO2 / ton = 7,500 tons CO2.

[0090] 2.5 Bayesian optimization of carbon emission baseline

[0091] Taking into account factors such as equipment updates and energy structure adjustments, the optimized carbon emission baseline is set as: 7,500 tons of CO2 (preliminary baseline) × 0.95 (optimization coefficient) = 7,125 tons of CO2.

[0092] 3. Carbon Emission Performance Calculation

[0093] 3.1 Carbon Emission Comparison

[0094] Actual carbon emissions: 8,506 tons CO2,

[0095] Carbon emissions baseline: 7,125 tons CO2,

[0096] Carbon emissions comparison difference: 8,506 tons - 7,125 tons = 1,381 tons of CO2.

[0097] 3.2 Carbon emission performance indicators

[0098] Carbon emission intensity: 0.8506 tons CO2 / ton (actual), 0.7125 tons CO2 / ton (baseline),

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

[0100] Emission reduction potential: 0.8506 tons CO2 / ton - 0.7125 tons CO2 / ton = 0.1381 tons CO2 / ton.

[0101] 3.3 Decision 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 the multi-criteria decision-making model, the overall performance score is 75 (out of 100 points).

[0105] Graded Assessment:

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

[0107] Production stage: 70 points (large room for energy efficiency improvement),

[0108] Transportation link: 60 points (carbon emissions exceed the standard, and there is much room for transportation optimization).

[0109] 4. Adjustment of Carbon Credit Allocation

[0110] 4.1 Calculation of basic carbon credit allocation

[0111] Initial market allocation rules: Partial rewards below the baseline, 10 carbon credits for every ton of CO2 reduced,

[0112] Reduction below baseline: 7,125 tonnes - 7,500 tonnes = 375 tonnes,

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

[0114] 4.2 Market carbon credit supply and demand data

[0115] Total carbon credit supply in the market: 100,000 credits,

[0116] Total carbon credit demand in the market: 90,000 credits,

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

[0118] 4.3 Calculation of distribution coefficient

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

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

[0121] 4.4 Adjustment of Carbon Credit Allocation

[0122] Adjusted carbon credit allocation: 3,750×1.08=4,050 carbon credits.

[0123] See also Figures 1 to 5 The present application provides a method for dynamically adjusting carbon credit evaluation and allocation, the method comprising:

[0124] S1. Obtain carbon emission data;

[0125] Specifically, relevant emission data is collected 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. Setting a carbon emission baseline according to the carbon emission data to obtain the carbon emission baseline data;

[0127] Specifically, determine the baseline for evaluating carbon emission performance, such as using the average carbon emissions of 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 type of different carbon emission sources. Finally determine the carbon emission baseline data, confirm and archive it in the system.

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

[0129] Specifically, the total emissions of each emission source are calculated based on the actual carbon emission data. The actual emissions are compared with the baseline data to calculate the difference. Based on the difference, the carbon emission performance indicators of each emission source are calculated, such as emission reduction rate, excess amount, etc. Different emission sources are graded according to the performance indicators, such as excellent, qualified and unqualified.

[0130] S4. Adjust the carbon credit allocation amount according to the carbon emission performance data to obtain the carbon credit allocation amount data.

[0131] Specifically, carbon credits are initially allocated based on carbon emission baseline data and performance grading results. Carbon credit allocations are dynamically adjusted based on real-time performance data of each emission source, such as reducing carbon credits for emission sources that exceed the standard and increasing carbon credits for emission sources with excellent performance. The adjusted carbon credit allocations are compared with actual usage to calculate the final carbon credit surplus or deficit. For emission sources with insufficient carbon credits, it is recommended to conduct carbon credit transactions and purchase the remaining credits from other emission sources; for emission sources with surplus, they can choose to sell their excess carbon credits.

[0132] In the present invention, through the Internet of Things (IoT) devices, smart sensors and automated data acquisition systems, it is possible to achieve real-time collection of carbon emission data of enterprises in all directions and at multiple levels. By dynamically adjusting the algorithm, the baseline emissions are updated in real time according to the volatility of the production activities of the enterprise and policy changes, which can adapt to the changes in the actual situation of the enterprise, flexibly adjust the baseline, and avoid the baseline misalignment 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 is dynamically adjusted according to the carbon emission performance data of the enterprise to ensure that the carbon credit allocation matches the emission reduction efforts of the enterprise. Enterprises with excellent emission reduction performance will receive more carbon credit incentives, while enterprises that fail to meet the emission reduction requirements will reduce their carbon credit quotas. This can effectively motivate enterprises to actively take emission reduction measures and promote the decline of overall carbon emissions.

[0133] Optionally, S1 includes:

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

[0135] Specifically, energy consumption monitoring sensors installed on production equipment are used to collect energy data consumed during the production process, such as electricity consumption and fuel consumption (coal, natural gas, oil, etc.). Equipment status monitoring systems are used to collect data such as wear and failure rates during equipment operation to estimate changes in equipment energy efficiency and additional losses.

[0136] S12, collecting 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 enterprise's overall energy consumption, including the consumption of electricity, natural gas, steam and other energy sources by each department.

[0138] S13, collecting transport carbon emission data from transport tools to obtain transport carbon emission data;

[0139] Specifically, the GPS and OBD (on-board diagnostic system) devices installed on the vehicle are used to collect real-time data such as vehicle mileage, fuel consumption, carbon emissions, etc. For vehicles that do not have automatic monitoring functions, drivers need to manually record vehicle mileage and fuel consumption after each transportation task is completed.

[0140] S14. Cross-screen the energy system consumption data according to the production energy consumption data to obtain energy system consumption screening data;

[0141] Specifically, the energy consumption data of production equipment is compared with the overall consumption data of the energy system to screen out the differences between the two. The energy utilization rate of each production unit is calculated to eliminate abnormal energy consumption data caused by equipment loss or uneven energy distribution.

[0142] S15. Calculate carbon emissions 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.

[0143] Specifically, carbon emissions are calculated based on various energy consumption data using different carbon emission coefficients (such as electricity, natural gas, fuel oil, etc.). The carbon emissions of various departments and transportation tools are accumulated to obtain the total carbon emissions of the enterprise.

[0144] The present invention directly collects production energy consumption data from the enterprise's production equipment, which can accurately record the actual energy consumption of each device and avoid 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, and high-energy-consuming links and potential energy-saving spaces can be identified. The collection of carbon emission data of transportation tools enables enterprises to fully grasp the carbon emissions of logistics and transportation links. By cross-screening the production energy consumption data and the energy system consumption data, it is possible to identify and eliminate duplicate energy consumption data to ensure the accuracy of the data and avoid the problem of overestimation or underestimation of carbon emissions caused by repeated calculations. Combining production energy consumption, equipment loss, transportation carbon emissions and screened energy system consumption data, the overall carbon emission level of the enterprise can be calculated more comprehensively and accurately, avoiding omissions or repeated calculations, and ensuring the scientificity and accuracy of the calculation results.

[0145] Optionally, equipment loss data collection includes:

[0146] The operation data is collected through sensors preset in the production equipment to obtain the equipment operation status data;

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

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

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

[0150] Equipment aging and loss assessment is performed based on equipment energy consumption characteristic data to obtain production equipment loss data.

[0151] Specifically, according to the design parameters and historical operation data of the equipment, set the energy consumption baseline value and loss threshold of the equipment. For example, the design energy efficiency ratio of a certain equipment is 0.9 and the average power is 5kW. Compare the current energy consumption characteristic data with the historical data of the equipment, and analyze the changing trends of indicators such as 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 increased loss.

[0152] Using the loss assessment model, the energy consumption characteristic data is input into the model to calculate the loss degree of the equipment, such as mechanical loss, electrical loss, etc. The loss assessment model can be based on the equipment aging curve (P(t) = P0×e -γt , P(t) is the performance of the equipment at time t, P0 is the initial performance, e is the natural exponential term, γ is the aging rate coefficient, t is the usage time data), vibration analysis model, temperature change model, etc. The loss data obtained from the evaluation is used to generate a loss report, including information such as the aging degree, loss type, and failure risk of the equipment. A production equipment loss data table is generated, including the aging degree, loss amount, loss type, and potential failure risk information of the equipment.

[0153] Specifically, a regression model is used to evaluate equipment aging and loss on equipment energy consumption characteristic data to obtain production equipment loss data, wherein the regression coefficient of the regression model is calculated by training historical data.

[0154] In the present invention, by conducting an in-depth analysis of the equipment operation status data, the energy consumption characteristic data of the equipment can be extracted, such as instantaneous energy consumption, average energy consumption, energy efficiency ratio, peak energy consumption and other indicators. The extraction of energy consumption characteristic data helps to identify the energy consumption fluctuations of the equipment under different working conditions, such as changes in energy consumption under different states such as startup, shutdown, full load, and low load. Through 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 is used for a longer time, its energy efficiency will gradually decrease. By evaluating the changing trend of the energy consumption characteristics, the aging process of the equipment can be quantitatively described. As a part of the carbon emission indicators, the equipment loss data can effectively reflect the additional energy consumption and corresponding carbon emissions caused by factors such as aging, failure or reduced operating efficiency during the use of the equipment.

[0155] Optionally, cross-screening includes:

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

[0157] Specifically, specific energy consumption information from each production equipment, such as the electricity and natural gas consumed by a production equipment in a specific time period. Feature information is extracted from the matching data, such as: the energy consumption trend of each equipment or system in different time periods. The fluctuation range and frequency of energy consumption in a specific time period. The operating time and downtime ratio of the equipment.

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

[0159] Specifically, by calculating the similarity between the energy consumption characteristics of production equipment and the overall energy system consumption characteristics, possible duplicate or abnormal energy consumption data can be identified. Energy consumption data of different units and magnitudes are standardized to ensure comparability between characteristics when similarity is calculated. Distance measurement methods (such as Euclidean distance, cosine similarity, etc.) are used to calculate the similarity between the energy consumption characteristics of production equipment and the energy system consumption characteristics. A similarity score (0-1) is assigned to each device based on the similarity calculation results. The higher the similarity, the more similar the energy consumption characteristics of the device are to the overall characteristics of the system. Total energy consumption information at the enterprise or workshop level includes total electricity consumption, gas consumption, etc. in each time period.

[0160] Energy consumption redundancy is removed based on the energy consumption feature similarity data to obtain energy system consumption screening data.

[0161] Specifically, for energy consumption data with similarity higher than a certain threshold (such as similarity>0.90), they are considered to be repeated or redundant, and their energy consumption values ​​are merged. For data that are significantly different from the overall characteristics (such as similarity<0.3), they are considered to be abnormal data and marked or removed. The data after removing redundancy or abnormality is corrected and the overall energy consumption distribution is recalculated. The results after de-redundancy are compared with the original data to ensure that the change in total energy consumption is within a reasonable range.

[0162] In the present invention, by matching and analyzing the production energy consumption data and the energy system consumption data, the corresponding relationship between the sources of each energy consumption data can be accurately identified, such as the corresponding relationship between the energy consumption of the 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, such as the energy consumption of the production equipment is simultaneously included in the production line energy consumption and the total energy consumption. These overlapping data can be eliminated through matching analysis 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 de-redundancy processing and avoiding energy consumption estimation errors caused by repeated calculation of data with high similarity. De-redundancy processing is performed based on feature similarity data, which can identify and eliminate highly similar energy consumption data and avoid repeated calculation of the same or similar energy consumption data.

[0163] Optionally, carbon emissions calculations include:

[0164] 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;

[0165] Specifically, the carbon emission factor refers to the emission of greenhouse gases such as carbon dioxide per unit of 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 (for example, 0.5kg CO2 / kWh). According to the fuel type (such as coal, natural gas, oil, etc.), extract the corresponding carbon emission factor (for example, 1.9kg CO2 / m 3 Natural gas). Estimate the carbon emission factor of the equipment during the loss period by the rate of change of its energy efficiency. For example, if the rated carbon emission factor of a certain equipment is 1kg CO2 / kWh, and the efficiency is reduced by 10% due to loss, the carbon emission factor during the loss period is 1.1kg CO2 / kWh. Extract 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 2.68kg CO2 / liter. Electricity consumption factor extraction: 0.5kg CO2 / kWh (local power grid data). Gas consumption factor extraction: 1.9kg CO2 / m 3(Natural gas standard factor). Transport factor extraction: 2.68kg CO2 / liter (diesel truck standard factor).

[0166] A carbon emission calculation model is constructed according to the carbon emission factor data to obtain a carbon emission calculation model;

[0167] Specifically, establish a mapping relationship between carbon emission factors and specific activity quantities (such as electricity consumption, fuel consumption, transportation mileage, etc.). Set the carbon emission calculation formula for each link: Carbon emission in the production process = production energy consumption × corresponding carbon emission factor. Carbon emission from equipment loss = equipment loss × corresponding carbon emission factor. Carbon emission in the transportation process = fuel consumption × fuel carbon emission factor. Based on historical data and empirical values, calibrate various parameters in the model, such as the efficiency change rate and fuel consumption rate of different equipment.

[0168] Calculate carbon emissions item by item according to the carbon emissions calculation model to obtain carbon emissions item by item data;

[0169] Specifically, calculate the carbon emissions of each item according to different energy types, equipment, and transportation tools, so as to facilitate further analysis and management. Calculate the carbon emissions of each production workshop and equipment. Calculate the items 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 causes an increase in energy consumption of 200kWh, and the corresponding carbon emissions are 200×0.5=100kg CO2. Calculate the carbon emissions of different transportation tools, including vehicles, ships, etc. Calculate the items according to the transportation tasks. For example, if a certain task consumes 100 liters of diesel, the carbon emissions are 100×2.68=268kg CO2. Calculate the overall carbon emissions of the energy system, minus the redundant carbon emissions after redundancy.

[0170] The carbon emission intensity is calculated based on the carbon emission sub-item data to obtain the carbon emission data.

[0171] Specifically, carbon emission intensity refers to the amount of carbon emissions generated per unit of product or output value. It is an important indicator to measure the carbon emission efficiency of an enterprise. The total carbon emissions are combined with the total production or output value of the enterprise to calculate the carbon emission intensity, which reflects the carbon emission efficiency of the enterprise. Select appropriate output indicators as the denominator of carbon emission intensity, such as total output value, total output, product weight, etc. Carbon emission intensity = total carbon emissions / total output. Calculate the corresponding carbon emission intensity according to the carbon emissions of each sub-item. For example: Carbon emission intensity of the production process = carbon emissions of the production process / total output. Take the weighted average of the intensities of each sub-item to obtain the overall carbon emission intensity of the enterprise.

[0172] In the present invention, carbon emission factors of various energy sources are extracted by analyzing multiple data sources (production energy consumption, equipment loss, transportation carbon emissions, energy system consumption), such as emission coefficients of different energy sources such as coal, natural gas, and electricity. Carbon emission factors are extracted from multi-source data, various emission sources in the production process are comprehensively considered, and information from different data sources is effectively integrated. The carbon emission calculation model can adjust and optimize parameters according to the actual situation of different enterprises, and is suitable for various types of production enterprises. The model can be adjusted for different process flows and energy usage, and has strong versatility and flexibility. Through item-by-item calculations, 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 in a targeted manner. Carbon emission intensity data (such as carbon emissions per unit of output value) can measure the carbon emissions corresponding to the unit output of an enterprise during the production process, reflecting the carbon emission efficiency of the enterprise.

[0173] Optionally, S2 includes:

[0174] S21. 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;

[0175] Specifically, historical carbon emission data from the past few years is obtained from the company'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. Real-time carbon emission data collected during the current production cycle includes production energy consumption data, equipment loss data, and transportation emission data. By comparing historical data with current data, the trend of changes in the company's carbon emissions can be identified to understand whether there is an upward, downward, or stable trend.

[0176] Arrange historical carbon emission data in chronological order, draw a time series graph, and observe the changing pattern of carbon emissions. Use linear regression, exponential smoothing, or moving average methods to build a trend model to predict future carbon emission trends. Identify abnormal points in the data (such as sudden increases or decreases in emissions), analyze their causes, and make data corrections. Extract trend data such as carbon emission growth rate, decline rate, etc. based on the model's prediction results.

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

[0178] Specifically, obtain carbon emission data of enterprises in the same industry from the industry database. The data includes information such as 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 to facilitate 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 input for cluster analysis. Use K-means, hierarchical clustering and other methods to cluster 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 between the enterprise and the industry benchmark within the cluster group to which it belongs, and understand the gap between the enterprise's carbon emission level and the industry average. Determine the industry benchmark data of the cluster group to which the enterprise belongs, 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 according to the carbon emission baseline 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 effectiveness of future carbon emission reduction measures. Combining carbon emission trend data and industry benchmark deviation data, the company's preliminary carbon emission baseline is determined to provide 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 the carbon emission baseline data.

[0184] Specifically, it includes production-related data such as production plans, actual output, equipment operating rates, energy consumption records, and 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 to make it closer to the actual situation of the enterprise.

[0185] The preliminary carbon emission baseline is taken as the mean of the prior distribution, and a certain distribution variance is set to characterize the uncertainty of the baseline. The actual production activity data of the enterprise is used as the observation value, and the prior distribution is continuously updated to 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. The prior information is continuously updated to converge to the optimal carbon emission baseline value. The posterior mean after Bayesian optimization is used 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: 0.6799kg CO2 / yuan output value. Actual output increased by 10%. Energy consumption increased by 5%. Optimized baseline: 0.7kg CO2 / yuan output value (the optimized baseline value reflects the actual situation of output and energy consumption growth).

[0186] In the present invention, by jointly analyzing historical carbon emission data and current carbon emission data, the long-term trend of carbon emission of enterprises can be identified, including rising, falling or fluctuating patterns. Through cluster analysis of carbon emission trend data, enterprises can be compared with other enterprises in the industry with similar carbon emission characteristics to identify industry best practices. Cluster analysis can divide enterprises into benchmark clusters similar to their own carbon emission characteristics, avoiding simple average or general benchmark comparison methods, providing more accurate and segmented industry benchmark positioning, and providing reliable reference data for baseline setting. Benchmark deviation calculation can quantify the gap between enterprises and industry benchmarks, helping enterprises to clarify the difference between their own carbon emissions and the average or optimal level of the industry. By considering 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 where the baseline is too high or too low. Bayesian optimization can dynamically respond to changes in production activities, such as production load, equipment operating status, energy usage, etc., and adjust the carbon emission baseline in real time to avoid baseline inaccuracy caused by fluctuations in actual conditions.

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

[0188] Obtain industry carbon emissions benchmark data;

[0189] Specifically, obtain industry-wide carbon emission data from the government, industry associations, and third-party data providers, including information on carbon emissions, production scale, energy consumption, product types, etc. of different industries. Obtain carbon emission information of companies in the same industry from public documents such as annual corporate environmental reports and carbon emission reports. 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 industry carbon emission benchmark data and carbon emission trend data to obtain carbon emission enterprise type benchmark clustering data;

[0191] Specifically, cluster analysis is performed on enterprises according to the industry type to which they belong (such as manufacturing, service industry, agriculture, etc.), and the carbon emission characteristics of different types of enterprises are identified to form benchmark clustering data of enterprise types. Features related to enterprise types are selected, such as carbon emission intensity (kg CO2 / yuan output value), energy consumption ratio (electricity, gas, coal, etc.), main product types, production processes, etc. Different feature data are standardized to ensure that the weights of different features in cluster analysis are consistent. K-means, hierarchical clustering and other algorithms are used to perform clustering calculations based on enterprise type-related features. According to the clustering results, enterprises are divided into different types, such as high-carbon emission manufacturing industry and low-carbon emission service industry. The clustering results are represented by cluster centers (Centroid), and each cluster center represents the benchmark feature data of an enterprise type. Cluster 1: High-carbon emission manufacturing industry, with an average carbon emission intensity of 0.75kg CO2 / yuan output value, the main energy structure is coal and natural gas, and includes 50 enterprises. Cluster 2: Low-carbon emission service industry, with an average carbon emission intensity of 0.2kg CO2 / yuan of output value, and the main energy structure is electricity, including 30 companies.

[0192] 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.

[0193] Specifically, cluster analysis is performed according to the scale of enterprises (such as large, medium, and small enterprises), and the carbon emission characteristics of enterprises of different scales are identified to form enterprise scale benchmark clustering data. Select features related to enterprise scale, such as total emissions (tons of CO2 / year), output value (yuan / year), number of employees, and production capacity (annual output, etc.). Standardize the characteristics of enterprises of different scales to eliminate the impact caused by scale differences between different enterprises. Use algorithms such as K-means or density clustering (DBSCAN) to perform clustering calculations based on enterprise scale-related features. According to the clustering results, the enterprises are divided into different scale categories, such as large enterprises, medium-sized enterprises, and small enterprises. The clustering results are represented by cluster centers, and each cluster center represents the benchmark characteristic data of an enterprise scale type. Cluster A: large enterprises, with an average emission of 100,000 tons of CO2 / year, an annual output value of 500 million yuan, and 5,000 employees, including 20 enterprises. Cluster B: medium-sized enterprises, with an average emission of 30,000 tons of CO2 / year, an annual output value of 100 million yuan, 1,000 employees, and 50 companies. Cluster C: small enterprises, with an average emission of 5,000 tons of CO2 / year, an annual output value of 20 million yuan, 200 employees, and 100 companies.

[0194] In the present invention, by collecting industry carbon emission benchmark data, a reliable benchmark based on industry-wide data can be established to ensure that subsequent cluster analysis has a solid data foundation, and provide an accurate benchmark reference for cluster calculations of enterprise types and scales. Through enterprise type cluster calculations, enterprises are clustered and analyzed according to subdivided industry types (such as manufacturing, service industry, chemical industry, energy industry, etc.), which can effectively avoid the impact of carbon emission differences between different industries on benchmark calculations 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 impact of differences in enterprise scale can be eliminated, making benchmark comparisons 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, which represents the reference carbon emission level of the enterprise under normal production conditions. The carbon emission data is the actual carbon emission data of the enterprise at present or in a specific period, including energy consumption and transportation emissions in 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, carbon emission performance indicators are important parameters for measuring the effectiveness of corporate carbon emission management. They can include aspects such as emission reduction effect, energy efficiency level, and emission intensity. Multiple performance indicators are generated based on carbon emission comparison data to comprehensively evaluate the performance of companies in carbon emission management;

[0200] Select performance indicators based on 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 by unit 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, performing multi-criteria decision analysis based on the carbon emission performance indicator data to obtain carbon emission performance indicator selection data;

[0203] Specifically, since carbon emission performance indicators are diverse and may conflict with each other, it is necessary to use multi-criteria decision analysis methods (such as AHP, TOPSIS, etc.) to comprehensively weigh different indicators and select key performance indicators. Assign a weight to each performance indicator based on the company's strategic goals, policy requirements and actual conditions. 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 of different dimensions so that they are between 0 and 1 for easy comparison and calculation. Use the weighted comprehensive method or other multi-criteria decision-making methods to comprehensively score each performance indicator and 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 impact on the company's carbon emission management. Generate carbon emission performance indicator selection data, including the score, weight and comprehensive performance score of each indicator.

[0204] S34. Perform a multi-level performance evaluation on the carbon emission data based on the selected data according to the carbon emission performance indicators to obtain the carbon emission performance data.

[0205] Specifically, based on the selected key performance indicators, conduct a systematic assessment of carbon emission performance at different levels (such as enterprises, departments, projects, etc.) to identify weak links and outstanding performance in carbon emission management. Set performance evaluation standards at different levels, such as enterprise level, department level, project level, etc. Based on the actual carbon emission data at different levels and the selected key performance indicator data, 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, with a total score of 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] The carbon emission comparison data in the present invention can identify whether the carbon emissions of an enterprise exceed the predetermined baseline or are lower than the baseline, helping enterprises to promptly discover emission excesses or savings, take corresponding measures, and reduce carbon emission management risks. By analyzing the carbon emission comparison data, multi-dimensional performance indicators are generated, such as emission intensity, emission reduction rate, excess rate, compliance rate, etc., so that the carbon emission performance evaluation is more comprehensive and three-dimensional, and can better reflect the comprehensive performance of the enterprise's carbon emission management. Through multi-criteria decision analysis, the most critical indicators for the company's carbon emission management are identified among many performance indicators to ensure that the company can focus on the most important areas of carbon emission management and improve management efficiency. Multi-level evaluation can help companies accurately identify bottlenecks and weak links in carbon emission management, such as low carbon emission efficiency in a department or workshop, or low energy efficiency of a certain equipment. Companies can formulate targeted improvement measures based on this to improve overall performance.

[0220] Optionally, S4 includes:

[0221] S41. Calculate the carbon credit allocation amount based on the carbon emission performance data to obtain preliminary carbon credit allocation amount 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 for carbon credits, such as enterprise production, historical emissions, industry benchmarks, etc. Based on the carbon emission performance of the enterprise, preliminarily determine the carbon credit allocation for each enterprise or department. Set the initial carbon credit benchmark quota according to industry standards, government policies or internal regulations of the enterprise. For example, the benchmark quota for an industry is 100 tons of CO2 / 10,000 yuan of output value. Initial carbon credit allocation = benchmark credit × (1-performance deviation coefficient). 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 initial carbon credit allocation for each enterprise according to the formula, positively incentivize low-emission enterprises, and negatively incentivize high-emission enterprises. Generate a preliminary carbon credit allocation data table, including the carbon credit allocation for each enterprise, department or project.

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

[0224] Specifically, the supply and demand data of carbon credits in the market are obtained from carbon exchanges or government platforms, including information such as the total supply of carbon credits, total demand, transaction price, and transaction volume. Carbon credit supply data includes the carbon credits owned by enterprises, free quotas allocated by the government, and excess credits that can be traded. Carbon credit demand data includes the carbon credits that enterprises need to purchase for exceeding emission standards and the demand of potential buyers.

[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 a preliminary carbon credit allocation, enterprises can clearly understand the relationship between their emission reduction efforts and the benefits they obtain in the carbon market, and the quantification method improves the transparency and fairness of carbon credit allocation. Obtaining market carbon credit supply and demand data can help enterprises understand the supply and demand of carbon credits in the current market, such as the total amount of carbon credits, market transaction prices, and the needs of buyers and sellers, etc., and provide key market background information for the company's carbon credit allocation strategy. By combining market supply and demand data with the company's preliminary carbon credit allocation data and calculating the carbon credit allocation coefficient, a balance can be found between the company's emission reduction performance and market supply and demand conditions, effectively avoiding the unfair distribution caused by simply based on emission reduction performance or market supply and demand. After considering the market supply and demand conditions, the company's preliminary allocation and the allocation coefficient, the carbon credit allocation is accurately adjusted to ensure that the carbon credit allocation obtained by each company can truly reflect its performance level and market supply and demand conditions, and improve the accuracy of the allocation.

[0233] The purpose of the present invention is to use Internet of Things sensors and intelligent data acquisition systems to obtain carbon emission data from multiple sources such as production equipment, energy consumption systems, and transportation tools in real time. Using the company's historical carbon emission data, industry benchmark data, and actual production activity data, combined with carbon emission trend analysis and industry benchmark cluster analysis, the carbon emission baseline is scientifically set. Through the Bayesian optimization method, the actual production activity data of the enterprise and the preliminary carbon emission baseline data are dynamically adjusted, so that the baseline can respond to the volatility and policy changes of the company's production activities in real time, and improve 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 the company's carbon emission performance data, but also takes into account the supply and demand of market carbon credits. By combining the preliminary allocation amount and market supply and demand data to calculate the allocation coefficient, the company's carbon credit allocation can more accurately reflect its relative position in the market and carbon emission performance.

[0234] Therefore, from any point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the attached application documents rather than the above description, and it is intended that all changes falling within the meaning and scope of equivalent elements of the application documents are included in the present invention.

[0235] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may 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 the embodiments shown herein, but should conform to the widest 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 the carbon credit allocation amount data.

2. The method according to claim 1, characterized in that 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 data is obtained by calculating carbon emissions based on production energy consumption data, production equipment loss data, transportation carbon emission data and energy system consumption screening data.

3. The method according to claim 2, 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.

4. The method according to claim 2, characterized in that: 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; Energy consumption redundancy is removed based on the energy consumption feature similarity data to obtain energy system consumption screening data.

5. The method according to claim 2, 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.

6. The method according to claim 1, characterized in that S2 include: 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; The actual production activity data of the enterprise is obtained, and Bayesian optimization is performed based on the actual production activity data of the enterprise and the preliminary carbon emission baseline data to obtain the carbon emission baseline data.

7. The method according to claim 6, characterized in that 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.

8. The method according to claim 1, characterized in that S3 include: Carry out carbon emission comparison 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.

9. 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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