Cost reduction method and device based on carbon asset full life cycle management

Abstract

The invention belongs to the technical field of environmental protection control, and relates to a cost reduction method and device based on carbon asset full life cycle management, and the method comprises the steps: obtaining the carbon emission data converted from the cost input of each stage of an enterprise; the carbon emission data is analyzed; formulating a carbon emission list based on the analysis result of the carbon emission data, the carbon emission thermodynamic diagram and the carbon quota of the enterprise; predicting the carbon emission of the enterprise based on the carbon emission list; establishing an enterprise carbon management personal incentive mechanism based on the result of predicting the enterprise carbon emission; and preposed carbon sink design is carried out, so that the full-life-cycle income of carbon assets is improved. The preposed carbon sink design is emphasized, and carbon sink projects such as afforestation and energy conservation and emission reduction technology application are reasonably planned, so that the carbon emission of enterprises is effectively counteracted, the full-life-cycle income of carbon assets can be improved, and the win-win situation of economic benefits and environmental benefits is realized.

Description

Technical Field

[0001] This invention relates to the field of environmental control technology, and in particular to a cost reduction method and apparatus based on the full life cycle management of carbon assets. Background Technology

[0002] In the power transformer industry, cost reduction methods based on the full lifecycle management of carbon assets are currently lacking. The full lifecycle of carbon assets involves multiple stages, including carbon generation, emission, trading, and emission reduction. Enterprises need to plan and manage the entire process from product design, raw material selection, production, transportation, and storage to use and disposal to achieve optimal carbon asset allocation and effective cost reduction. However, most enterprises currently lack awareness in this area, lack systematic management systems and professional technical methods, and have failed to fully explore the cost reduction potential inherent in the full lifecycle management of carbon assets.

[0003] The acquisition and integration of carbon emission data from cost inputs at each stage of a company's entire lifecycle are fragmented and difficult, making it impossible to accurately correlate costs with carbon emissions. There is a lack of intuitive ways to present carbon emission distribution, making it difficult to develop a scientific inventory based on carbon quotas. The accuracy of carbon emission prediction models is insufficient, affecting the formulation of emission reduction strategies. Employees are not very motivated to participate in cost reduction and emission reduction. The design of carbon sinks is lagging behind, failing to fully utilize CCER benefits to improve the benefits throughout the entire lifecycle. These problems all restrict the effectiveness of cost reduction based on the lifecycle management of carbon assets. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a cost reduction method based on the full lifecycle management of carbon assets, employing the following technical solution: Obtain carbon emission data converted from cost inputs at each stage of a company's operations; The carbon emission data were analyzed. Based on the analysis of the carbon emission data, the carbon emission heat map, and the carbon quotas of enterprises, a carbon emission inventory is developed. Based on carbon emission inventories, predict corporate carbon emissions; Based on the results of predicting corporate carbon emissions, establish an individual incentive mechanism for corporate carbon management; Design carbon sinks in advance to improve the returns of carbon assets throughout their entire life cycle.

[0005] Preferably, the step of obtaining carbon emission data converted from the cost inputs of an enterprise at each stage specifically includes: Raw data from procurement, production, and logistics processes are collected through IoT sensors installed on carbon asset devices. By using blockchain sharding, blockchain selection is performed to verify the authenticity of the original data and carbon asset emission responsibilities are assigned by department and production line.

[0006] Preferably, the step of analyzing the carbon emission data specifically includes: Data verification is conducted using an authoritative verification mechanism and a multi-node verification method. By combining federated learning with privacy-preserving computation and building a virtual mapping with digital twins, only the computation results are uploaded to the blockchain after 80% of the data analysis has been completed. A carbon emission model is built based on the uploaded data to dynamically display the carbon intensity of each stage. To conduct audits and trace back, query carbon data snapshots of any historical node and compare the differences between blockchain-based evidence and paper ledgers.

[0007] Preferably, the step of developing a carbon emission inventory based on the analysis results of the carbon emission data, the carbon emission heat map, and the enterprise's carbon quota specifically includes: Normalize corporate carbon cost data to intensity per unit output value ,in, Let be the carbon emission intensity (tCO2 / ten thousand yuan) of the i-th node. Value added to the enterprise (ten thousand yuan); Interpolation is performed using IDW, with color gradients representing high-medium-low emission zones; By overlaying quota data with heat maps, the spatial matching degree between quotas and actual emissions can be analyzed. ,in, Let i be the quota for node i. As an industry benchmark, For adjustment coefficients; The carbon emission intensity and deficit of enterprises are calculated by designing a structured inventory based on emission sources. The formula is: carbon emission intensity = total emissions / total output value, net emissions = actual emissions - quota. A negative net emission value indicates a quota surplus, and a positive net emission value indicates over-emission.

[0008] Preferably, the step of predicting the enterprise's carbon emissions based on the carbon emission inventory specifically includes: Select a base year, analyze carbon emission trends in past preset years, and identify driving factors; A predictive model was constructed using an ARIMA-LSTM hybrid model, and multiple policy scenarios, namely carbon tax gradient testing, were set up. Backtesting was conducted using historical data, and the parameters in the model were adjusted to make the prediction error less than 10%. Tornado diagrams were used to test the impact of variables on the results. Monte Carlo simulations are used to quantify data uncertainty.

[0009] Preferably, the step of establishing an individual incentive mechanism for corporate carbon management based on the prediction of corporate carbon emissions specifically includes: Based on the prediction of corporate carbon emissions, the carbon footprint of employees' work and innovation scenarios is modeled by combining an integrated OA system with IoT sensors. Carbon credit rules are designed based on the industry's per capita carbon emission levels. A tiered bonus and green option pool incentive system is adopted, and rules are set for obtaining corporate carbon trading revenue sharing.

[0010] Preferably, the step of designing a pre-emptive carbon sink to improve the life-cycle benefits of carbon assets specifically includes: Integrate the low-carbon supply chain and set rules for suppliers to participate in carbon sink projects, requiring suppliers to provide carbon sink forests for every ton of product they produce; Establish a carbon sink-energy efficiency offsetting mechanism, where for every 1kW reduction in transformer no-load loss, the corresponding carbon sink demand is reduced by 5 acres. Develop carbon sink projects in accordance with CCER standards and reserve 10% of carbon credits to cope with future carbon tariffs on product exports; Design a blockchain traceability system to link the carbon sequestration certificates of each transformer. To address the aforementioned technical problems, this invention also provides a cost reduction device based on carbon asset lifecycle management, employing the following technical solution, including: The acquisition module is used to acquire carbon emission data converted from the cost inputs of an enterprise at various stages; An analysis module is used to analyze the carbon emission data; The module is used to develop a carbon emission inventory based on the results of analysis of the carbon emission data, carbon emission heat map, and corporate carbon quotas. The forecasting module is used to predict corporate carbon emissions based on carbon emission inventories. Establish a module to create an individual incentive mechanism for corporate carbon management based on the prediction results of corporate carbon emissions; The design module is used for pre-carbon sink design to improve the full life cycle benefits of carbon assets.

[0011] To address the aforementioned technical problems, the present invention also provides a computer device that employs the technical solution described below, comprising a memory and a processor. The memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the aforementioned cost reduction method based on carbon asset lifecycle management.

[0012] To address the aforementioned technical problems, the present invention also provides a computer-readable storage medium, which employs the technical solution described below. The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the aforementioned cost reduction method based on the full life cycle management of carbon assets.

[0013] Compared with existing technologies, this invention has the following main advantages: First, by accurately measuring the carbon emission data converted from the cost input of enterprises at each stage, it lays a solid foundation for subsequent analysis; in-depth analysis of this data reveals carbon emission hotspots and potential emission reduction space, and by combining carbon emission heat maps and enterprise carbon quotas, a detailed carbon emission inventory is formulated, clarifying emission reduction targets and paths; predictive models are used to scientifically predict enterprise carbon emissions, providing a basis for formulating forward-looking emission reduction strategies; based on the prediction results, an individual incentive mechanism for enterprise carbon management is established to stimulate employees' enthusiasm and creativity in participating in carbon emission reduction, forming a positive emission reduction atmosphere from top to bottom; in addition, it emphasizes the design of carbon sinks in advance, and through the rational planning of carbon sink projects, such as afforestation and the application of energy-saving and emission-reduction technologies, it can not only effectively offset enterprise carbon emissions, but also improve the full life cycle benefits of carbon assets, achieving a win-win situation for economic and environmental benefits. Attached Figure Description

[0014] Figure 1 This is a flowchart of an embodiment of the cost reduction method based on the full life cycle management of carbon assets according to the present invention; Figure 2 This is a schematic diagram of an embodiment of the cost reduction device based on the full life cycle management of carbon assets according to the present invention; Figure 3 This is a schematic diagram of the structure of an embodiment of the computer device of the present invention. Detailed Implementation

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the specification is for the purpose of describing particular embodiments only and is not intended to limit the invention; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings are used to distinguish different objects and not to describe a particular order.

[0016] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0017] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0018] It should be noted that the cost reduction method based on carbon asset lifecycle management provided in the embodiments of the present invention is generally executed by a server / terminal device, and correspondingly, the cost reduction device based on carbon asset lifecycle management is generally installed in the server / terminal device.

[0019] It should be understood that the number of terminal devices, networks, and servers is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be used.

[0020] Example 1 Please refer to Figure 1 The flowchart illustrates an embodiment of the cost reduction method based on carbon asset lifecycle management according to the present invention. The cost reduction method based on carbon asset lifecycle management includes the following steps: Step S1: Obtain carbon emission data converted from the cost inputs of the enterprise at each stage.

[0021] In this embodiment, the electronic devices (e.g., servers / terminal devices) running on the cost reduction method based on carbon asset lifecycle management can receive cost reduction requests based on carbon asset lifecycle management via wired or wireless connections. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAXX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future-developed wireless connection methods.

[0022] In this embodiment, step S1, obtaining carbon emission data converted from the cost input of the enterprise at each stage, may specifically include the following steps: S11 collects raw data from procurement, production, and logistics processes through IoT sensors installed on carbon asset devices.

[0023] IoT sensors are devices that can sense and respond to environmental changes and convert these changes into transmittable data. They play a crucial role in the entire lifecycle management of carbon assets. Deployed across various stages such as procurement, production, and logistics, they collect real-time raw data related to carbon emissions, such as energy consumption, material usage, and equipment operating status.

[0024] IoT sensors connect to a central data processing system wirelessly or via wired connections to ensure the real-time nature and accuracy of the data.

[0025] The application of IoT sensors enables enterprises to have a comprehensive and detailed understanding of their carbon emissions, providing data support for subsequent cost control and emission reduction strategies.

[0026] The formula for calculating carbon emissions at a single node is: ,in, Let t be the total carbon emissions of node i (tCO2e). This represents the k-th type of direct activity data for node i (e.g., electricity consumption in kWh). For the kth type of emission factor, For node i, the m-th type of supply chain data (such as the tonnage of raw materials purchased). For range 3 (where 3 indicates carbon emissions occurring outside the scope of the enterprise's direct control), the emission factor is... This represents the total number of direct activity types. This represents the total number of supply chain types.

[0027] S12, using blockchain sharding, selects a blockchain, verifies the authenticity of the original data, and assigns carbon asset emission responsibilities by department and production line.

[0028] Blockchain sharding can improve the performance and scalability of blockchain systems. In traditional blockchain networks, all nodes need to process and store all transaction data, leading to performance bottlenecks and scalability challenges. Sharding technology, by dividing all network nodes into multiple subsets (shards), with each shard responsible for processing a portion of the transaction data, achieves parallel processing and distributed data storage.

[0029] The application of blockchain sharding is mainly reflected in the verification of the authenticity of original data and the allocation of carbon asset emission responsibility. Through sharding technology, enterprises can store carbon emission data from different departments and production lines in different shards, with each shard's data being verified and updated by the corresponding nodes. This approach not only improves data processing speed but also enhances data security and credibility.

[0030] Meanwhile, leveraging the immutability and transparency of blockchain, companies can ensure that each department and production line is responsible for the authenticity and accuracy of its carbon emission data. Any data tampering will be quickly identified and recorded by the system, effectively preventing data fraud and misconduct.

[0031] The methods employed in steps S11 and S12 complement and reinforce each other. IoT sensors provide massive amounts of raw data, offering a rich data source for the blockchain system; while the blockchain sharding method ensures the authenticity and credibility of this data, providing a solid data foundation for enterprises to implement cost control and emission reduction strategies.

[0032] Step S2: Analyze the carbon emission data.

[0033] In this embodiment, step S2, analyzing the carbon emission data, may specifically include the following steps: S21 employs an authoritative verification mechanism and adopts a multi-node verification method for data verification.

[0034] Proof of Authority (PoA) is a blockchain consensus mechanism that verifies the validity of transactions or data through pre-selected nodes with a certain level of authority and credibility. Compared to Proof of Work (PoW) or Proof of Stake (PoS), PoA offers faster transaction processing speeds and lower energy consumption, making it suitable for scenarios requiring high transaction speeds and where participants are relatively trustworthy. In carbon emission data verification, PoA ensures the speed and accuracy of the data verification process while reducing unnecessary energy consumption.

[0035] Multi-node verification refers to the process of cross-validating data using multiple independent and authoritative nodes during the data verification process. This approach effectively prevents data errors caused by the failure or tampering of a single node, improving data reliability and security. In the verification of carbon emission data, multi-node verification means that each piece of data will undergo rigorous review by multiple authoritative nodes to ensure its authenticity and integrity, providing a solid foundation for subsequent data analysis.

[0036] Step S21 integrates blockchain technology and an authoritative proof mechanism to achieve multi-node verification. Each authoritative node runs the same verification algorithm to independently verify the submitted carbon emission data. Once the data is confirmed as valid by a majority of nodes, it is recorded on the blockchain, forming an immutable data chain. This process not only enhances the credibility of the data but also provides a reliable basis for subsequent data tracking and auditing.

[0037] S22 combines federated learning with privacy-preserving computation and uses digital twins to build a virtual mapping. After 80% of the computation is completed, only the computation results are uploaded to the blockchain. Based on the uploaded data, a carbon emission model is built to dynamically display the carbon intensity at each stage.

[0038] Federated learning is a distributed machine learning framework that allows multiple stakeholders to collaboratively train a model without sharing the original data, thus protecting data privacy. Privacy-preserving computation, on the other hand, is a system of technologies designed to protect individual privacy when processing and analyzing data. Combining federated learning with privacy-preserving computation can enable cross-organizational sharing and analysis of carbon emission data while protecting sensitive corporate information, thereby promoting the optimization of carbon reduction strategies.

[0039] Digital twin technology creates virtual mirror images of the physical world, enabling real-time monitoring, simulation, and optimization of physical systems. In carbon emission management, digital twin technology can build virtual models of an enterprise's carbon emission system, simulating the effects of different emission reduction measures and providing support for decision-making. Combined with federated learning and privacy-preserving computation, it can construct carbon emission models based on multi-source data while protecting data privacy, dynamically displaying the carbon intensity at each stage and helping to identify emission reduction potential.

[0040] Step S22 utilizes federated learning and privacy-preserving computation technologies, combined with digital twin technology, to achieve in-depth analysis and model building of carbon emission data. After 80% of the computation is completed, only the results are uploaded to the blockchain, protecting the security of the original data while achieving efficient data utilization. The carbon emission model built based on this data can reflect a company's carbon footprint in real time, providing a scientific basis for developing targeted emission reduction strategies, thereby effectively reducing emission reduction costs.

[0041] S23, conduct audit traceability, query carbon data snapshots of any historical node, and compare the differences between blockchain evidence and paper ledgers.

[0042] Audit traceability is a crucial means of ensuring the accuracy and compliance of carbon emission data. It allows companies to review historical data at any time, verifying its integrity and authenticity, and providing legal safeguards for carbon asset management.

[0043] The formula for cross-node carbon flow tracing is: ,in, This represents the carbon emissions from subject i to subject j. This represents the trade frequency coefficient between i and j. Let represent the sum of the association coefficients between i and all K related entities (including j). This represents the carbon emissions from the export of subject i. This represents the amount of carbon emissions imported by subject j.

[0044] The carbon cost conversion formula is: ,in, Let be the carbon cost of node i (kgCO2 / yuan). The cost (in yuan) incurred by node i. Let be the carbon emissions of node i.

[0045] The dynamic carbon footprint allocation formula is: ,in, The unit carbon footprint of product f (kgCO2e / yuan). The number of products f produced by node i. Let f be the life cycle coefficient of product f. The number of nodes The number of products g produced by node i.

[0046] Step S23 utilizes blockchain technology to achieve data auditing and traceability. Each piece of data on the blockchain is timestamped and has a unique hash value, ensuring the immutability and traceability of the data. Enterprises can query carbon data snapshots at any historical node, compare the differences between blockchain-based evidence and paper ledgers, promptly identify and correct data errors, and ensure the accuracy and compliance of carbon asset management. This process not only improves the transparency of data management but also enhances the enterprise's credibility and sense of responsibility.

[0047] Step S3: Based on the analysis results of the carbon emission data, the carbon emission heat map, and the carbon quotas of enterprises, a carbon emission inventory is formulated.

[0048] In this embodiment, step S3, which involves developing a carbon emission inventory based on the analysis results of the carbon emission data, the carbon emission heat map, and the enterprise's carbon quota, may specifically include the following steps: S31 normalizes corporate carbon cost data to intensity per unit of output. ,in, Let be the carbon emission intensity (tCO2 / ten thousand yuan) of the i-th node. Let be the carbon cost of node i (kgCO2 / yuan). Value added to the enterprise (ten thousand yuan).

[0049] Normalization is a fundamental task in data mining. It transforms data with different dimensions to the same scale, facilitating comparison and analysis. Here, the purpose of normalization is to correlate a company's carbon cost data with its output value, thereby deriving the carbon emission intensity per unit of output value.

[0050] Collect carbon cost data and enterprise value-added data from enterprises. Carbon cost data may include costs directly related to carbon emissions, such as energy consumption and carbon emission credit purchase fees; enterprise value-added data reflects the enterprise's production scale and economic efficiency. Then, use the above formula to calculate the carbon intensity per unit of output.

[0051] Normalizing corporate carbon cost data to intensity per unit of output helps companies understand their carbon emission efficiency more intuitively. By comparing the carbon emission intensity of different production stages, companies can identify high-emission areas, providing a basis for formulating subsequent emission reduction strategies. Furthermore, carbon emission intensity per unit of output is also an important indicator for measuring a company's environmental performance, helping it establish a green brand image in the market.

[0052] S32 uses IDW for interpolation and uses color gradients to represent high-medium-low emission zones.

[0053] Inverse Distance Weighted (IDW) interpolation is a simple spatial interpolation method that uses the distance between the interpolation point and the sample points as weights for a weighted average, assigning greater weight to sample points closer to the interpolation point. This method assumes that closer values ​​are more relevant than farther values, making it very effective when processing spatially distributed data. In carbon emission management, IDW can be used to estimate the carbon emission intensity of unknown areas, thereby providing a more comprehensive understanding of a company's carbon emission situation.

[0054] The collected carbon emission data from various regions of the enterprises are used as sample points, and the IDW interpolation method is used to calculate the carbon emission intensity of unknown regions. The core idea of ​​the IDW interpolation method is that the sample points closer to the interpolation point are assigned greater weights, so the carbon emission intensity of unknown regions can be estimated by weighted averaging.

[0055] To visually represent the spatial distribution of carbon emission intensity, a color gradient can be used to indicate high-, medium-, and low-emission zones. For example, red can represent high-emission zones, yellow medium-emission zones, and green low-emission zones. This allows companies to clearly see the spatial distribution of their carbon emission intensity, providing visual support for developing subsequent emission reduction strategies.

[0056] Using IDW (Indexed Waveform) for interpolation and representing high-, medium-, and low-emission zones with color gradients helps companies gain a more comprehensive understanding of their carbon emissions. Through visualization, companies can intuitively see which regions have high carbon emission intensity and which have low intensity, enabling them to develop targeted emission reduction strategies. Furthermore, this visualization method helps companies showcase their environmental achievements to stakeholders, enhancing their environmental image.

[0057] S33, overlaying quota data with heat maps to analyze the spatial matching degree between quotas and actual emissions. ,in, Let i be the quota for node i. As an industry benchmark, This is for adjusting the coefficient.

[0058] The government authorities verify and collect carbon quota data from enterprises based on their historical emissions and industry characteristics. This carbon quota data is then overlaid with a carbon emissions heat map, and the spatial matching degree between quotas and actual emissions is analyzed by comparing the spatial distribution of quota data and actual emissions data.

[0059] To achieve this goal, Geographic Information System (GIS) software can be used. GIS software has powerful spatial analysis capabilities, allowing for the overlay, comparison, and analysis of data from different sources. Through GIS software, companies can intuitively see the spatial matching between quota data and actual emission data, providing a spatial basis for subsequent emission reduction strategies.

[0060] Overlaying quota data with heat maps and analyzing the spatial match between quotas and actual emissions helps companies more accurately understand the relationship between their carbon emissions and quotas. By comparing the spatial distribution of quotas and actual emissions, companies can identify which regions have emissions approaching or exceeding quota limits, and which regions still have significant room for emission reduction. This helps companies develop targeted emission reduction strategies, ensuring cost reduction and efficiency improvement while meeting quota requirements.

[0061] S34. The inventory is structured according to the emission sources to calculate the carbon emission intensity and deficit of enterprises. The formula is: carbon emission intensity = total emissions / total output value, net emissions = actual emissions - quota. A negative net emission value indicates a quota surplus, and a positive net emission value indicates over-emission.

[0062] Identify the company's main emission sources, such as energy consumption, industrial production processes, and transportation. Collect detailed emission data for each source, including emission volume and intensity. Construct a carbon emission inventory based on the emission data, which should include detailed information on each emission source and its contribution to total emissions.

[0063] Total emissions can be obtained by summing the emissions from each emission source in the emissions inventory; total output value reflects the production scale and economic benefits of enterprises; actual emissions are the actual carbon emissions of enterprises; and quotas are the carbon emissions that enterprises are allowed to emit as approved by the government authorities. By calculating carbon emission intensity and net emissions, enterprises can understand their carbon emission efficiency and whether they are exceeding emission limits.

[0064] Designing a structured carbon emission inventory based on emission sources and calculating corporate carbon intensity and net emissions helps companies more accurately understand their carbon emission situation. By constructing a carbon emission inventory, companies can clearly see the contribution of each emission source to the total emissions, thus enabling them to develop targeted emission reduction strategies. Simultaneously, calculating carbon intensity and net emissions helps companies assess their environmental performance and emission reduction effectiveness, providing a basis for setting more scientific emission reduction targets. Furthermore, carbon emission inventories and net emissions calculations are also important bases for companies to participate in carbon market trading and seek green financing.

[0065] Step S4: Based on the carbon emission inventory, predict the carbon emissions of enterprises.

[0066] In this embodiment, step S4, predicting the enterprise's carbon emissions based on the carbon emission inventory, may specifically include the following steps: S41. Select a base year and analyze carbon emission trends in past preset years to identify driving factors.

[0067] Driving factors refer to the key factors that affect changes in carbon emissions. These factors can include energy structure, production efficiency, technological processes, equipment energy efficiency, market size, and policy guidance.

[0068] Data collection and processing: Collect carbon emission data, energy consumption data, production and operation data of enterprises over the past 3-5 years (preset year) to ensure the accuracy and completeness of the data.

[0069] Trend analysis: Using time series analysis techniques, such as moving averages and exponential smoothing, we can perform trend analysis on carbon emission data to identify long-term trends and short-term fluctuations in carbon emissions.

[0070] Driver identification: Through statistical methods such as correlation analysis and regression analysis, we identify the driving factors that are significantly related to carbon emissions. These factors will serve as important input variables for subsequent prediction models.

[0071] Through step S41, companies can gain a clear understanding of their own carbon emission trends, identify key areas for emission reduction and potential areas for optimization, and lay a solid foundation for subsequent steps.

[0072] S42, construct a prediction model, adopt an ARIMA-LSTM hybrid model, and set up multiple policy scenarios, namely carbon tax gradient test.

[0073] The ARIMA (Autoregressive Integrated Moving Average) model is a statistical time series forecasting model suitable for capturing the long-term dependencies and periodicity of time series data. The LSTM (Long Short-Term Memory) model is a neural network-based model capable of handling nonlinear relationships and short-term fluctuations in time series data. The ARIMA-LSTM hybrid model combines the advantages of both, capturing both long-term trends and handling short-term fluctuations, thereby improving the accuracy and stability of forecasts.

[0074] Model Construction: The ARIMA model is used to preprocess carbon emission data to eliminate non-stationarity and extract trend components and residual sequences. Then, the residual sequences are used as input features for the LSTM model for training and optimization. Finally, the prediction results of the ARIMA model are combined with the prediction residuals of the LSTM model to obtain the final prediction result.

[0075] Multiple policy scenario settings: Multiple forecasting scenarios are set up based on different carbon tax policy gradients. By adjusting the parameters in the model, carbon emission changes under different policy scenarios are simulated, providing decision support for enterprises' carbon asset management.

[0076] Through step S42, the ARIMA-LSTM hybrid model can significantly improve the accuracy of carbon emission predictions, helping companies better cope with the uncertainties brought about by policy changes. At the same time, the setting of multiple policy scenarios allows companies to flexibly adjust under different policy environments and optimize their carbon asset management strategies.

[0077] S43. Use historical data for backtesting, adjust the parameters in the model to make the prediction error <10%, and use tornado diagrams to test the impact of variables on the results.

[0078] Backtesting the prediction model using historical data and adjusting model parameters (such as the order of the ARIMA model, the number of layers in the LSTM model, the number of hidden units, and the learning rate) to keep the prediction error within 10% is a crucial step in ensuring the accuracy of the model's predictions.

[0079] A tornado diagram is a sensitivity analysis tool used to illustrate the degree of influence of different variables on forecast results. By using a tornado diagram, it's easy to see which variables have the greatest impact on carbon emission forecasts, thus helping companies identify key areas for emission reduction.

[0080] Historical data backtesting: The model is backtested using carbon emission data from the past few years, and its predictive performance is evaluated through methods such as cross-validation. Model parameters are adjusted based on the prediction error until the error is controlled within an acceptable range.

[0081] Tornado Mapping: Based on the predictive model, the sensitivity of different variables to the predicted carbon emissions is calculated, and a tornado map is generated. Through the tornado map, companies can clearly understand which variables have the greatest impact on carbon emissions, thereby developing targeted emission reduction strategies.

[0082] Step S43 ensured the accuracy and stability of the prediction model through backtesting with historical data and parameter adjustments. Meanwhile, the application of tornado maps helped companies identify key emission reduction areas, providing strong support for subsequent emission reduction actions.

[0083] S44 uses Monte Carlo simulation to quantify data uncertainty.

[0084] Monte Carlo simulation is a numerical computation method guided by probability and statistics theory. It uses random sampling and statistical methods to numerically simulate problems. In carbon emission prediction, Monte Carlo simulation can be used to quantify the uncertainty of data and assess the reliability of prediction results.

[0085] Model building and parameter setting: Based on the prediction model, a Monte Carlo simulation model is built. Parameters such as the number of simulations and the random number generation algorithm are set to ensure the accuracy and reliability of the simulation results.

[0086] Uncertainty Quantification: Monte Carlo simulations generate a large number of carbon emission predictions. Statistical analysis of these results yields indicators such as the expected value, variance, and confidence interval of carbon emissions, thereby quantifying the uncertainty of the data.

[0087] Risk Assessment and Decision Support: Based on the results of Monte Carlo simulations, risk assessments are conducted. Risk factors that could lead to significant changes in carbon emissions are identified, and corresponding risk response strategies are developed. Simultaneously, decision support is provided for corporate carbon asset management, helping companies optimize their emission reduction strategies and investment portfolios.

[0088] Step S44, through the application of Monte Carlo simulation, enables companies to quantify the uncertainties in carbon emission forecasting, providing strong support for risk assessment and decision-making. This helps companies make more informed decisions in the face of uncertainty, optimize carbon asset management strategies, and reduce emission reduction costs.

[0089] Step S5: Based on the results of predicting corporate carbon emissions, establish an individual incentive mechanism for corporate carbon management.

[0090] In this embodiment, step S5, establishing an individual incentive mechanism for corporate carbon management based on the prediction results of corporate carbon emissions, may specifically include the following steps: S51, based on the prediction of corporate carbon emissions, uses an integrated OA system combined with IoT sensors to model the carbon footprint of employee work scenarios and innovation scenarios.

[0091] Intelligent data acquisition: By deploying IoT sensors, such as temperature and humidity sensors and energy consumption monitoring sensors, real-time energy consumption data of employee work areas and innovation projects is collected. This data includes, but is not limited to, electricity consumption, water resource usage, and waste generation, and forms the basis for carbon footprint calculation.

[0092] OA System Integration: Integrating the collected data into the enterprise's OA (Office Automation) system. As the enterprise's information management center, the OA system can efficiently process, store, and analyze this data, facilitating subsequent carbon footprint calculation and modeling.

[0093] Utilizing carbon footprint calculation models: Based on collected data, carbon footprint calculation models (such as IPCC guidelines or industry-specific carbon footprint calculation tools) are used to quantify the carbon emissions from employee work and innovation scenarios. This model considers various factors, including carbon emission factors from different energy types and employee behavior patterns, to ensure the accuracy of the calculation results.

[0094] 3D modeling and visualization: Use 3D modeling software (such as Rhino, ArcGIS, etc.) to transform carbon footprint data into intuitive 3D models or dynamic data dashboards. These models not only show the spatial distribution of carbon emissions, but also help employees better understand their own carbon footprint, thereby stimulating their awareness of emission reduction.

[0095] The implementation of step S51 has the following effects: it can improve data accuracy - by collecting data in real time through IoT sensors, errors caused by manual reporting are avoided, and the accuracy of carbon footprint calculation is improved; it can optimize resource allocation - by modeling the carbon footprint of employees' work scenarios and innovation scenarios, enterprises can identify high-emission areas and links, thereby optimizing resource allocation and reducing carbon emissions.

[0096] S52, referencing the industry's per capita carbon emission level, designs carbon credit rules.

[0097] Businesses need to collect and analyze carbon emission data for their industry, especially per capita carbon emissions. This data can be obtained through industry reports, government statistics, or third-party research institutions.

[0098] Based on the industry's average carbon emissions per employee, a formula for calculating carbon credits is designed. This formula should reflect the difference in an employee's carbon emissions relative to the industry average and provide corresponding credit rewards or penalties.

[0099] Develop a points management system to record, calculate, and display employees' carbon credits. This system should seamlessly integrate with the OA (Office Automation) system to ensure real-time data updates and accuracy.

[0100] The implementation of step S52 has the following effects: establishing a fair mechanism - by designing carbon credit rules with reference to the industry's per capita carbon emission level, the fairness and rationality of the mechanism are ensured, and the sense of unfairness among employees is avoided; stimulating emission reduction motivation - as a quantifiable emission reduction result, carbon credits can stimulate employees' emission reduction motivation and encourage them to take more low-carbon behaviors in their daily work.

[0101] S53 adopts a tiered bonus and green option pool incentive approach, and sets rules for obtaining corporate carbon trading revenue sharing.

[0102] A tiered bonus scheme will be designed based on employees' carbon credits. The bonus amount will increase as carbon credits accumulate, incentivizing employees to continue reducing emissions.

[0103] A green option pool will be established, with a certain percentage of the company's carbon trading revenue used as option rewards, to be awarded to employees who demonstrate outstanding performance in carbon management. These options can be converted into cash or company stock within a certain period in the future.

[0104] Detailed profit-sharing rules should be established, clearly defining the conditions, proportions, and methods by which employees receive carbon trading revenue. These rules should be open and transparent to ensure that employees' rights are protected.

[0105] By integrating carbon credits and profit-sharing rules with the company's performance management system, functions such as automated calculation and distribution of bonuses and stock options can be achieved.

[0106] The implementation of step S53 has the following effects: Strengthening emission reduction effectiveness – Tiered bonuses and green option pools can significantly enhance employees' motivation to reduce emissions, promoting the effectiveness of corporate carbon management; Improving employee satisfaction – By sharing carbon trading revenue, employees can directly feel the economic returns from their emission reduction behavior, thereby increasing their satisfaction and loyalty; Promoting corporate development – ​​The carbon trading revenue-sharing mechanism not only helps reduce the company's carbon emission costs but also enhances the company's environmental image and brand value, bringing long-term development opportunities to the company.

[0107] Step S6 involves designing a carbon sink in advance to improve the returns of carbon assets throughout their entire life cycle.

[0108] In this embodiment, step S6, designing a pre-carbon sink to improve the full life-cycle benefits of carbon assets, may specifically include the following steps: S61 integrates the low-carbon supply chain and sets rules for suppliers to participate in carbon sink projects, requiring suppliers to provide carbon sink forests for every ton of product.

[0109] In practice, for example, suppliers can be required to participate in carbon sink projects, and rules can be set that suppliers must provide carbon sink forests for every ton of product they produce.

[0110] Carbon emission accounting: Use carbon emission accounting tools to accurately calculate the carbon emissions of suppliers, ensuring the accuracy and comparability of the data.

[0111] Design and management of carbon sink forests: Collaborate with forestry agencies to design forest planting and management programs that meet carbon sink requirements, ensuring that carbon sink forests can effectively absorb carbon dioxide.

[0112] Supply chain collaborative management: Through the supply chain management system, the carbon emissions and carbon sink forest planting of suppliers are tracked in real time to ensure that both parties comply with the agreement.

[0113] Step S61 can reduce carbon emissions in the supply chain: By integrating a low-carbon supply chain, companies can encourage suppliers to take emission reduction measures, thereby reducing the overall carbon emission level of the supply chain; it can increase carbon asset returns: Carbon sink forests planted by suppliers can not only offset their carbon emissions, but can also be traded as carbon assets, bringing additional revenue to companies; it can enhance brand competitiveness: Taking proactive carbon management measures helps companies improve their brand image and enhance the confidence of consumers and investors.

[0114] S62 establishes a carbon sink-energy efficiency offsetting mechanism, whereby a reduction of 1kW in transformer no-load loss corresponds to a reduction of 5 acres in carbon sink demand.

[0115] Energy efficiency monitoring and optimization: Using intelligent sensors and monitoring systems, the energy efficiency indicators of transformers, such as no-load loss and load loss, are monitored in real time.

[0116] Energy efficiency upgrades: Based on monitoring results, energy efficiency upgrades are carried out on transformers, such as using energy-saving transformers and optimizing operating methods.

[0117] Calculate carbon sequestration demand: Based on the reduction in transformer energy consumption after energy efficiency upgrades, calculate the corresponding reduction in carbon sequestration demand.

[0118] Step S62 can improve energy efficiency: Through energy efficiency monitoring and optimization, enterprises can promptly identify and resolve energy efficiency issues, thereby improving equipment operating efficiency; reduce carbon sequestration demand: Energy efficiency upgrades can reduce equipment energy consumption, thereby reducing the corresponding carbon sequestration demand and lowering the enterprise's carbon asset management costs; and promote technological innovation: Establishing a carbon sequestration-energy efficiency hedging mechanism can incentivize enterprises to continuously innovate and develop more efficient and environmentally friendly equipment and technologies.

[0119] S63, develop carbon sink projects according to CCER standards, and reserve 10% carbon credits to cope with future carbon tariffs on product exports.

[0120] CCER (China Certified Emission Reduction) is the abbreviation for China's voluntary certified emission reductions. It is the process of quantifying, verifying, and selling the carbon dioxide absorbed by greenhouse gas emission reduction projects (such as renewable energy, forestry carbon sinks, and methane utilization). The main functions of CCER are as follows: promoting emission reduction: through market mechanisms, incentivizing enterprises to take emission reduction measures and reduce greenhouse gas emissions; increasing revenue: enterprises can trade CCERs generated from emission reduction projects to obtain additional revenue; and responding to policy changes: reserving CCERs can provide a buffer for enterprises to cope with possible future carbon tariff policies.

[0121] Project identification and design: Based on CCER requirements, identify projects that meet emission reduction conditions, such as forestry carbon sinks and renewable energy projects, and conduct detailed designs.

[0122] Project monitoring and reporting: Use monitoring tools and methods to monitor and report the emission reductions generated by the project in real time, ensuring the accuracy and traceability of the data.

[0123] Project verification and registration: Invite a third-party organization to verify the project and ensure that it meets the CCER standards before applying for registration with the relevant organization.

[0124] Through step S63, companies can obtain CCER benefits: by developing CCER projects, companies can obtain additional carbon asset benefits; cope with carbon tariffs: reserved CCERs can provide companies with an effective strategy to cope with carbon tariffs that they may face in the future; enhance corporate image: actively participating in CCER projects helps enhance the company's environmental image and social responsibility.

[0125] S64, design a blockchain traceability system to link the carbon sequestration certificates of each transformer.

[0126] A blockchain traceability system combines blockchain technology with a traceability system. It utilizes the decentralized, traceable, and tamper-proof characteristics of blockchain to ensure that information about goods or assets from production to circulation is truthfully and accurately recorded on the blockchain.

[0127] The main functions of a blockchain traceability system are as follows: Improving information transparency: Through blockchain technology, consumers can view the entire chain of information for goods or assets in real time, increasing information transparency; Preventing data tampering: Once data is written to the blockchain, it cannot be tampered with or deleted, ensuring the authenticity and reliability of the data; Enhancing consumer trust: A blockchain traceability system can increase consumers' trust in goods or assets, helping to improve brand image and market competitiveness.

[0128] Data is uploaded to the blockchain: Information related to transformer production, energy efficiency improvement, and carbon sink forest planting is stored on the blockchain to ensure the authenticity and immutability of the data.

[0129] Using smart contracts: By using smart contract technology, the generation, transfer, and trading of transformer carbon sequestration certificates can be executed automatically, improving the automation and intelligence level of the system.

[0130] Set up a user query interface: Provide users with a convenient query interface so that they can view the transformer's carbon sequestration certificates and related information in real time.

[0131] Step S64 improves information transparency: consumers can view the transformer's carbon sequestration certificates and related information in real time, enhancing transparency and consumer trust; it prevents data fraud: once data is written to the blockchain, it cannot be tampered with or deleted, ensuring the authenticity and reliability of the data and avoiding the risk of data fraud; and it promotes carbon asset management: through the blockchain traceability system, enterprises can more conveniently manage and track their carbon assets, improving the efficiency and accuracy of carbon asset management.

[0132] The beneficial effects of implementing this embodiment are as follows: First, by accurately measuring the carbon emission data converted from the enterprise's cost input at each stage, a solid foundation is laid for subsequent analysis; in-depth analysis of this data reveals carbon emission hotspots and potential emission reduction space, and by combining carbon emission heat maps and enterprise carbon quotas, a detailed carbon emission inventory is formulated, clarifying emission reduction targets and paths; predictive models are used to scientifically predict the enterprise's carbon emissions, providing a basis for formulating forward-looking emission reduction strategies; based on the prediction results, an individual incentive mechanism for enterprise carbon management is established to stimulate employees' enthusiasm and creativity in participating in carbon emission reduction, forming a positive emission reduction atmosphere from top to bottom; in addition, it emphasizes the design of carbon sinks in advance, and through the rational planning of carbon sink projects, such as afforestation and the application of energy-saving and emission-reduction technologies, not only can enterprise carbon emissions be effectively offset, but the full life-cycle benefits of carbon assets can also be improved, achieving a win-win situation for economic and environmental benefits.

[0133] This invention can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0134] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0135] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0136] Example 2 Further reference Figure 2 As a response to the above Figure 1 The present invention provides an embodiment of a cost reduction device based on carbon asset lifecycle management, which is similar to the method described above. Figure 1 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0137] like Figure 2 As shown, the cost reduction device 70 based on carbon asset lifecycle management in this embodiment includes: an acquisition module 71, an analysis module 72, a formulation module 73, a prediction module 74, a creation module 75, and a design module 76. Wherein: The acquisition module 71 is used to acquire carbon emission data converted from the cost inputs of enterprises at various stages; Analysis module 72 is used to analyze the carbon emission data; Module 73 is used to develop a carbon emission inventory based on the results of analysis of the carbon emission data, carbon emission heat map, and corporate carbon quotas. Prediction module 74 is used to predict corporate carbon emissions based on carbon emission inventories; Module 75 is established to create an individual incentive mechanism for corporate carbon management based on the results of forecasting corporate carbon emissions. Design module 76 is used for pre-carbon sink design to improve the full life cycle benefits of carbon assets.

[0138] The beneficial effects of implementing this embodiment are as follows: First, by accurately measuring the carbon emission data converted from the enterprise's cost input at each stage, a solid foundation is laid for subsequent analysis; in-depth analysis of this data reveals carbon emission hotspots and potential emission reduction space, and by combining carbon emission heat maps and enterprise carbon quotas, a detailed carbon emission inventory is formulated, clarifying emission reduction targets and paths; predictive models are used to scientifically predict the enterprise's carbon emissions, providing a basis for formulating forward-looking emission reduction strategies; based on the prediction results, an individual incentive mechanism for enterprise carbon management is established to stimulate employees' enthusiasm and creativity in participating in carbon emission reduction, forming a positive emission reduction atmosphere from top to bottom; in addition, it emphasizes the design of carbon sinks in advance, and through the rational planning of carbon sink projects, such as afforestation and the application of energy-saving and emission-reduction technologies, not only can enterprise carbon emissions be effectively offset, but the full life-cycle benefits of carbon assets can also be improved, achieving a win-win situation for economic and environmental benefits.

[0139] Example 3 To address the aforementioned technical problems, embodiments of the present invention also provide a computer device. Please refer to [link / reference needed]. Figure 3 , Figure 3 This is a basic structural block diagram of the computer device in this embodiment.

[0140] The aforementioned computer device 8 includes a memory 81, a processor 82, and a network interface 83 that are interconnected via a system bus. It should be noted that only the computer device 8 with components 81, 82, and 83 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described herein is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0141] The aforementioned computer devices can be desktop computers, laptops, handheld computers, and cloud servers, among other computing devices. These devices can facilitate human-computer interaction with users through keyboards, mice, remote controls, touchpads, or voice-activated devices.

[0142] The aforementioned memory 81 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the aforementioned memory 81 may be an internal storage unit of the aforementioned computer device 8, such as the hard disk or memory of the computer device 8. In other embodiments, the aforementioned memory 81 may also be an external storage device of the aforementioned computer device 8, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 8. Of course, the aforementioned memory 81 may also include both the internal storage unit and its external storage device of the aforementioned computer device 8. In this embodiment, the aforementioned memory 81 is typically used to store the operating system and various application software installed on the aforementioned computer device 8, such as computer-readable instructions based on cost reduction methods under the carbon asset life cycle management. In addition, the aforementioned memory 81 can also be used to temporarily store various types of data that have been output or will be output.

[0143] In some embodiments, the processor 82 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 82 is typically used to control the overall operation of the computer device 8. In this embodiment, the processor 82 is used to execute computer-readable instructions stored in the memory 81 or to process data, for example, to execute computer-readable instructions related to the cost reduction method based on the carbon asset lifecycle management.

[0144] The network interface 83 may include a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the computer device 8 and other electronic devices.

[0145] The beneficial effects of implementing this embodiment are as follows: First, by accurately measuring the carbon emission data converted from the enterprise's cost input at each stage, a solid foundation is laid for subsequent analysis; in-depth analysis of this data reveals carbon emission hotspots and potential emission reduction space, and by combining carbon emission heat maps and enterprise carbon quotas, a detailed carbon emission inventory is formulated, clarifying emission reduction targets and paths; predictive models are used to scientifically predict the enterprise's carbon emissions, providing a basis for formulating forward-looking emission reduction strategies; based on the prediction results, an individual incentive mechanism for enterprise carbon management is established to stimulate employees' enthusiasm and creativity in participating in carbon emission reduction, forming a positive emission reduction atmosphere from top to bottom; in addition, it emphasizes the design of carbon sinks in advance, and through the rational planning of carbon sink projects, such as afforestation and the application of energy-saving and emission-reduction technologies, not only can enterprise carbon emissions be effectively offset, but the full life-cycle benefits of carbon assets can also be improved, achieving a win-win situation for economic and environmental benefits.

[0146] Example 4 The present invention also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the cost reduction method based on carbon asset life cycle management as described above.

[0147] The beneficial effects of implementing this embodiment are as follows: First, by accurately measuring the carbon emission data converted from the enterprise's cost input at each stage, a solid foundation is laid for subsequent analysis; in-depth analysis of this data reveals carbon emission hotspots and potential emission reduction space, and by combining carbon emission heat maps and enterprise carbon quotas, a detailed carbon emission inventory is formulated, clarifying emission reduction targets and paths; predictive models are used to scientifically predict the enterprise's carbon emissions, providing a basis for formulating forward-looking emission reduction strategies; based on the prediction results, an individual incentive mechanism for enterprise carbon management is established to stimulate employees' enthusiasm and creativity in participating in carbon emission reduction, forming a positive emission reduction atmosphere from top to bottom; in addition, it emphasizes the design of carbon sinks in advance, and through the rational planning of carbon sink projects, such as afforestation and the application of energy-saving and emission-reduction technologies, not only can enterprise carbon emissions be effectively offset, but the full life-cycle benefits of carbon assets can also be improved, achieving a win-win situation for economic and environmental benefits.

[0148] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0149] Obviously, the embodiments described above are merely some embodiments of the present invention, not all embodiments. The accompanying drawings show preferred embodiments of the present invention, but do not limit the patent scope of the present invention. The present invention can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the patent protection scope of this invention.

Claims

1. A cost reduction method based on the full life cycle management of carbon assets, characterized in that, Includes the following steps: Obtain carbon emission data converted from cost inputs at each stage of a company's operations; The carbon emission data were analyzed. Based on the analysis of the carbon emission data, the carbon emission heat map, and the carbon quotas of enterprises, a carbon emission inventory is developed. Based on carbon emission inventories, predict corporate carbon emissions; Based on the results of predicting corporate carbon emissions, establish an individual incentive mechanism for corporate carbon management; Design carbon sinks in advance to improve the returns of carbon assets throughout their entire life cycle.

2. The cost reduction method based on carbon asset lifecycle management according to claim 1, characterized in that, The steps for obtaining carbon emission data converted from the cost inputs of enterprises at various stages specifically include: Raw data from procurement, production, and logistics processes are collected through IoT sensors installed on carbon asset devices. By using blockchain sharding, blockchain selection is performed to verify the authenticity of the original data and carbon asset emission responsibilities are assigned by department and production line.

3. The cost reduction method based on carbon asset lifecycle management according to claim 1, characterized in that, The steps for analyzing the carbon emission data specifically include: Data verification is conducted using an authoritative verification mechanism and a multi-node verification method. By combining federated learning with privacy-preserving computation and building a virtual mapping with digital twins, only the computation results are uploaded to the blockchain after 80% of the data analysis is completed. A carbon emission model is built based on the uploaded data to dynamically display the carbon intensity of each stage. To conduct audits and trace back, query carbon data snapshots of any historical node and compare the differences between blockchain-based evidence and paper ledgers.

4. The cost reduction method based on carbon asset lifecycle management according to claim 1, characterized in that, The steps for developing a carbon emission inventory based on the analysis of the carbon emission data, the carbon emission heat map, and the enterprise's carbon quota specifically include: Normalize corporate carbon cost data to intensity per unit output value ,in, Let be the carbon emission intensity (tCO2 / ten thousand yuan) of the i-th node. Value added to the enterprise (ten thousand yuan); Interpolation is performed using IDW, with color gradients representing high-medium-low emission zones; By overlaying quota data with heat maps, the spatial matching degree between quotas and actual emissions can be analyzed. ,in, Let i be the quota for node i. As an industry benchmark, For adjustment coefficients; The carbon emission intensity and deficit of enterprises are calculated by designing a structured inventory based on emission sources. The formula is: carbon emission intensity = total emissions / total output value, net emissions = actual emissions - quota. A negative net emission value indicates a quota surplus, and a positive net emission value indicates over-emission.

5. The cost reduction method based on carbon asset lifecycle management according to claim 1, characterized in that, The steps for predicting corporate carbon emissions based on carbon emission inventories specifically include: Select a base year, analyze carbon emission trends in past preset years, and identify driving factors; A predictive model was constructed using an ARIMA-LSTM hybrid model, and multiple policy scenarios, namely carbon tax gradient testing, were set up. Backtesting was conducted using historical data, and the parameters in the model were adjusted to make the prediction error less than 10%. Tornado diagrams were used to test the impact of variables on the results. Monte Carlo simulations are used to quantify data uncertainty.

6. The cost reduction method based on carbon asset lifecycle management according to claim 1, characterized in that, The steps for establishing an individual incentive mechanism for corporate carbon management based on the prediction of corporate carbon emissions specifically include: Based on the prediction of corporate carbon emissions, the carbon footprint of employees' work and innovation scenarios is modeled by combining an integrated OA system with IoT sensors. Carbon credit rules are designed based on the industry's per capita carbon emission levels. A tiered bonus and green option pool incentive system is adopted, and rules are set for obtaining corporate carbon trading revenue sharing.

7. The cost reduction method based on carbon asset lifecycle management according to any one of claims 1 to 6, characterized in that, The steps for designing upfront carbon sinks to improve the life-cycle returns of carbon assets specifically include: Integrate the low-carbon supply chain and set rules for suppliers to participate in carbon sink projects, requiring suppliers to provide carbon sink forests for every ton of product they produce; Establish a carbon sink-energy efficiency offsetting mechanism, where for every 1kW reduction in transformer no-load loss, the corresponding carbon sink demand is reduced by 5 acres. Develop carbon sink projects in accordance with CCER standards and reserve 10% of carbon credits to cope with future carbon tariffs on product exports; Design a blockchain traceability system to link the carbon sequestration certificates of each transformer.

8. A cost reduction device based on carbon asset lifecycle management, characterized in that, include: The acquisition module is used to acquire carbon emission data converted from the cost inputs of an enterprise at various stages; An analysis module is used to analyze the carbon emission data; The module is used to develop a carbon emission inventory based on the results of analysis of the carbon emission data, carbon emission heat map, and corporate carbon quotas. The forecasting module is used to predict corporate carbon emissions based on carbon emission inventories. Establish a module to create an individual incentive mechanism for corporate carbon management based on the prediction results of corporate carbon emissions; The design module is used for pre-carbon sink design to improve the full life cycle benefits of carbon assets.

9. A computer device, characterized in that, It includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the cost reduction method based on the carbon asset life cycle management as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the cost reduction method based on the full life cycle management of carbon assets as described in any one of claims 1 to 7.

Images