A carbon neutrality analysis method and carbon neutrality analysis system

Abstract

This invention discloses a carbon neutrality analysis method and system, relating to the field of carbon neutrality technology. The method includes the following steps: S1, Enterprise carbon emission tracking: setting up data collection points at various stages of an enterprise to collect carbon emission data in real time, and integrating and storing it through a blockchain platform; S2, Full gas coverage monitoring and accounting; S3, Dynamic modeling and prediction. This carbon neutrality analysis method and system employs system dynamics to construct a dynamic carbon neutrality analysis model, capable of capturing the dynamic interactions between factors such as carbon emissions, carbon absorption, technological progress, and policy changes. By simulating the evolution of carbon neutrality paths under different scenarios, the model can predict and analyze the achievement paths and feasibility of carbon neutrality targets under different scenarios, providing valuable decision support for policymakers and enterprises, and contributing to the successful achievement of climate goals.

Description

Technical Field

[0001] This invention relates to the field of carbon neutrality technology, specifically to a carbon neutrality analysis method and a carbon neutrality analysis system. Background Technology

[0002] Carbon neutrality refers to a state where net carbon dioxide emissions from human activities are zero through reducing carbon emissions and increasing carbon absorption. Its core objective is to balance emissions and absorption to mitigate climate change. Carbon neutrality analysis is crucial for quantifying carbon emissions, developing emission reduction pathways, and assessing their feasibility, thereby promoting the achievement of climate goals. Carbon neutrality analysis involves a systematic assessment of pathways to carbon neutrality for individuals, businesses, industries, or countries, including emissions accounting, emission reduction potential, techno-economic feasibility, and social impacts. Currently, mainstream methods can be divided into two categories: top-down macroeconomic policy modeling and bottom-up microeconomic technology assessment, specifically including carbon accounting, scenario modeling, marginal cost curves (MACC), and integrated assessment models (IAMs).

[0003] However, in practice, existing analytical methods often rely on estimated values ​​rather than actual measurements for carbon emission factors at various stages of a company's operations, leading to inaccurate data. Furthermore, due to the involvement of multiple stages and stakeholders, the willingness to share data among these stakeholders is low, resulting in incomplete data. At the same time, the credibility of existing carbon offset mechanisms is questionable; for example, fraudulent afforestation projects, which fail to achieve genuine emission reductions but obtain carbon offset credits through improper means, are also prevalent.

[0004] Furthermore, existing analytical models generally suffer from static assumptions, making overly idealistic assumptions and neglecting the impact of technological upheavals and policy iterations on carbon neutrality pathways. Moreover, existing models often simplify the treatment of non-CO2 greenhouse gas emissions such as methane and nitrogen oxides, but these have a higher global warming potential (GWP) and their impact on climate change cannot be ignored. Summary of the Invention

[0005] The purpose of this invention is to provide a comprehensive, accurate, and dynamic carbon neutrality analysis method and system to solve the problems of inaccurate and incomplete data, static model assumptions, and insufficient non-CO2 greenhouse gas accounting in existing carbon neutrality analysis methods, thereby promoting the smooth achievement of climate goals.

[0006] To achieve the above objectives, the present invention provides the following technical solution: On the one hand, a carbon neutrality analysis method is provided, including the following steps: S1. Enterprise Carbon Emission Tracking: Data collection points are set up at various stages of an enterprise, such as raw material procurement, production, transportation, and sales, to collect carbon emission data in real time. This data is then integrated and stored through a blockchain platform. By collecting and integrating carbon emission data from all stages of an enterprise in real time and utilizing the immutability of blockchain technology, the authenticity and integrity of the data are ensured, providing a solid data foundation for carbon neutrality analysis.

[0007] S2. Full Gas Coverage Monitoring and Accounting: Establish accounting standards for non-CO2 greenhouse gases and use monitoring equipment to monitor non-CO2 greenhouse gases such as CH4 and N2O in real time to improve the accounting of non-CO2 greenhouse gases. By establishing comprehensive non-CO2 greenhouse gas accounting standards and using high-precision monitoring equipment, accurate accounting of non-CO2 greenhouse gases is achieved, which helps to more accurately assess the total carbon emissions of enterprises and the global warming potential, and provides a scientific basis for formulating more effective emission reduction strategies.

[0008] S3. Dynamic Modeling and Prediction: Establish dynamic carbon neutrality analysis models and conduct analysis and prediction of various scenarios based on dynamic models to evaluate the achievement path and feasibility of carbon neutrality targets under different scenarios. By constructing dynamic carbon neutrality analysis models, we can capture the dynamic interaction between factors such as carbon emissions, carbon absorption, technological progress and policy changes, improve the foresight and flexibility of carbon neutrality analysis, and provide valuable decision support for policymakers and enterprises.

[0009] Furthermore, in step S1, the specific tracking scope of the enterprise includes, but is not limited to, raw material procurement, production, transportation, and sales. The specific tracking content for each stage is as follows: Raw material procurement: Track carbon emissions during the transportation of raw materials from suppliers to production enterprises, including the choice of transportation vehicles (such as trucks, trains, ships, etc.), transportation distance, and energy consumption during transportation. Production process: Tracking direct and indirect carbon emissions during the production process. Direct carbon emissions come from fuel combustion in production equipment, while indirect carbon emissions include electricity consumption, which is further subdivided into energy consumption of the production line and waste gas emissions during the process. Transportation: Tracking carbon emissions during the transportation of products from manufacturers to distributors or end users, similar to the raw material procurement process, including the choice of transportation vehicles, transportation distance, and energy consumption during transportation; Sales process: Tracking energy consumption in sales activities, including the use of store lighting, air conditioning, refrigeration equipment, etc., as well as carbon emissions in the production and processing of product packaging materials; By clearly defining the specific scope and content of corporate carbon emission tracking, the comprehensiveness and accuracy of carbon emission data were ensured, providing detailed data support for subsequent carbon neutrality analysis.

[0010] Furthermore, step S1 includes the following sub-steps: S11. Assign a unique digital identity: Register a unique digital identity for each emission source (raw material supplier, producer, transporter, distributor, etc.) in the enterprise on the blockchain platform, and verify the digital identity of the emission source through digital certificate or public-private key encryption technology to ensure its authenticity and legality. S12. Data Collection Point Setup: Data collection points are set up at various stages of the enterprise, including raw material procurement, production, transportation, and sales. These collection points can be sensors, IoT devices, or manual input interfaces, used to collect carbon emission data in real time. For example, in the production stage, sensors can be installed on key equipment on the production line to monitor energy consumption and exhaust emissions. In actual operation, the data collection frequency can be set according to the actual situation of the enterprise, such as once per minute, hour, or day, with sensor accuracy ±1%.

[0011] S13. Data on the blockchain: The collected carbon emission data is securely transmitted to the blockchain platform through encryption technology. On the blockchain, the digital identity of each emission source is associated with its carbon emission data, forming an immutable data record. S14. Distributed ledger recording: When new carbon emission data is generated, the new data is broadcast to the entire network and added to the blockchain after being verified by the consensus mechanism, forming a new block.

[0012] Furthermore, the blockchain automatically verifies and updates data through smart contracts (an automatically executed computer program that can automatically perform preset operations when specific conditions are met). Specifically, when new carbon emission data is uploaded to the blockchain, the smart contract automatically verifies the integrity and accuracy of the data. After verification, the carbon emission record on the blockchain is automatically updated. The data verification includes the data source, format, and rationality. Specifically, it involves checking whether the data comes from a registered emission source, whether the data format meets the preset standards, and whether the data is within a reasonable range. The data update includes, but is not limited to, adding new data to existing records or adjusting the total carbon emissions of relevant emission sources based on data changes.

[0013] Furthermore, in step S2, the accounting criteria include the following: Gas types: clearly defined non-CO2 greenhouse gases, including CH4 (methane), N2O (nitrous oxide), etc.; Scope of accounting: Determine the time and spatial scope of the accounting; Monitoring methods: The specific methods for monitoring non-CO2 greenhouse gases are specified, including the monitoring equipment used, the setting of sampling points, and the monitoring frequency. The monitoring frequency can be set according to the gas emission characteristics. Typically, the monitoring frequency for CH4 can be set once per hour, and the monitoring frequency for N2O can be set once every two hours.

[0014] The calculation formula is used to convert non-CO2 greenhouse gas emissions into CO2 equivalents and specifies the selection principles for parameters in the formula, such as the selection of GWP values ​​should be based on intermediate values ​​given in authoritative reports (such as IPCC assessment reports). Reporting and Verification Requirements: Specify the reporting format, content, and submission time for accounting results, and clarify the verification methods for accounting results, including internal audits and external third-party verifications, to ensure the accuracy and reliability of the accounting results.

[0015] Furthermore, in step S2, the monitoring equipment employs gas sensors, including but not limited to infrared sensors and laser spectrometers, for high-precision detection of CH4 and N2O gas concentrations, providing accurate data for calculation. The CH4 and N2O emissions are converted to CO2 equivalents using the following formula: ; in: The amount of methane emitted by an enterprise within a certain period of time (unit: tons or kilograms). The global warming potential of methane (specifically, the GWP of methane over a 100-year timescale given in the IPCC Fifth Assessment Report is approximately 28-36, taking an intermediate value, such as 32). The amount of nitrogen oxides emitted by an enterprise within a certain period of time (unit: tons or kilograms). Global warming potential of nitrogen oxides (GWP) is approximately 265-298 over a 100-year timescale, as given in the IPCC Fifth Assessment Report; we will take an intermediate value, such as 281.

[0016] Furthermore, step S3 includes the following sub-steps: S31. Establish a dynamic model: Using system dynamics, construct a carbon neutrality analysis model that reflects the dynamic interaction of carbon emissions, carbon absorption, technological progress, and policy changes. This model differs from the traditional static model and emphasizes capturing the changes in variables over time. S32. Scenario Analysis and Forecasting: Set up multi-dimensional scenarios, including but not limited to: the speed of technological breakthroughs (such as improving technological efficiency by 3%-5% per year), the intensity of policies (such as levying a carbon tax of 30-100 yuan per ton of CO2), and changes in market demand (such as an annual increase of 1%-3% in public transportation usage). Simulation operation: Input different scenario parameters into the dynamic model to simulate the evolution of the carbon neutrality pathway; through simulation operation, the changing trends of key indicators such as carbon emissions and carbon absorption under different scenarios can be observed intuitively.

[0017] Results Prediction: Output the changing trends of key indicators such as carbon emissions, carbon absorption, and time to achieve carbon neutrality targets under various scenarios; these predictions can provide valuable decision support for policymakers and businesses, helping them to better formulate emission reduction strategies and address climate change.

[0018] S33. Feasibility Assessment: Based on the forecast results, analyze the feasibility of achieving carbon neutrality under different scenarios, including dimensions such as techno-economics (e.g., emission reduction costs, rate of return on investment), social acceptance (e.g., public support for the policy, corporate participation), and policy coherence (e.g., policy stability, sustainability). A comprehensive assessment of the feasibility under different scenarios can provide policymakers and businesses with more comprehensive decision-making support, promoting the smooth achievement of climate goals.

[0019] Furthermore, in step S31, the relationship between the core modules and variables of the carbon neutrality analysis model is as follows: 1) Carbon emission submodule Input variables: intensity of economic activity (GDP, industrial output, etc.), energy structure (proportion of fossil fuels, proportion of renewable energy), technical efficiency (carbon emission coefficient per unit of output), policy intensity (carbon tax, subsidies, emission quotas); Dynamic relationships: Carbon emissions = intensity of economic activity × energy structure × technological efficiency × policy adjustment coefficient; Policy adjustment coefficients can have a reverse effect on energy structure and technological efficiency through carbon taxes or subsidies (e.g., increased carbon taxes → reduced use of fossil fuels → decreased carbon emissions). 2) Carbon absorption submodule Input variables: natural uptake (uptake by ecosystems such as forests and oceans), technology uptake (scale of CCUS technology deployment), and land use change (forest cover rate and urbanization rate). Dynamic relationships: Carbon absorption = Natural absorption baseline + Technological absorption increment × Policy support intensity; The strength of policy support (such as subsidies) promotes the deployment of CCUS technology, indirectly enhancing carbon absorption capacity; 3) Technological Progress Submodule Input variables: R&D investment (clean energy, CCUS, etc.), technology diffusion rate (market penetration rate of new technologies), policy incentives (R&D subsidies, patent protection); Dynamic relationships: Technical efficiency improvement rate = R&D investment × technology diffusion coefficient × policy incentive factor; The rate of improvement in technical efficiency has a converse effect on the carbon emission coefficient per unit output value in the carbon emission sub-module. 4) Policy Change Submodule Input variables: carbon price level (carbon tax / carbon trading price), emission reduction target intensity (such as carbon neutrality timetable), social acceptance (public support rate for the policy); Dynamic relationships: Policy strength = Emission reduction target strength × Social acceptance × Carbon price feedback; The policy intensity affects carbon emissions and technological progress by adjusting parameters such as carbon taxes and subsidies. The causal loop of carbon emissions, carbon absorption, technological progress, and policy changes is as follows: Positive feedback loop: Technological progress → carbon emission reduction: Increased R&D investment → improved technical efficiency → reduced carbon emissions per unit of output → decrease in total carbon emissions.

[0020] Policy incentives → Accelerated technology diffusion: Strengthened subsidy policies → Reduced CCUS technology costs → Increased market penetration → Enhanced carbon absorption capacity.

[0021] Negative feedback loop: Rising carbon emissions → Tightening policies: Exceeding carbon emission limits → Government increases carbon tax → Energy structure adjustment → Carbon emissions decline.

[0022] Insufficient carbon absorption → Policy adjustment: Natural absorption is lower than the target → Expand forest protection area or increase CCUS subsidies → Carbon absorption rebounds.

[0023] Furthermore, the carbon neutrality analysis model includes the following equations: 1) Dynamic equation for carbon emissions: ; In the formula, Let be the carbon emissions at time t. Let be the carbon emission coefficient of economic activity at time t. Let be the carbon emission coefficient of the energy structure at time t. Let be the carbon tax policy adjustment coefficient at time t, and 0 ≤ τ ≤ 1. Let t be the rate of improvement in technical efficiency; this equation reflects the dynamic impact of economic activity, energy structure, technical efficiency, and policy adjustments on carbon emissions.

[0024] 2) Dynamic equation for carbon absorption: ; In the formula, Let be the amount of carbon absorbed at time t. The natural absorption capacity at time t. The absorption capacity of CCUS technology at time t. Let t be the policy support intensity index at time t; this equation reflects the dynamic impact of natural absorption, technological absorption, and policy support on carbon absorption.

[0025] 3) Equation for improvement rate of technical efficiency: ; In the formula, Let be the rate of improvement in technical efficiency at time t. As the basic coefficient for technology diffusion, For the R&D investment at time t, Let be the technology diffusion rate at time t. Let t be the technology policy incentive factor at time t.

[0026] On the other hand, a carbon neutrality analysis system is provided, applicable to the carbon neutrality analysis method described above, including: Carbon emission tracking module: It is responsible for setting up data collection points at various stages of the enterprise (raw material procurement, production, transportation, sales, etc.), collecting carbon emission data in real time through high-precision sensors, IoT devices or manual input interfaces, and integrating and storing it through a blockchain platform; these data collection points can capture all carbon emission-related activities from the entry of raw materials into the enterprise to the sale of the final product.

[0027] The blockchain data management module is responsible for managing carbon emission data on the blockchain platform, including data verification, updates, and distributed ledger recording. This module uses smart contract technology to automatically execute data verification and update operations, ensuring the immutability and timeliness of the data. It also provides data query and access interfaces, facilitating authorized users to view and verify the data.

[0028] The full gas coverage monitoring module is responsible for establishing accounting standards for non-CO2 greenhouse gases (such as CH4 and N2O) and using monitoring equipment for real-time monitoring. This module aims to improve the accounting of non-CO2 greenhouse gases in order to more accurately assess the total carbon emissions of enterprises and the global warming potential.

[0029] The accounting standards development module is responsible for developing accounting standards for non-CO2 greenhouse gases, including gas types, accounting scope, monitoring methods, accounting formulas, and reporting and verification requirements. This module provides unified accounting standards to ensure comparability and consistency between different companies and projects. Furthermore, it enhances the transparency and credibility of accounting results through clear reporting and verification requirements. The dynamic model building module is responsible for constructing carbon neutrality analysis models that reflect the dynamic interactions of carbon emissions, carbon absorption, technological progress, and policy changes using system dynamics methods. By building dynamic models, it can more accurately predict and analyze the paths and feasibility of achieving carbon neutrality targets under different scenarios. It provides flexible scenario analysis tools to help policymakers and businesses develop targeted emission reduction strategies.

[0030] The predictive analytics module is responsible for analyzing and predicting various scenarios using dynamic models, and combining the prediction results to evaluate the pathways and feasibility of achieving carbon neutrality goals under different scenarios. It provides predictive analytics capabilities across multiple scenarios to help policymakers and businesses fully understand the potential impact of different strategies. Visualizing the prediction results enhances the intuitiveness and scientific rigor of the decision-making process.

[0031] Carbon offset implementation module: responsible for developing carbon offset standards and implementing carbon offset projects, including project selection, purchase of emission reductions, contract signing, transfer and cancellation of emission reductions; Third-party verification module: responsible for introducing independent third-party organizations to verify carbon offset projects, ensuring the authenticity, additionality, accuracy and verifiability of emission reductions.

[0032] Furthermore, the system also includes: The processor is used to execute computer instructions to enable the system to perform: a carbon emission tracking module, a blockchain data management module, a full gas coverage monitoring module, an accounting standard setting module, a dynamic model building module, a predictive analysis module, a carbon offsetting execution module, and a third-party verification module; A memory is used to store computer instructions. The memory stores a computer program that, when executed by a processor, implements the carbon neutrality analysis method described above, thereby automating and intelligentizing the carbon neutrality analysis method.

[0033] This invention provides a carbon neutrality analysis method and system, which have the following beneficial effects: 1. This invention introduces blockchain technology, setting up data collection points at various stages of an enterprise to collect and integrate carbon emission data in real time. Leveraging the immutability of blockchain and the automatic execution of smart contracts, it ensures the authenticity and integrity of the data, effectively solving the problem of difficult-to-track enterprise emissions. This transparent and accurate carbon emission tracking mechanism provides a solid data foundation for carbon neutrality analysis and improves the reliability of the analysis results.

[0034] 2. This invention establishes an accounting standard for non-CO2 greenhouse gases and uses high-precision monitoring equipment for real-time monitoring. By clarifying the accounting scope, monitoring methods, accounting formulas, and reporting and verification requirements, it achieves coverage and accurate accounting of non-CO2 greenhouse gases, which helps to more accurately assess the total carbon emissions of enterprises and the global warming potential, and provides a scientific basis for formulating more effective emission reduction strategies.

[0035] 3. This invention employs a system dynamics approach to construct a dynamic carbon neutrality analysis model. This model captures the dynamic interactions between factors such as carbon emissions, carbon absorption, technological progress, and policy changes. By simulating the evolution of carbon neutrality pathways under different scenarios, the model can predict and analyze the paths and feasibility of achieving carbon neutrality goals under various conditions. This dynamic modeling and prediction capability not only enhances the foresight and flexibility of carbon neutrality analysis but also provides valuable decision support for policymakers and businesses, contributing to the successful achievement of climate goals. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the steps of a carbon neutrality analysis method according to the present invention; Figure 2 This is a flowchart of step S1 of a carbon neutrality analysis method according to the present invention; Figure 3 This is a flowchart of step S3 of the carbon neutrality analysis method of the present invention; Figure 4 This is a logic block diagram of a carbon neutrality analysis system according to the present invention. Detailed Implementation

[0037] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0038] Example 1: Carbon Neutrality Analysis of Manufacturing Enterprises A manufacturing company, primarily producing metal products, has an annual output value of 1 billion RMB. The company has decided to conduct a carbon neutrality analysis to develop effective emission reduction strategies.

[0039] Step S1, Enterprise Carbon Emission Tracking: In the raw material procurement stage, carbon emissions during the transportation of iron ore from suppliers to production enterprises are tracked. The transportation method is trucks, the transportation distance is 500 kilometers, the annual transportation volume is 100,000 tons, and the carbon emission per unit distance is 0.2 kg CO2 / ton-kilometer. Carbon emissions in the raw material procurement stage: 100,000 tons * 500 kilometers * 0.2 kg CO2 / ton-kilometer = 1000 tons of CO2.

[0040] In the production process, tracking energy consumption on the production line shows an annual electricity consumption of 50 million kWh, with carbon emissions per unit of electricity at 0.6 kg CO2 / kWh. Total carbon emissions from the production process: 50 million kWh * 0.6 kg CO2 / kWh = 30,000 tons of CO2.

[0041] In the transportation segment, the process of transporting products from the manufacturer to the distributor is tracked. The mode of transport is train, with an annual transport volume of 80,000 tons and a transport distance of 1,000 kilometers. The carbon emission per unit distance is 0.05 kg CO2 / ton-kilometer. Therefore, the carbon emissions in the transportation segment are: 80,000 tons * 1,000 kilometers * 0.05 kg CO2 / ton-kilometer = 400 tons of CO2.

[0042] In the sales process, energy consumption for store lighting, air conditioning, etc., is tracked, with an annual electricity consumption of 2 million kWh. Carbon emissions from the sales process: 2 million kWh * 0.6 kg CO2 / kWh = 1200 tons of CO2.

[0043] Step S2, Full Gas Coverage Monitoring and Calculation: Infrared sensors were used to monitor CH4 emissions during the production process, with an annual emission of 2 tons and a GWP of 32. Laser spectrometers were used to monitor N2O emissions, with an annual emission of 0.5 tons and a GWP of 281. The CO2 equivalents of CH4 and N2O were calculated as follows: The CO2 equivalent of CH4: 2 tons * 32 = 64 tons of CO2 equivalent; The CO2 equivalent of N2O: 0.5 tons * 281 = 140.5 tons of CO2 equivalent.

[0044] Step S3, Dynamic Modeling and Prediction: A dynamic carbon neutrality analysis model was constructed, with the technological breakthrough speed set at 5% annual improvement in technological efficiency, the policy intensity set at a carbon tax of 50 yuan per ton of CO2, and the market demand change set at an annual growth of 2%.

[0045] After simulation, it is predicted that the company can reduce its carbon emissions year by year over the next 10 years through technological improvements and carbon tax policies, and is expected to achieve carbon neutrality in the 8th year.

[0046] Implementation results: 1. The company's total annual carbon emissions are 32,740.5 tons of CO2 (including all aspects of the company's operations and non-CO2 greenhouse gases).

[0047] 2. According to dynamic model predictions, companies can achieve carbon neutrality in the 8th year, provided they continue to make technological improvements and accept carbon tax policies.

[0048] Example 2: Carbon Neutrality Analysis of Urban Transportation Systems A major city has decided to conduct a carbon neutrality analysis of its transportation system in order to assess its emission reduction potential and formulate corresponding policies.

[0049] Step S1, Enterprise Carbon Emission Tracking (simplified here to carbon emission tracking in transportation operations): Track the annual mileage and carbon emissions per mile for buses, taxis, and private cars.

[0050] The annual mileage of buses is 50 million kilometers, and the carbon emission per unit mileage is 0.3 kg CO2 / km; the carbon emissions of buses: 50 million kilometers * 0.3 kg CO2 / km = 15,000 tons of CO2.

[0051] The annual mileage of taxis is 30 million kilometers, and the carbon emission per unit mileage is 0.25 kg CO2 / km; Taxi carbon emissions: 30 million kilometers * 0.25 kg CO2 / km = 7,500 tons of CO2.

[0052] Private cars travel 2 billion kilometers per year, with carbon emissions of 0.2 kg CO2 / km per unit mileage; carbon emissions from private cars: 2 billion kilometers * 0.2 kg CO2 / km = 400,000 tons of CO2.

[0053] Step S2, Full Gas Coverage Monitoring and Calculation: CH4 and N2O emission data from the transportation system were obtained through monitoring equipment. Annual CH4 emissions were 5 tons, with a GWP of 32; annual N2O emissions were 1 ton, with a GWP of 281. The CO2 equivalents of CH4 and N2O were calculated as follows: The CO2 equivalent of CH4: 5 tons * 32 = 160 tons of CO2 equivalent; The CO2 equivalent of N2O: 1 ton * 281 = 281 tons of CO2 equivalent.

[0054] Step S3, Dynamic Modeling and Prediction: A dynamic carbon neutrality analysis model was constructed, with the technological breakthrough rate set at 3% per year to improve the energy efficiency of transportation vehicles, the policy intensity set at levying an additional carbon tax on high-emission vehicles, and the market demand change set at 2% per year to increase the utilization rate of public transportation.

[0055] After simulation, it is predicted that over the next 15 years, the city's transportation system can reduce carbon emissions year by year through technological improvements, policy guidance, and changes in market demand, and is expected to achieve carbon neutrality in the 12th year.

[0056] Implementation results: The city's transportation system has an annual total carbon emission of 422,941 tons of CO2 (including all types of transportation and non-CO2 greenhouse gases). Dynamic models predict that the transportation system can achieve carbon neutrality in year 12, provided that continuous technological improvements are made, effective policies are implemented, and public transportation usage is increased.

[0057] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. A carbon neutrality analysis method, characterized in that, Includes the following steps: S1. Enterprise Carbon Emission Tracking: Set up data collection points at various stages of the enterprise to collect carbon emission data in real time, and integrate and store it through a blockchain platform; S2. Full gas coverage monitoring and accounting: Establish accounting standards for non-CO2 greenhouse gases and use monitoring equipment to monitor non-CO2 greenhouse gases in real time to improve the accounting of non-CO2 greenhouse gases. S3. Dynamic Modeling and Prediction: Establish a dynamic carbon neutrality analysis model, and conduct analysis and prediction of various scenarios based on the dynamic model to evaluate the achievement path and feasibility of the carbon neutrality target under different scenarios. S31. Establish a dynamic model: Using system dynamics methods, construct a carbon neutrality analysis model that reflects the dynamic interaction of carbon emissions, carbon absorption, technological progress, and policy changes. S32. Scenario Analysis and Forecasting: Set up multi-dimensional scenarios: including but not limited to the speed of technological breakthroughs, policy intensity, and changes in market demand; Simulation operation: Input different scenario parameters into the dynamic model to simulate the evolution of the carbon neutrality pathway; Results Prediction: Output the changing trends of carbon emissions, carbon absorption, and time to achieve carbon neutrality targets under each scenario; S33. Feasibility Assessment: Based on the forecast results, analyze the feasibility of achieving the carbon neutrality target under different scenarios, including techno-economic feasibility, social acceptance, and policy coherence.

2. The carbon neutrality analysis method according to claim 1, characterized in that, In step S1, the specific tracking scope of the enterprise includes, but is not limited to, the raw material procurement, production, transportation, and sales processes. The specific tracking content for each process is as follows: Raw material procurement: Track carbon emissions during the transportation of raw materials from suppliers to production enterprises, including the choice of transportation vehicles, transportation distance, and energy consumption during transportation; Production process: Tracking direct and indirect carbon emissions during the production process, where direct carbon emissions come from fuel combustion in production equipment, and indirect carbon emissions include electricity consumption; Transportation: Tracking carbon emissions during the transportation of products from manufacturers to distributors or end users, including the choice of transportation vehicles, transportation distance, and energy consumption during transportation; Sales process: Tracking energy consumption in sales activities, including the use of store lighting, air conditioning, and refrigeration equipment, as well as carbon emissions from the production and processing of product packaging materials.

3. The carbon neutrality analysis method according to claim 2, characterized in that, Step S1 includes the following sub-steps: S11. Assign a unique digital identity: Register a unique digital identity for each emission source in the enterprise on the blockchain platform, and verify the digital identity of the emission source through digital certificates or public-private key peer-to-peer encryption technology; S12. Data collection point setting: Set up data collection points in the enterprise's raw material procurement, production, transportation and sales processes. The collection points can be sensors, IoT devices or manual input interfaces. S13. Data on the blockchain: The collected carbon emission data is securely transmitted to the blockchain platform through encryption technology. On the blockchain, the digital identity of each emission source is associated with its carbon emission data, forming an immutable data record. S14. Distributed ledger recording: When new carbon emission data is generated, the new data is broadcast to the entire network and added to the blockchain after being verified by the consensus mechanism, forming a new block.

4. The carbon neutrality analysis method according to claim 3, characterized in that, The blockchain automatically verifies and updates data through smart contracts. Specifically, when new carbon emission data is uploaded to the blockchain, the smart contract automatically verifies the integrity and accuracy of the data. After verification, the carbon emission record on the blockchain is automatically updated. The data verification includes, but is not limited to, checking whether the data comes from a registered emission source, whether the data format meets preset standards, and whether the data is within a reasonable range; The data update includes, but is not limited to, adding new data to existing records or adjusting the total carbon emissions of relevant emission sources based on data changes.

5. The carbon neutrality analysis method according to claim 1, characterized in that, In step S2, the accounting criteria include the following: Gas types: clearly defined non-CO2 greenhouse gases, including CH4 and N2O; Scope of accounting: Determine the time and spatial scope of the accounting; Monitoring methods: Specify the specific methods for monitoring non-CO2 greenhouse gases, including the monitoring equipment used, sampling point settings, and monitoring frequency; The calculation formula is used to convert non-CO2 greenhouse gas emissions into CO2 equivalents and specifies the selection principles for the parameters in the formula. Reporting and Verification Requirements: Specify the reporting format, content, and submission time for accounting results, and clarify the verification methods for accounting results, including internal audits and external third-party verifications.

6. The carbon neutrality analysis method according to claim 1, characterized in that, In step S2, the monitoring equipment uses gas sensors, including but not limited to infrared sensors and laser spectrometers, to accurately detect the gas concentrations of CH4 and N2O. The emissions of CH4 and N2O are converted to CO2 equivalents using the following formula: ； in: The amount of methane emitted by a company within a certain period of time; Global warming potential of methane; The amount of nitrogen oxides emitted by an enterprise within a certain period of time; Global warming potential of nitrogen oxides.

7. The carbon neutrality analysis method according to claim 1, characterized in that, In step S31, the relationship between the core modules and variables of the carbon neutrality analysis model is as follows: 1) Carbon emission submodule Input variables: intensity of economic activity, energy structure, technological efficiency, and policy intensity; Dynamic relationships: Carbon emissions = intensity of economic activity × energy structure × technological efficiency × policy adjustment coefficient; Policy adjustment coefficients can indirectly affect energy structure and technological efficiency through carbon taxes or subsidies. 2) Carbon absorption submodule Input variables: natural absorption, technological absorption, land use change; Dynamic relationships: Carbon absorption = Natural absorption baseline + Technological absorption increment × Policy support intensity; Strong policy support promotes the deployment of CCUS technology, indirectly enhancing carbon sequestration capacity; 3) Technological Progress Submodule Input variables: R&D investment, technology diffusion rate, policy incentives; Dynamic relationships: Technical efficiency improvement rate = R&D investment × technology diffusion coefficient × policy incentive factor; The rate of improvement in technical efficiency has a converse effect on the carbon emission coefficient per unit output value in the carbon emission sub-module. 4) Policy Change Submodule Input variables: carbon price level, emission reduction target intensity, social acceptance; Dynamic relationships: Policy strength = Emission reduction target strength × Social acceptance × Carbon price feedback; The policy intensity affects carbon emissions and technological progress by adjusting parameters such as carbon taxes and subsidies.

8. The carbon neutrality analysis method according to claim 7, characterized in that, The carbon neutrality analysis model includes the following equations: 1) Dynamic equation for carbon emissions: ； In the formula, Let be the carbon emissions at time t. Let be the carbon emission coefficient of economic activity at time t. Let be the carbon emission coefficient of the energy structure at time t. Let be the carbon tax policy adjustment coefficient at time t, and 0 ≤ τ ≤ 1. Let t be the rate of improvement in technical efficiency. 2) Dynamic equation for carbon absorption: ； In the formula, Let be the amount of carbon absorbed at time t. The natural absorption capacity at time t. The absorption capacity of CCUS technology at time t. Let t be the policy support strength index. 3) Equation for improvement rate of technical efficiency: ； In the formula, Let be the rate of improvement in technical efficiency at time t. As the basic coefficient for technology diffusion, For the R&D investment at time t, Let be the technology diffusion rate at time t. Let t be the technology policy incentive factor at time t.

9. A carbon neutrality analysis system, applied to the carbon neutrality analysis method as described in any one of claims 1-8, characterized in that, include: Carbon emission tracking module: Responsible for setting up data collection points at various stages of the enterprise, collecting carbon emission data in real time, and integrating and storing it through a blockchain platform; Blockchain Data Management Module: Responsible for managing carbon emission data on the blockchain platform, including data verification, updates, and distributed ledger recording; Full gas coverage monitoring module: responsible for establishing accounting standards for non-CO2 greenhouse gases and using monitoring equipment for real-time monitoring; Accounting Standards Development Module: Responsible for developing accounting standards for non-CO2 greenhouse gases, including gas types, accounting scope, monitoring methods, accounting formulas, and reporting and verification requirements; Dynamic Model Building Module: Responsible for using system dynamics methods to build a carbon neutrality analysis model that reflects the dynamic interaction of carbon emissions, carbon absorption, technological progress, and policy changes; Predictive Analysis Module: Responsible for using dynamic models to analyze and predict various scenarios, and combining the prediction results to evaluate the path and feasibility of achieving the carbon neutrality target under different scenarios; Carbon offset implementation module: responsible for developing carbon offset standards and implementing carbon offset projects, including project selection, purchase of emission reductions, contract signing, transfer and cancellation of emission reductions; Third-party verification module: responsible for introducing independent third-party organizations to verify carbon offset projects, ensuring the authenticity, additionality, accuracy and verifiability of emission reductions.

10. A carbon neutrality analysis system according to claim 9, characterized in that, include: The processor is used to execute computer instructions to enable the system to perform: a carbon emission tracking module, a blockchain data management module, a full gas coverage monitoring module, an accounting standard setting module, a dynamic model building module, a predictive analysis module, a carbon offsetting execution module, and a third-party verification module; A memory for storing computer instructions, wherein the memory stores a computer program that, when executed by a processor, implements the carbon neutralization analysis method as described in any one of claims 1-8.

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