Intelligent management method and system for carbon emission reduction assetization and transaction mechanism of railway industry

Abstract

The present application relates to the technical field of carbon emission, in particular to an intelligent management method for carbon emission reduction assetization and transaction mechanism in railway industry. The intelligent management method integrates historical operation data and real-time operation data of railway, uses machine learning to train railway carbon emission feature recognition model and dynamic baseline model, can realize intelligent identification of potential emission reduction projects in railway industry and precise adaptation of CCER methodology, collects carbon accounting monitoring data of candidate projects, quantifies carbon emission reduction of each candidate project by dynamic baseline model, calculates carbon asset value of each emission reduction project automatically combined with quantified emission reduction, and develops systematic risk analysis and control strategy based on multi-dimensional data of carbon emission reduction, carbon asset value, market price and cost, realizes intelligent and integrated management of whole process of identification, emission reduction accounting, carbon asset value evaluation and risk control of carbon emission reduction projects in railway industry.

Description

Technical Field

[0001] This invention belongs to the field of voiceprint detection technology, specifically relating to an intelligent management method and system for the assetization and trading mechanism of carbon emission reduction in the railway industry. Background Technology

[0002] As one of the main sources of energy consumption and carbon emissions, the green and low-carbon transformation of the transportation sector has become a key link. With its advantages of high electrification rate and low carbon emission intensity per unit turnover, railway transportation has become a green and low-carbon backbone transportation mode in the comprehensive transportation system and has huge emission reduction potential in the deep decarbonization of the transportation sector.

[0003] Regarding carbon market mechanisms, the national carbon emission trading market has been officially launched, and the national certified voluntary emission reduction (CCER) mechanism has been restarted and is constantly being improved, providing a policy channel for the market-based realization of carbon emission reduction value in non-controlled emission industries. The railway industry mainly emits indirect emissions with a low proportion of direct emissions, making it difficult to be included in the national mandatory carbon market in the short term. Therefore, realizing the assetization of carbon emission reduction potential through the CCER mechanism has become the core path for the railway industry to achieve a balance between environmental and economic benefits.

[0004] However, under existing technologies and mechanisms, the railway industry still faces numerous bottlenecks in participating in carbon trading: First, the CCER methodology system lacks specialized methodologies tailored to the characteristics of railway operations. Particularly for typical scenarios such as the shift from road to rail freight structure adjustment, energy conservation in traction power supply systems, and distributed photovoltaic power generation in railways, there is a lack of unified and implementable accounting and verification standards, preventing many emission reduction practices from being transformed into compliant carbon assets. Second, railway carbon emissions exhibit characteristics of "electricity dependence and indirect emission dominance," involving data from multiple aspects such as traction power supply, station energy consumption, freight transfer, and equipment operation. Existing technologies struggle to achieve unified collection, dynamic monitoring, and accurate accounting of multi-source data. Third, carbon asset valuation relies on multiple dimensions such as emission reductions, carbon prices, development costs, and transaction costs. Existing technologies lack integrated capabilities for dynamic value calculation and risk quantification analysis, making it difficult to effectively manage policy risks, market price risks, monitoring data risks, and leakage risks. Fourth, the path for the railway industry to participate in the carbon market is unclear. Mechanisms for project development, investment and operation, carbon inclusion integration, and phased implementation have not yet formed a systematic plan, making it difficult to scale up and sustainably transform carbon emission reduction potential into tradable assets. Summary of the Invention

[0005] Based on the aforementioned problems in the existing technology, the purpose of this invention is to provide an intelligent management method and system for the assetization and trading mechanism of carbon emission reduction in the railway industry, so as to solve the problems of insufficient methodological adaptation, inaccurate data monitoring, incomplete value assessment, uncontrollable risks, and unclear trading paths in the existing technology.

[0006] The technical solution adopted by this invention to solve its technical problem is:

[0007] Firstly, this invention provides an intelligent management method for the assetization and trading mechanism of carbon emission reduction in the railway industry. The intelligent management method includes: Step S1: Obtain historical and real-time operational data of the railway and perform preprocessing to obtain historical sample data and real-time sample data; Step S2: Based on the historical sample data, a railway carbon emission characteristic identification model and a dynamic baseline model are obtained through machine learning training; Step S3: Input the real-time sample data into the railway carbon emission characteristic identification model to identify potential emission reduction projects, determine the adaptation strategy by benchmarking the CCER methodology library, and generate a candidate project list. Step S4: Based on each candidate project in the candidate project list, collect real-time monitoring data of railway carbon accounting corresponding to each candidate project in real time; Step S5: Input the real-time monitoring data of railway carbon accounting into the dynamic baseline model to calculate the carbon emission reduction of each candidate project. Step S6: Calculate the carbon asset value of each candidate project based on the carbon emission reduction of the project; Step S7: Based on the value of the carbon assets, the carbon emission reduction of the project, carbon market prices and cost data, conduct risk analysis and formulate risk management strategies.

[0008] Preferably, the intelligent management method further includes: Step S8: Automatically recommend three market participation paths: project owners, investment institutions, and carbon inclusion organizers, and make trading timing decisions; Step S9: Generate a three-step strategic roadmap centered on CCER development, encompassing near-term breakthroughs, mid-term promotion, and long-term integration.

[0009] Preferably, the historical and real-time operational data include traction power supply and station energy consumption data, freight turnover and freight flow transfer data, technical parameters of railway equipment and facilities, and CCER and carbon market trading price data.

[0010] Preferably, the railway carbon emission characteristic identification model is a machine learning classification model, which takes railway energy consumption, freight structure, and equipment parameters as inputs to identify various emission reduction project types in the railway field and match the corresponding CCER methodology; the dynamic baseline model is a machine learning regression model, which is used to calculate the difference between the baseline emission and the actual emission, and then obtain the carbon emission reduction of the project.

[0011] Preferably, the candidate projects include railway photovoltaic power generation projects, traction power supply energy-saving renovation projects, road-to-rail freight optimization projects, and new energy locomotive replacement projects.

[0012] Preferably, the dynamic baseline model is constructed based on railway operation-related monitoring data, emission factors, and industry carbon emission parameters. Through dynamic baseline emission rules, it quantifies the carbon emission reduction of each candidate project, ensuring the compliance and accuracy of emission reduction calculation.

[0013] Preferably, the step of calculating the carbon asset value of each of the candidate projects in step S6 includes: calculating the carbon asset value of each of the candidate projects using a carbon asset value calculation formula. The formula for calculating the value of carbon assets is as follows: ; in, The carbon emission reduction for a single candidate project. For carbon market trading prices, Costs for project development, monitoring and verification, and transaction operations.

[0014] Preferably, the step of conducting risk analysis and formulating risk management strategies in step S7 includes: policy and regulatory risks, market and price risks, technology and execution risks, and financial and management risks, which can be used to formulate targeted prevention and control measures to ensure the stable operation of railway carbon asset development and trading.

[0015] Secondly, this invention provides an intelligent management system for the assetization and trading mechanism of carbon emission reduction in the railway industry. The intelligent management system includes: The data acquisition and processing module is used to acquire historical and real-time operational data of the railway and perform preprocessing to obtain historical and real-time sample data. The model training module is used to obtain a railway carbon emission feature identification model and a dynamic baseline model through machine learning training based on the historical sample data. The project identification and matching module is used to input the real-time sample data into the railway carbon emission characteristic identification model, identify potential emission reduction projects, determine the adaptation strategy by benchmarking the CCER methodology library, and generate a candidate project list. The data monitoring and acquisition module is used to collect real-time monitoring data of railway carbon accounting corresponding to each candidate project in the candidate project list. The emission reduction calculation module is used to input the real-time monitoring data of railway carbon accounting into the dynamic baseline model and calculate the project carbon emission reduction corresponding to each of the candidate projects. The carbon asset value calculation module is used to calculate the carbon asset value of each of the candidate projects based on the carbon emission reductions of the projects. The risk analysis and management module is used to conduct risk analysis and formulate risk management strategies based on the value of the carbon assets, the carbon emission reduction of the project, carbon market prices and cost data.

[0016] Thirdly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the intelligent management method for the assetization and trading mechanism of carbon emission reduction in the railway industry as described in any one of the first aspects.

[0017] The beneficial effects of this invention are: This invention discloses an intelligent management method and system for the assetization and trading mechanism of carbon emission reduction in the railway industry. This intelligent management method integrates historical and real-time railway operation data, utilizes machine learning to train a railway carbon emission characteristic identification model and a dynamic baseline model, enabling intelligent identification of potential emission reduction projects in the railway industry and precise adaptation to CCER methodology. It collects targeted carbon accounting monitoring data based on candidate projects, accurately quantifies the carbon emission reduction of each candidate project using the dynamic baseline model, and automatically calculates the carbon asset value of each emission reduction project based on the quantified emission reduction. Furthermore, it conducts systematic risk analysis and formulates control strategies based on multi-dimensional data including carbon emission reduction, carbon asset value, market price, and cost. This comprehensive approach achieves intelligent and integrated management of the entire process of carbon emission reduction project identification, emission reduction accounting, carbon asset value assessment, and risk control in the railway industry. It effectively addresses issues such as inefficient carbon emission reduction project identification, inconsistent emission reduction accounting standards, inaccurate carbon asset quantification, and an imperfect risk control system in the railway industry. It enhances the scientific rigor, timeliness, and standardization of railway carbon emission reduction asset development and carbon trading management, fully taps the carbon emission reduction potential of the railway industry, and provides reliable technical support for the market-based value transformation and low-carbon development of railway carbon resources. Attached Figure Description

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Figure 1 This is a flowchart illustrating the intelligent management method of Embodiment 1 of the present invention.

[0020] Figure 2 This is another flowchart illustrating the intelligent management method of Embodiment 1 of the present invention.

[0021] Figure 3 This is a schematic diagram of the module structure of the intelligent pipe system in Embodiment 2 of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Example 1 like Figure 1-2 As shown in the figure, this embodiment provides an intelligent management method for the assetization and trading mechanism of carbon emission reduction in the railway industry. The intelligent management method includes: Step S1: Obtain historical and real-time operational data of the railway and perform preprocessing to obtain historical and real-time sample data.

[0024] In this embodiment, historical and real-time operational data include traction power supply and station energy consumption data, freight turnover and freight flow transfer data, technical parameters of railway equipment and facilities, CCER and carbon market trading price data; CCER refers to emission reductions issued after review, filing and verification in accordance with the methodology stipulated by the state, which can be used to offset part of the mandatory carbon market compliance obligations, or for enterprises to voluntarily achieve carbon neutrality.

[0025] As an optional implementation, historical operation data refers to comprehensive railway operation data from historical periods, which is used for training machine learning models and provides a foundation for learning model parameters; real-time operation data refers to real-time operation indicators across all dimensions, which are used by carbon emission characteristic identification models to identify emission reduction projects and match methodologies.

[0026] As an optional implementation, preprocessing includes data cleaning, outlier removal, missing data completion, and data standardization to meet the input requirements for model training and data recognition.

[0027] Step S2: Based on historical sample data, a railway carbon emission characteristic identification model and a dynamic baseline model are obtained through machine learning training.

[0028] In this embodiment, the railway carbon emission feature identification model is a machine learning classification model, which takes railway energy consumption, freight structure, and equipment parameters as inputs to identify various emission reduction project types in the railway field and match the corresponding CCER methodology; the dynamic baseline model is a machine learning regression model, which is used to calculate the difference between the baseline emission and the actual emission, and then obtain the project's carbon emission reduction.

[0029] As an optional embodiment, the railway carbon emission feature identification model is a machine learning classification model. It uses preprocessed historical energy consumption, freight structure, equipment parameters, and labeled emission reduction project data as training samples. The model learns the correspondence between railway carbon emission features and emission reduction project types and CCER methodologies through machine learning algorithms, thus completing model training. Specifically, the model uses preprocessed historical sample data as input, which includes historical railway energy consumption data, freight structure data, line and equipment technical parameters, historical emission reduction project types, and CCER methodology matching labels. The training objective is to establish a mapping relationship between railway operation characteristics and emission reduction project types and CCER methodologies, enabling automatic classification and identification of railway emission reduction scenarios. The specific training process involves: labeling historical sample data, mapping different energy consumption characteristics, freight modes, and equipment conditions to various railway emission reduction projects and suitable CCER methodologies; using machine learning classification algorithms for model training and iterative optimization, learning the correlation between key railway emission features and carbon emission reduction potential; and completing model training and parameter optimization to obtain a railway carbon emission feature identification model with the ability to identify carbon emission reduction projects and match methodologies.

[0030] As an optional implementation, the dynamic baseline model is a machine learning regression model. It uses historical railway operating conditions, energy consumption data, and carbon emission-related parameters as training data to fit the baseline emission patterns under normal railway operation. Through machine learning regression training, a quantitative calculation relationship for carbon emissions is established. This allows the model to calculate the difference between baseline emissions and actual emissions based on real-time monitoring data, thus obtaining the carbon emission reduction for each candidate project. Specifically, historical energy consumption, freight volume, operating parameters, power grid emission factors, and industry carbon emission benchmark parameters are used as the training set as input. The training objective is to learn the quantitative relationship between operating load, energy consumption level, and carbon emission level, and to construct a dynamic calculation pattern for baseline emissions. The specific training process is as follows: Based on historical operating time-series data, the model fits the baseline emission change patterns under normal railway operation; combined with industry carbon emission benchmarks and emission factor parameters, a machine learning regression algorithm is used for training to establish a quantitative baseline emission model; iterative training using historical operating condition data corrects the emission calculation parameters and stabilizes the model's calculation accuracy; after training, the model can calculate the baseline emissions and actual emissions based on real-time monitoring data, and quantify the carbon emission reduction for each project by using the difference between the two.

[0031] Step S3: Input real-time sample data into the railway carbon emission characteristic identification model to identify potential emission reduction projects, determine the adaptation strategy by benchmarking the CCER methodology library, and generate a candidate project list.

[0032] In this embodiment, candidate projects include railway photovoltaic power generation projects, traction power supply energy-saving renovation projects, road-to-rail freight optimization projects, and new energy locomotive replacement projects. Specifically, the core of railway photovoltaic power generation projects lies in the green transformation of relatively fixed energy consumption nodes within the railway system; this mainly includes the large-scale deployment of distributed photovoltaic power generation systems on vast spatial resources such as railway stations, depots, and slopes along the line, achieving self-consumption and grid connection of surplus electricity, directly reducing dependence on high-carbon electricity from the power grid; another important direction is to promote in-depth energy-saving renovations of large railway public buildings and passenger and freight stations, reducing building operating energy consumption through the application of comprehensive technologies such as high-efficiency HVAC, intelligent lighting control, and building insulation materials; such projects have relatively mature technologies and clear boundaries, making them the most direct and stable entry point for initiating railway carbon asset development.

[0033] The goal of traction power supply energy-saving renovation projects is to tap into the deep energy-saving potential of the traction power link, which is the core link of railway operation. Specifically, this includes promoting the application of advanced power supply technologies that can effectively reduce traction energy consumption in specific sections such as long and steep slopes and heavy-haul transportation. It also includes systematically reducing traction power consumption per unit of transportation turnover through transportation organization methods such as optimizing train timetables, increasing train density, promoting fixed traction capacity for freight trains, popularizing intelligent driving and optimizing operation strategies. Such projects have a high degree of technological integration and significant energy-saving effects, and are core carbon assets that reflect the high technology content of the railway industry.

[0034] The road-to-rail freight optimization project is the most distinctive and emission-reduction potential asset in the railway industry. Its core is to promote the shift of bulk and medium-to-long-distance freight transportation from road to rail. By leveraging the low-carbon comparative advantages of railways, it aims to achieve net emission reductions for the entire society while completing the same social transport turnover. The key to realizing the value of the road-to-rail freight optimization project lies in scientifically and rigorously setting the project baseline and establishing a measurable, reportable, and verifiable freight transfer tracking and accounting system to prove the additionality, authenticity, and accuracy of emission reductions.

[0035] The new energy locomotive replacement project aims to address emissions from mobile sources that are not yet electrified within the railway system. The focus is on replacing traditional diesel shunting locomotives with battery or hydrogen fuel cell locomotives in operational scenarios such as large marshalling yards, freight yards, and depots. Simultaneously, on non-electrified branch lines, the project explores the application of hybrid or entirely new locomotives powered by low-carbon energy sources such as hydrogen fuel cells and biodiesel. Furthermore, the electrification of railway fleets, including official vehicles and logistics delivery vehicles, also falls under this category. While the individual emission reductions from these projects may be relatively small, their demonstrative significance is substantial, representing a crucial step towards achieving near-zero emissions from mobile sources in the railway system.

[0036] Step S4: Based on each candidate project in the candidate project list, collect real-time monitoring data of railway carbon accounting corresponding to each candidate project.

[0037] In this embodiment, the real-time monitoring data for railway carbon accounting is dedicated monitoring data selected for carbon emission reduction accounting. It only includes the core indicators required for emission reduction calculation, such as real-time energy consumption data, freight volume data, power generation data, and equipment operating parameters, which are used by the dynamic baseline model to complete the accurate calculation of carbon emission reduction.

[0038] Step S5: Input the real-time monitoring data of railway carbon accounting into the dynamic baseline model to calculate the carbon emission reduction corresponding to each candidate project.

[0039] In this embodiment, the dynamic baseline model is constructed based on railway operation-related monitoring data, emission factors, and industry carbon emission parameters. Through dynamic baseline emission rules, the carbon emission reduction of each candidate project is quantified to ensure the compliance and accuracy of emission reduction calculation.

[0040] Step S6: Calculate the carbon asset value of each candidate project based on the project's carbon emission reduction.

[0041] In this embodiment, the carbon asset value of each candidate project is calculated using the carbon asset value calculation formula, which is as follows: ; in, Carbon emission reductions for a single candidate project. For carbon market trading prices, Costs for project development, monitoring and verification, and transaction operations.

[0042] As an alternative implementation, carbon assets are environmental and economic rights created under a specific policy framework through rigorously certified greenhouse gas emission reduction or removal activities. For the railway industry, the core form of carbon assets is Certified Emission Reductions (CCERs). CCER assets possess policy-derived characteristics, virtual commodity nature, and a dual nature of risk and opportunity; their value is not inherent but is determined by market supply and demand and institutional rules. The total carbon asset value (V) of a single CCER project can be expressed using the following theoretical model: ; In the formula, Let the project's average annual emission reduction in year i be (tons). The physical output of a project is determined by the type of technology, the scale of the project, and the rigor of its methodology. The expected carbon market price (yuan / ton) at the time of issuance of emission reductions in year i. The largest source of external uncertainty is the development cost (design, approval), monitoring report verification (MRV) cost, and transaction cost, which are affected by factors such as national policies, macroeconomic conditions, and market supply and demand. Cdevelopment,i, CMRV,i, and Ctransaction,i represent the development costs (design, approval), monitoring report verification (MRV) costs, and transaction costs allocated to the project in year i, respectively.

[0043] Therefore, the core of carbon asset management for railway enterprises lies in managing three variables: expanding emission reductions through technological innovation and large-scale application (Q); grasping carbon prices through market analysis and risk management (P); and reducing full-cycle costs through process optimization and digitalization (C). Carbon assets are evolving from environmental rights into green financial instruments with functions such as financing and pledging.

[0044] As an optional implementation, to specifically quantify the potential economic value of railway carbon assets, this implementation establishes a unified assessment framework to quantitatively estimate the emission reduction potential and monetary value of four major categories of key projects. The assessment framework covers areas such as clean infrastructure, transportation structure optimization, deep energy conservation in traction systems, and zero carbonization of mobile sources, and sets key parameters including activity level, emission factor, carbon price scenario, and technical efficiency. The emission factor is conservatively estimated using the current national power grid average emission factor and typical values ​​for diesel and road freight. For the carbon price, three scenarios—benchmark, conservative, and optimistic—are set to analyze sensitivity.

[0045] For railway photovoltaic power generation projects, assuming a cumulative development scale of 4 gigawatts and an annual equivalent utilization of 1,150 hours within a certain preset period, the annual emission reduction potential can reach approximately 2.67 million tons of carbon dioxide equivalent, calculated using the current grid emission factor. Under a benchmark carbon price of 60 yuan per ton, the corresponding annual carbon asset value is approximately 160 million yuan. The value of such projects is relatively stable, mainly depending on solar resources and operation and maintenance levels.

[0046] For the "road-to-rail" freight transfer project, assuming that a stable freight volume of 30 million tons of specific goods with an average transport distance of 450 kilometers can be transferred from road to rail each year; by comparing the carbon emission intensity of heavy-duty diesel trucks and electrified railways, the annual emission reduction potential can be calculated to be approximately 2.38 million tons of CO2 equivalent, with an annual value of approximately 143 million yuan under the benchmark carbon price; this type of project has the greatest potential for emission reduction, but its value realization is highly dependent on the rigor of the project boundary design, the monitorability of freight transfer, and the stability of the market.

[0047] For energy-saving projects related to flexible traction in-phase power supply systems for heavy-haul railways, taking a heavy-haul trunk line with an annual transport volume of 300 billion ton-kilometers as an example, and referring to engineering practice, it is assumed that a systematic transformation can achieve an 11% reduction in traction energy consumption. According to calculations, its annual emission reduction potential can reach approximately 2.6 million tons of carbon dioxide equivalent, with an annual value of approximately 156 million yuan under the benchmark carbon price. This project not only has huge individual value, but also has a highly replicable technical path. After being promoted on major heavy-haul trunk lines, it can generate a significant cluster effect, and its marginal emission reduction cost may have a competitive advantage.

[0048] For the project of replacing internal combustion locomotives with new energy locomotives, a scenario analysis was conducted with the replacement of 200 standard internal combustion shunting locomotives per year. Under the "oil-to-electricity" scenario, after considering the indirect emissions from the increased electricity consumption, the net annual emission reduction potential is about 16,700 tons, with a benchmark value of about 1 million yuan. If "green hydrogen" fuel cell locomotives are used, their carbon emissions throughout the entire life cycle are extremely low, and the net annual emission reduction potential can reach about 48,900 tons, with a benchmark value of about 2.93 million yuan.

[0049] Step S7: Based on carbon asset value, project carbon emission reduction, carbon market price and cost data, conduct risk analysis and formulate risk management strategies.

[0050] In this embodiment, step S7, which involves conducting risk analysis and formulating a risk management strategy, includes: policy and regulatory risks, market and price risks, technology and implementation risks, and financial and management risks. This allows for targeted preventative and control measures to ensure the stable operation of railway carbon asset development and trading. Specifically, policy and regulatory risks are the most critical systemic risks, including changes in CCER methodology standards, adjustments to project filing or issuance processes, reductions in market offset ratios, and even the future inclusion of the railway industry in the mandatory carbon market. These top-level institutional changes will fundamentally impact project compliance and profitability. Market and price risks refer to the fact that carbon prices are affected by multiple factors such as the macroeconomy, energy prices, and quota supply and demand, and may fluctuate significantly. A sharp drop in carbon prices will directly erode the project's economic viability. Technology and implementation risks specifically include monitoring risks due to deficiencies in the monitoring system leading to invalid data, performance risks where actual emission reductions fail to meet design expectations, and the potential leakage risk in "road-to-rail" projects where freight transport is merely transferred within the road system rather than resulting in actual emission reductions. Financial and management risks involve issues such as sunk costs, cost overruns, insufficient trading liquidity, and a shortage of professional capabilities and talent within the enterprise.

[0051] As an optional implementation, to effectively manage these risks, railway enterprises should establish a carbon asset project risk assessment matrix, combining qualitative and quantitative methods. Qualitatively, the risk matrix can be used to classify and position the probability and impact of various risks. Quantitatively, the focus should be on carbon price sensitivity analysis, and methods such as scenario stress testing and even Monte Carlo simulations should be used to assess the robustness of project economic indicators under different risk scenarios. In terms of response strategies, a multi-pronged approach is needed: regarding policy risks, proactive tracking and participation in rule-making are required, and conservative parameters and diversified project portfolios should be adopted in project design; regarding... To address market risks, long-term agreements with emission-controlled enterprises can be explored to lock in a portion of revenue, and hedging can be conducted using potentially mature carbon financial derivatives. Regarding technological risks, investment in a high-standard, tamper-proof MRV system is essential, along with exploring the introduction of innovative products such as carbon insurance. For financial and management risks, internal capacity building needs to be strengthened, refined budget management implemented, and the establishment of a risk reserve fund considered. Only through systematic value assessment and full-cycle risk management can the railway industry transform its enormous carbon asset potential into sustainable and predictable green economic benefits, robustly supporting its green transformation and high-quality development.

[0052] Step S8: Automatically recommend three market participation paths: project owners, investment institutions, and carbon inclusion organizers, and make trading timing decisions.

[0053] In this embodiment, the railway industry can choose from three differentiated paths to participate in the carbon market based on its own attributes, resource endowments, and strategic goals. These three paths each have their own emphasis in terms of participation depth, professional requirements, and profit models. The first path is to participate deeply as a voluntary emission reduction project owner. This is the core path to directly transform the railway's own green practices into economic benefits. Under this path, the railway operation or asset management unit, as the legal entity, leads the entire process of CCER project development and management, including project identification and design, entrusting third-party review, applying for filing with the national competent authority, strictly implementing the monitoring plan during the project operation period, regularly verifying emission reductions and applying for issuance, and ultimately using the obtained CCERs for market trading or for its own future compliance. This path requires enterprises to have clear property rights to project assets, a deep understanding of emission reduction technologies, the construction of a monitoring report verification system that meets methodological requirements, and long-term project management and financial capabilities. Its benefits come directly from the sale of carbon assets and can most effectively enhance the enterprise's green brand image.

[0054] As an optional implementation, the second approach involves participation as an investment institution. This is primarily applicable to financial or investment companies under the China State Railway Group with strong financial resources and risk tolerance. This path does not directly develop the railway's own emission reduction projects but rather operates through capital operations in the carbon market from a financial investment perspective. Specific methods include buying and selling carbon emission allowances or CCERs in the secondary market to profit from price differences, directly investing in high-quality domestic and international emission reduction projects as a financial investor to acquire carbon assets or dividends, or participating in investments in various carbon financial products based on carbon assets. The advantage of this path is that it allows for relatively rapid market entry, accumulation of trading experience, understanding of carbon price signals, and potential financial returns, while also providing market information and financial tools to support the group's core carbon asset development business. However, it does not directly address the assetization of the railway's own emission reductions and can serve as a supplementary or strategic development path.

[0055] As an optional implementation, the third approach is to participate as an organizer of carbon credit projects. This is an innovative path aimed at mobilizing public participation and shaping a green image for railways. Railway companies can leverage their large passenger volume and employee base to build their own carbon credit platforms. For example, they can integrate functions into railway ticketing mobile applications, quantifying low-carbon behaviors such as choosing e-tickets, taking high-speed rail, and traveling during off-peak hours into personal carbon credits, which can be redeemed for incentives such as ticket discounts and VIP services, thus forming a closed loop of low-carbon behavior - carbon credits - incentive benefits. Furthermore, railway companies can collaborate with provincial and municipal governments that are building local carbon credit markets, applying to include projects or behaviors that have clear emission reduction effects but have not yet been developed into national CCERs into local carbon credit systems. This allows for more flexible and faster conversion of some emission reductions into value and deepens green partnerships with local governments.

[0056] Step S9: Generate a three-step strategic roadmap centered on CCER development, encompassing near-term breakthroughs, mid-term promotion, and long-term integration.

[0057] In this embodiment, to achieve the strategic goal of developing railway carbon assets from scratch, expanding from specific points to a comprehensive scale, and ultimately achieving full integration, a three-step strategic roadmap centered on CCER development is proposed. This roadmap starts with a specific year and clarifies the core objectives and key tasks for each stage. The first stage is the near-term breakthrough and capacity building stage. The core objective of this stage is to achieve a breakthrough in CCER development for key project types, overcome core methodological barriers, and establish a basic capability system. Specifically, one to two technically mature heavy-haul railway trunk lines should be selected to simultaneously promote engineering demonstrations of energy-saving projects for flexible traction in-phase power supply systems in heavy-haul railways and the development and application of dedicated CCER methodologies, focusing on resolving the technical challenges of determining baselines and monitoring energy-saving effects for such systemic energy-saving projects. Simultaneously, concentrated demonstrations of projects replacing diesel locomotives with new energy locomotives should be carried out in typical stations or dedicated lines, and methodological adaptation and innovation should be promoted for different technical routes. While tackling high-tech projects, relatively mature rooftop photovoltaic projects at railway stations should be promoted in parallel. Sites with favorable conditions should be selected to quickly initiate the CCER development process, striving to achieve the first batch of project registration and emission reduction issuance, so as to generate early benefits, accumulate experience, and enhance confidence. In addition, it is necessary to begin designing 1-2 clearly defined road-to-rail demonstration project schemes. In terms of organizational support, the China State Railway Group should establish a physical carbon asset management center during this stage, each railway bureau should clarify the responsible department, and the construction of a professional talent team should be initiated.

[0058] As an optional implementation, the second phase is the mid-term promotion and value realization phase. The core objective of this phase is to promote the large-scale development of multiple types of projects, establish a market-oriented operation mechanism, and significantly increase the market share and influence of railway carbon assets. In terms of technology projects, the flexible traction power supply energy-saving technology and CCER development model, which have been proven in practice, should be replicated and promoted to other eligible trunk lines, and the large-scale replacement of new energy locomotives in more scenarios should be promoted. In terms of potential projects, railway photovoltaics should expand from individual station buildings to hub areas and areas along the line to form economies of scale. "Road-to-rail" projects should form replicable development templates based on the summary of demonstration experience and be promoted in major freight corridors to accelerate the transformation of their huge potential into carbon assets. At the same time, a professional carbon asset operation platform should be established or relied upon to uniformly manage, trade, and provide financial services for CCERs generated by various projects, and actively cooperate with exchanges and financial institutions to explore the application of innovative tools such as carbon asset pledge financing and carbon bonds.

[0059] As an optional implementation, the third stage is the long-term integration and ecosystem building stage. The core objective of this stage is to achieve a comprehensive integration of carbon asset management and enterprise operation and development, making the railway industry a standard setter and value leader in carbon emission reduction in the transportation sector. In this stage, project type barriers should be broken down to maximize the value of system emission reduction, such as developing comprehensive CCER projects that integrate photovoltaics, flexible power supply, and electric locomotives along the line. The railway industry should strive to elevate its emission reduction practices and methodologies in areas such as flexible power supply and new energy locomotives to industry and even national standards, and actively participate in the formulation of international rail transit carbon emission reduction rules. Ultimately, carbon asset management should be deeply integrated into the core strategy of railway enterprises, with carbon costs and carbon benefits becoming important considerations in investment decisions, and low carbon becoming a key carrier of the core competitiveness and brand value of railway services. The railway industry not only realizes the economic value of its own emission reduction through carbon trading, but also helps the whole society to deeply decarbonize by providing low-carbon logistics solutions and building a green supply chain ecosystem, achieving a unity of environmental, economic, and social benefits.

[0060] This embodiment provides an intelligent management method for the assetization and trading mechanism of carbon emission reduction in the railway industry. This intelligent management method integrates historical and real-time railway operation data, utilizes machine learning to train a railway carbon emission characteristic identification model and a dynamic baseline model, enabling intelligent identification of potential emission reduction projects in the railway industry and precise adaptation to the CCER methodology. It collects targeted carbon accounting and monitoring data based on candidate projects, accurately quantifies the carbon emission reduction of each candidate project using the dynamic baseline model, and automatically calculates the carbon asset value of each emission reduction project based on the quantified emission reduction. Furthermore, it leverages the carbon emission reduction and carbon asset... By conducting systematic risk analysis and formulating control strategies based on multi-dimensional data including value, market price, and cost, the system achieves intelligent and integrated management of the entire process of carbon emission reduction project identification, emission reduction calculation, carbon asset valuation, and risk control in the railway industry. This effectively addresses issues such as inefficient identification of carbon emission reduction projects, inconsistent emission reduction calculation standards, inaccurate carbon asset quantification, and an imperfect risk control system in the railway industry. It enhances the scientific, timely, and standardized development and management of railway carbon emission reduction assets and carbon trading, fully taps the carbon emission reduction potential of the railway industry, and provides reliable technical support for the market-based value transformation and low-carbon development of railway carbon resources.

[0061] Example 2 like Figure 3 As shown, this embodiment provides an intelligent management system 10 for the assetization and trading mechanism of carbon emission reduction in the railway industry. The intelligent management system 10 includes: a data acquisition and processing module 11, a model training module 12, a project identification and matching module 13, a data monitoring and collection module 14, an emission reduction calculation module 15, a carbon asset value calculation module 16, and a risk analysis and control module 17. The data acquisition and processing module 11 is used to acquire historical and real-time operational data of the railway and perform preprocessing to obtain historical and real-time sample data.

[0062] Model training module 12 is used to train a railway carbon emission characteristic identification model and a dynamic baseline model based on historical sample data through machine learning.

[0063] The project identification and matching module 13 is used to input real-time sample data into the railway carbon emission characteristic identification model, identify potential emission reduction projects, determine the adaptation strategy by benchmarking the CCER methodology library, and generate a candidate project list.

[0064] The data monitoring and acquisition module 14 is used to collect real-time monitoring data of railway carbon accounting corresponding to each candidate project in the candidate project list.

[0065] The emission reduction calculation module 15 is used to input real-time monitoring data of railway carbon accounting into the dynamic baseline model to calculate the project carbon emission reduction corresponding to each candidate project.

[0066] The carbon asset value calculation module 16 is used to calculate the carbon asset value of each candidate project based on the project's carbon emission reduction.

[0067] The Risk Analysis and Management Module 17 is used to conduct risk analysis and formulate risk management strategies based on carbon asset value, project carbon emission reduction, carbon market price and cost data.

[0068] As an optional embodiment, the intelligent management system further includes: a market path participation module 18 and a strategic route layout module 19; The Market Path Participation Module 18 is used to automatically recommend three market participation paths: project owners, investment institutions, and carbon inclusion organizers, and to make trading timing decisions.

[0069] The strategic roadmap module 19 is used to generate a three-step strategic roadmap centered on CCER development, which includes near-term breakthroughs, mid-term promotion, and long-term integration.

[0070] This embodiment provides an intelligent management system for the assetization and trading mechanism of carbon emission reduction in the railway industry. This intelligent management system is based on the intelligent management method for the assetization and trading mechanism of carbon emission reduction in the railway industry provided in Embodiment 1 above; specific details will not be repeated here. Specifically, this intelligent management system integrates historical and real-time railway operation data, uses machine learning to train a railway carbon emission characteristic identification model and a dynamic baseline model, and can achieve intelligent identification of potential emission reduction projects in the railway industry and precise adaptation to the CCER methodology. It collects targeted carbon accounting and monitoring data based on candidate projects, accurately quantifies the carbon emission reduction of each candidate project using the dynamic baseline model, and combines the quantified emission reduction to complete the self-assessment of the carbon asset value of each emission reduction project. The system employs automated calculations and leverages multi-dimensional data on carbon emission reductions, carbon asset value, market prices, and costs to conduct systematic risk analysis and formulate control strategies. This enables intelligent and integrated management of the entire process of carbon emission reduction project identification, emission reduction calculation, carbon asset value assessment, and risk control in the railway industry. It effectively addresses issues such as inefficient carbon emission reduction project identification, inconsistent emission reduction calculation standards, inaccurate carbon asset quantification, and an imperfect risk control system. This enhances the scientific rigor, timeliness, and standardization of railway carbon emission reduction asset development and carbon trading management, fully tapping the carbon emission reduction potential of the railway industry and providing reliable technical support for the market-based value transformation and low-carbon development of railway carbon resources.

[0071] Example 3 This embodiment relates to a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the intelligent management method for the assetization and trading mechanism of carbon emission reduction in the railway industry as described in Embodiment 1 above.

[0072] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0073] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the scope of the present invention. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. An intelligent management method for the assetization and trading mechanism of carbon emission reduction in the railway industry, characterized in that, The intelligent management method includes: Step S1: Obtain historical and real-time operational data of the railway and perform preprocessing to obtain historical sample data and real-time sample data; Step S2: Based on the historical sample data, a railway carbon emission characteristic identification model and a dynamic baseline model are obtained through machine learning training; Step S3: Input the real-time sample data into the railway carbon emission characteristic identification model to identify potential emission reduction projects, determine the adaptation strategy by benchmarking the CCER methodology library, and generate a candidate project list. Step S4: Based on each candidate project in the candidate project list, collect real-time monitoring data of railway carbon accounting corresponding to each candidate project in real time; Step S5: Input the real-time monitoring data of railway carbon accounting into the dynamic baseline model to calculate the carbon emission reduction of each candidate project. Step S6: Calculate the carbon asset value of each candidate project based on the carbon emission reduction of the project; Step S7: Based on the value of the carbon assets, the carbon emission reduction of the project, carbon market prices and cost data, conduct risk analysis and formulate risk management strategies.

2. The intelligent management method for the assetization and trading mechanism of carbon emission reduction in the railway industry according to claim 1, characterized in that, The intelligent management method also includes: Step S8: Automatically recommend three market participation paths: project owners, investment institutions, and carbon inclusion organizers, and make trading timing decisions; Step S9: Generate a three-step strategic roadmap centered on CCER development, encompassing near-term breakthroughs, mid-term promotion, and long-term integration.

3. The intelligent management method for the assetization and trading mechanism of carbon emission reduction in the railway industry according to claim 2, characterized in that, The historical and real-time operational data include traction power supply and station energy consumption data, freight turnover and freight flow transfer data, technical parameters of railway equipment and facilities, CCER and carbon market trading price data.

4. The intelligent management method for the assetization and trading mechanism of carbon emission reduction in the railway industry according to claim 1, characterized in that, The railway carbon emission feature identification model is a machine learning classification model that takes railway energy consumption, freight structure and equipment parameters as inputs to identify various emission reduction project types in the railway field and match the corresponding CCER methodology. The dynamic baseline model is a machine learning regression model used to calculate the difference between the baseline emissions and the actual emissions, thereby obtaining the carbon emission reduction of the project.

5. The intelligent management method for the assetization and trading mechanism of carbon emission reduction in the railway industry according to claim 1, characterized in that, The candidate projects include railway photovoltaic power generation projects, traction power supply energy-saving renovation projects, road-to-rail freight optimization projects, and new energy locomotive replacement projects.

6. The intelligent management method for the assetization and trading mechanism of carbon emission reduction in the railway industry according to claim 1, characterized in that, The dynamic baseline model is constructed based on railway operation-related monitoring data, emission factors, and industry carbon emission parameters. Through dynamic baseline emission rules, it quantifies the carbon emission reduction of each candidate project, ensuring the compliance and accuracy of emission reduction calculation.

7. The intelligent management method for the assetization and trading mechanism of carbon emission reduction in the railway industry according to claim 1, characterized in that, The step of calculating the carbon asset value of each candidate project in step S6 includes: calculating the carbon asset value of each candidate project using a carbon asset value calculation formula. The formula for calculating the value of carbon assets is as follows: ； in, The carbon emission reduction for a single candidate project. For carbon market trading prices, Costs for project development, monitoring and verification, and transaction operations.

8. The intelligent management method for the assetization and trading mechanism of carbon emission reduction in the railway industry according to claim 1, characterized in that, The steps in S7, which involve conducting risk analysis and formulating risk management strategies, include: policy and regulatory risks, market and price risks, technology and execution risks, and financial and management risks. These strategies enable the development of targeted preventive and control measures to ensure the stable operation of railway carbon asset development and trading.

9. An intelligent management system for the assetization and trading mechanism of carbon emission reduction in the railway industry, characterized in that, The intelligent management system includes: The data acquisition and processing module is used to acquire historical and real-time operational data of the railway and perform preprocessing to obtain historical and real-time sample data. The model training module is used to obtain a railway carbon emission feature identification model and a dynamic baseline model through machine learning training based on the historical sample data. The project identification and matching module is used to input the real-time sample data into the railway carbon emission characteristic identification model, identify potential emission reduction projects, determine the adaptation strategy by benchmarking the CCER methodology library, and generate a candidate project list. The data monitoring and acquisition module is used to collect real-time monitoring data of railway carbon accounting corresponding to each candidate project in the candidate project list. The emission reduction calculation module is used to input the real-time monitoring data of railway carbon accounting into the dynamic baseline model and calculate the project carbon emission reduction corresponding to each of the candidate projects. The carbon asset value calculation module is used to calculate the carbon asset value of each of the candidate projects based on the carbon emission reductions of the projects. The risk analysis and management module is used to conduct risk analysis and formulate risk management strategies based on the value of the carbon assets, the carbon emission reduction of the project, carbon market prices and cost data.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the intelligent management method of the railway industry carbon emission reduction assetization and trading mechanism as described in any one of claims 1 to 8.

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