Railway carbon emission multi-dimensional influence factor analysis method based on improved LMDI model

Abstract

The invention discloses a railway carbon emission multi-dimensional influence factor analysis method based on an improved LMDI model, and the method comprises the steps: firstly collecting the energy consumption, freight turnover, the total transportation turnover of passenger transportation and freight, the number of internal combustion locomotives, the number of electric locomotives, the total number of locomotives, the total investment of technical transformation, and the total investment of capital construction, and carrying out the preprocessing; then, constructing a railway carbon emission multi-dimensional influence factor index system comprising four dimensions of an energy system, transportation efficiency, technical innovation and capital construction driving; then, obtaining an improved LMDI model equation by expanding a Kaya identical equation, decomposing the total carbon emission variation into variation of each influence factor, calculating a contribution degree static evaluation value of each influence factor in each annual period, and calculating an average change speed of each influence factor in each annual period as a contribution degree dynamic evaluation value; the method has the advantages that the defects that in the prior art, heterogeneity of energy / transportation modes cannot be processed, the nonlinear threshold effect is difficult to analyze, and dynamic response capacity is lacked are overcome.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy conservation and emission reduction in transportation, and in particular relates to a method for analyzing multi-dimensional influencing factors of railway carbon emissions based on an improved LMDI (Logarithmic Mean Divisia Index) model. Background Art

[0002] As global climate change becomes increasingly severe, carbon emissions have become a core metric for measuring the environmental impact of various industries. In the transportation sector, rail, as a representative green and low-carbon mode of transport, has a significantly lower carbon emission intensity than roads and aviation. According to the International Energy Agency (IEA), the global transportation industry contributes approximately 24% of energy-related CO2 emissions. Rail transport's carbon emissions per unit passenger turnover are only one-tenth of aviation's, and its carbon emissions per unit freight turnover are only one-fifth of road's. However, as demand for rail transport continues to grow, its total carbon emissions continue to rise. Therefore, accurately analyzing the factors influencing railway carbon emissions is crucial for formulating scientific emission reduction policies.

[0003] The Log-Mean Dirichlet Index (LMDI) decomposition method has been widely used in carbon emission research at the national and industry levels due to its mathematical rigor (complete decomposition without residuals) and operational simplicity (Ang & Zhang, 2000). The basic principle of the LMDI is to quantify the contribution of each variable to changes in carbon emissions by decomposing factors such as energy intensity, economic scale, and energy structure. However, recent studies (Wang et al., 2021; Li et al., 2022) have found that the traditional LMDI model has the following key limitations when applied to the analysis of factors affecting railway carbon emissions:

[0004] First, railway carbon emissions have unique structural characteristics: (1) energy duality, i.e., the coexistence of electric traction (zero direct emissions) and internal combustion traction (diesel carbon emission coefficient 2.68 kgCO2 / L); (2) modal heterogeneity, with heavy-load freight unit energy consumption (28 kWh / (10 3t·km) is 3.8 times that of high-speed rail passenger transport (7.3kWh / thousand passenger-km) (UIC, 2021). (3) Network effect, road network density and hub layout affect locomotive idle rate and energy efficiency. For example, for every 10% increase in road network density, the locomotive idle rate can be reduced by 1.2-1.8 percentage points (Liu et al., 2022). The traditional LMDI model usually uses macroeconomic indicators (such as GDP per capita) for decomposition, which makes it difficult to quantify the impact of the above characteristics. For example, when the traditional LMDI model does not distinguish between the carbon emission factors of electric / diesel traction (electricity: 0.85kgCO2 / kWh, diesel: 2.68kgCO2 / L (national standard GB 2589-2020)), the emission reduction assessment of the energy efficiency improvement of a freight trunk line will produce significant deviations. According to the UIC standard, the actual emission reduction is 120,000 tons, while the traditional LMDI model, due to the extensive treatment of energy structure, obtains 168,000 tons, with a relative error of +40%.

[0005] Secondly, there is a significant threshold effect on railway carbon emissions: (1) The railway network density exceeds 500km / 10 3 km 2 When the electrification rate is between 70% and 80%, the marginal emission reduction efficiency decreases by 40% (Zhang et al., 2023). (2) When the electrification rate is between 70% and 80%, each 1% increase can reduce emissions by 0.8%, but the benefits decrease after exceeding 85% (Li et al., 2022). The linear assumption of the traditional LMDI decomposition method based on a fixed base period leads to an error of 30-45% in the policy effect evaluation (Chen et al., 2021).

[0006] Thirdly, the traditional LMDI decomposition method based on a fixed base period (Ang & Zhang, 2000) cannot effectively capture the multi-timescale dynamic changes in railway carbon emissions, specifically: (1) Short-term (less than or equal to 1 year) fluctuations: Unable to respond to seasonal operational adjustments such as the Spring Festival travel rush. (2) Medium-term (1-5 years) policy intervention: Underestimating the effects of policies such as electrification. After the policy was implemented, a certain group eliminated 380 diesel locomotives, and actually achieved an annual emission reduction of 120,000 tons, while the traditional LMDI decomposition method based on a fixed base period only reduced annual emissions by 90,000 tons (deviation -25%). (3) Long-term (greater than 5 years) technological evolution: Unable to adapt to the nonlinear cumulative effects of technological iteration. Research shows (Wang et al., 2020) that the traditional LMDI decomposition method based on a fixed base period will produce an average assessment bias of ±25% (confidence interval 95%) when evaluating the compound impact of policy and technology over a 3-5 year period.

[0007] Therefore, in view of the multi-dimensional defects of the traditional LMDI model in railway carbon emissions analysis: the inability to handle the heterogeneity of energy / transportation modes in the structural dimension, the difficulty in analyzing the nonlinear threshold effect in the mathematical dimension, and the lack of dynamic response capability in the time dimension, it is of great research value and practical significance to study a multi-dimensional influencing factor analysis method for railway carbon emissions based on the improved LMDI model. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to provide a method for analyzing the multi-dimensional influencing factors of railway carbon emissions based on the improved LMDI model, which solves the defects of the existing technology such as the inability to handle the heterogeneity of energy / transportation modes, difficulty in analyzing nonlinear threshold effects, and lack of dynamic response capabilities.

[0009] The technical solution adopted by the present invention to solve the above technical problems is: a method for analyzing the multi-dimensional influencing factors of railway carbon emissions based on an improved LMDI model, which is characterized by comprising the following steps:

[0010] Step 1: Collect data, including CO2 emissions for various energy types, energy consumption for various energy types, freight turnover, total passenger and freight transport turnover, number of diesel locomotives, number of electric locomotives, total number of locomotives, total investment in technological transformation, and total investment in capital construction. Energy types include raw coal, anthracite, diesel, gasoline, liquefied petroleum gas, natural gas from gas fields, purchased heat, and electricity. Then, pre-process the collected data.

[0011] Step 2: Construct an indicator system for multi-dimensional influencing factors of railway carbon emissions, covering four dimensions: energy system, transportation efficiency, technological innovation, and infrastructure drive. The energy system is composed of four influencing factors: energy structure, energy intensity, electric locomotive deployment rate, and locomotive energy dependence rate. Transportation efficiency is composed of two influencing factors: transportation structure and diesel locomotive transportation efficiency. Technological innovation is composed of two influencing factors: locomotive technology investment coverage rate and technological transformation investment ratio. Infrastructure drive is composed of two influencing factors: total capital construction investment and carbon emission intensity.

[0012] Step 3: Using the preprocessed data and the constructed railway carbon emissions multi-dimensional influencing factor indicator system, we conduct an analysis of the multi-dimensional influencing factors of railway carbon emissions based on the improved LMDI model. The specific process is as follows:

[0013] Step 3.1: Based on the Kaya identity, expand to obtain the LMDI model equation, which is described as:

[0014]

[0015] Among them, C represents carbon dioxide emissions, E represents energy consumption, FT represents freight turnover, TT represents the total transport turnover of passenger and freight, NICL represents the number of diesel locomotives, NEL represents the number of electric locomotives, TNL represents the total number of locomotives, TITR represents the total investment in technological transformation, and CIT represents the total investment in capital construction;

[0016] Step 3.2: Based on the LMDI model equation, expand to obtain the improved LMDI model equation, which is described as:

[0017]

[0018] Where i = 1, 2, ..., 8, C z Indicates the total carbon dioxide emissions, C i represents the carbon dioxide emissions corresponding to the i-th energy type, E i represents the energy consumption corresponding to the i-th energy type, E z Represents the total energy consumption, let CI i =C i / E i Denotes the carbon emission intensity corresponding to the i-th energy type, let ES i =E i / E z Represents the energy structure corresponding to the i-th energy type, let EI=E z / FT represents energy intensity, let TS = FT / TT represent transportation structure, let ITE = TT / NICL represent diesel locomotive transportation efficiency, let EDR = NICL / NEL represent locomotive energy dependence rate, let ECR = NEL / TNL represent electric locomotive configuration rate, let LTICR = TNL / TITR represent locomotive technology investment coverage rate, let TIR = TITR / CIT represent technology transformation investment ratio;

[0019] Step 3.3: Based on the improved LMDI model equation, express the change in total carbon emissions between the target year and the base year as ΔC. and will Decompose into in, represents the total CO2 emissions in the target year, represents the total carbon dioxide emissions in the base year, ΔCI i Indicates the change in carbon emission intensity corresponding to the i-th energy type between the target year and the base year, ΔES irepresents the change in energy structure corresponding to the i-th energy type between the target year and the base year, ΔEI represents the change in energy intensity between the target year and the base year, ΔTS represents the change in transport structure between the target year and the base year, ΔITE represents the change in diesel locomotive transport efficiency between the target year and the base year, ΔEDR represents the change in locomotive energy dependence rate between the target year and the base year, ΔECR represents the change in electric locomotive configuration rate between the target year and the base year, ΔLTICR represents the change in locomotive technology investment coverage rate between the target year and the base year, ΔTIR represents the change in technological transformation investment ratio between the target year and the base year, and ΔCIT represents the change in total capital construction investment between the target year and the base year;

[0020] Step 3.4: Divide the annual time period for analysis into Num annual segments. The first and last years of each annual segment serve as the base year and target year respectively. The base year of the latter annual segment is the target year of the previous annual segment, or the base year of the latter annual segment is the year after the target year of the previous annual segment.

[0021] Step 3.5: Follow the procedure in step 3.3 to obtain the ΔCI for each year segment in the same manner. i , ΔES i , ΔEI, ΔTS, ΔITE, ΔEDR, ΔECR, ΔLTICR, ΔTIR, ΔCIT; Then apply the mutation series theory to calculate the ΔCI of each annual segment. i , ΔES i , ΔEI, ΔTS, ΔITE, ΔEDR, ΔECR, ΔLTICR, ΔTIR, ΔCIT, corresponding to obtaining the carbon emission intensity corresponding to the i-th energy type in each annual period, the energy structure corresponding to the i-th energy type, energy intensity, transportation structure, diesel locomotive transportation efficiency, locomotive energy dependence rate, electric locomotive configuration rate, locomotive technology investment coverage rate, technology transformation investment ratio, and static evaluation value of contribution to the total capital construction investment;

[0022] Step 3.6: Calculate the average change rates of the carbon emission intensity corresponding to the i-th energy type, the energy structure, energy intensity, transportation structure, diesel locomotive transportation efficiency, locomotive energy dependence rate, electric locomotive configuration rate, locomotive technology investment coverage rate, technological transformation investment ratio, and total capital construction investment in each annual period, which correspond to the dynamic evaluation values ​​of the contribution of the carbon emission intensity corresponding to the i-th energy type, the energy structure, energy intensity, transportation structure, diesel locomotive transportation efficiency, locomotive energy dependence rate, electric locomotive configuration rate, locomotive technology investment coverage rate, technological transformation investment ratio, and total capital construction investment in each annual period. The average change rate of an influencing factor is defined as the change of the influencing factor divided by the difference between the target year and the base year.

[0023] The pretreatment includes cleaning, standardization and validation processes.

[0024] The energy structure is defined as the proportion of energy consumption corresponding to various energy types used by the railway sector in its total energy consumption; the energy intensity is defined as the energy consumption corresponding to unit freight turnover; the electric locomotive configuration rate is defined as the proportion of electric locomotives in the total number of locomotives; the locomotive energy dependence rate is defined as the ratio of the number of diesel locomotives to the number of electric locomotives; the transport structure is defined as the proportion of freight turnover in the total transport turnover of passenger and freight; the diesel locomotive transport efficiency is defined as the average transport turnover undertaken by each diesel locomotive; the locomotive technology investment coverage rate is defined as the ratio of the total number of locomotives to the total investment in technological transformation; the technological transformation investment ratio is defined as the proportion of the total investment in technological transformation to the total investment in capital construction; the total investment in capital construction is defined as the total investment in railway infrastructure construction in a certain period; the carbon emission intensity is defined as the unit energy consumption of various energy types used by the railway sector or the carbon dioxide emissions generated per unit transport activity.

[0025] The carbon emission intensity includes the carbon emission intensity of raw coal, the carbon emission intensity of anthracite, the carbon emission intensity of diesel, the carbon emission intensity of gasoline, the carbon emission intensity of liquefied petroleum gas, the carbon emission intensity of gas field natural gas, the carbon emission intensity of purchased heat, and the carbon emission intensity of electricity.

[0026] In step 3.3,

[0027]

[0028] in, represents the carbon dioxide emissions corresponding to the i-th energy type in the target year, represents the carbon dioxide emissions corresponding to the i-th energy type in the base year, represents the carbon emission intensity corresponding to the i-th energy type in the target year, represents the carbon emission intensity corresponding to the i-th energy type in the base year, represents the energy structure corresponding to the i-th energy type in the target year, Indicates the energy structure corresponding to the i-th energy type in the base year, EI t Indicates the energy intensity in the target year, EI 0 represents the energy intensity of the base year, TS t represents the transportation structure of the target year, TS 0 represents the transport structure of the base year, ITE t Indicates the diesel locomotive transportation efficiency in the target year, ITE 0 Denotes the diesel locomotive transport efficiency in the base year, EDR tIndicates the locomotive energy dependence rate in the target year, EDR 0 The locomotive energy dependence rate in the base year, ECR t Indicates the electric locomotive deployment rate in the target year, ECR 0 represents the electric locomotive deployment rate in the base year, LTICR t represents the locomotive technology investment coverage ratio in the target year, LTICR 0 TIR is the coverage rate of locomotive technology investment in the base year. t Indicates the technological transformation investment ratio in the target year, TIR 0 Indicates the technology transformation investment ratio in the base year, CIT t Indicates the total amount of capital construction investment in the target year, CIT 0 represents the total amount of capital construction investment in the base year,

[0029] Compared with the prior art, the advantages of the present invention are:

[0030] (1) The method of the present invention constructs a multi-dimensional influencing factor index system for railway carbon emissions covering four dimensions: energy system, transportation efficiency, technological innovation, and infrastructure drive. It subdivides ten influencing factors of railway carbon emissions (such as energy structure, electric locomotive configuration rate, transportation structure, etc.), covering eight energy types (such as raw coal, anthracite, etc.) and transportation mode differences. The traditional LMDI model has difficulty distinguishing the heterogeneous impacts of different energy types (such as electricity and diesel) and transportation modes (freight / passenger transport). The method of the present invention more accurately quantifies the independent contribution of each influencing factor to carbon emissions through the sub-item decomposition of the LMDI model (such as carbon emission intensity and energy structure by energy type), and can accurately identify high carbon emission sources, thereby providing a scientific basis for the railway department to formulate emission reduction strategies.

[0031] (2) The method of the present invention introduces the mutation series theory to analyze the static evaluation value of the contribution of each influencing factor and capture the nonlinear mutation characteristics. The traditional LMDI decomposition method based on a fixed base period cannot reflect the critical effect of influencing factors on carbon emissions (such as the accelerated emission reduction after the electrification rate exceeds the threshold). The method of the present invention evaluates the mutation nodes of the influencing factors within the annual period through the mutation series theory, revealing the nonlinear emission reduction law of the influencing factors.

[0032] (3) The method of the present invention divides the carbon emissions of railways into multi-year segments and calculates the dynamic evaluation value of the contribution, which can effectively capture the dynamic changes of railway carbon emissions at multiple time scales and realize continuous tracking in the time dimension. The traditional LMDI decomposition method based on a fixed base period cannot capture the delayed effect of policy or technology iteration. The method of the present invention reflects the cumulative effect and dynamic response of influencing factors through a dynamic evaluation sequence of contribution (such as the year-on-year impact trend of the investment ratio of technological transformation), which can reduce the error of policy effect evaluation and support the formulation of medium- and long-term emission reduction strategies. Studies have shown that the traditional LMDI model will produce an average evaluation deviation of ±25% when evaluating the compound impact of policies and technologies over a period of 3-5 years. The method of the present invention can significantly reduce this deviation and improve the accuracy of the evaluation by improving the LMDI model.

[0033] (4) The method of the present invention can be applied based on the existing railway system data, and the static evaluation value and dynamic evaluation value of the contribution of the influencing factors can be directly used to sort the policy priorities (such as focusing on energy structure optimization in the short term and relying on technological transformation investment in the long term) to guide the practical operation of energy conservation and emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a block diagram of the overall implementation of the method of the present invention;

[0035] Figure 2 This is a schematic diagram of the changes in various influencing factors in the total annual period of 2005-2023;

[0036] Figure 3 Schematic diagram of the static evaluation value of the contribution of each influencing factor in the four annual periods. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0038] This embodiment uses the carbon emissions of a railway enterprise in Central China from 2005 to 2023 as an example. Figure 1 The present invention discloses a method for analyzing the multi-dimensional influencing factors of railway carbon emissions based on an improved LMDI model. The overall implementation diagram is as follows: Figure 1 As shown, it includes the following steps:

[0039] Step 1: Collect data, including carbon dioxide emissions corresponding to various energy types, energy consumption corresponding to various energy types, freight turnover, total transport turnover of passenger and freight, the number of diesel locomotives, the number of electric locomotives, the total number of locomotives, the total investment in technological transformation, and the total investment in capital construction. The energy types include raw coal, anthracite, diesel, gasoline, liquefied petroleum gas, gas field natural gas, purchased heat, and electricity. The above-mentioned freight turnover and total transport turnover of passenger and freight constitute transport operation data, the number of diesel locomotives, the number of electric locomotives, and the total number of locomotives constitute transport equipment data, and the total investment in technological transformation and the total investment in capital construction constitute investment transformation data. Then, preprocess the collected data. The preprocessing includes cleaning, standardization, and verification processes to ensure the accuracy and completeness of the collected data.

[0040] Table 1 gives a list of the collected data.

[0041] Table 1 List of collected data

[0042]

[0043] Step 2: Construct an indicator system for multi-dimensional influencing factors of railway carbon emissions, which includes four dimensions: energy system, transportation efficiency, technological innovation, and infrastructure drive. The energy system is composed of four influencing factors: energy structure (ES), energy intensity (EI), electric locomotive configuration rate (ECR), and locomotive energy dependence rate (EDR). Transportation efficiency is composed of two influencing factors: transportation structure (TS) and diesel locomotive transportation efficiency (ITE). Technological innovation is composed of two influencing factors: locomotive technology investment coverage rate (LTICR) and technological transformation investment ratio (TIR). Infrastructure drive is composed of two influencing factors: total capital construction investment (CIT) and carbon emission intensity (CI).

[0044] Here, energy mix (ES) is defined as the contribution of each energy type used by a railway sector to its total energy consumption. Specifically, the energy mix reflects the relative contributions of different energy types (e.g., electricity, diesel, natural gas, etc.) to railway transportation, typically expressed as a percentage. For example, if electricity accounts for 70% of a railway sector's total energy consumption and diesel accounts for 30%, the energy mix can be expressed as ES = {electricity: 70%, diesel: 30%}. Energy mix not only changes dynamically over time (e.g., an increase in electrification leads to an increase in the share of electricity), but also varies regionally (e.g., eastern regions have a higher electrification rate, while western regions may still have a certain proportion of internal combustion traction). Changes in the energy mix, such as a shift from coal to natural gas or renewable energy, can significantly reduce carbon emissions, as these energy sources generally have lower carbon intensity.

[0045] Energy intensity (EI) is defined as the amount of energy consumed per unit of freight turnover. Energy intensity is commonly used to measure the efficiency of energy utilization during transportation. A higher energy intensity indicates that more energy is consumed to complete the same transportation task, reflecting lower transportation efficiency. Reducing energy intensity is an important way to improve transportation efficiency and reduce carbon emissions. This can be achieved through improved logistics management (such as optimizing transportation routes and increasing loading rates) and the introduction of more efficient technologies (such as adopting energy-efficient rolling stock and upgrading railway infrastructure), thereby reducing overall carbon emissions.

[0046] Here, the electric locomotive deployment ratio (ECR) is defined as the proportion of electric locomotives in the total number of locomotives. Increasing the electric locomotive deployment ratio can effectively reduce carbon emissions. Because electric locomotives do not produce direct carbon emissions during operation, they are more environmentally friendly than diesel locomotives. The carbon emission advantages of electric locomotives are particularly significant in power systems powered by non-fossil energy (such as renewable energy or nuclear energy).

[0047] Here, the locomotive energy dependency ratio (EDR) is defined as the ratio of the number of diesel locomotives to the number of electric locomotives. Reducing the locomotive energy dependency ratio, that is, reducing the proportion of diesel locomotives used and increasing the proportion of electric locomotives used, can reduce carbon emissions. This is because electric locomotives do not produce direct carbon emissions during operation, making them more environmentally friendly than diesel locomotives. The carbon emission advantages of electric locomotives are particularly significant when the electricity comes from renewable energy sources.

[0048] Here, transport structure (TS) is defined as the proportion of freight turnover in the total transport turnover of passenger and freight. Adjusting the transport structure, such as increasing the proportion of freight or optimizing transport modes, can reduce carbon emissions. This is because rail transport is generally more efficient and environmentally friendly than road transport. The unit energy consumption and carbon emission intensity of rail transport are significantly lower than those of road transport. The carbon emission advantages of rail transport are particularly evident when electrified railways and renewable energy are used.

[0049] Here, diesel locomotive transport efficiency (ITE) is defined as the average transport turnover per diesel locomotive, calculated based on the number of diesel locomotives and the total transport turnover of passengers and freight. Improving diesel locomotive transport efficiency means that each diesel locomotive can complete more transport tasks, thereby reducing energy consumption and carbon emissions per unit transport task. This can be achieved by optimizing diesel locomotive usage, improving diesel locomotive performance and maintenance, and adopting more efficient transport organization and management measures.

[0050] Here, the locomotive technology investment coverage ratio (LTICR) is defined as the ratio of the total number of locomotives to the total investment in technological transformation. Improving the locomotive technology investment coverage ratio means increasing investment in locomotive technological transformation, which usually involves introducing more efficient or cleaner technologies, thereby reducing carbon emissions.

[0051] Here, the technological renovation investment ratio (TIR) ​​is defined as the ratio of total technological renovation investment to total capital construction investment. A higher TIR indicates that the railway sector is placing greater emphasis on sustainable development and the application of environmentally friendly technologies, which helps reduce carbon emissions. By increasing technological renovations of existing facilities and equipment, energy efficiency can be improved, operating costs can be reduced, and negative environmental impacts can be minimized.

[0052] Here, total capital investment (CIT) is defined as the total investment in railway infrastructure over a given period. Increasing CIT can support the railway sector in introducing more environmentally friendly technologies and facilities, such as more efficient tracks, bridges, and rolling stock, thereby helping to reduce overall carbon emissions. By increasing investment in infrastructure, the railway sector can improve transportation efficiency, optimize energy use, and ultimately reduce carbon emissions.

[0053] Carbon intensity (CI) is defined as the amount of CO2 emitted per unit of energy consumption or transport activity for each energy type used by the railway sector. It is calculated based on the corresponding CO2 emissions and energy consumption for each energy type. Carbon intensity is a key indicator of carbon emission efficiency. Specifically, it reflects the amount of CO2 emitted when completing a specific transport task (such as a unit of transport turnover) or consuming a specific amount of energy. It is typically expressed in units such as "tons of CO2 per ton-kilometer" or "tons of CO2 per kilowatt-hour." Carbon emission intensity (CI) includes the carbon emission intensity of raw coal (carbon dioxide emissions generated per unit energy consumption of raw coal), the carbon emission intensity of anthracite (carbon dioxide emissions generated per unit energy consumption of anthracite), the carbon emission intensity of diesel (carbon dioxide emissions generated per unit energy consumption of diesel), the carbon emission intensity of gasoline (carbon dioxide emissions generated per unit energy consumption of gasoline), the carbon emission intensity of liquefied petroleum gas (carbon dioxide emissions generated per unit energy consumption of liquefied petroleum gas), the carbon emission intensity of gas field natural gas (carbon dioxide emissions generated per unit energy consumption of gas field natural gas), the carbon emission intensity of purchased heat (carbon dioxide emissions generated per unit energy consumption of purchased heat), and the carbon emission intensity of electricity (carbon dioxide emissions generated per unit energy consumption of electricity).

[0054] The construction of the multi-dimensional influencing factor indicator system follows the logical framework of "system deconstruction-action mechanism-transmission path", fully reflecting the multi-level driving characteristics of carbon emissions in the railway industry. The specific construction basis is as follows:

[0055] 1. Theoretical Support System

[0056] Energy transition theory: The energy system reveals the transmission mechanism of the low-carbon transformation of the energy system through the two-way regulatory effect of structural optimization of energy supply and improvement of consumption efficiency.

[0057] Transport economics theory: Transport efficiency is based on the transport elasticity coefficient theory, constructing a two-dimensional analysis framework of "transport demand elasticity-energy conversion elasticity" to quantify the elastic response relationship between transport organization optimization and energy consumption.

[0058] Technology Diffusion Theory: Technological innovation distinguishes between breakthrough innovation (locomotive technology investment coverage rate) and incremental innovation (technological transformation investment ratio) in terms of technology diffusion paths.

[0059] Life cycle theory: Infrastructure-driven development introduces the full life cycle assessment (LCA) method, forming a complete carbon footprint closed-loop analysis through the front-end driving effect of infrastructure investment and the back-end feedback mechanism of carbon emission intensity.

[0060] 2. System Coupling Relationship

[0061] Vertical transmission mechanism: forming a four-level transmission chain of "energy system (structure) → transportation efficiency (process) → technological innovation (power) → infrastructure drive (carrier)", which conforms to the law of material-energy-information flow in the railway system.

[0062] The energy system focuses on the core elements of energy supply and consumption. The energy structure and energy intensity constitute the energy fundamentals. The electric locomotive configuration rate and locomotive energy dependence rate reflect the characteristics of the energy consumption structure. The four together characterize the impact path of railway energy transformation on carbon emissions; transportation efficiency highlights the energy efficiency improvement brought about by the optimization of transportation organization, the transportation structure determines the basic energy consumption demand, and the transportation efficiency of diesel locomotives reflects the improvement of traditional power. The two form a closed-loop analysis of "transportation demand-energy conversion"; technological innovation emphasizes the driving role of technological iteration on emission reduction, the locomotive technology investment coverage rate reflects the speed of technology diffusion, and the technological transformation investment ratio reflects incremental innovation. The two together construct the "dual-path drive of railway technology innovation" analysis framework; infrastructure drive reveals the dynamic relationship between infrastructure and carbon emission intensity. The total amount of capital construction investment is the front-end drive, and carbon emission intensity is the final result indicator. The two form a "investment-facilities-emissions" railway system carbon emission causal transmission chain.

[0063] Step 3: Using the preprocessed data and the constructed railway carbon emissions multi-dimensional influencing factor indicator system, we conduct an analysis of the multi-dimensional influencing factors of railway carbon emissions based on the improved LMDI model. The specific process is as follows:

[0064] Step 3.1: Based on a full understanding of the essential characteristics and connotations of carbon emissions and their influencing factors, the Kaya identity (proposed by Japanese scholar Yoichi Kaya) is expanded to obtain the LMDI model equation, which is described as:

[0065]

[0066] Among them, C represents carbon dioxide emissions, E represents energy consumption, FT represents freight turnover, TT represents the total transport turnover of passenger and freight, NICL represents the number of diesel locomotives, NEL represents the number of electric locomotives, TNL represents the total number of locomotives, TITR represents the total investment in technological transformation, and CIT represents the total investment in capital construction.

[0067] Step 3.2: Based on the LMDI model equation, expand to obtain the improved LMDI model equation, which is described as:

[0068]

[0069] Where i = 1, 2, ..., 8, C z Indicates the total carbon dioxide emissions, C i represents the carbon dioxide emissions corresponding to the i-th energy type, E i represents the energy consumption corresponding to the i-th energy type, E z Represents the total energy consumption, let CI i =C i / E i Denotes the carbon emission intensity corresponding to the i-th energy type, let ES i =E i / E z Represents the energy structure corresponding to the i-th energy type, let EI=E z / FT represents energy intensity, TS = FT / TT represents transportation structure, ITE = TT / NICL represents diesel locomotive transportation efficiency, EDR = NICL / NEL represents locomotive energy dependence rate, ECR = NEL / TNL represents electric locomotive configuration rate, LTICR = TNL / TITR represents locomotive technology investment coverage rate, and TIR = TITR / CIT represents technological transformation investment ratio.

[0070] Step 3.3: Based on the improved LMDI model equation, express the change in total carbon emissions between the target year and the base year as ΔC. and will Decompose into in, represents the total CO2 emissions in the target year, represents the total carbon dioxide emissions in the base year, ΔCI iIndicates the change in carbon emission intensity corresponding to the i-th energy type between the target year and the base year, ΔES i represents the change in energy structure corresponding to the i-th energy type between the target year and the base year, ΔEI represents the change in energy intensity between the target year and the base year, ΔTS represents the change in transport structure between the target year and the base year, ΔITE represents the change in diesel locomotive transport efficiency between the target year and the base year, ΔEDR represents the change in locomotive energy dependence rate between the target year and the base year, ΔECR represents the change in electric locomotive configuration rate between the target year and the base year, ALTICR represents the change in locomotive technology investment coverage rate between the target year and the base year, ΔTIR represents the change in technological transformation investment ratio between the target year and the base year, and ΔCIT represents the change in total capital construction investment between the target year and the base year.

[0071] In this specific embodiment, in step 3.3,

[0072] in, represents the carbon dioxide emissions corresponding to the i-th energy type in the target year, represents the carbon dioxide emissions corresponding to the i-th energy type in the base year, represents the carbon emission intensity corresponding to the i-th energy type in the target year, represents the carbon emission intensity corresponding to the i-th energy type in the base year, represents the energy structure corresponding to the i-th energy type in the target year, Indicates the energy structure corresponding to the i-th energy type in the base year, EI t Indicates the energy intensity in the target year, EI 0 represents the energy intensity of the base year, TS t represents the transportation structure of the target year, TS 0 represents the transport structure of the base year, ITE t Indicates the diesel locomotive transportation efficiency in the target year, ITE 0 Denotes the diesel locomotive transport efficiency in the base year, EDR t Indicates the locomotive energy dependence rate in the target year, EDR 0 The locomotive energy dependence rate in the base year, ECR t Indicates the electric locomotive deployment rate in the target year, ECR 0 represents the electric locomotive deployment rate in the base year, LTICR t represents the locomotive technology investment coverage ratio in the target year, LTICR 0 TIR is the coverage rate of locomotive technology investment in the base year. t Indicates the technological transformation investment ratio in the target year, TIR 0 Indicates the technology transformation investment ratio in the base year, CIT tIndicates the total amount of capital construction investment in the target year, CIT 0 represents the total amount of capital construction investment in the base year,

[0073] Through the above decomposition, we can obtain the contribution of each influencing factor to the change in total carbon emissions, and analyze the specific impact of changes in carbon emission intensity, energy structure, energy intensity, transportation structure, diesel locomotive transportation efficiency, locomotive energy dependence rate, electric locomotive configuration rate, locomotive technology investment coverage rate, technological transformation investment ratio and total capital construction investment on carbon emissions.

[0074] Step 3.4: Divide the annual time period for analysis into Num annual segments. The first and last years of each annual segment serve as the base year and target year, respectively. The base year of the subsequent annual segment is the target year of the previous annual segment, or the base year of the subsequent annual segment is the year after the target year of the previous annual segment. When dividing the annual segments, all annual segments can contain the same or different number of years.

[0075] Step 3.5: Follow the procedure in step 3.3 to obtain the ΔCI for each year segment in the same manner. i , ΔES i , ΔEI, ΔTS, ΔITE, ΔEDR, ΔECR, ΔLTICR, ΔTIR, ΔCIT; Then apply the mutation series theory to calculate the ΔCI of each annual segment. i , ΔES i , ΔEI, ΔTS, ΔITE, ΔEDR, ΔECR, ΔLTICR, ΔTIR, ΔCIT, corresponding to the carbon emission intensity corresponding to the i-th energy type in each annual period, the energy structure corresponding to the i-th energy type, energy intensity, transportation structure, diesel locomotive transportation efficiency, locomotive energy dependence rate, electric locomotive configuration rate, locomotive technology investment coverage rate, technological transformation investment ratio, and static evaluation value of contribution to the total capital construction investment.

[0076] Step 3.6: Calculate the average change rate of the carbon emission intensity corresponding to the i-th energy type, the energy structure, energy intensity, transportation structure, diesel locomotive transportation efficiency, locomotive energy dependence rate, electric locomotive configuration rate, locomotive technology investment coverage rate, technological transformation investment ratio, and total capital construction investment for each annual period. The corresponding dynamic evaluation value of the carbon emission intensity corresponding to the i-th energy type, the energy structure, energy intensity, transportation structure, diesel locomotive transportation efficiency, locomotive energy dependence rate, electric locomotive configuration rate, locomotive technology investment coverage rate, technological transformation investment ratio, and total capital construction investment for each annual period is used. The average change rate of an influencing factor is defined as the change in the influencing factor divided by the difference between the target year and the base year. For example, the average change rate of the carbon emission intensity corresponding to the i-th energy type is equal to ΔCI i Divide by the difference between the target year and the base year. For any influencing factor, the dynamic contribution evaluation values ​​of this influencing factor in Num annual segments can be arranged in chronological order to form a dynamic contribution evaluation sequence, so that the contribution trend of this influencing factor to carbon emissions can be observed.

[0077] In order to further illustrate the feasibility and effectiveness of the method of the present invention, the impact of different influencing factors on carbon emissions of a certain group company was discussed and analyzed.

[0078] 1. The annual time period used for analysis is 2005-2023. Taking 2005-2023 as one annual segment, the changes in each influencing factor from 2005 to 2023 are calculated.

[0079] Figure 2 The changes of each influencing factor in the total annual period of 2005-2023 are given. Figure 2 Where ΔCI is the sum of the changes in carbon emission intensity corresponding to the eight energy types, and ΔES is the sum of the changes in energy structure corresponding to the eight energy types. Figure 2 It can be seen that:

[0080] The positive contributions of internal combustion engine transport efficiency (ITE) and electric locomotive deployment rate (ECR) to carbon emissions are the most significant, indicating that while improvements in internal combustion engine transport efficiency (ITE) and increases in electric locomotive deployment rate (ECR) have played an important role in improving transport capacity and modernization, they have led to an increase in carbon emissions. This may be because the efficiency improvements of internal combustion engine locomotives are usually accompanied by higher fuel consumption, especially when the fuel source still relies on high-carbon-emission fossil fuels, leading to an increase in carbon emissions. Similarly, although the use of electric locomotives is theoretically more environmentally friendly, if the main source of electricity is fossil fuel power generation, then its carbon emission intensity may still be high, thus offsetting the environmental advantages of electric locomotives themselves.

[0081] Influencing factors such as energy structure (ES), locomotive technology investment coverage ratio (LTICR), and technological transformation investment ratio (TIR) ​​also have a certain positive impact on carbon emissions, indicating that changes or adjustments in these aspects have increased carbon emissions to a certain extent.

[0082] The reduction in locomotive energy dependence rate (EDR) has the most significant negative contribution to carbon emissions, indicating that carbon emissions can be effectively reduced by reducing reliance on internal combustion locomotives, especially those that rely on high-carbon emission fuels. This shift is often accompanied by the promotion of electric locomotives, especially when the electricity supply comes from renewable or cleaner energy sources, the use of electric locomotives can significantly reduce carbon emissions.

[0083] Energy intensity (EI) and total capital investment (CIT) also made significant contributions. The reduction in energy intensity (EI) shows that the railway sector has made significant progress in energy efficiency. By introducing more efficient locomotive technology or improving logistics management, energy consumption per unit of freight turnover can be reduced, thereby reducing overall carbon emissions. The increase in total capital investment (CIT) has also played a positive role in reducing carbon emissions. This may include investment in more environmentally friendly technologies and facilities, such as more efficient track systems, new low-emission locomotives, and improving the overall efficiency of the transportation network. These technological transformations and infrastructure construction have not only improved the operational efficiency of the railway system, but also effectively reduced overall carbon emissions by reducing energy consumption and carbon emission intensity.

[0084] In summary, while some measures to improve transport efficiency and modernization (such as increasing the transport efficiency of internal combustion locomotives (ITE) and the electric locomotive deployment rate (ECR)) may have led to an increase in carbon emissions, overall carbon emissions have been effectively controlled by reducing reliance on high-carbon-emitting locomotives, improving energy efficiency, and increasing investment in environmentally friendly technologies. This demonstrates that modernization and efficiency improvements must be accompanied by consideration of environmentally friendly technologies and optimization of the energy mix to achieve true sustainable development.

[0085] 2. The annual time period used for analysis is 2006-2023, which is divided into four annual segments: 2006-2010 as the first annual segment, 2011-2015 as the second annual segment, 2016-2020 as the third annual segment, and 2021-2023 as the fourth annual segment. The static evaluation value of the contribution of each influencing factor in each annual segment from 2006 to 2023 is calculated.

[0086] Figure 3 The static evaluation values ​​of the contribution of each influencing factor in the four annual periods are given. Figure 3Where ΔCI is the sum of the changes in carbon emission intensity corresponding to the eight energy types, and ΔES is the sum of the changes in energy structure corresponding to the eight energy types. Figure 3 It can be seen that:

[0087] Between 2006 and 2010, rail transport carbon emissions increased, primarily due to factors including internal combustion engine efficiency (ITE), the technological improvement investment ratio (TIR), and the energy mix (ES). During this period, passenger and freight transport volumes increased significantly, leading to a substantial increase in demand for rail transport capacity. Despite improvements in internal combustion engine efficiency, the overall increase in transport volume did not fully offset the resulting increase in carbon emissions. Furthermore, the relatively low proportion of clean energy in the energy mix (ES) constrained overall carbon emissions. While internal combustion engine efficiency (ITE) was high, it continued to rely on high-carbon energy sources, such as coal and diesel, exacerbating the increase in carbon emissions. The relatively low technological improvement investment ratio (TIR) ​​also indicates that the pace of technological upgrades was insufficient to significantly improve energy efficiency or reduce carbon emissions, effectively failing to mitigate the growth of carbon emissions during this period.

[0088] Between 2011 and 2015, railway transport carbon emissions decreased significantly, primarily due to the combined effects of factors such as energy intensity (EI), diesel locomotive transport efficiency (ITE), technological improvement investment ratio (TIR), and total capital investment (CIT). During this period, energy intensity (EI) improved significantly. By improving transport efficiency and optimizing logistics management, energy consumption per unit of freight turnover was reduced, thereby reducing overall carbon emissions. Furthermore, improvements in diesel locomotive transport efficiency (ITE) effectively reduced energy consumption and carbon emissions per unit of transport. Furthermore, the increase in the technological improvement investment ratio (TIR) ​​indicates that the railway sector has placed greater emphasis on introducing efficient and environmentally friendly technologies, thus playing a positive role in reducing carbon emissions. Total capital investment (CIT) also played a key role. By increasing infrastructure investment, the railway sector was able to introduce more environmentally friendly technologies and facilities, such as efficient track and electric locomotives, further reducing carbon emissions. Overall, the combined impact of these factors led to significant control and reduction of carbon emissions during this period.

[0089] Between 2016 and 2020, rail transport carbon emissions increased slightly, primarily due to the transport structure (TS), diesel locomotive transport efficiency (ITE), and the technological transformation investment ratio (TIR). During this period, adjustments to the transport structure (TS) may not have fully optimized energy efficiency, particularly given the large proportion of high-carbon-emitting transport modes, leading to a slight increase in overall carbon emissions. However, the improvement in diesel locomotive transport efficiency (ITE) demonstrates the railway sector's efforts to improve locomotive efficiency. While this improvement did not fully offset the increase in carbon emissions, it still indicates a positive direction for efficiency improvement. The impact of the technological transformation investment ratio (TIR), while relatively limited, did provide support for the introduction of more efficient and environmentally friendly technologies. Overall, despite the increase in carbon emissions, the adjustments and investment efforts during this period mitigated the increase to some extent, demonstrating the railway sector's proactive efforts and gradual progress in addressing carbon emissions.

[0090] During the 2021-2023 period, railway transport carbon emissions saw a slight increase, primarily driven by the internal combustion engine transport efficiency (ITE), the locomotive technology investment coverage ratio (LTICR), and the technological transformation investment ratio (TIR). Despite this slight increase in carbon emissions, the overall situation remained relatively stable, demonstrating the railway sector's continued efforts in technological transformation and efficiency improvement. Improvements in the internal combustion engine transport efficiency (ITE) and the increase in the locomotive technology investment coverage ratio (LTICR) indicate significant progress in promoting technological advancement and enhancing locomotive performance. While the technological transformation investment ratio (TIR) ​​remained the most influential factor during this period, its positive impact lay in continuously promoting the application of environmentally friendly technologies and improving technological levels, thus laying the foundation for long-term emission reductions. Overall, despite the increase in carbon emissions during this period, the railway sector's technological investment and transformation measures continued to play a role, demonstrating stability in carbon emission control and the potential for long-term progress.

[0091] 3. The annual time period used for analysis is 2006-2023, which is divided into four annual sections: 2006-2010 is the first annual section, 2011-2015 is the second annual section, 2016-2020 is the third annual section, and 2021-2023 is the fourth annual section. The dynamic evaluation value (average change rate) of the contribution of each influencing factor in the first three annual sections of 2006-2023 and the entire annual section of 2006-2023 is calculated.

[0092] Table 2 gives the dynamic evaluation values ​​of the contribution of each influencing factor in the four annual periods.

[0093] Table 2 Dynamic evaluation values ​​of contribution of each influencing factor in four annual periods

[0094]

[0095] In Table 2, ΔCI is the sum of the changes in carbon emission intensity corresponding to the eight energy types, and ΔES is the sum of the changes in energy structure corresponding to the eight energy types.

[0096] Between 2006 and 2010, the contribution of the energy structure (ES) effect to carbon emissions decreased significantly, primarily due to the active promotion of natural gas and renewable energy, which in turn reduced the use of high-carbon energy sources such as coal. At the same time, the contribution of the transport structure (TS) effect to carbon emissions showed a clear upward trend, driven by an increase in the proportion of freight transport and the partial substitution of road transport by rail. However, due to the inherently high efficiency of rail transport, overall carbon emissions continued to decline. Furthermore, improvements in the transport efficiency of internal combustion locomotives (ITE) also played a significant role in reducing carbon emissions, primarily due to the retirement of older diesel locomotives and technological upgrades, such as optimizing diesel engine combustion efficiency. However, investment in technological transformation was insufficient, accounting for a relatively low proportion of infrastructure investment. This resulted in a short-term reliance on infrastructure expansion, while in the long term, technological upgrades lagged behind.

[0097] From 2011 to 2015, the energy mix (ES) rebounded, manifested in an increased contribution to carbon emissions, which to some extent offset the gains made in the previous period. This phenomenon may be due to a rebound in coal consumption (e.g., increased industrial demand amidst economic stimulus policies) and a slowdown in the promotion of clean energy. During this period, the transport mix (TS) also deteriorated, with a decreased contribution to carbon emissions, but actual carbon emissions may have increased. This may be due to an increase in the proportion of passenger transport (e.g., the continued expansion of the high-speed rail network) and a relatively decreased proportion of freight transport, resulting in a decrease in the efficiency of carbon emissions per unit.

[0098] During the 2016-2020 period, the optimization of the energy structure (ES) on carbon emissions was relatively limited. This may be due to the policy focus shifting to controlling carbon dioxide emissions, while the replacement of clean energy sources was relatively slow (for example, due to insufficient natural gas supply). At the same time, the contribution of energy intensity (EI) to carbon emissions was on the rise, mainly due to the surge in freight demand during this period (such as the booming e-commerce logistics industry), while technological upgrades failed to keep pace with demand. However, in terms of technological transformation, the contribution of the technological transformation investment ratio (TIR) ​​to carbon emissions during this period improved significantly, which may be due to the active policy guidance that promoted the pilot application of hydrogen locomotives and energy storage technologies.

[0099] An in-depth analysis of the dynamic trends of influencing factors from 2006 to 2023 reveals the following persistent contradictions: First, the locomotive energy dependence rate (EDR) continues to contribute significantly to carbon emissions, indicating that the proportion of diesel locomotives in railway companies remains high and the progress of clean energy transformation is relatively lagging. Second, the contribution of the electric locomotive deployment rate (ECR) to carbon emissions is declining, reflecting insufficient grid infrastructure and policy support, and the failure of electric power substitution to achieve the expected results. However, these dynamic trends also reveal some positive signals: the contribution of the transport structure (TS) to carbon emissions is gradually increasing, indicating that the proportion of rail freight is continuously increasing and its low-carbon advantages are gradually being realized. Furthermore, the contribution of the locomotive technology investment coverage ratio (LTICR) to carbon emissions is also gradually increasing, indicating that locomotive technology investment is gradually accumulating and has significant long-term emission reduction potential.

[0100] Overall, the period from 2006 to 2010 saw active policy promotion, the period from 2011 to 2015 saw a "game between economics and environmental protection," and the period from 2016 to 2020 saw "technical pilot exploration." Future development will require focusing on grid infrastructure and technology upgrades, and rationally adjusting policies based on the dynamics of freight demand to achieve "decoupling" of railway carbon emissions from the context of scale growth. A dynamic analysis of the contribution of influencing factors offers the following important insights for energy conservation and carbon reduction efforts:

[0101] 1. Policy consistency: To avoid a “campaign-style” emission reduction situation similar to the rebound in energy structure during the 2011-2015 period, a long-term and stable clean energy replacement plan should be formulated.

[0102] 2. Technical adaptation requirements: When freight volume surges, high-efficiency locomotive technologies, such as hybrid and hydrogen locomotives, must be upgraded simultaneously to prevent a rebound in energy consumption.

[0103] 3. Power infrastructure construction first: When promoting railway electrification in remote areas, it is necessary to ensure that it is carried out in parallel with the expansion of the power grid to avoid the embarrassing situation of electric locomotives having "trains but no electricity".

[0104] 4. Dynamic data monitoring: Establish and improve a phased carbon emissions accounting system, clearly distinguishing the contributions of freight and passenger transport, and different energy types to carbon emissions, to avoid indicators being too vague and affecting accurate decision-making.

Claims

1. A method for analyzing multi-dimensional influencing factors of railway carbon emissions based on an improved LMDI model, characterized by The following steps are involved: Step 1: Collect data, including CO2 emissions for various energy types, energy consumption for various energy types, freight turnover, total passenger and freight transport turnover, number of diesel locomotives, number of electric locomotives, total number of locomotives, total investment in technological transformation, and total investment in capital construction. Energy types include raw coal, anthracite, diesel, gasoline, liquefied petroleum gas, natural gas from gas fields, purchased heat, and electricity. Then, pre-process the collected data. Step 2: Construct an indicator system for multi-dimensional influencing factors of railway carbon emissions, covering four dimensions: energy system, transportation efficiency, technological innovation, and infrastructure drive. The energy system is composed of four influencing factors: energy structure, energy intensity, electric locomotive deployment rate, and locomotive energy dependence rate. Transportation efficiency is composed of two influencing factors: transportation structure and diesel locomotive transportation efficiency. Technological innovation is composed of two influencing factors: locomotive technology investment coverage rate and technological transformation investment ratio. Infrastructure drive is composed of two influencing factors: total capital construction investment and carbon emission intensity. Step 3: Using the preprocessed data and the constructed railway carbon emissions multi-dimensional influencing factor indicator system, we conduct an analysis of the multi-dimensional influencing factors of railway carbon emissions based on the improved LMDI model. The specific process is as follows: Step 3.1: Based on the Kaya identity, expand to obtain the LMDI model equation, which is described as: Among them, C represents carbon dioxide emissions, E represents energy consumption, FT represents freight turnover, TT represents the total transport turnover of passenger and freight, NICL represents the number of diesel locomotives, NEL represents the number of electric locomotives, TNL represents the total number of locomotives, TITR represents the total investment in technological transformation, and CIT represents the total investment in capital construction; Step 3.2: Based on the LMDI model equation, expand to obtain the improved LMDI model equation, which is described as: Where i = 1, 2, ..., 8, C z Indicates the total carbon dioxide emissions, C i represents the carbon dioxide emissions corresponding to the i-th energy type, E i represents the energy consumption corresponding to the i-th energy type, E z Represents the total energy consumption, let CI i =C i / E i Denotes the carbon emission intensity corresponding to the i-th energy type, let ES i =E i / E z Represents the energy structure corresponding to the i-th energy type, let EI=E z / FT represents energy intensity, let TS = FT / TT represent transportation structure, let ITE = TT / NICL represent diesel locomotive transportation efficiency, let EDR = NICL / NEL represent locomotive energy dependence rate, let ECR = NEL / TNL represent electric locomotive configuration rate, let LTICR = TNL / TITR represent locomotive technology investment coverage rate, let TIR = TITR / CIT represent technology transformation investment ratio; Step 3.3: Based on the improved LMDI model equation, express the change in total carbon emissions between the target year and the base year as ΔC. and will Decompose into in, represents the total CO2 emissions in the target year, represents the total carbon dioxide emissions in the base year, ΔCI i Indicates the change in carbon emission intensity corresponding to the i-th energy type between the target year and the base year, ΔES i represents the change in energy structure corresponding to the i-th energy type between the target year and the base year, ΔEI represents the change in energy intensity between the target year and the base year, ΔTS represents the change in transport structure between the target year and the base year, ΔITE represents the change in diesel locomotive transport efficiency between the target year and the base year, ΔEDR represents the change in locomotive energy dependence rate between the target year and the base year, ΔECR represents the change in electric locomotive configuration rate between the target year and the base year, ΔLTICR represents the change in locomotive technology investment coverage rate between the target year and the base year, ΔTIR represents the change in technological transformation investment ratio between the target year and the base year, and ΔCIT represents the change in total capital construction investment between the target year and the base year; Step 3.4: Divide the annual time period for analysis into Num annual segments. The first and last years of each annual segment serve as the base year and target year respectively. The base year of the latter annual segment is the target year of the previous annual segment, or the base year of the latter annual segment is the year after the target year of the previous annual segment. Step 3.5: Follow the procedure in step 3.3 to obtain the ΔCI for each year segment in the same manner. i , ΔES i , ΔEI, ΔTS, ΔITE, ΔEDR, ΔECR, ΔLTICR, ΔTIR, ΔCIT; then apply the mutation series theory, according to the ΔCI of each annual segment i , ΔES i , ΔEI, ΔTS, ΔITE, ΔEDR, ΔECR, ΔLTICR, ΔTIR, ΔCIT, corresponding to the carbon emission intensity corresponding to the i-th energy type in each annual period, the energy structure corresponding to the i-th energy type, energy intensity, transportation structure, diesel locomotive transportation efficiency, locomotive energy dependence rate, electric locomotive configuration rate, locomotive technology investment coverage rate, technology transformation investment ratio, and static evaluation value of contribution to the total capital construction investment; Step 3.6: Calculate the average change rates of the carbon emission intensity corresponding to the i-th energy type, the energy structure, energy intensity, transportation structure, diesel locomotive transportation efficiency, locomotive energy dependence rate, electric locomotive configuration rate, locomotive technology investment coverage rate, technological transformation investment ratio, and total capital construction investment in each annual period, which correspond to the dynamic evaluation values ​​of the contribution of the carbon emission intensity corresponding to the i-th energy type, the energy structure, energy intensity, transportation structure, diesel locomotive transportation efficiency, locomotive energy dependence rate, electric locomotive configuration rate, locomotive technology investment coverage rate, technological transformation investment ratio, and total capital construction investment in each annual period. The average change rate of an influencing factor is defined as the change of the influencing factor divided by the difference between the target year and the base year.

2. The method for analyzing multi-dimensional influencing factors of railway carbon emissions based on the improved LMDI model according to claim 1 is characterized in that The pretreatment includes cleaning, standardization and validation processes.

3. The method for analyzing multi-dimensional influencing factors of railway carbon emissions based on the improved LMDI model according to claim 1 is characterized in that The energy structure is defined as the proportion of energy consumption corresponding to various energy types used by the railway sector in its total energy consumption; the energy intensity is defined as the energy consumption corresponding to unit freight turnover; the electric locomotive configuration rate is defined as the proportion of electric locomotives in the total number of locomotives; and the locomotive energy dependence rate is defined as the ratio of the number of diesel locomotives to the number of electric locomotives. The transport structure is defined as the proportion of freight turnover in the total transport turnover of passenger and freight transport; the diesel locomotive transport efficiency is defined as the average transport turnover undertaken by each diesel locomotive; the locomotive technology investment coverage ratio is defined as the ratio of the total number of locomotives to the total investment in technological transformation; The technological transformation investment ratio is defined as the proportion of the total technological transformation investment to the total capital construction investment; the total capital construction investment is defined as the total investment in railway infrastructure construction within a certain period; and the carbon emission intensity is defined as the unit energy consumption of various energy types used by the railway sector or the carbon dioxide emissions generated per unit transportation activity.

4. The method for analyzing multi-dimensional influencing factors of railway carbon emissions based on the improved LMDI model according to claim 1 or 3 is characterized in that The carbon emission intensity includes the carbon emission intensity of raw coal, the carbon emission intensity of anthracite, the carbon emission intensity of diesel, the carbon emission intensity of gasoline, the carbon emission intensity of liquefied petroleum gas, the carbon emission intensity of gas field natural gas, the carbon emission intensity of purchased heat, and the carbon emission intensity of electricity.

5. The method for analyzing multi-dimensional influencing factors of railway carbon emissions based on the improved LMDI model according to claim 1 is characterized in that In step 3.3, in, represents the carbon dioxide emissions corresponding to the i-th energy type in the target year, represents the carbon dioxide emissions corresponding to the i-th energy type in the base year, represents the carbon emission intensity corresponding to the i-th energy type in the target year, represents the carbon emission intensity corresponding to the i-th energy type in the base year, represents the energy structure corresponding to the i-th energy type in the target year, Indicates the energy structure corresponding to the i-th energy type in the base year, EI t Indicates the energy intensity in the target year, EI 0 represents the energy intensity of the base year, TS t represents the transportation structure of the target year, TS 0 represents the transport structure of the base year, ITE t Indicates the diesel locomotive transportation efficiency in the target year, ITE 0 Denotes the diesel locomotive transport efficiency in the base year, EDR t Indicates the locomotive energy dependence rate in the target year, EDR 0 The locomotive energy dependence rate in the base year, ECR t Indicates the electric locomotive deployment rate in the target year, ECR 0 represents the electric locomotive deployment rate in the base year, LTICR t represents the locomotive technology investment coverage ratio in the target year, LTICR 0 TIR is the coverage ratio of locomotive technology investment in the base year. t Indicates the technological transformation investment ratio in the target year, TIR 0 Indicates the technology transformation investment ratio in the base year, CIT t Indicates the total amount of capital construction investment in the target year, CIT 0 represents the total amount of capital construction investment in the base year,

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