Traffic carbon emission effect value determination method, system and device, carbon emission evaluation method and economic unhooking relation evaluation method

Abstract

The invention discloses a traffic carbon emission effect value determination method, system and equipment considering passenger and freight indexes, and a carbon emission evaluation method and economic unhooking relationship evaluation method, and belongs to the field of traffic transportation engineering and environmental protection. According to the method, the problem of low judgment accuracy of the influence factors and the effect values of the traffic carbon emission caused by the fact that the influence of passenger and freight indexes on the traffic carbon emission is not considered in the prior art is solved, the LMDI influence factor decomposition model is constructed, the effect values of the influence factors are determined according to the traffic data, and the influence of traffic activities on the carbon emission is revealed; the contribution degree of each influence factor to the carbon emission variation is determined according to the effect value of each influence factor, so that the factors causing larger proportion of carbon growth can be identified; and decomposing the traffic carbon emission-economic unhooking coefficient according to the effect value of each influence factor to obtain an elastic coefficient of each influence factor, analyzing the influence of each factor on traffic carbon emission-economic unhooking, and realizing evaluation of the relationship between traffic carbon emission and economic unhooking.

Description

Technical Field

[0001] The present invention relates to a method, system, equipment and carbon emission evaluation method for determining a traffic carbon emission effect value taking into account passenger and freight transport indicators, and an evaluation method for decoupling the relationship with the economy, and belongs to the field of transportation engineering and environmental protection. Background Art

[0002] With the progress of society, the global economy has developed rapidly, leading to environmental problems such as a surge in global carbon emissions and global warming. Data released by the National Bureau of Statistics show that in recent years, the energy consumption and carbon emissions of the transportation industry have shown a rapid growth trend: energy consumption has increased nearly fourfold from 2000 to 2020, reaching a peak of 439.09 million tons of standard coal in 2019; in terms of total carbon emissions, the transportation industry accounts for nearly 10% of the national total. This shows that the transportation industry is an important source of carbon emissions and a key area that needs to be tackled in carbon emission reduction efforts.

[0003] There are many factors that affect the carbon emissions of the transportation industry, and the traditional carbon emission evaluation method focuses on studying economic, population, energy and other factors, without taking passenger and freight indicators into consideration. The large turnover and rapid growth of passenger and freight transportation are important factors that lead to the increase in carbon emissions from transportation. Passenger and freight volume, average transportation distance and passenger and freight turnover are closely related, so this factor also affects transportation carbon emissions. Therefore, to study how to reduce transportation carbon emissions and improve transportation carbon emission efficiency, it is necessary to study the mechanism and degree of influence of various passenger and freight indicators on China's transportation carbon emissions. Based on this background, the present invention provides a method for determining the effect value of transportation carbon emissions considering passenger and freight indicators. This method innovatively introduces passenger and freight indicators such as passenger volume, passenger structure, freight volume, and freight structure as variables, constructs a LMDI influencing factor decomposition model, and calculates the effect value of each influencing factor and the contribution to the change in carbon emissions. It can better reveal how transportation activities affect carbon emissions and changing trends, and help identify whether it is freight transportation or personnel flow that leads to a larger proportion of carbon growth. Then, the elasticity coefficient of each influencing factor is determined according to the effect value of each influencing factor, and the impact of each factor on carbon emission-economic decoupling is analyzed. Summary of the invention

[0004] The present invention aims to solve the problem that the prior art does not consider the impact of passenger and freight indicators on transportation carbon emissions, resulting in low accuracy in determining transportation carbon emission influencing factors and effect values, and provides a transportation carbon emission effect value determination method, system, equipment and carbon emission evaluation method that considers passenger and freight indicators, and an evaluation method that is decoupled from the economy.

[0005] A method for determining the effect value of transportation carbon emissions considering passenger and freight indicators, firstly obtaining transportation data, inputting it into the LMDI influencing factor decomposition model, and determining the effect value of each influencing factor based on the LMDI influencing factor decomposition model, the LMDI influencing factor decomposition model is as follows:

[0006]

[0007] Among them, C is the carbon emission of transportation, i represents two modes of transportation: passenger transportation and freight transportation, j represents four modes of transportation: road transportation, railway transportation, water transportation and aviation transportation, M ij is the passenger and freight volume, O ij is the average transport distance, F ij is the carbon emission factor of the transport mode, B i For passenger and freight transport structure, B ij is the transport mode structure, T ij is the number of people supplied per unit of transport production, Q is the energy intensity, and X is the energy efficiency.

[0008] Taking the logarithm of both sides of formula (1) and taking the derivative with respect to time, and then taking the definite integral, we get:

[0009]

[0010] Determine the weight function It is expressed as:

[0011]

[0012] Formula (3) is derived into the form of formula (5):

[0013]

[0014] Bundle Disassemble and C t -C t-1 Put it on the left side of the formula, and we get formula (6):

[0015]

[0016] From formula (6), we can get the decomposition formula based on addition, as shown in formula (7):

[0017]

[0018] ΔC is the total change in carbon emissions, is the effect of passenger and freight volume on carbon emissions, is the effect of average transport distance on carbon emission changes, is the effect value of the carbon emission factor of the transport mode on the change of carbon emissions, is the effect of passenger and freight transport structure on carbon emission changes, is the effect of the transport mode structure on carbon emission changes, The effect value of the number of people per unit of transport production supply on the change of carbon emissions, ΔC Q is the effect of energy intensity on carbon emission changes, ΔC X is the effect value of energy efficiency on carbon emission changes; the calculation formula of the effect value of each influencing factor is as follows:

[0019]

[0020] Based on formula (7) and formulas (8)-(15), the effect value of each influencing factor is determined according to its influence on the total change of carbon emissions.

[0021] A system for determining the effect value of transportation carbon emissions taking into account passenger and freight indicators, comprising:

[0022] Transportation data acquisition unit: acquire transportation data;

[0023] Influencing factor effect value calculation unit: use the LMDI influencing factor decomposition model and transportation data to calculate the effect value of each influencing factor;

[0024] The LMDI influencing factor decomposition model used is as follows:

[0025]

[0026] The formula used is as follows:

[0027]

[0028]

[0029] Therefore, the effect value of each influencing factor is determined according to its impact on the total carbon emissions.

[0030] A computer storage medium stores at least one instruction, which is loaded and executed by a processor to determine a traffic carbon emission effect value taking into account passenger and freight indicators.

[0031] A device for determining the carbon emission effect value of transportation taking into account passenger and freight indicators, the device comprising a processor and a memory, the memory storing at least one instruction, the at least one instruction being loaded and run by the processor to determine the carbon emission effect value of transportation taking into account passenger and freight indicators.

[0032] A method for evaluating transportation carbon emissions that takes passenger and freight indicators into consideration. The method for determining the effect value of transportation carbon emissions that takes passenger and freight indicators into consideration obtains the effect value of each influencing factor, and the contribution of each influencing factor to the change in carbon emissions is obtained by dividing the effect value by the total change in carbon emissions. The effect of each influencing factor on carbon emissions is analyzed to evaluate transportation carbon emissions.

[0033] A method for evaluating the relationship between transportation carbon emissions and economic decoupling taking into account passenger and freight transport indicators. The method for determining the effect value of transportation carbon emissions taking into account passenger and freight transport indicators obtains the effect value of each influencing factor, and uses the effect value to decompose the transportation carbon emission-economic decoupling coefficient to obtain the elasticity coefficient of each influencing factor, analyzes the impact of each factor on carbon emission and economic decoupling, and realizes the evaluation of the relationship between transportation carbon emissions and economic decoupling.

[0034] Beneficial effects of the present invention:

[0035] The method for determining the effect value of transportation carbon emissions considering passenger and freight transport indicators of the present invention not only innovatively introduces passenger and freight transport indicators such as passenger volume, passenger structure, freight volume, and freight structure to construct a LMDI influencing factor decomposition model, but also innovatively determines the LMDI influencing factor decomposition model based entirely on physical factors. It is found through verification that the model does not produce residuals, thereby being able to accurately determine each influencing factor and effect value, and being able to effectively improve the accuracy of determining the influencing factors and effect values ​​of transportation carbon emissions, which not only ensures the accuracy of carbon emission prediction, but also effectively adjusts the influencing factors according to carbon emission requirements; at the same time, a transportation carbon emission effect value determination system and equipment are formed according to this method, which can determine the effect value of each influencing factor based on transportation data; the contribution of each influencing factor to the change in carbon emissions is determined based on the effect value of each influencing factor, which reveals the impact of transportation activities on carbon emissions and the changing trend, and helps to identify whether it is freight transportation or personnel flow that leads to a larger proportion of carbon growth, thereby being able to provide effective advice for transportation.

[0036] In addition, in response to the problem that the prior art does not consider the impact of passenger and freight index factors on transportation carbon emissions-economic decoupling, the present invention also decomposes the transportation carbon emissions-economic decoupling coefficient according to the effect value of each influencing factor to obtain the elasticity coefficient of each influencing factor, analyzes the impact of each factor on carbon emissions and economic decoupling, and realizes the evaluation of the relationship between transportation carbon emissions and economic decoupling. It not only ensures the objectivity of transportation carbon emissions, but also can give effective opinions on transportation carbon emissions based on the physical world. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a box plot of the contribution of passenger and freight transport indicators;

[0038] Figure 2 This is the decomposition result of the transportation carbon emission-economy decoupling coefficient. DETAILED DESCRIPTION Specific implementation method one:

[0040] This implementation method is a method for determining the effect value of transportation carbon emissions taking into account passenger and freight indicators. First, transportation data is obtained, and a LMDI influencing factor decomposition model is constructed by adding passenger and freight indicators based on the traditional LMDI decomposition method. Then, the transportation data is input into the LMDI influencing factor decomposition model to determine the effect value of each influencing factor.

[0041] The LMDI influencing factor decomposition model is as follows:

[0042]

[0043] Among them, C is the carbon emission of transportation, i represents two modes of transportation: passenger transportation and freight transportation, j represents four modes of transportation: road transportation, railway transportation, water transportation and aviation transportation, M ij is the passenger and freight volume, O ij is the average transport distance, F ij is the carbon emission factor of the transport mode, B i For passenger and freight transport structure, B ij is the transport mode structure, T ij is the number of people supplied per unit of transport production, W is the energy intensity, and X is the energy efficiency.

[0044] The traditional LMDI decomposition method is as follows:

[0045]

[0046] Among them, C is the carbon emission of transportation, TS i For transportation structures, TI i is the transportation intensity, Q is the energy intensity, Y i is the energy structure, F i is the carbon emission factor.

[0047] Taking the logarithm of both sides of formula (1) and taking the derivative with respect to time, and then taking the definite integral, we get:

[0048]

[0049] Determine the weight function It is expressed as:

[0050]

[0051] Formula (3) can be derived into the form of formula (5):

[0052]

[0053] Will Disassemble and C t-C t-1 Put it on the left side of the formula, and we get formula (6):

[0054]

[0055] The addition-based decomposition formula is obtained from formula (6), as shown in formula (7):

[0056]

[0057] ΔC is the total change in carbon emissions.

[0058] The calculation method of the effect value of each influencing factor is as follows:

[0059]

[0060]

[0061] Based on formula (7) and formulas (8)-(15), the effect value of each influencing factor is determined according to its influence on the total change of carbon emissions.

[0062] Verify the residuals of the LMDI influencing factor decomposition model:

[0063]

[0064] ΔC rad is the residual, ΔC 实际 is the actual change in traffic carbon emissions, ΔC 计算 is the calculated change in transportation carbon emissions;

[0065] The residual of the LMDI influencing factor decomposition model is zero, that is, the sum of the carbon emission changes of each influencing factor is consistent with the actual traffic carbon emission changes and meets the constraints of formula (17). Formula (17) is as follows:

[0066]

[0067] Where η represents the passenger and freight volume M ij , average transport distance O ij , Carbon emission factor F of transport mode ij 、Passenger and freight structure B i 、Transportation mode structure B ij 、Unit transportation production supply number T ij , energy intensity Q, energy efficiency X and other influencing factors;

[0068] The calculation results of formula (16) prove that the LMDI influencing factor method will not produce residuals, that is, the actual carbon emission change value will be fully allocated to all indicators, and it also meets the constraints of formula (17), proving the rationality of the method. Specific implementation method 2:

[0070] This embodiment is a system for determining the carbon emission effect value of transportation taking into account passenger and freight indicators, including:

[0071] Transportation data acquisition unit: acquire transportation data;

[0072] Influencing factor effect value calculation unit: Use the LMDI influencing factor decomposition model and transportation data to determine the effect value of each influencing factor;

[0073] The LMDI influencing factor decomposition model used is as follows:

[0074]

[0075] The formula used is as follows:

[0076]

[0077]

[0078] Therefore, the effect value of each influencing factor is determined according to its impact on the total carbon emissions. Specific implementation method three:

[0080] This embodiment is a computer storage medium, in which at least one instruction is stored. The at least one instruction is loaded by a processor to run a system for determining the traffic carbon emission effect value taking into account passenger and freight indicators.

[0081] It should be understood that the instructions include computer program products, software or computerized methods corresponding to any method described in the present invention; the instructions can be used to program a computer system or other electronic device. Computer storage media may include readable media on which instructions are stored, including but not limited to magnetic storage media, optical storage media; magneto-optical storage media include read-only memory ROM, random access memory RAM, erasable programmable memory (e.g., EPROM and EEPROM) and flash memory layers, or other types of media suitable for storing electronic instructions. Specific implementation method four:

[0083] This embodiment is a device for determining the traffic carbon emission effect value taking into account passenger and freight indicators, the device includes a processor and a memory, and it should be understood that including any device including a processor and a memory described in the present invention, the device may also include other units and modules that perform display, interaction, processing, control, etc. and other functions through signals or instructions;

[0084] The memory stores at least one instruction, which is loaded and executed by the processor to provide a system for determining the traffic carbon emission effect value taking into account passenger and freight indicators.

[0085] Those skilled in the art will appreciate that at least one instruction stored is a computer program product corresponding to the method or system. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0086] The present application is described with reference to the flowcharts and / or block diagrams of the methods, systems, and computer program products according to the embodiments of the present application, and may also be used for corresponding devices. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0087]

[0088] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0090] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0091] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations. Specific implementation method five:

[0093] This implementation method is a method for evaluating transportation carbon emissions taking into account passenger and freight indicators. According to the method for determining the effect value of transportation carbon emissions taking into account passenger and freight indicators, the contribution of each influencing factor to the change in carbon emissions is calculated by dividing the effect value by the total change in carbon emissions, and the effect of each influencing factor on carbon emissions is analyzed to evaluate transportation carbon emissions.

[0094] 1. Determine the effect value of each influencing factor. Since this application does not consider the change of carbon emission factors, is 0, and the effect values ​​are shown in Table 1;

[0095] Table 1 Effect value calculation results

[0096]

[0097] Table 1 combines the effects of passenger and freight transport modes, showing the effects of transport volume structure, average transport distance structure, carbon emission factor effect, unit transport production supply number structure effect, passenger and freight structure effect, transport mode structure effect, energy intensity effect and energy efficiency effect. The contribution is determined according to the effect value, as shown in Table 2:

[0098] Table 2 Contribution calculation results

[0099]

[0100] There are data anomalies in the calculation results. A vertical comparison shows that some data deviates from the effective range of the overall data. The reasons include:

[0101] (1) Errors in data collection and processing lead to deviations from the actual data.

[0102] (2) An influential event occurred in a certain year, such as the epidemic in the past three years, which caused the data in these three years to show different trends or data anomalies from the previous ones.

[0103] 2. Outlier processing: Since the effect value and contribution are affected by abnormal data, it is not suitable to perform statistical analysis directly. The data is processed through the box plot method. Figure 1 This is a box plot of the contribution of passenger and freight indicators. Figure 1 The following conclusions can be drawn:

[0104] (1) The contribution of passenger and freight volume and transport mode structure to the change of carbon emissions is positive, and the effect value of passenger and freight volume is at the forefront of all indicators.

[0105] (2) The contribution of the number of people per unit of transport production and supply, industrial structure, and energy intensity to the change in carbon emissions is negative, indicating that their structural changes have an inhibitory effect on the growth of carbon emissions. The inhibitory effect is arranged from large to small as follows: the number of people per unit of transport production and supply, energy intensity, and industrial structure.

[0106] Furthermore, the contribution of influencing factors is analyzed;

[0107] 1. Cumulative contribution analysis. The cumulative contribution and average annual contribution are shown in Table 3:

[0108] Table 3 Cumulative contribution

[0109]

[0110] The following conclusions can be drawn from Table 3:

[0111] (1) The contribution of passenger and freight volume is at the forefront of all indicators and is a positive driving factor, with an average annual contribution of 4.80%. This means that the increase in China’s passenger and freight volume has significantly contributed to the increase in carbon emissions.

[0112] (2) The contribution of average transport distance is relatively low and is a positive driving factor, with an average annual contribution of 1.20%. That is, the increase in China’s average transport distance promotes an increase in carbon emissions.

[0113] (3) Carbon emission factor: Since this application does not consider the change of carbon emission factor, it is calculated uniformly based on the carbon emission factor in 2018, and the contribution is 0.

[0114] (4) The contribution of the number of people per unit of transport production and supply ranks first among all indicators and is a negative driving factor, with an average annual contribution of -4.58%. This means that the increase in the number of people per unit of transport production and supply in China has significantly suppressed the increase in carbon emissions.

[0115] (5) The contribution of the passenger and freight transport structure is very low and is a negative driving factor, with an average annual contribution of -0.35%. That is, the change in China's passenger and freight transport structure has suppressed the increase in carbon emissions.

[0116] (6) The contribution of the transport mode structure is relatively low and is a positive driving factor, with an average annual contribution of 1.39%. This means that changes in China’s transport mode structure have promoted an increase in carbon emissions.

[0117] (7) Energy intensity is a negative driving factor, with an average annual contribution of -2.36%, which means that changes in China's energy intensity have suppressed the increase in carbon emissions. Energy intensity is the energy consumption per unit of transportation output value. The higher the value, the greater the energy consumption per unit of output value and the greater the carbon emissions. China's carbon emission intensity has dropped from 163 in 2000 to 9,900 tons of standard coal per 100 million yuan in 2020. The reduction in carbon emission intensity has effectively suppressed the increase in carbon emissions.

[0118] (8) The higher the energy efficiency, the less energy is consumed and the smaller the carbon emissions. According to the data, its average annual contribution is -0.46%, which means that the increase in China's transportation energy efficiency has suppressed the increase in carbon emissions, but the contribution is very small. This is because the change in energy efficiency is not obvious and is in a state of ups and downs.

[0119] 2. Interval cumulative contribution analysis

[0120] Since it is difficult to judge the detailed differences in the cumulative contribution, we take a 3-year interval to analyze the interval cumulative contribution. The interval contribution is shown in Table 4:

[0121] Table 4 Interval contribution

[0122]

[0123] The following conclusions can be drawn from Table 4:

[0124] (1) Passenger and freight volume have always been positive drivers of carbon emissions before 2017, and their contribution was large. Before 2011, the contribution increased rapidly, and the contribution rate from 2011 to 2017 was stable between 15% and 20%. However, due to the impact of the epidemic, the contribution rate from 2018 to 2020 varied greatly. This is because passenger volume was halved and freight volume was also affected.

[0125] (2) Average haul distance was a positive driver of carbon emissions from 2003 to 2014, but its contribution was negative before 2002 and after 2015, which was related to the fluctuation of average haul distance.

[0126] (3) The number of people per unit of transport production supply has always been the main negative driving factor affecting carbon emissions before 2017, but due to the impact of the epidemic, it changed to 17.65% from 2018 to 2020, which was caused by a sharp decline in traffic turnover.

[0127] (4) The transport mode structure was a positive driving factor with a small contribution in most years, but it made a large contribution from 2006 to 2008. This was caused by the rapid increase in the proportion of road in freight transport.

[0128] (5) The passenger and freight transport structure is a negative driving factor with a small contribution in most years. This is because the transport mode structure changed very little from 2000 to 2020, while it changed significantly from 2018 to 2020. This was caused by the rapid decrease in the proportion of passenger transport due to the epidemic.

[0129] (6) Energy intensity has always been a negative driving factor, and its contribution has increased from 2000 to 2020, indicating that increasing energy intensity is an effective means to reduce carbon emissions.

[0130] (7) Energy efficiency has always been a driving factor with a small contribution, but its positive and negative values ​​show different states in different intervals, indicating that energy efficiency is in a fluctuating state, not a gradually rising trend.

[0131] 3. Analysis of the contribution of passenger and freight indicators to carbon emissions changes

[0132] The above analysis is based on the contribution of indicators such as passenger and freight transportation and transportation technology level, without considering the effect values ​​and contributions of passenger and freight transportation respectively. Here, the passenger and freight transportation indicators will be analyzed separately, and their respective contributions to the change in carbon emissions will be calculated and analyzed, as shown in Table 5:

[0133] Table 5: Contribution of carbon emissions changes by passenger and freight transport

[0134]

[0135] From Table 5, we can draw the following conclusions:

[0136] (1) Compared with passenger transport, freight transport contributes more to carbon emissions, but the trends of the two are similar, both gradually increasing from 2000 to 2011, and reaching the maximum contribution from 2009 to 2011. Affected by the epidemic, the contribution of both has turned negative.

[0137] (2) The contribution of the average passenger transport distance is smaller than that of the average freight transport distance. The contribution of the average freight transport distance reached a peak of 25.31% between 2006 and 2008. This was caused by the rapid growth of the average freight road transport distance between 2006 and 2008.

[0138] (3) For the number of people per unit of transport production, the contribution of passenger and freight transport is negative, but the contribution of passenger transport is much smaller than that of freight transport, which means that the increase in the number of people per unit of transport production in China's freight transport will significantly suppress the growth of carbon emissions.

[0139] (4) For passenger and freight transport modes, the contribution of freight transport is significantly higher than that of passenger transport. The contribution of freight transport mode reached 26.42% from 2006 to 2008, indicating that during this period, the freight transport structure changed greatly, which greatly promoted the increase of carbon emissions.

[0140] 4. Analysis of the contribution of passenger and freight transport mode indicators to carbon emissions changes

[0141] Passenger and freight transport are divided into four modes of transport: road, rail, water and air. The contribution of each mode to the change in carbon emissions is calculated and analyzed. The contribution of the four modes of transport to carbon emissions is shown in Table 6:

[0142] Table 6 Contribution of four modes of transport to transport volume

[0143]

[0144] In terms of transport volume, among passenger transport, air transport contributes the most to the change in carbon emissions, followed by rail and road, and water transport's contribution is 0; among freight transport, road contributes the most, followed by water, rail and air transport.

[0145] The contribution of the average transport distance of the four modes of transportation to carbon emissions is shown in Table 7:

[0146] Table 7 Average transport distance contribution of four transport modes

[0147]

[0148] In terms of average transport distance, passenger transport modes contribute relatively little to carbon emissions, with roads having the largest positive driving force and railways having the largest negative driving force. In freight transport, roads contribute the most and are a positive driving force, followed by air transport, while railways and water transport are negative driving forces and their contributions are relatively small.

[0149] The contribution of the number of people supplied per unit turnover of the four modes of transportation to carbon emissions is shown in Table 8:

[0150] Table 8 Contribution of the number of people per unit turnover of four modes of transportation

[0151]

[0152] For the number of people supplied per unit turnover, aviation and railways are negative driving forces in passenger transport, but their contribution is relatively small; the number of people supplied per unit turnover in freight transport by road transport is relatively large, which is a negative driving force, and the contribution of other modes of transport is relatively small, all of which are negative driving forces. This shows that the more people supplied per unit turnover, the smaller the carbon emissions. Specific implementation method three:

[0154] The decoupling method can characterize the connection between the environment and the economy. Achieving carbon neutrality also requires the decoupling of transportation carbon emissions from economic development. The elasticity coefficient of each influencing factor is calculated by the Tapio decoupling method to determine whether each factor promotes or inhibits the decoupling of transportation carbon emissions from the economy. The Tapio decoupling method is shown in formula (19):

[0155]

[0156] In the formula, t (C,T) is the transportation carbon emission-economic elasticity coefficient, t (Z,T) is the development elasticity coefficient of the transportation industry, t (C,Z) is the carbon emission elasticity coefficient of transportation, ΔZ is the change in transportation turnover, Z is the transportation turnover, ΔT is the change in economic growth, and T is the economic growth;

[0157] In this way, the carbon emission-economic elasticity coefficient is decomposed into the transportation industry development elasticity coefficient and the transportation carbon emission elasticity coefficient. According to formula (7) and formula (18), the transportation carbon emission-economic elasticity coefficient can be decomposed into the elasticity coefficients of various influencing factors, as shown in formula (19):

[0158]

[0159] According to the decomposition of the carbon emission changes in formulas (8) to (15), the elasticity coefficient of each influencing factor is determined, so as to analyze the reasons why the transportation carbon emission-economy decoupling state has not been achieved. The decomposition results of the transportation carbon emission-economy decoupling elasticity coefficient are shown in Table 9 and Figure 2 As shown:

[0160] Table 9 Decomposition of transportation carbon emissions-economic decoupling elasticity coefficient

[0161]

[0162] The following conclusions can be drawn from Table 9:

[0163] (1) In most years, carbon emissions from transportation and the economy cannot achieve a strong decoupling state, which is consistent with reality, because the current development of China's transportation industry is still at the expense of the environment.

[0164] (2) Among all the influencing factors, passenger and freight volume are the factors that inhibit the decoupling of transportation carbon emissions and the economy. The number of people per unit of transportation production supply, energy efficiency and energy intensity are the factors that promote the decoupling of transportation carbon emissions and the economy. Other influencing factors are in a fluctuating state between promotion and inhibition.

[0165] (3) Improving the coordination between passenger and freight volume and the environment, reducing its elasticity with carbon emissions, and continuing to promote the development of the number of people per unit of transport production, energy efficiency, and energy intensity are all beneficial to the decoupling of transportation carbon emissions and the economy.

[0166] The above calculation examples of the present invention are only used to illustrate the design concept and process of the present invention in detail, and are not intended to limit the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for determining the carbon emission effect value of transportation considering passenger and freight indicators, characterized in that: The transportation data is obtained and input into the LMDI influencing factor decomposition model, and the effect value of each influencing factor is determined based on the LMDI influencing factor decomposition model; the LMDI influencing factor decomposition model is as follows: Among them, C is the carbon emission of transportation, i represents the two modes of transportation, passenger transportation and freight transportation, j represents the four modes of transportation, road transportation, railway transportation, water transportation and aviation transportation, M ij is the passenger and freight volume, O ij is the average transport distance, F ij is the carbon emission factor of the transport mode, B i For passenger and freight transport structure, B ij is the transport mode structure, T ij is the number of people supplied per unit of transport production, Q is energy intensity, and X is energy efficiency; Taking the logarithm of both sides of formula (1) and taking the derivative with respect to time, and then taking the definite integral, we get: Determine the weight function It is expressed as: Formula (3) is derived into the form of formula (5): Will Disassemble and C t -C t-1 Put it on the left side of the formula, and we get the decomposition formula (7) based on addition: ΔC is the total change in carbon emissions, is the effect of passenger and freight volume on carbon emissions, is the effect of average transport distance on carbon emission changes, is the effect value of the carbon emission factor of the transport mode on the change of carbon emissions, is the effect of passenger and freight transport structure on carbon emission changes, is the effect of the transport mode structure on carbon emission changes, The effect value of the number of people per unit of transport production supply on the change of carbon emissions, ΔC Q is the effect of energy intensity on carbon emission changes, ΔC X is the effect value of energy efficiency on carbon emission changes; the calculation formula of the effect value of each influencing factor is as follows: Based on formula (7) and formulas (8)-(15), the effect value of each influencing factor is determined according to its influence on the total change of carbon emissions.

2. A method for determining the carbon emission effect value of transportation considering passenger and freight indicators, characterized in that: Bundle Disassemble and C t -C t-1 The derivation process on the left side of the formula is shown in formula (6):

3. A system for determining the carbon emission effect value of transportation considering passenger and freight indicators, characterized in that: include: Transportation data acquisition unit: acquire transportation data; Influencing factor effect value calculation unit: Use the LMDI influencing factor decomposition model and transportation data to determine the effect value of each influencing factor; The LMDI influencing factor decomposition model used is as follows: The formula used is as follows: Therefore, the effect value of each influencing factor is determined according to its impact on the total carbon emissions.

4. A computer storage medium, characterized in that: The storage medium stores at least one instruction, and the at least one instruction is loaded by the processor and runs the system for determining the traffic carbon emission effect value taking into account passenger and freight indicators as described in claim 3.

5. A device for determining the traffic carbon emission effect value considering passenger and freight indicators, characterized in that: The device includes a processor and a memory, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to execute the system for determining the traffic carbon emission effect value taking into account passenger and freight indicators as described in claim 3.

6. A method for evaluating transportation carbon emissions taking into account passenger and freight indicators, characterized in that: According to the method for determining the transportation carbon emission effect value considering passenger and freight transport indicators described in claim 1, the effect value of each influencing factor is obtained, and the contribution of each influencing factor to the change in carbon emissions is obtained by dividing the effect value by the total change in carbon emissions. The effect of each influencing factor on carbon emissions is analyzed, and transportation carbon emissions are evaluated.

7. An evaluation method for the relationship between transportation carbon emissions and economic decoupling considering passenger and freight indicators, characterized in that: According to the method for determining the effect value of transportation carbon emissions considering passenger and freight transport indicators described in claim 1, the effect value of each influencing factor is obtained, and the transportation carbon emission-economic decoupling coefficient is decomposed using the effect value to obtain the elasticity coefficient of each influencing factor, and the impact of each factor on carbon emissions and economic decoupling is analyzed to achieve the evaluation of the relationship between transportation carbon emissions and economic decoupling.

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