A carbon emissions prediction method

Abstract

The present application discloses a carbon emission prediction method, including: obtaining the annual average growth rate of the turnover volume of different transportation modes, the annual average growth rate of the construction mileage of traffic routes, and the annual average change rate of carbon emissions in the target area, as well as obtaining the annual average growth rate of the GDP and the annual average growth rate of the permanent population in the target area; obtaining the predicted annual average growth rate of the turnover volume of different transportation modes in the target area; obtaining the annual average energy use optimization rate of different existing energy sources in the target area; obtaining the annual average energy structure optimization rate of different new energy sources in the target area; obtaining the predicted annual average optimization rate of the carbon emission intensity of different transportation modes in the target area; obtaining the predicted carbon emissions of different transportation modes in the target year in the target area; and obtaining the predicted development trend of the carbon emissions in the target area. The present application presets weight factors according to the actual situation of the target area, and the current carbon emission data of different transportation modes obtained are reliable, so as to accurately predict future carbon emissions.

Description

Technical Field

[0001] The present application relates to the technical field of carbon emission prediction, and in particular to a carbon emission prediction method. Background Art

[0002] Railway passenger transport, railway freight, air passenger transport, and air freight are part of the transportation sector. The energy used by these different modes of transportation includes existing energy and new energy. Existing energy mainly includes coal, oil and natural gas, and new energy mainly includes solar energy, biomass energy, wind energy, geothermal energy, wave energy, ocean current energy and tidal energy. With the development of science and technology, more types of new energy will be put into use in the future. The carbon emissions generated when these energy sources are consumed not only affect transportation, but also the total carbon emissions of the entire industry in society. Therefore, it is necessary to predict future carbon emissions.

[0003] At present, when predicting future carbon emissions, it is necessary to first calculate the current status of carbon emissions. In the prior art, the current status of carbon emissions is generally calculated by splitting the energy usage in the energy balance table, obtaining the type and amount of energy consumed by railway and air transportation, and estimating the current carbon emissions in the railway and air transportation sectors based on the standard coal conversion coefficient of each energy and the carbon emission intensity factor. The limitation of this accounting method is that there is a certain degree of subjectivity in the proportion used when splitting the total energy usage of society into railway and air transportation, and the carbon emission calculation depends on the actual energy usage data. The data obtained using this accounting method will result in inaccurate predictions. Summary of the invention

[0004] In order to solve the problem in the prior art that when predicting future carbon emissions, the current carbon emissions data obtained are highly subjective, resulting in inaccurate prediction results, the present application discloses a carbon emissions prediction method.

[0005] The present application discloses a carbon emission prediction method, comprising:

[0006] Obtain the turnover, transportation route construction mileage and carbon emission intensity of different modes of transportation in the target area over the years, including air passenger transportation, air freight transportation, rail passenger transportation and rail freight transportation;

[0007] Obtain historical GDP information and permanent population information of the target area;

[0008] Obtain the annual average growth rate of turnover of different modes of transportation in the target area, the annual average growth rate of transportation route construction mileage and the annual average change rate of carbon emissions, as well as the annual average growth rate of GDP and the annual average growth rate of permanent population in the target area;

[0009] Obtain the predicted average annual growth rate of the turnover volume of different transportation modes in the target area according to the first preset formula;

[0010] Obtain the average annual energy use optimization rate of different existing energy sources in the target area;

[0011] Obtain the average annual energy structure optimization rate of different new energy sources in the target area;

[0012] Obtain the predicted average annual optimization rate of the carbon emission intensity of different transportation modes in the target area according to the second preset formula;

[0013] Obtain the predicted carbon emissions of different transportation modes in the target area in the target year according to the third preset formula;

[0014] Obtain the predicted development trend of carbon emissions in the target area.

[0015] Optionally, the step of obtaining the predicted average annual growth rate of the turnover volume of different transportation modes in the target area according to the first preset formula includes:

[0016] The first preset formula is determined according to the following formula:

[0017] R i =w X R X +w G R G +w P R P +w L R L ;

[0018] where i represents the i-th transportation mode, R i represents the predicted average annual growth rate of the turnover volume of the i-th transportation mode, R X represents the average annual growth rate of the turnover volume of the i-th transportation mode, R G represents the average annual growth rate of the GDP of the target area, R P represents the average annual growth rate of the permanent population of the target area, R L represents the average annual growth rate of the construction mileage of the transportation route of the i-th transportation mode, where w X 、w G 、w P and w L are preset weight factors, and w X +w G +w P +w L =1.

[0019] Optionally, the step of obtaining the predicted average annual optimization rate of the carbon emission intensity of different transportation modes in the target area according to the second preset formula includes:

[0020] The second preset formula is determined according to the following formula:

[0021] E i = w C R C + w k R k + w L R L ;

[0022] where E i represents the predicted annual average optimization rate of the carbon emission intensity of the i-th transportation mode, R C represents the annual average change rate of the carbon emissions of the i-th transportation mode, R k represents the predicted annual average energy use optimization rate of different existing energy sources, R L represents the predicted annual average energy structure optimization rate of different new energy sources, w C , w k and w L are preset weight factors, and w C + w k + w L = 1.

[0023] Optionally, according to the third preset formula, obtaining the predicted carbon emissions of different transportation modes in the target area in the target year includes:

[0024] The third preset formula is determined according to the following formula:

[0025] Y i (q) = C i × E i q × X i × R i q ;

[0026] where q represents the q-th year in the future, Y i (q) represents the predicted carbon emissions of the i-th transportation mode in the q-th year in the future, C i represents the carbon emission intensity of the i-th transportation mode in the most recent year, E i q represents the predicted annual average optimization rate of the carbon emission intensity of the i-th transportation mode in the q-th year, X i represents the turnover volume of the i-th transportation mode in the most recent year, R i q represents the predicted annual average growth rate of the turnover volume of the i-th transportation mode in the q-th year.

[0027] Optionally, the obtaining of the predicted annual average energy use optimization rate of different existing energy sources in the target area includes:

[0028] Obtain the types and development status of existing energy sources in the target area;

[0029] Obtain the energy utilization efficiency of different existing energy sources in the target area over the years.

[0030] Optionally, obtaining the annual average energy structure optimization rate of different new energy sources in the target area includes:

[0031] Obtain the types and development status of new energy sources in the target area;

[0032] Obtain the proportion of energy use of different new energy sources in the target area over the years.

[0033] Optionally, before obtaining the predicted development trend of carbon emissions in the target area, the method further includes:

[0034] Obtain the predicted carbon emissions of all transportation modes in the target area in the target year.

[0035] Optionally, the preset weight factors w X 、w G 、w P 、w L 、w C 、w k and w L are obtained according to the economic development of the target area, the geographical location characteristics of the target area, the availability and usability of renewable energy sources in the target area, as well as according to the saturation effect of resident consumption and the growth slowdown effect on the demand side.

[0036] Optionally, the preset weight factors w X 、w G 、w P 、w L 、w C 、w k and w L Take different values according to different transportation modes and different years.

[0037] The present application discloses a carbon emission prediction method, including: obtaining the turnover volume, traffic route construction mileage, and carbon emission intensity of different transportation modes in a target area over the years, where the different transportation modes include air passenger transportation, air freight transportation, railway passenger transportation, and railway freight transportation; obtaining the GDP information and permanent population information of the target area over the years; obtaining the average annual growth rate of the turnover volume of different transportation modes in the target area, the average annual growth rate of the traffic route construction mileage, and the average annual change rate of carbon emissions, as well as obtaining the average annual growth rate of the GDP and the average annual growth rate of the permanent population in the target area; obtaining the predicted average annual growth rate of the turnover volume of different transportation modes in the target area according to a first preset formula; obtaining the average annual energy use optimization rate of different existing energy sources in the target area; obtaining the average annual energy structure optimization rate of different new energy sources in the target area; obtaining the predicted average annual optimization rate of the carbon emission intensity of different transportation modes in the target area according to a second preset formula; obtaining the predicted carbon emissions of different transportation modes in the target area in a target year according to a third preset formula; and obtaining the predicted development trend of the carbon emissions in the target area.

[0038] The present application analyzes the historical statistical data such as the turnover volume, traffic line construction mileage, and carbon emission intensity in the passenger and freight fields of railways and aviation in the target area, combines the characteristics of economic and social development such as the GDP and permanent population in the target area, analyzes the process of technological progress and energy structure optimization in related transportation fields, judges the expected years and influence scope of the promotion and application of new technologies and new energy sources, and predicts and calculates the future carbon emissions in the passenger and freight fields of railways and aviation in the target area by integrating various factors, and judges the development trend of the total carbon emissions. The present application presets weight factors according to the actual situation of the target area, and the obtained current carbon emission data of different transportation modes are reliable, so as to accurately predict the future carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0040] Figure 1 It is a schematic flowchart of a carbon emission prediction method disclosed in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] In order to solve the problem that in the prior art, when predicting future carbon emissions, the obtained current carbon emission data is highly subjective, resulting in inaccurate prediction results, the present application discloses a carbon emission prediction method.

[0042] The present application discloses a carbon emission prediction method, see Figure 1 the schematic flowchart shown, including:

[0043] Step 101, obtain the turnover volume, traffic route construction mileage, and carbon emission intensity of different transportation modes in the target area A over the years. The different transportation modes include air passenger transportation, air freight transportation, railway passenger transportation, and railway freight transportation. Among them, different transportation modes are represented by the i-th transportation mode, where i = 1 represents railway passenger transportation, i = 2 represents railway freight transportation, i = 3 represents air passenger transportation, and i = 4 represents air freight transportation. The years are at least five years. The unit of passenger turnover volume is "ten thousand person-kilometers", the unit of freight turnover volume is "ten thousand ton-kilometers", the unit of traffic route construction mileage is "kilometers", the unit of passenger carbon emission intensity is "kilograms of CO2 per ten thousand person-kilometers", and the unit of freight carbon emission intensity is "kilograms of CO2 per ten thousand ton-kilometers". For the i-th transportation field, according to the annual turnover volume X obtained by year j j , traffic route construction mileage L j and carbon emission intensity C j .

[0044] Step 102, obtain the annual GDP information and permanent population information of the target area over the years. Among them, the unit of GDP is "ten thousand yuan", and the unit of the permanent population is "ten thousand people". The GDP obtained by year j is represented as G j , the permanent population is represented as P j .

[0045] Step 103, obtain the average annual growth rate R of the turnover volume of different transportation modes in the target area i , the average annual growth rate R of the traffic route construction mileage L and the average annual change rate R of the carbon emission volume C , and obtain the average annual growth rate R of the GDP of the target area G and the average annual growth rate R of the permanent population P .

[0046] Step 104, according to the first preset formula, obtain the predicted average annual growth rate of the turnover volume of different transportation modes in the target area;

[0047] The first preset formula is determined according to the following formula:

[0048] R i = w X R X + w G R G + w P R P + w L R L ;

[0049] Among them, i represents the i-th transportation mode, and R i represents the predicted average annual growth rate of the turnover volume of the i-th transportation mode, and RX Denote the average annual growth rate of the turnover volume of the \(i\)-th transportation mode as \(R G Denote the average annual growth rate of the GDP of the target area as \(R P Denote the average annual growth rate of the permanent population of the target area as \(R L Denote the average annual growth rate of the construction mileage of the transportation routes of the \(i\)-th transportation mode, where \(w X 、\(w G 、\(w P and \(w L are preset weight factors, and \(w X +w G +w P +w L = 1.

[0050] Step 105: Obtain the types and development status of the existing energy sources in the target area; for the \(i\)-th transportation field, obtain the current research status, research stage, and research bottlenecks of the use of existing energy sources and new energy sources for transportation tools in this field, and judge the expected completion year and commercialization scope of relevant technologies.

[0051] Obtain the energy use efficiency of different existing energy sources in the target area over the years.

[0052] Obtain the average annual energy use optimization rate of different existing energy sources in the target area; specifically, according to the judgment that the energy use efficiency of the \(k\)-th existing energy source in the \(m\)-th year is currently \(f k %, calculate the average annual energy use optimization rate \(R k .

[0053] Step 106: Obtain the types and development status of new energy sources in the target area.

[0054] Obtain the energy use proportion of different new energy sources in the target area over the years.

[0055] Obtain the average annual energy structure optimization rate of different new energy sources in the target area; according to the judgment that the energy use proportion of the \(L\)-th new energy source, electric energy, reaches \(f L % in the \(n\)-th year, calculate the average annual energy structure optimization rate \(R L .

[0056] Step 107: According to the second preset formula, obtain the predicted average annual optimization rate of the carbon emission intensity of different transportation modes in the target area.

[0057] The second preset formula is determined according to the following formula:

[0058] E i = w C R C +w k R k +wL R L ;

[0059] Among them, E i represents the predicted annual average optimization rate of the carbon emission intensity of the i-th transportation mode, R C represents the annual average change rate of the carbon emissions of the i-th transportation mode, R k represents the predicted annual average energy use optimization rate of different existing energy sources, R L represents the predicted annual average energy structure optimization rate of different new energy sources, w C 、w k and w L are preset weight factors, and w C +w k +w L = 1.

[0060] Step 108: Obtain the predicted carbon emissions of different transportation modes in the target area in the target year according to the third preset formula.

[0061] The third preset formula is determined according to the following formula:

[0062] Y i (q) = C i × E i q × X i × R i q ;

[0063] Among them, q represents the q-th year in the future, Y i (q) represents the predicted carbon emissions of the i-th transportation mode in the q-th year in the future, C i represents the carbon emission intensity of the i-th transportation mode in the most recent year, E i q represents the predicted annual average optimization rate of the carbon emission intensity of the i-th transportation mode in the q-th year, X i represents the turnover volume of the i-th transportation mode in the most recent year, R i q represents the predicted annual average growth rate of the turnover volume of the i-th transportation mode in the q-th year.

[0064] Step 109: Obtain the predicted carbon emissions of all transportation modes in the target area in the target year. Add the predicted carbon emissions Yi(q) in each field in the q-th year to calculate the predicted total carbon emissions Y(q) in the passenger and freight transportation fields of railways and aviation in Area A. The calculation formula is Y(q) = Y1(q) + Y2(q) + Y3(q) + Y4(q). Arrange the predicted total carbon emissions Y(q) for each year in the chronological order of the year q to obtain the development trend of the total carbon emissions over time.

[0065] Obtain the predicted development trend of carbon emissions in the target area.

[0066] This application discloses a carbon emission prediction method, including: obtaining the turnover volume, traffic route construction mileage, and carbon emission intensity of different transportation modes in the target area over the years, where the different transportation modes include air passenger transport, air freight transport, railway passenger transport, and railway freight transport; obtaining the GDP information and resident population information of the target area over the years; obtaining the average annual growth rate of the turnover volume of different transportation modes in the target area, the average annual growth rate of the traffic route construction mileage, and the average annual change rate of carbon emissions, as well as obtaining the average annual growth rate of the GDP and the average annual growth rate of the resident population in the target area; according to the first preset formula, obtaining the predicted average annual growth rate of the turnover volume of different transportation modes in the target area; obtaining the average annual energy use optimization rate of different existing energy sources in the target area; obtaining the average annual energy structure optimization rate of different new energy sources in the target area; according to the second preset formula, obtaining the predicted average annual optimization rate of the carbon emission intensity of different transportation modes in the target area; according to the third preset formula, obtaining the predicted carbon emissions of different transportation modes in the target year in the target area; obtaining the predicted development trend of carbon emissions in the target area.

[0067] This application obtains historical statistical data such as the turnover volume, traffic line construction mileage, and carbon emission intensity in the passenger and freight fields of railways and aviation in the target area, combines the characteristics of economic and social development such as the GDP and resident population of the target area, analyzes the process of technological progress and energy structure optimization in the relevant transportation fields, judges the expected years and influence scope of the promotion and application of new technologies and new energy sources, and comprehensively predicts and calculates the future carbon emissions in the passenger and freight fields of railways and aviation in the target area considering multiple factors, and judges the development trend of the total carbon emissions. This application presets weight factors according to the actual situation of the target area, and the obtained current carbon emission data of different transportation modes is reliable, so as to accurately predict future carbon emissions.

[0068] Further, the preset weight factors w X 、w G 、w P 、w L 、w C 、w k and w L are obtained according to the economic development of the target area, the geographical location characteristics of the target area, the availability and usability of renewable energy in the target area, as well as according to the saturation effect of residents' consumption and the growth slowdown effect on the demand side.

[0069] Further, the preset weight factors w X 、w G 、w P 、w L 、w C 、wk and w L It takes different values according to different transportation modes and different years. Specifically, the weight factor w needs to be studied according to the actual situation of Region A. The saturation effect of economic development and residents' consumption needs to be considered. The slowdown effect of growth on the demand side needs to be considered. The weight factor w is not a fixed value over time and the corresponding value of w needs to be calculated according to the predicted year. The characteristics of the local geographical location need to be considered. The availability and usability of renewable energy such as local water resources, wind resources, and solar resources need to be considered. The future government policy orientation and intensity need to be considered and judged. The weight factor w is not a fixed value over time and the corresponding value of w needs to be calculated according to the predicted year.

[0070] Specifically, the calculation process of carbon emission intensity involves three mathematical quantities: energy in-kind per unit mass (or unit volume), energy conversion coefficient to standard coal, and energy carbon emission factor. Among them, the unit of energy in-kind is kilogram (kg) or cubic meter (m 3 ), the unit of energy conversion coefficient to standard coal is kilogram of standard coal per unit of energy in-kind (kgce / kg, or kgce / m 3 ), and the unit of energy carbon emission factor is kilogram of CO2 per kilogram of standard coal (kgCO2 / kgce). For the energy conversion coefficient to standard coal and the energy carbon emission factor, there are internationally recommended values for use. At the same time, there are also recommended values for use within a specific country. In this case, the recommended values of the specific country are preferentially used. When making predictions, it is necessary to judge whether the energy conversion coefficient to standard coal and the energy carbon emission factor will change and the corresponding change range. According to the specific predicted year, the corresponding values of the energy conversion coefficient to standard coal and the energy carbon emission factor are used to calculate the required energy carbon emission intensity.

[0071] The present application has been described in detail above in combination with specific embodiments and exemplary examples. However, these descriptions should not be construed as limiting the present application. Those skilled in the art understand that without departing from the spirit and scope of the present application, various equivalent substitutions, modifications or improvements can be made to the technical solutions and their implementation manners of the present application, and all of these fall within the scope of the present application. The protection scope of the present application shall be subject to the appended claims.

Claims

1. A carbon emission prediction method, characterized in that, Including: Obtaining the turnover volume, the construction mileage of transportation routes, and the carbon emission intensity of different transportation modes in the target area over the years, where the different transportation modes include air passenger transport, air freight transport, railway passenger transport, and railway freight transport; Obtaining the GDP information and the permanent population information of the target area over the years; Obtain the average annual growth rate \(R\) of the turnover volume of different transportation modes in the target area X and the average annual growth rate \(R\) of the mileage of transportation route construction L and the average annual change rate \(R\) of carbon emissions C , and obtain the average annual growth rate \(R\) of the GDP in the target area G and the average annual growth rate \(R\) of the permanent population P ; According to the first preset formula R i = w X R X + w G R G + w P R P + w L R L , obtain the predicted average annual growth rate R of the turnover volume of different transportation modes in the target area i ; Obtain the annual average energy usage optimization rate R of different existing energy sources in the target area k ; Obtain the annual average energy structure optimization rate R of different new energy sources in the target area l ; According to the second preset formula E i = w C R C + w k R k + w l R l , obtain the predicted annual average optimization rate E of the carbon emission intensity of different transportation modes in the target area i ; According to the third preset formula Y i (q) = C i × E i q × X i × R i q , obtain the predicted carbon emissions of different transportation modes in the target area in the target year Obtaining the predicted development trend of the carbon emissions in the target area; Among them, i represents the i-th transportation mode, w X 、w G 、w P 、w L、 w C 、w k and w l are preset weight factors, and w X +w G +w P +w L = 1; w C +w k +w l = 1; q represents the q-th year in the future, Y i (q) represents the predicted carbon emissions of the i-th transportation mode in the q-th year in the future, C i represents the carbon emission intensity of the i-th transportation mode in the most recent year, E i q represents the predicted annual average optimization rate of the carbon emission intensity of the i-th transportation mode in the q-th year, X i represents the turnover volume of the i-th transportation mode in the most recent year, R i q represents the predicted annual average growth rate of the turnover volume of the i-th transportation mode in the q-th year.

2. The carbon emission prediction method according to claim 1, characterized in that, The obtaining of the annual average energy use optimization rate of different existing energy sources in the target area includes: Obtaining the types and development status of the existing energy sources in the target area; Obtaining the energy use efficiency of different existing energy sources in the target area over the years.

3. The carbon emission prediction method according to claim 1, characterized in that, The obtaining of the annual average energy structure optimization rate of different new energy sources in the target area includes: Obtaining the types and development status of the new energy sources in the target area; Obtaining the proportion of energy use of different new energy sources in the target area over the years.

4. The carbon emission prediction method according to claim 1, characterized in that, Before obtaining the predicted development trend of the carbon emissions in the target area, the method further includes: Obtaining the predicted carbon emissions of all transportation modes in the target area in the target year.

5. The carbon emission prediction method according to claim 1, characterized in that, The preset weight factors w X , w G , w P , w L , w C , w k and w l are obtained according to the economic development of the target region, the geographical location characteristics of the target region, the availability and usability of renewable energy in the target region, as well as according to the saturation effect of resident consumption and the growth slowdown effect on the demand side.

6. The carbon emission prediction method according to claim 5, characterized in that, The preset weight factor w X , w G , w P , w L , w C , w k and w l Take different values according to different transportation modes and different years respectively.

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