Resident low-carbon travel carbon cost quantification method based on travel chain

Abstract

The invention discloses a resident low-carbon travel carbon cost quantification method based on a travel chain, and the method is characterized in that the method comprises the steps: 1, collecting resident travel data, carrying out the recognition and classification of traffic modes in the resident travel chain, and obtaining a recognition and classification result; 2, collecting traffic data and energy consumption data of various traffic travel modes, and carrying out beforehand key parameter calculation; and step 3, performing quantitative calculation and total carbon emission reduction calculation on various low-carbon travel links in the resident travel chain based on the beforehand key parameters. According to the method, the carbon popularity quantification method for resident travel is measured and calculated from the angle of the travel chain, carbon emission and emission reduction generated by mixing of multiple travel modes are more completely calculated, key parameters are calculated according to published data, statistical data and data of authoritative research institutions of government related departments, the credibility is higher, and the calculation efficiency is higher. And the accuracy and the reliability of the emission and emission reduction results are effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation, specifically a method for quantifying carbon benefits for residents' low-carbon travel based on travel chains. Background Technology

[0002] Carbon inclusion refers to quantifying and assigning value to carbon reduction behaviors of government agencies, enterprises, public institutions, social organizations, other social organizations, or individuals in areas such as green travel, energy conservation, resource recycling, and renewable energy utilization, based on published carbon inclusion methodologies. It then utilizes commercial incentives, policy support, and market transactions to promote a positive incentive mechanism for establishing green and low-carbon production and lifestyles. The effective implementation of the carbon inclusion mechanism relies on the quantification and verification of carbon emission reductions, requiring the establishment of scientifically sound basic data and quantitative accounting.

[0003] Currently, similar technical solutions to this invention mainly consist of publicly available low-carbon travel methodologies from various cities. In 2019, Guangdong Province issued the "Guangdong Province Bicycle Cycling Carbon Inclusive Methodology," in 2021, Shenzhen issued the "Shenzhen Low-Carbon Public Transportation Carbon Inclusive Methodology," and in 2023, Chongqing issued the "Urban Public Transportation Vehicle Travel Greenhouse Gas Emission Reduction Methodology." While these three methodologies cover different modes of transportation, their main calculation methods are similar. The core of the calculation process framework is to calculate the baseline scenario emissions and the low-carbon scenario emissions, and then subtract the two to obtain the project's emission reduction.

[0004] The accuracy of existing methods and parameters for calculating carbon reduction benefits from low-carbon travel for residents still faces challenges. Specifically, existing methods mainly involve single modes of travel, and there are gaps in the calculation methods and parameters between different modes of travel. However, in reality, a single trip by a resident is a travel chain composed of multiple modes of transportation, including buses, subways, and pedestrians. Quantifying the carbon reduction effect of these travel behaviors requires considering various factors such as the carbon emission factors of different modes of transportation and travel distance. Therefore, it is necessary to develop a carbon reduction calculation method based on the complete travel chain to ensure the accuracy and reliability of the reduction. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for quantifying the carbon reduction effects of residents' low-carbon travel based on travel chains. Quantifying the carbon reduction effects of these behaviors requires considering various factors such as the carbon emission factors of different behaviors and travel distances.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for quantifying carbon-based low-carbon travel for residents based on travel chains, comprising:

[0007] Step 1: Identify and classify the modes of transportation in the residents' travel chain, and obtain the identification and classification results;

[0008] Step 2: Collect traffic data and energy consumption data for various modes of transportation, and conduct pre-trip key parameter calculations;

[0009] Step 3: Quantify and calculate the total carbon emission reduction of various low-carbon travel links in the residents' travel chain, so as to systematically calculate and evaluate the actual emission reduction benefits of their low-carbon behavior.

[0010] Preferably, the resident travel data to be collected in step one includes the number of trips, the origin and destination of the trip, the mode of transportation in the travel chain, and the travel mileage of different modes of transportation in the travel chain.

[0011] Preferably, the modes of transportation for residents' travel in step one include rail transit, ground public transport, ferry travel, and slow-moving transportation.

[0012] Preferably, the traffic data in step two includes rail transit passenger turnover, including rail transit, ground public transport passenger turnover, ferry passenger turnover, taxi passenger turnover, and private passenger car turnover; energy consumption data includes total electricity consumption of rail transit, total electricity consumption of ground public transport, total diesel consumption of ground public transport, total natural gas consumption of ground public transport, total diesel consumption of ferry, total electricity consumption of taxi, total gasoline consumption of taxi, total electricity consumption of private passenger cars, and total gasoline consumption of private passenger cars.

[0013] Preferably, the key parameters in step two include the average carbon emission factor per passenger-kilometer of the baseline scenario, the path conversion coefficient, the average carbon emission factor per passenger-kilometer of rail transit, the average carbon emission factor per passenger-kilometer of ground public transport, and the average carbon emission factor per passenger-kilometer of ferry.

[0014] Preferably, the quantitative calculation of various low-carbon travel links in the resident travel chain in step three includes the quantitative calculation of rail transit travel links, the quantitative calculation of ground public transport travel links, the quantitative calculation of ferry travel links, the quantitative calculation of slow-moving travel links, and the quantitative calculation of private car travel links.

[0015] Preferably, the baseline carbon emissions of the resident travel chain in step three are quantitatively calculated.

[0016] Preferably, the calculation of total carbon emission reduction in step three includes summing the differences between baseline carbon emissions and carbon emissions at each low-carbon travel stage in the travel chain to obtain the total amount of carbon dioxide emissions reduced by residents through various low-carbon travel methods.

[0017] Beneficial effects of the present invention

[0018] 1. This invention provides specific quantitative steps and calculation formulas for carbon emission reduction of green and low-carbon travel modes, which can accurately assess the carbon emissions of different travel modes, thereby more effectively formulating public transport priority strategies, optimizing resource allocation, and reducing operating costs.

[0019] 2. This invention, through specific low-carbon travel behaviors and real-time, simple, and effective carbon emission reduction measures, is easy for residents to understand and implement. It can enhance residents' awareness of their own energy-saving and carbon-reducing behaviors, reduce urban transportation carbon emissions, contribute to sustainable transportation development, and foster a good trend of green and low-carbon travel, thus having positive social benefits. Attached Figure Description

[0020] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0021] The present invention will be further described below with reference to embodiments, but the scope of protection of the present invention is not limited thereto:

[0022] Combination Figure 1 This invention discloses a method for quantifying carbon benefits for residents' low-carbon travel based on travel chains, specifically including:

[0023] Step 1: Identify and classify transportation modes in the residents' travel chain. Low-carbon travel modes include rail transit, public transport, ferry travel, and cycling. Rail transit data is primarily obtained using ticketing data (card swiping at stations), or calculated using relevant map measurement algorithms based on the origin, destination, and trajectory data. Public transport data is primarily calculated using relevant map measurement algorithms based on the origin, destination, and trajectory data. When only ticketing data is available or only the existence of travel behavior can be determined, the average one-way travel distance of public transport can be used as a substitute. The average travel distance can be provided by the public transport management department or obtained through sampling surveys. Ferry travel data is primarily obtained using ferry ticketing data, or calculated using relevant map measurement algorithms based on the origin, destination, and trajectory data. Cycling data can be calculated using relevant map measurement algorithms based on data collected from internet-based bicycle rental platforms, including data on vehicle rental and return point locks.

[0024] Step Two: Collect traffic and energy consumption data for various modes of transportation and conduct pre-trip key parameter calculations. Urban passenger transport turnover data, including rail transit passenger turnover, surface public transport passenger turnover, ferry passenger turnover, and taxi passenger turnover, are obtained from statistics compiled by the Shanghai Municipal Transportation Commission. Passenger turnover of private cars and slow-moving traffic is obtained from data from the Shanghai Comprehensive Transportation Survey and cross-validation of traffic models. Energy consumption data for the urban passenger transport industry, including total electricity consumption of rail transit, total electricity consumption of surface public transport, total diesel consumption of surface public transport, total natural gas consumption of surface public transport, total diesel consumption of ferries, total electricity consumption of taxis, and total gasoline consumption of taxis, are sourced from statistics compiled by the Shanghai Municipal Transportation Commission. Total electricity consumption of private cars is from monitoring data of the Shanghai New Energy Vehicle Public Data Collection and Monitoring Research Center (EVDATA), and total gasoline consumption of private cars is from the Energy Statistics Yearbook of the Shanghai Municipal Bureau of Statistics.

[0025] The key parameters calculated beforehand include the average passenger-kilometer carbon emission factor, route conversion coefficient, passenger-kilometer carbon emission factor for rail transit, passenger-kilometer carbon emission factor for public transport, and passenger-kilometer carbon emission factor for ferry travel under the baseline scenario. The formulas are as follows:

[0026] (1) Carbon emission factor per person-kilometer of trip under baseline scenario

[0027] The baseline scenario represents the expected carbon emissions scenario for all modes of transportation, i.e., the average emissions level of residents using private cars, taxis, rail transit, ground public transport, ferries, and slow-moving vehicles.

[0028]

[0029] —Emission factor per person-kilometer (kgCO2 / PKM) for baseline scenario trips;

[0030] A j —Annual energy consumption (kg, kWh, m³) of urban passenger transport industry (rail transit, public transport, ferries, taxis) 3 );

[0031] C j —Total annual energy consumption of Class J passenger vehicles in the city (kg, kWh);

[0032] j — Energy type, which can be gasoline, diesel, electricity, natural gas, etc.;

[0033] EF j —Emission factors of energy type j (kgCO2 / kg, kgCO2 / kWh, kgCO2 / m³) 3 );

[0034] Q sum —Annual passenger turnover of all modes of transportation (PKM), including rail transit passenger turnover, ground public transport passenger turnover, ferry passenger turnover, taxi passenger turnover, private car passenger turnover and slow traffic passenger turnover.

[0035] (2) Path conversion coefficient based on travel chain

[0036] The path conversion factor is defined as the ratio of the actual travel distance of different modes of transportation to the shortest road distance from the perspective of the travel chain.

[0037]

[0038] R k —The average route conversion coefficient of mode of travel k;

[0039] k – Mode of transportation, including rail transit, public transport, ferry, and pedestrian / bicycle travel.

[0040] D k —The travel distance (kg) of travel mode k in the sample travel data;

[0041] D d —The shortest road distance (kg) from the origin to the destination in the sample travel data.

[0042] (3) Carbon emission factors per person-kilometer of rail transit, ground public transport and ferry travel

[0043]

[0044] —The average carbon emission factor per person-kilometer of low-carbon travel using mode k (kgCO2 / PKM);

[0045] j — Energy type, which can be gasoline, diesel, electricity, natural gas, etc.;

[0046] k – Low-carbon travel modes, including rail transit, public transport, and ferry travel.

[0047] A k,j —Energy consumption (kg, kWh, m³) of industry category j for low-carbon travel mode k in the baseline year 3 );

[0048] EF j —Emission factors of energy type j (kgCO2 / kg, kgCO2 / kWh, kgCO2 / m³) 3 );

[0049] Qk —Annual passenger turnover (PKM) of low-carbon travel mode k in the baseline year.

[0050] (4) Average per-kilometer carbon emission factor for slow travel: The per-kilometer carbon emission factor for slow travel is 0.

[0051] Step 3: Quantify and calculate the total carbon emission reduction of various low-carbon travel links in the residents' travel chain, so as to systematically calculate and evaluate the actual emission reduction benefits of their low-carbon behavior.

[0052] (1) Baseline scenario travel carbon emissions

[0053] The baseline scenario travel carbon emissions are calculated by multiplying the baseline emission factor by the baseline travel mileage.

[0054] BE=∑ i (E PKM,i,b,m ×D i,b )

[0055] In the formula:

[0056] BE – Baseline carbon emissions (kgCO2);

[0057] —Emission factor per person-kilometer (kgCO2 / PKM) for baseline scenario trips;

[0058] D i,b —The baseline travel distance (km) for the i-th trip;

[0059] i — Number of times residents take low-carbon trips (times).

[0060] In practical applications, in some cases, complete coordinates of the origin and destination of a trip can be obtained, such as with navigation software. In other cases, only data on some points along the route can be obtained, such as subway station entry and exit data and bus passenger boarding and alighting data. Therefore, the calculation methods will differ.

[0061] Among them, when the coordinates of the starting point and destination of residents' low-carbon travel can be obtained, D i,b The shortest path mileage calculated using the Dijkstra algorithm by map service providers such as Gaode Maps was used as the baseline mileage for this low-carbon trip.

[0062] When the coordinates of the origin and destination of residents' low-carbon travel cannot be obtained, or in other situations where the shortest path cannot be calculated, the baseline travel mileage is calculated by dividing the travel mileage of each low-carbon travel segment by the average path conversion coefficient of that travel mode. The calculation steps are as follows:

[0063] D i,b =PD i,k / Rk

[0064] In the formula:

[0065] D i,b : The baseline travel distance (km) for the i-th trip;

[0066] PD i,k : Actual travel distance (km) for the i-th trip using k different modes of transportation;

[0067] R k Under the current road network conditions, the path conversion coefficient and average value of method k are defined as the ratio of the actual mileage of method k to the shortest road mileage.

[0068] (2) Carbon emissions from low-carbon travel scenarios

[0069] Low-carbon travel scenario carbon emissions include the total carbon emissions of various low-carbon travel links in the resident travel chain, including quantitative calculations of the total carbon emissions of rail transit, ground public transport, ferries, and slow-moving travel.

[0070] The carbon emissions from low-carbon travel scenarios are calculated as follows:

[0071] PE=∑ i ∑ k (E PKM,i,k ×PD i,k )

[0072] In the formula:

[0073] PE – Carbon emissions from low-carbon travel scenarios (kgCO2);

[0074] E PKM,i,k —Emission factor per kilometer of low-carbon travel by mode k for the i-th time of a resident in the baseline year (kgCO2 / PKM);

[0075] PD i,k —The travel distance (km) of the i-th trip in the k-mode segment;

[0076] i — Number of low-carbon trips by residents (times);

[0077] (3) Carbon emission reduction of low-carbon travel scenarios

[0078] The carbon emission reduction of low-carbon travel is calculated as follows:

[0079] ER = BE - PE

[0080] In the formula:

[0081] ER – Carbon emission reduction from low-carbon travel (kgCO2);

[0082] BE – Baseline Emissions (kg CO2);

[0083] PE – Emissions from rail transit trips (kgCO2).

[0084] In this embodiment, the calculated data sources are integrated from multiple sources. Urban passenger transport turnover data, including rail transit passenger turnover, ground public transport passenger turnover, ferry passenger turnover, and taxi passenger turnover, are obtained from statistics compiled by the Shanghai Municipal Transportation Commission. Passenger turnover of private cars and slow-moving traffic is obtained from data from the Shanghai Comprehensive Transportation Survey and cross-validation of traffic models. Urban passenger transport energy consumption data, including total electricity consumption of rail transit, total electricity consumption of ground public transport, total diesel consumption of ground public transport, total natural gas consumption of ground public transport, total diesel consumption of ferries, total electricity consumption of taxis, and total gasoline consumption of taxis, are sourced from statistics compiled by the Shanghai Municipal Transportation Commission. Total electricity consumption of private cars is sourced from monitoring data of the Shanghai New Energy Vehicle Public Data Collection and Monitoring Research Center (EVDATA), and total gasoline consumption of private cars is sourced from the Energy Statistics Yearbook of the Shanghai Municipal Bureau of Statistics.

[0085] This invention provides a quantitative method for measuring the carbon benefits of residents' travel from the perspective of the travel chain. It more comprehensively calculates the carbon emissions and emission reductions generated by the combination of multiple travel modes. Furthermore, the calculation of key parameters is based on data released by relevant government departments, statistical data, and data from authoritative research institutions, ensuring higher credibility and effectively improving the accuracy and reliability of emission and emission reduction results. The calculation results can provide technical support for the issuance and verification of carbon emission reductions from residents' low-carbon travel in the implementation of carbon benefits in various provinces and cities. The total carbon emission reduction index quantifies residents' low-carbon behavior into actual economic gains, contributing to energy conservation and environmental protection in transportation planning. Simultaneously, this index can be used for carbon market trading, providing value guidance.

[0086] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A low-carbon travel carbon-prime-quantification method for residents based on travel chains, characterized by The method comprises the following steps: Step 1: Collecting resident travel data, identifying and classifying the traffic modes in the resident travel chain, and obtaining the identification and classification results; Step 2: Collecting traffic data and energy consumption data of various traffic modes, and carrying out pre-key parameter calculation; Step 3: Based on the pre-key parameters, the various low-carbon travel links in the resident travel chain are quantitatively calculated, and the total carbon emission reduction amount is calculated.

2. The method of claim 1, wherein The resident travel data in step 1 includes the number of trips, the start and end points of the trip, the traffic modes in the travel chain, and the travel mileage of different traffic modes in the travel chain.

3. The method of claim 1, wherein The traffic modes of the resident travel in step 1 include rail transit travel, ground bus travel, ferry travel and slow travel.

4. The method of claim 1, wherein The traffic data in step 2 includes rail transit passenger turnover, including rail transit, ground bus passenger turnover, ferry passenger turnover, taxi passenger turnover, and social small passenger car turnover; the energy consumption data includes rail transit power consumption, ground bus power consumption, ground bus diesel consumption, ground bus natural gas consumption, ferry diesel consumption, taxi power consumption, taxi gasoline consumption, social small passenger car power consumption, and social small passenger car gasoline consumption.

5. The method of claim 1, wherein The pre-key parameters in step 2 include the average person-kilometer carbon emission factor of the baseline scenario trip, the path conversion coefficient, the average person-kilometer carbon emission factor of the rail transit travel mode, the average person-kilometer carbon emission factor of the ground bus travel mode, the average person-kilometer carbon emission factor of the ferry travel mode, and the average person-kilometer carbon emission factor of the slow travel.

6. The method of claim 5, wherein: 1) the average person-kilometer carbon emission factor of the baseline scenario trip is obtained by the following formula: wherein, represents the average person-kilometer carbon emission factor of the baseline scenario trip; A j represents the total annual energy consumption of the urban passenger transport industry of the energy type j; C j represents the total annual energy consumption of the social small passenger car of the energy type j; j represents the energy type, including gasoline, diesel, electricity, and natural gas; EF j represents the emission factor of the energy type j; Q sum represents the annual turnover of all travel modes, including rail transit passenger turnover, including rail transit passenger turnover, ground bus passenger turnover, ferry passenger turnover, taxi passenger turnover, social small passenger car passenger turnover, and slow traffic passenger turnover; 2) the path conversion coefficient is obtained by the following formula: In the formula, R k indicates the average path conversion coefficient of the travel mode k; k indicates the travel mode, including rail transit travel, ground bus travel, ferry travel, and slow travel; D k indicates the travel distance using the travel mode k in the sample travel data; D d indicates the shortest road mileage from the starting point to the ending point in the sample travel data; 3) the average person-kilometer carbon emission factor of rail transit, ground bus and ferry travel is obtained by the following formula: wherein, represents the average person-kilometer carbon emission factor of k-way low-carbon travel; j represents the energy type, taking gasoline, diesel, electricity, and natural gas; k represents the low-carbon travel mode, including rail transit travel, ground bus travel, and ferry travel; A k,j represents the energy consumption of industry j of low-carbon travel mode k in the base year; EF j represents the emission factor of energy type j; Q k represents the annual passenger turnover of low-carbon travel mode k in the base year; 4) the average person-kilometer carbon emission factor of slow travel is 0.

7. The method of claim 1, wherein In step 3, the various low-carbon travel links in the resident travel chain are quantitatively calculated as follows: 1) the baseline scenario trip carbon emission amount is calculated by multiplying the baseline emission factor by the baseline trip mileage: where: BE represents baseline carbon emissions; represents the average person-kilometer emission factor of baseline scenario travel; D i,b represents the baseline travel distance of the ith trip; i represents the number of low-carbon trips of the resident; 2) the low-carbon scenario trip carbon emission amount includes the total carbon emission amount of various low-carbon travel links in the resident travel chain, The low-carbon scenario trip carbon emission amount is calculated as follows: In the formula, PE represents the low-carbon scenario travel carbon emission; E PKM,i,k represents the reference year resident's i-th k-mode low-carbon travel person-kilometer emission factor; PD i,k represents the i-th travel k-mode link travel mileage; i represents the number of times of resident low-carbon travel.

8. The method of claim 7, wherein When the resident low-carbon travel start and end point coordinates cannot be obtained, or in other cases where the shortest path cannot be calculated, the baseline trip mileage is calculated by dividing the travel mileage of each low-carbon travel link by the average path conversion coefficient of the travel mode, and the calculation steps are as follows: D i,b = PD i,k / R k In the formula, D i,b represents the reference line trip mileage of the ith trip; PD i,k represents the k-mode actual trip mileage of the ith trip; P k represents the path conversion coefficient of the k-mode under the current road network condition, the average value, defined as the ratio of the actual mileage of the k-mode to the shortest road mileage.

9. The method of claim 1, wherein The total carbon emission reduction amount in step 3 includes the difference between the baseline carbon emission amount and the carbon emission amount of each low-carbon travel link in the travel chain, and the total carbon dioxide emission reduction amount of the residents through various low-carbon travel modes is obtained.

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