Calculation method of carbon emissions from residents' medical travel

Abstract

The present invention discloses a method for calculating carbon emissions of residents' medical travel. Based on the bottom-up carbon emissions calculation method, the present invention takes into account the difference in choices between outpatient medical treatment and inpatient medical treatment for the specific travel behavior of residents seeking medical treatment, and establishes a refined calculation model for carbon emissions of residents' medical travel. The operation is simple and fast, and the model is suitable for efficient and rapid calculation of carbon emissions of residents' medical travel in different geographical areas and time ranges, and can effectively support the refined construction of carbon emissions inventories.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission calculation, and specifically relates to a method for calculating carbon emission when residents travel to seek medical treatment. Background Art

[0002] Low-carbon development has become an international consensus, and carbon dioxide emissions are a major issue that cannot be ignored by countries around the world in the 21st century. Carbon emissions from the transportation sector account for about 10% of China's total carbon emissions, of which carbon emissions caused by residents' travel are an important part of carbon emissions in the transportation sector.

[0003] Domestic and foreign scholars have divided their research on carbon emission accounting methods into two categories: direct carbon emission accounting and indirect carbon emission accounting. Among them, the direct carbon emission accounting method has easy data acquisition and simple calculation process, and this method is widely used, while the indirect carbon emission accounting method is relatively complex to calculate and inconvenient to obtain data, so it is currently less used.

[0004] There are two main methods for calculating direct carbon emissions: top-down and bottom-up. The top-down method refers to calculating the total carbon emissions from travel based on energy consumption statistics, combined with the carbon emission factors of different fuels for different travel modes. The bottom-up method is to calculate based on the mileage, turnover, road network, etc. of residents' travel, and calculate the carbon emissions of different travels based on travel differences, such as travel mode, vehicle model, mileage and other data, combined with the corresponding carbon dioxide emission factors.

[0005] There are two main methods for calculating indirect carbon emissions from residents' travel: the input-output method and the life cycle method. The input-output method calculates the total amount based on the indirect and complete consumption coefficients of each product department per unit of total output based on the input-output table, thereby analyzing in detail the carbon emissions of residents for different travel purposes. The life cycle method calculates the carbon emissions of residents' travel at different stages by counting the energy consumption within the life cycle of each activity, including the mining, processing, utilization, and reuse of resources.

[0006] my country has not yet formed a unified standard for the detailed calculation of carbon emissions from medical travel. At present, the calculation of transportation carbon emissions is mostly based on the perspective of overall energy consumption of road traffic, relying on statistical data or survey data. The data is rough and the calculation results lack timeliness. Existing technology cannot perform detailed real-time calculation of transportation carbon emissions generated by a specific behavior in a certain period of time, and the authenticity, accuracy and precision of the estimated results cannot be guaranteed.

[0007] Medical treatment is a basic life need of residents, and medical travel is a daily basic behavior that cannot be ignored. Due to the urgency and irreplaceability of medical travel, residents are more inclined to choose high-carbon tools such as taxis and private cars in actual situations. As people's living standards improve, residents' demand for medical facilities becomes stronger, the frequency of medical travel increases, and the resulting carbon emissions also rise. Refined carbon emissions accounting for residents' medical travel behavior is convenient for more realistic and accurate evaluation of the carbon emission characteristics of medical travel behavior, exploring the potential for carbon reduction and emission reduction, and proposing targeted guidance strategies for medical travel.

[0008] In summary, the current carbon emissions accounting of residents’ medical travel has the following shortcomings and deficiencies:

[0009] 1. Currently, most of the calculations on carbon emissions from travel are based on the overall energy consumption, which is a comprehensive calculation of carbon emissions from transportation during travel. There is a lack of detailed calculation of carbon emissions generated by the specific travel behavior of seeking medical treatment.

[0010] 2. The calculation process of travel carbon emissions based on statistical data or questionnaire data is relatively extensive, inaccurate, not timely, and has obvious lag;

[0011] 3. The existing technology does not have sufficient accuracy in calculating carbon emissions from travel and has low spatial resolution, which cannot effectively meet the needs of precise spatial control;

[0012] 4. The existing data cannot reflect the temporal and spatial differences and changing patterns of carbon emissions from travel in different periods. Summary of the invention

[0013] In view of the deficiencies of the prior art, the purpose of the present invention is to provide a method for calculating the carbon emissions of residents traveling for medical treatment.

[0014] The purpose of the present invention is achieved through the following technical solutions.

[0015] A method for calculating carbon emissions of residents' medical travel includes the following steps:

[0016] Step 1, determine the geographical scope and time range for calculating carbon emissions, obtain the latitude and longitude coordinate information of all hospitals within the geographical scope or all hospitals with certain characteristics, and convert each of the above hospitals into a POI vector point displayed on the map, where i represents the number of the POI vector point, i=1, ..., n, and n is the number of POI vector points within the geographical scope for calculating carbon emissions;

[0017] Get the population spatial distribution kilometer grid dataset of the time range. The data format of the population spatial distribution kilometer grid dataset is raster. Each grid in the raster is a pixel. Each pixel is converted into a vector point. The centroid of each pixel is the position of the vector point converted from the pixel. The population of the mth pixel is N. m , m = 1, ..., g, g represents the number of vector points within the geographical scope of carbon emissions accounting;

[0018] Step 2, calculating the proportion of each vector point selecting each POI vector point when seeing a doctor in the outpatient clinic and the proportion of each vector point selecting each POI vector point when seeing a doctor in the inpatient clinic;

[0019] The proportion of the mth vector point choosing the ith POI vector point when seeking medical treatment in the outpatient clinic D mi is the travel distance from the mth vector point to the i-th POI vector point within the region;

[0020] The proportion of the mth vector point choosing the ith POI vector point when hospitalized Among them, P iB is the bed ratio of the i-th POI vector point;

[0021] Step 3, including: 3-1 and 3-2;

[0022] 3-1, use the Gaussian function to calculate the distance attenuation coefficient of each vector point when seeking medical treatment. The distance attenuation coefficient G of the mth vector point when seeking medical treatment is m :

[0023]

[0024] Where D max is the maximum tolerance distance for outpatient and inpatient medical treatment, D mmin is the shortest travel distance from the mth vector point to all POI vector points within the region where carbon emissions are calculated, D mmax The longest travel distance from the mth vector point to all POI vector points within the region where carbon emissions are calculated;

[0025] 3-2, calculate the probability of each vector point choosing each POI vector point when seeking medical treatment after distance attenuation. The probability of the mth vector point choosing the i-th POI vector point when seeking medical treatment is R mi :

[0026] R mi =P mi ×G m

[0027] Among them, when it is an outpatient visit, R mi For Rmi Ⅰ , P mi P mi Ⅰ ; When hospitalized, R mi For R mi Ⅱ , P mi P mi Ⅱ ;

[0028] Step 4: Calculate the transportation carbon emission coefficient for a single medical trip for each POI vector point:

[0029] 4-1, according to the travel speed of each medical travel trajectory, the transportation mode of each medical travel trajectory is determined, and the transportation modes include five categories;

[0030] 4-2, divide the travel distance into intervals with d km as the modulus: calculate the probability of choosing the tth type of transportation mode within the interval ((k-1)d,kd] for the travel trajectory of seeking medical treatment k = 1, 2, 3, ..., s, s is the number of intervals;

[0031] 4-3, obtain the carbon emission factor of each type of transportation, the carbon emission factor of the tth type of transportation is C t ;

[0032] For each travel distance D mi , according to the travel distance D mi The interval ((k-1)d,kd] that falls into will Substitute into formula (7) to calculate the transportation carbon emission coefficient of each vector point for each POI vector point. The transportation carbon emission coefficient of the mth vector point for the i-th POI vector point is T mi :

[0033]

[0034] Step 5, including: 5-1 to 5-4:

[0035] 5-1, calculate the parameter λ of residents seeking medical treatment within the geographical scope and time scope:

[0036]

[0037] In formula (8), when the patient is seeking medical treatment in an outpatient clinic, the resident medical treatment parameter λ is Ⅰ , when the patient is hospitalized, the resident medical parameter λ is λ Ⅱ ; Q is the proportion of residents seeking medical treatment within the geographical scope and time range for calculating carbon emissions, In order to calculate the average frequency of residents’ medical visits within the geographical scope and time range of carbon emissions, is the average proportion of outpatient or inpatient medical treatment within the geographical scope and time range of carbon emissions accounting; when λ is λ Ⅰ hour, Average proportion of outpatient visits When λ is λ Ⅱ , The average proportion of hospitalization

[0038] 5-2. Calculate the carbon emissions of medical travel for each vector point for each POI vector point within the geographical scope and time range of the accounting carbon emissions. The carbon emissions of medical travel for the mth vector point for the i-th POI vector point is CE mi , C.E. mi The carbon emissions CE of the outpatient medical trip for the mth vector point at the i-th POI vector point within the geographical scope and time range of the carbon emissions calculation is mi Ⅰ Or calculate the carbon emissions CE of hospitalization and medical travel for the mth vector point within the geographical scope and time range of carbon emissions for the i-th POI vector point mi Ⅱ :CE mi =N m ×T mi ×R mi ×λ;

[0039] Among them, when CE mi For CE mi Ⅰ When λ is λ Ⅰ , R mi For R mi Ⅰ ; When CE mi For CE mi Ⅱ When λ is λ Ⅱ , R mi For R mi Ⅱ ; N m is the population of the mth pixel;

[0040] 5-3, respectively summarize the carbon emissions of outpatient medical travel and inpatient medical travel at each vector point within the geographical scope and time range of carbon emissions. The carbon emissions of outpatient medical travel at the mth vector point is CE mⅠ , the carbon emission of hospitalization and medical treatment travel at the mth vector point is CE mⅡ :

[0041] Among them, CE m For CE m Ⅰ or CE mⅡ , when CE m For CE m Ⅰ When CE mi For CE mi Ⅰ ; When CEm For CE mⅡ When CE mi For CE mi Ⅱ ;

[0042] Calculate the total carbon emissions of medical travel for each vector point. The total carbon emissions of medical travel for the mth vector point is CE msum :CE msum =CE m Ⅰ +CE m Ⅱ .

[0043] 5-4. Calculate the carbon emissions CE of residents’ medical travel for all vector points within the geographical scope and time range of carbon emissions accounting sum :

[0044] In the above technical solution, when a hospital includes multiple campuses, each campus is calculated according to a POI vector point.

[0045] In the above technical solution, the travel distance D from the mth vector point to the i-th POI vector point within the geographical range is obtained. mi The method is:

[0046]

[0047] Where D mLon , D mLat are the longitude and latitude of the mth vector point, D iLon , D iLat are respectively the longitude and latitude of the i-th POI vector point; p is the distance ratio coefficient.

[0048] In the above technical solution, the value range of p is [1,2].

[0049] In the above technical solution, It is the sum of the reciprocals of the travel distances from the mth vector point to each POI vector point within the geographical area where carbon emissions are calculated.

[0050] In the above technical solution, the bed ratio of the i-th POI vector point is B i is the number of beds used for hospitalization at the i-th POI vector point, The number of inpatient beds at all POI vector points within the geographical area for calculating carbon emissions.

[0051] In the above technical solution, sd≥D mmax .

[0052] The beneficial effects of the present invention compared with the prior art are:

[0053] 1. Based on the bottom-up carbon emission accounting method, the present invention takes into account the difference between outpatient and inpatient medical treatment for the specific travel behavior of residents seeking medical treatment, and establishes a refined accounting model for carbon emissions of residents seeking medical treatment. The operation is simple and fast, and it is suitable for efficient and rapid accounting of carbon emissions of residents seeking medical treatment in different geographical scopes and time ranges, and can effectively support the refined construction of carbon emission inventories;

[0054] 2. Considering the uncertainty of questionnaire data and the lag of statistical data, the present invention uses the residents' mobile phone signaling travel data that can reflect the travel behavior characteristics in real time. The data is complete, true and reliable. According to the changes in residents' travel, a medical travel database is established and updated in real time, which improves the accuracy and timeliness of the calculation results;

[0055] 3. The present invention increases the spatial resolution of carbon emissions from medical travel to 1km, spatializes the calculation results and converts them into different spatial scales as needed, solving the problem of high-precision spatialization of carbon emissions from medical travel. It helps to screen out areas that need to be focused on according to emission grids, realize precise spatial management, and propose more targeted emission reduction targets.

[0056] 4. The present invention can select the calculation area and time range by itself, realize the extraction of carbon emissions from residents' medical treatment and travel in any grid area and any time period, identify the heterogeneity and change law of carbon emissions in space and time, and has strong scientificity. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 For CE mi Ⅰ (i=1) and CE mi Ⅱ (i=1) The grid dataset after spatial processing;

[0058] Figure 2 It is a grid dataset after spatial processing of carbon emissions of outpatient medical travel of all vector points and carbon emissions of inpatient medical travel of all vector points;

[0059] Figure 3 This is a grid dataset of the total carbon emissions from medical trips for all vector points. DETAILED DESCRIPTION

[0060] The technical solution of the present invention is further described below in conjunction with specific embodiments.

[0061] Example 1

[0062] A method for calculating carbon emissions of residents' medical travel includes the following steps:

[0063] Step 1, determine the geographical scope and time scope for calculating carbon emissions, extract the vector boundary of the geographical scope based on the map, obtain the latitude and longitude coordinate information of all hospitals or all hospitals with certain characteristics within the geographical scope based on the online map application program interface (Web Mapping Application Program Interface, this embodiment uses Baidu Map API, Spatial Accessibility to Hospitals Based on Web Mapping API: An Empirical Study in Kaifeng, China), map the locations of the aforementioned hospitals to the map based on GIS software (refer to the patent "Urban Functional Area Detection Method Based on Mobile Phone Signaling Data", CN116797936A), so as to convert each of the aforementioned hospitals into a POI vector point displayed on the map, wherein, when a hospital includes multiple campuses, each campus is calculated as a POI vector point (it is assumed that the medical level of all hospitals is equivalent), and i is used to represent the number of the POI vector point, i=1, ..., n, and n is the number of POI vector points within the geographical scope for calculating carbon emissions;

[0064] Obtain a population spatial distribution kilometer grid dataset within a time range. This embodiment uses the Chinese population spatial distribution kilometer grid dataset released by the Resource and Environmental Science Data Center of the Chinese Academy of Sciences (Xu Xinliang. Chinese population spatial distribution kilometer grid dataset. Resource and Environmental Science Data Registration and Publishing System (http: / / www.resdc.cn / DOI), 2017.DOI:10.12078 / 2017121101, https: / / www.resdc.cn / DOI / DOI.aspx?DOIID=32). The kilometer grid dataset of population spatial distribution includes 6 periods, namely 1995, 2000, 2005, 2010, 2015 and 2019. The data of the year close to the time range is selected (such as setting 2022 as the specified time range in Example 2, then selecting the kilometer grid dataset of population spatial distribution in China in 2019). The resolution of the kilometer grid dataset of population spatial distribution in China is 1km×1km. The data format of the kilometer grid dataset of population spatial distribution in China is a grid. Each grid in the grid is a pixel. The pixel value of each pixel represents the population in the grid. Based on GIS software, the vector boundary of the regional scope is used for clipping to obtain the kilometer grid dataset of population spatial distribution in 2019 within the regional scope (CN110580386A, a spatial gridding method for carbon dioxide emissions in the transportation sector). The total population POP in 2019 and 2022 within the regional scope in the statistical yearbook is used. 2019 、POP 2022, the population growth rate θ=(POP 2022 -POP 2019 ) / (2022-2019), using the pixel value N of each pixel in the 2019 population spatial distribution kilometer grid dataset m2019 and population growth rate θ (in person / year) to calculate the pixel value of a pixel in the kilometer grid dataset of population spatial distribution in 2022 within the region, that is, N m =N m2019 ×(1+θ) (2022-2019) .

[0065] Based on GIS software, each pixel in the kilometer grid dataset of the spatial distribution of population in 2022 within the regional scope is converted into a vector point (one-to-one correspondence between pixels and vector points), and a mapping relationship between pixels and vector points is established. The centroid of each pixel is the position of the vector point converted from the pixel, and the population of the mth pixel is N. m (unit: person), m = 1, ..., g, g represents the number of vector points within the geographical scope of carbon emissions calculation;

[0066] Step 2, calculating the proportion of each vector point selecting each POI vector point when seeing a doctor in the outpatient clinic and the proportion of each vector point selecting each POI vector point when seeing a doctor in the inpatient clinic;

[0067] Calculate the proportion P of the mth vector point choosing the i-th POI vector point when seeing a doctor in the outpatient clinic mi Ⅰ The steps include: selecting a hospital based on distance when seeking outpatient treatment, the closer the hospital is, the easier it is to choose; calculating the travel distance D from the mth vector point to the i-th POI vector point within the geographical scope of carbon emissions calculation based on GIS software mi , the unit is km (reference publication number is CN116233757A, title is: A method for calculating carbon emissions from residents' travel based on mobile phone signaling data):

[0068]

[0069] Where D mLon , D mLat are the longitude and latitude of the mth vector point, D iLon , D iLat are respectively the longitude and latitude of the i-th POI vector point; p is the distance-to-distance ratio coefficient, and the value range of p is [1,2]. In this embodiment, considering that the traffic distance between two points in an urban area is usually 1.5 times the straight-line distance, p is set to 1.5.

[0070] Calculate the sum of the reciprocals of the travel distances from the mth vector point to each POI vector point within the region where carbon emissions are calculated The proportion P of the mth vector point choosing the i-th POI vector point when seeing a doctor in the outpatient clinic is calculated by formula (2): mi Ⅰ :

[0071]

[0072] Calculate the proportion P of the mth vector point choosing the i-th POI vector point when hospitalized mi Ⅱ The steps include: when seeking medical treatment in hospital, selecting a hospital based on the distance and the size of the hospital. The closer the distance and the larger the scale of the hospital, the easier it is to be selected. Calculate the bed ratio P of the i-th POI vector point iB :

[0073]

[0074] In the formula, B i is the number of beds used for hospitalization at the i-th POI vector point (in units), The number of beds used for hospitalization at all POI vector points within the geographical area for calculating carbon emissions (in units);

[0075] Calculate the proportion P of the mth vector point choosing the i-th POI vector point when hospitalized mi Ⅱ :

[0076]

[0077] Step 3: It is known that there is a maximum tolerance distance for medical treatment. After the hospital location exceeds the maximum tolerance distance for medical treatment, the probability of the hospital being selected decreases as the distance increases. The Gaussian function is used to calculate the distance attenuation coefficient of the hospital location beyond the maximum tolerance distance for medical treatment, and then the probability of each POI vector point being selected in outpatient medical treatment and inpatient medical treatment after distance attenuation is calculated, which specifically includes: 3-1 and 3-2;

[0078] 3-1, use the Gaussian function to calculate the distance attenuation coefficient of each vector point when seeking medical treatment. The distance attenuation coefficient G of the mth vector point when seeking medical treatment is m :

[0079]

[0080] Where D max is the maximum tolerance distance for outpatient and inpatient medical treatment, which is obtained based on the residents' medical travel space database (the maximum tolerance distance is the maximum value of the travel distance of outpatient and inpatient medical travel trajectories in the residents' medical travel space database), D mmin is the shortest travel distance from the mth vector point to all POI vector points within the region where carbon emissions are calculated, D mmin =min(Dmi ), D mmax is the longest travel distance from the mth vector point to all POI vector points within the region where carbon emissions are calculated, D mmax =max(D mi ),D mmin and D mma The GIS software is used to input the POI vector point and the longitude and latitude coordinates of the vector point to obtain the result. e is a constant, and e is taken as 2.718281828459045;

[0081] The spatial database of residents' medical travel includes: medical travel trajectories and the characteristics of medical travel trajectories. The characteristics include: date, user identification code, latitude and longitude coordinates of the starting point O, latitude and longitude coordinates of the destination point D, travel start time, travel end time, travel duration (travel duration = travel end time - travel start time, in h), travel distance (in km), and travel speed (travel speed = travel distance / travel duration, in km / h).

[0082] Among them, the method for constructing a spatial database of residents' medical travel is as follows: collecting residents' mobile phone signaling travel data within the geographical scope and time scope of calculating carbon emissions, involving negotiation and data acquisition with network providers, mobile operators or other data providers; parsing the original data, processing missing values ​​and outliers, cleaning and eliminating invalid data (reference publication number CN116797936A, named Urban Functional Area Detection Method Based on Mobile Phone Signaling Data), including removing noise, correcting erroneous data, processing duplicate records and outliers; performing geographic matching based on the mutual mapping relationship between mobile phone base station signals and urban geographic space, that is, converting residents' mobile phone signaling travel data into travel trajectories displayed on the map based on GIS software, identifying the characteristics of the travel trajectories, and screening travel trajectories with the starting point O or the destination point D within 1km around the POI vector point as medical travel trajectories, and using the medical travel trajectories and their characteristics as the residents' medical travel spatial database.

[0083] 3-2, calculate the probability of each vector point choosing each POI vector point when seeking medical treatment after distance attenuation, and the probability R of the mth vector point choosing the i-th POI vector point when seeking medical treatment mi :

[0084] R mi =P mi ×G m (6)

[0085] Among them, when it is an outpatient visit, R mi For R mi Ⅰ , P mi P mi Ⅰ ; When hospitalized, Rmi For R mi Ⅱ , P mi P mi Ⅱ ;

[0086] Step 4: Calculate the transportation carbon emission coefficient for a single medical trip for each POI vector point:

[0087] 4-1. According to the travel duration and travel distance of each medical travel trajectory in the resident medical travel space database, the travel speed of each medical travel trajectory is calculated, and the transportation mode of each medical travel trajectory is determined according to the travel speed (reference publication number CN116233757A, a patent entitled a method for calculating carbon emissions of resident travel based on mobile phone signaling data). The transportation modes include the following five categories: walking / bicycle, electric vehicle, bus, subway, taxi / private car; among them, the determination of the two types of transportation modes, "bus" and "subway", in addition to the travel speed, also needs to be determined based on whether the travel trajectory overlaps with the subway line: if there is overlap, the transportation mode is "subway", otherwise it is "bus";

[0088] Table 1 Traffic mode identification method (for reference)

[0089] Transportation <![CDATA[Travel speed (km·h -1 )]]> Walking / Bicycle [0,5] Electric Vehicles (5,10] the bus (10,30] subway (10,30] Taxi / Private Car (30,120]

[0090] 4-2, divide the travel distance into intervals with d km as the modulus: calculate the travel distance of medical travel trajectories (including outpatient and inpatient) in ((k-1)d,kd](k=1,2,3,……,s;s is the number of intervals, sd≥D based on the residents' medical travel spatial database mmax ) The probability of choosing the tth type of transportation within the interval t=1、……、5;

[0091] 4-3, the carbon emission factors of five types of transportation modes were obtained through the China Product Life Cycle Greenhouse Gas Emission Coefficient Database (http: / / lca.cityghg.com / ) released by the China City Greenhouse Gas Working Group, as shown in Table 2. t is the carbon emission factor of the tth mode of transportation;

[0092] Table 2 Carbon emission factors of five types of transportation / kgCO2·person -1 km -1

[0093]

[0094] For each travel distance D mi , according to the travel distance D mi The interval ((k-1)d,kd] that falls into will Substitute into formula (7) to calculate the transportation carbon emission coefficient of each vector point for each POI vector point. The transportation carbon emission coefficient of the mth vector point for the i-th POI vector point is T mi , unit is kgCO2·person -1 ·Second-rate -1 :

[0095]

[0096] Step 5, including 5-1 to 5-4:

[0097] 5-1, calculate the parameter λ of residents seeking medical treatment within the geographical scope and time scope:

[0098]

[0099] In formula (8), when the patient is seeking medical treatment in an outpatient clinic, the resident medical treatment parameter λ is Ⅰ , when the patient is hospitalized, the resident medical parameter λ is λ Ⅱ ; Q is the proportion of residents seeking medical treatment within the geographical scope and time range for calculating carbon emissions, The average frequency of residents' medical visits within the geographical scope and time range for calculating carbon emissions (times), Q, All of them are obtained by characteristic classification based on the spatial database of residents’ medical travel. The average proportion of outpatient or inpatient medical treatment within the geographical scope and time range of carbon emissions is calculated based on the number of outpatient and inpatient visits published by the hospital; when λ is λ Ⅰ hour, Average proportion of outpatient visits When λ is λ Ⅱ , The average proportion of hospitalization (The average proportion of outpatient visits = outpatient visits / total visits, the average proportion of inpatient visits = inpatient visits / total visits, total visits = outpatient visits + inpatient visits, Obtained through hospital public data).

[0100] According to the spatial database of residents' medical travel, the proportion of users with any of the medical travel trajectories of outpatient medical treatment and inpatient medical treatment among all users is the residents' medical travel ratio Q. If the starting point O and the destination point D are the same (the same: the distance between the starting point O of the two medical travel trajectories does not exceed 500m and the distance between the destination points D of the two medical travel trajectories does not exceed 500m), they are determined to be the same travel trajectory. The number of repetitions of the same medical travel trajectory of the same user is the travel frequency of the user. The average travel frequency of all users is the average travel frequency. .

[0101] 5-2. Calculate the carbon emissions (in kgCO2) of each vector point for medical travel for each POI vector point within the geographical scope and time range of carbon emissions accounting. The carbon emissions of medical travel for the mth vector point for the i-th POI vector point is CE mi , C.E. mi The carbon emissions CE of the outpatient medical trip for the mth vector point at the i-th POI vector point within the geographical scope and time range of the carbon emissions calculation is mi Ⅰ Or calculate the carbon emissions CE of hospitalization and medical travel for the mth vector point within the geographical scope and time range of carbon emissions for the i-th POI vector point mi Ⅱ :

[0102]

[0103] Among them, when CE mi For CE mi Ⅰ When λ is λ Ⅰ , R mi For R mi Ⅰ ; When CE mi For CE mi Ⅱ When λ is λ Ⅱ , R mi For R mi Ⅱ ; N m is the population of the mth pixel (people);

[0104] 5-3, respectively summarize the carbon emissions of outpatient medical travel and inpatient medical travel at each vector point within the geographical scope and time range of carbon emissions. The carbon emissions of outpatient medical travel at the mth vector point is CE m Ⅰ , the carbon emission of hospitalization and medical treatment travel at the mth vector point is CE m Ⅱ (Unit: kgCO2):

[0105]

[0106] Among them, CE m For CE m Ⅰ or CE m Ⅱ , when CE m For CE m Ⅰ When CE mi For CE mi Ⅰ ; When CE m For CE m Ⅱ When CE mi For CE mi Ⅱ ;

[0107] Calculate the total carbon emissions of medical travel for each vector point. The total carbon emissions of medical travel for the mth vector point is CEmsum , unit is kgCO2:

[0108] CE msum =CE m Ⅰ +CE m Ⅱ (11)

[0109] 5-4. Calculate the carbon emissions CE of residents’ medical travel for all vector points within the geographical scope and time range of carbon emissions accounting sim , unit is kgCO2:

[0110]

[0111] Example 2

[0112] On the basis of Example 1, the Tianjin area is set as the geographical scope for calculating carbon emissions, with a total of 11,924 pixels, i.e., g=11,924, and the whole year of 2022 is the time range. The hospitals are selected as tertiary hospitals (all hospitals with certain characteristics) that can provide medical treatment for children aged 0 to 14 years old. A total of 27 hospitals are selected, including 2 children's hospital campuses and 25 comprehensive tertiary hospitals with pediatrics, i.e., n=27;

[0113] Set p = 1.5, d = 3, and calculate D mmax =164.69km, so s=55, the 55 intervals are [0,3], (3,6], (6,9], (9,12], (12,15], (15,18], (18,21], ..., (162,165]. The probability of choosing a transportation mode does not change significantly after the travel distance exceeds 15km, so the travel distance intervals exceeding 15km are combined and counted;

[0114] Table 3 Probability of choosing different transportation modes in different intervals

[0115]

[0116] The proportion of residents seeking medical treatment within the geographical scope and time range of carbon emissions calculation is Q = 94.48%, and the average travel frequency of residents seeking medical treatment within the geographical scope and time range of carbon emissions calculation is Average proportion of outpatient visits Average proportion of hospitalizations but CE sum =5.35×10 7 kgCO2.

[0117] The carbon emissions CE of outpatient medical travel for all vector points within the geographical scope and time range of the carbon emissions calculation is calculated for the first POI vector pointmi Ⅰ (i=1) and the carbon emissions CE of hospitalization and medical travel for all vector points within the geographical scope and time range of the carbon emissions calculation for the first POI vector point mi Ⅱ (i=1) performs spatial processing (spatial processing: convert each vector point into 1 pixel, and convert the CE of each vector point into mi Ⅰ (i=1) and CE mi Ⅱ (i=1) as the pixel value of its corresponding pixel, and the visible graph is obtained as a grid data set), such as Figure 1 As shown in Figure 1, the carbon emissions of outpatient medical travel of all vector points and the carbon emissions of inpatient medical travel of all vector points are spatially processed (spatial processing: convert each vector point into 1 pixel, and use the carbon emissions of outpatient medical travel / inpatient medical travel of each vector point as the pixel value of its corresponding pixel, and obtain a visual graph as a grid dataset), as shown in Figure 1. Figure 2 As shown; the total carbon emissions of medical travel of all vector points CE msum Perform spatial processing (spatial processing: convert each vector point into 1 pixel, take the total carbon emissions of each vector point as the pixel value of its corresponding pixel, and obtain a visual graph as a grid dataset) Figure 3 As shown. Figures 1 to 3 , since most hospitals are concentrated in the central urban area of ​​Tianjin (the six districts in the city), the carbon emissions in the central urban area are at the highest level; the carbon emissions in the southeast and north, which are far away from the central urban area, are higher than those in other areas, because the medical travel distances in these areas are generally longer, resulting in higher carbon emissions per single trip; the carbon emissions of outpatient medical treatment are higher than those of inpatient medical treatment, because the average proportion of outpatient medical treatment is higher than that of inpatient medical treatment. Overall, the spatial distribution of the carbon emissions of children's medical travel in Tianjin is consistent with the expected results, which can help verify the accuracy of the calculation results to a certain extent.

[0118] The present invention is described above by way of example. It should be noted that, without departing from the core of the present invention, any simple deformation, modification or other equivalent replacement that can be made by those skilled in the art without inventive effort falls within the protection scope of the present invention.

Claims

1. A method for calculating carbon emissions of residents' medical travel, characterized in that: The following steps are involved: Step 1, determine the geographical scope and time range for calculating carbon emissions, obtain the latitude and longitude coordinate information of all hospitals within the geographical scope or all hospitals with certain characteristics, and convert each of the above hospitals into a POI vector point displayed on the map, where i represents the number of the POI vector point, i=1, ..., n, and n is the number of POI vector points within the geographical scope for calculating carbon emissions; Get the population spatial distribution kilometer grid dataset of the time range. The data format of the population spatial distribution kilometer grid dataset is raster. Each grid in the raster is a pixel. Each pixel is converted into a vector point. The centroid of each pixel is the position of the vector point converted from the pixel. The population of the mth pixel is N. m , m = 1, ..., g, g represents the number of vector points within the geographical scope of carbon emissions accounting; Step 2, calculating the proportion of each vector point selecting each POI vector point when seeing a doctor in the outpatient clinic and the proportion of each vector point selecting each POI vector point when seeing a doctor in the inpatient clinic; The proportion of the mth vector point choosing the ith POI vector point when seeking medical treatment in the outpatient clinic D mi is the travel distance from the mth vector point to the i-th POI vector point within the region; The proportion of the mth vector point choosing the ith POI vector point when hospitalized Among them, P iB is the bed ratio of the i-th POI vector point; Step 3, including: 3-1 and 3-2; 3-1, use the Gaussian function to calculate the distance attenuation coefficient of each vector point when seeking medical treatment. The distance attenuation coefficient G of the mth vector point when seeking medical treatment is m : Where D max The maximum distance for outpatient and inpatient medical treatment; D mmin is the shortest travel distance from the mth vector point to all POI vector points within the region where carbon emissions are calculated, D mmax The longest travel distance from the mth vector point to all POI vector points within the region where carbon emissions are calculated; 3-2, calculate the probability of each vector point choosing each POI vector point when seeking medical treatment after distance attenuation, and the probability R of the mth vector point choosing the i-th POI vector point when seeking medical treatment mi : R mi =P mi ×G m Among them, when it is an outpatient visit, R mi For R mi Ⅰ , P mi P mi Ⅰ ; When hospitalized, R mi For R mi Ⅱ , P mi P mi Ⅱ ; Step 4: Calculate the transportation carbon emission coefficient for a single medical trip for each POI vector point: 4-1, according to the travel speed of each medical travel trajectory, the transportation mode of each medical travel trajectory is determined, and the transportation modes include five categories; 4-2, divide the travel distance into intervals with d kilometers as the modulus: calculate the probability of choosing the tth type of transportation mode in the interval ((k-1)d,kd] for the travel trajectory of seeking medical treatment k = 1, 2, 3, ..., s, s is the number of intervals; 4-3, obtain the carbon emission factor of each type of transportation, the carbon emission factor of the tth type of transportation is C t ; For each travel distance D mi , according to the travel distance D mi The interval ((k-1)d, kd] that it falls into is used to calculate the transportation carbon emission coefficient of each vector point for each POI vector point. The transportation carbon emission coefficient of the mth vector point for the i-th POI vector point is T mi : Step 5, including: 5-1 to 5-4: 5-1, calculate the parameter λ of residents seeking medical treatment within the geographical scope and time scope: When the patient is seeking medical treatment in an outpatient clinic, the resident medical treatment parameter λ is λ Ⅰ , when the patient is hospitalized, the resident medical parameter λ is λ Ⅱ ; Q is the proportion of residents seeking medical treatment within the geographical scope and time range for calculating carbon emissions, In order to calculate the average frequency of residents’ medical visits within the geographical scope and time range of carbon emissions, is the average proportion of outpatient or inpatient medical treatment within the geographical scope and time range of carbon emissions accounting; when λ is λ Ⅰ hour, Average proportion of outpatient visits When λ is λ Ⅱ , The average proportion of hospitalization 5-2. Calculate the carbon emissions of medical travel for each vector point for each POI vector point within the geographical scope and time range of the accounting carbon emissions. The carbon emissions of medical travel for the mth vector point for the i-th POI vector point is CE mi , C.E. mi The carbon emissions CE of the outpatient medical trip for the mth vector point at the i-th POI vector point within the geographical scope and time range of the carbon emissions calculation is mi Ⅰ Or calculate the carbon emissions CE of hospitalization and medical travel for the mth vector point within the geographical scope and time range of carbon emissions for the i-th POI vector point mi Ⅱ :CE mi =N m ×T mi ×R mi ×λ; Among them, when CE mi For CE mi Ⅰ When λ is λ Ⅰ , R mi For R mi Ⅰ ; When CE mi For CE mi Ⅱ When λ is λ Ⅱ , R mi For R mi Ⅱ ; N m is the population of the mth pixel; 5-3, respectively summarize the carbon emissions of outpatient medical travel and inpatient medical travel at each vector point within the geographical scope and time range of carbon emissions. The carbon emissions of outpatient medical travel at the mth vector point is CE m Ⅰ , the carbon emission of hospitalization and medical treatment travel at the mth vector point is CE m Ⅱ : Among them, CE m For CE m Ⅰ or CE m Ⅱ , when CE m For CE m Ⅰ When CE mi For CE mi Ⅰ ; When CE m For CE m Ⅱ When CE mi For CE mi Ⅱ ; Calculate the total carbon emissions of medical travel for each vector point. The total carbon emissions of medical travel for the mth vector point is CE msum :CE msum =CE m Ⅰ +CE m Ⅱ ; 5-4. Calculate the carbon emissions CE of residents’ medical travel for all vector points within the geographical scope and time range of carbon emissions accounting sum :

2. The carbon emissions calculation method according to claim 1, characterized in that: When a hospital includes multiple campuses, each campus is calculated as a POI vector point.

3. The carbon emission calculation method according to claim 1, characterized in that: Get the travel distance D from the mth vector point to the i-th POI vector point within the region mi The method is: Where D mLon , D mLat are the longitude and latitude of the mth vector point, D iLon , D iLat are respectively the longitude and latitude of the i-th POI vector point; p is the distance ratio coefficient.

4. The carbon emission calculation method according to claim 3, characterized in that: The value range of p is [1,2].

5. The carbon emission calculation method according to claim 1, characterized in that: It is the sum of the reciprocals of the travel distances from the mth vector point to each POI vector point within the geographical area where carbon emissions are calculated.

6. The carbon emission calculation method according to claim 1, characterized in that: The proportion of beds at the i-th POI vector point B i is the number of beds used for hospitalization at the i-th POI vector point, The number of inpatient beds at all POI vector points within the geographical area for calculating carbon emissions.

7. The carbon emission calculation method according to claim 1, characterized in that: s*d≥D mmax 。

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