Carbon emission locking measurement and calculation method for regional building energy facilities

Abstract

The invention discloses a carbon emission locking calculation method for regional building energy facilities, and the method comprises the steps: screening buildings in a target region, and obtaining the basic information of the buildings; classifying and identifying the energy infrastructure and the annual carbon emission value corresponding to the reference year of the energy infrastructure; the average service life is set for various facilities, the difference value between the current year and the equipment building year is calculated, and the remaining emission potential period of the current facility in the reference year serves as the time range of the carbon emission locking effect; calculating a carbon locking effect value in the reference year; and associating the carbon locking effect value with the building space position, and constructing a carbon locking thermodynamic diagram. According to the method, on the basis of considering the types, the service cycles and the spatial distribution of the buildings and the energy equipment of the buildings, the future inevitable total carbon emission accumulation in the regional building field can be systematically measured and calculated.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of carbon emission assessment, in particular to a method for calculating carbon emission lock of regional building energy facilities. BACKGROUND

[0002] With the increasing pressure of global climate change, the building industry, as an important source of carbon emissions, its future long-term emission trend has gradually attracted attention. Carbon lock-in effect refers to the decision of current energy infrastructure and technology path, which makes future carbon emissions inevitable or difficult to reverse, that is, "locks" the future carbon emission space.

[0003] The existing carbon emission assessment system focuses on annual emissions, life cycle assessment or energy efficiency accounting of specific buildings, and less on systematic assessment of future carbon emission "stock" risk. First, there is a lack of emission potential estimation means for the "residual life" perspective of infrastructure, which leads to the inability to quantify future stock emission space. Second, it ignores the influence of building spatial distribution characteristics on carbon lock-in agglomeration effect, making it difficult to guide regional differentiated governance. Third, there is a lack of calculation mechanism linked to actual strategy scenarios (such as retirement replacement and updating plan), making it difficult to support dynamic path assessment.

[0004] Therefore, in order to solve the above problems, a method for calculating carbon emission lock of regional building energy facilities is needed, which can systematically calculate the total amount of inevitable carbon emissions in the future of the regional building field based on considering the type, service period and spatial distribution of buildings and their energy equipment. SUMMARY

[0005] Therefore, the purpose of the present application is to overcome the defects in the prior art, and to provide a method for calculating carbon emission lock of regional building energy facilities, which can systematically calculate the total amount of inevitable carbon emissions in the future of the regional building field based on considering the type, service period and spatial distribution of buildings and their energy equipment.

[0006] The method for calculating carbon emission lock of regional building energy facilities of the present application comprises:

[0007] Screening buildings in the target region to obtain building basic information;

[0008] Classifying and identifying energy infrastructure and corresponding annual carbon emission values of benchmark years;

[0009] Setting average service life for each type of facility, calculating the difference between the current year and the equipment construction year, and taking the remaining emission potential period of the current facility in the benchmark year as the time range of carbon emission lock effect;

[0010] Calculating the carbon lock-in effect value under the benchmark year;

[0011] The carbon lock-in effect value is associated with the building space position, and a carbon lock-in heat map is constructed.

[0012] Further, building basic information is acquired, specifically including:

[0013] The geographical boundary of the target area is delineated, and existing buildings in the area are taken as objects;

[0014] The building type, construction year, energy equipment configuration and energy use type are collected;

[0015] The collected information is standardized and entered into the database using a unified coding system;

[0016] The GIS geographic coding is bound to the building space position.

[0017] Further, the energy infrastructure and the corresponding annual carbon emission value of the benchmark year are classified and identified, specifically including:

[0018] Based on field investigation, design drawings, energy audit data, energy consumption statistical annual report or intelligent metering data, the energy infrastructure information of the buildings in the area is collected;

[0019] According to the installation time of the energy equipment and the construction year of the building, the corresponding benchmark year is determined, and the buildings are classified and sorted according to the functional type, forming a three-dimensional structure data table of energy infrastructure-building type-benchmark year;

[0020] According to the operating parameters of various energy equipment and the carbon emission factor of the used energy, the annual carbon emission value is calculated; wherein the operating parameters include installed capacity, usage frequency and energy efficiency coefficient.

[0021] Further, the carbon lock-in effect value CE in the benchmark year is calculated according to the following formula y :

[0022] CE y =∑E i,y ×W i ×P(t)×(lifetime-T i,y );

[0023] Wherein, i is the energy infrastructure, y is the benchmark year, E i,y is the benchmark year carbon emission value, lifetime is the assumed average service life, T i,y is the service life of facility i, then (lifetime-T i,y ) represents the remaining operating time under a certain service life assumption, i.e. survival curve; W i is the carbon emission intensity correction coefficient; P(t) is the strategy adjustment function, which reflects the nonlinear evolution of future emission path.

[0024] Further, the policy adjustment function P(t) is determined according to the following formula:

[0025] P(t)=e -k(t-t0) , t≥t0;

[0026] wherein k is a policy decay rate coefficient, representing the policy strength or replacement speed; t is a future year, t0 is the starting year of the policy or technical intervention; when t < t0, P(t) = 1.

[0027] Further, the policy adjustment function P(t) is determined according to the following formula:

[0028] P(t)=max(0,1-α(t-t0));

[0029] wherein a is an annual reduction ratio, taking a value in the range of 0-1; t is a future year, t0 is the starting year of the policy or technical intervention.

[0030] Further, the policy adjustment function P(t) is determined according to the following formula:

[0031]

[0032] wherein 0 < d < c < b < a ≤ 1; t0 is the starting year of the policy or technical intervention; t is the target year currently under consideration; t1 is the turning year of the second-stage policy adjustment; t2 is the target year of the third-stage or final-stage adjustment.

[0033] Further, the carbon lock-in effect value is associated with the building space position to construct a carbon lock-in heat map, specifically including:

[0034] Binding the carbon lock-in effect value with the spatial coordinate information of the building in the regional map;

[0035] Establishing a carbon lock-in space database of building units; wherein the fields of the carbon lock-in space database include building unique number, building type, construction year, energy system type, unit area carbon lock-in value and spatial coordinates;

[0036] Using GIS for spatial analysis, projecting the carbon lock-in intensity value of each building into a two-dimensional map space, using interpolation algorithm or raster reclassification method to fit and render the spatial distribution trend of the carbon lock-in intensity;

[0037] Outputting the carbon lock-in heat map result, superimposed on the city basic base map, building contour map layer or administrative district map layer, forming a regional building carbon lock-in atlas.

[0038] The beneficial effects of the present application are: the carbon emission locking calculation method of the regional building energy facility disclosed by the present application, by constructing an accounting system including building energy infrastructure characteristics, service cycle, historical emissions and other elements, the carbon emission locking value of different years, different types of buildings and energy facilities in the region is dynamically calculated. The present application can consider the information of building types, service cycle and spatial distribution of energy equipment, systematically calculate the inevitable cumulative total carbon emission of the regional building field in the future, and display its regional distribution characteristics in the form of spatial heat map, which provides technical support for urban low-carbon update, infrastructure reconstruction priority and carbon neutralization path design. BRIEF DESCRIPTION OF DRAWINGS

[0039] The present application will be further described below in combination with the drawings and examples:

[0040] Figure 1 The carbon emission locking calculation method flowchart of the present application is shown in the figure. DETAILED DESCRIPTION

[0041] The present application will be further described below in combination with the drawings and examples:

[0042] The present application discloses a carbon emission locking calculation method of regional building energy facility, including the following steps:

[0043] Screening the buildings in the target region to obtain the building basic information;

[0044] Classifying and identifying the energy infrastructure and the annual carbon emission value corresponding to the benchmark year;

[0045] Setting the average service life for each type of facility, calculating the difference between the current year and the equipment construction year, and taking the remaining emission potential period of the current facility in the benchmark year as the time range of the carbon emission locking effect;

[0046] Calculate the carbon locking effect value under the benchmark year;

[0047] Correlate the carbon locking effect value with the building space position, and construct a carbon locking heat map.

[0048] In this embodiment, in order to realize the carbon emission locking effect calculation of regional building energy infrastructure, it is necessary to first screen and collect all the buildings in the target region to obtain the building basic information. This step is the basis for the construction of the entire carbon locking model, and the integrity and accuracy of the information directly affect the accuracy of the calculation results. The specific steps are as follows:

[0049] According to the research or analysis target, the geographical boundary of the target area is delineated, which can cover city administrative areas, functional areas, street blocks, or development zones, etc. The building screening takes the existing buildings in the region as the object, including residential buildings, office buildings, commercial buildings, public buildings, industrial buildings, etc.

[0050] The building type, construction year, energy equipment configuration, and energy use type are collected. The building type refers to the building purpose category, including residential, office, commercial, education, medical, cultural, industrial, and other mixed use buildings. This information can be obtained through building registration data from the housing management bureau, urban planning database, or field reconnaissance. The construction year refers to the year when the building is completed and put into use, which is used to estimate the commissioning time and operating life of the supporting energy equipment. This data can be extracted from real estate registration archives, completion records, or historical satellite image interpretation. If missing, it can be estimated by interpolation of adjacent samples or identification of construction period building style.

[0051] The energy equipment configuration refers to the type and scale of energy equipment supported by the building, including heating equipment, air conditioning system, domestic hot water equipment, lighting system, etc. This information can be collected and verified through property management unit data, energy use audit report, field household questionnaire, or remote sensing image recognition means. The energy use type refers to the actual energy type used by the building, including electricity, gas, coal, biomass, central heating, etc. This data can be comprehensively judged in combination with municipal access records, user energy consumption bills, and pipe network access point distribution, etc.

[0052] The collected information is standardized and entered into the database using a unified coding system. For example: the building type is based on the national standard GB / T 51179 classification; the construction year is divided into ten-year periods (such as 1980-1989, 1990-1999, etc.); the energy equipment configuration establishes a device classification tree structure to distinguish between main equipment and auxiliary systems; the energy use type is classified according to the dominant energy and marked for complex energy use situations.

[0053] All information is bound to the spatial location of the building through GIS geographic coding, providing data support for subsequent carbon emission locking heat map generation and spatial analysis.

[0054] Through the above steps, it is ensured that the regional building object has a clear energy system composition and operation background before measurement, laying a detailed data foundation for locking effect modeling.

[0055] In this embodiment, the classification and identification of energy infrastructure in regional buildings and the determination of their baseline year carbon emission values are the pre-step for carbon locking effect calculation, which needs to be combined with building attribute data and energy system operation characteristics to carry out system identification and emission accounting, including the following steps:

[0056] Collecting the information of energy infrastructure configured by buildings in the region based on field investigation, design drawings, energy audit data, energy consumption statistical annual report or intelligent metering data; wherein, the energy infrastructure includes building heating system, domestic hot water system, refrigeration and ventilation equipment, and lighting and power system.

[0057] According to the installation time of energy equipment and the building completion year, determine the reference year to which it belongs, and classify and organize it in combination with the functional type of the building (such as residence, office, business), to form a three-dimensional structure data table of energy infrastructure-building type-reference year;

[0058] According to the operating parameters of various energy equipment and the carbon emission factor of the energy used, calculate the annual carbon emission value; wherein, the operating parameters include installed capacity, usage frequency and energy efficiency coefficient.

[0059] The annual carbon emission value can be processed by the following two methods: method one: according to the “Greenhouse Gas Emission Accounting Method and Reporting Guide” or the IPCC recommended method, the carbon emission factor method is used for estimation; method two: if there is building energy monitoring system data or regional energy consumption bill data, based on the measured annual energy consumption data, the carbon emission can be allocated according to the equipment attribution.

[0060] Finally, the energy infrastructure carbon emission database is formed by type and year, which provides technical support for subsequent remaining service life estimation and carbon lock-in effect calculation. This process ensures the accuracy, difference and dynamic adaptability of the accounting system, which meets the actual needs of building carbon emission management at regional scale.

[0061] In this embodiment, in order to realize the dynamic estimation of regional building carbon emission lock-in effect, the service life of energy infrastructure needs to be parameterized and set, and based on the actual operation period, the remaining emission potential period of the infrastructure in the reference year is reasonably defined as the time range of carbon lock-in effect. The process specifically includes the following steps:

[0062] For each type of energy infrastructure i identified, according to the national building energy saving standard, industry technical specification, manufacturer's instruction and existing literature research data, determine the average technical service life lifetime, unit: year.

[0063] Collect the operation time of the target equipment or the building completion year as the facility start-up reference year, and take the specified estimation reference year (such as 2025) as the current year y, calculate the served period T of the facility to y year i,y . Among them, if the equipment start-up time is uncertain, the building completion year + average configuration lag year can be used for approximate estimation.

[0064] Combine the above service life lifetime and served period Ti,y , the computing facility remaining service life lifetime-T in the base year y i,y .

[0065] In this embodiment, the carbon lock-in effect value CE in the base year is calculated according to the following formula y :

[0066] CE y =∑E i,y ×W i ×P(t)×(lifetime-T i,y );

[0067] Wherein, i is an energy infrastructure, y is the base year, E i,y is the carbon emission value in the base year, lifetime is the assumed average service life, T i,y is the service life of the facility i, then (lifetime-T i,y ) represents the remaining running time under a certain service life assumption, that is, the survival curve; W i is the carbon emission intensity correction coefficient; P(t) is a strategy adjustment function, which reflects the nonlinear evolution of the future emission path.

[0068] W i is the carbon emission intensity correction coefficient, which is used to quantitatively correct the differences in operating efficiency of energy facilities of different types or different service life. For example, a newly built high-efficiency gas boiler can be set W i = 0.8, while an old coal-fired boiler with more than 20 years of service life can be set W i = 1.2, so as to reflect the weighted influence of equipment aging or high emission properties in the carbon lock-in calculation. This coefficient can be obtained according to energy efficiency standards, measured energy consumption data or expert scoring method.

[0069] The strategy adjustment function P(t) is an important function variable for dynamically adjusting the calculation of carbon emission lock-in effect, and its core purpose is to introduce the influence of future strategy adjustment, technology update or equipment elimination on the carbon emission path, and to reflect the nonlinear evolution characteristics of the emission trend;

[0070] The traditional carbon lock-in calculation model often assumes that the emission intensity of energy facilities remains unchanged during their remaining service life, which cannot reflect the actual situation of gradual replacement, elimination or efficiency improvement after the implementation of future carbon emission reduction strategies. Therefore, in the present application, the strategy adjustment function is set to correct the carbon emission value according to the strategy or technology scenario corresponding to different time nodes, forming an evolutionary path of dynamic decay or segmented change.

[0071] The strategy adjustment function P(t) can be determined according to the following formula:

[0072]

[0073] where k is the policy decay rate coefficient, representing the policy strength or replacement speed; t is the future year, and t0 is the starting year of the policy or technical intervention; when t < t0, P(t) = 1.

[0074] This function represents the exponential decline of the carbon emission contribution of a unit facility after the implementation of the policy, which is used to simulate the emission path compression process under rapid replacement, such as mandatory retirement or the implementation of high carbon tax.

[0075] The policy adjustment function P(t) can also be determined according to the following formula:

[0076] P(t) = max(0, 1 - a(t - t0));

[0077] where a is the annual reduction ratio, ranging from 0 to 1; t is the future year, and t0 is the starting year of the policy or technical intervention. When P(t) = 0, it represents that the emissions of this type of facility have been completely eliminated.

[0078] This function is more suitable for medium and long-term updating plans, such as annual updating of old equipment plans, gradual energy-saving renovation, etc.

[0079] The policy adjustment function P(t) can also be determined according to the following formula:

[0080]

[0081] where t0 is the starting year of the policy or technical intervention; t is the target year under consideration; t1 is the turning point of the second stage policy adjustment; and t2 is the target year of the third stage or final adjustment.

[0082] This function is more suitable for explicitly planned time nodes, such as phased elimination plans, three-stage policy progressive implementation, etc., and can be designed synchronously with the annual targets of the regional double carbon path.

[0083] The P(t) function constructed in the above form can be flexibly embedded in the carbon lock-in estimation model, making the emission lock-in effect have a controllable convergence trend on the time axis, effectively improving the sensitivity and adaptability of the estimation results to future strategies.

[0084] In this embodiment, the carbon lock-in effect value is associated with the building space position, and the steps for constructing the carbon lock-in heat map are as follows:

[0085] Based on the carbon lock-in effect values CE calculated in the above steps, the carbon lock-in heat map of the target region can be constructed as follows: yBind it with the spatial coordinate information of the building in the regional map. The spatial coordinate information can be obtained from the vector layer of the built buildings in the GIS platform or generated by combining remote sensing images and geocoding technology.

[0086] Establish a carbon lock-in space database for building units. The fields of the carbon lock-in space database include building unique number, building type, construction year, energy system type, carbon lock-in value per unit area, and spatial coordinates.

[0087] Use GIS for spatial analysis to project the carbon lock-in intensity values of each building onto a two-dimensional map space. Use interpolation algorithms or raster reclassification methods to fit and render the spatial distribution trend of carbon lock-in intensity. To enhance the readability of the heat map, the following heat classification standards can be introduced: carbon lock-in value ≤10tCO2: low intensity area, marked in green; 10< carbon lock-in value ≤30tCO2: medium intensity area, marked in yellow; carbon lock-in value >30tCO2: high intensity area, marked in red.

[0088] Output the carbon lock-in heat map results and superimpose them on the city base map, building contour map layer, or administrative division map layer to form a regional building carbon lock-in atlas, thereby supporting further spatial decision analysis, such as: carbon lock-in areas are given priority in urban renewal plans; medium intensity areas are combined with the renewal rhythm and strategy window period to develop phased reconstruction paths; low intensity areas are kept for green building promotion incentives and long-term supervision.

[0089] Through the above steps, the visualization of carbon lock-in spatial information is effectively realized, which provides technical support for fine identification of regional carbon risk aggregation points, reasonable allocation of renewal resources, and determination of low-carbon update priorities.

[0090] Finally, it should be pointed out that the above examples are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the purpose and scope of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A method for calculating locked-in carbon emissions from regional building energy facilities, characterized by: including: screening buildings in the target area to obtain basic building information; classifying and identifying energy infrastructure and its annual carbon emission value corresponding to the base year; setting an average service life for each type of facility, calculating the difference between the current year and the year when the equipment was built, and taking the remaining emission potential period of the current facility in the base year as the time range of the carbon lock-in effect; calculating the carbon lock-in effect value in the base year; associating the carbon lock-in effect value with the building spatial location to construct a carbon lock-in heat map.

2. The method for calculating locked-in carbon emissions of regional building energy facilities according to claim 1, characterized in that: Obtaining basic building information, specifically including: defining the geographical boundary of the target area with the existing buildings in the area as the object; collecting building type, construction year, energy equipment configuration, and energy consumption type; standardizing the above-mentioned collected information and entering it into the database using a unified coding system; binding it to the building spatial location through GIS geocoding.

3. The method for calculating locked-in carbon emissions of regional building energy facilities according to claim 1, characterized in that: Classifying and identifying energy infrastructure and its annual carbon emission value corresponding to the base year, specifically including: collecting energy infrastructure information configured in the buildings in the area based on on-site investigations, design drawings, energy audit materials, annual energy consumption statistics reports, or intelligent metering data; determining the base year to which it belongs according to the installation time of the energy equipment and the construction year of the building, and classifying and sorting it in combination with the functional type of the building to form a three-dimensional structure data table of energy infrastructure - building type - base year; calculating its annual carbon emission value based on the operating parameters of various energy equipment and the carbon emission factors of the energy used; among them, the operating parameters include installed capacity, usage frequency, and energy efficiency coefficient.

4. The method for calculating locked-in carbon emissions of regional building energy facilities according to claim 1, characterized in that: The carbon lock-in effect value CE in the base year is calculated according to the following formula y : CE y =∑E i,y ×W i ×P(t)×(lifetime-T i,y ); Where i is energy infrastructure, y is the base year, E i,y is the base year carbon emission value, lifetime is the assumed average service life, T i,y is the service life of facility i, then (lifetime-T i,y ) represents the remaining operating time under a certain service life assumption, that is, the survival curve; W i is the carbon emission intensity correction coefficient; P(t) is the strategy adjustment function, which reflects the nonlinear evolution of the future emission path.

5. The method for calculating locked-in carbon emissions of regional building energy facilities according to claim 4, characterized in that: Determine the strategy adjustment function P(t) according to the following formula: where k is the strategy attenuation rate coefficient, representing the strategy intensity or substitution speed; t is the future year, t0 is the start year of the strategy or technology intervention; when t < t0, P(t) = 1.

6. The method for calculating locked-in carbon emissions of regional building energy facilities according to claim 4, characterized in that: Determine the strategy adjustment function P(t) according to the following formula: P(t) = max(0, 1 - α(t - t0)); where α is the annual decreasing ratio, with a value range of 0 to 1; t is the future year, t0 is the start year of the strategy or technology intervention.

7. The method for calculating locked-in carbon emissions of regional building energy facilities according to claim 4, characterized in that: Determine the strategy adjustment function P(t) according to the following formula: where 0 < d < c < b < a ≤ 1; t0 is the start year of the strategy or technology intervention; t is the target year being examined currently; t1 is the turning year of the second-stage strategy adjustment; t2 is the target year of the third-stage or final adjustment.

8. The method for calculating locked-in carbon emissions of regional building energy facilities according to claim 1, characterized in that: Associating the carbon lock-in effect value with the building spatial location to construct a carbon lock-in heat map, specifically including: binding the carbon lock-in effect value to the spatial coordinate information of the building in the regional map; establishing a building unit carbon lock-in spatial database; among them, the fields of the carbon lock-in spatial database include building unique number, building type, construction year, energy system type, carbon lock-in value per unit area, and spatial coordinates; using GIS for spatial analysis, projecting the carbon lock-in intensity values of each building into the two-dimensional map space, and using interpolation algorithms or raster reclassification methods to fit and render the distribution trend of the carbon lock-in intensity in space; Output the carbon lock heat map results and overlay them on the city base map, building outline layer or administrative division layer to form a regional building carbon lock atlas.

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