Two-way estimation method of carbon emission reduction contribution of zero-carbon cloud library and carbon intensity reduction of GDP

Abstract

The present application relates to carbon emission intensity accounting and public institution carbon emission data processing technical field, especially to a kind of zero carbon cloud library emission reduction contribution and domestic product total value carbon intensity reduction range two-way calculation method.The method receives local historical domestic product total value data, energy consumption data, carbon emission data, zero carbon cloud library emission reduction, public institution basic characteristic data, public institution carbon emission data and parameter configuration data, generates two-way calculation task data by data source writing, accounting period collection, field standardization and integrity check;Based on the task data, local domestic product total value carbon emission intensity baseline data, public institution carbon emission intensity baseline data and project emission reduction collection data are generated, and local intensity reduction range contribution value, public institution intensity improvement contribution value and two-way quantitative calculation report are generated.The present application improves the consistency and traceability of emission reduction contribution calculation.

Description

Technical Field

[0001] This invention relates to the fields of carbon emission intensity accounting and carbon emission data processing of public institutions, and in particular to a two-way calculation method for the emission reduction contribution of zero-carbon cloud libraries and the reduction in carbon intensity of GDP. Background Technology

[0002] In the fields of carbon emission intensity accounting and public institution carbon emission data processing, existing solutions for zero-carbon cloud library emission reductions typically involve collecting local historical GDP data, energy consumption data, carbon emission data, public institution carbon emission data, and emission reduction data. This data is then processed through field standardization, accounting cycle aggregation, and report generation. However, these solutions suffer from limitations such as insufficient correlation between project emission reduction data and local GDP carbon emission intensity benchmark data, a lack of a unified generation link for public institution carbon emission intensity benchmark data, and difficulty in simultaneously including the annual emission reduction of the same project into two intensity calibers. Existing methods often rely on single emission reduction statistics, single carbon emission intensity calculations, or single contribution value aggregations, which can easily lead to the fragmented generation of local GDP carbon emission intensity reduction contribution values ​​and public institution carbon emission intensity improvement contribution values.

[0003] In scenarios where zero-carbon cloud library emission reductions and public institution carbon emission data are jointly used in calculations, if the local GDP carbon emission intensity benchmark data, public institution carbon emission intensity benchmark data, and project emission reduction data lack a corresponding relationship for the same year and data version, inconsistencies may arise between the emission reduction path contribution records and the library's annual contribution records. If public institution carbon emission data is recorded only according to the annual total, without combining building area, number of office staff, scale of energy-consuming equipment, institution type, regional climate, and functional positioning to form public institution carbon emission intensity benchmark data, it will be difficult to meet the requirement of stable generation of two-way quantitative calculation reports.

[0004] Regarding the joint processing of project emission reduction data and baseline data, existing technologies generally lack a method for synchronously inputting local GDP carbon emission intensity baseline data, public institution carbon emission intensity baseline data, and project emission reduction data into the contribution value generation chain. They also lack a process for linking local contribution data areas and public institution contribution data areas through a contribution value version field. Existing technologies struggle to establish a consistent process for data source writing, accounting cycle collection, baseline generation, contribution value generation, report generation, and version updates in the two-way calculation scenario of emission reduction contributions and GDP carbon intensity reduction in the Zero Carbon Cloud Library. This results in a lack of continuous data links between the local GDP carbon emission intensity reduction contribution value and the public institution carbon emission intensity improvement contribution value in the two-way quantitative calculation report. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a two-way calculation method for the emission reduction contribution of zero-carbon cloud libraries and the reduction in GDP carbon intensity, including:

[0006] S100: Receive information from multiple sources, and generate bidirectional measurement task data through data source writing, accounting cycle aggregation, field standardization and integrity verification;

[0007] The multi-source information includes local historical GDP data, energy consumption data, carbon emission data, local GDP data for the current year, emission reduction data from the zero-carbon cloud library, basic characteristic data of public institutions, carbon emission data of public institutions, and parameter configuration data.

[0008] S200. Based on the bidirectional measurement task data, perform intensity benchmark calculation and emission reduction collection processing to generate local GDP carbon emission intensity benchmark data, public institution carbon emission intensity benchmark data, and project emission reduction collection data.

[0009] S300. Based on the local GDP carbon emission intensity baseline data, the public institution carbon emission intensity baseline data and the project emission reduction data, calculate the contribution value to generate the contribution value of the reduction in local GDP carbon emission intensity and the contribution value of the improvement in public institution carbon emission intensity.

[0010] S400. Based on the contribution value of the reduction in carbon emission intensity of local GDP and the contribution value of the improvement in carbon emission intensity of public institutions, perform sensitivity analysis and report encapsulation to generate a two-way quantitative calculation report.

[0011] Furthermore, the emission reduction of the zero-carbon cloud library includes the library site identifier, emission reduction path, accounting cycle, annual emission reduction total of the project, data source, verification status and data version; S100 writes the library site identifier, emission reduction path, accounting cycle, annual emission reduction total of the project, data source, verification status and data version into the project emission reduction collection data.

[0012] Furthermore, the accounting cycle aggregation mentioned in S100 includes grouping the local historical GDP data, energy consumption data, carbon emission data, local GDP data for the current year, emission reduction data from the zero-carbon cloud library, and carbon emission data from public institutions into the same year; if the integrity verification fails, an abnormal data marker is written into the bidirectional calculation task data.

[0013] Furthermore, S200 also includes: calculating the carbon emission intensity per unit of GDP based on local historical carbon emission data and local historical GDP data for each year, generating a local GDP carbon emission intensity sequence sorted by year, and generating local GDP carbon emission intensity baseline data containing baseline period intensity value, target year intensity value and baseline version based on the local GDP carbon emission intensity sequence.

[0014] Furthermore, the basic characteristic data of the public institution includes building area, number of office staff, scale of energy-consuming equipment, institution type, regional climate, and functional positioning; S200 generates standardized carbon emission intensity data of the public institution based on the carbon emission data, building area, number of office staff, and scale of energy-consuming equipment, and generates baseline data of carbon emission intensity of the public institution based on the institution type, regional climate, and functional positioning.

[0015] Furthermore, S300 also includes: reading the total annual emission reduction (ER) of the project in the project emission reduction collection data and the GDP in the local GDP data of the current year, and calculating the contribution value ΔCI of the local GDP carbon emission intensity reduction according to ΔCI=(ER / GDP).

[0016] Furthermore, S300 also includes: reading the project emission reduction aggregated data and the public institution carbon emission intensity baseline data, generating the public institution carbon emission intensity before emission reduction, the public institution carbon emission intensity after emission reduction, and the public institution carbon emission intensity improvement contribution value, and writing the public institution carbon emission intensity before emission reduction, the public institution carbon emission intensity after emission reduction, and the public institution carbon emission intensity improvement contribution value into the public institution contribution data area.

[0017] Furthermore, in S400, the two-way quantitative calculation report includes emission reduction, contribution value of local GDP carbon emission intensity reduction, contribution value of public institution carbon emission intensity improvement, data source, accounting cycle, baseline parameters, caliber explanation, abnormal data marker and report version identifier.

[0018] Furthermore, S400 also includes: reading the emission reduction fluctuation range, the annual local GDP change range, and the public institution basic characteristic change range from the parameter configuration data, generating sensitivity analysis results, and writing the sensitivity analysis results into the two-way quantitative calculation report.

[0019] Furthermore, S400 also includes: when the data versions of the zero-carbon cloud library's emission reduction, annual local GDP data, public institution basic characteristic data, or public institution carbon emission data change, reading the changed data version, returning to execute S100 to S400, and generating an updated two-way quantitative calculation report.

[0020] The key innovations of this invention include:

[0021] (1) Input the project emission reduction data into the local GDP carbon emission intensity baseline data and the public institution carbon emission intensity baseline data to generate the local GDP carbon emission intensity reduction contribution value and the public institution carbon emission intensity improvement contribution value.

[0022] (2) Generate local GDP carbon emission intensity baseline data, public institution carbon emission intensity baseline data and project emission reduction collection data based on bidirectional measurement task data, so that the baseline data and project emission reduction collection data form a corresponding relationship under the same accounting year and data version.

[0023] (3) Link the local contribution data area and the public institution contribution data area through the contribution value version field, and write the baseline version, data version, venue identifier, emission reduction path and verification status into the two-way quantitative calculation report.

[0024] The following are its main beneficial effects:

[0025] (1) To address the problem that the annual emission reduction of the same project is difficult to be included in two intensity calibers simultaneously, the local GDP carbon emission intensity baseline data and the public institution carbon emission intensity baseline data are processed in two directions, so that the contribution value of the reduction of local GDP carbon emission intensity and the contribution value of the improvement of public institution carbon emission intensity are generated from the emission reduction data of the same project.

[0026] (2) To address the problem of insufficient correlation between project emission reduction data and baseline data, three types of intermediate data objects are generated by bidirectional calculation of task data, so that the local GDP carbon emission intensity baseline data, public institution carbon emission intensity baseline data and project emission reduction data maintain the same year and version input relationship in the contribution value generation process.

[0027] (3) To address the issue of dispersed contribution value sources, the contribution value version field is used to link the local contribution data area and the public institution contribution data area, so that the contribution value of the local GDP carbon emission intensity reduction and the contribution value of the public institution carbon emission intensity improvement in the two-way quantitative calculation report have the same version record.

[0028] (4) To address the issue that carbon emission data of public institutions is only recorded in annual totals, baseline data on carbon emission intensity of public institutions is generated by taking into account building area, number of office staff, scale of energy-consuming equipment, type of institution, regional climate and functional positioning, so that the contribution value of public institutions to improving carbon emission intensity has a corresponding basic characteristic caliber.

[0029] (5) To address the inconsistency between the emission reduction path contribution record and the annual contribution record of the venue, the project emission reduction data is generated by using the venue identifier, emission reduction path, accounting cycle, annual emission reduction amount of the project, data source, verification status and data version, so that the two-way quantitative calculation report retains the corresponding record between the emission reduction path and the annual venue. Attached Figure Description

[0030] Figure 1A flowchart illustrating the method for bidirectional calculation of the emission reduction contribution of the zero-carbon cloud library and the reduction in GDP carbon intensity provided in this application embodiment;

[0031] Figure 2 The structural block diagram of the two-way calculation method for the emission reduction contribution of the zero-carbon cloud library and the reduction in GDP carbon intensity provided in the embodiments of this application is shown. Detailed Implementation

[0032] Example 1: Refer to Figure 1 This is a flowchart illustrating the method for bidirectional calculation of the emission reduction contribution of the zero-carbon cloud library and the reduction in GDP carbon intensity provided in this embodiment of the invention. The process may include at least steps S100-S400:

[0033] S100 receives local historical GDP data, energy consumption data, carbon emission data, local GDP data for the current year, emission reduction data from the zero-carbon cloud library, basic characteristic data of public institutions, carbon emission data of public institutions, and parameter configuration data. After data source writing, accounting cycle aggregation, field standardization, and integrity verification, it generates bidirectional measurement task data.

[0034] S200. Based on the bidirectional measurement task data, generate local GDP carbon emission intensity baseline data, public institution carbon emission intensity baseline data, and project emission reduction collection data;

[0035] S300. Based on the local GDP carbon emission intensity baseline data, the public institution carbon emission intensity baseline data, and the project emission reduction data, generate the contribution value of the local GDP carbon emission intensity reduction and the contribution value of the public institution carbon emission intensity improvement.

[0036] S400. Based on the contribution value of the reduction in carbon emission intensity of local GDP and the contribution value of the improvement in carbon emission intensity of public institutions, generate a two-way quantitative calculation report.

[0037] S100 receives local historical GDP data, energy consumption data, carbon emission data, local GDP data for the current year, emission reduction data from the zero-carbon cloud library, basic characteristic data of public institutions, carbon emission data of public institutions, and parameter configuration data. After data source writing, accounting cycle aggregation, field standardization, and integrity verification, it generates bidirectional measurement task data.

[0038] Specifically, S100 is executed by the bidirectional measurement task data generation unit, which receives local historical Gross Domestic Product (GDP) data, energy consumption data, carbon emission data, current year's local GDP data, zero-carbon cloud library emission reduction data, basic characteristic data of public institutions, carbon emission data of public institutions, and parameter configuration data. The local historical GDP data is a field representing local GDP recorded annually; the energy consumption data is a field representing consumption volume recorded by energy type, year, and statistical region; the carbon emission data is a field representing total carbon emissions recorded annually; and the current year's local GDP data is a field representing local GDP for the year to be measured. The zero-carbon cloud library emission reduction data includes the library's identification, emission reduction path, accounting cycle, total annual emission reduction for the project, data source, verification status, and data version. The basic characteristic data of public institutions includes building area, number of office staff, scale of energy-consuming equipment, institution type, regional climate, and functional positioning. The carbon emission data of public institutions is a field representing the carbon emissions generated by the corresponding public institution within the same year. The parameter configuration data includes accounting cycle configuration, field name configuration, unit conversion configuration, baseline configuration, report version configuration, emission reduction fluctuation range, annual local GDP change range, and public institution basic characteristic change range.

[0039] Specifically, the bidirectional measurement task data generation unit initiates this step upon receiving manually imported files, data interface reporting records, ledger data update records, or data version change records. The manually imported files enter the field reading link, the data interface reporting records enter the interface field parsing link, the ledger data update records enter the emission reduction field aggregation link, and the data version change records enter the version comparison link. After different input sources enter the same field standardization processing link, they are all converted to a unified field name. The field name includes statistical region, accounting year, data source, data version, local historical GDP, energy consumption, carbon emissions, current year's local GDP, venue identifier, emission reduction path, project's annual emission reduction total, verification status, building area, number of office staff, scale of energy-consuming equipment, institution type, regional climate, and functional positioning.

[0040] Furthermore, the "data source writing" refers to writing the data source, import time, interface name, file name, ledger number, or verification status corresponding to each piece of input data into the source field. The source field is stored together with the local historical GDP data, energy consumption data, carbon emission data, current year's local GDP data, zero-carbon cloud library emission reductions, public institution basic characteristic data, and public institution carbon emission data. The data source writing does not change the original values ​​of the input data; instead, it adds a source field and a data version field to the original values. The source field is read in S200 and used to generate the baseline version, data source, and data version in the local GDP carbon emission intensity baseline data, public institution carbon emission intensity baseline data, and project emission reduction aggregation data.

[0041] Furthermore, the accounting cycle aggregation refers to grouping the local historical GDP data, energy consumption data, carbon emission data, current year's local GDP data, zero-carbon cloud library emission reductions, and public institution carbon emission data into the same year. The bidirectional measurement task data generation unit first reads the accounting cycle configuration in the parameter configuration data, and then reads the year field from each input data. If the zero-carbon cloud library emission reductions are recorded monthly, they are aggregated into the project's annual emission reduction total based on the library's identification and emission reduction path within the same year. If the public institution carbon emission data are recorded quarterly, they are aggregated into annual public institution carbon emission data within the same year. If the local historical GDP data and the carbon emission data have already been recorded annually, they are directly written into the annual aggregation field. Understandably, the output fields of the accounting cycle aggregation are the accounting year, the pre-aggregation cycle, the post-aggregation cycle, and the annual aggregation value, where the annual aggregation value serves as the input for the local GDP carbon emission intensity sequence, public institution carbon emission intensity standardized data, and project emission reduction aggregation data in S200.

[0042] Furthermore, the field standardization includes field name standardization, data type standardization, and unit of measurement standardization. Field name standardization unifies "Gross Domestic Product," "Regional Gross Domestic Product," and "GDP" from different sources into a single field representing local historical GDP or current year's local GDP; it unifies "Total Carbon Emissions," "Total Carbon Emissions," and "Annual Carbon Emissions" into a single field representing carbon emission data; and it unifies "Emission Reductions," "Annual Emission Reductions," and "Project Emission Reductions" into a single field representing annual project emission reductions. Data type standardization converts annual fields to annual types, numerical fields to numerical types, and source fields, verification status, and data versions to text fields. Unit of measurement standardization unifies annual project emission reductions to tCO2, local historical GDP and current year's local GDP to 10,000 yuan, building area to m2, number of office staff to people, and energy-consuming equipment scale to an equipment scale field. This field standardization generates a standard field set, which is written into the bidirectional measurement task data along with the source fields and the accounting year.

[0043] Furthermore, the integrity verification is performed by an integrity verification link. This link reads a standard field set and checks whether local historical GDP data, carbon emission data, current year's local GDP data, zero-carbon cloud library emission reductions, public institution basic characteristic data, and public institution carbon emission data contain empty fields, inconsistent year fields, unconverted unit fields, or missing data version fields. If the integrity verification fails, an abnormal data marker is written into the bidirectional measurement task data. The abnormal data markers include missing field markers, period conflict markers, unit conflict markers, version missing markers, and verification status abnormal markers. The abnormal data markers do not delete the original fields; they retain the original field content and the abnormal reason field. The abnormal data markers are read in S200, and data with abnormal data markers does not enter the corresponding baseline calculation link but still enters the report record link along with the bidirectional measurement task data.

[0044] In the engineering implementation, after the annual accounting is completed, the public institution management platform imports the annual emission reduction data of the Zero Carbon Cloud Library and the carbon emission data of public institutions, and accesses local historical GDP data, energy consumption data, carbon emission data, and the local GDP data for the current year. The bidirectional calculation task data generation unit reads the library site identifier, emission reduction path, accounting cycle, annual emission reduction total of the project, data source, verification status, and data version from the emission reduction data of the Zero Carbon Cloud Library, aggregates the monthly emission reduction data into the annual project emission reduction total, and groups the local historical GDP data, carbon emission data, and public institution carbon emission data into the same year. If a library site identifier lacks a verification status, the integrity verification link writes an abnormal verification status mark in the bidirectional calculation task data; if the basic characteristic data of public institutions lacks the scale of energy-consuming equipment, the integrity verification link writes a missing field mark. The above fields enter S200 along with the bidirectional calculation task data, and S200 reads and generates local GDP carbon emission intensity baseline data, public institution carbon emission intensity baseline data, and project emission reduction aggregated data respectively.

[0045] Understandably, the minimum set of core parameters for S100 includes local historical GDP data, carbon emission data, current year's local GDP data, emission reduction data from the Zero Carbon Cloud Library, basic characteristic data of public institutions, carbon emission data of public institutions, accounting year, data source, data version, and abnormal data markers. Energy consumption data is a preferred input field, used to supplement local carbon emission data sources and baseline parameter records. Parameter configuration data is a runtime configuration field used to control the accounting cycle, unit conversion, report version, and sensitivity analysis. The bidirectional measurement task data output by S100 includes a standard field set, source field, accounting year, data version, abnormal data markers, and annual aggregated values. This bidirectional measurement task data serves as direct input to the "local GDP carbon emission intensity baseline data, public institution carbon emission intensity baseline data, and project emission reduction aggregated data" in S200.

[0046] In summary, the technical effects of this step are as follows: S100 organizes local GDP, energy consumption, carbon emissions, zero-carbon cloud library emission reductions, and public institution data from multiple sources into bidirectional measurement task data under the same year, field name, and data version. S100 places data source writing, accounting cycle aggregation, field standardization, and integrity verification into the same processing link, ensuring that the data read by S200 has a unified field structure. S100 retains abnormal fields and their reasons through abnormal data marking, providing continuous fields for subsequent baseline generation and report recording.

[0047] S200. Based on the bidirectional measurement task data, generate local GDP carbon emission intensity baseline data, public institution carbon emission intensity baseline data, and project emission reduction collection data;

[0048] Specifically, step S200 is executed by the baseline data generation unit. This unit receives the bidirectional measurement task data output from step S100 and reads local historical GDP data, local historical carbon emission data, basic characteristic data of public institutions, carbon emission data of public institutions, emission reduction data from the zero-carbon cloud library, accounting year, data source, data version, and abnormal data markers from the bidirectional measurement task data. The baseline data generation unit performs data routing based on the abnormal data markers. Data without abnormal data markers enters the baseline calculation link, while data with abnormal data markers enters the abnormal record link. The abnormal record link writes the abnormal field name, abnormal reason, data source, and data version into the abnormal data marker field of the subsequent bidirectional quantitative measurement report, and does not participate in the numerical generation of the local GDP carbon emission intensity baseline data and the public institution carbon emission intensity baseline data.

[0049] Specifically, the local GDP carbon emission intensity baseline data is generated by a local intensity baseline link. This link reads historical local GDP data and historical local carbon emission data for each year, and divides the historical local carbon emission data by the historical local GDP data annually to generate a unit GDP carbon emission intensity. The unit GDP carbon emission intensity is a field representing the ratio between the local carbon emission data and the historical local GDP data for the same year, expressed in tCO2 / 10,000 yuan. Subsequently, the local intensity baseline link arranges the unit GDP carbon emission intensities for each year in chronological order, generating a local GDP carbon emission intensity sequence. This sequence includes an annual field, a unit GDP carbon emission intensity field, a data source field, and a data version field. The local intensity baseline link then reads the baseline period intensity value and the target year intensity value from the local GDP carbon emission intensity sequence according to the baseline configuration in the parameter configuration data, and writes them into the baseline version to generate the local GDP carbon emission intensity baseline data.

[0050] Further, the baseline intensity value is the unit GDP carbon emission intensity field corresponding to the baseline year, and the target year intensity value is the unit GDP carbon emission intensity field corresponding to the year to be measured or obtained by sequence extension. The baseline version is generated by the baseline data generation unit according to the accounting year, data version, and report version configuration in the parameter configuration data. If there are multiple data versions for the same year, the local intensity baseline link reads the data version field, selects the current version according to the version update time, and writes the old version into the version record field. If there is an annual breakpoint in the local historical GDP data or local historical carbon emission data, the local intensity baseline link writes an annual breakpoint marker into the local GDP carbon emission intensity sequence and passes the marker to the baseline parameter field in the local GDP carbon emission intensity baseline data.

[0051] Specifically, the carbon emission intensity baseline data for public institutions is generated by the public institution intensity baseline link. This link reads carbon emission data, building area, number of office staff, and scale of energy-consuming equipment from public institutions to generate standardized carbon emission intensity data. This standardized data includes carbon emission intensity per unit building area (based on building area), carbon emission intensity per capita (based on number of office staff), and carbon emission intensity per unit scale (based on the scale of energy-consuming equipment). The link then reads the institution type, regional climate, and functional positioning, and writes these information into a correction field. This correction field, together with the standardized carbon emission intensity data, generates the public institution carbon emission intensity baseline data. This baseline data includes the public institution identifier, accounting year, standardized carbon emission intensity data, institution type, regional climate, functional positioning, baseline parameters, and data version.

[0052] Furthermore, the institution type is used to distinguish public institution categories, the regional climate is used to record the climate attributes of the region where the public institution is located, and the functional positioning is used to record the service function attributes of the public institution. The public institution intensity baseline link does not change the original values ​​of the public institution carbon emission data. Instead, it uses the building area, number of office staff, and scale of energy-consuming equipment as standardized denominator fields to generate multiple intensity fields, and writes the institution type, regional climate, and functional positioning as caliber description fields into the public institution carbon emission intensity baseline data. If any field of building area, number of office staff, or scale of energy-consuming equipment is missing, the public institution intensity baseline link writes a missing field marker and stops generating the corresponding standardized intensity field; standardized intensity fields with other complete fields continue to be generated, and the missing field marker is passed to the public institution carbon emission intensity baseline data.

[0053] Specifically, the project emission reduction data is generated by the project emission reduction collection link. This link reads the library's site identifier, emission reduction path, accounting period, total annual emission reduction for the project, data source, verification status, and data version from the zero-carbon cloud library's emission reduction data, and collects the data according to the site identifier, emission reduction path, and accounting year. If multiple emission reduction paths exist for the same site identifier in the same year, the project emission reduction collection link records the total annual emission reduction for each emission reduction path, and then generates the total annual emission reduction for the site. If multiple data versions exist for the same emission reduction path, the project emission reduction collection link reads the data version field and writes it to the version record field. The project emission reduction data includes the site identifier, emission reduction path, accounting period, total annual emission reduction for the project, total annual emission reduction for the site, data source, verification status, and data version. This project emission reduction data serves as a common input in S300 for both the contribution value of local GDP carbon emission intensity reduction and the contribution value of public institution carbon emission intensity improvement.

[0054] In an engineering implementation, a public institution operates multiple zero-carbon cloud library sites within the same year. S100 has already generated bidirectional measurement task data. S200 reads the local historical GDP data and local historical carbon emission data for consecutive years, generates carbon emission intensity per unit GDP year by year, then arranges them by year to generate a local GDP carbon emission intensity sequence, and writes the baseline intensity value, target year intensity value, and baseline version. S200 also reads the public institution's carbon emission data, building area, number of office staff, and energy-consuming equipment scale, generating carbon emission intensity per unit building area, per capita carbon emission intensity, and equipment scale carbon emission intensity, and writes the institution type, regional climate, and functional positioning into the public institution carbon emission intensity baseline data. S200 also reads the total annual emission reduction of each site, forming project emission reduction aggregation data according to site identification and emission reduction path. The aforementioned local GDP carbon emission intensity baseline data, public institution carbon emission intensity baseline data, and project emission reduction aggregation data are all entered into S300.

[0055] Understandably, the minimum set of core parameters in S200 includes local historical GDP data, local historical carbon emission data, public institution carbon emission data, building area, number of office staff, scale of energy-consuming equipment, total annual emission reduction of projects, site identification, emission reduction path, accounting year, and data version. The institution type, regional climate, and functional positioning are explanatory fields for the scope of the public institution carbon emission intensity baseline data. The output fields of the local GDP carbon emission intensity baseline data include the local GDP carbon emission intensity sequence, baseline period intensity value, target year intensity value, baseline version, and baseline parameters. The output fields of the public institution carbon emission intensity baseline data include standardized public institution carbon emission intensity data, institution type, regional climate, functional positioning, baseline parameters, and data version. The output fields of the project emission reduction aggregation data include site identification, emission reduction path, total annual emission reduction of projects, total annual emission reduction of sites, verification status, and data version. These three types of data are read in S300 to generate the contribution value of local GDP carbon emission intensity reduction and the contribution value of public institution carbon emission intensity improvement.

[0056] In summary, the technical effects of this step are as follows: Step S200 breaks down the bidirectional measurement task data into three categories of data objects: local intensity baseline, public institution intensity baseline, and project emission reduction aggregation. Step S200 converts local historical GDP data and local historical carbon emission data into a local GDP carbon emission intensity sequence, and converts public institution carbon emission data into standardized public institution carbon emission intensity data. Step S200 ensures that the annual emission reduction of the same project is simultaneously included in the calculation of the contribution value of local GDP carbon emission intensity reduction and the calculation of the contribution value of public institution carbon emission intensity improvement in Step S300.

[0057] S300. Based on the local GDP carbon emission intensity baseline data, the public institution carbon emission intensity baseline data, and the project emission reduction data, generate the contribution value of the local GDP carbon emission intensity reduction and the contribution value of the public institution carbon emission intensity improvement.

[0058] Specifically, step S300 is executed by the bidirectional contribution value generation unit, which receives the local GDP carbon emission intensity baseline data, the public institution carbon emission intensity baseline data, and the project emission reduction aggregation data output by step S200. The local GDP carbon emission intensity baseline data provides, in this step, the local GDP carbon emission intensity sequence, baseline period intensity value, target year intensity value, baseline version, and baseline parameters. The public institution carbon emission intensity baseline data provides, in this step, standardized data on public institution carbon emission intensity, institution type, regional climate, functional positioning, baseline parameters, and data version. The project emission reduction aggregation data provides, in this step, the site identifier, emission reduction path, annual total emission reduction for the project, annual total emission reduction for the site, verification status, and data version. The bidirectional contribution value generation unit initiates the contribution value generation link when the data versions of the three types of data are consistent, the accounting year is consistent, and abnormal data markers do not block the calculation.

[0059] Specifically, the contribution value of local GDP carbon emission intensity reduction is generated by the local contribution value generation link. This link reads the total annual emission reduction (ER) of the project from the project emission reduction aggregation data and the GDP from the local GDP data for the current year. ER represents the total annual emission reduction of the project, in tCO2; GDP represents the local GDP for the current year, in ten thousand yuan. The local contribution value generation link calculates the contribution value ΔCI of local GDP carbon emission intensity reduction according to ΔCI=(ER / GDP), where ΔCI is in tCO2 / ten thousand yuan. The link then writes ΔCI, along with the baseline intensity value, target year intensity value, and baseline version from the local GDP carbon emission intensity baseline data, into the local contribution data area. The local contribution data area includes the site identifier, emission reduction path, total annual emission reduction (ER) of the project, local GDP for the current year, local GDP carbon emission intensity reduction contribution value ΔCI, baseline intensity value, target year intensity value, baseline version, and data version.

[0060] Furthermore, when multiple emission reduction paths exist for the same venue identifier, the local contribution value generation link reads the project's annual emission reduction total (ER) for each emission reduction path, calculates the corresponding local GDP carbon emission intensity reduction contribution value (ΔCI) for each path, and generates an emission reduction path contribution record. Subsequently, the local contribution value generation link reads the venue's annual emission reduction total, generates the venue's annual local GDP carbon emission intensity reduction contribution value, and writes each emission reduction path contribution record and the venue's annual contribution record into the same local contribution data area. If the verification status shows "pending verification," the local contribution value generation link writes this verification status into the verification status field of the local contribution data area and displays the verification status in the subsequent S400 bidirectional quantitative calculation report. If the project's annual emission reduction total (ER) or the local GDP for the year is missing, the local contribution value generation link does not generate ΔCI, writes an abnormal data marker, and transmits the abnormal data marker to S400.

[0061] Specifically, the contribution value for improving the carbon emission intensity of public institutions is generated by the public institution contribution value generation link. This link reads the standardized carbon emission intensity data of public institutions from the baseline data and the total annual emission reduction of projects or venues from the project emission reduction aggregation data. The link first reads the pre-reduction carbon emission intensity of the public institution corresponding to the carbon emission data, then deducts the total annual emission reduction of projects from the carbon emission data to generate the post-reduction carbon emission data. Subsequently, the link generates the post-reduction carbon emission intensity of public institutions according to building area, number of office staff, and scale of energy-consuming equipment. The difference between the pre-reduction and post-reduction carbon emission intensity of public institutions is written into the public institution carbon emission intensity improvement contribution value. This contribution value, along with the institution type, regional climate, functional positioning, and baseline parameters, is written into the public institution contribution data area.

[0062] Furthermore, the carbon emission intensity of public institutions before emission reduction includes the carbon emission intensity per unit building area, the carbon emission intensity per capita, and the carbon emission intensity per unit of equipment size before emission reduction. The carbon emission intensity of public institutions after emission reduction includes the carbon emission intensity per unit building area, the carbon emission intensity per capita, and the carbon emission intensity per unit of equipment size after emission reduction. The contribution value for improving carbon emission intensity of public institutions includes the contribution value per unit building area, the contribution value per capita, and the contribution value per unit of equipment size. When generating the above fields, the public institution contribution value generation link reads the institution type, regional climate, and functional positioning from the public institution carbon emission intensity baseline data and writes them as caliber description fields into the public institution contribution data area. If the fields corresponding to building area, number of office staff, or energy-consuming equipment size have missing field markers, the public institution contribution value generation link stops generating the improvement contribution value for the corresponding dimension and retains the improvement contribution values ​​corresponding to other complete fields.

[0063] Furthermore, the bidirectional contribution value generation unit associates the local contribution data area and the public institution contribution data area with versions within the same accounting year. This version association refers to writing the data versions of the local GDP carbon emission intensity reduction contribution value, the public institution carbon emission intensity improvement contribution value, the project emission reduction collection data, the baseline version of the local GDP carbon emission intensity baseline data, and the public institution carbon emission intensity baseline data into the same contribution value version field. This contribution value version field is read in S400 and used to generate the report version identifier for the bidirectional quantitative calculation report. If the data version of the local GDP carbon emission intensity baseline data is inconsistent with the data version of the public institution carbon emission intensity baseline data, the bidirectional contribution value generation unit writes a version inconsistency flag and passes this flag to the abnormal data flag field of S400.

[0064] In the engineering implementation, S200 has generated local GDP carbon emission intensity baseline data, public institution carbon emission intensity baseline data, and project emission reduction aggregation data for a certain year. S300 reads the project's annual total emission reduction (ER) and the local GDP for that year, generates a local GDP carbon emission intensity reduction contribution value ΔCI according to ΔCI=(ER / GDP), and writes ΔCI, along with the baseline intensity value, target year intensity value, and baseline version, into the local contribution data area. S300 simultaneously reads standardized carbon emission intensity data, building area, number of office staff, and energy-consuming equipment scale of public institutions, generating public institution carbon emission intensity before emission reduction, public institution carbon emission intensity after emission reduction, and public institution carbon emission intensity improvement contribution value, and writes the institution type, regional climate, and functional positioning into the public institution contribution data area. The local contribution data area and the public institution contribution data area together serve as the input source for S400.

[0065] Understandably, the minimum set of core parameters for S300 includes the project's annual total emission reduction (ER), the local GDP for the year, local GDP carbon emission intensity baseline data, standardized carbon emission intensity data for public institutions, building area, number of office staff, scale of energy-consuming equipment, and data version. The institution type, regional climate, and functional positioning are descriptive fields for the public institution contribution data area. The output products of S300 include the contribution value of local GDP carbon emission intensity reduction, the local contribution data area, the public institution carbon emission intensity before emission reduction, the public institution carbon emission intensity after emission reduction, the contribution value of public institution carbon emission intensity improvement, and the public institution contribution data area. The local contribution data area and the public institution contribution data area serve as direct inputs to "generating a two-way quantitative calculation report" in S400.

[0066] In summary, the technical effects of this step are as follows: S300 inputs the annual emission reduction total of the same project into the local contribution value generation link and the public institution contribution value generation link, respectively, to form the contribution value for the reduction of local GDP carbon emission intensity and the contribution value for the improvement of public institution carbon emission intensity. S300 writes the emission reduction path contribution record, the annual contribution record of the venue, and the public institution contribution data area into the same contribution value version field, forming a common version basis for bidirectional calculation. S300 generates two contribution value outputs within the same accounting year, enabling the bidirectional quantitative calculation report of S400 to have traceable input fields.

[0067] S400. Based on the contribution value of the reduction in carbon emission intensity of local GDP and the contribution value of the improvement in carbon emission intensity of public institutions, generate a two-way quantitative calculation report.

[0068] Specifically, step S400 is executed by the two-way quantitative calculation report generation unit. This unit receives the local GDP carbon emission intensity reduction contribution value, local contribution data area, public institution carbon emission intensity improvement contribution value, and public institution contribution data area output from step S300, and reads the data source, accounting cycle, abnormal data markers, and parameter configuration data from the two-way calculation task data output from step S100. The two-way quantitative calculation report generation unit establishes a report encapsulation link according to the report version configuration. The two-way quantitative calculation report includes emission reductions, local GDP carbon emission intensity reduction contribution value, public institution carbon emission intensity improvement contribution value, data source, accounting cycle, baseline parameters, caliber description, abnormal data markers, and report version identifier. The report version identifier is generated jointly by the accounting year, contribution value version field, baseline version, and data version.

[0069] Specifically, the two-way quantitative calculation report generation unit first establishes a local carbon intensity contribution data area. This local carbon intensity contribution data area reads the site identifier, emission reduction path, annual emission reduction (ER) of the project, local GDP for the current year, local GDP carbon emission intensity reduction contribution value (ΔCI), baseline period intensity value, target year intensity value, baseline version, and data version from the local contribution data area. The local carbon intensity contribution data area writes the annual emission reduction (ER) of the project into the emission reduction amount field, writes the local GDP carbon emission intensity reduction contribution value (ΔCI) into the local GDP carbon emission intensity reduction contribution value field, and writes the baseline period intensity value, target year intensity value, and baseline version into the baseline parameter field. If the local contribution data area contains a verification status field or an abnormal data marker, the two-way quantitative calculation report generation unit writes it into the corresponding field of the local carbon intensity contribution data area.

[0070] Furthermore, the two-way quantitative calculation report generation unit establishes a public institution contribution data area. This public institution contribution data area reads the following data from the public institution contribution data area: pre-reduction carbon emission intensity, post-reduction carbon emission intensity, public institution carbon emission intensity improvement contribution value, institution type, regional climate, functional positioning, baseline parameters, and data version. The public institution contribution data area writes the public institution carbon emission intensity improvement contribution value into the public institution carbon emission intensity improvement contribution value field, writes the institution type, regional climate, and functional positioning into the caliber description field, and writes the baseline parameters from the public institution carbon emission intensity baseline data into the baseline parameter field. If there are missing field markers for building area, number of office staff, or scale of energy-consuming equipment, the two-way quantitative calculation report generation unit writes the missing field markers into the abnormal data marker field and retains the already generated public institution carbon emission intensity improvement contribution value field.

[0071] Specifically, S400 reads the emission reduction fluctuation range, the annual GDP change range, and the public institution basic characteristic change range from the parameter configuration data to generate sensitivity analysis results. These results are generated by a sensitivity analysis link. The link first reads the emission reduction fluctuation range and generates an emission reduction change field based on the project's annual emission reduction (ER) according to this range; then it reads the annual GDP change range and generates a GDP change field based on this range; subsequently, it reads the public institution basic characteristic change range and generates a public institution basic characteristic change field based on the building area, number of office workers, and energy-consuming equipment scale according to this range. Based on these change fields, the sensitivity analysis link regenerates records of changes in the contribution value of local GDP carbon emission intensity reduction and public institution carbon emission intensity improvement, and writes these records into the sensitivity analysis result field. The sensitivity analysis result field is then entered into the two-way quantitative calculation report.

[0072] Further, the bidirectional quantitative calculation report generation unit performs report encapsulation processing. The report encapsulation process reads the local carbon intensity contribution data area, the public institution contribution data area, sensitivity analysis results, data source, accounting cycle, baseline parameters, caliber description, abnormal data markers, and report version identifier, and writes them into a unified report object. The unified report object is saved as a structured data record. The structured data record includes a main report table, a local carbon intensity contribution data area, a public institution contribution data area, an abnormal data marker area, and a version record area. The main report table records the report version identifier, accounting cycle, data source, and generation time. The local carbon intensity contribution data area records the contribution value of the reduction in local GDP carbon emission intensity. The public institution contribution data area records the contribution value of the improvement in public institution carbon emission intensity. The abnormal data marker area records the abnormal data markers transmitted by S100, S200, and S300. The version record area records the data versions of the zero-carbon cloud library emission reduction, current year's local GDP data, public institution basic characteristic data, public institution carbon emission data, local GDP carbon emission intensity baseline data, and public institution carbon emission intensity baseline data.

[0073] Furthermore, when the data versions of the zero-carbon cloud library's emission reduction data, annual local GDP data, public institution basic characteristic data, or public institution carbon emission data change, the version monitoring link reads the changed data version and generates a version change trigger record. This version change trigger record is written to the bidirectional calculation task data generation unit, returning to execute steps S100 to S400. Upon returning to execution, S100 regenerates the bidirectional calculation task data, S200 regenerates the local GDP carbon emission intensity baseline data, public institution carbon emission intensity baseline data, and project emission reduction aggregation data, S300 regenerates the local GDP carbon emission intensity reduction contribution value and the public institution carbon emission intensity improvement contribution value, and S400 generates an updated bidirectional quantitative calculation report. The updated bidirectional quantitative calculation report includes a new report version identifier while retaining the old report version identifier and old data version fields.

[0074] In the engineering implementation, after the annual accounting of public institutions is completed, S400 receives the local contribution data area and the public institution contribution data area, and generates a two-way quantitative calculation report containing emission reductions, local GDP carbon emission intensity reduction contribution value, public institution carbon emission intensity improvement contribution value, data source, accounting cycle, baseline parameters, caliber explanation, abnormal data markers, and report version identifier. If a revised version of the local GDP data for the current year is subsequently released, the version monitoring link reads the changed data version and triggers a return to execute S100 to S400. The system re-collects the accounting cycle, regenerates the baseline data and contribution value, and writes the new report version identifier into the updated two-way quantitative calculation report. The old report version continues to be stored in the version record area. If the building area in the basic characteristic data of public institutions changes subsequently, the version monitoring link also reads the changed data version, returns to execute S100 to S400, and updates the public institution carbon emission intensity improvement contribution value and sensitivity analysis results.

[0075] Understandably, the minimum set of core parameters for S400 includes the contribution value of local GDP carbon emission intensity reduction, the contribution value of public institutions to carbon emission intensity improvement, data source, accounting cycle, baseline parameters, caliber explanation, abnormal data markers, and report version identifier. The sensitivity analysis results are extended fields, generated from the emission reduction fluctuation range, the annual local GDP change range, and the public institution basic characteristic change range in the parameter configuration data. The two-way quantitative calculation report serves as the final output of this round of S100 to S400 operation. Simultaneously, its report version identifier, abnormal data markers, and version record area enter the version comparison link of the next round of S100 when the data version changes, forming a cross-round closed loop.

[0076] In summary, the technical effects of this step are as follows: S400 encapsulates the contribution value of local GDP carbon emission intensity reduction and the contribution value of public institution carbon emission intensity improvement into a single two-way quantitative calculation report. S400 writes the data source, accounting period, baseline parameters, caliber description, abnormal data markers, and report version identifier into the report object, ensuring continuity between the report fields and the inputs and outputs of S100 to S300. When the data version changes, S400 triggers a return to execute S100 to S400, generating an updated two-way quantitative calculation report.

[0077] Example 2: Figure 2 A structural block diagram is shown for a method for bidirectionally calculating the emission reduction contribution of a zero-carbon cloud library and the reduction in GDP carbon intensity according to an embodiment of the present invention. Figure 2 As shown, the structure may include:

[0078] The bidirectional measurement task data generation module 01 receives local historical GDP data, energy consumption data, carbon emission data, current year's local GDP data, zero-carbon cloud library emission reduction data, basic characteristic data of public institutions, carbon emission data of public institutions, and parameter configuration data. After data source writing, accounting cycle aggregation, field standardization, and integrity verification, it generates bidirectional measurement task data. Specifically, the bidirectional measurement task data generation module is equipped with a data receiving interface, a field processing component, a cycle aggregation component, and a verification component. The data receiving interface receives local historical GDP data, energy consumption data, carbon emission data, current year's local GDP data, zero-carbon cloud library emission reduction data, basic characteristic data of public institutions, carbon emission data of public institutions, and parameter configuration data. After the zero-carbon cloud library emission reduction data enters the field processing component, the library location identifier, emission reduction path, accounting cycle, project annual emission reduction total, data source, verification status, and data version are written into the same data record. The periodic aggregation component reads the accounting period and aggregates local historical GDP data, energy consumption data, carbon emission data, current year's local GDP data, zero-carbon cloud library emission reduction data, and public institution carbon emission data into the same year. The field processing component converts field names, data types, and units of measurement into unified fields. The verification component checks for missing fields, period conflicts, unit conflicts, version errors, and verification status anomalies, and sends the corresponding abnormal fields to the anomaly marking module. The bidirectional measurement task data generation module outputs bidirectional measurement task data and provides the bidirectional measurement task data to the baseline data generation module and the project emission reduction aggregation module.

[0079] The baseline data generation module 02, connected to the bidirectional calculation task data generation module, is used to generate local GDP carbon emission intensity baseline data and public institution carbon emission intensity baseline data based on the bidirectional calculation task data. Specifically, the baseline data generation module receives bidirectional calculation task data from the bidirectional calculation task data generation module and reads local historical GDP data, carbon emission data, public institution basic characteristic data, public institution carbon emission data, accounting period, data source, and data version. The baseline data generation module is equipped with a local intensity processing component and a public institution intensity processing component. The local intensity processing component reads local historical GDP data and carbon emission data annually, generates a unit GDP carbon emission intensity record, and generates a local GDP carbon emission intensity sequence in annual order. The local intensity processing component reads the baseline period intensity value, the target year intensity value, and the baseline version to generate local GDP carbon emission intensity baseline data. The public institution intensity processing component reads carbon emission data, building area, number of office staff, and scale of energy-consuming equipment from public institutions, generates standardized carbon emission intensity data for public institutions, and writes the institution type, regional climate, and functional positioning into the caliber field to generate baseline data for carbon emission intensity of public institutions. The baseline data generation module transmits the local GDP carbon emission intensity baseline data and the public institution carbon emission intensity baseline data to the contribution value generation module.

[0080] The project emission reduction aggregation module 03, connected to the bidirectional calculation task data generation module, is used to generate project emission reduction aggregation data based on the site identifier, emission reduction path, accounting cycle, annual project emission reduction total, data source, verification status, and data version in the bidirectional calculation task data. Specifically, the project emission reduction aggregation module receives bidirectional calculation task data from the bidirectional calculation task data generation module and reads the site identifier, emission reduction path, accounting cycle, annual project emission reduction total, data source, verification status, and data version. The project emission reduction aggregation module is equipped with a site aggregation component, an emission reduction path aggregation component, and a version recording component. The site aggregation component aggregates the annual project emission reduction total according to the site identifier and accounting cycle. The emission reduction path aggregation component records the corresponding annual project emission reduction total according to the emission reduction path and generates the annual emission reduction total for the site. The version recording component reads the data source, verification status, and data version and writes the data source, verification status, and data version into the project emission reduction aggregation data. When the verification status is abnormal or the data version is missing, the project emission reduction collection module sends the verification status abnormality field or version missing field to the anomaly marking module. The project emission reduction collection module outputs the project emission reduction collection data and transmits the project emission reduction collection data to the contribution value generation module.

[0081] The contribution value generation module 04 is connected to both the baseline data generation module and the project emission reduction collection module. It is used to generate a contribution value for the reduction of local GDP carbon emission intensity and a contribution value for the improvement of public institution carbon emission intensity based on the local GDP carbon emission intensity baseline data, the public institution carbon emission intensity baseline data, and the project emission reduction collection data. Specifically, the contribution value generation module receives local GDP carbon emission intensity baseline data and public institution carbon emission intensity baseline data from the baseline data generation module, and receives project emission reduction collection data from the project emission reduction collection module. The contribution value generation module includes a local contribution processing component and a public institution contribution processing component. The local contribution processing component reads the project's annual emission reduction total, the local GDP data for the current year, the baseline period intensity value, the target year intensity value, and the baseline version, generates a contribution value for the reduction of local GDP carbon emission intensity, and writes it to the local contribution data area. The public institution contribution processing component reads the project's annual total emission reduction, public institution carbon emission intensity baseline data, building area, number of office staff, and energy-consuming equipment scale, generates the public institution carbon emission intensity before emission reduction, the public institution carbon emission intensity after emission reduction, and the public institution carbon emission intensity improvement contribution value, and writes them into the public institution contribution data area. The contribution value generation module transmits the local GDP carbon emission intensity reduction contribution value, the local contribution data area, the public institution carbon emission intensity improvement contribution value, and the public institution contribution data area to the report generation module.

[0082] The report generation module 05, connected to the contribution value generation module, is used to generate a two-way quantitative calculation report based on the contribution value of the reduction in local GDP carbon emission intensity and the contribution value of the improvement in carbon emission intensity of public institutions. Specifically, the report generation module receives the contribution value of the reduction in local GDP carbon emission intensity, the local contribution data area, the contribution value of the improvement in carbon emission intensity of public institutions, and the public institution contribution data area from the contribution value generation module, and reads the data source, calculation cycle, parameter configuration data, and data version from the two-way calculation task data generation module. The report generation module sets up a main report table component, a local contribution writing component, a public institution contribution writing component, and a version writing component. The main report table component writes the data source, calculation cycle, and report version identifier. The local contribution writing component writes the emission reduction, the contribution value of the reduction in local GDP carbon emission intensity, the baseline parameters, and the data version. The public institution contribution writing component writes the contribution value of the improvement in carbon emission intensity of public institutions, the institution type, regional climate, functional positioning, and caliber description. The version writing component writes the report version identifier and the data version. The report generation module receives the sensitivity analysis results written by the sensitivity analysis module and the anomaly flag field written by the anomaly flag module, and generates a two-way quantitative measurement report. The two-way quantitative measurement report is then transmitted to the version update module for version registration.

[0083] Sensitivity analysis module 06, connected to the report generation module, is used to read the emission reduction fluctuation range, the annual GDP change range, and the public institution basic characteristic change range from the parameter configuration data, generate sensitivity analysis results, and write them into the two-way quantitative calculation report. Specifically, the sensitivity analysis module receives a call request from the report generation module and reads the emission reduction fluctuation range, the annual GDP change range, and the public institution basic characteristic change range from the parameter configuration data. The sensitivity analysis module is equipped with an emission reduction change processing component, a GDP change processing component, and a public institution basic characteristic change processing component. The emission reduction change processing component generates emission reduction change records according to the emission reduction fluctuation range. The GDP change processing component generates GDP change records according to the annual GDP change range. The public institution basic characteristic change processing component generates building area change records, office staff number change records, and energy-consuming equipment scale change records according to the public institution basic characteristic change range. The sensitivity analysis module generates sensitivity analysis results based on the above change records and writes the sensitivity analysis results into the two-way quantitative calculation report in the report generation module.

[0084] Anomaly marking module 07, connected to the bidirectional measurement task data generation module, the baseline data generation module, the contribution value generation module, and the report generation module, is used to write missing field markers, periodic conflict markers, unit conflict markers, version missing markers, and verification status anomaly markers. Specifically, the anomaly marking module receives anomaly fields from the bidirectional measurement task data generation module, the baseline data generation module, the contribution value generation module, and the report generation module. The anomaly marking module sets up field marking components, periodic marking components, unit marking components, version marking components, and verification status marking components. The field marking component writes missing field markers, the periodic marking component writes periodic conflict markers, the unit marking component writes unit conflict markers, the version marking component writes version missing markers, and the verification status marking component writes verification status anomaly markers. The anomaly marking module writes the above markers back to the bidirectional measurement task data, local GDP carbon emission intensity baseline data, public institution carbon emission intensity baseline data, project emission reduction collection data, local contribution data area, public institution contribution data area, and bidirectional quantitative measurement report. The report generation module reads the flag field written back by the anomaly flag module and writes the flag field into the bidirectional quantization calculation report.

[0085] Version update module 08, connected to the bidirectional measurement task data generation module and the report generation module, is used to read the changed data version and generate an updated bidirectional quantitative measurement report when the data versions of the zero-carbon cloud library emission reduction, current year's local GDP data, public institution basic characteristic data, or public institution carbon emission data change. Specifically, the version update module receives the data version from the bidirectional measurement task data generation module and the report version identifier from the report generation module. The version update module is equipped with a version reading component, a version comparison component, and an update registration component. The version reading component reads the data versions of the zero-carbon cloud library emission reduction, current year's local GDP data, public institution basic characteristic data, and public institution carbon emission data. The version comparison component compares the current data version with the registered data versions and generates a version change record when the data version changes. The version change record is transmitted to the bidirectional measurement task data generation module, which regenerates the bidirectional measurement task data based on the changed data version. The report generation module generates an updated bidirectional quantitative measurement report based on the regenerated bidirectional measurement task data and the contribution value generation result. The update registration component saves the updated bidirectional quantitative measurement report, the report version identifier, and the changed data version.

Claims

1. A two-way calculation method for the emission reduction contribution of zero-carbon cloud libraries and the reduction in GDP carbon intensity, characterized in that... include: S100: Receive information from multiple sources, and generate bidirectional measurement task data through data source writing, accounting cycle aggregation, field standardization and integrity verification; The multi-source information includes local historical GDP data, energy consumption data, carbon emission data, local GDP data for the current year, emission reduction data from the zero-carbon cloud library, basic characteristic data of public institutions, carbon emission data of public institutions, and parameter configuration data. S200. Based on the bidirectional measurement task data, perform intensity benchmark calculation and emission reduction collection processing to generate local GDP carbon emission intensity benchmark data, public institution carbon emission intensity benchmark data, and project emission reduction collection data. S300. Based on the local GDP carbon emission intensity baseline data, the public institution carbon emission intensity baseline data and the project emission reduction data, calculate the contribution value to generate the contribution value of the reduction in local GDP carbon emission intensity and the contribution value of the improvement in public institution carbon emission intensity. S400. Based on the contribution value of the reduction in carbon emission intensity of local GDP and the contribution value of the improvement in carbon emission intensity of public institutions, perform sensitivity analysis and report encapsulation to generate a two-way quantitative calculation report.

2. The method for bidirectionally calculating the emission reduction contribution of zero-carbon cloud libraries and the reduction in GDP carbon intensity according to claim 1, characterized in that, The emission reduction of the zero-carbon cloud library includes the library site identifier, emission reduction path, accounting cycle, annual emission reduction total of the project, data source, verification status and data version; S100 writes the library site identifier, emission reduction path, accounting cycle, annual emission reduction total of the project, data source, verification status and data version into the project emission reduction collection data.

3. The method for bidirectionally calculating the emission reduction contribution of zero-carbon cloud libraries and the reduction in GDP carbon intensity according to claim 1, characterized in that, The accounting cycle aggregation mentioned in S100 includes aggregating the local historical GDP data, energy consumption data, carbon emission data, local GDP data for the current year, emission reduction data from the zero-carbon cloud library, and carbon emission data from public institutions into the same year; if the integrity verification fails, an abnormal data marker is written into the bidirectional calculation task data.

4. The method for bidirectionally calculating the emission reduction contribution of zero-carbon cloud libraries and the reduction in GDP carbon intensity according to claim 1, characterized in that, S200 also includes: calculating the carbon emission intensity per unit of GDP based on local historical carbon emission data and local historical GDP data for each year, generating a local GDP carbon emission intensity sequence sorted by year, and generating local GDP carbon emission intensity baseline data containing baseline period intensity value, target year intensity value and baseline version based on the local GDP carbon emission intensity sequence.

5. The method for bidirectionally calculating the emission reduction contribution of zero-carbon cloud libraries and the reduction in GDP carbon intensity according to claim 1, characterized in that, The basic characteristic data of the public institution includes building area, number of office staff, scale of energy-consuming equipment, institution type, regional climate and functional positioning; S200 generates standardized carbon emission intensity data of the public institution based on the carbon emission data, building area, number of office staff and scale of energy-consuming equipment, and generates baseline data of carbon emission intensity of the public institution based on the institution type, regional climate and functional positioning.

6. The method for bidirectionally calculating the emission reduction contribution of zero-carbon cloud libraries and the reduction in GDP carbon intensity according to claim 2, characterized in that, S300 also includes: reading the total annual emission reduction (ER) of the project in the project emission reduction collection data and the GDP in the local GDP data of the current year, and calculating the contribution value ΔCI of the local GDP carbon emission intensity reduction according to ΔCI=(ER / GDP).

7. The method for bidirectionally calculating the emission reduction contribution of the zero-carbon cloud library and the reduction in GDP carbon intensity according to claim 5, is characterized in that, S300 further includes: reading the project's emission reduction aggregated data and the public institution's carbon emission intensity baseline data, generating the public institution's carbon emission intensity before emission reduction, the public institution's carbon emission intensity after emission reduction, and the public institution's carbon emission intensity improvement contribution value, and writing the public institution's carbon emission intensity before emission reduction, the public institution's carbon emission intensity after emission reduction, and the public institution's carbon emission intensity improvement contribution value into the public institution contribution data area.

8. The method for bidirectionally calculating the emission reduction contribution of zero-carbon cloud libraries and the reduction in GDP carbon intensity according to claim 1, characterized in that, In S400, the two-way quantitative calculation report includes emission reduction, contribution value of local GDP carbon emission intensity reduction, contribution value of public institutions to carbon emission intensity improvement, data source, accounting cycle, baseline parameters, caliber explanation, abnormal data marking and report version identifier.

9. The method for bidirectionally calculating the emission reduction contribution of the zero-carbon cloud library and the reduction in GDP carbon intensity according to claim 8, characterized in that, S400 also includes: reading the emission reduction fluctuation range, the annual local GDP change range, and the public institution basic characteristic change range from the parameter configuration data, generating sensitivity analysis results, and writing the sensitivity analysis results into the two-way quantitative calculation report.

10. The method for bidirectionally calculating the emission reduction contribution of the zero-carbon cloud library and the reduction in GDP carbon intensity according to claim 8, characterized in that, S400 also includes: when the data versions of the zero-carbon cloud library's emission reduction, local GDP data for the year, basic characteristic data of public institutions, or carbon emission data of public institutions change, read the changed data version, return to execute S100 to S400, and generate an updated two-way quantitative calculation report.

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