Carbon emission prediction method and system

Abstract

The invention discloses a carbon emission prediction method. The method comprises the following steps: acquiring carbon emission data information of a target area; performing carbon emission data accounting of the target area based on the carbon emission offset data; constructing a carbon emission prediction model of the target area; and performing carbon emission prediction of the target area based on the set scene information by adopting the carbon emission prediction model. The invention also discloses a system for realizing the carbon emission prediction method. According to the carbon emission prediction method and system provided by the invention, through the acquisition of the historical data, the accounting of the carbon emission data and the construction and evaluation of the prediction model, the prediction of the carbon emission of the target object in the whole life cycle is realized, and the reliability is higher and the accuracy is better.

Description

Technical Field

[0001] The present invention belongs to the technical field of green energy, and in particular relates to a carbon emission prediction method and system. Background Art

[0002] With the development of economy and technology and the improvement of people's living standards, people are paying more and more attention to environmental issues. Carbon emissions are crucial to environmental issues. Therefore, the prediction of carbon emissions has become the focus of researchers.

[0003] At present, the carbon emission prediction schemes for emission entities such as cities and buildings are mainly based on the historical emission data of the carbon emission entities. However, such carbon emission prediction schemes based on historical emission data cannot accurately predict the carbon emission capacity of the emission entity throughout its life cycle, especially in the actual scenario of the operation stage. Therefore, the existing carbon emission prediction schemes have the problems of poor reliability and poor accuracy. Summary of the invention

[0004] One of the purposes of the present invention is to provide a carbon emission prediction method with high reliability and good accuracy.

[0005] A second objective of the present invention is to provide a system for implementing the carbon emission prediction method.

[0006] The carbon emission prediction method provided by the present invention comprises the following steps:

[0007] S1. Obtain carbon emission data information of the target area;

[0008] S2. Calculate the carbon emission data of the target area based on the carbon emission offset data according to the carbon emission data information obtained in step S1;

[0009] S3. Constructing a carbon emission prediction model for the target area based on the carbon emission data information obtained in step S1;

[0010] S4. Using the carbon emission prediction model constructed in step S3, based on the set scenario information and the calculation results obtained in step S2, the carbon emission prediction of the target area is performed.

[0011] The step S1 of obtaining the carbon emission data information of the target area specifically includes the following steps:

[0012] Obtain carbon emission data information of carbon emission entities within a set historical period;

[0013] Acquire scale factor data, weather factor data, economic factor data, user behavior factor data, physical site and building form factor data, and green building and energy factor data; the scale factor data include population size data, number of households data, number of tenants data, site area data, and total building floor area data; the weather factor data include humidity data, average temperature data, rainfall data, heat wave data, blizzard data, air quality data, and extreme weather data; the economic factor data include occupancy rate data, vacancy rate data, rental level data, property valuation data, building construction cost data, industrial output, and tenant or user business nature data; the user The user behavior factor data include traffic flow data, pedestrian flow data, operation time data, space or facility usage pattern data and user socio-economic characteristics data; the physical place and building form factor data include street network density data, open space coverage or ratio data, open space ventilation duct data, building window orientation data, building facade window ratio data, building form and shape data, building height data, building material data, building age data and parking space data; the green building and energy factor data include building certification level data, building energy-saving design standard data, renewable or clean energy use percentage data, energy use time series pattern data and historical trend data.

[0014] Step S2, based on the carbon emission data information obtained in step S1 and based on the carbon emission offset data, calculates the carbon emission data of the target area, and specifically includes the following steps:

[0015] Calculate carbon emission data: including building operation energy carbon emission data, building embodied energy carbon emission data, mobility and transportation carbon emission data, solid waste carbon emission data, water resource carbon emission data, wastewater and sewage treatment carbon emission data, industrial and manufacturing activities carbon emission data, and public facilities and street lighting carbon emission data;

[0016] The aforementioned building operation energy carbon emission data include the building operation energy carbon emission data after the statutory building design standards have been added with additional building energy conservation requirements; the aforementioned building embodied energy carbon emission data include the building embodied energy carbon emission data after the statutory reference value standards have been additionally reduced; the aforementioned mobility and transportation carbon emission data include the mobility and transportation carbon emission data after measures such as the adoption of transportation-oriented urban form development, highly accessible public transportation services, the distance from the site to the nearest public transportation service, increasing the proportion of green travel by residents or visitors, and increasing the percentage of electric vehicle travel; the aforementioned solid waste carbon emission data include the solid waste carbon emission data after increasing the proportion of waste converted into energy and reducing the volume of solid waste ; The water resources carbon emission data mentioned include the water resources carbon emission data after increasing water resources protection and reducing the baseline per capita water consumption rate; the wastewater and sewage treatment carbon emission data mentioned include the wastewater and sewage treatment carbon emission data after increasing the proportion of sewage converted into energy and reducing the wastewater volume; the industrial and manufacturing activities carbon emission data include the industrial and manufacturing activities carbon emission data after increasing the proportion of GDP in the service industry from the baseline, reducing the energy consumption per unit of industrial GDP and added value from the baseline, reducing the energy consumption savings in industrial processes and increasing the on-site clean or renewable energy consumption; the public facilities and street lighting carbon emission data include the public facilities and street lighting carbon emission data after increasing the percentage of lighting using LED and renewable electricity;

[0017] Calculate carbon offset data: including carbon offset data after increasing vegetation coverage of forests, green spaces and tree planting;

[0018] Calculates carbon avoided figures: including carbon avoided figures from increasing renewable energy use for water heating within a set range, increasing renewable energy use for electricity supply, adding off-site renewable energy production projects and programs from a baseline, purchasing renewable energy certificates to offset fossil fuel electricity use, and purchasing voluntary certified carbon reduction credits;

[0019] The set scopes include scope 1, scope 2 and scope 3; scope 1 refers to direct emissions from sources controlled by the entity within its physical boundaries and removal of carbon sinks, including combustion of fuel in stationary sources to produce electricity, heat or steam, and fuel combustion in mobile sources controlled by the emitting entity and used within the physical boundaries of the emitting entity’s related parties or the emitting entity; scope 2 refers to indirect energy emissions from purchased electricity and gas; scope 3 refers to other indirect emissions, including greenhouse gas emissions generated in landfills due to waste treatment by the emitting entity, greenhouse gas emissions caused by electricity and energy used for water supply, drainage, and wastewater treatment, embodied carbon in buildings generated from the extraction and production, transportation, construction and demolition of building materials, and carbon emissions from cross-border passenger and cargo transportation and travel generated by the operation of the emitting entity;

[0020] Finally, the following formula is used to calculate the carbon emission data of the target area:

[0021] C=∑C adi ×c efi

[0022] Where C is the carbon emission data calculation result of the target area; C adi is the calculated carbon emission data, carbon offset data and carbon avoidance data, where carbon emission data is positive data, carbon offset data and carbon avoidance data are negative data; c efi is the carbon emission factor;

[0023] The calculated carbon emission data accounting results of the target area provide basic data for building a carbon emission prediction model in step S4.

[0024] Step S3, based on the carbon emission data information obtained in step S1, constructs a carbon emission prediction model for the target area, specifically including the following steps:

[0025] In the carbon emission data information obtained in step S1, select relevant variables;

[0026] According to the results of multicollinearity diagnosis, a multiple regression analysis model was constructed:

[0027] Model 1:

[0028] Model 2: Where C1 is the output result of the first multivariate regression analysis model constructed, corresponding to the logarithmic form of the nonlinear model, that is, Model 1; C2 is the output result of the second multivariate regression analysis model constructed, corresponding to the linear model, that is, Model 2; α0 is the set constant; α i is the regression coefficient of the model; Z i represents the carbon emission data of building operation energy, carbon emission data of embodied energy in buildings, carbon emission data of mobility and transportation, carbon emission data of solid waste, carbon emission data of water resources, carbon emission data of wastewater and sewage treatment, carbon emission data of industrial and manufacturing activities, as well as the vegetation coverage rate of public facilities and street lighting, forests, green spaces and tree planting, and the level of renewable energy use; γ is the error residual term of the model;

[0029] The carbon emission prediction model for the target area takes the optimal results of Model 1 and Model 2;

[0030] Finally, based on the constructed multivariate regression model, the carbon emission prediction model of the target area was obtained.

[0031] The carbon emission prediction model constructed in step S3 described in step S4 predicts the carbon emission of the target area based on the set scenario information and the calculation result obtained in step S2, and specifically includes the following steps:

[0032] Select carbon emission scenarios and carbon reduction scenarios for the forecast year;

[0033] Set carbon emission reduction data based on the carbon emission scenario data and carbon emission reduction scenario data of the selected year;

[0034] The carbon emission prediction model constructed in step S3 is used to predict the carbon emission data of the target area in the selected prediction year.

[0035] The carbon emission prediction method further includes the following steps:

[0036] S5. Evaluate the carbon emission prediction results obtained in step S4 and generate an evaluation report.

[0037] Step S5 of evaluating the carbon emission prediction result obtained in step S4 and generating an evaluation report specifically includes the following steps:

[0038] Evaluate the carbon emission forecast results obtained in step S4 in the areas of operational and embedded emissions, real estate financial portfolio, property management and green leasing, certified carbon emission reduction, and spatial planning, urban planning and urban design indicators;

[0039] Assessments in the areas of operations and embedded emissions, including assessments of total carbon emissions for gross annual, monthly and weekly operations, assessments of carbon emissions by source, assessments of carbon emissions per unit of site area, assessments of carbon emissions per unit of gross floor area of ​​buildings, assessments of carbon emissions per capita, assessments of carbon emissions per unit of rental income, assessments of carbon emissions per unit of land price, assessments of carbon emissions per unit of gross floor area of ​​buildings and assessments of carbon emissions per unit of construction cost;

[0040] Assessments in the area of ​​real estate finance portfolios, including assessments of carbon emissions in the real estate portfolio generated through lending or investment, assessments of carbon emissions removed or offset in the real estate portfolio through lending or investment, and assessments of carbon emissions avoided in the real estate portfolio through lending or investment;

[0041] Property management and green lease area assessments, including annual and monthly assessments of the property's total energy consumption, water consumption and waste, assessments of carbon emissions from the building's mechanical and electrical systems, and annual and monthly assessments of the leased area's total energy consumption, water consumption and waste;

[0042] Certified carbon emission reduction area assessments, including assessments of energy efficiency measures in non-conventional renewable biomass for thermal applications, assessments of energy efficiency and renewable energy measures in new residential buildings, and assessments of energy efficiency and fuel switching measures in buildings;

[0043] Assessment of indicators in the field of spatial planning, urban planning and urban design, including assessment of activity data per unit of land use area for different statutory land use types, assessment of energy consumption per unit of land use area for different statutory land use types, assessment of carbon emissions per unit of land use area for different statutory land use types, assessment of carbon sequestration per unit of land use area, assessment of carbon sequestration per unit of green area, assessment of carbon emissions per unit of road length, assessment of renewable energy use per unit of land use area, assessment of total annual carbon emissions in the planning area and assessment of total expected life cycle carbon emissions in the planning area during the planning period;

[0044] Generate an assessment report based on the assessment results.

[0045] The present invention also provides a system for implementing the carbon emission prediction method, including a data acquisition module, a carbon emission accounting module, a model construction module, a carbon emission prediction module and a carbon emission evaluation module; the data acquisition module, the carbon emission accounting module, the model construction module, the carbon emission prediction module and the carbon emission evaluation module are connected in series in sequence; the data acquisition module is used to acquire the carbon emission data information of the target area, and upload the data information to the carbon emission accounting module; the carbon emission accounting module is used to perform carbon emission data accounting of the target area based on the received data information, the acquired carbon emission data information, and the carbon emission offset data, and upload the data information to the model construction module; the model construction module is used to construct a carbon emission prediction model for the target area based on the received data information and the acquired carbon emission data information, and upload the data information to the carbon emission prediction module; the carbon emission prediction module is used to perform carbon emission prediction for the target area based on the received data information, the constructed carbon emission prediction model, the set scenario information and the obtained calculation results, and upload the data information to the carbon emission evaluation module; the carbon emission evaluation module is used to evaluate the obtained carbon emission prediction results based on the received data information and generate an evaluation report.

[0046] The carbon emission prediction method and system provided by the present invention, through the acquisition of historical data, calculation of carbon emission data, construction and evaluation of prediction models, not only realizes the prediction of carbon emissions of the target object throughout its life cycle, but also has higher reliability and better accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The figure is a schematic diagram of the method flow of the present invention.

[0048] Figure 2 Schematic diagram of the functional modules of the system of the present invention. DETAILED DESCRIPTION

[0049] like Figure 1 The method flow diagram of the method of the present invention is shown as follows: The carbon emission prediction method disclosed in the present invention comprises the following steps:

[0050] S1. Obtaining carbon emission data information of the target area; specifically including the following steps:

[0051] Obtain carbon emission data information of carbon emission entities within a set historical period;

[0052] Acquire scale factor data, weather factor data, economic factor data, user behavior factor data, physical site and building form factor data, and green building and energy factor data; the scale factor data include population size data, number of households data, number of tenants data, site area data, and total building floor area data; the weather factor data include humidity data, average temperature data, rainfall data, heat wave data, blizzard data, air quality data, and extreme weather data; the economic factor data include occupancy rate data, vacancy rate data, rental level data, property valuation data, building construction cost data, industrial output, and tenant or user business nature data; the user The user behavior factor data include traffic flow data, pedestrian flow data, operation time data, space or facility use pattern data and user socio-economic characteristics data; the physical place and building form factor data include street network density data, open space coverage or ratio data, open space ventilation duct data, building window orientation data, building facade window ratio data, building form and shape data, building height data, building material data, building age data and parking space data; the green building and energy factor data include building certification level data, building energy-saving design standard data, renewable or clean energy use percentage data, energy use time series pattern data and historical trend data;

[0053] S2. Calculate the carbon emission data of the target area based on the carbon emission offset data according to the carbon emission data information obtained in step S1; specifically, the following steps are included:

[0054] Calculate carbon emission data: including building operation energy carbon emission data, building embodied energy carbon emission data, mobility and transportation carbon emission data, solid waste carbon emission data, water resource carbon emission data, wastewater and sewage treatment carbon emission data, industrial and manufacturing activities carbon emission data, and public facilities and street lighting carbon emission data;

[0055] The aforementioned building operation energy carbon emission data include the building operation energy carbon emission data after the statutory building design standards are supplemented with building energy conservation requirements (including 10% additional energy conservation requirements, 20% additional energy conservation requirements, 30% additional energy conservation requirements and 40% additional energy conservation requirements); the aforementioned building embodied energy carbon emission data include the building embodied energy carbon emission data after the statutory reference value standards are supplemented with additional carbon reduction (including 10% additional carbon reduction, 20% additional carbon reduction and 30% additional carbon reduction); the aforementioned mobility and transportation carbon emission data include the use of transportation Mobility and transportation carbon emissions data after measures such as access-oriented urban form development (including the percentage of building floor area within 500 meters exceeding 100%), high accessibility to public transportation services, distance from the site to the nearest public transportation service (including 100 meters), increasing the proportion of green travel by residents or visitors (including 70%, 60% and 50%) and increasing the percentage of electric vehicle travel (including 20%, 15%, 10% and 5%); solid waste carbon emissions data, including increasing the proportion of waste conversion into energy (including 100%) The carbon emission data of solid waste mentioned above include the carbon emission data of water resources after increasing water resource protection and reducing the baseline per capita water consumption rate (including a reduction of 5%, 10%, 15% and 20%); the carbon emission data of wastewater and sewage treatment include the carbon emission data of wastewater after increasing the proportion of sewage conversion into energy (including 100%, 90%, 80%, 50% or 20%) and reducing the volume of wastewater (including a reduction of 5%, 10%, 15% or 20%). 10%, 15% or 20% by volume) and sewage treatment carbon emissions data; the industrial and manufacturing activities carbon emissions data, including the increase in the service industry GDP proportion (including 10%, 20%, 30% and 40%) from the baseline, the reduction of energy consumption per unit of industrial GDP and added value from the baseline, the reduction of industrial process energy consumption savings and the increase of on-site clean or renewable energy consumption; the public facilities and street lighting carbon emissions data, including the public facilities and street lighting carbon emissions data after increasing the percentage of lighting using LED and renewable electricity;

[0056] Calculation of carbon offset data: including carbon offset data after increasing vegetation coverage of forests, green spaces and tree planting (including an increase of 40%, 45%, 50%, 55% or 60%);

[0057] Calculates carbon avoided figures: including carbon avoided figures from increasing renewable energy use for water heating within a set range, increasing renewable energy use for electricity supply, adding off-site renewable energy production projects and programs from a baseline, purchasing renewable energy certificates to offset fossil fuel electricity use, and purchasing voluntary certified carbon reduction credits;

[0058] The set scope includes scope 1, scope 2 and scope 3; scope 1 is direct emissions from sources controlled by the entity within the physical boundary and removal of carbon sinks, including burning fuel in fixed sources (excluding electronic equipment) to generate electricity, heat or steam (for example: power generators, boilers, gas stoves, etc.), and fuel combustion of mobile sources (such as mobile energy of motor vehicles) controlled by the emitting entity and used within the related parties of the emitting entity or the physical boundary of the emitting entity (for example, local shuttle services are within the physical boundary of the emitting entity); scope 2 is indirect energy emissions generated by purchased electricity and gas, which are generated by equipment controlled by the emitting entity or its physical Scope 3 is other indirect emissions, including greenhouse gas emissions generated in landfills due to waste treatment by the emitting entity, greenhouse gas emissions caused by electricity and energy used for water supply, drainage, and wastewater treatment, carbon embodied in buildings generated from the extraction and production, transportation, construction, and demolition of building materials, and carbon emissions generated by cross-border passenger and freight transportation and trips generated by the emitting entity's operations. These trips are within or outside the physical boundary of the emitting entity, and the emitting entity is the starting point or end point of the trip (for example, employees commuting to get off work, residents commuting to get off work, leisure shopping, visiting companies within the emitting entity, etc., excluding transportation that does not start or end at the emitting entity);

[0059] Finally, the following formula is used to calculate the carbon emission data of the target area:

[0060] C=∑C adi ×c efi

[0061] Where C is the carbon emission data calculation result of the target area; C adi is the calculated carbon emission data, carbon offset data and carbon avoidance data, where carbon emission data is positive data, carbon offset data and carbon avoidance data are negative data; c efi is the carbon emission factor;

[0062] The calculated carbon emission data accounting results of the target area provide basic data for building a carbon emission prediction model in step S4;

[0063] S3. Based on the carbon emission data information obtained in step S1, a carbon emission prediction model for the target area is constructed; specifically, the following steps are included:

[0064] In the carbon emission data information obtained in step S1, select relevant variables;

[0065] According to the results of multicollinearity diagnosis, a multiple regression analysis model was constructed:

[0066] Model 1:

[0067] Model 2: Where C1 is the output result of the first multivariate regression analysis model constructed, corresponding to the logarithmic form of the nonlinear model, that is, Model 1; C2 is the output result of the second multivariate regression analysis model constructed, corresponding to the linear model, that is, Model 2; α0 is the set constant; α i is the regression coefficient of the model; Z i represents the carbon emission data of building operation energy, carbon emission data of embodied energy in buildings, carbon emission data of mobility and transportation, carbon emission data of solid waste, carbon emission data of water resources, carbon emission data of wastewater and sewage treatment, carbon emission data of industrial and manufacturing activities, as well as the vegetation coverage rate of public facilities and street lighting, forests, green spaces and tree planting, and the level of renewable energy use; γ is the error residual term of the model;

[0068] The carbon emission prediction model for the target area takes the optimal results of Model 1 and Model 2;

[0069] Finally, the carbon emission prediction model of the target area was obtained based on the constructed multivariate regression model;

[0070] S4. Using the carbon emission prediction model constructed in step S3, based on the set scenario information and the calculation results obtained in step S2, the carbon emission prediction of the target area is performed; specifically, the steps include:

[0071] Select carbon emission scenarios and carbon reduction scenarios for the forecast year;

[0072] Set carbon emission reduction data based on the carbon emission scenario data and carbon emission reduction scenario data of the selected year;

[0073] Using the carbon emission prediction model constructed in step S3, predicting carbon emission data of the target area in the selected prediction year;

[0074] S5. Evaluate the carbon emission prediction results obtained in step S4 and generate an evaluation report; generating the evaluation report specifically includes the following steps:

[0075] Evaluate the carbon emission forecast results obtained in step S4 in the areas of operational and embedded emissions, real estate financial portfolio, property management and green leasing, certified carbon emission reduction, and spatial planning, urban planning and urban design indicators;

[0076] Assessments in the areas of operations and embedded emissions, including assessments of total carbon emissions for gross annual, monthly and weekly operations, assessments of carbon emissions by source (generated, removed or offset, avoided emissions), assessments of carbon emissions per unit of site area, assessments of carbon emissions per unit of gross floor area of ​​the building, assessments of carbon emissions per capita (employees, residents, visitors), assessments of carbon emissions per unit of rental income, assessments of carbon emissions per unit of land price, assessments of carbon emissions per unit of gross floor area of ​​the building and assessments of carbon emissions per unit of construction cost;

[0077] Assessments in the area of ​​real estate finance portfolios, including assessments of carbon emissions in the real estate portfolio generated through lending or investment, assessments of carbon emissions removed or offset in the real estate portfolio through lending or investment, and assessments of carbon emissions avoided in the real estate portfolio through lending or investment;

[0078] In practice, in the assessment of carbon emissions from a real estate portfolio generated through loans or investments, total portfolio emissions are derived from total annual and monthly emissions, and financing emissions are calculated by adjusting total portfolio emissions by an attribution factor that reflects the outstanding percentage of the loan or investment in terms of total value;

[0079] Assessment of property management and green leasing areas, including assessment of total annual and monthly energy consumption, water consumption and waste volume of the property, assessment of carbon emissions from building mechanical and electrical systems (including air conditioning, ventilation, lighting, elevators and mobile equipment, electrical and electronic equipment, etc.), and assessment of total annual and monthly energy consumption, water consumption and waste volume of the leased area;

[0080] Assessment of the field of certified carbon emission reduction, including energy efficiency measures in non-conventional renewable biomass for thermal applications (including distribution of high-efficiency light bulbs to households; solar water heating systems; use of biogas / biomass thermal applications by households / small users; efficiency of household refrigerators and HFC-134a recovery; demand-side activities for efficient outdoor and street lighting technologies; solar cookers for households; promotion of efficient household appliances, etc.), assessment of energy efficiency and renewable energy measures in new residential buildings (including energy efficiency technologies and fuel switching in new buildings; mechanical energy with or without electricity by users; thermal energy production with or without electricity; user conversion from non-renewable biomass thermal applications; user electricity generation; electricity saving by replacing energy-efficient chillers; avoiding methane emissions through composting; electricity saving by replacing energy-efficient chillers, etc.), and assessment of energy efficiency and fuel switching measures in buildings (including for efficient lighting technologies; and demand-side energy efficiency activities for specific technologies, etc.);

[0081] Assessments in the areas of spatial planning, urban planning and urban design indicators, including assessments of activity data per unit of land use area for different statutory land use types (including residential, commercial, industrial, public facilities, transportation, roads, etc.), assessments of energy consumption per unit of land use area for different statutory land use types, assessments of carbon emissions per unit of land use area for different statutory land use types, assessments of carbon sequestration per unit of land use area, assessments of carbon sequestration per unit of green space area, assessments of carbon emissions per unit of road length, assessments of renewable energy use per unit of land use area, assessments of total annual carbon emissions in the planning area and assessments of total expected life cycle carbon emissions in the planning area during the planning period;

[0082] Generate an assessment report based on the assessment results.

[0083] like Figure 2 The functional module schematic diagram of the system of the present invention is shown as follows: the system for implementing the carbon emission prediction method disclosed in the present invention comprises a data acquisition module, a carbon emission accounting module, a model construction module, a carbon emission prediction module and a carbon emission evaluation module; the data acquisition module, the carbon emission accounting module, the model construction module, the carbon emission prediction module and the carbon emission evaluation module are connected in series in sequence; the data acquisition module is used to acquire the carbon emission data information of the target area, and upload the data information to the carbon emission accounting module; the carbon emission accounting module is used to perform carbon emission data accounting of the target area based on the received data information, the acquired carbon emission data information and the carbon emission offset data, and upload the data information to the model construction module; the model construction module is used to construct a carbon emission prediction model for the target area based on the received data information and the acquired carbon emission data information, and upload the data information to the carbon emission prediction module; the carbon emission prediction module is used to perform carbon emission prediction for the target area based on the received data information, the constructed carbon emission prediction model and the set scenario information, and upload the data information to the carbon emission evaluation module; the carbon emission evaluation module is used to evaluate the obtained carbon emission prediction results based on the received data information and generate an evaluation report.

Claims

1. A carbon emission prediction method, comprising the following steps: S1. Obtain carbon emission data information of the target area; S2. Calculate the carbon emission data of the target area based on the carbon emission offset data according to the carbon emission data information obtained in step S1; S3. Constructing a carbon emission prediction model for the target area based on the carbon emission data information obtained in step S1; S4. Using the carbon emission prediction model constructed in step S3, based on the set scenario information and the calculation results obtained in step S2, the carbon emission prediction of the target area is performed.

2. The carbon emission prediction method according to claim 1, characterized in that The step S1 of obtaining the carbon emission data information of the target area specifically includes the following steps: Obtain carbon emission data information of carbon emission entities within a set historical period; Acquire scale factor data, weather factor data, economic factor data, user behavior factor data, physical site and building form factor data, and green building and energy factor data; the scale factor data include population size data, number of households data, number of tenants data, site area data, and total building floor area data; the weather factor data include humidity data, average temperature data, rainfall data, heat wave data, blizzard data, air quality data, and extreme weather data; the economic factor data include occupancy rate data, vacancy rate data, rental level data, property valuation data, building construction cost data, industrial output, and tenant or user business nature data; the user The user behavior factor data include traffic flow data, pedestrian flow data, operation time data, space or facility usage pattern data and user socio-economic characteristics data; the physical place and building form factor data include street network density data, open space coverage or ratio data, open space ventilation duct data, building window orientation data, building facade window ratio data, building form and shape data, building height data, building material data, building age data and parking space data; the green building and energy factor data include building certification level data, building energy-saving design standard data, renewable or clean energy use percentage data, energy use time series pattern data and historical trend data.

3. The carbon emission prediction method according to claim 2, characterized in that Step S2, based on the carbon emission data information obtained in step S1 and based on the carbon emission offset data, calculates the carbon emission data of the target area, and specifically includes the following steps: Calculate carbon emission data: including building operation energy carbon emission data, building embodied energy carbon emission data, mobility and transportation carbon emission data, solid waste carbon emission data, water resource carbon emission data, wastewater and sewage treatment carbon emission data, industrial and manufacturing activities carbon emission data, and public facilities and street lighting carbon emission data; The aforementioned building operation energy carbon emission data include the building operation energy carbon emission data after the statutory building design standards have been added with additional building energy conservation requirements; the aforementioned building embodied energy carbon emission data include the building embodied energy carbon emission data after the statutory reference value standards have been additionally reduced; the aforementioned mobility and transportation carbon emission data include the mobility and transportation carbon emission data after measures such as the adoption of transportation-oriented urban form development, highly accessible public transportation services, the distance from the site to the nearest public transportation service, increasing the proportion of green travel by residents or visitors, and increasing the percentage of electric vehicle travel; the aforementioned solid waste carbon emission data include the solid waste carbon emission data after increasing the proportion of waste converted into energy and reducing the volume of solid waste ; The water resources carbon emission data mentioned include the water resources carbon emission data after increasing water resources protection and reducing the baseline per capita water consumption rate; the wastewater and sewage treatment carbon emission data mentioned include the wastewater and sewage treatment carbon emission data after increasing the proportion of sewage converted into energy and reducing the wastewater volume; the industrial and manufacturing activities carbon emission data include the industrial and manufacturing activities carbon emission data after increasing the proportion of GDP in the service industry from the baseline, reducing the energy consumption per unit of industrial GDP and added value from the baseline, reducing the energy consumption savings in industrial processes and increasing the on-site clean or renewable energy consumption; the public facilities and street lighting carbon emission data include the public facilities and street lighting carbon emission data after increasing the percentage of lighting using LED and renewable electricity; Calculate carbon offset data: including carbon offset data after increasing vegetation coverage of forests, green spaces and tree planting; Calculates carbon avoided figures: including carbon avoided figures from increasing renewable energy use for water heating within a set range, increasing renewable energy use for electricity supply, adding off-site renewable energy production projects and programs from a baseline, purchasing renewable energy certificates to offset fossil fuel electricity use, and purchasing voluntary certified carbon reduction credits; The set scopes include scope 1, scope 2 and scope 3; scope 1 refers to direct emissions from sources controlled by the entity within its physical boundaries and removal of carbon sinks, including combustion of fuel in stationary sources to produce electricity, heat or steam, and fuel combustion in mobile sources controlled by the emitting entity and used within the physical boundaries of the emitting entity’s related parties or the emitting entity; scope 2 refers to indirect energy emissions from purchased electricity and gas; scope 3 refers to other indirect emissions, including greenhouse gas emissions generated in landfills due to waste treatment by the emitting entity, greenhouse gas emissions caused by electricity and energy used for water supply, drainage, and wastewater treatment, embodied carbon in buildings generated from the extraction and production, transportation, construction and demolition of building materials, and carbon emissions from cross-border passenger and cargo transportation and travel generated by the operation of the emitting entity; Finally, the following formula is used to calculate the carbon emission data of the target area: C=∑C adi ×c efi Where C is the carbon emission data calculation result of the target area; C adi is the calculated carbon emission data, carbon offset data and carbon avoidance data, where carbon emission data is positive data, carbon offset data and carbon avoidance data are negative data; c efi is the carbon emission factor; The calculated carbon emission data accounting results of the target area provide basic data for building a carbon emission prediction model in step S4.

4. The carbon emission prediction method according to claim 3, characterized in that Step S3, based on the carbon emission data information obtained in step S1, constructs a carbon emission prediction model for the target area, specifically including the following steps: In the carbon emission data information obtained in step S1, select relevant variables; According to the results of multicollinearity diagnosis, a multiple regression analysis model was constructed: Model 1: Model 2: Where C1 is the output result of the first multivariate regression analysis model constructed, corresponding to the logarithmic form of the nonlinear model, that is, Model 1; C2 is the output result of the second multivariate regression analysis model constructed, corresponding to the linear model, that is, Model 2; α0 is the set constant; α i is the regression coefficient of the model; Z i represents the carbon emission data of building operation energy, carbon emission data of embodied energy in buildings, carbon emission data of mobility and transportation, carbon emission data of solid waste, carbon emission data of water resources, carbon emission data of wastewater and sewage treatment, carbon emission data of industrial and manufacturing activities, as well as the vegetation coverage rate of public facilities and street lighting, forests, green spaces and tree planting, and the level of renewable energy use; γ is the error residual term of the model; The carbon emission prediction model for the target area takes the optimal results of Model 1 and Model 2; Finally, based on the constructed multivariate regression model, the carbon emission prediction model of the target area was obtained.

5. The carbon emission prediction method according to claim 4, characterized in that The carbon emission prediction model constructed in step S3 described in step S4 performs carbon emission prediction for the target area based on the set scenario information, specifically including the following steps: Select carbon emission scenarios and carbon reduction scenarios for the forecast year; Set carbon emission reduction data based on the carbon emission scenario data and carbon emission reduction scenario data of the selected year; The carbon emission prediction model constructed in step S3 is used to predict the carbon emission data of the target area in the selected prediction year.

6. The carbon emission prediction method according to any one of claims 1 to 4, characterized in that The carbon emission prediction method further includes the following steps: S5. Evaluate the carbon emission prediction results obtained in step S4 and generate an evaluation report.

7. The carbon emission prediction method according to claim 6, characterized in that Step S5 of evaluating the carbon emission prediction result obtained in step S4 and generating an evaluation report specifically includes the following steps: Evaluate the carbon emission forecast results obtained in step S4 in the areas of operational and embedded emissions, real estate financial portfolio, property management and green leasing, certified carbon emission reduction, and spatial planning, urban planning and urban design indicators; Assessments in the areas of operations and embedded emissions, including assessments of total carbon emissions for gross annual, monthly and weekly operations, assessments of carbon emissions by source, assessments of carbon emissions per unit of site area, assessments of carbon emissions per unit of gross floor area of ​​buildings, assessments of carbon emissions per capita, assessments of carbon emissions per unit of rental income, assessments of carbon emissions per unit of land price, assessments of carbon emissions per unit of gross floor area of ​​buildings and assessments of carbon emissions per unit of construction cost; Assessments in the area of ​​real estate finance portfolios, including assessments of carbon emissions in the real estate portfolio generated through lending or investment, assessments of carbon emissions removed or offset in the real estate portfolio through lending or investment, and assessments of carbon emissions avoided in the real estate portfolio through lending or investment; Property management and green lease area assessments, including annual and monthly assessments of the property's total energy consumption, water consumption and waste, assessments of carbon emissions from the building's mechanical and electrical systems, and annual and monthly assessments of the leased area's total energy consumption, water consumption and waste; Certified carbon emission reduction area assessments, including assessments of energy efficiency measures in non-conventional renewable biomass for thermal applications, assessments of energy efficiency and renewable energy measures in new residential buildings, and assessments of energy efficiency and fuel switching measures in buildings; Assessment of indicators in the field of spatial planning, urban planning and urban design, including assessment of activity data per unit of land use area for different statutory land use types, assessment of energy consumption per unit of land use area for different statutory land use types, assessment of carbon emissions per unit of land use area for different statutory land use types, assessment of carbon sequestration per unit of land use area, assessment of carbon sequestration per unit of green area, assessment of carbon emissions per unit of road length, assessment of renewable energy use per unit of land use area, assessment of total annual carbon emissions in the planning area and assessment of total expected life cycle carbon emissions in the planning area during the planning period; Generate an assessment report based on the assessment results.

8. A system for implementing the carbon emission prediction method according to any one of claims 1 to 7, characterized in that It includes a data acquisition module, a carbon emission accounting module, a model building module, a carbon emission prediction module and a carbon emission evaluation module; the data acquisition module, the carbon emission accounting module, the model building module, the carbon emission prediction module and the carbon emission evaluation module are connected in series in sequence; the data acquisition module is used to acquire the carbon emission data information of the target area, and upload the data information to the carbon emission accounting module; the carbon emission accounting module is used to calculate the carbon emission data of the target area based on the received data information, the acquired carbon emission data information and the carbon emission offset data, and upload the data information to the model building module; the model building module is used to construct a carbon emission prediction model for the target area based on the received data information and the acquired carbon emission data information, and upload the data information to the carbon emission prediction module; the carbon emission prediction module is used to predict the carbon emissions of the target area based on the received data information, the set scenario information and the obtained calculation results, using the constructed carbon emission prediction model, and upload the data information to the carbon emission evaluation module; the carbon emission evaluation module is used to evaluate the obtained carbon emission prediction results based on the received data information and generate an evaluation report.

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