Greenhouse gas and atmospheric pollutant collaborative emission reduction benefit evaluation method based on energy activity horizontal dynamic coupling

Abstract

The invention discloses a greenhouse gas and atmospheric pollutant collaborative emission reduction benefit evaluation method based on energy activity horizontal dynamic coupling, and relates to the technical field of environmental protection and climate change coping intersection. According to the method, real-time accurate accounting of the emission amount is realized by constructing a high-temporal-spatial-resolution energy activity dynamic matrix and a dynamic emission factor model library. And designing different policy scenes, simulating a linkage effect by applying an integrated energy system model, and calculating the dynamic emission reduction amount. The carbon emission reduction monetization value and the health benefit value based on the atmospheric diffusion model are weighted and integrated, and the total policy cost is combined, so that a cooperative emission reduction comprehensive benefit index (SRI) is created for the first time as a core evaluation index. According to the method, the problems of static accounting distortion and multi-dimensional benefit splitting in a traditional method are solved, a complete technical path from dynamic process simulation to comprehensive benefit quantitative comparison is realized, and a scientific decision basis is provided for cost-benefit optimization of a cooperative control policy.

Description

Technical Field

[0001] This invention relates to the field of interdisciplinary technology of environmental protection and climate change response, and in particular to a method for evaluating the synergistic emission reduction benefits of greenhouse gases and air pollutants based on the dynamic coupling of energy activity levels. Background Technology

[0002] Climate change and air pollution have become major global challenges, both primarily stemming from fossil fuel consumption. Therefore, promoting the coordinated control of greenhouse gases (GHGs) and air pollutants (APs) is an efficient path to achieving environmental improvement and climate goals, and a core strategy for current national environmental governance. In this context, scientifically and accurately quantifying and assessing the benefits of coordinated emission reduction is crucial for formulating cost-optimal and highly effective environmental policies.

[0003] However, existing assessment methods have significant limitations in supporting refined decision-making. First, the accounting basis is static. Mainstream methods generally use fixed emission factors based on annual averages or design conditions, coupled with dynamic changes in energy activity levels. In reality, generator load rates, traffic congestion, and renewable energy output all exhibit significant time-varying characteristics, and their corresponding emission intensities (such as carbon emissions per unit of electricity generated and pollutant emissions per unit of driving distance) are not constant. This "static factor matching dynamic activity" model leads to distorted emission inventories, making it difficult to accurately reflect real-time emission reduction effects, and especially failing to capture the true environmental impact of transient processes such as peak shaving and start-up / shutdown.

[0004] Secondly, benefit assessments are fragmented. Existing studies typically calculate and present climate benefits (measured in carbon dioxide equivalents) separately from environmental and health benefits (measured in terms of changes in pollutant concentrations or health end-stage events). Due to the different dimensions, policymakers find it difficult to make a direct cost-benefit comparison between "reducing one ton of carbon dioxide" and "avoiding one premature death." The lack of a comprehensive indicator that integrates multiple benefits and has clear economic significance makes multi-objective optimization decision-making lack a unified quantitative benchmark.

[0005] Furthermore, there is a lack of systemic feedback. Traditional assessments often treat energy activity levels as exogenous givens, neglecting the fact that emission reduction policies themselves profoundly alter energy production and consumption patterns through market signals and technological substitution. For example, rising carbon prices not only directly affect emission costs but also incentivize power generation restructuring and energy efficiency improvements, thereby dynamically changing the activity levels and emission factors of the entire system. This dynamic feedback mechanism between "policy-activity-emissions" is not fully reflected in the existing assessment framework.

[0006] In summary, developing an evaluation method that can couple the dynamics of energy activities, quantify the comprehensive benefits of synergy, and embed a system feedback mechanism is an urgent need to overcome current technological bottlenecks and achieve precise environmental governance. This invention aims to solve the above-mentioned problems. Summary of the Invention

[0007] The purpose of this invention is to overcome the aforementioned shortcomings in the existing technology and provide a method for evaluating the synergistic emission reduction benefits of greenhouse gases and air pollutants based on the dynamic coupling of energy activity levels. This method constructs a dynamic emission accounting system with high spatiotemporal resolution, establishes a dynamic factor model that reflects the real-time state of the system, and pioneers the standardized indicator of "Synergistic Emission Reduction Comprehensive Benefit Index (SRI)," which unifies climate benefits and environmental health benefits within a cost-benefit analysis framework. This provides reliable quantitative decision support for formulating precise, efficient, and economical synergistic control policies for greenhouse gases and air pollutants.

[0008] To achieve the above-mentioned objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for evaluating the synergistic emission reduction benefits of greenhouse gases and air pollutants based on the dynamic coupling of energy activity levels, comprising the following steps: S1. Acquire and process high spatiotemporal resolution energy activity data of the target area during the assessment period, and construct the dynamic matrix A(x,y,t) of energy activity level, where x,y are spatial coordinates and t is time; S2. Establish a dynamic emission factor mapping model library that includes dynamic carbon emission factor functions and dynamic pollution emission factor functions, wherein the dynamic emission factor functions are configured such that their output factor values ​​depend on the real-time input of the corresponding energy activity level or operating condition parameters. S3. Input the energy activity level dynamic matrix A(x,y,t) into the dynamic emission factor mapping model library for mapping calculation to generate a high spatiotemporal resolution greenhouse gas dynamic emission inventory E. GHG (x,y,t) and the dynamic emission inventory of air pollutants E AP (x,y,t); S4. Design at least one coordinated control policy scenario, simulate the dynamic matrix A'(x,y,t) of energy activity levels under the scenario using an integrated energy system model, and calculate the scenario emission inventory E' based on this matrix. GHG and E' AP Then, the dynamic emission reduction ΔE relative to the baseline scenario is calculated. GHG (x,y,t) and ΔE AP (x,y,t); S5. Based on the aforementioned dynamic emission reduction, quantify the monetized climate benefits of greenhouse gas emission reduction. GHGAnd the monetization of health and environmental benefits of air pollutant emission reduction B AP And combined with the scenario total cost (Cost), the comprehensive benefit index (SRI) for synergistic emission reduction is calculated, where SRI = α * (B GHG / Cost)+β*(B AP / Cost), where α and β are weighting coefficients and α+β=1; S6. Based on the SRI (Synergistic Emission Reduction Comprehensive Benefit Index) and its sub-benefits, conduct scenario comparison and policy optimization analysis, and output decision support information.

[0009] Furthermore, in step S1, the high spatiotemporal resolution energy activity data includes at least one of the following: generator output data at the hourly or more refined time scale, fossil fuel consumption data by industry / technology, traffic flow data, renewable energy power generation data, and cross-regional power flow data; the construction of the dynamic matrix of energy activity level includes coupling the energy activity data with geographic information system spatial data and assigning it spatial grid attributes.

[0010] Furthermore, the dynamic carbon emission factor function EF GHG ,i(load i (t) is constructed for a specific device or process i, whose carbon emission factor varies with the load rate of that device or process at time t. i (t) is a continuous or piecewise function of variation; the dynamic pollution emission factor function EF AP , The data is configured to be specific to emission source j, and its atmospheric pollutant emission factor varies with the operating parameters of that emission source at time t. The changing function, wherein the operating condition parameters include at least one of combustion temperature, load rate, pollution control equipment operation status, and fuel quality parameters.

[0011] Furthermore, in step S3, the emission E of the k-th emission source located within the (x,y) spatial grid at time t is calculated. k The formula for (x,y,t) is: E k (x,y,t)=A k (x,y,t)×EF k (A k (x,y,t),t), where EF k To match activity level A k A dynamic emission factor related to time t; the dynamic emission inventory is generated by integrating the emissions of all source categories, spatial grids, and time steps.

[0012] Furthermore, in step S4, the parameters of the coordinated control policy scenario include at least one of carbon price, renewable energy installed capacity target, electricity substitution rate, end-of-pipe treatment technology upgrade plan, and industrial capacity adjustment plan; the integrated energy system model is used to simulate the chain changes in the activity levels of each link of energy production, conversion, transmission and consumption under the drive of the policy parameters.

[0013] Furthermore, in step S5, the monetized health and environmental benefits E are quantified. AP include: S51, Dynamic emission reduction of air pollutants ΔE AP Inputting (x,y,t) into the atmospheric chemical transport model, the pollutant concentration change field ΔC(x,y,t) is simulated. S52. Based on population spatial distribution data, baseline incidence data, and exposure-response coefficients, use a concentration-response model to quantify the changes in the number of health endpoints (including premature death, hospitalization, and illness) caused by concentration changes. S53. Using the willingness-to-pay method or the human capital method, the change in the number of health terminals is monetized to obtain the value of health benefits.

[0014] Furthermore, in step S5, the values ​​of the weighting coefficients α and β are determined by the expert Delphi method, the analytic hierarchy process, or a preset rule based on policy objectives, in order to reflect the relative importance of climate benefits and local environmental benefits in the comprehensive assessment.

[0015] Secondly, the present invention provides a collaborative emission reduction benefit evaluation system for implementing the above method, comprising: a data acquisition and processing module configured to execute the above step S1 and construct the dynamic matrix A(x,y,t) of the energy activity level; and a dynamic factor model library module for storing and managing the dynamic emission factor mapping model library established in the above step S2. The dynamic emission accounting engine is configured to execute step S3, call the dynamic factor model library module to perform calculations, and generate a dynamic emission inventory. The scenario simulation and analysis module is configured to execute step S4, integrate the integrated energy system model, and calculate dynamic emission reductions under different scenarios. The comprehensive benefit quantification module is configured to perform step S5 and calculate the monetized climate benefit B. GHG Monetization of Health and Environmental Benefits B AP and the aforementioned synergistic emission reduction comprehensive benefit index SRI; The decision support output module is configured to perform step S6 to generate a visual report that includes a benefit-cost comparison and spatiotemporal benefit distribution.

[0016] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0017] Fourthly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the steps of the method described above.

[0018] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. Significantly improved the spatiotemporal accuracy and realism of the assessment. By constructing a high-resolution dynamic activity matrix and a dynamic emission factor library with real-time operating condition response, a fundamental shift from "average static accounting" to "process dynamic tracking" has been achieved. This enables the accurate capture of the real emissions and emission reduction benefits of transient processes such as peak shaving start-up and shutdown, and intermittent renewable energy access, making the assessment results more consistent with the actual operating status of the energy system; 2. It innovatively achieves unified quantification and intuitive comparison of synergistic benefits. The proposed "Synergistic Emission Reduction Comprehensive Benefit Index (SRI)" creatively integrates climate benefits (monetized carbon emission reduction value) and local environmental health benefits (monetized health gains and losses) within the same cost-efficiency framework, solving the decision-making problem of difficulty in directly weighing benefits of different dimensions, and providing clear and comparable core indicators for multi-objective optimization; 3. A complete policy-system dynamic feedback mechanism is embedded. The method incorporates an integrated energy system model into the assessment chain, which can simulate the chain changes in the level and structure of energy activities under policy intervention and their secondary impact on emission intensity. It realizes a closed-loop dynamic assessment of "policy-driven → activity response → emission change → benefit feedback", making policy scenario analysis more forward-looking and systematic. 4. Enhanced the targeting and operability of decision support. The final output of high spatiotemporal resolution benefit distribution maps, cost-benefit curves, and SRI rankings can clearly identify hotspots, key periods, and priority sectors for emission reduction benefits, supporting the formulation of differentiated and refined regional and industry policies, and significantly improving the cost-effectiveness of environmental governance measures. Attached Figure Description

[0019] Figure 1 This is an overall flowchart of the method of the present invention; Figure 2 This is a schematic diagram of dynamic emission factor mapping (showing the curve of emission factor change with equipment load rate); Figure 3 This diagram illustrates the calculation of the Synergistic Emission Reduction Comprehensive Benefit Index (SRI) and scenario comparison. Detailed Implementation

[0020] The present invention will be further described in detail below with reference to experimental examples and specific embodiments. However, this should not be construed as limiting the scope of the above-mentioned subject matter of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention. Example 1

[0021] This invention provides a method for evaluating the synergistic emission reduction benefits of greenhouse gases and air pollutants based on the dynamic coupling of energy activity levels, comprising the following steps: S1. Acquire and process high spatiotemporal resolution energy activity data of the target area during the assessment period, and construct the dynamic matrix A(x,y,t) of energy activity level, where x,y are spatial coordinates and t is time; S2. Establish a dynamic emission factor mapping model library that includes dynamic carbon emission factor functions and dynamic pollution emission factor functions, wherein the dynamic emission factor functions are configured such that their output factor values ​​depend on the real-time input of the corresponding energy activity level or operating condition parameters. S3. Input the energy activity level dynamic matrix A(x,y,t) into the dynamic emission factor mapping model library for mapping calculation to generate a high spatiotemporal resolution greenhouse gas dynamic emission inventory E. GHG (x,y,t) and the dynamic emission inventory of air pollutants E AP (x,y,t); S4. Design at least one coordinated control policy scenario, simulate the dynamic matrix A'(x,y,t) of energy activity levels under the scenario using an integrated energy system model, and calculate the scenario emission inventory E' based on this matrix. GHG and E' AP Then, the dynamic emission reduction ΔE relative to the baseline scenario is calculated. GHG (x,y,t) and ΔE AP (x,y,t); S5. Based on the aforementioned dynamic emission reduction, quantify the monetized climate benefits of greenhouse gas emission reduction. GHG And the monetization of health and environmental benefits of air pollutant emission reduction B AP And combined with the scenario total cost (Cost), the comprehensive benefit index (SRI) for synergistic emission reduction is calculated, where SRI = α * (B GHG / Cost)+β*(B AP / Cost), where α and β are weighting coefficients and α+β=1; S6. Based on the SRI (Synergistic Emission Reduction Comprehensive Benefit Index) and its sub-benefits, conduct scenario comparison and policy optimization analysis, and output decision support information.

[0022] Specifically, such as Figure 1As shown, the overall implementation process of this invention takes a provincial region (such as "a province in East China") as the evaluation object, the evaluation period is a future planning year (such as 2025), and the time resolution is 1 hour.

[0023] Step S1 (Data Acquisition and Processing Module): The system automatically collects real-time and statistical data from the provincial power dispatch center, statistics department, and traffic management department. Through data cleaning and spatial allocation algorithms (such as allocating the province's total power generation to corresponding grids based on generator unit locations), it generates data such as... Figure 2 (a) shows a dynamic heat map of energy activity levels, which displays the power generation activity intensity matrix A for each grid in the province at 12:00 noon on a certain day (t=12). k,(x,y),12 .

[0024] Step S2 (Dynamic Factor Model Library Module): In the system's backend database, the following modules are pre-configured for the main 600MW supercritical coal-fired power units in the province: Figure 2 (b) shows the dynamic carbon emission factor function curve. The curve shows that the CO2 emission factor per unit of electricity generated by the unit is about 8% higher at 50% load than at 100% load. A dynamic model was also configured for the unit to show the NOx emission factor as a function of load and the inlet temperature of the SCR denitrification equipment.

[0025] Step S3 (Dynamic Emissions Calculation Engine): The engine reads the unit load data of a power plant at time t=12 (e.g., load rate of 70%), retrieves the corresponding dynamic factor from the model library (e.g., CO2 factor is 0.82tCO2 / MWh), calculates the CO2 emissions of the power plant at that time, and adds it to the emissions inventory of its grid. 12. The calculation is performed iteratively across all sources, all grids, and all time points to ultimately generate a dynamic emissions inventory for the entire province for the whole year.

[0026] Step S4 (Scenario Simulation and Analysis Module): Design the "Baseline Scenario" (continuing current policies) and the "Enhanced PV Scenario" (adding 10GW of distributed PV). Input the scenario parameters into the integrated energy system model. The model simulates that under the "Enhanced PV Scenario," a surge in midday PV power generation leads to a general decrease in coal-fired power load to 55%, and updates the activity level matrix to A′. k,(x,y),t Re-execute S3 to obtain a new emissions inventory, and subtract it from the baseline scenario to obtain a dynamic spatial distribution map of emissions reductions. ,(x,y),12, intuitively shows that pollutant emission reduction is mainly concentrated in the coal-fired power plant cluster area.

[0027] Step S5 (Comprehensive Benefit Quantification Module): Total CO2 emission reductions Multiply by the province's average trading price in the carbon market (e.g., 60 yuan / ton) to obtain the climate benefit B. GHG ; Pollutant emission reduction inventory ΔE AP Input the atmospheric diffusion model and simulate PM2.5. 2.5 Annual average concentration decline distribution map. Combining population raster data and epidemiological parameters, a Poisson regression model was used to calculate the number of preventable premature deaths. Based on the province's statistical willingness-to-pay study (e.g., value-based life (VSL) of 1 million yuan), the health benefits B were quantified. AP ; Calculate the total incremental cost (including photovoltaic investment, operation and maintenance, and grid transformation costs) for the "enhanced photovoltaic scenario"; Let the policy preference weights be set as α = 0.4 (climate-oriented) and β = 0.6 (health-oriented), and substitute them into the formula SRI = 0.4 * (B GHG / Cost)+0.6*(B AP The comprehensive benefit index of synergistic emission reduction in this scenario is calculated using / Cost.

[0028] Step S6 (Decision Support Output Module): The system generates outputs such as... Figure 3 The radar chart and SRI bar chart show a comparison of the scenarios. The radar chart shows that the "enhanced PV scenario" scores significantly higher than the baseline scenario in terms of health benefits and air quality improvement. The bar chart visually compares the SRI values ​​of different scenarios, showing that the "enhanced PV scenario" has the highest SRI, indicating that it offers the greatest overall benefit per unit cost and is the optimal recommended option. The report also points out that the benefits are highest during midday in summer and recommends implementing demand-side response measures to maximize benefits. Example 2

[0029] Furthermore, in step S1, the high spatiotemporal resolution energy activity data includes at least one of the following: generator output data at the hourly or more refined time scale, fossil fuel consumption data by industry / technology, traffic flow data, renewable energy power generation data, and cross-regional power flow data; the construction of the dynamic matrix of energy activity level includes coupling the energy activity data with geographic information system spatial data and assigning it spatial grid attributes.

[0030] Specifically, in combination Figure 1In the S1 stage, the specific data construction method is as follows: Hourly power generation output data exported from the SCADA system of the provincial power company is obtained. Each data point includes a timestamp, power plant ID, unit ID, and output value. Online monitoring data of key pollution sources, including hourly fuel consumption, is obtained from the environmental protection department. Using checkpoint data from the transportation department, license plate recognition and trajectory tracking are combined with a vehicle environmental label database to estimate the hourly traffic flow and vehicle type composition on major roads. All data are spatially correlated with a pre-divided 1km×1km GIS grid through their latitude and longitude coordinates. For example, all activity data of a thermal power plant located within grid (102,58) are merged into matrix element A. 火电,(102,58),t middle. Example 3

[0031] Furthermore, the dynamic carbon emission factor function EF GHG ,i(load i (t) is constructed for a specific device or process i, whose carbon emission factor varies with the load rate of that device or process at time t. i (t) is a continuous or piecewise function of variation; the dynamic pollution emission factor function The data is configured to be specific to emission source j, and its atmospheric pollutant emission factor varies with the operating parameters of that emission source at time t. The changing function, wherein the operating condition parameters include at least one of combustion temperature, load rate, pollution control equipment operation status, and fuel quality parameters.

[0032] like Figure 2 As shown in (b), the process of establishing the dynamic factor function is as follows: For a certain type of gas turbine, by reviewing one year's historical data from its Continuous Emission Monitoring System (CEMS), the hourly NOx emission concentration (mg / m³) is determined. 3 ) and emission flow (m 3 The emission factor for that hour is obtained by converting the emission rate (kg / h) to the emission rate (kg / h) and then dividing by the hourly power generation (MWh). A scatter plot is then created with the unit load rate (%) on the x-axis and the emission factor (kg / MWh) on the y-axis. The function curve E is obtained by fitting the curve using the nonlinear least squares method. NOx (Load) = a*Load 2 +b*Load+c. This function indicates that in the 40%-80% load range, the emission factor increases sharply as the load decreases, and this function is stored in the dynamic factor model library. Example 4

[0033] Furthermore, in step S3, the emission E of the k-th emission source located within the (x,y) spatial grid at time t is calculated. k The formula for (x,y,t) is: Ek (x,y,t)=A k (x,y,t)×EF k (A k (x,y,t),t), where EF k To match activity level A k A dynamic emission factor related to time t; the dynamic emission inventory is generated by integrating the emissions of all source categories, spatial grids, and time steps.

[0034] Taking the calculation of SO2 emissions from a specific boiler in an industrial park at time t as an example, the activity level data A of the grid containing the boiler is extracted from the matrix. 燃煤锅炉 (x0, y0),t represents the coal consumption (tons) at that moment. The corresponding dynamic emission factor function is called from the model library. Where St is the current furnace temperature (obtained from the DCS system), and S% is the sulfur content of the current batch of coal (correlated with fuel warehousing detection data). Substituting the real-time parameters St = 950℃ and S% = 0.8% into the function, the real-time emission factor EF = 1.2 kg SO2 / ton of coal is calculated under this operating condition. Final emissions... Example 5

[0035] Furthermore, in step S4, the parameters of the coordinated control policy scenario include at least one of carbon price, renewable energy installed capacity target, electricity substitution rate, end-of-pipe treatment technology upgrade plan, and industrial capacity adjustment plan; the integrated energy system model is used to simulate the chain changes in the activity levels of each link of energy production, conversion, transmission and consumption under the drive of the policy parameters.

[0036] Design a scenario of "full electrification of transportation". In the integrated energy system model, set parameters: by 2030, the electrification rates of private cars, buses, and logistics vehicles will reach 40%, 100%, and 30%, respectively. Based on a traffic activity baseline, vehicle energy consumption model, and charging behavior model, the model simulates the future hourly increase in charging load curve ΔLoad. ev,t This load curve will be superimposed on the total grid load. Based on the new load curve, the power system optimization submodule will reschedule generator units (prioritizing clean energy and increasing the peak-shaving depth of coal-fired power plants), thereby outputting a new dynamic matrix A′ of energy activity levels that includes electric vehicle charging activities. k,(x,y),t This matrix reflects the cascading effects of transportation policies on power generation. Example 6

[0037] Furthermore, in step S5, the monetized health and environmental benefits E are quantified. AP include: S51, Dynamic emission reduction of air pollutants ΔE AP Inputting (x,y,t) into the atmospheric chemical transport model, the pollutant concentration change field ΔC(x,y,t) is simulated. S52. Based on population spatial distribution data, baseline incidence data, and exposure-response coefficients, use a concentration-response model to quantify the changes in the number of health endpoints (including premature death, hospitalization, and illness) caused by concentration changes. S53. Using the willingness-to-pay method or the human capital method, the change in the number of health terminals is monetized to obtain the value of health benefits.

[0038] Specifically, in combination Figure 1 The health benefit quantification sub-process of S5: Atmospheric simulation: ΔE PM2.5 and ΔE NOX (As a precursor) input into the WRF-CMAQ atmospheric chemical transport coupling model. After the model runs, the output is the PM2.5 concentration across the entire province. 2.5 The annual average concentration change field ΔC(x,y) shows that the average concentration in urban areas decreased by 3.8 μg / m³. 3 ; Exposure-response calculation: The exposure-response coefficient from the Global Burden of Disease (GBD) study, i.e., PM2.5, was used. 2.5 For every 10 μg / m³ increase in long-term exposure concentration 3 The risk of death from cardiopulmonary diseases increases by X%. The formula is: Δmortality = baseline mortality rate * exposed population * (1 - exp(-β * ΔC)), where β is a coefficient. Substituting the baseline mortality rate and population raster data by age and disease for each province and city, the number of preventable premature deaths in the province is calculated to be ΔM = 3200 people / year. Monetization: The health value calculated based on the willingness-to-pay approach in the "National Health Account Research" published by the National Bureau of Statistics is used for monetization. At the same time, the market approach was used to assess the increased crop yields resulting from reduced acid rain. Example 7

[0039] Furthermore, in step S5, the values ​​of the weighting coefficients α and β are determined by the expert Delphi method, the analytic hierarchy process, or a preset rule based on policy objectives, in order to reflect the relative importance of climate benefits and local environmental benefits in the comprehensive assessment.

[0040] Specifically, in the province's environmental planning decision-making, the Analytic Hierarchy Process (AHP) was used to determine the weights α and β. First, ten experts in the fields of environment, climate, economy, and public health were invited to conduct pairwise comparisons and scoring of the importance of the two criteria: "climate change mitigation" and "local public health." Second, a judgment matrix was constructed, and after a consistency test, the weight vectors for each criterion were calculated. For example, experts generally believed that in the current phase of prominent air quality problems, "local public health" was slightly more important than "climate change mitigation," ultimately resulting in α = 0.45 and β = 0.55. These weights were incorporated into the province's assessment system configuration file and used for SRI calculations across all scenarios, ensuring that the assessment results are consistent with local strategic priorities. Example 8

[0041] This invention provides a synergistic emission reduction benefit evaluation system for implementing the above-described method, comprising: The data acquisition and processing module is configured to execute step S1 and construct the dynamic matrix A(x,y,t) of the energy activity level; the dynamic factor model library module stores and manages the dynamic emission factor mapping model library established in step S2. The dynamic emission accounting engine is configured to execute step S3, call the dynamic factor model library module to perform calculations, and generate a dynamic emission inventory. The scenario simulation and analysis module is configured to execute step S4, integrate the integrated energy system model, and calculate dynamic emission reductions under different scenarios. The comprehensive benefit quantification module is configured to perform step S5 and calculate the monetized climate benefit B. GHG Monetization of Health and Environmental Benefits B AP and the aforementioned synergistic emission reduction comprehensive benefit index SRI; The decision support output module is configured to perform step S6 to generate a visual report that includes a benefit-cost comparison and spatiotemporal benefit distribution.

[0042] Specifically, such as Figure 1 As shown overall, the collaborative emission reduction benefit assessment system in this embodiment is deployed on the cloud server of the provincial environmental monitoring center and adopts a microservice architecture: Data Acquisition and Processing Module: As an independent service, it periodically extracts data from external APIs and databases, executes the process of Example 2, and outputs standardized A. k,(x,y),t Data flow; The dynamic factor model library module is a relational database and model file server that stores various function curves and model parameters established as in Example 3. Dynamic Emissions Calculation Engine: This is a high-performance computing service that receives data streams and calls model libraries, performing parallel calculations according to the formula in Example 4; Scenario Simulation and Analysis Module: Integrates open-source energy system models (such as OSeMOSYS) and provides a web interface for users to set scenario parameters and start simulations as in Example 5; Comprehensive benefit quantification module: It encapsulates the atmospheric model call interface and health assessment algorithm package, and automatically executes the quantification process as described in Example 6; Decision Support Output Module: This is a visual web application that reads results from the database and generates outputs as shown in Example 1. Figure 3 The interactive charts and downloadable reports are shown. Users can log in to the system through a browser to complete the entire evaluation process, from data uploading and scenario design to report generation. Example 9

[0043] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0044] Fourthly, the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the steps of the method described above.

[0045] The computer program of this invention is stored in a computer-readable storage medium (such as an SSD hard disk array) of the system server described in Embodiment 8. When the program is executed by a processor (such as the CPU of a server cluster), it controls the various modules to work together and automatically implements all the method steps of Embodiments 1-7 described above. The electronic device refers to the hardware system that includes a server, storage device, and network device.

[0046] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating the synergistic emission reduction benefits of greenhouse gases and air pollutants based on the dynamic coupling of energy activity levels, characterized in that, Includes the following steps: S1. Acquire and process high spatiotemporal resolution energy activity data of the target area during the assessment period, and construct the dynamic matrix A(x,y,t) of energy activity level, where x,y are spatial coordinates and t is time; S2. Establish a dynamic emission factor mapping model library that includes dynamic carbon emission factor functions and dynamic pollution emission factor functions, wherein the dynamic emission factor functions are configured such that their output factor values ​​depend on the real-time input of the corresponding energy activity level or operating condition parameters. S3. Input the energy activity level dynamic matrix A(x,y,t) into the dynamic emission factor mapping model library for mapping calculation to generate a high spatiotemporal resolution greenhouse gas dynamic emission inventory E. GHG (x,y,t) and the dynamic emission inventory of air pollutants E AP (x,y,t); S4. Design at least one coordinated control policy scenario, simulate the dynamic matrix A'(x,y,t) of energy activity levels under the scenario using an integrated energy system model, and calculate the scenario emission inventory E' based on this matrix. GHG and E' AP Then, the dynamic emission reduction ΔE relative to the baseline scenario is calculated. GHG (x,y,t) and ΔE AP (x,y,t); S5. Based on the aforementioned dynamic emission reduction, quantify the monetized climate benefits of greenhouse gas emission reduction. GHG And the monetization of health and environmental benefits of air pollutant emission reduction B AP And combined with the scenario total cost (Cost), the comprehensive benefit index (SRI) for synergistic emission reduction is calculated, where SRI = α * (B GHG / Cost)+β*(B AP / Cost), where α and β are weighting coefficients and α+β=1; S6. Based on the SRI (Synergistic Emission Reduction Comprehensive Benefit Index) and its sub-benefits, conduct scenario comparison and policy optimization analysis, and output decision support information.

2. The method according to claim 1, characterized in that, In step S1, the high spatiotemporal resolution energy activity data includes at least one of the following: generator output data at hourly or more refined time scales, fossil fuel consumption data by industry / technology, traffic flow data, renewable energy power generation data, and cross-regional power flow data; the construction of the dynamic matrix of energy activity level includes coupling the energy activity data with geographic information system spatial data and assigning it spatial grid attributes.

3. The method according to claim 1, characterized in that, In step S2, the dynamic carbon emission factor function EF GHG ,i(load i (t) is constructed for a specific device or process i, whose carbon emission factor varies with the load rate of that device or process at time t. i (t) is a continuous or piecewise function of variation; the dynamic pollution emission factor function The data is configured to be specific to emission source j, and its atmospheric pollutant emission factor varies with the operating parameters of that emission source at time t. The changing function, wherein the operating condition parameters include at least one of combustion temperature, load rate, pollution control equipment operation status, and fuel quality parameters.

4. The method according to claim 1, characterized in that, In step S3, the emission E of the k-th emission source located within the (x,y) spatial grid at time t is calculated. k The formula for (x,y,t) is: E k (x,y,t)=A k (x,y,t)×EF k (A k (x,y,t),t), where EF k To match activity level A k A dynamic emission factor related to time t; the dynamic emission inventory is generated by integrating the emissions of all source categories, spatial grids, and time steps.

5. The method according to claim 1, characterized in that, In step S4, the parameters of the coordinated control policy scenario include at least one of carbon price, renewable energy installed capacity target, electricity substitution rate, end-of-pipe treatment technology upgrade plan and industrial capacity adjustment plan; the integrated energy system model is used to simulate the chain changes in the activity levels of each link of energy production, conversion, transmission and consumption under the drive of the policy parameters.

6. The method according to claim 1, characterized in that, In step S5, the monetized health and environmental benefits E are quantified. AP include: S51, Dynamic emission reduction of air pollutants ΔE AP Inputting (x,y,t) into the atmospheric chemical transport model, the pollutant concentration change field ΔC(x,y,t) is simulated. S52. Based on population spatial distribution data, baseline incidence data, and exposure-response coefficients, use a concentration-response model to quantify the changes in the number of health endpoints (including premature death, hospitalization, and illness) caused by concentration changes. S53. Using the willingness-to-pay method or the human capital method, the change in the number of health terminals is monetized to obtain the value of health benefits.

7. The method according to claim 1, characterized in that, In step S5, the values ​​of the weighting coefficients α and β are determined by the expert Delphi method, the analytic hierarchy process, or a preset rule based on policy objectives, in order to reflect the relative importance of climate benefits and local environmental benefits in the comprehensive assessment.

8. A system for evaluating the synergistic emission reduction benefits of implementing the method according to any one of claims 1 to 7, characterized in that, include: The data acquisition and processing module is configured to execute step S1 to construct the dynamic matrix A(x,y,t) of the energy activity level; The dynamic factor model library module stores and manages the dynamic emission factor mapping model library established in step S2. The dynamic emission accounting engine is configured to execute step S3, call the dynamic factor model library module to perform calculations, and generate a dynamic emission inventory. The scenario simulation and analysis module is configured to execute step S4, integrate the integrated energy system model, and calculate dynamic emission reductions under different scenarios. The comprehensive benefit quantification module is configured to perform step S5 and calculate the monetized climate benefit B. GHG Monetization of Health and Environmental Benefits B AP and the aforementioned synergistic emission reduction comprehensive benefit index SRI; The decision support output module is configured to perform step S6 to generate a visual report that includes a benefit-cost comparison and spatiotemporal benefit distribution.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

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