A method and system for quantifying the effect of climate policy carbon emissions and simulating and evaluating heat wave risk

Abstract

The present application belongs to the technical field of carbon emission policy simulation, and relates to a method and system for quantifying the influence of carbon emission of climate policy and simulating and evaluating heat wave risk. The present application can automatically analyze policy text to generate quantitative parameters, correct model deviation by combining measured carbon emission data, and efficiently complete multi-emission reduction scenario comparison and calculation. Based on local historical samples, the present application fits exclusive carbon-temperature response rules, accurately deduces extreme climate by fusing regional inherent climate base, distinguishes normal illness and newly increased illness caused by extreme weather, quantifies health benefits corresponding to different policies, and supports the development of low-carbon policies and public health planning. The present application is equipped with a whole-process data traceability storage mechanism, and the operation process can be completely reproduced to meet the audit requirements. Each link does not depend on special models, but relies on general tools, which can be applied to various regional evaluation scenarios.

Description

Technical Field

[0001] This invention relates to the field of carbon emission policy simulation technology, and specifically discloses a method and system for quantifying the effectiveness of climate policy carbon emission impact and simulating and assessing heat wave risk. Background Technology

[0002] Under the dual carbon targets, a series of emission policies, including national-level carbon emission control, energy structure optimization, industrial low-carbon transformation, and ecological carbon sink enhancement, have been continuously implemented. The implementation intensity, control scope, and technical constraints of different policy scenarios directly drive the dynamic evolution of the energy consumption structure and total carbon emissions of the whole society, thereby affecting the process of regional temperature rise and the frequency and intensity of climate events such as extreme heat waves and extreme rainfall, ultimately producing differentiated health risk effects on the population.

[0003] Existing carbon, climate, and health assessment tools operate on a fragmented technological chain, capable of calculating carbon emissions or roughly estimating regional warming independently. They fail to provide an integrated assessment of the extreme weather events and their associated health impacts caused by emission reduction policies. Traditional solutions require manual input of policy constraints, involve significant multi-scenario modeling, and lack the ability to correct model errors using local measured data, resulting in low simulation accuracy. Carbon-temperature conversions often employ universal fixed coefficients without incorporating localized patterns based on regional samples, leading to poor alignment between climate simulations and extreme weather identification. Conventional health assessments only count the total number of morbidities, failing to account for the additional health burden caused by extreme weather events and hindering guidance on healthcare resource allocation. Furthermore, existing tools lack a unified data traceability and management mechanism, resulting in mixed storage of multiple versions of data, making the computation process untraceable and difficult to reproduce, and posing significant challenges to compliance audits. Most systems are also tied to dedicated models, leading to high implementation costs and limited applicability to various scenarios. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for quantifying the effectiveness of climate policy carbon emission impacts and simulating and assessing heat wave risks, in order to solve the aforementioned problems in the prior art; the specific solution is as follows: In a first aspect, the present invention provides a method for quantifying the effectiveness of climate policy carbon emission impacts and simulating and assessing heat wave risks, including: Collect and standardize preprocessed carbon emission policy text data, annual time-series carbon emission statistics data, temperature observation data, and multi-source meteorological data; Semantic parsing and parameter transformation are performed on carbon emission policy text data to generate standardized parameter files; Carbon emission scenario calculations are performed based on standardized parameter files to obtain initial carbon emission calculation results. Dynamic deviation corrections are applied to the initial carbon emission calculation results, and carbon emission data corresponding to different carbon emission policies are output. Based on the correlation and variation patterns between annual time-series carbon emission statistics and temperature observation data, a carbon-temperature correlation dataset is constructed. By integrating carbon emission data, carbon-temperature correlation datasets, and multi-source meteorological data, a comprehensive climate dataset is generated. Based on the comprehensive climate dataset, extreme climate simulations are carried out, and daily extreme climate prediction data are output. Based on daily extreme climate forecast data, quantitative indicators of population health risks under corresponding carbon emission policies are established.

[0005] Preferably, the semantic parsing and parameterization transformation of the carbon emission policy text data includes: Extract target indicators from carbon emission policy text data, convert the target indicators into numerical parameters, and encapsulate them into a standardized parameter file.

[0006] Preferably, the carbon emission scenario calculation based on the standardized parameter file includes: Based on standardized parameter files and regional socio-economic data, the energy demand of each industry is calculated. The carbon emissions of each industry are then calculated using the corresponding carbon emission accounting benchmark coefficients. The initial carbon emission calculation results are obtained by summing up the carbon emissions of each industry.

[0007] Preferably, the dynamic deviation correction of the initial carbon emission calculation results includes: Obtain measured carbon emission data, compare the measured carbon emission data with the initial carbon emission calculation result to obtain the deviation value, and dynamically correct the initial carbon emission calculation result based on the deviation value to obtain the corrected carbon emission data.

[0008] Preferably, the construction of the carbon-temperature correlation dataset includes: The annual time-series carbon emission statistics and temperature observation data are matched and calibrated in terms of time and space. Carbon emission samples and temperature samples of the same time period and the same region are paired one by one to generate paired samples. The quantitative correlation between carbon emission change and temperature change was obtained by fitting paired samples. The paired samples and the correlation were integrated to generate a carbon-temperature correlation dataset.

[0009] Preferably, the generation of the comprehensive climate dataset includes: Based on the aforementioned carbon-temperature correlation dataset and carbon emission data corresponding to different carbon emission policies, and by overlaying regional topography and annual baseline meteorological data, the medium- and long-term meteorological changes under different carbon emission policies are deduced and integrated to generate a comprehensive climate dataset.

[0010] Preferably, the output daily extreme climate prediction data includes: The comprehensive climate dataset is broken down into daily meteorological sequences. Extreme climate events are identified based on preset extreme climate thresholds, and the regions and dates corresponding to the extreme events are marked. Daily extreme climate prediction data are then output.

[0011] Preferred indicators for quantifying population health risks under carbon emission policies include: By introducing regional population distribution and local disease baseline data, combined with daily extreme climate prediction data, the scale of additional illnesses and hospitalizations caused by extreme climate is calculated, and the population health risk indicators corresponding to each carbon emission policy are quantified.

[0012] Preferably, a hierarchical data matching mechanism is established, and a unique traceability identifier is configured and centrally stored for standardized parameter files, carbon emission data, carbon-temperature correlation datasets, comprehensive climate datasets, and daily extreme climate prediction data.

[0013] Secondly, the present invention also provides a system for quantifying the effectiveness of carbon emission impacts of climate policies and simulating and assessing heat wave risks, used to perform the steps of the method for quantifying the effectiveness of carbon emission impacts of climate policies and simulating and assessing heat wave risks described in any of the preceding claims, including: The data processing module is used to collect and standardize preprocessed carbon emission policy text data, annual time-series carbon emission statistics data, temperature observation data, and multi-source meteorological data; The policy parsing module is used to perform semantic parsing and parameterization transformation on carbon emission policy text data to generate standardized parameter files; The carbon emission simulation and correction module is used to perform carbon emission scenario calculations based on standardized parameter files, obtain initial carbon emission calculation results, perform dynamic deviation corrections on the initial carbon emission calculation results, and output carbon emission data corresponding to different carbon emission policies. The carbon-temperature correlation mining module is used to construct a carbon-temperature correlation dataset based on the correlation and change patterns between annual time-series carbon emission statistics and temperature observation data. The climate fusion simulation module is used to fuse carbon emission data, carbon-temperature correlation datasets, and multi-source meteorological data to generate a comprehensive climate dataset. Based on the comprehensive climate dataset, extreme climate simulations are carried out, and daily extreme climate prediction data are output. The health risk quantification module is used to quantify the health risk indicators of the population under the corresponding carbon emission policies based on daily extreme climate forecast data.

[0014] The technical solution of the present invention has at least the following advantages and beneficial effects: This invention establishes a complete calculation process encompassing data preprocessing, policy parameter conversion, carbon emission correction simulation, carbon-temperature pattern fitting, extreme climate event extrapolation, and health risk quantification. It also includes a comprehensive data traceability and storage mechanism, effectively addressing the shortcomings of existing technologies such as fragmented assessment chains, insufficient simulation accuracy, and lack of traceability of calculation results. This invention integrates policy, climate, and health data for assessment, automatically parsing various emission reduction policy texts to generate quantitative parameters. It corrects model biases using measured carbon emission data, efficiently completing comparative calculations across multiple policy scenarios. It fits specific carbon-temperature correlation patterns based on local historical data, integrating regional topography and seasonal climate baselines for refined simulations, accurately identifying extreme climate events under different policies. This invention distinguishes between pre-existing pre-existing conditions and the increase in new cases due to extreme weather, quantifying health burden based on susceptible population distribution, and intuitively demonstrating the health benefits of various emission reduction schemes, supporting the formulation of low-carbon policies and public health planning. Furthermore, this invention assigns unique traceability identifiers to all data throughout the process, supporting complete reproduction of the calculation process and meeting compliance audit requirements. Each stage is not bound to a dedicated model or database, allowing for implementation using general-purpose tools, adapting to various regional assessment scenarios, and offering greater versatility and practicality. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the system modules of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0017] like Figure 1 As shown, this invention discloses a method for quantifying the effectiveness of climate policy carbon emission impacts and simulating and assessing heat wave risks. The specific steps are as follows: S1: Collect and standardize preprocessed carbon emission policy text data, annual time-series carbon emission statistics data, temperature observation data, and multi-source meteorological data; In this embodiment, carbon emission policy text data refers to various written policy documents related to carbon emission control issued by the national and local governments at all levels, such as official documents like the Dual Carbon Action Plan, the Industrial Energy Consumption Control Regulations, and new energy development support policies. Annual time-series carbon emission statistics refer to the carbon emissions of different industries within the same region, calculated year by year. All values ​​have been verified by the industry and by officials, serving as a reliable historical sample for calculating the long-term patterns of carbon emissions and temperature changes. Temperature observation data comes from continuous monitoring records of various meteorological stations throughout the year, completely preserving relevant information on annual temperature fluctuations. Multi-source meteorological data refers to fixed climatic baseline information such as regional topography and elevation, annual average precipitation, annual wind speed changes, and seasonal climate rhythms.

[0018] This step involves collecting the four types of raw data in batches through a unified data interface. The raw data often suffers from missing or anomaly values, so standardized preprocessing, such as data cleaning, is required. The final output is a standardized basic dataset, providing compliant and usable data support for subsequent simulation operations throughout the entire process.

[0019] S2: Perform semantic parsing and parameterization on carbon emission policy text data to generate standardized parameter files; In this embodiment, semantic parsing and parameterization are the core steps in converting carbon emission policy text into calculable numerical parameters. Specifically, target control indicators are extracted from the preprocessed carbon emission policy text data. These target control indicators are categorized into several types: one is the energy consumption reduction target to be achieved by each industry (e.g., requiring an annual reduction of 3% in industrial energy consumption); another is the required replacement ratio of clean energy (e.g., the proportion of new energy installed capacity in the power industry reaching 50%); and the third is the capacity control limit corresponding to high-energy-consuming industries (e.g., setting production limits for high-energy-consuming industries to restrict the maximum number of products that enterprises can produce, controlling carbon emissions at the source). Other similar carbon emission reduction constraints are also included. For the extracted textual policy indicators, quantitative conversion is performed in conjunction with industry accounting benchmarks, transforming the vague policy descriptions into precise and calculable numerical parameters. Simultaneously, all numerical parameters are standardized in format and uniformly packaged to generate standardized parameter files that can be recognized by machines.

[0020] The text parsing and parameter conversion logic in this step can be implemented using an industry-standard text semantic processing model, or other similar tools with text indicator extraction and quantification capabilities can be selected. This invention is not limited to a single model to complete this step.

[0021] S3: Perform carbon emission scenario calculations based on standardized parameter files to obtain initial carbon emission calculation results, perform dynamic deviation corrections on the initial carbon emission calculation results, and output carbon emission data corresponding to different carbon emission policies. In this embodiment, carbon emission scenario calculation is a complete core calculation process. The process uses two types of basic data for calculation. One type is the regional economic development baseline, which is used to understand the original production scale of various industries in the area. The other type is the policy parameters generated above, which are used to superimpose various emission reduction and control requirements, and calculate the carbon emission scale under the corresponding policies for each industry, thereby simulating the carbon emission level of each industry after the implementation of different emission reduction policies.

[0022] The specific calculation steps are as follows: First, based on the regional socio-economic data, the baseline energy demand corresponding to the original production scale of each industry is calculated. Then, the energy conservation and carbon reduction constraint parameters for the corresponding industries are retrieved from the standardized parameter file, and the baseline energy demand is corrected according to policy requirements to calculate the actual energy demand of each industry under the corresponding emission reduction policy constraints. Subsequently, the pre-set carbon emission accounting benchmark coefficients for each industry are matched, and the carbon emissions are calculated separately for each industry. After summing all industry values, the initial carbon emission calculation results for the region are obtained. This initial result relies solely on the model to complete the policy scenario simulation, and there is a systematic deviation between it and the actual carbon emission status of the region. Therefore, this step introduces regional measured carbon emission data to carry out dynamic deviation correction. The actual monitored measured carbon emission data is matched and compared with the initial carbon emission calculation results output by the model in a spatiotemporal dimension to calculate the model simulation deviation value. Based on the deviation value, the initial carbon emission calculation results are dynamically calibrated and the deviation is compensated to eliminate the systematic error of the model and the scenario simulation error. Finally, multiple sets of high-precision and differentiated carbon emission simulation data corresponding to different carbon emission policies are output.

[0023] The industry carbon emission accounting and deviation correction logic in this step can be implemented using a general industry energy assessment model combined with an error correction algorithm. Alternatively, other similar tools with multi-scenario carbon emission simulation and adaptive deviation calibration capabilities can be selected. This invention is not limited to using a single model to complete the calculation in this step.

[0024] S4: Construct a carbon-temperature correlation dataset based on the correlation and change patterns between annual time-series carbon emission statistics and temperature observation data; In this embodiment, the carbon-temperature correlation dataset is the core foundational dataset for mining the quantitative response relationship between regional carbon emissions and temperature changes, and is used to support subsequent meteorological change projections under different carbon scenarios.

[0025] First, preprocessed annual time-series carbon emission statistics and synchronous historical temperature observation data are retrieved. Precise matching and calibration of these two types of heterogeneous data in both temporal and spatial dimensions are performed, eliminating invalid records with spatiotemporal misalignment and sample anomalies. Carbon emission samples and temperature samples from the same time period and region are precisely paired one-to-one, generating a large number of standardized carbon-temperature pairing samples. Based on all paired samples, mathematical fitting analysis is conducted to uncover the intrinsic correlation between regional carbon emission increases / decreases and temperature fluctuations, constructing a quantitative correlation law between carbon emission changes and temperature changes. Finally, all standardized paired samples and the fitted quantitative correlation law are integrated, uniformly organized, and stored to generate a complete regional carbon-temperature correlation dataset, realizing a quantifiable and computable mapping relationship between "carbon change" and "temperature change."

[0026] The spatiotemporal sample matching and carbon-temperature law fitting logic in this step can be implemented using a common industry time series correlation fitting model, or other similar tools with multi-scale variable correlation mining capabilities can be selected. This invention is not limited to a single model to complete this step of the analysis.

[0027] S5: Integrates carbon emission data, carbon-temperature correlation datasets, and multi-source meteorological data to generate a comprehensive climate dataset. Based on the comprehensive climate dataset, it conducts extreme climate simulations and outputs daily extreme climate prediction data. In this embodiment, the comprehensive climate dataset is a composite simulation dataset. The dataset integrates three types of content: carbon emission simulation results corresponding to different policies, the corresponding change patterns of carbon emissions and temperature, and the region's fixed topography and perennial climate basis. It no longer stores only carbon emission values, but can build a complete scenario of future climate change in the region.

[0028] This comprehensive climate dataset is built in three steps: The first step is to read the multi-policy carbon emission data that has been corrected in the previous steps, as well as the carbon-temperature correlation dataset that has been built in advance; then, topographic and annual baseline meteorological information from multi-source meteorological data are introduced to build the inherent basic climate background of the region itself.

[0029] The second step involves using the numerical correspondences determined in the carbon-temperature dataset to separately extrapolate the local climate changes over the next few years after the implementation of each emission reduction policy, and to calculate how much regional warming will result from the increase in carbon emissions under each policy.

[0030] The third step involves combining the warming changes caused by carbon emissions with the region's original, fixed basic climate background. This includes incorporating information such as topographical differences, seasonal variation patterns, and long-term climate fluctuations, ultimately generating a standardized, comprehensive climate dataset. This data can be retrieved separately for different regions, time periods, and emission reduction policy scenarios.

[0031] After obtaining the comprehensive climate dataset, the data is further refined: First, the climate data, initially measured in annual and quarterly units, is broken down into continuous daily meteorological records. Then, all daily meteorological records are screened against pre-defined extreme weather criteria to identify extreme weather events such as high temperatures and torrential rain. Simultaneously, the location, time, and weather type of each extreme weather event are recorded. Finally, complete and accurate daily extreme weather prediction data is output as the climate basis for subsequent calculations of population health risks.

[0032] The climate fusion and extreme event identification logic in this step can be implemented using industry-standard regional climate simulation models, or other similar tools with medium- and long-term meteorological projection and daily extreme weather screening capabilities can be selected. This invention is not limited to using a single model to complete this simulation.

[0033] S6: Based on daily extreme climate forecast data, quantify the health risk indicators of the population under the corresponding carbon emission policies.

[0034] In this embodiment, the quantification of population health risk is the final application output of this invention, achieving a quantitative assessment of the entire chain from carbon emission policy to extreme climate and population health risk. The system pre-imports two types of foundational data to support the assessment as the calculation basis. One type is regional population distribution data, recording the overall population size and population density of each area, while separately calculating the proportion and spatial distribution of climate-vulnerable groups such as the elderly, children, and patients with chronic diseases. The other type is local disease baseline data, recording the stable number of illnesses and hospitalizations in the region under conditions of no extreme heat or heavy rain, representing the basic health level unaffected by climate impacts.

[0035] Retrieve the daily extreme weather forecast data generated in the previous step. First, locate the specific date and region of each extreme weather event, and match it with the corresponding population distribution information to determine the total population affected by the extreme weather and the number of susceptible individuals. Compare this to the baseline hospitalization rate for common illnesses recorded in the local disease database to calculate the baseline number of patients in the region during the same period without extreme weather. Then, considering the degree of disease-inducing impact corresponding to extreme weather, calculate the additional number of illnesses and hospitalizations that will result from the extreme weather.

[0036] The calculated increase in illness and hospitalization is used as the core data to form standardized population health risk indicators. Different carbon emission policies correspond to extreme weather events of varying frequencies and intensities, ultimately generating their own independent risk indicators. Staff can then compare the health burdens corresponding to multiple policies and intuitively distinguish the differences in health benefits and risks brought about by various emission reduction plans.

[0037] The logic for calculating health losses due to extreme climate in this step can be implemented using a common industry-standard climate health correlation assessment model, or other similar tools with the ability to calculate the incremental burden of population health can be selected. This invention is not limited to a single model to complete the quantification of this step.

[0038] In one exemplary embodiment of the present invention, the entire calculation process is equipped with a hierarchical data matching and unified traceability storage mechanism. A unique traceability identifier is assigned to each type of core data generated throughout the process, covering all types of data, including standardized parameter files, policy-specific carbon emission data, carbon-temperature correlation datasets, comprehensive climate datasets, and daily extreme climate prediction data.

[0039] Each unique traceability identifier is linked to multi-dimensional supplementary information, including the data generation time, the matching carbon emission policy scenario, the model calculation version, the corresponding functional module generated, and the operator's record. Based on this identifier system, all intermediate data and output results generated throughout the entire process can be fully traced and reviewed, and subsequent verification and checking are also supported.

[0040] Simultaneously, all core datasets are classified and centrally stored, which can prevent different versions of data from being mixed and disordered, ensuring the standardized operation of the entire calculation process from the data base level, and also allows the original data to be retrieved repeatedly to reproduce the complete calculation process, meeting the needs of compliance auditing and document verification.

[0041] This data matching and traceability storage mechanism can be implemented using general-purpose distributed data management tools, and can be replaced by various database systems with data identification, classification storage, and log retention functions.

[0042] Secondly, this invention also provides a system for quantifying the effectiveness of climate policy carbon emission impacts and simulating and assessing heat wave risks. This system can execute the steps of the aforementioned method for quantifying the effectiveness of climate policy carbon emission impacts and simulating and assessing heat wave risks. The system internally comprises multiple functionally independent yet collaboratively linked modules, which work together to complete the entire process from data collection, policy analysis, carbon emission simulation, carbon temperature analysis, climate simulation to health risk quantification. Specifically: The data processing module is used to collect and standardize preprocessed carbon emission policy text data, annual time-series carbon emission statistics data, temperature observation data and multi-source meteorological data, complete data cleaning, spatiotemporal calibration, format normalization and anomaly removal, and output standardized multi-source basic datasets to provide a compliant data foundation for the entire process operation.

[0043] The policy analysis module is used to perform semantic analysis, indicator extraction, and parameterization transformation on carbon emission policy text data. It converts unstructured policy provisions into structured numerical parameters, encapsulates them to generate standardized parameter files, and provides policy constraint rules for multi-scenario carbon emission simulation.

[0044] The carbon emission simulation and correction module is used to retrieve standardized parameter files and regional socio-economic basic data to complete industry-specific carbon emission scenario calculations and summarize the initial carbon emission calculation results; it also combines regional measured carbon emission data to complete dynamic deviation correction and output high-precision, multi-scenario differentiated carbon emission data.

[0045] The carbon-temperature correlation mining module is used to complete the spatiotemporal matching and pairing of samples based on annual time-series carbon emission statistics and temperature observation data, mine the quantitative correlation patterns of carbon and temperature, construct and output a localized carbon-temperature correlation dataset, and realize the quantitative relationship modeling between carbon emissions and temperature changes.

[0046] The climate fusion simulation module is used to integrate multi-scenario carbon emission data, carbon-temperature correlation datasets, and multi-source meteorological baseline data to extrapolate medium- and long-term meteorological changes under different policies and generate a comprehensive climate dataset. It also performs daily detailed decomposition of climate data, identifies and marks extreme climate events, and outputs standardized daily extreme climate prediction data.

[0047] The health risk quantification module is used to quantitatively calculate the scale of additional illnesses and hospitalizations induced by extreme weather based on regional population distribution, local disease baseline data, and daily extreme weather forecast data. It quantifies the population health risk indicators corresponding to each carbon emission policy and completes the final policy health impact assessment output.

[0048] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for quantifying the effectiveness of climate policy carbon emissions and simulating and assessing heat wave risk, characterized in that, include: Collect and standardize preprocessed carbon emission policy text data, annual time-series carbon emission statistics data, temperature observation data, and multi-source meteorological data; Semantic parsing and parameter transformation are performed on carbon emission policy text data to generate standardized parameter files; Carbon emission scenario calculations are performed based on standardized parameter files to obtain initial carbon emission calculation results. Dynamic deviation corrections are applied to the initial carbon emission calculation results, and carbon emission data corresponding to different carbon emission policies are output. Based on the correlation and variation patterns between annual time-series carbon emission statistics and temperature observation data, a carbon-temperature correlation dataset is constructed. By integrating carbon emission data, carbon-temperature correlation datasets, and multi-source meteorological data, a comprehensive climate dataset is generated. Based on the comprehensive climate dataset, extreme climate simulations are carried out, and daily extreme climate prediction data are output. Based on daily extreme climate forecast data, quantitative indicators of population health risks under corresponding carbon emission policies are established.

2. The method for quantifying the carbon emission impact effectiveness of climate policies and simulating and assessing heat wave risks according to claim 1, characterized in that, The semantic parsing and parameterization of carbon emission policy text data includes: Extract target indicators from carbon emission policy text data, convert the target indicators into numerical parameters, and encapsulate them into a standardized parameter file.

3. The method for quantifying the carbon emission impact effectiveness of climate policies and simulating and assessing heat wave risks according to claim 2, characterized in that, The carbon emission scenario calculation based on the standardized parameter file includes: Based on standardized parameter files and regional socio-economic data, the energy demand of each industry is calculated. The carbon emissions of each industry are then calculated using the corresponding carbon emission accounting benchmark coefficients. The initial carbon emission calculation results are obtained by summing up the carbon emissions of each industry.

4. The method for quantifying the carbon emission impact effectiveness of climate policy and simulating and assessing heat wave risk according to claim 1, characterized in that, The dynamic deviation correction of the initial carbon emission calculation results includes: Obtain measured carbon emission data, compare the measured carbon emission data with the initial carbon emission calculation result to obtain the deviation value, and dynamically correct the initial carbon emission calculation result based on the deviation value to obtain the corrected carbon emission data.

5. The method for quantifying the effectiveness of climate policy carbon emissions and simulating and assessing heat wave risk according to claim 1, characterized in that, The construction of the carbon-temperature correlation dataset includes: The annual time-series carbon emission statistics and temperature observation data are matched and calibrated in terms of time and space. Carbon emission samples and temperature samples of the same time period and the same region are paired one by one to generate paired samples. The quantitative correlation between carbon emission change and temperature change was obtained by fitting paired samples. The paired samples and the correlation were integrated to generate a carbon-temperature correlation dataset.

6. The method for quantifying the carbon emission impact effectiveness of climate policy and simulating and assessing heat wave risk according to claim 1, characterized in that, The generated comprehensive climate dataset includes: Based on the aforementioned carbon-temperature correlation dataset and carbon emission data corresponding to different carbon emission policies, and by overlaying regional topography and annual baseline meteorological data, the medium- and long-term meteorological changes under different carbon emission policies are deduced and integrated to generate a comprehensive climate dataset.

7. The method for quantifying the effectiveness of climate policy carbon emissions and simulating and assessing heat wave risk according to claim 1, characterized in that, The output daily extreme climate prediction data includes: The comprehensive climate dataset is broken down into daily meteorological sequences. Extreme climate events are identified based on preset extreme climate thresholds, and the regions and dates corresponding to the extreme events are marked. Daily extreme climate prediction data are then output.

8. The method for quantifying the carbon emission impact effectiveness of climate policy and simulating and assessing heat wave risk according to claim 1, characterized in that, The indicators for quantifying the health risks to the population under the corresponding carbon emission policies include: By introducing regional population distribution and local disease baseline data, combined with daily extreme climate prediction data, the scale of additional illnesses and hospitalizations caused by extreme climate is calculated, and the population health risk indicators corresponding to various carbon emission policies are quantified.

9. The method for quantifying the carbon emission impact effectiveness of climate policy and simulating and assessing heat wave risk according to claim 1, characterized in that, Establish a hierarchical data matching mechanism, assign unique traceability identifiers to standardized parameter files, carbon emission data, carbon-temperature correlation datasets, comprehensive climate datasets, and daily extreme climate prediction data, and store them centrally.

10. A system for quantifying the effectiveness of climate policy carbon emissions and simulating and assessing heat wave risk, characterized in that, The steps for implementing the method for quantifying the effectiveness of climate policy carbon emission impacts and simulating and assessing heat wave risks as described in any one of claims 1-9 include: The data processing module is used to collect and standardize preprocessed carbon emission policy text data, annual time-series carbon emission statistics data, temperature observation data, and multi-source meteorological data; The policy parsing module is used to perform semantic parsing and parameterization transformation on carbon emission policy text data to generate standardized parameter files; The carbon emission simulation and correction module is used to perform carbon emission scenario calculations based on standardized parameter files, obtain initial carbon emission calculation results, perform dynamic deviation corrections on the initial carbon emission calculation results, and output carbon emission data corresponding to different carbon emission policies. The carbon-temperature correlation mining module is used to construct a carbon-temperature correlation dataset based on the correlation and change patterns between annual time-series carbon emission statistics and temperature observation data. The climate fusion simulation module is used to fuse carbon emission data, carbon-temperature correlation datasets, and multi-source meteorological data to generate a comprehensive climate dataset. Based on the comprehensive climate dataset, extreme climate simulations are carried out, and daily extreme climate prediction data are output. The health risk quantification module is used to quantify the health risk indicators of the population under the corresponding carbon emission policies based on daily extreme climate forecast data.

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