Urban heat exposure risk optimization method and system based on carbon emission dynamic regulation and control

Abstract

The invention discloses an urban thermal exposure risk optimization method and system based on carbon emission dynamic regulation and control, and belongs to the technical field of urban climate adaptation and low-carbon planning, and the method comprises the steps: obtaining real-time carbon emission data and a weather short-time forecast result; and inputting the obtained real-time carbon emission data and the weather short-time forecast result into the trained thermal exposure risk transfer model, outputting a future thermal exposure risk level, and combining an early warning threshold value to realize the optimization of the urban thermal exposure risk by adopting differential regulation and control strategy matching. The method solves the problems that a dynamic correlation mechanism of carbon emission and thermal exposure is not clear, a regulation strategy lacks multi-scene adaptability and real-time performance and the like, supports rapid early warning, accurate regulation and effect verification of thermal exposure risks, provides scientific support for urban thermal environment management, low-carbon development planning and resident health guarantee, and is suitable for popularization and application. The method is suitable for climate toughness optimization of multiple scenes such as industrial parks and high-density residential areas.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of urban climate adaptation and low-carbon planning, and particularly relates to a city heat exposure risk optimization method based on dynamic regulation of carbon emissions, especially to a method that fuses high-resolution carbon emission data, local climate zoning, and multi-scenario climate prediction models, realizes real-time assessment of city heat exposure risk, intelligent generation and optimization of carbon emission regulation strategies, and provides technical support for city heat environment governance, low-carbon development planning, and resident health protection by constructing a dynamic correlation model of carbon emissions and heat exposure risk. BACKGROUND

[0002] With the acceleration of global warming and urbanization, extreme high temperature events occur frequently, and the problem of urban heat exposure is becoming increasingly serious, which seriously threatens the health of residents and the sustainable development of cities. Studies have shown that climate warming driven by carbon emissions and urban heat island effect superimpose, further exacerbating heat exposure risk. Current governance technologies for urban heat exposure mainly focus on greening expansion, building energy-saving renovation, etc., but there are still significant deficiencies in the dynamic regulation of carbon emissions and the coordinated optimization of heat exposure risk, which are specifically manifested as follows:

[0003] 1. The correlation mechanism between carbon emissions and heat exposure has not been fully quantified: existing heat exposure assessment methods mainly focus on direct meteorological factors such as temperature and humidity, or static urban surface characteristics (such as impervious surface coverage), but fail to systematically integrate the dynamic influence of carbon emission data, resulting in a lack of precise response to human activity carbon emissions in heat exposure risk analysis.

[0004] 2. The regulation strategy lacks multi-scenario adaptability: traditional heat environment optimization schemes rely on historical data analysis and do not combine carbon emission path prediction under SSP-RCP and other climate scenarios, making it difficult to develop differentiated regulation strategies to match the long-term heat exposure trends of different emission reduction targets.

[0005] 3. Insufficient management of spatial heterogeneity: existing technologies lack sufficient analysis of the spatial differentiation characteristics of carbon emissions and heat exposure risk within cities (such as the difference in carbon emission contribution of industrial, commercial, and residential areas), and cannot achieve precise regulation by zone.

[0006] 4. Lack of real-time dynamic regulation capability: most regulation methods rely on periodic manual intervention and have not established a dynamic feedback mechanism based on real-time carbon emission monitoring and weather forecasting, resulting in low response efficiency in dealing with sudden high temperature events.

[0007] Therefore, there is an urgent need for an intelligent regulation method that can integrate dynamic carbon emission data, multi-scale climate models, and spatial heterogeneity analysis to scientifically quantify the driving effect of carbon emissions on heat exposure and generate real-time optimization strategies that adapt to different emission reduction targets, thereby improving the climate resilience of cities. SUMMARY

[0008] The present application aims to provide a city heat exposure risk optimization method based on dynamic regulation of carbon emissions, which realizes real-time quantitative evaluation of city heat exposure risk, intelligent generation and optimization of carbon emission regulation strategy, so as to reduce high temperature health risk and help city low-carbon development and climate resilience improvement, by fusing high-resolution carbon emission data, local climate zones (LCZ) and multi-scenario climate prediction model to build a dynamic correlation model of carbon emission and heat exposure risk.

[0009] According to an aspect of the present application, a city heat exposure risk optimization method based on dynamic regulation of carbon emissions is provided, comprising:

[0010] obtaining real-time carbon emission data and meteorological short-term forecast results;

[0011] inputting the obtained real-time carbon emission data and meteorological short-term forecast results into a fitted heat exposure risk transfer function, outputting future heat exposure risk level, and combining with early warning threshold to realize optimization of city heat exposure risk by adopting differentiated regulation strategy matching; wherein the fitting process of the heat exposure risk transfer function comprises:

[0012] collecting multi-source data and performing time and space scale alignment; the multi-source data includes carbon emission data, local climate zone data, meteorological and population data, map interest point and night light data, and normalized vegetation index;

[0013] calculating heat exposure based on meteorological and population data, and simultaneously performing spatial heterogeneity analysis on heat exposure risk based on carbon emission data, local climate zone data and normalized vegetation index by adopting geographic detector algorithm;

[0014] based on local climate zone data and map interest point and night light data, constructing carbon emission weight contribution matrix through Spearman correlation coefficient;

[0015] according to the spatial heterogeneity analysis result and the carbon emission weight contribution matrix, performing dynamic correlation modeling of carbon emission-heat exposure, and calculating the interdecadal high temperature day average under different shared socio-economic path-representative concentration path combination scenarios by adopting multi-model integration method, to obtain heat exposure risk transfer function together.

[0016] Further, the geographic detector algorithm is adopted to perform spatial heterogeneity analysis on heat exposure risk, comprising:

[0017] the geographic detector algorithm is adopted to quantize the spatial differentiation explanation force of carbon emission data, local climate zone data and normalized vegetation index on heat exposure risk.

[0018] Further, the carbon emission weight contribution matrix is constructed, comprising:

[0019] Based on local climate zoning data and map points of interest and night light data, the city is divided into different functional areas;

[0020] Based on different functional areas, the carbon emission weight contribution weight is constructed by Spearman correlation coefficient, and the expression is:

[0021]

[0022] Among them, is the carbon emission contribution weight of the kth functional area, is the Spearman correlation coefficient of the kth functional area, m is the total number of functional areas, and the carbon emission contribution weight of each functional area is combined into a carbon emission weight contribution matrix, and the expression is:

[0023] .

[0024] Further, the heat exposure risk transfer function is formula:

[0025]

[0026] Among them, is the base period heat exposure value, is the temperature change, is the carbon emission intensity change, , is the model calibration parameter.

[0027] Further, the optimization of urban heat exposure risk is realized by differentiated regulation and control strategy matching, including:

[0028] Monte Carlo simulation and NSGA-II multi-objective optimization algorithm are used to generate a Pareto optimal strategy set with emission reduction cost, heat exposure risk reduction, and resident acceptance as optimization objectives;

[0029] For different local climate zoning and carbon emission intensity areas, combined with the Pareto optimal strategy set, differentiated regulation and control strategy matching is carried out;

[0030] By comparing the heat exposure risk index and carbon emission intensity change before and after the implementation of differentiated regulation and control strategy matching, the effectiveness of differentiated regulation and control strategy matching is evaluated.

[0031] Further, differentiated regulation and control strategy matching is carried out, including:

[0032] For industrial areas, trigger peak-shaving production, distributed photovoltaic expansion and waste heat recovery;

[0033] For high-density residential areas, implement roof greening subsidies, public space spray cooling and air conditioning energy consumption limits;

[0034] For traffic hubs, dynamic congestion pricing, new energy bus priority scheduling, and sunshade canopy full coverage are implemented.

[0035] Further, the effectiveness of the differentiated regulation strategy matching is evaluated, including:

[0036] Remote sensing inversion verification, using Landsat satellite image surface temperature data to verify the heat island intensity reduction effect;

[0037] Carbon emission audit, combined with enterprise energy consumption data and Internet of Things monitoring equipment, to calculate the actual emission reduction;

[0038] Resident health data correlation, analysis of the spatiotemporal matching degree of heatstroke cases in hospitals and heat exposure warning areas.

[0039] Further, the collection of multi-source data also includes the following preprocessing process:

[0040] Adopting spatial resampling and projection conversion technology to unify the carbon emission data to the target city coordinate system, and filtering the abnormal values;

[0041] By mode resampling, the local climate zoning data is adapted to the set kilometer grid, and the spatial distribution characteristics of building types and natural surface types are extracted;

[0042] By bilinear interpolation, the spatial resolution of the meteorological and population data is unified, and a time-consistent spatiotemporal dataset is constructed;

[0043] By coordinate conversion, the point of interest data is unified to the target projection coordinate system, the effective labels are screened and the redundant items are removed, in addition, the night light data is radiometrically calibrated and denoised, and after generating the annual composite data, it is resampled to the set kilometer grid by bilinear interpolation.

[0044] Further, the city is divided into different functional areas, including:

[0045] The area with local climate zoning type as heavy industrial area and map point of interest containing "factory" and "chemical park" labels is divided into industrial area;

[0046] The area with map label as "subway station" and "train station" and daily passenger flow ≥ set number of times is divided into traffic hub;

[0047] The area with local climate zoning type as compact medium-rise building area and commercial related map point of interest density > set area is divided into commercial center;

[0048] The area with local climate zoning type as compact high-rise building area or compact low-rise building area and night light intensity index ≥ set size is divided into residential area.

[0049] According to one aspect of this specification, a system for optimizing urban heat exposure risk based on dynamic carbon emission control is provided, comprising:

[0050] The data acquisition module is used to acquire real-time carbon emission data and short-term weather forecast results;

[0051] The heat exposure risk optimization module is used to input the acquired real-time carbon emission data and short-term meteorological forecast results into a fitted heat exposure risk transfer function, output the future heat exposure risk level, and combine it with the early warning threshold to optimize the urban heat exposure risk using differentiated control strategies; wherein, the fitting process of the heat exposure risk transfer function includes:

[0052] Collect multi-source data and align it to spatiotemporal scales; the multi-source data includes: carbon emission data, local climate zoning data, meteorological and population data, map points of interest and nighttime light data, and normalized vegetation index;

[0053] Heat exposure is calculated based on meteorological and population data. At the same time, based on carbon emission data, local climate zoning data and normalized vegetation index, the geographic detector algorithm is used to conduct spatial heterogeneity analysis of heat exposure risk.

[0054] Based on local climate zoning data and map points of interest and nighttime light data, a carbon emission weight contribution matrix was constructed using Spearman correlation coefficient.

[0055] Based on the spatial heterogeneity analysis results and the carbon emission weight contribution matrix, a dynamic correlation model of carbon emission-heat exposure was constructed. The interdecadal mean number of high-temperature days under different shared socio-economic path-representative concentration path combination scenarios was calculated using a multi-model ensemble method, and the heat exposure risk transfer function was obtained together.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] 1. This invention constructs a standardized spatiotemporal database by integrating high-resolution carbon emission data, local climate zoning, multi-source meteorological data, population distribution, and points of interest, among other multi-dimensional data. Based on the geographic detector algorithm, it quantifies the spatial differentiation explanatory power of carbon emissions, local climate zoning, and vegetation cover on heat exposure risk. Combined with the Spearman correlation coefficient, it dynamically calibrates the carbon emission weight contribution matrix and quantifies the differentiated carbon emission impact of functional areas such as industrial zones and transportation hubs, thus solving the problem of unclear dynamic correlation mechanism between carbon emissions and heat exposure.

[0058] 2, The application adopts a multi-model integration method to predict the thermal exposure trend under different SSP-RCP scenarios, embeds a thermal exposure risk transfer function; a Pareto optimal control strategy set is generated by Monte Carlo simulation and NSGA-II multi-objective optimization algorithm, differential partition control of industrial park peak-shaving emission reduction, residential area three-dimensional greening and transportation hub dynamic scheduling is realized, the problems of lack of multi-scenario adaptability and real-time of control strategy are solved, rapid early warning, accurate control and effect verification of thermal exposure risk are supported, scientific support is provided for urban thermal environment governance, low-carbon development planning and resident health protection, and the application is suitable for climate resilience optimization of industrial parks, high-density residential areas and other scenes. BRIEF DESCRIPTION OF DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0060] Figure 1 The overall structure schematic diagram of the urban thermal exposure risk optimization method based on carbon emission dynamic regulation provided by the embodiment of the present application.

[0061] Figure 2 The multi-scenario thermal exposure prediction model construction flowchart provided by the embodiment of the present application.

[0062] Figure 3 The dynamic regulation flowchart provided by the embodiment of the present application. DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0064] As Figure 1As shown, the embodiment of the present application provides a city heat exposure risk optimization method based on carbon emission dynamic regulation, comprising: acquiring real-time carbon emission data and meteorological short-term forecast results; inputting the acquired real-time carbon emission data and meteorological short-term forecast results into a fitted heat exposure risk transfer function, outputting future heat exposure risk levels, and combining a warning threshold to realize the optimization of city heat exposure risk by using a differentiated regulation strategy; wherein the fitting process of the heat exposure risk transfer function comprises: collecting multi-source data and performing time and space scale alignment; the multi-source data includes: carbon emission data, local climate zoning data, meteorological and population data, map interest point and night light data, and normalized vegetation index; heat exposure is calculated based on meteorological and population data, and spatial heterogeneity analysis of heat exposure risk is performed based on carbon emission data, local climate zoning data, and normalized vegetation index using a geographic detector algorithm; based on local climate zoning data and map interest point and night light data, a carbon emission weight contribution matrix is constructed through a Spearman correlation coefficient; according to the spatial heterogeneity analysis result and the carbon emission weight contribution matrix, carbon emission-heat exposure dynamic correlation modeling is performed, and multi-model integration method is used to calculate the interdecadal high temperature day average under different shared social economic path-representative concentration path combination scenarios, and the heat exposure risk transfer function is obtained.

[0065] Specifically, the embodiment of the present application also provides multi-source data standardization preprocessing, comprising:

[0066] 1. Carbon emission data preprocessing: based on the 1-kilometer resolution carbon emission data of the open data list and the global emission map ODIAC, spatial resampling and projection conversion technology is used to unify the original data to the target city coordinate system (such as Albers equal-area projection), and abnormal value filtering processing is performed to eliminate the noise interference caused by instantaneous emission of industrial facilities.

[0067] 2. Local climate zoning data integration: fusion of global local climate zoning LCZ data set, 100-meter resolution LCZ data is adapted to 1-kilometer grid through mode resampling, and the spatial distribution characteristics of building types (such as compact high-rise, open middle layer) and natural surface types (such as water, low vegetation) are extracted.

[0068] 3. Meteorological and population data alignment: integrating the historical daily maximum temperature and relative humidity data of China high-resolution multi-element meteorological driving product ChinaMet, and the WorldPop grid population distribution data, realizing spatial resolution unification through bilinear interpolation, and constructing a time span consistent spatio-temporal data set.

[0069] 4. POI and night light data preprocessing: The POI data is converted to the target projection coordinate system Albers through coordinate conversion, and effective labels such as industry, transportation and commerce are screened out and redundant entries are removed. The night light data of Luojia-1 is radiometrically calibrated and denoised, and after generating annual synthetic data, it is resampled to a 1-kilometer grid through bilinear interpolation, finally spatially aligned with the LCZ data, and the boundaries of the functional area are verified by combining kernel density analysis and DN threshold segmentation, to ensure the spatial consistency of multi-source data, and extract high-intensity lighting areas with night light intensity index ≥80.

[0070] 5. Normalized Difference Vegetation Index (NDVI) acquisition and resampling: Extract the NDVI dataset in the study area from 2000 to 2020 through the Google Earth Engine platform, and calculate the maximum NDVI value of each year, then project the maximum NDVI value data of each year in ArcGIS, and resample it from 250m resolution to 1km resolution.

[0071] Specifically, the embodiment of the present application also provides multi-scenario climate prediction data integration, which integrates the National Aeronautics and Space Administration Earth Exchange Global Daily Scale Downscaling Climate Prediction Data NEX-GDDP and future climate data of the Sixth Coupled Model Comparison Project CMIP6 to form a NEX-GDDP-CMIP6 multi-model climate data set, which includes five types of climate models, as shown in Table 1 below. The multi-model integration method can effectively reduce the uncertainty of a single model and improve the stability and reliability of the prediction results by integrating the outputs of multiple climate models. For shared social economic path SSP and representative concentration path RCP combined scenarios (such as SSP126, SSP585), extract daily maximum temperature and relative humidity prediction data from 2030 to 2100, and use the bias correction method to eliminate systematic errors of the model.

[0072] Table 1 NEX-GDDP-CMIP6 climate model description

[0073] Model name Country of origin Developing institution CanESM5 Canada Canadian Centre for Climate Modelling and Analysis (CCCma) GFDL-ESM4 United States Geophysical Fluid Dynamics Laboratory (GFDL) IPSL-CM6A-LR France Institut Pierre-Simon Laplace (IPSL) MPI-ESM1-2-HR Germany Max Planck Institute for Meteorology (MPI-M) MRI-ESM2-0 Japan Meteorological Research Institute (MRI)

[0074] Specifically, as shown in Figure 2 the embodiment of the present application also provides specific steps for building a multi-scenario heat exposure prediction model, as follows:

[0075] 1. Carbon emission-heat exposure dynamic correlation modeling

[0076] 1) Population heat exposure calculation: The population heat exposure of urban built-up areas at the scale of 1 km grid is quantified, and the population heat exposure of each grid is the product of the high temperature days of the grid and the population of the grid. Among them, the grid high temperature days are calculated by absolute high temperature days. According to the provisions of the meteorological department of our country, the absolute high temperature day is determined by the daily maximum temperature exceeding 35℃, that is, when the daily maximum temperature is greater than or equal to 35℃, it is recorded as an absolute high temperature day. The calculation data sources of high temperature days and grid population are ChinaMet historical daily maximum temperature and WorldPop grid population distribution data, and the population heat exposure (hereinafter referred to as heat exposure) calculation formula is as follows:

[0077] (1)

[0078] (2)

[0079] Among them, the heat exposure of the i-th grid, represents the total population of the i-th grid, represents the high temperature days of the grid in a year, is the per capita heat exposure of each study area, and N is the total number of grids in the corresponding area.

[0080] 2) Spatial heterogeneity analysis: Based on the GeoDetector algorithm, the spatial differentiation explanation power (q value) of carbon emission, local climate zoning, normalized vegetation index (Normalized Difference Vegetation Index, NDVI) and other factors on heat exposure risk is quantified, and the formula is:

[0081] (3)

[0082] Among them, is the total grid number, is the grid number of the hth zoning, is the heat exposure risk variance in the zoning, is the global variance.

[0083] 3) Carbon emission weight contribution matrix construction: The carbon emission weight contribution matrix (Carbon Emission Contribution Weight Matrix, CECWM) is introduced to distinguish the differentiated influence of carbon emission of functional areas such as industrial area, transportation hub and commercial center on heat exposure. The matrix weight is dynamically calibrated by Spearman's rank correlation coefficient (Spearman's Rank Correlation Coefficient), and the formula is:

[0084] (4)

[0085] (5)

[0086] where, is the rank difference of carbon intensity and heat exposure risk of the i-th grid, is the total number of grids. is the carbon contribution weight of the k-th functional zone, is the Spearman correlation coefficient of the k-th functional zone, and m is the total number of functional zones.

[0087] The carbon contribution weights of each functional zone are then combined into a carbon emission weight contribution matrix, expressed as:

[0088] (6)

[0089] Specifically, the division of functional zones is based on local climate data, combined with Gaode POI data and night light data, and the classification rules are as follows in Table 2.

[0090] Table 2 Classification rules of functional zones

[0091] 2. Multi-scenario heat exposure prediction model

[0092] A multi-model integration method is used to weight average the prediction results of climate models such as CanESM5, GFDL-ESM4, and IPSL-CM6A-LR, to generate the interannual average of heat exposure days under different SSP-RCP scenarios. The heat exposure risk transfer function is embedded in the prediction model, and the formula is:

[0093] (7)

[0094] where, is the base heat exposure value, representing the average heat exposure risk level of the region in the historical reference period, which is the baseline for all future prediction changes. It is calculated using the ChinaMet historical meteorological data and WorldPop population distribution data processed by the data integration layer. is the temperature change, representing the average temperature change of the future period relative to the historical baseline period, which is obtained by weighted averaging according to the prediction results of the climate model. is the carbon intensity change, directly derived from the selected SSP-RCP scenario path itself. Each RCP path corresponds to a specific greenhouse gas concentration trajectory, which is driven by a specific global carbon emission path. 、 is the model calibration parameter, which is obtained by statistical calibration using historical data through carbon emission-heat exposure dynamic correlation modeling.

[0095] Specifically, as shown in Figure 3 the embodiment of the present application provides specific steps of dynamic regulation, including:

[0096] 1、Real-time heat exposure risk warning generation

[0097] Based on real-time carbon emission data and short-term weather forecast results, the dynamic correlation model is used to calculate the grid scale heat exposure risk level (low, medium, high, and extremely high) in the next 24-72 hours, and a heat map is generated through the GIS platform. The warning threshold is set according to the statistical value of the historical extreme event, and the red warning is triggered when the predicted heat exposure risk exceeds the 95th percentile.

[0098] 2、Differential regulation strategy matching

[0099] 1) Spatial weight regulation: For different LCZ types and carbon emission intensity areas, match priority regulation strategies: trigger peak shaving production in industrial areas, expand distributed photovoltaic, and recover waste heat; Start the roof greening subsidy in high-density residential areas, public space spray cooling, air conditioning energy consumption limit; Implement dynamic congestion tolling, new energy bus priority scheduling, and sunshade full coverage in transportation hubs.

[0100] 2) Multi-objective optimization decision: Use Monte Carlo simulation and NSGA-II multi-objective optimization algorithm to generate a Pareto optimal strategy set with emission reduction cost, heat exposure risk reduction, and resident acceptance as optimization objectives, and the formula is:

[0101] (8)

[0102] Wherein, 、 、 are the cost function, risk function and acceptance function respectively.

[0103] 3) Regulation effect verification and feedback

[0104] 3、By comparing the heat exposure risk index (Heat Exposure Index, HEI) and carbon emission intensity changes before and after the implementation of regulation, the effectiveness of the strategy is evaluated. The verification method includes:

[0105] 1) Remote sensing inversion verification: Use Landsat land surface temperature (Land Surface Temperature, LST) data to verify the heat island intensity reduction effect;

[0106] 2) Carbon emission audit: Combined with enterprise energy consumption data and Internet of Things monitoring equipment, calculate the actual emission reduction amount;

[0107] 3) Resident health data correlation: analyze the spatiotemporal matching degree of the number of heatstroke cases in the hospital and the heat exposure early warning area.

[0108] Specifically, the embodiment of the present application also provides system output and interaction, including:

[0109] 1. Multi-level data visualization

[0110] Through the interactive GIS platform, a heat exposure risk map, a carbon emission hotspot distribution, and a comparison chart of the implementation effect of the regulation and control strategy are output, and a time axis sliding function, a scenario switching function (such as SSP126 vs. SSP585), and a layer superposition analysis function are supported.

[0111] 2. Cross-departmental collaborative interface

[0112] A standardized API interface is provided to realize data intercommunication with the urban planning department, the environmental protection monitoring platform, and the public health system, and joint deployment and effect tracking of the regulation and control strategy are supported.

[0113] Specifically, the present application also provides embodiment 1: dynamic regulation and control of carbon emissions and optimization of heat exposure in an industrial zone. The application scenario is an industrial park with an area of about 50 square kilometers, and the zone contains high-carbon-emission chemical and steel enterprises and supporting residential areas. The specific work flow is as follows:

[0114] 1. Data integration and model construction

[0115] 1-kilometer resolution carbon emission data is obtained through ODIAC, and steel plants (annual carbon emission about 1.2 million tons) and chemical plants (annual carbon emission about 800,000 tons) in the park are identified as core emission sources. Combined with the functional area division method:

[0116] ① Industrial zone: based on LCZ data, LCZ10 compact industrial zone is identified, and POI data is integrated to screen the areas labeled as “steel plant” and “chemical plant”;

[0117] ② Residential area: combined with LCZ3 compact low-rise building area and DN value ≥80 in Luoja No. 1 night light data, the boundary of the supporting residential area is delineated;

[0118] ③ Transportation hub: the areas with POI labels of “logistics center” and “freight station” and daily vehicle flow ≥5000 vehicles are extracted.

[0119] Integrate the historical meteorological data of ChinaMet products from 2000 to 2020 and the SSP585 scenario prediction data (2030-2100) of NEX-GDDP-CMIP6 to build a standardized spatiotemporal database. Based on the geographic detector algorithm analysis, the spatial explanation q value of carbon emission of the industrial zone to the heat exposure risk is 0.42, which is significantly higher than that of other regions. Further, a carbon emission weight contribution matrix is constructed, and based on the monthly carbon emission data of the steel plant and the chemical plant in the past 5 years and the corresponding regional heat exposure risk index, the Spearman rank correlation coefficient of the two is calculated, and the correlation coefficient is normalized to generate a dynamic weight.

[0120] 2. Multi-scenario prediction and strategy generation

[0121] Using a multi-model integration method, it is predicted that under the SSP585 scenario, the summer average absolute high temperature days of the park in 2100 will reach 68 days (an increase of 160% compared with 2020), and the dynamic model shows that if the current emission level is maintained, the high-risk area of heat exposure in the park will expand to 85%. Differentiated regulation strategies are generated for different functional areas:

[0122] ① Industrial area: on the high temperature warning day (predicted temperature ≥ 38℃), the steel plant implements a 15% reduction in production capacity, and the chemical plant starts the night production mode;

[0123] ② Transportation hub: dynamically adjust the passing time of freight vehicles to avoid the high temperature peak period (12:00-16:00)

[0124] ③ Residential area: deploy roof photovoltaic panels with a coverage rate of ≥60%, which is expected to reduce carbon dioxide emissions by 150,000 tons per year, and install waste heat power generation equipment to reduce the factory environment temperature by 1.2-1.8℃.

[0125] 3. Effect verification

[0126] After one year of implementation, the carbon emission intensity of the park decreased by 18%, and the high-risk area of heat exposure reduced by 22%. Landsat land surface temperature inversion shows that the daily average temperature in the industrial core area in summer decreased by 2.3℃, and the number of heatstroke cases in the hospital decreased by 37% compared with the previous year.

[0127] Specifically, the present application also provides Example 2: Real-time heat exposure emergency regulation of high-density residential area, wherein the application scenario is a high-density residential area in Hangzhou City, with a population of about 200,000, and the LCZ type is LCZ1 compact high-rise building area. The specific work flow is as follows:

[0128] 1. Real-time monitoring and early warning triggering

[0129] Real-time access to Hangzhou Environmental Protection Bureau's 15-minute carbon emission data and meteorological bureau's 72-hour short-term forecast data, combined with WorldPop population dynamic distribution data, to identify morning and evening peak population gathering areas. On July 2023, the model predicted that the next day's maximum temperature would reach 41°C, triggering a red warning (risk value ≥ 99th percentile) for heat exposure risk level. The GIS platform generated a heat map showing that the area within 500 meters of the subway station was a high-risk area (population density > 30,000 people / km2).

[0130] 2. Dynamic control strategy execution

[0131] Deploy a modular vertical green wall on the subway station roof within 24 hours, and simultaneously start the roof misting system, with a coverage rate of about 75%. Through multi-objective optimization algorithm calculation, it is concluded that the above measures can reduce the local apparent temperature by 3-4°C and the cost-benefit ratio is 1:2.5. At the same time, dynamically adjust the bus departure frequency, increase by 30% to reduce the exhaust emissions caused by private car congestion, and start the community air conditioning energy consumption limit, so that the peak power decreases by 15%, and through the subsidy mechanism to guide residents to use energy-saving mode.

[0132] 3. Effect feedback and optimization

[0133] After the implementation of the control, the surface temperature around the subway station was measured to be reduced by 3.8°C, and the heat exposure risk level was reduced from "extremely high" to "medium". The carbon emission intensity on that day was reduced by 12% compared to the predicted value. The system automatically records the control parameters and uses them for subsequent optimization of similar scenarios.

[0134] The implementation basis of each embodiment of the present application is realized through programmed processing of a device with processor function. Therefore, in engineering practice, the technical solutions and functions of each embodiment of the present application are packaged into various modules. Based on this actual situation, on the basis of the above embodiments, the embodiments of the present application provide a city heat exposure risk optimization method system based on dynamic carbon emission regulation, which is used to execute the city heat exposure risk optimization method based on dynamic carbon emission regulation in the above method embodiments.

[0135] The system comprises: a data acquisition module for acquiring real-time carbon emission data and meteorological short-term forecast results; a heat exposure risk optimization module for inputting the acquired real-time carbon emission data and meteorological short-term forecast results into a fitted heat exposure risk transfer function, outputting future heat exposure risk levels, and combining with early warning thresholds to realize the optimization of urban heat exposure risk by using a differentiated regulation strategy; wherein the fitting process of the heat exposure risk transfer function comprises: collecting multi-source data and performing time-space scale alignment; the multi-source data comprises: carbon emission data, local climate zoning data, meteorological and population data, map interest point and night light data, and normalized vegetation index; heat exposure is calculated based on meteorological and population data, and heat exposure risk is analyzed for spatial heterogeneity based on carbon emission data, local climate zoning data, and normalized vegetation index using a geographic detector algorithm; based on local climate zoning data and map interest point and night light data, a carbon emission weight contribution matrix is constructed through a Spearman correlation coefficient; according to the spatial heterogeneity analysis result and the carbon emission weight contribution matrix, carbon emission-heat exposure dynamic correlation modeling is performed, and multi-model integration method is used to calculate the interdecadal high temperature day average under different shared social economic path-representative concentration path combination scenarios, to obtain the heat exposure risk transfer function.

[0136] The urban heat exposure risk optimization method system based on carbon emission dynamic regulation provided by the embodiment of the application solves the problems that the correlation mechanism of carbon emission and heat exposure is not fully quantified, the regulation strategy lacks multi-scenario adaptability, spatial heterogeneity management is insufficient, and real-time dynamic regulation capability is lacking, etc., adopts a plurality of modules, constructs a dynamic correlation model of carbon emission and heat exposure risk by fusing high-resolution carbon emission data, local climate zoning and multi-scenario climate prediction model, realizes real-time quantitative evaluation of urban heat exposure risk, intelligent generation and optimization of carbon emission regulation strategy, thereby reducing high temperature health risk and helping urban low-carbon development and climate resilience improvement.

[0137] In summary, the technical framework of the present application adopts a three-layer architecture of "data integration-model construction-dynamic regulation", including a data integration layer, a model construction layer and a dynamic regulation layer. The data integration layer constructs a standardized spatio-temporal database by integrating the open data inventory and high-resolution carbon emission data of the emission global map (Open-source Data Inventory for Anthropogenic CO2, ODIAC), local climate zoning data, multi-source meteorological data such as China Meteorological Administration High-Resolution Multi-element Meteorological Forcing Dataset (ChinaMet) historical meteorological data, NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) and Coupled Model Intercomparison Project Phase 6 (CMIP6) future climate prediction data, as well as population distribution data, Point of Interest (POI) data and night light data; the model construction layer analyzes the correlation mechanism of carbon emissions and heat exposure risk based on geographic detector and Spearman correlation coefficient, and predicts the heat exposure trend under different Shared Socioeconomic Pathways (SSP) and Representative Concentration Pathways (RCP) combined scenarios (such as SSP126, SSP245, SSP370 and SSP585) by combining multi-model integration method; the dynamic regulation layer dynamically generates heat exposure risk warning according to real-time carbon emission data and meteorological prediction results, and matches differential regulation strategies (such as industrial area staggered emission reduction and high-density residential area vertical greening) through spatial heterogeneity weight factor matching, to realize the bidirectional optimization of carbon emission and heat exposure risk.

[0138] To achieve the above object, the optimization method mainly includes the following steps: data standardization pretreatment, carbon emission-thermal exposure dynamic correlation modeling, multi-scenario thermal exposure prediction, spatial heterogeneity analysis, real-time regulation strategy generation and effect verification. In the model construction, the carbon emission weight contribution matrix is innovatively introduced to quantify the differentiated influence of carbon emissions in different functional areas (such as industrial areas, transportation hubs and commercial centers) on thermal exposure risk; in the regulation strategy generation, the Monte Carlo simulation and multi-objective optimization algorithm are combined to balance the emission reduction cost and thermal exposure risk relief benefit, and ensure the economy and feasibility of the strategy.

[0139] Finally, it should be noted that the above specific embodiments are only representative examples of the present application. Obviously, the present application is not limited to the above specific embodiments, but can have many variations. Any simple modification, equivalent change and modification made to the above specific embodiments according to the technical essence of the present application shall be considered as falling within the protection scope of the present application.

Claims

1. A method for optimizing urban heat exposure risk based on dynamic carbon emission control, characterized in that, include: Obtain real-time carbon emission data and short-term weather forecast results; The acquired real-time carbon emission data and short-term meteorological forecast results are input into a fitted heat exposure risk transfer function, which outputs the future heat exposure risk level. Combined with a warning threshold, a differentiated control strategy is used to optimize the urban heat exposure risk. The fitting process of the heat exposure risk transfer function includes: Collect multi-source data and align it to spatiotemporal scales; the multi-source data includes: carbon emission data, local climate zoning data, meteorological and population data, map points of interest and nighttime light data, and normalized vegetation index; Heat exposure is calculated based on meteorological and population data. At the same time, based on carbon emission data, local climate zoning data and normalized vegetation index, the geographic detector algorithm is used to conduct spatial heterogeneity analysis of heat exposure risk. Based on local climate zoning data and map points of interest and nighttime light data, a carbon emission weight contribution matrix was constructed using Spearman correlation coefficient. Based on the spatial heterogeneity analysis results and the carbon emission weight contribution matrix, a dynamic correlation model of carbon emission-heat exposure was constructed. The interdecadal mean number of high-temperature days under different shared socio-economic path-representative concentration path combination scenarios was calculated using a multi-model ensemble method, and the heat exposure risk transfer function was obtained together.

2. The method for optimizing urban heat exposure risk based on dynamic carbon emission control according to claim 1, characterized in that, Spatial heterogeneity analysis of heat exposure risk was performed using a geographic detector algorithm, including: Using a geographic detector algorithm, the spatial differentiation explanatory power of carbon emission data, local climate zoning data, and normalized vegetation index on heat exposure risk is quantified.

3. The method for optimizing urban heat exposure risk based on dynamic carbon emission control according to claim 1, characterized in that, Construct a carbon emission weighting contribution matrix, including: Based on local climate zoning data and map points of interest and nighttime light data, the city is divided into different functional zones; Based on different functional zones, the carbon emission contribution weight is calculated using the Spearman correlation coefficient, and the expression is as follows: , in, The weighting of carbon emissions contribution for the k-th functional zone. Let be the Spearman correlation coefficient for the k-th functional zone, and m be the total number of functional zones. The carbon emission contribution weights of each functional zone are combined to form a carbon emission weight contribution matrix, expressed as: 。 4. The method for optimizing urban heat exposure risk based on dynamic carbon emission control according to claim 1, characterized in that, The heat exposure risk transmission model is defined by the following formula: , in, Base period heat exposure value, This refers to the change in temperature. This represents the change in carbon emission intensity. , These are the parameters for model calibration.

5. The method for optimizing urban heat exposure risk based on dynamic carbon emission control according to claim 3, characterized in that, Optimizing urban heat exposure risk through differentiated control strategies includes: Monte Carlo simulation and NSGA-II multi-objective optimization algorithm were used to generate Pareto optimal strategy set with emission reduction cost, reduction of heat exposure risk and residents' acceptance as optimization objectives. Differentiated regulation strategies are matched based on Pareto optimal strategy sets for different local climate zones and carbon emission intensity areas. The effectiveness of the differentiated regulation strategy matching is evaluated by comparing the changes in heat exposure risk index and carbon emission intensity before and after the implementation of the differentiated regulation strategy matching.

6. The method for optimizing urban heat exposure risk based on dynamic carbon emission control according to claim 5, characterized in that, Implement differentiated control strategies, including: For industrial zones, measures such as staggered production, expansion of distributed photovoltaic capacity, and waste heat recovery from exhaust gases are being implemented. For high-density residential areas, subsidies will be provided for rooftop greening, public space misting cooling, and air conditioning energy consumption limits will be implemented. For transportation hubs, dynamic congestion pricing, priority scheduling of new energy buses, and full coverage of sunshades will be implemented.

7. The method for optimizing urban heat exposure risk based on dynamic carbon emission control according to claim 5, characterized in that, The effectiveness of matching differentiated control strategies is evaluated, including: Remote sensing inversion verification: Landsat satellite imagery surface temperature data is used to verify the effect of reducing heat island intensity. Carbon emission audits combine enterprise energy consumption data with IoT monitoring equipment to calculate actual emission reductions. By correlating residents' health data, we can analyze the spatiotemporal correlation between the number of heatstroke cases in hospitals and the areas under heat exposure warnings.

8. The method for optimizing urban heat exposure risk based on dynamic carbon emission control according to claim 1, characterized in that, The collection of multi-source data also includes the following preprocessing steps: Spatial resampling and projection transformation techniques are used to unify the carbon emission data to the target city coordinate system, and outliers are filtered out. The local climate zoning data is adapted to a set kilometer grid by mode resampling to extract the spatial distribution characteristics of building type and natural surface type. The spatial resolution of the meteorological and population data is unified by bilinear interpolation. The point of interest data is unified to the target projection coordinate system through coordinate transformation, valid labels are filtered and redundant entries are removed. In addition, radiometric calibration and noise reduction are performed on the nighttime light data. After generating annual synthetic data, it is resampled to the set kilometer grid through bilinear interpolation.

9. The method for optimizing urban heat exposure risk based on dynamic carbon emission control according to claim 3, characterized in that, The city is divided into different functional zones, including: Areas with a local climate zone type of heavy industry and map points of interest labeled "factory" or "chemical park" are classified as industrial zones. Areas labeled "subway station" or "train station" on the map and with an average daily passenger flow greater than or equal to a set number of people are designated as transportation hubs; The area with a local climate zone type of compact mid-rise building zone and a density of commercial-related map points of interest greater than the set area is designated as a commercial center. Areas with a local climate zoning type of compact high-rise building area or compact low-rise building area and a nighttime light intensity index ≥ a set value are designated as residential areas.

10. A system for assessing urban heat exposure risk based on dynamic carbon emission control, characterized in that, include: The data acquisition module is used to acquire real-time carbon emission data and short-term weather forecast results; The heat exposure risk optimization module is used to input the acquired real-time carbon emission data and short-term meteorological forecast results into a fitted heat exposure risk transfer function, output the future heat exposure risk level, and combine it with the early warning threshold to optimize the urban heat exposure risk using differentiated control strategies; wherein, the fitting process of the heat exposure risk transfer function includes: Collect multi-source data and align it to spatiotemporal scales; the multi-source data includes: carbon emission data, local climate zoning data, meteorological and population data, map points of interest and nighttime light data, and normalized vegetation index; Heat exposure is calculated based on meteorological and population data. At the same time, based on carbon emission data, local climate zoning data and normalized vegetation index, the geographic detector algorithm is used to conduct spatial heterogeneity analysis of heat exposure risk. Based on local climate zoning data and map points of interest and nighttime light data, a carbon emission weight contribution matrix was constructed using Spearman correlation coefficient. Based on the spatial heterogeneity analysis results and the carbon emission weight contribution matrix, a dynamic correlation model of carbon emission-heat exposure was constructed. The interdecadal mean number of high-temperature days under different shared socio-economic path-representative concentration path combination scenarios was calculated using a multi-model ensemble method, and the heat exposure risk transfer function was obtained together.

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