A method for determining a county low-carbon target trend

Abstract

The application relates to the technical field of low-carbon development planning and ecological environment protection, and provides a county low-carbon target optimization determination method. The method compares the population scale corresponding to the resource environment ecological carrying capacity with the predicted population value, determines the population prediction scenario of the predicted population value being less than the population scale corresponding to the resource environment ecological carrying capacity, calculates the total carbon emission of the county by using the list method, analyzes the predicted total carbon emission of the county in combination with the carbon emission driving factor, calculates and predicts the carbon emission per capita of the county according to the determined population prediction scenario and the predicted total carbon emission of the county, constructs the correlation model of the carbon emission per capita and the per capita gross domestic product, calculates the per capita carbon emission international advanced level threshold corresponding to the per capita gross domestic product, compares the predicted carbon emission per capita of the county with the per capita carbon emission international advanced level threshold, and obtains the county low-carbon target optimal solution. The application constructs a double constraint system of the resource environment ecological carrying capacity and the per capita carbon emission advanced level.

Description

Technical Field

[0001] This application relates to the interdisciplinary field of low-carbon development planning and ecological environmental protection, and in particular to a method for determining the improvement of low-carbon goals at the county level. Background Technology

[0002] Current quantitative targets and pilot programs for carbon emission reduction are mainly concentrated at the provincial and municipal levels, with insufficient research on the division of responsibilities and target setting for carbon emission reduction at the county level. This makes it difficult to implement low-carbon development strategies in practice. Furthermore, existing methods for setting low-carbon targets do not take resource, environmental, and ecological carrying capacity as a core constraint, failing to achieve systematic synergy between low-carbon development and ecological protection. Summary of the Invention

[0003] This application provides a method for determining the improvement of low-carbon goals at the county level, aiming to solve the problem that existing methods mostly adopt static optimization models, lack dynamic adjustment mechanisms, and are difficult to achieve continuous improvement in the level of low-carbon development while ensuring ecological sustainability.

[0004] In a first aspect, embodiments of this application provide a method for determining the improvement of low-carbon targets at the county level, the method comprising: Compare the population size corresponding to the resource, environment and ecological carrying capacity with the predicted population value to determine the population prediction scenario where the predicted population value is less than the population size corresponding to the resource, environment and ecological carrying capacity; use the inventory method to calculate the total carbon emissions of the county, and combine the analysis and prediction of the total carbon emissions of the county with the carbon emission driving factors. Based on the determined population projection scenario and the predicted total carbon emissions in the county, calculate and predict the per capita carbon emissions in the county; construct a correlation model between per capita carbon emissions and per capita GDP, and calculate the international advanced level threshold for per capita carbon emissions under the corresponding per capita GDP. The predicted per capita carbon emissions in the county are compared with the international advanced level threshold for per capita carbon emissions. If the predicted value is less than the threshold, the scheme with the minimum per capita carbon emissions is selected. If the predicted value is greater than the threshold, the carbon emission driving parameters are adjusted in multiple rounds to improve the situation until the adjusted per capita carbon emissions are less than the threshold, thus obtaining the optimal solution for the county's low-carbon target.

[0005] In some embodiments, the step of comparing the population size corresponding to the resource, environment, and ecological carrying capacity with the predicted population value to determine the population prediction scenario where the predicted population value is less than the population size corresponding to the resource, environment, and ecological carrying capacity includes: obtaining the predicted population values ​​corresponding to the low, medium, and high population prediction models respectively; comparing the predicted population value of each model with the population size corresponding to the resource, environment, and ecological carrying capacity one by one; and retaining all population prediction models where the predicted population value is less than the population size corresponding to the resource, environment, and ecological carrying capacity.

[0006] In some embodiments, the method of calculating the total carbon emissions of a county using an inventory method and analyzing and predicting the total carbon emissions of a county using carbon emission driving factors includes: calculating the total carbon emissions of a county using an inventory method, the scope of which includes carbon sources generated by energy activities, industrial production, agricultural production, land use, and waste disposal, as well as carbon sinks generated by photosynthesis of crops, forest land, and green space, the total carbon emissions being the total amount of carbon sources minus the total amount of carbon sinks, logarithmic processing of carbon emission driving factors, analysis of the degree of influence of different driving factors, and prediction of the total carbon emissions of a county.

[0007] In some embodiments, the step of calculating and predicting per capita carbon emissions in a county based on a determined population forecast scenario and the predicted total carbon emissions in the county includes: dividing the year-end total population corresponding to each eligible population forecast scenario by the corresponding predicted total carbon emissions in the county to obtain the per capita carbon emissions in the county under each population forecast scenario; setting three per capita carbon emission level scenarios of low, medium and high; and predicting the per capita carbon emissions in the county under different level scenarios.

[0008] In some embodiments, constructing the correlation model between per capita carbon emissions and per capita GDP includes: obtaining per capita GDP data expressed in constant US dollars for a preset year, eliminating the impact of population factors, exchange rate factors, and inflation factors on per capita GDP, and constructing a cubic polynomial correlation model between per capita carbon emissions and per capita GDP using a logarithmic form.

[0009] In some embodiments, the calculation of the internationally advanced threshold for per capita carbon emissions under the corresponding per capita GDP includes: based on Singapore's per capita carbon emissions and per capita GDP correlation model, substituting the county's per capita GDP forecast data into the correlation model to calculate the internationally advanced threshold for per capita carbon emissions under the corresponding per capita GDP.

[0010] In some embodiments, comparing the predicted per capita carbon emissions in the county with the international advanced level threshold for per capita carbon emissions, and if the predicted value is less than the threshold, includes: comparing the predicted per capita carbon emissions in the county with the international advanced level threshold for per capita carbon emissions under each population prediction scenario and each combination of per capita carbon emission level scenarios, and selecting all combinations with predicted values ​​less than the threshold.

[0011] In some embodiments, selecting the scheme with the minimum per capita carbon emissions involves, if the predicted value is greater than a threshold, performing multiple rounds of optimization adjustments on the carbon emission driving parameters until the adjusted per capita carbon emissions are less than the threshold, thus obtaining the optimal solution for the county's low-carbon target. This includes: horizontally adjusting the urbanization rate and industrial proportion among different prediction scenarios within the same prediction period to obtain the adjusted per capita carbon emissions; if the adjusted per capita carbon emissions are still greater than the threshold, horizontally adjusting the energy intensity, the carbon dioxide emission coefficient of the power sector, and coal consumption among different prediction scenarios within the same prediction period to obtain the adjusted per capita carbon emissions; if the adjusted per capita carbon emissions are still greater than the threshold, vertically adjusting the energy intensity, the carbon dioxide emission coefficient of the power sector, and coal consumption among different prediction periods to obtain the adjusted per capita carbon emissions; if the adjusted per capita carbon emissions are still greater than the threshold, vertically adjusting the urbanization rate and industrial proportion among different prediction periods to obtain the adjusted per capita carbon emissions; and if the adjusted per capita carbon emissions are still greater than the threshold, finely adjusting the energy intensity, the carbon dioxide emission coefficient of the power sector, and coal consumption downwards until the adjusted per capita carbon emissions are less than the threshold.

[0012] In some embodiments, the method further includes: using a genetic algorithm to globally optimize the adjustment order and adjustment magnitude of carbon emission driving parameters, generating multiple sets of parameter adjustment schemes, calculating the per capita carbon emissions corresponding to each set of schemes, and selecting the parameter adjustment scheme with the minimum per capita carbon emissions that meets the resource, environmental and ecological carrying capacity constraints.

[0013] In some embodiments, the method further includes: constructing a machine learning prediction model based on historical carbon emission data and parameter adjustment data, inputting a parameter adjustment scheme to be verified, predicting the per capita carbon emission and peak carbon emission time of the corresponding county, and outputting the prediction results as a reference for the formulation of county-level low-carbon targets.

[0014] This application fills the technological gap in quantifying low-carbon targets at the county level by constructing a dual constraint system of resource, environmental, and ecological carrying capacity and the internationally advanced level of per capita carbon emissions, thus achieving precise transmission of emission reduction targets to grassroots administrative units. By using resource, environmental, and ecological carrying capacity as a pre-existing constraint, it avoids damage to the ecological environment during low-carbon development from the source, ensuring the coordinated advancement of county-level economic and social development and ecological protection. Employing a multi-round optimization mechanism, it can continuously reduce the per capita carbon emissions level at the county level without exceeding the ecological carrying capacity through gradient optimization of carbon emission driving parameters, thereby enhancing the scientific rigor, feasibility, and foresight of the county-level low-carbon transformation plan.

[0015] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic flowchart illustrating the steps of a method for determining the improvement of low-carbon targets in a county, as provided in an embodiment of this application. Figure 2 A conceptual diagram of a parameterized adjustment and optimization model for per capita carbon emissions provided by this invention; Figure 3 Adjust the logic flowchart to improve performance; Figure 4 A schematic diagram showing the results of adjusting parameters to improve per capita carbon emissions; Figure 5 This is a schematic block diagram of a county-level low-carbon target improvement determination system provided in an embodiment of this application; Figure 6 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0021] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0022] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0023] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0024] Current carbon emission reduction targets and pilot programs are mainly concentrated at the provincial and municipal levels, with insufficient research on the division of carbon emission reduction responsibilities and target setting at the county level. This makes it difficult to implement low-carbon development strategies in practice. Furthermore, existing methods for setting low-carbon targets do not take resource, environmental, and ecological carrying capacity as a core constraint, failing to achieve systematic synergy between low-carbon development and ecological protection. In addition, existing methods mostly employ static optimization models, lacking dynamic adjustment mechanisms, making it difficult to achieve continuous improvement in low-carbon development levels while ensuring ecological sustainability.

[0025] like Figure 1 As shown in the embodiments of this application, a method for determining the improvement of low-carbon goals at the county level is provided, which is applied to computer equipment. The computer equipment can be deployed on a single server or a server cluster. It can also be deployed on handheld terminals, laptops, wearable devices, or robots, etc. All data is collected and extracted in accordance with regulations under the legal authorization of the data holding unit and the competent administrative department. The collection process complies with the requirements of the Data Security Law and relevant laws and regulations on personal information protection, and there is no situation of unauthorized capture of confidential information or illegal acquisition of user privacy data.

[0026] The provided method for determining the improvement of county-level low-carbon targets includes steps S101 to S103. Details are as follows: Step S101. Compare the population size corresponding to the resource, environment and ecological carrying capacity with the predicted population value, and determine the population prediction scenario where the predicted population value is less than the population size corresponding to the resource, environment and ecological carrying capacity; use the inventory method to calculate the total carbon emissions of the county, and combine the analysis of carbon emission driving factors to predict the total carbon emissions of the county.

[0027] Specifically, this step includes two parallel computer-automated calculation subtasks. The first subtask relies on county-level resource, environmental, and ecological carrying capacity data and multiple versions of population forecast data to screen compliant population calculation scenarios. The second subtask relies on the IPCC internationally accepted carbon emission inventory accounting rules to complete the current carbon emission accounting for the entire county and achieve full-cycle carbon emission total prediction through quantitative decomposition of driving factors.

[0028] The sub-task of population forecast scenario screening is implemented as follows: The computer's local database pre-stores the annual county-level resource-carrying population limit data calculated by relevant departments using the ecological carrying capacity comprehensive evaluation model, and simultaneously accesses multiple versions of population forecast data derived from the county-level medium- and long-term population development plans compiled by relevant local departments; the computer uses a loop comparison program to compare the total predicted population value for each year with the resource-environment-carrying population scale for the same year. The system automatically eliminates population scenarios where the predicted population value exceeds the resource carrying capacity limit, and only stores the population scenario data that meets the condition of the predicted population being less than the carrying capacity limit in a dedicated cache data table. This data table serves as the benchmark population data source for subsequent carbon emission calculations throughout the entire process.

[0029] The sub-task of county-level carbon emission accounting and total forecasting was implemented: The computer retrieved five types of official statistical data from the county: annual energy statistics reports, industrial production ledgers, land use change survey statistics tables, agricultural and forestry production reports, and annual urban and rural solid waste disposal reports. Carbon sources and carbon sinks were divided into two major accounting categories according to the IPCC list. The scope of carbon source accounting was limited to five types of production activities: energy consumption activities, industrial product production activities, agricultural planting and breeding production activities, land use change activities, and solid waste disposal activities. Carbon sink accounting only counted the carbon sequestration formed by vegetation photosynthesis in county-level crop planting land, contiguous forest land, and urban public green space. The system summarized all carbon source emissions and carbon sink carbon sequestration separately, and the total carbon source emissions were subtracted from the total carbon sink emissions to obtain the actual total carbon emissions of the county in the current period. After completing the current period's accounting, the computer extracts the raw data of seven categories of carbon emission driving indicators: population size, urbanization rate, GDP per capita, industrial scale, energy intensity, thermal power emission coefficient, and coal consumption. It automatically performs logarithmic standardization preprocessing on all indicators and uses the LMDI decomposition algorithm to quantify the contribution of each driving factor to the increase or decrease in carbon emissions. Combining the future annual growth rate indicators of each indicator in the local national economic development plan, the system iterates year by year to generate the annual carbon emission forecast data for the county within the target period. The accounting and forecast results are uniformly classified and archived.

[0030] Step S102. Based on the determined population projection scenario and the projected total carbon emissions in the county, calculate and predict the per capita carbon emissions in the county; construct a correlation model between per capita carbon emissions and per capita GDP, and calculate the international advanced level threshold for per capita carbon emissions under the corresponding per capita GDP.

[0031] Specifically, this step consists of two sequential subtasks. The first subtask is to calculate per capita carbon emissions under multiple scenarios based on the compliant population scenario data and total carbon emission prediction data output by S101. The second subtask is to construct a quantitative correlation model between per capita carbon emissions and per capita GDP, and to calculate the internationally advanced threshold for per capita carbon emissions under the corresponding economic level based on mature models in international benchmark regions.

[0032] The sub-task of calculating and predicting per capita carbon emissions in counties is implemented as follows: The computer retrieves compliant year-end total population data and corresponding annual total carbon emission prediction data from the S101 archive storage. Through built-in division operation logic, it calculates the basic per capita carbon emission values ​​for each population scenario and each natural year. The system combines the different implementation paces of three types of policies—county industrial upgrading, energy-saving technological transformation, and clean energy substitution—and configures three levels of county economic development assumptions (low, medium, and high) in the background. It then corrects the change range of each carbon emission driving parameter and iteratively calculates the per capita carbon emission prediction values ​​corresponding to the three development scenarios based on the corrected parameters. All calculation results are uniformly generated into a structured data table for storage.

[0033] The implementation of the correlation model construction and international threshold calculation sub-tasks: The computer imports the original data of nominal per capita GDP of the county over the years. The system has built-in exchange rate conversion and inflation deflator programs. The data is corrected by using the constant US dollar price in 2010 as the pricing benchmark to eliminate the data distortion caused by population changes, exchange rate fluctuations, and inflation. The processed data of per capita GDP and per capita carbon emissions are uniformly logarithmically transformed. Based on the cubic polynomial function, the per capita carbon emissions-per capita GDP EKC correlation model is built in the modeling module. The system pre-stores the parameters of a mature correlation model of the same type in Singapore that has been empirically verified as an international advanced level benchmark. The standardized per capita GDP data of the county by year is batch substituted into the benchmark model, and the international advanced threshold for per capita carbon emissions under the corresponding economic level is automatically solved year by year. The threshold results are archived separately.

[0034] Step S103. Compare the predicted per capita carbon emissions of the county with the international advanced level threshold for per capita carbon emissions. If the predicted value is less than the threshold, select the scheme with the minimum per capita carbon emissions. If the predicted value is greater than the threshold, perform multiple rounds of optimization adjustment on the carbon emission driving parameters until the adjusted per capita carbon emissions are less than the threshold, and obtain the optimal solution for the county's low-carbon target.

[0035] Specifically, this step is the verification and optimization phase of the entire process. The overall logic is a serial computer operation. First, it performs a batch comparison and judgment of the per capita carbon emission calculation value with the international advanced threshold. Then, based on the judgment result, it executes the optimal solution selection or multi-round graded parameter optimization in two branches. The computer retrieves the full-scenario per capita carbon emission prediction data table from the S102 archive and the corresponding annual international advanced threshold for per capita carbon emissions, performing a line-by-line comparison of the two indicators. Branch 1: If the per capita carbon emission calculation value under any combination of scenarios is lower than the international threshold for the same period, the system automatically selects the configuration combination with the lowest per capita carbon emission value from all compliant solutions and directly locks this combination as the county's basic low-carbon target solution. Branch Two: If the per capita carbon emissions for all calculated scenarios are higher than the corresponding annual threshold, the system will automatically initiate a preset five-level gradient optimization parameter adjustment program. Only five types of parameters will be adjusted: urbanization rate, industrial proportion, energy intensity, electricity carbon emission coefficient, and coal consumption proportion. The total population and per capita GDP will remain fixed. After each round of parameter adjustment, the system will automatically recalculate the per capita carbon emissions for all scenarios, iterating the adjustment process until the per capita carbon emissions after recalculation are lower than the international advanced threshold. The final parameter configuration scheme that meets the target is the optimal solution for the county's low-carbon goal.

[0036] In some embodiments, the step of comparing the population size corresponding to the resource, environment, and ecological carrying capacity with the predicted population value to determine the population prediction scenario where the predicted population value is less than the population size corresponding to the resource, environment, and ecological carrying capacity includes: obtaining the predicted population values ​​corresponding to the low, medium, and high population prediction models respectively; comparing the predicted population value of each model with the population size corresponding to the resource, environment, and ecological carrying capacity one by one; and retaining all population prediction models where the predicted population value is less than the population size corresponding to the resource, environment, and ecological carrying capacity.

[0037] By splitting the population forecasting models into three independent tiers—low, medium, and high—and comparing each model's predicted population with the resource and environmental carrying capacity limits, exceeding the limits is eliminated, and compliant population forecasting models are retained. The computer pre-enters three sets of independent population forecasting reports compiled by local development and reform departments: low-tier (low-speed), medium-tier (medium-speed), and high-tier (high-speed). These reports include the estimated total population at the end of each target year. Simultaneously, the maximum carrying capacity population data for each county, calculated by the natural resources department using a comprehensive resource and environmental carrying capacity evaluation model, is also entered. The computer program is set up with three independent loop operation logics to sequentially compare the low-tier, medium-tier, and high-tier population data with the corresponding carrying capacity data. The program automatically marks and eliminates any plans where the predicted population exceeds the resource carrying capacity limit for any given year. All population models that meet the requirement of a predicted population less than the resource carrying capacity threshold are categorized, collected, and stored in an independent database. Subsequent carbon emission-related calculations only use the population parameters selected and retained in this embodiment. Taking the actual measurement in County A as an example, the total population data for the three phases of low, middle and high schools in 2020 and 2035 were all verified by the rules in this embodiment and met the constraints and were retained.

[0038] In some embodiments, the method of calculating the total carbon emissions of a county using an inventory method and analyzing and predicting the total carbon emissions of a county using carbon emission driving factors includes: calculating the total carbon emissions of a county using an inventory method, the scope of which includes carbon sources generated by energy activities, industrial production, agricultural production, land use, and waste disposal, as well as carbon sinks generated by photosynthesis of crops, forest land, and green space, the total carbon emissions being the total amount of carbon sources minus the total amount of carbon sinks, logarithmic processing of carbon emission driving factors, analysis of the degree of influence of different driving factors, and prediction of the total carbon emissions of a county.

[0039] Based on the IPCC inventory method, the system comprehensively distinguishes between carbon sources and carbon sinks to complete the actual carbon emission accounting. It logarithmically processes carbon emission driving indicators and uses the LMDI factor decomposition method to analyze driving weights, enabling long-term total carbon emission prediction. The system connects to five data sources: county-level official statistical yearbooks, energy consumption statistical ledgers, annual land use change survey reports, agricultural census data, and statistical reports on the harmless disposal of domestic waste. On the carbon source side, the system separately extracts fossil energy consumption, key industrial product capacity, agricultural and forestry planting and breeding scale, newly added construction land area, and solid waste disposal volume, calculates the carbon emissions for each item, and then summarizes the total carbon sources for the entire region. On the carbon sink side, it retrieves the confirmed cultivated land area, forest land survey area, and measured urban green space area, matches the corresponding annual carbon sequestration quota coefficient per unit area of ​​vegetation, and summarizes the total carbon sinks for the entire region. The system subtracts the summarized carbon sink values ​​from the summarized carbon source values ​​to obtain the county's actual total carbon emissions for the current period. After the calculation is completed, the system extracts seven driving indicators: population, urbanization rate, GDP per capita, industrial added value, energy intensity, electricity carbon emission coefficient, and total coal consumption, and performs a unified natural logarithmic transformation. The system then uses the built-in LMDI calculation module to break down the contribution ratio of each indicator to the change in carbon emissions. Based on the annual growth rate of each indicator marked in the local five-year development plan, the system iteratively extrapolates the total carbon emissions of the county from 2020 to 2035 over a long period. The pilot project in County A uses this example to complete the calculation of total carbon emissions for five development scenarios, including the baseline scenario and the enhanced low-carbon scenario.

[0040] In some embodiments, the step of calculating and predicting per capita carbon emissions in a county based on a determined population forecast scenario and the predicted total carbon emissions in the county includes: dividing the year-end total population corresponding to each eligible population forecast scenario by the corresponding predicted total carbon emissions in the county to obtain the per capita carbon emissions in the county under each population forecast scenario; setting three per capita carbon emission level scenarios of low, medium and high; and predicting the per capita carbon emissions in the county under different level scenarios.

[0041] Based on filtered multi-version population data and output total carbon emission data, the system calculates the baseline per capita carbon emission under different scenarios through division operations. It also adds three development assumptions—low, medium, and high—to predict differentiated per capita carbon emission results. The computer retrieves compliant population data from a dedicated database and annual total carbon emission forecast data from an archive repository. With a built-in division program, it uses a single year and a single population scenario as the calculation unit, dividing the corresponding total carbon emission by the total population at the end of the same year to automatically output the baseline per capita carbon emission value. Operators configure three development parameters—low-speed, medium-speed, and high-speed—in the computer backend, based on the progress of county-level industrial transformation, energy-saving renovation, and clean energy promotion. Low-speed parameters represent lagging implementation of low-carbon policies, medium-speed parameters represent policies strictly following the planned timeline, and high-speed parameters represent proactive implementation of carbon reduction policies. The system uses these three parameters to adjust the fluctuation range of various driving indicators, recalculating the per capita carbon emission indicators under the three development conditions three times, generating a multi-dimensional calculation form as the original data source for subsequent threshold comparisons.

[0042] In some embodiments, constructing the correlation model between per capita carbon emissions and per capita GDP includes: obtaining per capita GDP data expressed in constant US dollars for a preset year, eliminating the impact of population factors, exchange rate factors, and inflation factors on per capita GDP, and constructing a cubic polynomial correlation model between per capita carbon emissions and per capita GDP using a logarithmic form.

[0043] The per capita GDP was uniformly converted using 2010 constant-price US dollars. After eliminating three types of interference factors—population, exchange rate, and inflation—the data was logarithmically transformed, and a cubic polynomial EKC correlation model was constructed. The system imported the original historical nominal per capita GDP data for the county in local currency. Using a built-in fixed exchange rate conversion function, the currency was converted to the 2010 official RMB / USD benchmark exchange rate, uniformly converting it to constant-price US dollars. The system automatically deflated the historical consumer price index to reduce inflation-related price inflation, simultaneously eliminating the disturbances to per capita indicators caused by annual fluctuations in the resident population, resulting in standardized per capita GDP series data. The system uniformly performed natural logarithmic transformations on both the standardized per capita GDP and the corresponding annual per capita carbon emissions data. In the modeling module, with per capita carbon emissions as the dependent variable and the linear, quadratic, and cubic terms of per capita GDP as independent variables, the least squares method was used to automatically fit the coefficients of each term in the model, completing the construction of the cubic polynomial EKC correlation model. The international threshold calculation for County A directly reused the model framework constructed in this embodiment.

[0044] In some embodiments, the calculation of the internationally advanced threshold for per capita carbon emissions under the corresponding per capita GDP includes: based on Singapore's per capita carbon emissions and per capita GDP correlation model, substituting the county's per capita GDP forecast data into the correlation model to calculate the internationally advanced threshold for per capita carbon emissions under the corresponding per capita GDP.

[0045] By using Singapore's mature EKC correlation model as an international benchmark, and substituting standardized per capita GDP data for counties, the per capita carbon emission constraint thresholds under the corresponding economic levels are calculated year by year. The computer locally stores Singapore's per capita carbon emission-per capita GDP correlation parameters, verified by academic literature, which serve as a benchmark for international low-carbon advancements. The processed, annualized standardized per capita GDP forecast data for counties is batch-imported into the benchmark model's calculation port. The system automatically calculates the per capita carbon emission upper limit threshold for each forecast year, matching the county's economic development level. In the pilot phase for County A, this example yields a mid-term threshold of 12.95 tCO2e / person in 2020 and a mid-term threshold of 13.31 tCO2e / person in 2035. This set of thresholds is used as a fixed international constraint judgment standard throughout the entire process.

[0046] In some embodiments, comparing the predicted per capita carbon emissions in the county with the international advanced level threshold for per capita carbon emissions, and if the predicted value is less than the threshold, includes: comparing the predicted per capita carbon emissions in the county with the international advanced level threshold for per capita carbon emissions under each population prediction scenario and each combination of per capita carbon emission level scenarios, and selecting all combinations with predicted values ​​less than the threshold.

[0047] By combining all population scenarios and carbon emission development scenarios, the system compares the calculated per capita carbon emissions with international thresholds item by item, and collects all possible combinations that meet the standards. A two-dimensional data matching matrix is ​​constructed, with low, medium, and high population prediction scenarios arranged horizontally and low, medium, and high per capita carbon emission development scenarios arranged vertically. The matrix cells are automatically filled with the predicted per capita carbon emissions under the corresponding combination conditions. The system then iterates through all cells, comparing the per capita carbon emission value in each cell with the corresponding international advanced threshold for the year. The program marks all combinations where per capita carbon emissions are lower than the threshold for the same period, and collects all compliant combinations, storing them in a candidate solution database. If valid data exists in the candidate solution database, the system directly proceeds to the optimal solution selection step.

[0048] In some embodiments, selecting the scheme with the minimum per capita carbon emissions involves, if the predicted value is greater than a threshold, performing multiple rounds of optimization adjustments on the carbon emission driving parameters until the adjusted per capita carbon emissions are less than the threshold, thus obtaining the optimal solution for the county's low-carbon target. This includes: horizontally adjusting the urbanization rate and industrial proportion among different prediction scenarios within the same prediction period to obtain the adjusted per capita carbon emissions; if the adjusted per capita carbon emissions are still greater than the threshold, horizontally adjusting the energy intensity, the carbon dioxide emission coefficient of the power sector, and coal consumption among different prediction scenarios within the same prediction period to obtain the adjusted per capita carbon emissions; if the adjusted per capita carbon emissions are still greater than the threshold, vertically adjusting the energy intensity, the carbon dioxide emission coefficient of the power sector, and coal consumption among different prediction periods to obtain the adjusted per capita carbon emissions; if the adjusted per capita carbon emissions are still greater than the threshold, vertically adjusting the urbanization rate and industrial proportion among different prediction periods to obtain the adjusted per capita carbon emissions; and if the adjusted per capita carbon emissions are still greater than the threshold, finely adjusting the energy intensity, the carbon dioxide emission coefficient of the power sector, and coal consumption downwards until the adjusted per capita carbon emissions are less than the threshold.

[0049] Following a five-level adjustment sequence, the parameters are adjusted horizontally for industry and urbanization, then horizontally for energy technology, then vertically for energy technology, then vertically for industry and urbanization, and finally fine-tuned for energy parameters. Per capita carbon emissions are recalculated after each round of adjustments until the calculated value falls below the international threshold. The computer uses a pre-set, fixed-sequence five-level parameter optimization trigger logic. Adjustable parameters are limited to five categories: urbanization rate, industrial share, energy intensity, carbon emission coefficient of the power sector, and coal consumption share. Total population and per capita GDP are locked throughout the process and do not participate in parameter changes. The first round, Run1, involves switching urbanization rate and industrial proportion parameters across low, medium, and high development scenarios within the same forecast period. After parameter changes, the system automatically recalculates per capita carbon emissions. If the calculation meets the target, the entire adjustment process terminates. If the calculation exceeds the target, it automatically proceeds to Run2: horizontal adjustments are made to the three energy parameters within the same forecast period, and the indicators are recalculated. If the targets are still exceeded, it proceeds to Run3: vertical changes are made to energy technology parameters across multiple forecast years (2020, 2025, 2030, and 2035). If the targets are still exceeded, it proceeds to Run4: vertical adjustments are made to urbanization rate and industrial proportion parameters across multiple years. If the targets are still exceeded, it proceeds to Run5: minor downward fine-tuning is performed on the three energy parameters. Per capita carbon emissions are recalculated after each fine-tuning, and this cycle continues until the calculated indicators are below the threshold, at which point the entire set of optimized parameters is locked. County A achieved its per capita carbon emission targets falling below the threshold standard in both 2020 and 2035 based on this five-level adjustment logic.

[0050] In some embodiments, the method further includes: using a genetic algorithm to globally optimize the adjustment order and adjustment magnitude of carbon emission driving parameters, generating multiple sets of parameter adjustment schemes, calculating the per capita carbon emissions corresponding to each set of schemes, and selecting the parameter adjustment scheme with the minimum per capita carbon emissions that meets the resource, environmental and ecological carrying capacity constraints.

[0051] The system utilizes a genetic algorithm to traverse parameter fluctuation ranges and generate multiple sets of alternative adjustment schemes. It then calculates the per capita carbon emissions for each scheme in batches, selecting the optimal parameter configuration that simultaneously satisfies resource carrying capacity constraints and minimizes per capita carbon emissions. An independent genetic algorithm computation module is embedded within the computer, using the reasonable fluctuation range of five types of adjustable parameters as the population encoding boundary. The algorithm iteration count and population size are pre-set. The algorithm randomly generates multiple initial sets of parameter adjustment schemes. Each scheme automatically calls the aforementioned carbon emission calculation logic to calculate the corresponding per capita carbon emissions, simultaneously verifying that population parameters do not exceed resource, environmental, and ecological carrying capacity constraints. With minimizing per capita carbon emissions as the optimization objective, the system iteratively generates a new generation of parameter schemes through selection, crossover, and mutation operations. After the iteration cycle, the system selects the parameter combination with the lowest per capita carbon emissions from all compliant and feasible schemes, adding it as a candidate optimization scheme to the county-level low-carbon target scheme library.

[0052] In some embodiments, the method further includes: constructing a machine learning prediction model based on historical carbon emission data and parameter adjustment data, inputting a parameter adjustment scheme to be verified, predicting the per capita carbon emission and peak carbon emission time of the corresponding county, and outputting the prediction results as a reference for the formulation of county-level low-carbon targets.

[0053] Based on historical carbon emission data of the county and historical samples of past parameter adjustments, a machine learning prediction model is trained. After the new parameter scheme is entered, the per capita carbon emission trend and the specific year of carbon emission peaking are predicted after the scheme is implemented.

[0054] The computer collects historical carbon emission statistics, records of multiple rounds of historical parameter adjustments, and actual per capita carbon emission data after implementation in the corresponding year for the county. It splits the training dataset and validation dataset according to the proportion, and uses a regression-based machine learning algorithm to complete model training and accuracy verification. After a new parameter optimization scheme is generated, all parameters in the scheme are imported into the input of the trained machine learning model in batches. The model automatically outputs the long-term per capita carbon emission change trend curve after the scheme is implemented, and simultaneously outputs the specific natural year corresponding to the peak carbon emission of the county. The pilot project in County A calculated through this example that the peak time of carbon emissions in the whole region falls in the range of 2027 to 2028. The calculation results are directly used for the local government's low-carbon industry policy formulation and medium- and long-term emission reduction planning.

[0055] In some embodiments, such as Figure 2 , Figure 3 and Figure 4 As shown, refer to Figure 2 The method includes the following steps: 1. Compare the carrying capacity of resources, environment, and ecology with the projected population to determine the population projection scenario; 2. Calculation of total carbon emissions at the county level; 3. Based on the population projection scenario determined in step 1 and the total county carbon emissions calculated in step 2, calculate the per capita carbon emissions in the county. 4. Construct a per capita carbon emission-per capita GDP EKC model to calculate the per capita carbon emission threshold; 5. Compare the per capita carbon emissions at the county level determined in steps 3 and 4. X i,t Is it less than the per capita carbon emission threshold? X s,g To be subject to internationally advanced standards; if so, then select one. X i,t The optimal solution; if not, then optimization parameters need to be adjusted. 6. Adjustment of parameters for improving per capita carbon emissions at the county level. Based on the internationally advanced level of carbon emission thresholds, the parameters in the county-level total carbon emission accounting process in step 2 are continuously improved to ensure that the per capita carbon emissions in step 3 meet the threshold standard of the internationally advanced level of per capita carbon emissions in step 4, thus finding the optimal solution. X i,t Minimum value.

[0056] The population size that County A can support under different development scenarios (low, medium, and high) is calculated using a comprehensive evaluation model of resource, environment, and ecological carrying capacity. P w,t ) and population projections for County A ( P j.t The 2020 population projection scenarios were determined as follows: high scenario 647,434 people, medium scenario 644,598 people, and low scenario 643,373 people. The 2035 population projection scenarios were determined as follows: high scenario 656,437 people, medium scenario 643,000 people, and low scenario 639,364 people.

[0057] Table 1 Comparison between the resource, environmental, and ecological carrying capacity scheme and the total population scheme. Simulation of total CO2 emissions based on scenario analysis: (1) Scenario design The study included five scenarios: a baseline scenario, an enhanced low-carbon scenario, a gradual transition scenario, a policy scenario for industrially strong counties, and a scenario for low-carbon development in the power sector. The differences in scenario analysis lie in the gradient design and emphasis preferences for key parameters in different areas, as well as the consideration of the implementation and enforcement of policy combinations (Table 2). The base period for the scenarios is 2017, and the outlook period is 2018-2035. The goal of the scenario analysis is to predict the total energy-related CO2 emissions of County A in each scenario, and whether there is a possibility of peaking within the prediction period.

[0058] Table 2 Parameter level settings in five cases (2) Parameter design: The parameter selection for scenario analysis depends on the LMDI method's deconstruction of CO2 driving factors; the parameter settings are shown in Table 3. These parameters are based on County A's own development level, the level of policy constraints on the parameters, and the planning objectives for the parameters.

[0059] Table 3 Parameter settings for low-speed (L), medium-speed (M), and high-speed (H) development (3) Prediction of total CO2 emissions in County A based on STIRPA scenario analysis: Based on the STIRPAT model, a fitting analysis was performed on the total CO2 emissions. The resulting functional relationship is shown in Equation 7.

[0060] (Formula 7) In the formula, C Total carbon emissions, in units of 10 4 tCO2; P (Population) refers to population size, expressed as total population, with units of 10. 4 People; U (Urbanization) represents the level of urbanization, characterized by the urbanization rate, expressed as a percentage (%). A Affluence represents the level of social wealth, expressed as GDP per capita, with units of 10. 4 Yuan / person; T (Technology) refers to the level of technology, characterized by energy intensity, with units of t standard coal / 10 4 Yuan; I (Industry) refers to industrial development, represented by the output value of the secondary industry, with units of 10. 4 Yuan; E (energy) represents energy utilization, characterized by total coal consumption, with units of 10. 4 t standard coal.

[0061] Per capita carbon emission accounting and forecasting for County A: After determining the population projection results for County A ( P j.t After predicting the total CO2 (ECi,t) and the total CO2 (ECi,t), substituting the data into Formula 4 yields the predicted per capita CO2 for County A. X i,t The results are shown in Table 5.

[0062] Construct a per capita carbon emission-per capita GDP EKC model to calculate the per capita carbon emission threshold: Based on the Singapore EKC model, the per capita GDP forecast data of County A is substituted into Formula 5 to calculate the threshold standard for the international advanced level of per capita carbon emissions in County A. The results are shown in Table 4.

[0063] Table 4. Forecast of per capita GDP in County A and Calculation Results of International Advanced Level Threshold As shown in Table 4, the per capita GDP of County A shows an increasing trend under all scenarios. Since the per capita GDP predicted by the medium scenario has a relatively small deviation in each forecast period, we select the threshold results corresponding to the per capita GDP predicted by the medium scenario in each forecast period as the constraint condition for the international advanced level. Therefore, the selected threshold standard for the medium term (2020) is 12.95, and the threshold standard for the long term (2035) is 13.31.

[0064] Compare the predicted per capita CO2 of County A ( X i,t ) and the internationally advanced threshold standard for per capita carbon emissions ( X s,g ): As shown in Table 5, per capita carbon emissions in County A X i,t, Greater than the per capita carbon emission threshold X s,g， The optimization parameters need to be adjusted. Table 5 Comparison of per capita CO2 schemes and threshold standards According to constraint (1) P j,t, <P w,t The high, medium, and low scenarios for total population projections in 2020 and 2035 all meet the requirements of this constraint. (Consider constraint (II)) X i,t <X s,g Therefore, we can conclude that: (1) The total population projection scenarios for 2020 and 2035 should be selected within the range of resource, environmental and ecological carrying capacity, and the population high scheme should be selected to minimize per capita carbon emissions.

[0065] (2) Per capita CO2 prediction X ,t The coefficients of the highest-valued scheme need to be adjusted to make the predicted per capita CO2 value ( X i,t Less than the threshold standard ( X s,g ).

[0066] The per capita carbon emission parameters in County A are trending towards improvement: After selecting the predicted scenario, the plan is adjusted. Adjustments to the optimization parameters can be categorized into two types: advancing the target year while maintaining the same quantitative adjustment magnitude; or keeping the target year unchanged while increasing the adjustment requirements. Specific adjustments are as follows: (1) In 2020, adjustments to population, technological level, and energy structure are key indicators, with the goal of parametrically adjusting based on thresholds to reduce per capita carbon emissions. Adjustment plan: ① Adjust the urbanization rate to a lower level; ② Increase energy intensity T1 and CO2 emission coefficient T2 of the power sector to the 2030 level; ③ The research report shows that industrial restructuring has great potential for energy conservation and carbon reduction.

[201] Therefore, it is set that the rate of decline in the industrial proportion of County A reaches the high-speed development level of 2025; ④ Advances in energy technology play a significant role in reducing per capita carbon emissions before carbon emission intensity peaks.

[104] Therefore, the energy structure needs to be adjusted. According to the "Z Province Low-Carbon Development Plan", by 2020, the proportion of non-fossil energy will increase to about 20%, and the proportion of coal consumption (excluding coal used for external thermal power generation) will decrease to about 42%, adjusting the proportion of coal consumption in primary energy to 40%, thus approaching the threshold.

[0067] Table 6. Adjustments to parameters for improving per capita carbon emissions (High-probability scenario, 2020) (2) Adjustment plan for 2035: ① The "A County Master Plan (2017-2035)" stipulates that energy consumption per unit of GDP should be reduced to about 0.3 by 2035, which serves as the highest constraint level for energy intensity in A County by 2035; ② The proportion of coal consumption (excluding coal used for external thermal power generation) dropped to 22%.

[0068] Table 7. Adjustments to parameters for improving per capita carbon emissions (High Scheme, 2035) Table 8 Results of Adjustments to Parameters for Improving Per Capita Carbon Emissions After optimization, the adjustments to the high-performance scheme resulted in an inverted U-shaped Environmental Kuznets Curve (EKC) pattern, as shown in [reference needed]. Figure 4 It is estimated that the per capita carbon emissions in County A will peak around 2027-2028. Relevant departments of the County A government can make reasonable arrangements for economic activities and formulate reasonable policies to further force the transformation of economic development mode and industrial structure adjustment, accelerate the transformation to a low-carbon economy, and coordinate economic development with energy conservation, emission reduction and low-carbon development. This has practical guiding significance for the research on per capita carbon emission control in the county.

[0069] Please see Figure 5 As shown, Figure 5 This is a schematic diagram of the structure of the county-level low-carbon target improvement determination system 200 provided in this application embodiment. The county-level low-carbon target improvement determination system 200 is used to execute the steps of the county-level low-carbon target improvement determination method shown in the above embodiments. The county-level low-carbon target improvement determination system 200 can be a single server or a server cluster, or it can be a terminal, such as a handheld terminal, laptop computer, wearable device, or robot.

[0070] like Figure 5 As shown, the county-level low-carbon target improvement determination system 200 includes: Population comparison unit 201 is used to compare the population size corresponding to the resource, environment and ecological carrying capacity with the predicted population value, and to determine the population prediction scenario where the predicted population value is less than the population size corresponding to the resource, environment and ecological carrying capacity; the total carbon emissions of the county are calculated using the inventory method, and the total carbon emissions of the county are predicted in combination with the analysis of carbon emission driving factors. The horizontal calculation unit 202 is used to calculate and predict the per capita carbon emissions of a county based on the determined population prediction scenario and the predicted total carbon emissions of the county; to construct a correlation model between per capita carbon emissions and per capita GDP, and to calculate the international advanced level threshold of per capita carbon emissions under the corresponding per capita GDP. The optimal unit 203 is used to compare the predicted per capita carbon emissions in the county with the international advanced level threshold for per capita carbon emissions. If the predicted value is less than the threshold, the scheme with the minimum per capita carbon emissions is selected. If the predicted value is greater than the threshold, the carbon emission driving parameters are adjusted in multiple rounds to improve the performance until the adjusted per capita carbon emissions are less than the threshold, thus obtaining the optimal solution for the county's low-carbon target.

[0071] In some embodiments, the step of comparing the population size corresponding to the resource, environment, and ecological carrying capacity with the predicted population value to determine the population prediction scenario where the predicted population value is less than the population size corresponding to the resource, environment, and ecological carrying capacity includes: obtaining the predicted population values ​​corresponding to the low, medium, and high population prediction models respectively; comparing the predicted population value of each model with the population size corresponding to the resource, environment, and ecological carrying capacity one by one; and retaining all population prediction models where the predicted population value is less than the population size corresponding to the resource, environment, and ecological carrying capacity.

[0072] In some embodiments, the method of calculating the total carbon emissions of a county using an inventory method and analyzing and predicting the total carbon emissions of a county using carbon emission driving factors includes: calculating the total carbon emissions of a county using an inventory method, the scope of which includes carbon sources generated by energy activities, industrial production, agricultural production, land use, and waste disposal, as well as carbon sinks generated by photosynthesis of crops, forest land, and green space, the total carbon emissions being the total amount of carbon sources minus the total amount of carbon sinks, logarithmic processing of carbon emission driving factors, analysis of the degree of influence of different driving factors, and prediction of the total carbon emissions of a county.

[0073] In some embodiments, the step of calculating and predicting per capita carbon emissions in a county based on a determined population forecast scenario and the predicted total carbon emissions in the county includes: dividing the year-end total population corresponding to each eligible population forecast scenario by the corresponding predicted total carbon emissions in the county to obtain the per capita carbon emissions in the county under each population forecast scenario; setting three per capita carbon emission level scenarios of low, medium and high; and predicting the per capita carbon emissions in the county under different level scenarios.

[0074] In some embodiments, constructing the correlation model between per capita carbon emissions and per capita GDP includes: obtaining per capita GDP data expressed in constant US dollars for a preset year, eliminating the impact of population factors, exchange rate factors, and inflation factors on per capita GDP, and constructing a cubic polynomial correlation model between per capita carbon emissions and per capita GDP using a logarithmic form.

[0075] In some embodiments, the calculation of the internationally advanced threshold for per capita carbon emissions under the corresponding per capita GDP includes: based on Singapore's per capita carbon emissions and per capita GDP correlation model, substituting the county's per capita GDP forecast data into the correlation model to calculate the internationally advanced threshold for per capita carbon emissions under the corresponding per capita GDP.

[0076] In some embodiments, comparing the predicted per capita carbon emissions in the county with the international advanced level threshold for per capita carbon emissions, and if the predicted value is less than the threshold, includes: comparing the predicted per capita carbon emissions in the county with the international advanced level threshold for per capita carbon emissions under each population prediction scenario and each combination of per capita carbon emission level scenarios, and selecting all combinations with predicted values ​​less than the threshold.

[0077] In some embodiments, selecting the scheme with the minimum per capita carbon emissions involves, if the predicted value is greater than a threshold, performing multiple rounds of optimization adjustments on the carbon emission driving parameters until the adjusted per capita carbon emissions are less than the threshold, thus obtaining the optimal solution for the county's low-carbon target. This includes: horizontally adjusting the urbanization rate and industrial proportion among different prediction scenarios within the same prediction period to obtain the adjusted per capita carbon emissions; if the adjusted per capita carbon emissions are still greater than the threshold, horizontally adjusting the energy intensity, the carbon dioxide emission coefficient of the power sector, and coal consumption among different prediction scenarios within the same prediction period to obtain the adjusted per capita carbon emissions; if the adjusted per capita carbon emissions are still greater than the threshold, vertically adjusting the energy intensity, the carbon dioxide emission coefficient of the power sector, and coal consumption among different prediction periods to obtain the adjusted per capita carbon emissions; if the adjusted per capita carbon emissions are still greater than the threshold, vertically adjusting the urbanization rate and industrial proportion among different prediction periods to obtain the adjusted per capita carbon emissions; and if the adjusted per capita carbon emissions are still greater than the threshold, finely adjusting the energy intensity, the carbon dioxide emission coefficient of the power sector, and coal consumption downwards until the adjusted per capita carbon emissions are less than the threshold.

[0078] In some embodiments, the method further includes: using a genetic algorithm to globally optimize the adjustment order and adjustment magnitude of carbon emission driving parameters, generating multiple sets of parameter adjustment schemes, calculating the per capita carbon emissions corresponding to each set of schemes, and selecting the parameter adjustment scheme with the minimum per capita carbon emissions that meets the resource, environmental and ecological carrying capacity constraints.

[0079] In some embodiments, the method further includes: constructing a machine learning prediction model based on historical carbon emission data and parameter adjustment data, inputting a parameter adjustment scheme to be verified, predicting the per capita carbon emission and peak carbon emission time of the corresponding county, and outputting the prediction results as a reference for the formulation of county-level low-carbon targets.

[0080] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the county-level low-carbon target improvement determination system and its modules described above can be referred to the corresponding content in the various embodiments of the county-level low-carbon target improvement determination method, and will not be repeated here.

[0081] The aforementioned method for determining the optimal low-carbon targets at the county level can be implemented as a computer program, which can be used in various ways, such as... Figure 5 It runs on the system shown.

[0082] Please see Figure 6 , Figure 6 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application. The computer device includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and internal memory.

[0083] The storage medium can store operating devices and computer programs. The computer program includes program instructions that, when executed, cause the processor to perform any method for determining the improvement of county-level low-carbon goals.

[0084] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0085] Internal memory provides an environment for the execution of computer programs in non-volatile storage media. When the computer program is executed by the processor, it enables the processor to execute any method for determining the improvement of county-level low-carbon goals.

[0086] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the terminal to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0087] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0088] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps: Compare the population size corresponding to the resource, environment and ecological carrying capacity with the predicted population value to determine the population prediction scenario where the predicted population value is less than the population size corresponding to the resource, environment and ecological carrying capacity; use the inventory method to calculate the total carbon emissions of the county, and combine the analysis and prediction of the total carbon emissions of the county with the carbon emission driving factors. Based on the determined population projection scenario and the predicted total carbon emissions in the county, calculate and predict the per capita carbon emissions in the county; construct a correlation model between per capita carbon emissions and per capita GDP, and calculate the international advanced level threshold for per capita carbon emissions under the corresponding per capita GDP. The predicted per capita carbon emissions in the county are compared with the international advanced level threshold for per capita carbon emissions. If the predicted value is less than the threshold, the scheme with the minimum per capita carbon emissions is selected. If the predicted value is greater than the threshold, the carbon emission driving parameters are adjusted in multiple rounds to improve the situation until the adjusted per capita carbon emissions are less than the threshold, thus obtaining the optimal solution for the county's low-carbon target.

[0089] In some embodiments, the step of comparing the population size corresponding to the resource, environment, and ecological carrying capacity with the predicted population value to determine the population prediction scenario where the predicted population value is less than the population size corresponding to the resource, environment, and ecological carrying capacity includes: obtaining the predicted population values ​​corresponding to the low, medium, and high population prediction models respectively; comparing the predicted population value of each model with the population size corresponding to the resource, environment, and ecological carrying capacity one by one; and retaining all population prediction models where the predicted population value is less than the population size corresponding to the resource, environment, and ecological carrying capacity.

[0090] In some embodiments, the method of calculating the total carbon emissions of a county using an inventory method and analyzing and predicting the total carbon emissions of a county using carbon emission driving factors includes: calculating the total carbon emissions of a county using an inventory method, the scope of which includes carbon sources generated by energy activities, industrial production, agricultural production, land use, and waste disposal, as well as carbon sinks generated by photosynthesis of crops, forest land, and green space, the total carbon emissions being the total amount of carbon sources minus the total amount of carbon sinks, logarithmic processing of carbon emission driving factors, analysis of the degree of influence of different driving factors, and prediction of the total carbon emissions of a county.

[0091] In some embodiments, the step of calculating and predicting per capita carbon emissions in a county based on a determined population forecast scenario and the predicted total carbon emissions in the county includes: dividing the year-end total population corresponding to each eligible population forecast scenario by the corresponding predicted total carbon emissions in the county to obtain the per capita carbon emissions in the county under each population forecast scenario; setting three per capita carbon emission level scenarios of low, medium and high; and predicting the per capita carbon emissions in the county under different level scenarios.

[0092] In some embodiments, constructing the correlation model between per capita carbon emissions and per capita GDP includes: obtaining per capita GDP data expressed in constant US dollars for a preset year, eliminating the impact of population factors, exchange rate factors, and inflation factors on per capita GDP, and constructing a cubic polynomial correlation model between per capita carbon emissions and per capita GDP using a logarithmic form.

[0093] In some embodiments, the calculation of the internationally advanced threshold for per capita carbon emissions under the corresponding per capita GDP includes: based on Singapore's per capita carbon emissions and per capita GDP correlation model, substituting the county's per capita GDP forecast data into the correlation model to calculate the internationally advanced threshold for per capita carbon emissions under the corresponding per capita GDP.

[0094] In some embodiments, comparing the predicted per capita carbon emissions in the county with the international advanced level threshold for per capita carbon emissions, and if the predicted value is less than the threshold, includes: comparing the predicted per capita carbon emissions in the county with the international advanced level threshold for per capita carbon emissions under each population prediction scenario and each combination of per capita carbon emission level scenarios, and selecting all combinations with predicted values ​​less than the threshold.

[0095] In some embodiments, selecting the scheme with the minimum per capita carbon emissions involves, if the predicted value is greater than a threshold, performing multiple rounds of optimization adjustments on the carbon emission driving parameters until the adjusted per capita carbon emissions are less than the threshold, thus obtaining the optimal solution for the county's low-carbon target. This includes: horizontally adjusting the urbanization rate and industrial proportion among different prediction scenarios within the same prediction period to obtain the adjusted per capita carbon emissions; if the adjusted per capita carbon emissions are still greater than the threshold, horizontally adjusting the energy intensity, the carbon dioxide emission coefficient of the power sector, and coal consumption among different prediction scenarios within the same prediction period to obtain the adjusted per capita carbon emissions; if the adjusted per capita carbon emissions are still greater than the threshold, vertically adjusting the energy intensity, the carbon dioxide emission coefficient of the power sector, and coal consumption among different prediction periods to obtain the adjusted per capita carbon emissions; if the adjusted per capita carbon emissions are still greater than the threshold, vertically adjusting the urbanization rate and industrial proportion among different prediction periods to obtain the adjusted per capita carbon emissions; and if the adjusted per capita carbon emissions are still greater than the threshold, finely adjusting the energy intensity, the carbon dioxide emission coefficient of the power sector, and coal consumption downwards until the adjusted per capita carbon emissions are less than the threshold.

[0096] In some embodiments, the method further includes: using a genetic algorithm to globally optimize the adjustment order and adjustment magnitude of carbon emission driving parameters, generating multiple sets of parameter adjustment schemes, calculating the per capita carbon emissions corresponding to each set of schemes, and selecting the parameter adjustment scheme with the minimum per capita carbon emissions that meets the resource, environmental and ecological carrying capacity constraints.

[0097] In some embodiments, the method further includes: constructing a machine learning prediction model based on historical carbon emission data and parameter adjustment data, inputting a parameter adjustment scheme to be verified, predicting the per capita carbon emission and peak carbon emission time of the corresponding county, and outputting the prediction results as a reference for the formulation of county-level low-carbon targets.

[0098] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the steps of the county-level low-carbon target improvement determination method provided in any embodiment of this application.

[0099] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0100] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for determining the improvement of low-carbon targets at the county level, characterized in that, include: Compare the population size corresponding to the resource, environment and ecological carrying capacity with the predicted population value, and determine the population prediction scenario where the predicted population value is less than the population size corresponding to the resource, environment and ecological carrying capacity. The total carbon emissions of a county are calculated using an inventory method, and the total carbon emissions of a county are predicted by combining the analysis of carbon emission driving factors. Based on the determined population projection scenario and the predicted total carbon emissions in the county, calculate and predict the per capita carbon emissions in the county; construct a correlation model between per capita carbon emissions and per capita GDP, and calculate the international advanced level threshold for per capita carbon emissions under the corresponding per capita GDP. The predicted per capita carbon emissions in the county are compared with the international advanced level threshold for per capita carbon emissions. If the predicted value is less than the threshold, the scheme with the minimum per capita carbon emissions is selected. If the predicted value is greater than the threshold, the carbon emission driving parameters are adjusted in multiple rounds to improve the situation until the adjusted per capita carbon emissions are less than the threshold, thus obtaining the optimal solution for the county's low-carbon target.

2. The method according to claim 1, characterized in that, The comparison of the population size corresponding to the resource, environmental, and ecological carrying capacity with the predicted population value, and the determination of population prediction scenarios where the predicted population value is less than the population size corresponding to the resource, environmental, and ecological carrying capacity, include: Obtain the predicted population values ​​corresponding to the low, medium, and high population prediction models respectively, compare the predicted population value of each model with the population size corresponding to the resource, environment, and ecological carrying capacity, and retain all population prediction models whose predicted population value is less than the population size corresponding to the resource, environment, and ecological carrying capacity.

3. The method according to claim 1, characterized in that, The method of using an inventory approach to calculate total county-level carbon emissions, combined with analysis of carbon emission driving factors to predict total county-level carbon emissions, includes: The total carbon emissions of a county are calculated using the inventory method. The scope of the calculation includes carbon sources generated by energy activities, industrial production, agricultural production, land use and waste disposal, as well as carbon sinks generated by photosynthesis of crops, forest land and green space. The total carbon emissions are the total carbon sources minus the total carbon emissions. Logarithmic processing of carbon emission drivers was performed to analyze the degree of influence of different drivers and predict the total carbon emissions in the county.

4. The method according to claim 1, characterized in that, The calculation and prediction of per capita carbon emissions in the county, based on the determined population projection scenario and the predicted total carbon emissions in the county, includes: The year-end total population corresponding to each eligible population projection scenario is divided by the corresponding projected total carbon emissions of the county to obtain the per capita carbon emissions of the county under each population projection scenario. Three scenarios with low, medium, and high per capita carbon emission levels were set up to predict the per capita carbon emission of counties under different scenarios.

5. The method according to claim 1, characterized in that, The construction of the correlation model between per capita carbon emissions and per capita GDP includes: Obtain GDP per capita data in constant-price US dollars for a preset year; The impact of population factors, exchange rate factors, and inflation on GDP per capita was eliminated. A cubic polynomial correlation model between per capita carbon emissions and per capita GDP is constructed by taking the logarithm.

6. The method according to claim 5, characterized in that, The calculation of the internationally advanced threshold for per capita carbon emissions under the corresponding per capita GDP includes: Based on the correlation model between per capita carbon emissions and per capita GDP in Singapore, the predicted per capita GDP data of counties are substituted into the correlation model to calculate the internationally advanced threshold for per capita carbon emissions under the corresponding per capita GDP.

7. The method according to claim 1, characterized in that, The step of comparing the predicted per capita carbon emissions in the county with the international advanced level threshold for per capita carbon emissions, and if the predicted value is less than the threshold, includes: We compared the predicted per capita carbon emissions of counties with the international advanced level threshold for per capita carbon emissions under each population projection scenario and each combination of per capita carbon emission level scenarios, and selected all combinations with predicted values ​​less than the threshold.

8. The method according to claim 1, characterized in that, The method of selecting the scheme with the minimum per capita carbon emissions involves, if the predicted value is greater than a threshold, performing multiple rounds of optimization adjustments on the carbon emission driving parameters until the adjusted per capita carbon emissions are less than the threshold, thus obtaining the optimal solution for the county's low-carbon target, including: The urbanization rate and the proportion of industry are adjusted horizontally among different forecast scenarios within the same forecast period to obtain the adjusted per capita carbon emissions. If the adjusted per capita carbon emissions are still greater than the threshold, the energy intensity, the carbon dioxide emission coefficient of the power sector, and the coal consumption are adjusted horizontally among different forecast scenarios within the same forecast period to obtain the adjusted per capita carbon emissions. If the adjusted per capita carbon emissions are still greater than the threshold, the energy intensity, the carbon dioxide emission coefficient of the power sector, and coal consumption are adjusted longitudinally between different forecast periods to obtain the adjusted per capita carbon emissions. If the adjusted per capita carbon emissions are still greater than the threshold, the urbanization rate and the proportion of industry are adjusted longitudinally between different forecast periods to obtain the adjusted per capita carbon emissions. If the adjusted per capita carbon emissions are still greater than the threshold, the energy intensity, the carbon dioxide emission coefficient of the power sector, and coal consumption are slightly adjusted downward until the adjusted per capita carbon emissions are less than the threshold.

9. The method according to claim 1, characterized in that, The method further includes: A genetic algorithm is used to globally optimize the adjustment order and magnitude of carbon emission driving parameters, generating multiple sets of parameter adjustment schemes. The per capita carbon emission corresponding to each scheme is calculated, and the parameter adjustment scheme with the minimum per capita carbon emission and that meets the resource, environmental and ecological carrying capacity constraints is selected.

10. The method according to claim 1, characterized in that, The method further includes: A machine learning prediction model based on historical carbon emission data and parameter adjustment data is constructed. The input parameter adjustment scheme to be verified is used to predict the per capita carbon emission and carbon emission peak time of the corresponding county. The prediction results are output as a reference for the county's low-carbon target setting.

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