Evaluation method for low-carbon city development under multi-condition constraint

Abstract

The invention discloses a method for evaluating the development of a low-carbon city under multi-condition constraints, and the method comprises the steps: S1, building a carbon emission prediction model: employing a gray correlation analysis method, taking the carbon emission over the years as a reference sequence, taking each influence factor as a comparison sequence, selecting social and economic factors which affect the carbon emission as constraint conditions, and carrying out the prediction of the carbon emission; the correlation degree between the influence factors is measured, and a regional carbon emission prediction model is constructed; s2, constructing a regional low-carbon development chasing model, analyzing urban low-carbon planning influence factors, and predicting the carbon emission of the region through an IPAT model; s3, determining a carbon emission prediction function, performing ridge regression analysis through a variance expansion factor (VIF), fitting a ridge regression result to determine a ridge regression equation, and establishing an STIRPAT expansion model; and S4, analyzing a result calculated by the STIRPAT expansion model, and planning an urban low-carbon development route under a double-control target in combination with problems existing in the urban development process.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission prediction, and in particular, relates to an evaluation method for low-carbon city development under multiple condition constraints. Background Art

[0002] With the development of industry, the total amount of carbon emissions will inevitably continue to show an upward trend, but through technological reform, scientific and technological innovation, and energy substitution, the intensity of carbon emissions can be gradually reduced, and economic growth can exceed the growth rate of carbon emissions. Therefore, for the low-carbon development plan of the city, considering the use of renewable energy, the reduction of energy consumption intensity and carbon emission intensity can be considered as one of the goals of the city's low-carbon development. In order to stabilize economic development and promote technological innovation of enterprises, factors such as scientific and technological investment and energy consumption should be taken into account, and the city's low-carbon development plan should be constructed from multiple fields such as industry, agriculture, and transportation.

[0003] In the Chinese public invention patent with application number CN202410494380.2, a regional carbon emission prediction method based on carbon emission flow and big data analysis is disclosed, and the method includes: in S1 regional division, the target area is divided into multiple sub-areas, each sub-area is labeled, and the main information set and secondary information set of each sub-area are formed according to different labels; S2 performs MIC correlation analysis to rank the correlation of the features affecting carbon emissions in the sub-area secondary information set; S3 constructs a sub-area carbon emission prediction model; S4 performs simulation calculation based on the constructed carbon emission prediction models of each sub-area, and outputs the regional carbon emission prediction results. This design can not only obtain different prediction models according to different data types, so as to make differentiated predictions for different regions, but also effectively reduce the difficulty of solving through labeling processing, and shorten the regional carbon emission prediction time.

[0004] The defect in the existing technology is that although the above patent has achieved differentiated predictions of carbon emissions for different regions, it lacks a low-carbon development route for cities under the dual control targets of carbon emission planning in different fields. Summary of the invention

[0005] In view of the problem that the existing carbon emission prediction methods lack urban low-carbon development routes under the dual control targets of carbon emission planning in different fields, the present invention provides an evaluation method for low-carbon urban development under multi-condition constraints.

[0006] In order to achieve the above technical objectives, the technical solution adopted by the present invention is as follows:

[0007] An evaluation method for low-carbon city development under multiple constraints includes the following steps:

[0008] S1. Construct a carbon emission prediction model: Use the grey correlation analysis method, take the carbon emissions of previous years as the reference series, and the influencing factors as the comparison series. Select the socioeconomic factors that affect carbon emissions as constraints, measure the correlation between the influencing factors, and construct a regional carbon emission prediction model.

[0009] S2. Construct a regional low-carbon development catch-up model, analyze the factors affecting urban low-carbon planning, and predict the carbon emissions of a region through the IPAT model; expand the model from the perspective of population size, economic level, and carbon emission intensity, and construct an urban low-carbon development planning model with multiple constraints.

[0010] S3. The carbon emission prediction function is determined by performing ridge regression analysis through the variance inflation factor (VIF). The results of ridge regression are fitted to determine the ridge regression equation and establish the STIRPAT extended model. It is found that carbon emissions can be reduced by adjusting the energy consumption structure, while also balancing the relationship between economic growth and emission reduction.

[0011] S4. Analyze the results calculated by the above-mentioned STIRPAT extended model, combine the existing problems in the urban development process, plan the urban low-carbon development route under the dual control objectives, and propose control methods for urban-level low-carbon development planning from the fields of energy, industry, transportation, construction, and ecosystem carbon sinks.

[0012] Furthermore, the detailed steps of constructing the carbon emission prediction model in step S1 include:

[0013] S101. Obtain the carbon emissions of previous years as a reference series and various influencing factors as a comparison series;

[0014] S102, respectively performing dimensionless processing on the reference number series and the comparison number series;

[0015] S103. According to the grey correlation model established above, the correlation between carbon emissions and various influencing factors is obtained.

[0016] Furthermore, in step S101, the raw carbon emission data is processed to form a reference sequence x0, x0 = {x0(1), x0(2), x0(3), ..., x0(n)}, and the length of the sequence is n; the industrial structure, energy structure, transportation, urbanization level, population size, lifestyle and other influencing factors are processed to form a comparison sequence set X, X = {x i |i=1,2,...,n}, where x i ={x i (1),x i (2),x i (3),...,x i (m)}, the length of the sequence is m; γ(x0(k),xi (k)) represents x0 and x i The correlation coefficient at time k.

[0017] Furthermore, the reference series and the comparison series are dimensionlessly processed and calculated as follows:

[0018]

[0019] Where x(min) = min minΔ 0i (k), x(max)=max·maxΔ 0i (k), Δ 0i (k)=|x0(k)-x i (k)|, take δ = 0.5; let R i is the comparison sequence x i The correlation degree of the reference sequence x0. The greater the correlation degree, the greater the influence of the comparison sequence on the reference sequence. The expression of the correlation degree Ri is as follows:

[0020]

[0021] Since the prediction accuracy of the grey GM (1, 1) model is closely related to the growth trend of the original data, the original data sequence should be logarithmically transformed. (0) =[Y (0) (1),Y (0) (2),Y (0) (n)], where Y (0) (k) = lnX (0) (k), k = 1, 2, ..., n. The differential equation is established based on the sequence generated by one accumulation:

[0022]

[0023] Let the vector α = [a,u]T be called the GM (1,1) model x (0) (k)+az (1) (k) = whitening equation of u. In which, the background value z (1) (k) = 1 / 2[z (1) (k)+z (1) (k-1)] constructs the nearest mean generation sequence a, b which can be fitted by the least squares method, and a smoother sequence is used for prediction. Finally, the predicted data is inversely transformed to obtain the predicted value of the original data sequence.

[0024] Furthermore, according to the exponential characteristics of the grey GM (1, 1) model, the interval integral method is adopted to optimize the background value, and the new background value is as follows:

[0025]

[0026] Among them, k=1, 2...n replaces the original background value, and the remaining steps are consistent with the traditional GM (1, 1) model.

[0027] At the same time, carbon emissions are estimated based on relevant data. The formula in the emission guidelines issued by the IPCC (Intergovernmental Panel on Climate Change) is as follows:

[0028]

[0029] Where:

[0030] C—Total carbon emissions from fuel combustion, in 10,000 tons;

[0031] E—fuel consumption, converted into standard coal using the standard coal conversion coefficient, in units of 10,000 tons of standard coal;

[0032] NCV s —The calorific value of the fuel, in TJ / Gg;

[0033] CEC—Carbon emission factor, in kg / GJ;

[0034] COF—carbon oxidation rate of the fuel, IPCC recommends 1;

[0035] 44 and 12 – the molecular weights of CO2 and carbon respectively.

[0036] Through Matlab programming, the gray prediction model fitting value is inversely transformed to obtain carbon emission prediction data;

[0037] According to the obtained simulated reduction value, the residual between it and the actual value of per capita carbon emissions of energy consumption is calculated respectively, and the calculation results of the above model are tested. The formula is as follows:

[0038]

[0039] In the formula, k = 1, 2, 3, 4, 5.

[0040] Furthermore, the specific formula of the IPAT equation of the IPAT model is as follows:

[0041]

[0042] Energy and carbon sink variables are introduced to expand the model and logarithmize it to reduce the heteroscedasticity in the data. The specific calculation formula is as follows:

[0043] i s =a0+a1lnP s +a2lnA s +a3lnTs +a4lnE s +a5ln s +a6lnL s

[0044] In the formula, a1 is population size, a2 is GDP per capita, a3 is carbon emission intensity, a4 is energy structure, a5 is energy intensity, and a6 is industrial structure.

[0045] Furthermore, carbon emissions include carbon emissions from direct energy consumption and carbon emissions from electricity consumption. Carbon emissions from direct energy consumption are calculated using the relevant conversion factors provided by IPCC2006, by adding the product of each energy consumption and its carbon emission factor, and calculated according to the basic carbon accounting formula provided by IPCC:

[0046] C=E i ×η×f i

[0047] Where C is the carbon emissions caused by energy consumption, E i is the consumption of the i-th energy of terminal energy consumption, η is the reference coefficient of the i-th energy converted into standard coal, f i is the carbon emission coefficient of the i-th type of energy, which refers to the amount of carbon corresponding to the heat released by burning fossil energy;

[0048] The carbon emissions generated by electricity consumption are calculated using the baseline emission factors of each regional power grid and the city's electricity consumption.

[0049] Furthermore, according to the established STIRPAT extended model, lnI was used as the dependent variable and the rest as independent variables, and ridge regression was used to estimate the parameters. The model F value was 450, and the corresponding p values ​​were all less than 5%. The prediction equation was as follows:

[0050] i s =1.4348+0.5599lnP s +0.2381lnA s +0.0559lnT s -0.0859lnE s +0.0291lnN s +0.1545lnL s ;

[0051] Furthermore, according to the calculation results of carbon emissions, the carbon emissions in future years are predicted based on this. The specific formula is as follows:

[0052]

[0053] Furthermore, the results calculated by the above-mentioned STIRPAT extended model are analyzed, and the DPSIR model is introduced to construct an evaluation index system for low-carbon city development;

[0054] The original data obtained in the index system is dimensionless, b ij is the index value of the jth index in the i-th year after standardization, a ij is the original value of the jth indicator of the i-th city, a ij is the original value of the jth indicator of the i-th city, and are the maximum and minimum values ​​of the jth indicator respectively. For positive indicators, For negative indicators,

[0055] Furthermore, for the processed indicator data, set P ij is the weight of the jth indicator in the i-th year, m is the number of years involved in the assessment, E j is the entropy value of the jth indicator, and the weight is calculated by the entropy weight method:

[0056]

[0057] Based on the obtained indicator weights and original data, the topsis model is used to evaluate the pros and cons of the low-carbon development level in different years.

[0058] Furthermore, let the positive ideal solution and the negative ideal solution be and C ij is the index of the jth indicator in the i-th year after weighted calculation. and are the distances from the indicators of each year to the optimal solution, f i is the relative closeness of the evaluated year. The larger the value, the better the low-carbon development level of that year. Different indicators of low-carbon development level in different years are expressed as follows:

[0059]

[0060]

[0061] Furthermore, the obstacles affecting the low-carbon development of cities are analyzed and W i is the index weight of the ith criterion layer; V ij is the weight of the ith single indicator of the jth criterion layer indicator; R i is the standardized value of a single indicator; ij is the obstacle degree of a single indicator, then the weight of a single factor on the overall goal: F i =Wi +V ij ; The difference between the evaluation value of a single indicator and 100% is I i =1-R i , and calculate the impact of each annual single indicator and criterion-level indicator on the city's low-carbon development plan:

[0062]

[0063] U j =∑O ij ;

[0064] The model calculates the factors affecting the city's low-carbon development in current urban planning and the total obstacle degree in different years, thereby determining the effectiveness of past planning policies and assisting planners in formulating the next strategy.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] The method of the present invention adopts the grey correlation analysis method to construct a carbon emission prediction model, measures the correlation degree between various influencing factors, and then performs ridge regression analysis through variance inflation factor (VIF), determines the ridge regression equation, and establishes the STIRPAT extended model; then, the results calculated by the above model are analyzed, and combined with the problems existing in the urban development process, the urban low-carbon development route under the dual control objectives of energy and carbon is planned, and a control method for the city-level low-carbon development plan is proposed from the fields of energy, transportation, construction, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is an overall flow chart of an evaluation method for low-carbon city development under multiple condition constraints in an embodiment of the present invention;

[0068] Figure 2 The following is a detailed flow chart of constructing a carbon emission prediction model in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] In order to facilitate the understanding of those skilled in the art, the present invention is further described below in conjunction with embodiments and drawings. The contents mentioned in the implementation modes are not intended to limit the present invention.

[0070] like Figure 1 As shown, this embodiment provides an evaluation method for low-carbon city development under multiple condition constraints, including the steps of:

[0071] S1. Construct a carbon emission prediction model: Use the grey correlation analysis method, take the carbon emissions of previous years as the reference series, and the influencing factors as the comparison series. Select the socioeconomic factors that affect carbon emissions as constraints, measure the correlation between the influencing factors, and construct a regional carbon emission prediction model.

[0072] S2. Construct a regional low-carbon development catch-up model, analyze the factors affecting urban low-carbon planning, and predict the carbon emissions of a region through the IPAT model; expand the model from the perspective of population size, economic level, and carbon emission intensity, and construct an urban low-carbon development planning model with multiple constraints.

[0073] S3. The carbon emission prediction function is determined by performing ridge regression analysis through the variance inflation factor (VIF). The results of ridge regression are fitted to determine the ridge regression equation and establish the STIRPAT extended model. It is found that carbon emissions can be reduced by adjusting the energy consumption structure, while also balancing the relationship between economic growth and emission reduction.

[0074] S4. Analyze the results calculated by the above model, combine the existing problems in the urban development process, plan the city's low-carbon development route under the dual control goals of energy and carbon, and propose control methods for city-level low-carbon development planning from the fields of energy, industry, transportation, construction, and ecosystem carbon sinks.

[0075] like Figure 2 As shown, the detailed steps of step S1 to construct a carbon emission prediction model include:

[0076] S101. Obtain the carbon emissions of previous years as a reference series and various influencing factors as a comparison series;

[0077] S102, respectively performing dimensionless processing on the reference number series and the comparison number series;

[0078] S103. According to the grey correlation model established above, the correlation between carbon emissions and various influencing factors is obtained.

[0079] In step S101, the original carbon emission data is processed to form a reference sequence x0, x0 = {x0(1), x0(2), x0(3), ..., x0(n)}, and the length of the sequence is n; the industrial structure, energy structure, transportation, urbanization level, population size, lifestyle and other influencing factors are processed to form a comparison sequence set X, X = {x i |i=1,2,...,n}, where x i ={x i (1),x i (2),x i (3),...,x i (m)}, the length of the sequence is m; γ(x0(k),x i (k)) represents x0 and x i The correlation coefficient at time k.

[0080] The calculation method of dimensionless processing the reference series and the comparison series is as follows:

[0081]

[0082] Where x(min) = min minΔ 0i (k), x(max)=max·maxΔ 0i (k), Δ 0i (k)=|x0(k)-x i (k)|, take δ = 0.5; let R i is the comparison sequence x i The correlation degree of the reference sequence x0. The greater the correlation degree, the greater the influence of the comparison sequence on the reference sequence. The expression of the correlation degree Ri is as follows:

[0083]

[0084] Since the prediction accuracy of the grey GM (1, 1) model is closely related to the growth trend of the original data, the original data sequence should be logarithmically transformed. (0) =[Y (0) (1),Y (0) (2),Y (0) (n)], where Y (0) (k) = lnX (0) (k), k = 1, 2, ..., n. The differential equation is established based on the sequence generated by one accumulation:

[0085]

[0086] Let the vector α = [a,u]T be called the GM (1,1) model x (0) (k)+az (1) (k) = whitening equation of u. In which, the background value z (1) (k) = 1 / 2[z (1) (k)+z (1) (k-1)] constructs the nearest mean generation sequence a, b which can be fitted by the least squares method, and a smoother sequence is used for prediction. Finally, the predicted data is inversely transformed to obtain the predicted value of the original data sequence.

[0087] According to the exponential characteristics of the grey GM (1, 1) model, the interval integral method is adopted to optimize the background value. The new background value is as follows:

[0088]

[0089] Among them, k=1, 2...n replaces the original background value, and the remaining steps are consistent with the traditional GM (1, 1) model.

[0090] At the same time, carbon emissions are estimated based on relevant data. The formula in the emission guidelines issued by the IPCC (Intergovernmental Panel on Climate Change) is as follows:

[0091]

[0092] Where:

[0093] C—Total carbon emissions from fuel combustion, in 10,000 tons;

[0094] E—fuel consumption, converted into standard coal using the standard coal conversion coefficient, in units of 10,000 tons of standard coal;

[0095] NCV s —The calorific value of the fuel, in TJ / Gg;

[0096] CEC—Carbon emission factor, in kg / GJ;

[0097] COF—carbon oxidation rate of the fuel, IPCC recommends 1;

[0098] 44 and 12 – the molecular weights of CO2 and carbon respectively.

[0099] Through Matlab programming, the gray prediction model fitting value is inversely transformed to obtain carbon emission prediction data;

[0100] According to the obtained simulated reduction value, the residual between it and the actual value of per capita carbon emissions of energy consumption is calculated respectively, and the calculation results of the above model are tested. The formula is as follows:

[0101]

[0102] In the formula, k = 1, 2, 3, 4, 5.

[0103] First, the main socioeconomic factors that affect carbon emissions are selected: industrial structure, energy structure, transportation, urbanization level, population size and lifestyle, among which carbon emissions are calculated by the energy consumption of Jilin Province. Secondly, the initial data is transformed (dimensionless) using the following formula to generate reference series and comparison series: k = 1, 2, ... 5; i = 0, 1, 2, ... , 6. Finally, according to the grey correlation model established above, the correlation between carbon emissions and various influencing factors is obtained.

[0104] The specific formula of the IPAT equation of the IPAT model is as follows:

[0105]

[0106] Table 1 STIPRAT variable indicators and descriptions

[0107]

[0108]

[0109] The indicators and descriptions of each variable of STIPRAT are shown in Table 2. The model includes the non-proportional impact of population size, economic level, and carbon emission intensity on carbon emissions.

[0110] Energy and carbon sink variables are introduced to expand the model and logarithmize it to reduce the heteroscedasticity in the data. The specific calculation formula is as follows:

[0111] i s =a0+a1lnP s +a2lnA s +a3lnT s +a4lnE s +a5ln s +a6lnL s

[0112] In the formula, a1 is population size, a2 is GDP per capita, a3 is carbon emission intensity, a4 is energy structure, a5 is energy intensity, and a6 is industrial structure.

[0113] Carbon emissions include carbon emissions from direct energy consumption and carbon emissions from electricity consumption. Carbon emissions from direct energy consumption are calculated using the relevant conversion factors provided by IPCC 2006, by adding the product of each energy consumption and its carbon emission factor, and calculated according to the basic carbon accounting formula provided by IPCC:

[0114] C=E i ×η×f i

[0115] Where C is the carbon emissions caused by energy consumption, E i is the consumption of the i-th energy of terminal energy consumption, η is the reference coefficient of the i-th energy converted into standard coal, f i is the carbon emission coefficient of the i-th type of energy, which refers to the amount of carbon corresponding to the heat released by burning fossil energy;

[0116] The carbon emissions generated by electricity consumption are calculated using the baseline emission factors of each regional power grid and the city's electricity consumption.

[0117] The main energy data of this embodiment includes the consumption of eight energy sources: raw coal, coke, crude oil, fuel oil, gasoline, diesel, kerosene and natural gas. The carbon emission coefficient of each energy source is shown in Table 2.

[0118] Table 2 Carbon emission coefficients of relevant energy sources

[0119]

[0120]

[0121] The carbon emissions generated by electricity consumption are relatively complex, that is, each regional power grid has only one emission factor. China's power grid is divided into six major regional power grids: North China, Northeast China, East China, Central China, Northwest China and South China. The baseline emission factors of each regional power grid have been released over the years. The carbon emissions generated by urban electricity consumption are calculated using the baseline emission factors of each regional power grid and the city's electricity consumption. The total carbon emissions of the city are obtained by adding the carbon emissions generated by electricity, gas, liquefied petroleum gas and thermal energy consumption.

[0122] The effect of the above model was evaluated by using the mean absolute percentage error (MAPE) and the coefficient of determination (R 2 ) are used as indicators of prediction accuracy. MAPE represents the difference between the model prediction value and the measured value. If these two indicators are small enough, it means that the model error is smaller. The coefficient of determination R2 usually takes values ​​between (0, 1), indicating that the model fit is good. The closer the calculated value is to 1, the better the model fit is. The calculation formula is as follows:

[0123]

[0124] By substituting historical data into the above formula and testing it, the function of predicting regional carbon emissions is obtained. The carbon emission values ​​of historical years calculated by the function are compared with the actual carbon emission values ​​for error analysis. If it is small enough, it means that this function can be used for carbon emission prediction. The closer the coefficient of determination is to 1, the more significant the effect is. After the prediction function is obtained, it is restored to the original carbon emission prediction function to predict the values ​​of future years.

[0125] The carbon emission prediction function is determined. It can be seen from the variance inflation factor (VIF) that the VIF of all independent variable coefficients is mostly greater than 10, indicating that there is serious multicollinearity between the explanatory variables of this regression equation. It can be seen that the ordinary least squares method for linear regression is not suitable for the data series investigated in this study. In order to obtain more reliable fitting results, the influence of multicollinearity between variables must be eliminated. The results of the multiple regression analysis are shown in Table 3.

[0126] Table 3 Results of multiple regression analysis

[0127]

[0128] The data were subjected to ridge regression analysis. The ridge regression results are shown in Table 5. The ridge regression results when k = 0.2 were selected for fitting to determine the ridge regression equation.

[0129] According to the established STIRPAT extended model, lnI was used as the dependent variable and the rest as independent variables. Ridge regression was used to estimate the parameters. The model F value was 450, and the corresponding p values ​​were all less than 5%. The prediction equation was as follows:

[0130] i s =1.4348+0.5599lnP s +0.2381lnA s +0.0559lnT s -0.0859lnE s +0.0291lnN s +0.1545lnL s ;

[0131] Table 4 Ridge regression fitting results

[0132]

[0133] According to the calculation results of carbon emissions, and based on this, the carbon emissions in future years are predicted. The specific formula is as follows:

[0134]

[0135] The results calculated by the above model are analyzed, the DPSIR model is introduced, and an evaluation index system for low-carbon city development is constructed;

[0136] Starting from the five dimensions of driving force-pressure-state-impact-response, the driving force indicators include economic and social development, the pressure indicators include social pressure, resource consumption and pollution emissions, the state indicators include enterprise development and talent exchange, the impact includes environmental impact and social impact, and the response indicators are capital investment, environmental protection and policy guidance. On this basis, a city-level low-carbon development indicator system is constructed. The carbon cost management and control level evaluation indicator set is shown in Table 5.

[0137] Table 5 Carbon cost management and control level evaluation index set

[0138]

[0139] The original data obtained in the index system is dimensionless, b ij is the index value of the jth index in the i-th year after standardization, a ij is the original value of the jth indicator of the i-th city, a ij is the original value of the jth indicator of the i-th city, and are the maximum and minimum values ​​of the jth indicator respectively. For positive indicators, For negative indicators,

[0140] For the processed indicator data, set P ij is the weight of the jth indicator in the i-th year, m is the number of years involved in the assessment, E j is the entropy value of the jth indicator, and the weight is calculated by the entropy weight method:

[0141]

[0142] Based on the obtained indicator weights and original data, the topsis model is used to evaluate the pros and cons of the low-carbon development level in different years.

[0143] Assume that the positive ideal solution and the negative ideal solution are and C ij is the index of the jth indicator in the i-th year after weighted calculation. and are the distances from the indicators of each year to the optimal solution, f i is the relative closeness of the evaluated year. The larger the value, the better the low-carbon development level of that year. Different indicators of low-carbon development level in different years are expressed as follows:

[0144]

[0145]

[0146] Analyze the obstacles that affect the low-carbon development of cities and set W i is the index weight of the ith criterion layer; V ij is the weight of the ith single indicator of the jth criterion layer indicator; R i is the standardized value of a single indicator; ij is the obstacle degree of a single indicator, then the weight of a single factor on the overall goal: F i =W i +V ij ; The difference between the evaluation value of a single indicator and 100% is I i =1-R i , and calculate the impact of each annual single indicator and criterion-level indicator on the city's low-carbon development plan:

[0147]

[0148] U j =∑O ij ;

[0149] The model calculates the factors affecting the city's low-carbon development in current urban planning and the total obstacle degree in different years, thereby determining the effectiveness of past planning policies and assisting planners in formulating the next strategy.

[0150] Compared with the prior art, the present invention has the following beneficial effects:

[0151] The method of the present invention adopts the grey correlation analysis method to construct a carbon emission prediction model, measures the correlation degree between various influencing factors, and then performs ridge regression analysis through variance inflation factor (VIF), determines the ridge regression equation, and establishes the STIRPAT extended model; then, the results calculated by the above model are analyzed, and combined with the problems existing in the urban development process, the urban low-carbon development route under the dual control objectives of energy and carbon is planned, and a control method for the city-level low-carbon development plan is proposed from the fields of energy, transportation, construction, etc.

[0152] The above is a detailed introduction to the evaluation method of low-carbon city development under multiple condition constraints provided by this application. The description of the specific embodiment is only used to help understand the method and its core idea of ​​this application. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of this application, several improvements and modifications can be made to this application, and these improvements and modifications also fall within the scope of protection of the claims of this application.

Claims

1. An evaluation method for low-carbon city development under multiple constraints, characterized in that: Includes steps: S1. Construct a carbon emission prediction model: Use the grey correlation analysis method, take the carbon emissions of previous years as the reference series, and the influencing factors as the comparison series. Select the socioeconomic factors that affect carbon emissions as constraints, measure the correlation between the influencing factors, and construct a regional carbon emission prediction model. S2. Construct a regional low-carbon development catch-up model, analyze the factors affecting urban low-carbon planning, and predict regional carbon emissions through the IPAT model; S3. The carbon emission prediction function is determined by performing ridge regression analysis through the variance inflation factor (VIF), fitting the results of ridge regression to determine the ridge regression equation and establish the STIRPAT extended model; S4. Analyze the results calculated by the above-mentioned STIRPAT extended model, combine the existing problems in the urban development process, and plan the urban low-carbon development route under the dual control objectives.

2. The evaluation method for low-carbon city development under multiple constraints according to claim 1 is characterized in that: The detailed steps of step S1 to construct a carbon emission prediction model include: S101. Obtain the carbon emissions of previous years as a reference series and various influencing factors as a comparison series; S102, respectively performing dimensionless processing on the reference number series and the comparison number series; S103. According to the grey correlation model established above, the correlation between carbon emissions and various influencing factors is obtained.

3. The evaluation method for low-carbon city development under multiple constraints according to claim 2 is characterized in that: In step S101, the original carbon emission data is processed to form a reference sequence x0, x0 = {x0(1), x0(2), x0(3), ..., x0(n)}, and the length of the sequence is n; the industrial structure, energy structure, transportation, urbanization level, population size, lifestyle and other influencing factors are processed to form a comparison sequence set X, X = {x i |i=1,2,...,n}, where x i ={x i (1),x i (2),x i (3),...,x i (m)}, the length of the sequence is m; γ(x0(k),x i (k)) represents x0 and x i The correlation coefficient at time k.

4. The evaluation method for low-carbon city development under multiple constraints according to claim 3 is characterized in that: The calculation method of dimensionless processing the reference series and the comparison series is as follows: Where x(min) = min minΔ 0i (k), x(max)=max·maxΔ 0i (k), Δ 0i (k)=|x0(k)-x i (k)|, take δ = 0.5; let R i is the comparison sequence x i The correlation degree of the reference sequence x0. The greater the correlation degree, the greater the influence of the comparison sequence on the reference sequence. The expression of the correlation degree Ri is as follows: Since the prediction accuracy of the grey GM (1, 1) model is related to the growth trend of the original data, the original data sequence is logarithmically transformed and Y (0) =[Y (0) (1),Y (0) (2),Y (0) (n)], where Y (0) (k) = ln X (0) (k), k = 1, 2, ..., n; establish the differential equation based on the sequence generated by one accumulation: Let the vector α = [a,u]T be called the GM (1,1) model x (0) (k)+az (1) (k) = whitening equation of u. In which, the background value z (1) (k) = 1 / 2[z (1) (k)+z (1) (k-1)] constructs the nearest mean generation sequence a, b which can be fitted by the least squares method, and a smoother sequence is used for prediction. Finally, the predicted data is inversely transformed to obtain the predicted value of the original data sequence.

5. The evaluation method for low-carbon city development under multiple constraints according to claim 4 is characterized in that: According to the exponential characteristics of the grey GM (1, 1) model, the interval integral method is adopted to optimize the background value. The new background value is as follows: Among them, k = 1, 2...n replaces the original background value, and the remaining steps are consistent with the traditional GM (1, 1) model; Carbon emissions are estimated based on relevant data. The estimation formula is as follows: Where: C—Total carbon emissions from fuel combustion, in 10,000 tons; E—fuel consumption, converted into standard coal using the standard coal conversion coefficient, in units of 10,000 tons of standard coal; NCV s —The calorific value of the fuel, in TJ / Gg; CEC—Carbon emission factor, in kg / GJ; COF—carbon oxidation rate of the fuel, IPCC recommends 1; 44 and 12—the molecular weights of CO2 and carbon, respectively; Through Matlab programming, the grey prediction model fitting value is inversely transformed to obtain carbon emission prediction data; According to the obtained simulated reduction value, the residuals with the actual value of per capita carbon emissions of energy consumption are calculated respectively, and the calculation results of the above model are tested. The formula is as follows: In the formula, k = 1, 2, 3, 4, 5.

6. The evaluation method for low-carbon city development under multiple constraints according to claim 5 is characterized in that: The specific formula of the IPAT equation of the IPAT model is as follows: Energy and carbon sink variables are introduced to expand the model and logarithmize it to reduce the heteroscedasticity in the data. The specific calculation formula is as follows: lnI s =a0+a1lnP s +a2lnA s +a3lnT s +a4lnE s +a5lnN s +a6lnL s In the formula, a1 is population size, a2 is GDP per capita, a3 is carbon emission intensity, a4 is energy structure, a5 is energy intensity, and a6 is industrial structure.

7. The evaluation method for low-carbon city development under multiple constraints according to claim 6 is characterized in that: Carbon emissions include carbon emissions from direct energy consumption and carbon emissions from electricity consumption. Carbon emissions from direct energy consumption are calculated using the relevant conversion factors provided by IPCC 2006, by adding the product of each energy consumption and its carbon emission factor, and calculated according to the basic carbon accounting formula provided by IPCC: C=E i ×η×f i Where C is the carbon emissions caused by energy consumption, E i is the consumption of the i-th energy of terminal energy consumption, η is the reference coefficient of the i-th energy converted into standard coal, f i is the carbon emission coefficient of the i-th type of energy, which refers to the amount of carbon corresponding to the heat released by burning fossil energy; The carbon emissions generated by electricity consumption are calculated using the baseline emission factors of each regional power grid and the city's electricity consumption.

8. The evaluation method for low-carbon city development under multiple constraints according to claim 7 is characterized in that: According to the established STIRPAT extended model, lnI was used as the dependent variable and the rest as independent variables. Ridge regression was used to estimate the parameters. The model F value was 450, and the corresponding p values ​​were all less than 5%. The prediction equation was as follows: lnI = 1.4348 + 0.5599lnP + 0.2381lnA + 0.0559lnT - 0.0859lnE + 0.0291lnN + 0.1545lnL According to the calculation results of carbon emissions, and based on this, the carbon emissions in subsequent years are predicted. The specific formula is as follows: The results calculated by the above-mentioned STIRPAT extended model are analyzed, and the DPSIR model is introduced to construct an evaluation index system for low-carbon city development; The original data obtained in the index system is dimensionless, b ij is the index value of the jth index in the i-th year after standardization, a ij is the original value of the jth indicator of the i-th city, a ij is the original value of the jth indicator of the i-th city, and are the maximum and minimum values ​​of the jth indicator respectively. For positive indicators, For negative indicators, 9. The evaluation method for low-carbon city development under multiple constraints according to claim 8 is characterized in that: For the processed indicator data, set P ij is the weight of the jth indicator in the i-th year, m is the number of years involved in the assessment, E j is the entropy value of the jth indicator, and the weight is calculated by the entropy weight method: Based on the obtained indicator weights and original data, the topsis model is used to evaluate the pros and cons of low-carbon development levels in different years; Assume that the positive ideal solution and the negative ideal solution are and C ij is the index of the jth indicator in the i-th year after weighted calculation. and are the distances from the indicators of each year to the optimal solution, f i is the relative closeness of the evaluated year. The larger the value, the better the low-carbon development level of that year. Different indicators of low-carbon development level in different years are expressed as follows:

10. The evaluation method for low-carbon city development under multiple constraints according to claim 9 is characterized in that: Analyze the obstacles that affect the low-carbon development of cities and set W i is the index weight of the ith criterion layer; V ij is the weight of the ith single indicator of the jth criterion layer indicator; R i is the standardized value of a single indicator; ij is the obstacle degree of a single indicator, then the weight of a single factor on the overall goal is: i =W i +V ij ; The difference between the evaluation value of a single indicator and 100% is I i =1-R i , and calculate the impact of each annual single indicator and criterion-level indicator on the city's low-carbon development plan: IN j =∑O ij ; The model calculates the factors affecting the city's low-carbon development in current urban planning and the total obstacle degree in different years, thereby determining the effectiveness of past planning policies and assisting planners in formulating the next strategy.

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