CN115907177A - Urban carbon emission prediction method and system - Google Patents
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Abstract
The invention discloses a method for predicting urban carbon emission, which comprises the following steps: constructing an urban carbon emission factor decomposition model and an ARMA prediction model; establishing a carbon emission combined prediction model by determining the weights of an urban carbon emission factor decomposition model and an ARMA prediction model by taking the minimum prediction error as a target; determining the driving factor change rate of urban carbon emission; the method comprises the steps of predicting the future carbon emission of a city through a carbon emission combined prediction model and the change rate of the urban carbon emission driving factor, calculating the weight of a factor decomposition model and an ARMA prediction model in carbon meal prediction to obtain a combined model, predicting the carbon emission of the city through the combined model more reasonably, analyzing the driving factor of the carbon emission from the urban level to determine the influence degree of different factors on the carbon emission of urban groups, improving the accuracy of measurement and calculation through data calculation verification, and providing reliable basis for provincial side measurement and calculation of the carbon peak reaching time in each scene.
Description
Technical Field
The invention belongs to the technical field of carbon emission prediction, and particularly relates to a method and a system for predicting urban carbon emission.
Background
In recent years, industrial structures mainly based on heavy industry and energy consumption structures mainly based on stone energy sources such as coal, petroleum and the like are important power for the high-speed development of Chinese economy, and simultaneously, the carbon emission is at the top of the world, the carbon emission reduction faces huge pressure, and the contradiction between the economic development and the environmental protection is increasingly prominent. Under the constraint of the carbon peak-reaching target, the relevant problems of various cities in the Jilin province in the aspects of coping with climate change, carbon emission reduction and the like need to be deeply analyzed, particularly, the Jilin province should be responsible for carbon reduction development since planning and the issuance of a distant view target outline in 2035 years, and by combining the industrial structure of the Jilin province, a carbon peak-reaching realization path is effectively explored, a carbon peak-reaching development scheme is made, and a sample case is provided for the development of carbon peak-reaching work. Meanwhile, by combining the current change situation of carbon emission at home and abroad and the relevant energy-saving and emission-reducing policies, it can be found that various carbon peak-reaching relevant implementation schemes may appear for different research objects. Therefore, the variation situation of the local carbon emission in the Jilin province is needed, and the variation trend of the carbon emission in different scenes is analyzed and the correlation path of the carbon peak is realized by setting various possible development scenes. The article utilizes carbon emission data of Jilin city level in the past year, constructs a factor decomposition model of urban carbon emission and a situation prediction method, analyzes driving factors for realizing carbon peak reaching and variation conditions of carbon emission under different development situations, discusses the overall variation trend of the Jilin city group under different situations through a nuclear density estimation method, and provides a relevant reference for realizing paths of carbon peak reaching.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method and a system for predicting urban carbon emission, which can accurately predict the future carbon emission of an urban.
The technical problem to be solved by the invention is realized by the following technical scheme:
in a first aspect, a method for predicting urban carbon emission is provided, including:
constructing an urban carbon emission factor decomposition model and an ARMA prediction model;
establishing a carbon emission combined prediction model by determining the weights of an urban carbon emission factor decomposition model and an ARMA prediction model by taking the minimum prediction error as a target;
determining the driving factor change rate of urban carbon emission;
and predicting the future carbon emission of the city through a carbon emission combination prediction model and the city carbon emission driving factor change rate. With reference to the first aspect, further, the urban carbon emission factor decomposition model is represented by formula (1)
C ＝ ΔC T + ΔC L + ΔC S + ΔC Q (1)
Where C is the total carbon emission, Δ C, driven by various factors T 、ΔC L 、ΔC S And Δ C Q Respectively represents carbon emission driven by population scale effect, carbon emission driven by man-average yield effect, and carbon emission driven by industrial structure effectAnd carbon emissions driven by industrial carbon intensity effects;
wherein, CE t And CE 0 Carbon emissions, T, for the T and current periods, respectively t And T 0 Population effects in the t and current periods, L t And L 0 The per-capita yield effect, S, of the t period and the current period respectively t And S 0 The respective industry structure effects of the t period and the current period, Q t And Q 0 The industrial carbon intensity effect of the t period and the current period respectively.
With reference to the first aspect, further, the ARIMA prediction model is shown in formula (3)
Wherein τ is a constant, δ 1 、δ 2 …δ q Is a white noise sequence in the regression model; sigma i Which is a residual error, is determined,
is a fitting parameter; g i-1 、g i-2 、...g i-q Is a residual sequence; y is i-q C (i) is the carbon emission amount of the i-th period to be predicted, which is predicted by the ARIMA model, to be predicted for the carbon emission amount of q periods before the year.
With reference to the first aspect, further, the constructing a carbon emission combined prediction model includes:
obtaining the optimized weight coefficients of the urban carbon emission factor decomposition model and the ARMA prediction model according to the formula (4);
P＝E -1 R/R T E -1 R (4)
wherein P is the optimized weight coefficient of the model
Wherein e is ji Represents the carbon emission amount of the ith period calculated by the jth model, and R = [1,1]T and E are error matrixes;
obtained according to formula (6)
C”＝aH 1 +bH 2 (6)
Wherein, a and b are respectively optimized weight coefficients of an urban carbon emission factor decomposition model and an ARMA prediction model which are calculated by the formula (4), and C' represents the urban carbon emission calculated by the carbon emission combined prediction model; h 1 、H 2 And the urban carbon emission is calculated by an urban carbon emission factor decomposition model and an ARMA prediction model respectively.
With reference to the first aspect, further, the determining the city carbon emission driver change rate includes:
obtaining the driving factor change rate of urban carbon emission by the formula (7)
ω＝(1+δ)·(1+χ)·(1-β)·(1-α)(7)
Wherein, delta, chi, beta and alpha respectively represent population scale growth rate, per capita yield growth rate, industrial structure change rate and industrial carbon strength change rate, and omega represents the urban carbon emission driving factor change rate.
With reference to the first aspect, further, the predicting the future urban carbon emission comprises:
predicting the carbon emission of the next period of the city according to the formula (8)
C” t+1 ＝ω t ·C” t (8)
Wherein, ω is t Represents the city carbon emission driver rate of change, C, of period t " t And C' t+1 Respectively representing the city carbon emission during the period t and the next period of t.
In a second aspect, a multi-scenario urban carbon emission prediction system is provided, comprising:
the combined model building module is used for building an urban carbon emission factor decomposition model and an ARMA prediction model;
establishing a carbon emission combined prediction model by determining the weights of an urban carbon emission factor decomposition model and an ARMA prediction model by taking the minimum prediction error as a target;
the carbon emission driving factor change rate calculation module is used for determining the urban carbon emission driving factor change rate;
and the carbon emission prediction module is used for predicting the future carbon emission of the city through the carbon emission combination prediction model and the city carbon emission driving factor change rate.
The invention has the beneficial effects that: according to the method, the combined model is obtained by calculating the weight of the factor decomposition model and the ARMA prediction model in the carbon cooking prediction, so that the carbon emission of the city is more reasonably predicted through the combined model, the driving factors of the carbon emission are analyzed from the city level, the influence degree of different factors on the carbon emission of the city group is determined, the measuring and calculating accuracy is improved through data calculation verification, and a reliable basis is provided for the provincial side to measure and calculate the carbon peak reaching time under each scene.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
To further describe the technical features and effects of the present invention, the present invention will be further described with reference to the accompanying drawings and detailed description.
Example 1
As shown in fig. 1, a method for predicting urban carbon emission includes the following steps:
step one, constructing an urban carbon emission factor decomposition model and an ARMA prediction model
1) Construction of urban carbon emission factor decomposition model
The specific resolution of the different driving factors can be expressed as follows:
where C is the total carbon emission, Δ C, driven by various factors T 、ΔC L 、ΔC S And Δ C Q Respectively represents carbon emission and yield driven by population scale effect and per-capita yield effectCarbon emissions driven by industrial structure effects and carbon emissions driven by industrial carbon intensity effects; CE t And CE 0 Carbon emissions, T, for the T and current periods, respectively t And T 0 Population effects in the t and current periods, L t And L 0 The per-capita yield effect, S, of the t period and the current period respectively t And S 0 The respective industry structure effects of the t period and the current period, Q t And Q 0 The commercial carbon intensity effect for the t period and the current period, respectively.
The decomposition model of urban carbon emission factor is shown as formula (2)
C ＝ ΔC T + ΔC L + ΔC S + ΔC Q (2)
Where C is the total carbon emissions driven by each factor.
2) Construction of ARMA prediction model
Model construction is shown in formula (3)
Where τ is a constant, δ 1 、δ 2 …δ q Is a white noise sequence in the regression model; sigma i Which is a residual error, is determined,
is a fitting parameter; g is a radical of formula i-1 、g i-2 、...g i-q Is a residual sequence; y is i-q The carbon emissions for the q periods before the year to be predicted (one year for each period in this application) and C (i) the carbon emissions for the i-th period to be predicted, predicted by the ARIMA model.
Step two, constructing a combined model
Different prediction models have advantages and disadvantages, the information extraction angles are different, different prediction methods are comprehensively considered for combination, and the combination of multiple prediction methods can effectively enhance the information extraction of the prediction models. Methods of combined prediction can be generally classified into two types, linear and nonlinear. The research adopts an optimal weighting combination prediction model, and obtains an optimal weighting coefficient according to a Lagrange multiplier method under a constraint condition by constructing an error matrix, wherein the optimal weighting coefficient is shown as a formula (4)
P＝E -1 R/R T E -1 R (4)
Wherein, P is an optimized weight coefficient of the model;
wherein e is ji Represents the carbon emission in the i-th period calculated by the j-th model, R = [1, \8230;, 1]T and E are error matrixes;
then according to formula (6) to obtain
C”＝aH 1 +bH 2 (6)
Wherein, a and b are respectively P calculated by formula (4) which is the optimized weight coefficient of the urban carbon emission factor decomposition model and ARMA prediction model calculated by formula (4), and C' represents the urban carbon emission calculated by the carbon emission combination prediction model; h 1 、H 2 And the urban carbon emission is calculated by an urban carbon emission factor decomposition model and an ARMA prediction model respectively.
Step three, determining the driving factor change rate of urban carbon emission
The driving factor change rate of urban carbon emission is obtained by the formula (7)
ω＝(1+δ)·(1+χ)·(1-β)·(1-α) (7)
Wherein, delta, chi, beta and alpha respectively represent the population size growth rate, the per-capita yield growth rate, the industrial structure change rate and the industrial carbon strength change rate, and omega represents the urban carbon emission driving factor change rate.
Step four, predicting the future carbon emission of the city
The carbon emission in the next period (year) is the carbon emission in the current year multiplied by the change rate
C” t+1 ＝ω t ·C” t (8)
Wherein, ω is t Represents the city carbon emission driver rate of change, C, of period t " t And C' t+1 Respectively representing the urban carbon emission during the period t and the period next to t.
Taking the Jilin province as an example, the direction of the rate of change of the carbon emission driving factor of the Jilin province is set according to the following principle: when the growth rate of the population-scale effect and the per-capita yield effect is reduced, the related carbon emission is reduced due to the inhibition of the scale effect; secondly, when the proportion of the second industry in the total GDP is reduced, the related carbon emission is also reduced, namely the optimization of the industrial structure effect can promote the development of low-carbon economy; finally, as the industrial carbon strength effect increases, the carbon emissions implied per unit of industrial increase also increase.
According to the recent emission reduction situation of each city in Jilin province, the development target and the growth constraint are established by combining the development plan and the policy file of each city, three scenes of benchmark, low-carbon emission and technical breakthrough are established, and the change rate of carbon emission driving factors in Jilin province under the three scenes is set respectively. The possible change situation of urban carbon emission of Jilin province in the future is predicted by setting the change rate of the related driving factors under three conditions in combination with the existing data of the change rate of each driving factor, and the possible change situation of the change rate of each driving factor in the related years under three conditions is set as shown in Table 1 in combination with the statistics yearbook of Jilin.
TABLE 1 setting of rates of change of respective driving factors under three scenarios
Example 2
The utility model provides a many scenarios city carbon emission prediction system, includes:
the combined model building module is used for building an urban carbon emission factor decomposition model and an ARMA prediction model;
establishing a carbon emission combined prediction model by determining the weights of an urban carbon emission factor decomposition model and an ARMA prediction model by taking the minimum prediction error as a target;
the carbon emission driving factor change rate calculation module is used for determining the urban carbon emission driving factor change rate;
and the carbon emission prediction module is used for predicting the future carbon emission of the city through the carbon emission combination prediction model and the city carbon emission driving factor change rate.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (7)
1. A method for predicting urban carbon emission is characterized by comprising the following steps:
constructing an urban carbon emission factor decomposition model and an ARMA prediction model;
establishing a carbon emission combined prediction model by determining the weights of an urban carbon emission factor decomposition model and an ARMA prediction model by taking the minimum prediction error as a target;
determining the driving factor change rate of urban carbon emission;
and predicting the future carbon emission of the city through a carbon emission combination prediction model and the city carbon emission driving factor change rate.
2. The method for predicting urban carbon emission according to claim 1, wherein the urban carbon emission factor decomposition model is represented by formula (1)
C ＝ ΔC T + ΔC L + ΔC S + ΔC Q (1)
Where C is the total carbon emission, Δ C, driven by various factors T 、ΔC L 、ΔC S And Δ C Q Respectively representing the carbon emission driven by population scale effect, the carbon emission driven by man-average yield effect, the carbon emission driven by industrial structure effect and the carbon emission driven by industrial carbon intensity effect;
wherein, CE t And CE 0 Carbon emissions, T, for the T and current periods, respectively t And T 0 Population effects, L, for the t and current periods, respectively t And L 0 The per-capita yield effect, S, of the t period and the current period respectively t And S 0 The respective industry structure effects of the t period and the current period, Q t And Q 0 The industrial carbon intensity effect of the t period and the current period respectively.
3. The method as claimed in claim 1, wherein the ARIMA prediction model is represented by formula (3)
Where τ is a constant, δ 1 、δ 2 …δ q Is a white noise sequence in the regression model; sigma i Which is a residual error, is calculated,
is a fitting parameter; g i-1 、g i-2 、...g i-q Is a residual sequence; y is i-q C (i) is the carbon emission amount of the i-th period to be predicted, which is predicted by the ARIMA model, to be predicted for the carbon emission amount of q periods before the year.
4. The urban carbon emission prediction method according to claim 1, wherein the constructing of the carbon emission combination prediction model comprises:
obtaining the optimized weight coefficients of the urban carbon emission factor decomposition model and the ARMA prediction model according to the formula (4);
P＝E -1 R/R T E -1 R (4)
wherein P is the optimized weight coefficient of the model
Wherein e is ji Represents the carbon emission amount in the i-th period calculated by the j-th model, and R = [1,.. 1., 1 =]T and E are error matrixes;
obtained according to formula (6)
C”＝aH 1 +bH 2 (6)
Wherein, a and b are respectively the optimized weight coefficients of the urban carbon emission factor decomposition model and the ARMA prediction model obtained by the formula (4), and C' represents the urban carbon emission calculated by the carbon emission combined prediction model; h 1 、H 2 And the urban carbon emission is calculated by an urban carbon emission factor decomposition model and an ARMA prediction model respectively.
5. The method of claim 1, wherein determining the city carbon emission driver change rate comprises:
obtaining the driving factor change rate of urban carbon emission by the formula (7)
ω＝(1+δ)·(1+χ)·(1-β)·(1-α) (7)
Wherein, delta, chi, beta and alpha respectively represent population scale growth rate, per capita yield growth rate, industrial structure change rate and industrial carbon strength change rate, and omega represents the urban carbon emission driving factor change rate.
6. The urban carbon emission prediction method according to claim 5, wherein the predicting urban future carbon emission comprises:
predicting carbon emission in the next city period according to equation (8)
C” t+1 ＝ω t ·C” t (8)
Wherein, ω is t Represents the city carbon emission driver rate of change, C, of period t " t And C' t+1 Respectively representing the urban carbon emission during the period t and the period next to t.
7. A multi-scenario urban carbon emission prediction system, comprising:
the combined model building module is used for building an urban carbon emission factor decomposition model and an ARMA prediction model;
establishing a carbon emission combined prediction model by determining the weights of an urban carbon emission factor decomposition model and an ARMA prediction model by taking the minimum prediction error as a target;
the carbon emission driving factor change rate calculation module is used for determining the urban carbon emission driving factor change rate;
and the carbon emission prediction module is used for predicting the future carbon emission of the city through the carbon emission combination prediction model and the city carbon emission driving factor change rate.
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| CN117057516B (en) * | 2023-10-12 | 2024-04-09 | 浙江大有实业有限公司配电工程分公司 | Carbon accounting parameter analysis and prediction method based on system dynamics |
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