Evaluation method and device for cooperative influence of policy on atmospheric pollution reduction and carbon reduction, and product

Abstract

The invention provides a method, a device and a product for evaluating the collaborative influence of policies on atmospheric pollution reduction and carbon reduction. The method comprises the following steps: researching and analyzing the influence effect of urban agglomeration global scale quantitative environmental policy intensity on pollution reduction and carbon reduction by adopting a panel fixing effect model; when multiple policies are implemented at the same time, the effect of multiple types of pollution reduction and carbon reduction policies in the urban scale is calculated through the factor analysis model. By means of the technical scheme, the defects of single-type and single-scale policy evaluation are overcome, and atmospheric pollution reduction and carbon reduction cooperative influence evaluation can be rapidly, accurately and stably achieved.

Description

Technical Field

[0001] This invention relates to the field of atmospheric monitoring and assessment, and in particular to an assessment method, device, and product for the synergistic impact of policies on atmospheric pollution reduction and carbon reduction. Background Technology

[0002] With the promulgation and implementation of a series of environmental policies, carbon emission reduction has been significant. However, volatile organic compounds (VOCs), as key precursors to the secondary formation of tropospheric ozone (O3) and fine particulate matter, continue to see increasing emissions. Currently, several important environmental policies have been implemented to address the dual pressures of reducing carbon emissions and mitigating air pollution. The synergistic effect of these policies has effectively promoted the integrated management of carbon pollution. Therefore, the synergistic impact of policies on pollution reduction and carbon reduction in the atmospheric environment has become a research hotspot in the field of environmental governance.

[0003] Currently, assessment methods for policy impact mainly focus on the effect of a single environmental policy on carbon emission reduction, or assess the role of an energy policy in pollutant emission reduction, failing to comprehensively consider the synergistic effect of policies on pollution reduction and carbon reduction. Summary of the Invention

[0004] The embodiments of the present invention provide a method, apparatus and product for evaluating the synergistic impact of policies on atmospheric pollution reduction and carbon reduction, so as to comprehensively consider the synergistic impact of environmental policies on atmospheric pollution reduction and carbon reduction from the perspective of multiple policy types.

[0005] To achieve the above objectives, on the one hand, a method for evaluating the synergistic impact of policies on air pollution reduction and carbon reduction is provided, including:

[0006] Panel fixed-effects models were used to analyze the effects of N selected environmental policies on CO2 emission reduction and VOCs emission reduction at the city cluster scale, where N is greater than or equal to 2.

[0007]

[0008] VOCs it =β i +β1X 1it +β2X 2it +β3X 3it +…+β N X Nit +ε i +ε t +ε i,t ;

[0009] in, This indicates the impact of environmental policies on CO2 emission reduction in city i at a selected time t; X represents the impact of environmental policies on the reduction of volatile organic compounds (VOCs) in city i at time t; i represents any city in the city cluster; X1 represents the intensity of policy 1, X2 represents the intensity of policy 2, X3 represents the intensity of policy 3, and X... N X represents the strength of policy N; 1it X represents the intensity of policy 1 for city i at time t. 2it X represents the intensity of policy 2 for city i at time t. 3it X represents the intensity of policy 3 for city i at time t. Nit ε represents the intensity of policy N targeting city i at time t; i For the fixed effects of city i, ε t For a fixed effect of time t, ε i,t Let α0, α1, α2…α be the random error of city i at time t; N β represents the predetermined carbon reduction estimation coefficient for each of the N policies; i ,β1,β2…β N These are predetermined pollution reduction estimation coefficients for each of the N policies;

[0010] Specifically, when the carbon reduction estimation coefficient or pollution reduction estimation coefficient is greater than zero, it indicates that the policy corresponding to the carbon reduction estimation coefficient or pollution reduction estimation coefficient has aggravated carbon emissions or pollution emissions; when the carbon reduction estimation coefficient or pollution reduction estimation coefficient is less than zero, it indicates that the policy corresponding to the carbon reduction estimation coefficient or pollution reduction estimation coefficient has a carbon reduction or pollution reduction effect; when the carbon reduction estimation coefficient corresponding to the same policy is greater than the pollution reduction estimation coefficient, it indicates that the carbon reduction effect of the policy is greater than the pollution reduction effect; when the carbon reduction estimation coefficient corresponding to the same policy is less than the pollution reduction estimation coefficient, it indicates that the carbon reduction effect of the policy is less than the pollution reduction effect.

[0011] Preferably, in the evaluation method, the selected time t is a selected year.

[0012] Preferably, the evaluation method further includes:

[0013] When the N policies are implemented simultaneously, the effects of the N policies on carbon reduction and pollution reduction at the city scale are calculated using a factor analysis model.

[0014] Among them, the effects of the N policies on carbon reduction and pollution control at the city scale, calculated using a factor analysis model, include:

[0015] Extract at least one policy variable from the policy text;

[0016] The extracted policy variables are transformed into multiple quantifiable comprehensive indicators to obtain multiple factors. Among them, different variables whose correlation meets the predetermined correlation degree are classified into the same factor.

[0017] The factor analysis model is used to analyze the obtained factors, and the factors whose variance contribution rate reaches the predetermined variance contribution rate threshold and whose eigenvalue is greater than or equal to 1 are identified as the main factors for analyzing the policy effect.

[0018] The scores of each principal factor are calculated by representing the principal factors as linear combinations of the original indicator variables.

[0019] Using the variance contribution rate of each principal factor as a weight, the comprehensive score of each policy is obtained by summing the product of the variance contribution rate of the principal factor and the principal factor.

[0020] Preferably, the evaluation method uses R language software to perform factor analysis and calculation; wherein, the KMO statistic is used as the coefficient for testing the correlation between variables.

[0021] On the other hand, an evaluation device for the synergistic impact of policies on air pollution reduction and carbon reduction is provided, including a memory and a processor, wherein the memory stores at least one program, which is executed by the processor to implement the evaluation method as described above.

[0022] In another aspect, a computer-readable storage medium is provided, wherein at least one program is stored therein, the at least one program being executed by a processor to implement the evaluation method as described in any of the above descriptions.

[0023] In another aspect, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the steps of any of the evaluation methods described above.

[0024] The above technical solution has the following technical effects:

[0025] The technical solution of this invention uses a panel fixed effects model to analyze the impact of N selected environmental policies on CO2 emission reduction and VOCs emission reduction at the city cluster scale. This overcomes the shortcomings of evaluating policies of a single type and scale, and can quickly, accurately and stably achieve the synergistic impact assessment of air pollution reduction and carbon reduction. It has practical significance for the green, low-carbon and sustainable development of city clusters. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating an embodiment of the present invention of a method for evaluating the synergistic impact of policies on air pollution reduction and carbon reduction. Detailed Implementation

[0027] To further illustrate the various embodiments, the present invention provides accompanying drawings. These drawings are part of the disclosure of the present invention, primarily used to illustrate the embodiments and to explain the operating principles of the embodiments in conjunction with the relevant descriptions in the specification. With reference to these drawings, those skilled in the art should be able to understand other possible implementations and the advantages of the present invention. Components in the drawings are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0028] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments.

[0029] Example 1:

[0030] Figure 1 This is a flowchart illustrating an embodiment of the evaluation method for the synergistic impact of policies on air pollution reduction and carbon reduction according to the present invention. The evaluation method of this embodiment includes:

[0031] Panel fixed-effects models were used to analyze the effects of N selected environmental policies on CO2 emission reduction and VOCs emission reduction at the city-wide scale, where N is greater than or equal to 2; and,

[0032] When N policies are implemented simultaneously, the effects of the N policies on carbon reduction and pollution reduction at the city scale are calculated using a factor analysis model.

[0033] The specific implementation of the embodiments of the present invention will be described below.

[0034] I. Constructing a model of the synergistic impact of environmental policies on pollution reduction and carbon reduction

[0035] (1) Collinearity analysis

[0036] Before building a multiple regression model, it is crucial to diagnose multicollinearity in the explanatory variables to ensure that the model's accuracy is not affected. All explanatory variables had variance inflation factors less than 10, indicating the absence of severe multicollinearity issues, and therefore these variables could be safely included in the regression model.

[0037] (2) Model Validation

[0038] After testing and analyzing the fixed effects (FE) model, random effects (RE) model, and pooled model, the optimal model was selected.

[0039] (3) Constructing a panel fixed effects model

[0040] A panel fixed effects model was used to study and analyze the effect of policy intensity on CO2 emission reduction, and to evaluate the effect of environmental policies on CO2 emission reduction, as shown in the following equation (1):

[0041]

[0042] Similarly, the policy intensity that has a significant impact on VOCs emission reduction is selected, as shown in equation (2):

[0043] VOCs it =β i +β1X 1it +β2X 2it +β3X 3it +…+β N X Nit +ε i +ε t +ε i,t (2)

[0044] in, This indicates the impact of environmental policies on CO2 emission reduction in city i at a selected time t; VOCs it X represents the impact of environmental policies on the reduction of volatile organic compounds (VOCs) in city i at time t; i represents any city in the city cluster; X1 represents the intensity of policy 1, X2 represents the intensity of policy 2, X3 represents the intensity of policy 3, and X... N X represents the strength of policy N; 1it X represents the intensity of policy 1 for city i at time t. 2it X represents the intensity of policy 2 for city i at time t. 3it X represents the intensity of policy 3 for city i at time t. Nit ε represents the intensity of policy N targeting city i at time t; i For the fixed effects of city i, ε t For a fixed effect of time t, ε i,t Let α0, α1, α2…α be the random error of city i at time t; N β represents the predetermined carbon reduction estimation coefficient for each of the N policies; i ,β1,β2…β N These are predetermined pollution reduction estimation coefficients for each of the N policies.

[0045] The estimated coefficients α1 and α2 measure the effects of policy 1 intensity and policy 2 intensity on carbon reduction, respectively. Other α-type coefficients have similar meanings. Similarly, the values ​​of β1 and β2 assess the effects of policy 2 intensity and policy 2 intensity on pollution reduction, respectively. Other β-type coefficients have similar meanings.

[0046] When the carbon reduction or pollution reduction estimation coefficient is greater than zero, it indicates that the policy corresponding to the carbon reduction or pollution reduction estimation coefficient has exacerbated carbon emissions or pollution emissions; when the carbon reduction or pollution reduction estimation coefficient is less than zero, it indicates that the policy corresponding to the carbon reduction or pollution reduction estimation coefficient has a carbon reduction or pollution reduction effect; when the carbon reduction estimation coefficient corresponding to the same policy is greater than the pollution reduction estimation coefficient, it indicates that the carbon reduction effect of the policy is greater than the pollution reduction effect; when the carbon reduction estimation coefficient corresponding to the same policy is less than the pollution reduction estimation coefficient, it indicates that the carbon reduction effect of the policy is less than the pollution reduction effect. Specifically, for example, if α1 is significantly negative, it indicates that policy 1 has a significant carbon reduction effect; if it is positive, it indicates that policy 1 has exacerbated carbon emissions; if it is not significant, it indicates that its impact on carbon reduction is not obvious.

[0047] II. Analysis of the Synergistic Impact of Environmental Policies on Pollution Reduction and Carbon Reduction

[0048] The environmental policy intensity effect estimation results obtained using the fixed-effects panel model show that the estimated coefficient of a certain policy at the 1% significance level represents the change in CO2 and VOCs by the same multiple as the estimated coefficient for every unit change in Policy 1. The estimated coefficient of a certain policy intensity in the carbon reduction effect model is larger than that in the pollution reduction effect model, indicating that the carbon reduction effect of the policy is greater than its pollution reduction effect. In other words, Policy 1 in this urban cluster is primarily focused on carbon reduction, with pollution reduction as a secondary objective.

[0049] III. Construction of an Environmental Policy Effect Model at the Urban Scale

[0050] The above analysis shows that the impact of environmental policies on pollution and carbon reduction is not a deterministic linear function, but is influenced by a variety of internal and external factors. Therefore, it is essential to quantify the effectiveness of environmental policies on pollution and carbon reduction from an empirical perspective. This involves extracting policy variables from policy texts and using factor analysis models to transform multiple variables in urban-scale environmental policies into a few comprehensive indicators.

[0051] In one embodiment, M policy variables are set for quantitative analysis of policy effects. These M variables are determined by analyzing the keyword frequencies of policy texts at the city scale within the urban agglomeration. The extracted M policy variables are then transformed into m quantifiable indicators. For example, the high-frequency term "elimination of outdated production capacity" in the policy text can be transformed into "energy intensity".

[0052] Factor analysis is used to quantitatively evaluate the effects of environmental policies. This method groups highly correlated variables into the same factor to reduce the number of factors and thus reflect most of the information in the original data. The steps of the factor analysis model are as follows:

[0053] First, construct the original data matrix C for environmental policies, including n policy indicators for m cities:

[0054]

[0055] In the formula, P represents the policy indicator, m represents the number of cities, and n represents the number of policy indicators.

[0056] Then, based on the mean of the original data and standard deviation σ j Standardize the data using Z-scores:

[0057]

[0058] In this formula, It is the mean of the j-th indicator, σ j Z is the standard deviation of the j-th indicator. ij These are the standardized variable values.

[0059] Next, calculate the correlation coefficient matrix R = (r ij ) m×n And find its characteristic roots ε j (ε1,ε2,…,ε j >0) and eigenvectors I1,I2,…,I j :

[0060] F = I T ×C (5)

[0061] In the formula, F is the extracted factor score, and C is the original data matrix.

[0062] Determine the number of factors; in one specific implementation, the number of factors to retain is determined by calculating the variance contribution rate and the cumulative contribution rate; for example, factors with a variance contribution rate > 70% are retained.

[0063] Find the initial factor loading matrix P, and rotate it to better interpret the meaning of each factor. Find the initial factor loading matrix Q.

[0064] Calculate the score for each factor, and express the factors as a linear combination of the original index variables:

[0065] F k =b k0 +b k1 P1+b k2 P2+…+b kn P n (6)

[0066] In the formula, F k Let b be the score of the extracted k-th factor, b be the indicator coefficient, and P be the policy indicator variable.

[0067] Finally, the variance contribution rate g of each factor is used. kAs weights, a linear formula for the overall score is obtained:

[0068] F = g1F1 + g2F2 + ... + g k F k (7)

[0069] In the formula, F is the comprehensive score of the factors, and g k This represents the variance contribution rate of the k-th factor.

[0070] IV. Analysis Process of Environmental Policy Effects Model at the Urban Scale

[0071] Factor analysis and calculations were performed using R software. First, a test analysis was conducted. The KMO statistic is a coefficient used to test the correlation between variables; its value ranges from 0 to 1. A higher value indicates a better effect of the factor analysis. The KMO statistic must be greater than the minimum standard of 0.5, and the Bartlett significance p = 0 < 0.001. Therefore, the selected environmental policy variable is suitable for factor analysis. Table 1 shows the KMO and Bartlett test results for environmental policy in an example.

[0072]

[0073] Table 1

[0074] After passing the inspection, the total variance interpretation table is obtained, as shown in Table 2.

[0075]

[0076]

[0077] Table 2

[0078] The total variance calculated here explains the percentage contribution of each factor. When the first cumulative variance contribution rate reaches 70%, a principal factor is extracted based on the principle that the variance contribution rate extracted from the principal factor reaches 70% and the eigenvalue is greater than or equal to 1. When the second cumulative variance contribution rate reaches more than 90%, the first two principal factors are selected and denoted as Factor 1 and Factor 2. They can represent more than 90% of the information in the environmental policy indicator data.

[0079] The specific analysis and solution steps are illustrated using City 1 as an example: As shown in the table above, the initial eigenvalue of the first principal component of City 1 is 5.340, with a cumulative variance contribution rate of 76.283%. Based on the principle that the variance contribution rate of the extracted principal factors reaches 70% and the eigenvalue is greater than or equal to 1, one principal factor is extracted. The initial eigenvalue of the second principal component is 1.097, with a cumulative variance contribution rate of 91.961%. Therefore, the first two principal factors are selected, denoted as Factor 1 and Factor 2, which can represent more than 90% of the information in the environmental policy indicator data. The first principal component is the component with the largest contribution obtained after principal component analysis; here, it refers to a specific policy type.

[0080] After determining the first two principal factors, the component score matrices of the two principal factors are obtained. In order to better interpret the meaning of each factor, the rotated component transformation matrix is ​​obtained, as shown in Table 3.

[0081]

[0082]

[0083] Table 3

[0084] Based on the component score coefficient matrix in the table above, the score of each principal factor is calculated using formula (6), and then the overall score of environmental policy is calculated using formula (7).

[0085] In one embodiment, multiple variables of the policies of various cities in a certain urban agglomeration are transformed into a few comprehensive indicators, namely, energy intensity (EI), industrial energy intensity (RIS), technological progress (TP), number of civilian vehicles (C), number of air quality monitoring substations (S), civilian coal consumption (RT), and population density (Popd). The calculation process of the policy factor scores is shown in formulas (8) to (20). 1 city:

[0086] Factor score 1 = -0.167×EI + (-0.200)×RIS + 0.192×TP + 0.165×C + 0.188×S + 0.075×RT + 0.158×Popd(8)

[0087] Factor score 2 = -0.107×EI + 0.175×RIS + (-0.040)×TP + 0.157×C + (-0.258)×S + 0.863×RT + 0.143×Popd(9)

[0088] Total factor score = 0.819 × factor score 1 + 0.181 × factor score 2 (10)

[0089] 2 cities:

[0090] Factor score 1 = -0.170×EI + (-0.172)×RIS + 0.176×TP + 176×C + 0.152×S + (-0.101)×RT + 0.156×Popd(11)

[0091] Total factor score = 1 × factor score 1 (12)

[0092] 3 cities:

[0093] Factor score 1 = -0.204×EI + (-0.188)×RIS + 0.191×TP + 0.176×C + 0.133×S + 0.090×RT + 0.189×Popd(13)

[0094] Factor score 2 = -0.130×EI + 0.002×RIS + (-0.001)×TP + (-0.085)×C + (-0.265)×S + 0.840×RT + 0.266×Popd(14)

[0095] Total factor score = 0.808 × factor score 1 + 0.192 × factor score 2 (15)

[0096] 4 cities:

[0097] Factor score 1 = -0.161×EI + (-0.161)×RIS + 0.179×TP + 172×C + 0.159×S + 0.142×RT + 0.149×Popd(16)

[0098] Total factor score = 1 × factor score 1 (17)

[0099] 5 cities:

[0100] Factor score 1 = -0.232×EI + (-0.086)×RIS + 0.175×TP + 0.285×C + 0.011×S + 0.235×RT + 0.304×Popd(18)

[0101] Factor score 2 = -0.015×EI + 0.230×RIS + (-0.091)×TP + 0.167×C + (-0.355)×S + 0.586×RT + 0.235×Popd(19)

[0102] Total factor score = 0.656 × factor score 1 + 0.344 × factor score 2 (20)

[0103] Based on the above formula, the detailed information on the main factor scores of urban environmental policies for the five selected cities from 2005 to 2020 is shown in Table 4 below.

[0104]

[0105] Table 4

[0106] Example 2:

[0107] The present invention also provides an evaluation device for the synergistic impact of policies on air pollution reduction and carbon reduction, including a memory and a processor. The memory stores at least one program, which is executed by the processor to implement the evaluation method as described above.

[0108] Example 3:

[0109] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method described in the embodiments of the present invention.

[0110] If the modules / units integrated in the computer unit are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0111] Example 4:

[0112] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps described above.

[0113] Although the invention has been specifically shown and described in conjunction with preferred embodiments, those skilled in the art should understand that various changes in form and detail may be made to the invention without departing from the spirit and scope of the invention as defined in the appended claims, all of which shall be within the scope of protection of the invention.

Claims

1. A method for evaluating the synergistic impact of policies on air pollution reduction and carbon reduction, characterized in that, include: Panel fixed-effects models were used to analyze the effects of N selected environmental policies on CO2 emission reduction and VOCs emission reduction at the city cluster scale, where N is greater than or equal to 2. VOCs it =b i +β1X 1it +β2X 2it +β3X 3it +…+b N X Nit +e i +e t +e i,t ; in, This indicates the impact of environmental policies on CO2 emission reduction in city i at a selected time t; VOCs it X represents the impact of environmental policies on the reduction of volatile organic compounds (VOCs) in city i at time t; i represents any city in the city cluster; X1 represents the intensity of policy 1, X2 represents the intensity of policy 2, X3 represents the intensity of policy 3, and X... N X represents the strength of policy N; 1it X represents the intensity of policy 1 for city i at time t. 2it X represents the intensity of policy 2 for city i at time t. 3it X represents the intensity of policy 3 for city i at time t. Nit ε represents the intensity of policy N targeting city i at time t; i For the fixed effects of city i, ε t For a fixed effect of time t, ε i,t Let α0, α1, α2…α be the random error of city i at time t; N β represents the predetermined carbon reduction estimation coefficient for each of the N policies; i ,β1,β2…β N These are predetermined pollution reduction estimation coefficients for each of the N policies; Specifically, when the carbon reduction estimation coefficient or pollution reduction estimation coefficient is greater than zero, it indicates that the policy corresponding to the carbon reduction estimation coefficient or pollution reduction estimation coefficient has aggravated carbon emissions or pollution emissions; when the carbon reduction estimation coefficient or pollution reduction estimation coefficient is less than zero, it indicates that the policy corresponding to the carbon reduction estimation coefficient or pollution reduction estimation coefficient has a carbon reduction or pollution reduction effect; when the carbon reduction estimation coefficient corresponding to the same policy is greater than the pollution reduction estimation coefficient, it indicates that the carbon reduction effect of the policy is greater than the pollution reduction effect; when the carbon reduction estimation coefficient corresponding to the same policy is less than the pollution reduction estimation coefficient, it indicates that the carbon reduction effect of the policy is less than the pollution reduction effect.

2. The evaluation method according to claim 1, characterized in that, The selected time t is the selected year.

3. The evaluation method according to claim 1, characterized in that, Also includes: When the N policies are implemented simultaneously, the effects of the N policies on carbon reduction and pollution reduction at the city scale are calculated using a factor analysis model. Among them, the effects of the N policies on carbon reduction and pollution control at the city scale, calculated using a factor analysis model, include: Extract at least one policy variable from the policy text; The extracted policy variables are transformed into multiple quantifiable comprehensive indicators to obtain multiple factors. Among them, different variables whose correlation meets the predetermined correlation degree are classified into the same factor. The factor analysis model is used to analyze the obtained factors, and the factors whose variance contribution rate reaches the predetermined variance contribution rate threshold and whose eigenvalue is greater than or equal to 1 are identified as the main factors for analyzing the policy effect. The scores of each principal factor are calculated by representing the principal factors as linear combinations of the original indicator variables. Using the variance contribution rate of each principal factor as a weight, the comprehensive score of each policy is obtained by summing the product of the variance contribution rate of the principal factor and the principal factor.

4. The evaluation method according to claim 3, characterized in that, Factor analysis and calculations were performed using R software; the KMO statistic was used as the coefficient to test the correlation between variables.

5. An evaluation device for the synergistic impact of policies on air pollution reduction and carbon reduction, characterized in that, It includes a memory and a processor, the memory storing at least one program, the at least one program being executed by the processor to implement the evaluation method as described in any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that, The storage medium stores at least one program, which is executed by a processor to implement the evaluation method as described in any one of claims 1 to 4.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the evaluation method as described in any one of claims 1 to 4.

Images