A basin industry-region-cross-region three-level pollution reduction and carbon reduction collaborative evaluation method and system

Abstract

This invention discloses a three-tiered (industry-regional-cross-regional) collaborative evaluation method and system for pollution reduction and carbon reduction within a watershed. The method involves: first, collecting multi-source basic data of the watershed, calculating pollutant and carbon emissions, and constructing a multi-year time-series benchmark dataset; after cleaning and standardizing the basic driving factors, performing correlation and significance tests using emission data as the response variable and the basic driving factors as independent variables to screen out key driving factors and form a candidate indicator set; deriving secondary characterization factors based on the candidate indicator set, and constructing a collaborative evaluation indicator system for pollution reduction and carbon reduction according to the dual dimensions of pollution reduction and carbon reduction and three spatial scales; using an objective weighting method to determine the weights of the dual-dimensional indicators, constructing a pollution reduction subsystem and a carbon reduction subsystem, and calculating the comprehensive evaluation value of the pollution reduction subsystem and the carbon reduction subsystem; and using coupling degree, coordination index, and coupling coordination degree models to quantitatively measure the collaborative level and adaptation level of pollution reduction and carbon reduction at different spatial scales at three levels.

Description

Technical Field

[0001] This invention relates to the field of pollution reduction and carbon reduction synergy evaluation technology, specifically to a three-level (industry-regional-cross-regional) pollution reduction and carbon reduction synergy evaluation method and system within a river basin. Background Technology

[0002] Because pollutants and greenhouse gases within a watershed share significant common origins, watershed-level pollution reduction and carbon reduction actions can produce positive synergistic effects in most scenarios. However, there are also trade-offs and even conflicts between these two objectives. For example, the high energy consumption of end-of-pipe treatment facilities can increase carbon emissions, thus hindering the achievement of carbon reduction targets. Therefore, scientifically and quantitatively revealing the coupling and synergistic relationship between pollution reduction and carbon reduction within a watershed is of crucial theoretical value and practical guiding significance for formulating precise and efficient comprehensive watershed ecological and environmental governance policies.

[0004] Currently, numerous studies have been conducted on evaluating the synergistic effects of pollution reduction and carbon reduction. Existing evaluation methods largely focus on single spatial scales: one type of research emphasizes the correlation characteristics between carbon emissions and major pollutant emissions at the macro-level (national and provincial administrative levels), while another type focuses on the environmental technology efficiency assessment of individual enterprises and specific pollution control projects at the micro-level. Regarding the selection of evaluation methods, existing studies often employ simple indicator comparison analysis, regression analysis, or single efficiency evaluation models (such as data envelopment analysis and DEA). These methods provide a theoretical basis and data support for understanding the preliminary correlation characteristics between pollution reduction and carbon reduction.

[0005] However, the existing evaluation methods mentioned above still have many insurmountable technical flaws, as follows:

[0006] First, existing evaluation methods cannot achieve multi-scale collaborative coverage and interactive analysis, making it difficult to simultaneously cover different spatial and management scales such as industry, administrative region, and cross-basin. Furthermore, they fail to effectively reveal the transmission mechanism and interactive impact of pollution reduction and carbon reduction synergy between different scales, resulting in significant bias in the final evaluation results and failing to provide comprehensive and reliable data support for basin-wide integrated governance decisions.

[0007] Secondly, the existing evaluation system has insufficient coverage of indicators. Most studies have failed to incorporate key driving factors that affect the synergistic effect of pollution reduction and carbon reduction, such as resource utilization efficiency, industrial structure characteristics, and spatial planning layout, into a unified evaluation framework. As a result, the evaluation results are difficult to fully and completely reflect the rich connotation of the synergistic effect of pollution reduction and carbon reduction in the basin, and the completeness and universality of the evaluation system are insufficient.

[0008] Third, existing evaluation methods mostly adopt static evaluation models, which make it difficult to accurately capture the spatiotemporal evolution of the synergy between pollution reduction and carbon reduction in watersheds. At the same time, they lack the ability to conduct efficient spatial visualization and targeted diagnostic analysis of the evaluation results, resulting in the evaluation conclusions being out of touch with the actual governance scenarios of watersheds and making it difficult to directly transform them into feasible and operable watershed ecological environment governance paths and control measures. Summary of the Invention

[0009] To overcome the shortcomings of existing technologies, this invention provides a three-tiered (industry-regional-cross-regional) collaborative evaluation method for pollution reduction and carbon reduction within a watershed. This method is more reasonable in evaluating the collaborative nature of pollution reduction and carbon reduction in a watershed, better identifies the collaborative nature of pollution reduction and carbon reduction, and provides quantitative basis and decision support for the collaborative management and control of pollution reduction and carbon reduction in a watershed.

[0010] The second objective of this invention is to provide a three-tiered collaborative evaluation system for pollution reduction and carbon reduction within a watershed, encompassing industry, region, and cross-regional levels.

[0011] The technical solution of the present invention to solve the above-mentioned technical problems is:

[0012] A three-tiered (industry-regional-cross-regional) collaborative evaluation method for pollution and carbon reduction within a watershed includes the following steps:

[0013] Step S1: Collect multi-source basic data related to pollutant emissions and carbon emissions within the target watershed; perform annual time-series accounting of pollutant emissions and carbon dioxide emissions within the target watershed to construct a multi-year time-series benchmark dataset of pollutants and carbon emissions.

[0014] Step S2: Perform data cleaning and dimensionless standardization on the multi-source basic data to obtain a multi-source basic driving factor dataset; use the multi-year time series benchmark dataset as the response variable and the multi-source basic driving factor dataset as the independent variable to perform correlation and significance tests, screen out key driving factors with significant correlation, and classify and collect them according to the two dimensions of pollution reduction and carbon reduction to form a candidate indicator set.

[0015] Step S3: Based on the candidate index set, secondary characterization factors are derived, and corresponding index groups are divided according to the dual attributes of pollution reduction and carbon reduction. At the same time, three-level hierarchical adaptation is completed according to industry scale, regional scale, and cross-regional scale to construct a pollution reduction and carbon reduction collaborative evaluation index system that connects the three spatial levels throughout the process and distinguishes the dual dimensions of pollution reduction and carbon reduction.

[0016] Step S4: Objectively assign weights to the pollution reduction and carbon reduction synergistic evaluation index system according to the hierarchy and the two dimensions, and independently complete the weight calculation within the pollution reduction dimension group and the carbon reduction dimension group;

[0017] Step S5: Divide the basin's pollution reduction and carbon reduction synergistic evaluation index system into interrelated pollution reduction subsystems and carbon reduction subsystems, and calculate the comprehensive evaluation values ​​of the pollution reduction subsystem and carbon reduction subsystem respectively by combining the corresponding weights; based on the comprehensive evaluation values ​​of the pollution reduction subsystem and carbon reduction subsystem, calculate the coupling degree, coordination index and coupling coordination degree of the pollution reduction subsystem and carbon reduction subsystem, and conduct independent quantitative calculations for the three levels of industry, region and cross-region to obtain the degree of correlation between pollution reduction and carbon reduction, the overall development level and the synergistic adaptation level at different spatial levels.

[0018] Preferably, in step S1, the multi-source basic data related to pollutants and carbon emissions includes natural background factor data, socio-economic driving factor data, pollutant and carbon emission factor data, administrative division data, and watershed boundary data. The natural background factor data reflects the environmental capacity and carbon sink capacity of the watershed, including average annual precipitation, average annual river flow, vegetation cover, regional forestry carbon sink increment, and river section water quality category. The socio-economic driving factor data reflects the scale, structure, efficiency, and cleanliness level of economic activities, including regional GDP, population, urbanization rate, the proportion of the tertiary industry, energy consumption, the proportion of high-tech industry output, and road freight volume. The pollutant and carbon emission factor data includes pollutant emission factor data and carbon emission factor data. The pollutant emission factor data measures environmental pressure and characterizes the effectiveness of pollution control, including wastewater discharge and exhaust gas emissions. The carbon emission factor data is used to calculate carbon emissions, analyze energy structure optimization paths, and characterize carbon reduction effectiveness, including total carbon dioxide emissions and fossil fuel consumption.

[0019] Preferably, in step S1, the calculation of carbon dioxide emissions adopts a calculation method based on energy statistics data, and the calculation of pollutant emissions adopts an equivalent calculation method.

[0020] Preferably, in step S2, the step of screening out the key driving factors with significant correlation is as follows:

[0021] Using the standardized multi-source basic driving factor dataset as independent variables, and the pollutant emission time series data and carbon emission time series data divided from the multi-year time series benchmark dataset as two sets of independent response variables, the Pearson correlation coefficients between the natural background factors, socio-economic driving factors and their corresponding independent response variables are calculated and significance tests are performed. Based on the preset significance level threshold, key driving factors that pass the significance test for both pollutant emission and carbon emission independent response variables are selected and aggregated to form the candidate index set.

[0022] Preferably, in step S3, based on the candidate index set, secondary characterization factors including carbon emission intensity and energy output rate are obtained through data derivation processing; based on the secondary characterization factors, a pollution reduction and carbon reduction collaborative evaluation index system covering industry scale, regional scale, and cross-regional scale is constructed, and the pollution reduction and carbon reduction collaborative evaluation index system is divided into a pollution reduction subsystem and a carbon reduction subsystem; wherein, the industry scale indicators are selected from four nodes: source reduction, process control, end-of-pipe treatment, and migration and transformation; the regional scale indicators are selected from three nodes: natural process, socio-economic process, and management process; the cross-regional scale indicators are used to measure the synergy and spatial correlation of pollution reduction and carbon reduction between upstream and downstream areas of a watershed or between different economic sectors.

[0023] Preferably, in step S4, the entropy weight method is used to independently and objectively assign weights to the pollution reduction and carbon reduction collaborative evaluation index system at three levels: industry, region, and cross-region, as well as the pollution reduction dimension group and the carbon reduction dimension group, and independently complete the intra-group weight calculation of the pollution reduction dimension group and the carbon reduction dimension group.

[0024] Preferably, in step S5, pollution reduction subsystems and carbon reduction subsystems are independently constructed for the three scales of industry, region, and cross-region. Based on the comprehensive evaluation value of the pollution reduction subsystems and carbon reduction subsystems at each scale, the coupling degree and coordination index of the pollution reduction subsystems and carbon reduction subsystems at the corresponding scales are calculated in sequence to finally obtain the coupling coordination degree corresponding to each scale, thereby quantitatively characterizing the interaction strength and coordinated development level of pollution reduction and carbon reduction at the corresponding scales.

[0025] Preferably, in step S5, the corresponding scale of collaborative development level is defined according to the preset coupling coordination degree numerical range, and the qualitative classification interpretation of the evaluation results at each level is completed.

[0026] Preferably, in step S5, the coordination level is divided according to the numerical range of coupling coordination degree: coupling coordination degree [0,2] is severe mismatch, (2,4] is mild mismatch, (4,6] is primary coordination, (6,8] is moderate coordination, and (8,10] is excellent coordination.

[0027] A three-tiered collaborative evaluation system for pollution and carbon reduction within a watershed, encompassing industry, region, and cross-regional levels, includes:

[0028] The data acquisition and time series accounting module is used to collect multi-source basic data related to pollutants and carbon emissions within the target watershed, conduct year-by-year time series accounting of pollutant emissions and carbon dioxide emissions within the target watershed, and construct a multi-year time series benchmark dataset of pollutants and carbon emissions.

[0029] The factor screening and preprocessing module is connected to the data acquisition and time series accounting module. It is used to perform data cleaning and dimensionless standardization on multi-source basic data to obtain a multi-source basic driving factor dataset. Using the multi-year time series benchmark dataset as the response variable and the multi-source basic driving factor dataset as the independent variable, correlation and significance tests are carried out to screen key driving factors with significant correlation. These factors are then classified and grouped according to two dimensions: pollution reduction and carbon reduction, to form a candidate indicator set.

[0030] The multi-level indicator system construction module is connected to the factor screening and preprocessing module. It is used to derive secondary characterization factors based on the candidate indicator set, divide the indicator groups according to the dual-dimensional attributes of pollution reduction and carbon reduction, and complete the three-level hierarchical adaptation according to industry scale, regional scale and cross-regional scale to construct a pollution reduction and carbon reduction collaborative evaluation indicator system that connects the three spatial levels throughout the process and distinguishes the dual dimensions of pollution reduction and carbon reduction.

[0031] The hierarchical dual-dimensional weighting module is communicatively connected to the multi-level indicator system construction module. It is used to objectively weight the pollution reduction and carbon reduction collaborative evaluation indicator system according to the level and the dual dimensions, and independently complete the weight calculation of the pollution reduction dimension group and the carbon reduction dimension group within the group.

[0032] The scale-based coupling evaluation module is communicatively connected to the multi-level indicator system construction module and the hierarchical two-dimensional weighting module. It is used to divide the pollution reduction and carbon reduction synergistic evaluation indicator system into interrelated pollution reduction subsystems and carbon reduction subsystems, and to obtain the comprehensive evaluation value of the two subsystems by combining the corresponding weights. Based on the comprehensive evaluation value of the pollution reduction subsystem and the carbon reduction subsystem, the coupling degree, coordination index and coupling coordination degree are calculated, and the independent quantitative calculations are performed for the three levels of industry, region and cross-region, and the correlation between pollution reduction and carbon reduction, the overall development level and the synergistic adaptation level are output at different spatial levels.

[0033] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0034] 1. This invention constructs a comprehensive evaluation index system for pollution reduction and carbon reduction across three levels: industry, region, and cross-region. It deeply integrates the entropy weight method and the coupling coordination degree model with this unique architecture, achieving an objective, dynamic, and precise quantitative assessment of the synergistic development level of watershed pollution reduction and carbon reduction systems. Unlike traditional evaluation methods that suffer from limitations such as single-scale constraints and subjective weighting biases, this invention is data-driven. It identifies key driving factors through significance testing and objectively determines the index weights of these key driving factors using the principle of information entropy, thereby effectively avoiding biases caused by subjective human intervention.

[0035] 2. This invention, through joint analysis of coupling degree and coupling coordination degree, can not only accurately characterize the interaction strength between the two major systems of pollution reduction and carbon reduction, but also clearly identify the collaborative development stage of the two major systems of pollution reduction and carbon reduction at different spatial scales. This greatly improves the scientific nature, objectivity and practical policy guidance value of the evaluation results, and provides accurate quantitative support for the coordinated management and control of pollution reduction and carbon reduction in watersheds.

[0036] 3. This invention provides an in-depth analysis of the synergistic mechanism of pollution reduction and carbon reduction in watersheds, offering a practical and scalable quantitative tool for cross-regional environmental collaborative governance. Existing related studies mostly focus on the accounting of total pollutant or carbon emissions and single-dimensional efficiency calculations, lacking a systematic characterization and in-depth analysis of the interactive relationship between the two major systems of pollution reduction and carbon reduction, making it difficult to support cross-regional collaborative governance decisions. This invention, through the decomposition, comparison, and coupling analysis of the comprehensive evaluation values ​​of the pollution reduction subsystem and the carbon reduction subsystem, can accurately pinpoint the shortcomings of collaborative development at different scales (such as the carbon reduction process lagging behind pollution reduction work in a certain region, or the insufficient collaborative management effectiveness in a certain industry), providing clear guidance for formulating differentiated and targeted pollution reduction and carbon reduction policies.

[0037] 4. This invention has good versatility and scalability, and can be adapted to the evaluation needs of different watersheds and different periods. It can realize horizontal comparison and vertical analysis between watersheds and different time periods, and provide scientific and reliable decision-making basis for establishing cross-regional ecological compensation mechanisms for watersheds, unifying collaborative management and control standards, and improving joint supervision systems, thereby helping to improve the systematicness and synergy of watershed environmental governance.

[0038] 5. This invention integrates objective weighting with system coupling analysis, avoiding subjective bias, and can scientifically identify collaborative shortcomings and dynamically evaluate collaborative effectiveness, providing quantitative support and decision-making basis for formulating differentiated watershed governance strategies and promoting cross-regional integrated pollution reduction and carbon reduction policies. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the process for the three-level (industry-regional-cross-regional) collaborative evaluation method for pollution reduction and carbon reduction within a watershed, as per the present invention.

[0040] Figure 2 This is a schematic diagram showing the calculation results of the Pearson correlation coefficients among the driving factors of city A.

[0041] Figure 3 For the three tiers from 2018 to 2022 , A schematic diagram of the calculation results.

[0042] Figure 4 This is a schematic diagram showing the calculation results of the three-level coupling coordination degree from 2018 to 2022. Detailed Implementation

[0043] The present invention will be further described in detail below with reference to the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0044] See Figures 1-4 The present invention provides a three-tiered (industry-regional-cross-regional) collaborative evaluation method for pollution and carbon reduction within a watershed, comprising the following steps:

[0045] Step S1: Collect multi-source basic data related to pollutants and carbon emissions within the target watershed; perform annual time-series accounting of pollutant emissions and carbon dioxide emissions within the target watershed to construct a multi-year time-series benchmark dataset for pollutants and carbon emissions, whereby...

[0046] The multi-source basic data related to pollutant and carbon emissions (as shown in Table 1) includes natural background factor data, socioeconomic driving factor data, pollutant and carbon emission factor data, administrative division data, and watershed boundary data.

[0047] The natural background factor data are used to reflect the environmental capacity and carbon sequestration capacity of the watershed, including average annual precipitation, average annual river flow, vegetation coverage, regional forestry carbon sequestration increment, and river section water quality category, etc.

[0048] The socioeconomic driving factor data are used to reflect the scale, structure, efficiency and cleanliness of economic activities, including regional GDP, population, urbanization rate, the proportion of the tertiary industry, energy consumption, the proportion of high-tech industry output, and road freight volume.

[0049] The pollutant and carbon emission factor data includes pollutant emission factor data and carbon emission factor data. The pollutant emission factor data is used to measure environmental pressure and characterize the effectiveness of pollution control, including wastewater discharge and exhaust gas emissions. The carbon emission factor data is used to calculate carbon emissions, analyze energy structure optimization paths, and characterize carbon reduction effectiveness, including total carbon dioxide emissions and fossil fuel consumption.

[0050] Table 1: Multi-source basic data table

[0051]

[0052] In this embodiment, it is assumed that city A and city B are two different types of cities, and Table 2 is the multi-source basic data from 2018 to 2022;

[0053] Table 2: Multi-source factor data for City A and City B, 2018-2022

[0054]

[0055]

[0056]

[0057]

[0058] Carbon dioxide accounting uses a calculation method based on energy statistics, while pollutant emission accounting uses equivalent calculations. The specific formulas are as follows:

[0059] ;

[0060] ;

[0061] In the formula, This refers to carbon dioxide emissions. To convert to total energy consumption in standard coal equivalent, the emission factor for standard coal combustion is taken as 0.68. For pollutant emissions, These are the emissions of sulfur dioxide, nitrogen oxides, COD, and ammonia nitrogen, respectively. For pollutants ( The equivalent weighting coefficient is taken as the reciprocal of the "pollution equivalent value" of each pollutant listed in the Appendix "Table of Taxable Pollutants and Equivalent Values" of the Environmental Protection Tax Law of the People's Republic of China (effective in 2018), as shown in Table 3.

[0062] Table 3: Equivalent Weight Values ​​Table

[0063] Total pollutants and carbon emissions (t) in City A:

[0064] =138.25×0.68×44 / 12=344.89;

[0065] =1.053×0.51+1.053×0.61+1.0×9.52+1.25×0.95=11.81;

[0066] Based on the above calculation principle, we obtain:

[0067] =336.15; =328.17; =320.19; =313.45;

[0068] =11.36; =10.95; =10.59; =10.23;

[0069] Pollutants and total carbon emissions (t) in City B:

[0070] =779.88; =763.15; =744.69; =726.22; =709.50;

[0071] =10.36; =9.93; =9.52; =9.15; =8.75;

[0072] Step S2: Perform data cleaning and dimensionless standardization on the multi-source basic data to obtain a multi-source basic driving factor dataset; using the multi-year time series benchmark dataset as the response variable and the multi-source basic driving factor dataset as the independent variable, conduct correlation and significance tests (P-value tests) to screen out key driving factors that can characterize pollutant emissions and carbon emissions that are significantly correlated, and classify and collect them according to the two dimensions of pollution reduction and carbon reduction to form a candidate indicator set, thereby ensuring the scientificity and objectivity of the indicator system subsequently constructed;

[0073] In this embodiment, the specific formula for standardization is as follows:

[0074] ;

[0075] ;

[0076] ;

[0077] in, Let be the mean of the j-th indicator; This represents the original value of the i-th sample on the j-th indicator; Let j be the standard deviation of the j-th indicator; The value of the i-th sample after standardization on the j-th indicator; The total number of samples.

[0078] According to the standardized formula, the multi-source fundamental driving factor dataset for city A can be obtained as follows:

[0079] ;

[0080] Using a standardized multi-source baseline driving factor dataset as independent variables, and pollutant emission time-series data and carbon emission time-series data from a multi-year time-series benchmark dataset as two sets of independent response variables, Pearson correlation coefficients were calculated between natural background factors, socioeconomic driving factors, and their corresponding independent response variables, and significance tests were performed. Based on a preset significance level threshold, key driving factors that passed the significance test with both pollutant emission and carbon emission independent response variables were selected, irrelevant or weakly correlated factors were eliminated, and the key driving factors were aggregated to form the candidate index set, as shown in the following formula:

[0081] ;

[0082] in, The correlation coefficient has a range of values. ; These are the standardized values ​​of the two indicators on the i-th sample; , These are the means of the two indicators across all samples; m is the number of samples.

[0083] In this embodiment, it is only necessary to determine whether there is a significant correlation between each driving factor and pollutant emissions and carbon emissions. Figure 2 This shows the absolute values ​​of the Pearson correlation coefficients for each driving factor in city A; (The rest of the text appears to be a fragment and requires further context for accurate translation.) Figure 2 It can be seen that the three driving factors of average annual precipitation, river network density, and total water resources are not significantly correlated with the total regional pollutant emissions and total regional carbon dioxide emissions. Therefore, these three factors are removed. The remaining driving factors are the key driving factors and together constitute the candidate index set.

[0084] Step S3: Based on the candidate index set, secondary characterization factors such as carbon emission intensity and energy output rate are derived. According to the dual-dimensional attributes of pollution reduction and carbon reduction, corresponding index groups are divided (i.e., pollution reduction dimension group and carbon reduction dimension group). At the same time, a three-level hierarchical adaptation is completed according to industry scale, regional scale and cross-regional scale to construct a pollution reduction and carbon reduction collaborative evaluation index system that connects the three spatial levels throughout the process and distinguishes the dual dimensions of pollution reduction and carbon reduction.

[0085] In this embodiment, based on the candidate index set, secondary characterization factors, including carbon emission intensity and energy output rate, are obtained through data derivation processing. Based on the secondary characterization factors, a pollution reduction and carbon reduction synergy evaluation index system covering industry scale, regional scale, and cross-regional scale is constructed. The pollution reduction and carbon reduction synergy evaluation index system is divided into a pollution reduction subsystem and a carbon reduction subsystem. Among them, the industry scale indicators are selected from four nodes: source reduction, process control, end-of-pipe treatment, and migration and transformation. The selected indicators are used to measure the synergy of pollution reduction and carbon reduction within different industries, such as energy, industry, agriculture, construction, transportation, and service industries, which are high-pollution, high-emission, and high-energy-consumption sectors. The regional scale indicators are selected from three nodes: natural process, socio-economic process, and management process. The selected indicators are used to measure the synergy of pollution reduction and carbon reduction within different administrative regions of the basin, such as cities and counties. The cross-regional scale indicators are used to measure the synergy and spatial correlation of pollution reduction and carbon reduction between upstream and downstream areas of the basin or different economic sectors. The pollution reduction and carbon reduction synergy evaluation index system is shown in Table 4.

[0086] Table 4: Evaluation Index System for Collaborative Pollution Reduction and Carbon Reduction

[0087]

[0088] Step S4: Using the entropy weight method, the pollution reduction and carbon reduction collaborative evaluation index system is independently and objectively weighted at three levels: industry, region, and cross-region, as well as the pollution reduction dimension group and the carbon reduction dimension group, and the weight calculation within the pollution reduction dimension group and the carbon reduction dimension group is completed independently.

[0089] In this embodiment, pollution reduction and carbon reduction synergy evaluations are conducted at the industrial sector of city A, the regional level of city A, and the cross-regional level between city A and city B. Table 5 shows the specific indicators corresponding to the industrial sector of city A. The indicators used at the regional level of city A and the cross-regional level between city A and city B are the regional and cross-regional indicators in the evaluation indicator system constructed in step S3.

[0090] Table 5: Specific Indicators of the Industrial Sector in City A

[0091]

[0092] This embodiment employs the entropy weighting method for weighting. This method determines the weight based on the amount of information reflected by the degree of variation in each indicator value, effectively avoiding bias caused by subjective factors and making the weight allocation more scientific and reasonable. The weights for each indicator are calculated below:

[0093] First, the secondary characterization factors selected in step S3 are converted into non-negative data with uniform direction and consistent dimensions to meet the input requirements of the entropy weight method. The specific formula is as follows:

[0094] Positive indicators:

[0095] ;

[0096] Negative indicators:

[0097] ;

[0098] in, Let i be the original observation value of the i-th indicator in year j. The converted index value is given, where n is the number of observation years.

[0099] Based on the formula, we can obtain the industrial sector of city A after the transformation, the index matrix of city A, and the relationship between city A and city B:

[0100] ;

[0101] ;

[0102] ;

[0103] Based on the transformed indicator matrix, the entropy value of each indicator (i.e., secondary characterization factor) is calculated to determine its objective weight. The core idea of ​​the entropy weight method is that if the data variation of an indicator is greater (i.e., the information entropy is smaller), the more information it provides, the greater its utility in the comprehensive evaluation, and the higher its weight should be. The specific formula is as follows:

[0104] ;

[0105] ;

[0106] ;

[0107] ;

[0108] in, Let be the feature weight of the i-th sample on the j-th indicator; The value of the i-th sample after standardization on the j-th indicator; Let be the entropy value of the j-th index; Let be the difference coefficient of the j-th indicator; Let be the weight of the j-th indicator; m is the number of samples, and n is the number of indicators; the weight values ​​of each indicator are shown in Tables 6-8.

[0109] Table 6: Weight Calculation Results of Various Indicators within the Industry

[0110]

[0111] Table 7: Calculation Results of Weights for Various Indicators within the Region

[0112]

[0113] Table 8: Weight Calculation Results of Various Indicators Across Regions

[0114] Step S5: Divide the watershed pollution reduction and carbon reduction synergistic evaluation index system into interrelated pollution reduction subsystems and carbon reduction subsystems, and calculate the comprehensive evaluation value of each subsystem based on the corresponding weights. Based on the comprehensive evaluation value of the pollution reduction and carbon reduction subsystems, calculate the coupling degree, coordination index and coupling coordination degree of the pollution reduction and carbon reduction subsystems, and conduct independent quantitative calculations for the three levels of industry, region and cross-region to obtain the degree of correlation between pollution reduction and carbon reduction, the overall development level and the synergistic adaptation level at different spatial levels.

[0115] In this embodiment, pollution reduction subsystems and carbon reduction subsystems are independently constructed for three scales: industry, region, and cross-region. Based on the comprehensive evaluation values ​​of the pollution reduction and carbon reduction subsystems at each scale, the coupling degree and coordination index of the pollution reduction and carbon reduction subsystems at the corresponding scale are calculated sequentially to obtain the coupling coordination degree corresponding to each scale. This quantitatively characterizes the interaction strength and coordinated development level of pollution reduction and carbon reduction at the corresponding scale. According to the preset coupling coordination degree value range, the collaborative development level of the corresponding scale is defined, and the qualitative classification and interpretation of the evaluation results at each level are completed.

[0116] In this embodiment, the specific formula is as follows:

[0117] Comprehensive evaluation value of pollution reduction system :

[0118] ;

[0119] Comprehensive evaluation value of carbon reduction system :

[0120] ;

[0121] in, As the indicator weight, These are standardized values.

[0122] Coupling degree (C):

[0123] ;

[0124] Coordination Index (T):

[0125]

[0126] Among them, usually taken This indicates that the pollution reduction subsystem is just as important as the carbon reduction subsystem.

[0127] Coupling Coordination Degree (D):

[0128] ;

[0129] Coordination levels are classified according to the coupling coordination degree (D) value range: [0,2] indicates severe misalignment, (2,4] indicates mild misalignment, (4,6] indicates basic coordination, (6,8] indicates moderate coordination, and (8,10] indicates excellent coordination.

[0130] The degree of synergy and coordinated development of pollution reduction and carbon reduction in the industrial sector of City A each year:

[0131] =(0.12×0.400+0.10×0.667+0.07×0.249+0.05×0.272+0.06×0.245+0.06×0.274+0.07×0.248)×100=19.43;

[0132] =(0.10×0.250+0.08×0.249+0.09×0.235+0.12×0.000+0.08×0.249)×100=8.61 (the same applies to others);

[0133] =37.76; =29.48; =33.87; =34.07; =47.72; =47.00;

[0134] =0.92 (the same applies to others); =0.96; =1.00; =1.00;

[0135] =0.5×19.43+0.5×8.61=14.02 (the same applies to others); =29.48; =33.97; =47.13;

[0136] =3.60 (the same applies to others); =5.32; =5.83; =6.87;

[0137] The degree of synergy and coordinated development between pollution reduction and carbon reduction in City A each year:

[0138] =(0.06×0.283+0.06×0.280+0.04×0.362+0.02×0.370+0.02×0.266+0.03×0.182+0.06×0.726+0.04×0.306+0.02×0.265+0.05×0.267+0.02×0.250+0.03×0.056)×100=14.76;

[0139] =(0.03×0.241+0.03×0.246+0.06×0.278+0.06×0.222+0.04×0.250+0.05×0.207+0.03×0.210+0.03×0.255+0.03×0.235+0.06×0.055+0.03×0.221+0.03×0.285+0.03×0.224+0.04×0.200)×100=11.92 (the others are similar);

[0140] =23.44; =24.55; =29.62; =42.43; =39.99; =51.48;

[0141] =0.99 (the same applies to others); =1.00; =0.98; =0.99;

[0142] =0.5×14.76+0.5×11.92=13.34 (the same applies to others); =23.99; =36.02; =45.73;

[0143] =3.64 (the same applies to others); =4.90; =5.95; =6.74;

[0144] The degree of synergy and coordinated development in pollution reduction and carbon reduction between City A and City B each year:

[0145] =(0.07×0.306+0.07×0.250+0.07×0.212+0.007×0.182+0.007×0.715+0.007×0.249+0.007×0.200)×100=14.79;

[0146] =(0.07×0.291+0.007×0.200+0.007×0.167+0.007×0.158+0.008×0.167+0.007×0.167+0.008×0.179)×100=9.63 (the same applies to others);

[0147] =23.11; =22.09; =31.24; =34.72; =42.00; =51.00;

[0148] =0.98 (the same applies to others); =1.00; =1.00; =1.00;

[0149] =0.5×14.79+0.5×9.63=12.21 (the same applies to others); =22.60; =32.98; =46.50;

[0150] =3.46 (the same applies to others); =4.75; =5.74; =6.80;

[0151] Based on the evaluation results and Figure 3 , Figure 4The data shows that from 2019 to 2022, the comprehensive evaluation values ​​of the pollution reduction subsystems at all three levels showed a significant upward trend, indicating that substantial progress has been made in pollution reduction efforts at the industrial, urban, and regional collaborative levels. Meanwhile, the comprehensive evaluation value of the carbon reduction subsystem also showed a steady upward trend, but its growth trajectory differed significantly from that of the pollution reduction subsystem. Carbon reduction efforts achieved leapfrog development during the study period, with particularly outstanding improvements in the industrial sector and regional collaborative levels. The coupling coordination degree at all three levels has shown a continuous upward trend, and the level of coordinated development has achieved a leapfrog improvement: In 2019, the coupling coordination degree (D value) at all three levels was in the (2,4] range, belonging to the "slightly imbalanced" state, indicating that the coordination between the pollution reduction subsystem and the carbon reduction subsystem was insufficient at this stage, and there was a problem of development disconnect; 2020 became a key turning point, with the coupling coordination degree (D value) at all three levels breaking through the 4.0 mark, officially entering the "basic coordination" state, marking a substantial breakthrough in the coordinated development of pollution reduction and carbon reduction in the basin; In 2022, the coupling coordination degree (D value) at all three levels further broke through 6.0, entering the "medium coordination" state, and the level of coordinated development was significantly improved. In just four years, all three levels have achieved a two-level leapfrog development from "slightly imbalanced" to "medium coordination", which fully demonstrates the effectiveness and scientific nature of the implementation of the pollution reduction and carbon reduction coordinated governance policy.

[0152] Example 2

[0153] The present invention provides a three-tiered collaborative evaluation system for pollution and carbon reduction within a watershed, encompassing industry, region, and cross-regional levels, including:

[0154] The data acquisition and time series accounting module is used to collect multi-source basic data related to pollutants and carbon emissions within the target watershed, conduct year-by-year time series accounting of pollutant emissions and carbon dioxide emissions within the target watershed, and construct a multi-year time series benchmark dataset of pollutants and carbon emissions.

[0155] The factor screening and preprocessing module is connected to the data acquisition and time series accounting module. It is used to perform data cleaning and dimensionless standardization on multi-source basic data to obtain a multi-source basic driving factor dataset. Using the multi-year time series benchmark dataset as the response variable and the multi-source basic driving factor dataset as the independent variable, correlation and significance tests are carried out to screen key driving factors with significant correlation. These factors are then classified and grouped according to two dimensions: pollution reduction and carbon reduction, to form a candidate indicator set.

[0156] The multi-level indicator system construction module is connected to the factor screening and preprocessing module. It is used to derive secondary characterization factors based on the candidate indicator set, divide the indicator groups according to the dual-dimensional attributes of pollution reduction and carbon reduction, and complete the three-level hierarchical adaptation according to industry scale, regional scale and cross-regional scale to construct a pollution reduction and carbon reduction collaborative evaluation indicator system that connects the three spatial levels throughout the process and distinguishes the dual dimensions of pollution reduction and carbon reduction.

[0157] The hierarchical dual-dimensional weighting module is communicatively connected to the multi-level indicator system construction module. It is used to objectively weight the pollution reduction and carbon reduction collaborative evaluation indicator system according to the level and the dual dimensions, and independently complete the weight calculation of the pollution reduction dimension group and the carbon reduction dimension group within the group.

[0158] The scale-based coupling evaluation module is communicatively connected to the multi-level indicator system construction module and the hierarchical two-dimensional weighting module. It is used to divide the pollution reduction and carbon reduction synergistic evaluation indicator system into interrelated pollution reduction subsystems and carbon reduction subsystems, and to obtain the comprehensive evaluation value of the two subsystems by combining the corresponding weights. Based on the comprehensive evaluation value of the pollution reduction subsystem and the carbon reduction subsystem, the coupling degree, coordination index and coupling coordination degree are calculated, and the independent quantitative calculations are performed for the three levels of industry, region and cross-region, and the correlation between pollution reduction and carbon reduction, the overall development level and the synergistic adaptation level are output at different spatial levels.

[0159] The above are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above content. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for evaluating the synergy of pollution reduction and carbon reduction in a three-level industry-region-cross-region of a river basin, characterized in that, Includes the following steps: Step S1: Collect multi-source basic data related to pollutant emissions and carbon emissions within the target watershed; The pollutant emissions and carbon dioxide emissions in the target watershed are calculated on an annual time-series basis to construct a multi-year time-series benchmark dataset of pollutants and carbon emissions. Step S2: Perform data cleaning and dimensionless standardization on the multi-source basic data to obtain a multi-source basic driving factor dataset; use the multi-year time series benchmark dataset as the response variable and the multi-source basic driving factor dataset as the independent variable to perform correlation and significance tests, screen out key driving factors with significant correlation, and classify and collect them according to the two dimensions of pollution reduction and carbon reduction to form a candidate indicator set. Step S3: Based on the candidate index set, secondary characterization factors are derived, and corresponding index groups are divided according to the dual attributes of pollution reduction and carbon reduction. At the same time, three-level hierarchical adaptation is completed according to industry scale, regional scale, and cross-regional scale to construct a pollution reduction and carbon reduction collaborative evaluation index system that connects the three spatial levels throughout the process and distinguishes the dual dimensions of pollution reduction and carbon reduction. Step S4: Objectively assign weights to the pollution reduction and carbon reduction synergistic evaluation index system according to the hierarchy and the two dimensions, and independently complete the weight calculation within the pollution reduction dimension group and the carbon reduction dimension group; Step S5: Divide the basin's pollution reduction and carbon reduction synergistic evaluation index system into interrelated pollution reduction subsystems and carbon reduction subsystems, and calculate the comprehensive evaluation values ​​of the pollution reduction subsystem and carbon reduction subsystem respectively by combining the corresponding weights; based on the comprehensive evaluation values ​​of the pollution reduction subsystem and carbon reduction subsystem, calculate the coupling degree, coordination index and coupling coordination degree of the pollution reduction subsystem and carbon reduction subsystem, and conduct independent quantitative calculations for the three levels of industry, region and cross-region to obtain the degree of correlation between pollution reduction and carbon reduction, the overall development level and the synergistic adaptation level at different spatial levels.

2. The industry-region-cross-region three-level collaborative evaluation method for pollution reduction and carbon reduction in a river basin according to claim 1, characterized in that, In step S1, the multi-source basic data related to pollutants and carbon emissions includes natural background factor data, socio-economic driving factor data, pollutant and carbon emission factor data, administrative division data, and watershed boundary data. The natural background factor data reflects the environmental capacity and carbon sink capacity of the watershed, including average annual precipitation, average annual river flow, vegetation cover, regional forestry carbon sink increment, and river section water quality category. The socio-economic driving factor data reflects the scale, structure, efficiency, and cleanliness level of economic activities, including regional GDP, population, urbanization rate, the proportion of the tertiary industry, energy consumption, the proportion of high-tech industry output, and road freight volume. The pollutant and carbon emission factor data includes pollutant emission factor data and carbon emission factor data. The pollutant emission factor data measures environmental pressure and characterizes the effectiveness of pollution control, including wastewater discharge and exhaust gas emissions. The carbon emission factor data is used to calculate carbon emissions, analyze energy structure optimization paths, and characterize carbon reduction effectiveness, including total carbon dioxide emissions and fossil fuel consumption.

3. The industry-region-cross-region three-level collaborative evaluation method for pollution reduction and carbon reduction in a river basin according to claim 1, characterized in that, In step S1, the calculation of carbon dioxide emissions adopts a calculation method based on energy statistics data, and the calculation of pollutant emissions adopts an equivalent calculation method.

4. The three-tiered (industry-regional-cross-regional) collaborative evaluation method for pollution and carbon reduction within a watershed, as described in claim 1, is characterized in that... In step S2, the step of screening out the key drivers with significant correlations is as follows: The standardized multi-source basic driving factor dataset was used as the independent variable, and the pollutant emission time series data and carbon emission time series data divided from the multi-year time series benchmark dataset were used as two sets of independent response variables. The Pearson correlation coefficients between the natural background factor, the socio-economic driving factor and the corresponding independent response variable are calculated and significance tests are performed. Based on the preset significance level threshold, key driving factors that pass the significance test for both pollutant emissions and carbon emissions are selected and aggregated to form the candidate index set.

5. The three-tiered (industry-regional-cross-regional) collaborative evaluation method for pollution and carbon reduction within a watershed, as described in claim 1, is characterized in that... In step S3, based on the candidate index set, secondary characterization factors including carbon emission intensity and energy output rate are obtained through data derivation processing. Based on the aforementioned secondary characterization factors, a collaborative evaluation index system for pollution reduction and carbon reduction is constructed, covering industry-scale, regional-scale, and cross-regional-scale evaluation. This system is divided into a pollution reduction subsystem and a carbon reduction subsystem. The industry-scale indicators are selected from four nodes: source reduction, process control, end-of-pipe treatment, and migration and transformation. The regional-scale indicators are selected from three nodes: natural processes, socio-economic processes, and management processes. The cross-regional-scale indicators are used to measure the synergy and spatial correlation of pollution reduction and carbon reduction between upstream and downstream areas of a river basin or between different economic sectors.

6. The three-tiered (industry-regional-cross-regional) collaborative evaluation method for pollution and carbon reduction within a watershed, as described in claim 1, is characterized in that... In step S4, the entropy weight method is used to independently and objectively assign weights to the pollution reduction and carbon reduction collaborative evaluation index system at three levels: industry, region, and cross-region, as well as the pollution reduction dimension group and the carbon reduction dimension group, and independently complete the intra-group weight calculation of the pollution reduction dimension group and the carbon reduction dimension group.

7. The three-tiered (industry-regional-cross-regional) collaborative evaluation method for pollution and carbon reduction within a watershed, as described in claim 1, is characterized in that... In step S5, pollution reduction subsystems and carbon reduction subsystems are independently constructed for the three scales of industry, region, and cross-region. Based on the comprehensive evaluation value of the pollution reduction subsystems and carbon reduction subsystems at each scale, the coupling degree and coordination index of the pollution reduction subsystems and carbon reduction subsystems at the corresponding scales are calculated in sequence. Finally, the coupling coordination degree corresponding to each scale is obtained, which quantitatively characterizes the interaction strength and coordinated development level of pollution reduction and carbon reduction at the corresponding scales.

8. The three-tiered (industry-regional-cross-regional) collaborative evaluation method for pollution and carbon reduction within a watershed, as described in claim 7, is characterized in that... In step S5, based on the preset range of coupling coordination degree values, the corresponding scale of collaborative development level is defined, and the qualitative classification interpretation of the evaluation results at each level is completed.

9. The three-tiered (industry-regional-cross-regional) collaborative evaluation method for pollution and carbon reduction within a watershed, as described in claim 8, is characterized in that... In step S5, the coordination level is divided according to the numerical range of coupling coordination degree: coupling coordination degree [0,2] is severe mismatch, (2,4] is mild mismatch, (4,6] is primary coordination, (6,8] is moderate coordination, and (8,10] is excellent coordination.

10. A three-tiered collaborative evaluation system for pollution and carbon reduction within a watershed, encompassing industry, region, and cross-regional levels, characterized in that... include: The data acquisition and time series accounting module is used to collect multi-source basic data related to pollutants and carbon emissions within the target watershed, conduct year-by-year time series accounting of pollutant emissions and carbon dioxide emissions within the target watershed, and construct a multi-year time series benchmark dataset of pollutants and carbon emissions. The factor screening and preprocessing module is connected to the data acquisition and time series accounting module. It is used to perform data cleaning and dimensionless standardization on multi-source basic data to obtain a multi-source basic driving factor dataset. Using the multi-year time series benchmark dataset as the response variable and the multi-source basic driving factor dataset as the independent variable, correlation and significance tests are carried out to screen key driving factors with significant correlation. These factors are then classified and grouped according to two dimensions: pollution reduction and carbon reduction, to form a candidate indicator set. The multi-level indicator system construction module is connected to the factor screening and preprocessing module. It is used to derive secondary characterization factors based on the candidate indicator set, divide the indicator groups according to the dual-dimensional attributes of pollution reduction and carbon reduction, and complete the three-level hierarchical adaptation according to industry scale, regional scale and cross-regional scale to construct a pollution reduction and carbon reduction collaborative evaluation indicator system that connects the three spatial levels throughout the process and distinguishes the dual dimensions of pollution reduction and carbon reduction. The hierarchical dual-dimensional weighting module is communicatively connected to the multi-level indicator system construction module. It is used to objectively weight the pollution reduction and carbon reduction collaborative evaluation indicator system according to the level and the dual dimensions, and independently complete the weight calculation of the pollution reduction dimension group and the carbon reduction dimension group within the group. The scale-based coupling evaluation module is communicatively connected to the multi-level indicator system construction module and the hierarchical two-dimensional weighting module. It is used to divide the pollution reduction and carbon reduction synergistic evaluation indicator system into interrelated pollution reduction subsystems and carbon reduction subsystems, and to obtain the comprehensive evaluation value of the two subsystems by combining the corresponding weights. Based on the comprehensive evaluation value of the pollution reduction subsystem and the carbon reduction subsystem, the coupling degree, coordination index and coupling coordination degree are calculated, and the independent quantitative calculations are performed for the three levels of industry, region and cross-region, and the correlation between pollution reduction and carbon reduction, the overall development level and the synergistic adaptation level are output at different spatial levels.

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