A multi-scale integrated energy pollution reduction and carbon reduction collaborative path analysis method and system

Abstract

The patent of this invention discloses a multi-scale integrated energy pollution reduction and carbon reduction collaborative path analysis method and system, which specifically relates to the field of environmental management technology. It includes: integrating the pollution carbon emission intensity accounting model and learning curve of the whole life cycle of energy technology to obtain the pollution carbon emission intensity prediction model of the whole life cycle of energy technology; establishing the cross-influence matrix of energy technology to obtain the micro-scale energy technology development scenario set with systematic correlation; establishing the energy system system dynamics model to obtain the macro- and meso-scale energy system development scenario set with systematic correlation; integrating the micro-scale energy technology development scenario set with the macro- and meso-scale energy system development scenario set to obtain the time profile of pollution carbon emissions in the whole life cycle of energy; establishing the technology-industry-region multi-scale integrated energy pollution reduction and carbon reduction collaborative factor decomposition model to obtain the technology-industry-region multi-scale integrated energy pollution reduction and carbon reduction collaborative path.

Description

Technical Field

[0001] The present invention relates to the field of environmental management technology, and in particular to a multi-scale integrated energy pollution reduction and carbon reduction collaborative path analysis method and system. Background Art

[0002] The development and utilization of global fossil energy has led to intensive emissions of atmospheric pollutants and greenhouse gases, causing serious environmental pollution and climate warming. Collaboratively promoting energy pollution reduction and carbon reduction is an important guarantee for the sustainable development of mankind. Energy resource endowment, supply and demand structure, and technological application have significant regional heterogeneity characteristics, and there are many uncertainties in future trends. Therefore, energy development will derive infinite possibilities at different times and in different regions in the future. Determining the key collaborative path of pollution reduction and carbon reduction requires scenario analysis and factor decomposition.

[0003] Scenario analysis usually focuses on the development trend of energy systems at the macro (regional) or meso (industry) scale, and does not go further down to the micro (technical) scale to analyze the development trend of energy technology. The same is true for factor decomposition, which often focuses on the synergistic effects of macro and meso scale factors such as economy, population, energy structure, and industrial structure, and rarely considers the pollution reduction and carbon reduction synergistic effects of energy technology at the micro scale. The above limitations lead to the lack of refinement and operability of the determined energy pollution reduction and carbon reduction synergistic paths, making it difficult to accurately guide the implementation of energy pollution reduction and carbon reduction synergistic measures.

[0004] In addition, the development scenarios of different energy technologies are analyzed relatively isolated at the micro-scale, and the mutual influence of development trends among various energy technologies is not systematically considered. For specific energy technologies, their carbon emission levels are usually analyzed from a static perspective, ignoring the pollution reduction and carbon reduction potential brought about by technological progress, and only focusing on the carbon emissions directly caused by the energy technology itself, and less considering the carbon emissions indirectly caused by the upstream and downstream industrial chains of energy technologies. In terms of the coordinated elements of pollution reduction and carbon reduction, the coordinated emission reduction of atmospheric pollutants and greenhouse gases is usually focused on, while the coordinated emission reduction of wastewater pollutants, solid waste and greenhouse gases is ignored. The above limitations lead to the lack of accuracy, incomplete boundaries, and incomplete elements in the determined energy pollution reduction and carbon reduction coordinated paths, making it difficult to scientifically support the formulation of energy pollution reduction and carbon reduction coordinated policies.

[0005] Therefore, based on the above-mentioned practical needs, a collaborative path analysis method for energy pollution reduction and carbon reduction that integrates multiple scales of technology, industry and region is needed. Summary of the invention

[0006] The present invention aims to provide a multi-scale integrated energy pollution reduction and carbon reduction collaborative path analysis method and system to solve the problems of insufficient refinement, poor operability, insufficient accuracy, incomplete boundaries, and incomplete elements in the existing energy pollution reduction and carbon reduction collaborative paths.

[0007] In order to achieve the above object, the technical solution of the present invention is as follows: a multi-scale fusion energy pollution reduction and carbon reduction collaborative path analysis method, comprising the following steps:

[0008] S1. Integrate the carbon emission intensity accounting model and learning curve of energy technology throughout its life cycle to obtain a prediction model for carbon emission intensity throughout its life cycle;

[0009] S2. Establish an energy technology cross-impact matrix to obtain a set of micro-scale energy technology development scenarios with systematic correlations;

[0010] S3. Establish a system dynamics model of the energy system to obtain a set of macro- and meso-scale energy system development scenarios with systematic correlations;

[0011] S4. Integrate the micro-scale energy technology development scenario set with the macro- and meso-scale energy system development scenario set to obtain the time profile of carbon emissions over the entire energy life cycle that integrates technology, industry, and region at multiple scales;

[0012] S5. Establish a decomposition model of energy pollution reduction and carbon reduction synergy factors that integrates technology, industry and region at multiple scales, and obtain a synergistic path for energy pollution reduction and carbon reduction that integrates technology, industry and region at multiple scales.

[0013] Furthermore, the method of step S1 is as follows:

[0014] S1.1. Determine the boundaries of the carbon emission intensity accounting system for the entire life cycle based on the characteristics of energy technology, covering energy technology and its upstream materials, energy supply stage and downstream waste treatment stage involving air pollutants, wastewater pollutants, solid waste, and greenhouse gas emissions, and establish a carbon emission intensity accounting model for the entire life cycle of energy technology;

[0015] S1.2. Collect dynamic data on energy technology material consumption, energy consumption, and environmental emissions, combine them with the localized carbon emission life cycle background database, and substitute them into the energy technology life cycle carbon emission intensity accounting model established in S1.1 to obtain the dynamic energy technology life cycle carbon emission intensity;

[0016] S1.3. Establish a learning curve for the carbon emission intensity of energy technology throughout its life cycle, collect dynamic data on the production scale of energy technology, combine it with the dynamic carbon emission intensity of energy technology throughout its life cycle obtained in S1.2, calculate the "learning by doing" coefficient of the carbon emission intensity of energy technology throughout its life cycle and extrapolate its changing trend to obtain a prediction model for the carbon emission intensity of energy technology throughout its life cycle.

[0017] Furthermore, the method of step S2 is as follows:

[0018] S2.1. Determine the qualitative logical relationship between various energy technologies based on their characteristics;

[0019] S2.2. Determine the quantitative logical relationship between various energy technologies based on the qualitative logical relationship between various energy technologies determined in S2.1 and combined with expert research and judgment;

[0020] S2.3. Based on the quantitative logical relationship between various energy technologies determined in S2.2, a correction model for the input rates of various energy technologies is established. The initial input rates of various energy technologies given by relevant policy planning or expert research are substituted, and the corrected input rates of various energy technologies are calculated and a time profile is formed to obtain a set of micro-scale energy technology development scenarios with systematic correlations.

[0021] Furthermore, the method of step S3 is as follows:

[0022] S3.1. Determine key indicators based on the characteristics of the energy system, combine expert research and judgment, identify the causal relationship between key indicators, and establish a causal relationship diagram;

[0023] S3.2. Based on the cause-effect relationship diagram established in S3.1, determine the flow and stock types of key indicators, supplement auxiliary indicators, determine industry structure, energy production, and energy consumption as dependent variables, and other indicators as independent variables, and establish a flow-stock diagram;

[0024] S3.3. According to the flow and stock diagram established in S3.2, establish the correlation calculation equations between various indicators, form the system dynamics model of the energy system, and carry out intuitive testing, operation testing, and historical testing;

[0025] S3.4. Based on the system dynamics model of the energy system established in S3.3, substitute the independent variable time profile given by relevant policy planning or expert research and judgment to form the dependent variable time profile, and obtain a set of macro- and meso-scale energy system development scenarios with systematic correlation.

[0026] Furthermore, the method of step S4 is as follows:

[0027] S4.1. By multiplying the time profiles of the modified input rates of various energy technologies formed by S2.3 by the time profiles of the industry structure, energy production, and energy consumption formed by S3.4, the micro-scale energy technology development scenario set and the macro- and meso-scale energy system development scenario set are integrated to obtain the time profiles of the production scales of various energy technologies;

[0028] S4.2. Based on the production scale time profiles of various energy technologies obtained in S4.1, calculate the cumulative production scale time profiles of various energy technologies, substitute them into the energy technology life cycle carbon emission intensity prediction model established in S1.3, and obtain the carbon emission intensity time profile of the micro-scale energy technology life cycle;

[0029] S4.3. Based on the time profile of carbon emission intensity over the entire life cycle of micro-scale energy technologies obtained in S4.2, the time profiles of production scales of various energy technologies obtained in S4.1 are aggregated from bottom to top at the meso- and macro-scales to obtain a time profile of carbon emission over the entire life cycle of energy that integrates multiple scales of technology, industry, and region.

[0030] Further, the method of step S5 is as follows:

[0031] S5.1. Based on the characteristics of energy system and energy technology, decompose the synergistic driving factors of energy pollution reduction and carbon reduction step by step from macroscopic, mesoscopic to microscopic scales, and establish a decomposition model of energy pollution reduction and carbon reduction synergistic factors integrating technology, industry and region at multiple scales;

[0032] S5.2. Substitute the time profile of carbon emissions in the energy life cycle obtained by the technology-industry-region multi-scale integration in S4.3 into the energy pollution reduction and carbon reduction synergy factor decomposition model established in S5.1 by the technology-industry-region multi-scale integration to quantify the pollution reduction and carbon reduction synergy effect of the macro- and meso-scale energy system and the pollution reduction and carbon reduction synergy effect of the micro-scale energy technology;

[0033] S5.3. Analyze the positive and negative effects of the synergistic effects of pollution reduction and carbon reduction of macro- and meso-scale energy systems and the synergistic effects of pollution reduction and carbon reduction of micro-scale energy technologies quantified in S5.2, determine the positive synergistic effects of pollution reduction and carbon reduction of macro- and meso-scale energy systems and the positive synergistic effects of pollution reduction and carbon reduction of micro-scale energy technologies, and obtain the synergistic path of energy pollution reduction and carbon reduction that integrates multiple scales of technology, industry and region.

[0034] Another technical solution provided by the present invention is a multi-scale integrated energy pollution reduction and carbon reduction collaborative path analysis system, comprising:

[0035] Carbon emission prediction model module: used to integrate the carbon emission intensity accounting model and learning curve of the energy technology throughout its life cycle to obtain the carbon emission intensity prediction model of the energy technology throughout its life cycle;

[0036] Energy technology development scenario module: used to establish the energy technology cross-impact matrix and obtain a set of micro-scale energy technology development scenarios with systematic correlations;

[0037] Energy system development scenario module: used to establish a system dynamics model of the energy system and obtain a set of macro- and meso-scale energy system development scenarios with systematic correlations;

[0038] Energy development scenario integration module: used to integrate the micro-scale energy technology development scenario set with the macro- and meso-scale energy system development scenario set to obtain the time profile of carbon emissions over the entire energy life cycle with the technology-industry-region multi-scale integration;

[0039] Pollution reduction and carbon reduction collaborative path module: used to establish an energy pollution reduction and carbon reduction collaborative factor decomposition model with multi-scale integration of technology, industry and region, and obtain an energy pollution reduction and carbon reduction collaborative path with multi-scale integration of technology, industry and region.

[0040] Furthermore, the carbon emission prediction model module includes:

[0041] Carbon emission modeling module: used to determine the boundary of the carbon emission intensity accounting system for the entire life cycle according to the characteristics of energy technology, and to establish a carbon emission intensity accounting model for the entire life cycle of energy technology;

[0042] Carbon emission accounting module: used to manage the dynamic data of energy technology material consumption, energy consumption, and environmental emissions, as well as the localized carbon emission life cycle background database, run the carbon emission intensity accounting model for the entire life cycle of energy technology, and obtain the dynamic carbon emission intensity of the entire life cycle of energy technology;

[0043] Learning curve quantification module: used to establish the learning curve of carbon emission intensity over the entire life cycle of energy technology, manage the dynamic data of energy technology production scale and dynamic data of carbon emission intensity over the entire life cycle of energy technology, calculate the "learning by doing" coefficient of carbon emission intensity over the entire life cycle of energy technology and extrapolate its changing trend, and obtain a prediction model for carbon emission intensity over the entire life cycle of energy technology.

[0044] Furthermore, the energy technology development scenario module includes:

[0045] Qualitative logic identification module: used to determine the qualitative logic relationship between various energy technologies based on energy technology characteristics;

[0046] Quantitative logic identification module: used to determine the quantitative logic relationship between various energy technologies based on the qualitative logic relationship between them and combined with expert research and judgment;

[0047] Investment probability correction module: It is used to establish an investment rate correction model for various energy technologies based on the quantitative logical relationship between them, manage the initial investment rates of various energy technologies given by relevant policy planning or expert research and judgment, calculate the corrected investment rates of various energy technologies and form a time profile, and obtain a set of micro-scale energy technology development scenarios with systematic correlations.

[0048] Furthermore, the energy system development scenario module includes:

[0049] Causal relationship quantification module: used to determine key indicators based on energy system characteristics, identify the causal relationship between key indicators in combination with expert research and judgment, and establish a causal relationship diagram;

[0050] Flow and stock quantification module: used to determine the flow and stock types of key indicators based on the cause-effect relationship diagram, supplement auxiliary indicators, determine industry structure, energy production, and energy consumption as dependent variables, and other indicators as independent variables, and establish a flow and stock diagram;

[0051] System modeling and testing module: used to establish the correlation calculation equations between various indicators based on the flow and stock diagram, form the system dynamics model of the energy system, and carry out intuitive testing, operation testing, and historical testing;

[0052] System model prediction module: used to manage the time profile of independent variables given by relevant policy planning or expert research and judgment, run the system dynamics model of the energy system, form the time profile of dependent variables, and obtain a set of macro- and meso-scale energy system development scenarios with systematic correlation.

[0053] Furthermore, the energy development scenario integration module includes:

[0054] Development scenario integration module: used to integrate the micro-scale energy technology development scenario set with the macro- and meso-scale energy system development scenario set to obtain the time profile of the production scale of various energy technologies;

[0055] Micro emission prediction module: used to manage the production scale time profiles of various energy technologies, calculate the cumulative production scale time profiles of various energy technologies, run the carbon emission intensity prediction model for the entire life cycle of energy technologies, and obtain the carbon emission intensity time profiles of the entire life cycle of energy technologies at the micro scale;

[0056] Macro emission aggregation module: It is used to manage the time profile of carbon emission intensity throughout the life cycle of energy technology at the micro scale, and aggregate it from bottom to top at the meso and macro scales to obtain the time profile of carbon emission throughout the life cycle of energy that integrates technology, industry and region at multiple scales.

[0057] Furthermore, the pollution reduction and carbon reduction collaborative pathway module includes:

[0058] Factor decomposition modeling module: It is used to decompose the synergistic driving factors of energy pollution reduction and carbon reduction step by step from macroscopic, mesoscopic to microscopic scales according to the characteristics of energy system and energy technology, and establish a synergistic factor decomposition model of energy pollution reduction and carbon reduction that integrates technology, industry and region at multiple scales;

[0059] Synergy effect quantification module: used to manage the time profile of carbon emissions in the entire life cycle of energy with multi-scale integration of management technology, industry and region, operate the energy pollution reduction and carbon reduction synergy factor decomposition model with multi-scale integration of operation technology, industry and region, and quantify the pollution reduction and carbon reduction synergy of macro- and meso-scale energy systems and the pollution reduction and carbon reduction synergy of micro-scale energy technologies;

[0060] Collaborative path analysis module: used to analyze the positive and negative effects of the synergistic effects of pollution reduction and carbon reduction in macro- and meso-scale energy systems and the synergistic effects of pollution reduction and carbon reduction in micro-scale energy technologies, determine the positive synergistic effects of pollution reduction and carbon reduction in macro- and meso-scale energy systems and the positive synergistic effects of pollution reduction and carbon reduction in micro-scale energy technologies, and obtain the energy pollution reduction and carbon reduction synergistic path that integrates multiple scales of technology, industry and region.

[0061] Compared with the prior art, this solution has the following beneficial effects:

[0062] 1. The present invention establishes a learning curve for the carbon emission intensity of energy technology throughout its life cycle, identifies the potential for pollution reduction and carbon reduction brought about by technological progress, and considers not only the carbon emissions directly caused by the energy technology itself, but also the carbon emissions indirectly caused by the upstream and downstream industrial chains of energy technology when determining the boundaries of the carbon emission intensity accounting system throughout its life cycle. Carbon emissions cover not only atmospheric pollutants and greenhouse gas emissions, but also wastewater pollutants and solid waste emissions, making the collaborative path for energy pollution reduction and carbon reduction more accurate, with more complete boundaries and more comprehensive elements, and can scientifically support the formulation of collaborative policies for energy pollution reduction and carbon reduction.

[0063] 2. The present invention establishes an energy technology cross-impact matrix and identifies the mutual influence of development trends among various energy technologies. Compared with the relatively isolated development scenarios of different energy technologies, it is more scientific and systematic and can truly reflect the process of mutual competition and development of various energy sources. At the same time, in the scenario analysis and factor decomposition, it sinks from the macro-regional scale and the meso-industry scale to the micro-technical scale, making the energy pollution reduction and carbon reduction collaborative path more refined and more operational, and can accurately guide the implementation of energy pollution reduction and carbon reduction collaborative measures.

[0064] 3. The present invention is simple to operate and has strong practical applicability. It can scientifically, completely and accurately analyze the collaborative path of energy pollution reduction and carbon reduction, effectively promote the improvement of air quality and respond to climate change. It has a wide range of applications and can be widely used in the field of collaborative governance of pollution reduction and carbon reduction in the energy industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a flow chart of a multi-scale integrated energy pollution reduction and carbon reduction collaborative path analysis method of the present invention. DETAILED DESCRIPTION

[0066] The present invention is further described in detail below through specific embodiments:

[0067] Example 1

[0068] like Figure 1 As shown in the figure, a multi-scale integrated energy pollution reduction and carbon reduction collaborative path analysis method includes the following steps:

[0069] S1. Integrate the carbon emission intensity accounting model and learning curve of energy technology throughout its life cycle to obtain a prediction model for carbon emission intensity throughout its life cycle. The method is as follows:

[0070] S1.1. Determine the boundaries of the carbon emission intensity accounting system for the entire life cycle based on the characteristics of energy technology, covering energy technology and its upstream materials, energy supply stage and downstream waste treatment stage involving air pollutants, wastewater pollutants, solid waste, and greenhouse gas emissions, and establish a carbon emission intensity accounting model for the entire life cycle of energy technology;

[0071] LCT ijk =∑ l UTA ijl ·UTE jkl +TE ijk +∑ m DTA ijm ·DTE jkm

[0072] Among them, i represents the type of energy technology, j represents the year, k represents the type of carbon emission, l represents the upstream process of energy technology, and m represents the downstream process of energy technology; LCT represents the carbon emission intensity of energy technology throughout its life cycle, UTA represents the activity level of the upstream process of energy technology, UTE represents the carbon emission factor of the upstream process of energy technology, TE represents the carbon emission intensity of energy technology, DTA represents the activity level of the downstream process of energy technology, and DTE represents the carbon emission factor of the downstream process of energy technology.

[0073] S1.2. Collect dynamic data on energy technology material consumption, energy consumption, and environmental emissions, combine them with the localized carbon emission life cycle background database, and substitute them into the energy technology life cycle carbon emission intensity accounting model established in S1.1 to obtain the dynamic energy technology life cycle carbon emission intensity;

[0074] S1.3. Establish a learning curve for the carbon emission intensity of energy technology over its entire life cycle, collect dynamic data on the production scale of energy technology, and combine it with the dynamic carbon emission intensity of energy technology over its entire life cycle obtained in S1.2. Calculate the "learning by doing" coefficient of the carbon emission intensity of energy technology over its entire life cycle (the larger the cumulative production scale of the energy technology, the lower the carbon emission intensity over its entire life cycle) and extrapolate its changing trend to obtain a prediction model for the carbon emission intensity of energy technology over its entire life cycle.

[0075] log 10 LCT ijk =α ik +β ik ·log 10 Q ij

[0076] Among them, α represents the residual coefficient, β represents the "learning by doing" coefficient, and Q represents the cumulative production scale of energy technology. According to the historical development of energy technology, it is divided into different stages. The "learning by doing" coefficients of different stages are fitted through historical data, and its future change trend is extrapolated. That is, the residual coefficient and the "learning by doing" coefficient are known numbers in the future. With the cumulative production scale of energy technology as the independent variable and the carbon emissions of energy technology throughout its life cycle as the dependent variable, a prediction model for the carbon emissions intensity of energy technology throughout its life cycle is constructed.

[0077] S2. Establish an energy technology cross-impact matrix to obtain a set of micro-scale energy technology development scenarios with systematic correlations. The method is as follows:

[0078] S2.1. Determine the qualitative logical relationship between various energy technologies based on their characteristics, i.e. the direction of cross-impact: positive impact, negative impact or no impact;

[0079] S2.2. Based on the qualitative logical relationship between various energy technologies determined in S2.1, combined with expert research and judgment, determine the quantitative logical relationship between various energy technologies, that is, the degree of cross-influence: very strong, relatively strong or relatively weak;

[0080] S2.3. Based on the quantitative logical relationship between various energy technologies determined in S2.2, a correction model for the input rates of various energy technologies (i.e., the adversarial explanatory structural model) is established. The initial input rates of various energy technologies given by relevant policy planning or expert research are substituted, and the corrected input rates of various energy technologies are calculated and a time profile is formed to obtain a set of micro-scale energy technology development scenarios with systematic correlations.

[0081] γ ij =δ ij +KS ijn ·(δ ij -1)·δ ij

[0082] Among them, n represents the type of energy technology different from i; γ represents the energy technology correction investment rate, δ represents the energy technology initial investment rate, and KS represents the quantitative impact of energy technology n on energy technology i. The Monte Carlo simulation of the energy technology correction investment rate is carried out until convergence.

[0083] S3. Establish a system dynamics model of the energy system to obtain a set of macro- and meso-scale energy system development scenarios with systematic correlations. The method is as follows:

[0084] S3.1. Determine key indicators such as population, urbanization rate, gross domestic product, industry structure, energy production, and energy consumption based on the characteristics of the energy system, identify the causal relationship between key indicators based on expert research and judgment, and establish a causal relationship diagram;

[0085] S3.2. Based on the cause-effect relationship diagram established in S3.1, determine the flow and stock types of key indicators, supplement auxiliary indicators, determine industry structure, energy production, and energy consumption as dependent variables, and other indicators as independent variables, and establish a flow-stock diagram;

[0086] S3.3. According to the flow and stock diagram established in S3.2, establish the correlation calculation equations between various indicators, form the system dynamics model of the energy system, and carry out intuitive testing, operation testing, and historical testing;

[0087] S3.4. Based on the system dynamics model of the energy system established in S3.3, substitute the independent variable time profile given by relevant policy planning or expert research and judgment to form the dependent variable time profile, and obtain a set of macro- and meso-scale energy system development scenarios with systematic correlation.

[0088] S4. Integrate the micro-scale energy technology development scenario set with the macro- and meso-scale energy system development scenario set to obtain the time profile of carbon emissions over the entire energy life cycle that integrates technology, industry, and region at multiple scales. The method is as follows:

[0089] S4.1. By multiplying the time profiles of the modified input rates of various energy technologies formed by S2.3 by the time profiles of the industry structure, energy production, and energy consumption formed by S3.4, the micro-scale energy technology development scenario set and the macro- and meso-scale energy system development scenario set are integrated to obtain the time profiles of the production scales of various energy technologies;

[0090] S4.2. Based on the production scale time profiles of various energy technologies obtained in S4.1, calculate the cumulative production scale time profiles of various energy technologies, substitute them into the energy technology life cycle carbon emission intensity prediction model established in S1.3, and obtain the carbon emission intensity time profile of the micro-scale energy technology life cycle;

[0091] S4.3. Based on the time profile of carbon emission intensity over the entire life cycle of micro-scale energy technologies obtained in S4.2, the time profiles of production scales of various energy technologies obtained in S4.1 are aggregated from bottom to top at the meso- and macro-scales to obtain a time profile of carbon emission over the entire life cycle of energy that integrates multiple scales of technology, industry, and region.

[0092] S5. Establish a decomposition model of energy pollution reduction and carbon reduction synergy factors for technology-industry-region multi-scale integration, and obtain the energy pollution reduction and carbon reduction synergy path for technology-industry-region multi-scale integration. The method is as follows:

[0093] S5.1. Based on the characteristics of energy system and energy technology, decompose the synergistic driving factors of energy pollution reduction and carbon reduction step by step from macroscopic, mesoscopic to microscopic scales, and establish a decomposition model of energy pollution reduction and carbon reduction synergistic factors integrating technology, industry and region at multiple scales;

[0094] Firstly, the microscopic, mesoscopic and macroscopic influencing factors are selected to establish the Kaya identity.

[0095] LCE jk =∑ iop LCT ijk γ ij ·ES jop ·EI jp IS jp GDP j

[0096] Among them, o represents energy type; p represents industry type; LCE represents the total carbon emissions in the region, ES represents energy structure, EI represents energy intensity, IS represents industry structure, and GDP represents regional gross domestic product.

[0097] Secondly, the logarithmic mean Dirichlet index method was used to decompose the contribution of influencing factors.

[0098]

[0099]

[0100]

[0101]

[0102]

[0103]

[0104] Among them, q represents the base year; r represents the final year; Δ represents the change in carbon emissions caused by influencing factors.

[0105] S5.2. Substitute the time profile of carbon emissions in the energy life cycle obtained by the technology-industry-region multi-scale integration in S4.3 into the energy pollution reduction and carbon reduction synergy factor decomposition model established in S5.1 by the technology-industry-region multi-scale integration to quantify the pollution reduction and carbon reduction synergy effect of the macro- and meso-scale energy system and the pollution reduction and carbon reduction synergy effect of the micro-scale energy technology;

[0106] S5.3. Analyze the positive and negative effects of the synergistic effects of pollution reduction and carbon reduction of macro- and meso-scale energy systems and the synergistic effects of pollution reduction and carbon reduction of micro-scale energy technologies quantified in S5.2, determine the positive synergistic effects of pollution reduction and carbon reduction of macro- and meso-scale energy systems and the positive synergistic effects of pollution reduction and carbon reduction of micro-scale energy technologies, and obtain the synergistic path of energy pollution reduction and carbon reduction that integrates multiple scales of technology, industry and region.

[0107] Example 2

[0108] A multi-scale fusion energy pollution reduction and carbon reduction collaborative path analysis system, characterized by:

[0109] Carbon emission prediction model module: used to integrate the carbon emission intensity accounting model and learning curve of the energy technology throughout its life cycle to obtain the carbon emission intensity prediction model of the energy technology throughout its life cycle;

[0110] Energy technology development scenario module: used to establish the energy technology cross-impact matrix and obtain a set of micro-scale energy technology development scenarios with systematic correlations;

[0111] Energy system development scenario module: used to establish a system dynamics model of the energy system and obtain a set of macro- and meso-scale energy system development scenarios with systematic correlations;

[0112] Energy development scenario integration module: used to integrate the micro-scale energy technology development scenario set with the macro- and meso-scale energy system development scenario set to obtain the time profile of carbon emissions over the entire energy life cycle with the technology-industry-region multi-scale integration;

[0113] Pollution reduction and carbon reduction collaborative path module: used to establish an energy pollution reduction and carbon reduction collaborative factor decomposition model with multi-scale integration of technology, industry and region, and obtain an energy pollution reduction and carbon reduction collaborative path with multi-scale integration of technology, industry and region.

[0114] The carbon emission prediction model module includes:

[0115] Carbon emission modeling module: used to determine the boundary of the carbon emission intensity accounting system for the entire life cycle according to the characteristics of energy technology, and to establish a carbon emission intensity accounting model for the entire life cycle of energy technology;

[0116] Carbon emission accounting module: used to manage the dynamic data of energy technology material consumption, energy consumption, and environmental emissions, as well as the localized carbon emission life cycle background database, run the carbon emission intensity accounting model for the entire life cycle of energy technology, and obtain the dynamic carbon emission intensity of the entire life cycle of energy technology;

[0117] Learning curve quantification module: used to establish the learning curve of carbon emission intensity over the entire life cycle of energy technology, manage the dynamic data of energy technology production scale and dynamic data of carbon emission intensity over the entire life cycle of energy technology, calculate the "learning by doing" coefficient of carbon emission intensity over the entire life cycle of energy technology and extrapolate its changing trend, and obtain a prediction model for carbon emission intensity over the entire life cycle of energy technology.

[0118] Energy technology development scenario modules include:

[0119] Qualitative logic identification module: used to determine the qualitative logic relationship between various energy technologies based on energy technology characteristics;

[0120] Quantitative logic identification module: used to determine the quantitative logic relationship between various energy technologies based on the qualitative logic relationship between them and combined with expert research and judgment;

[0121] Investment probability correction module: It is used to establish a correction model for the investment rates of various energy technologies (i.e., the adversarial explanatory structural model) based on the quantitative logical relationship between various energy technologies, manage the initial investment rates of various energy technologies given by relevant policy planning or expert research and judgment, calculate the corrected investment rates of various energy technologies and form a time profile, and obtain a set of micro-scale energy technology development scenarios with systematic correlations.

[0122] The energy system development scenario module includes:

[0123] Causal relationship quantification module: used to determine key indicators related to the energy system, such as population, urbanization rate, gross domestic product, industrial structure, energy production, and energy consumption, based on the characteristics of the energy system. Combined with expert research and judgment, it identifies the causal relationship between key indicators and establishes a causal relationship diagram;

[0124] Flow and stock quantification module: used to determine the flow and stock types of key indicators based on the cause-effect relationship diagram, supplement auxiliary indicators, determine industry structure, energy production, and energy consumption as dependent variables, and other indicators as independent variables, and establish a flow and stock diagram;

[0125] System modeling and testing module: used to establish the correlation calculation equations between various indicators based on the flow and stock diagram, form the system dynamics model of the energy system, and carry out intuitive testing, operation testing, and historical testing;

[0126] System model prediction module: used to manage the time profile of independent variables given by relevant policy planning or expert research and judgment, run the system dynamics model of the energy system, form the time profile of dependent variables, and obtain a set of macro- and meso-scale energy system development scenarios with systematic correlation.

[0127] The energy development scenario integration module includes:

[0128] Development scenario integration module: used to integrate the micro-scale energy technology development scenario set with the macro- and meso-scale energy system development scenario set to obtain the time profile of the production scale of various energy technologies;

[0129] Micro emission prediction module: used to manage the production scale time profiles of various energy technologies, calculate the cumulative production scale time profiles of various energy technologies, run the carbon emission intensity prediction model for the entire life cycle of energy technologies, and obtain the carbon emission intensity time profiles of the entire life cycle of energy technologies at the micro scale;

[0130] Macro emission aggregation module: It is used to manage the time profile of carbon emission intensity throughout the life cycle of energy technology at the micro scale, and aggregate it from bottom to top at the meso and macro scales to obtain the time profile of carbon emission throughout the life cycle of energy that integrates technology, industry and region at multiple scales.

[0131] The pollution reduction and carbon reduction collaborative pathway modules include:

[0132] Factor decomposition modeling module: It is used to decompose the synergistic driving factors of energy pollution reduction and carbon reduction step by step from macroscopic, mesoscopic to microscopic scales according to the characteristics of energy system and energy technology, and establish a synergistic factor decomposition model of energy pollution reduction and carbon reduction that integrates technology, industry and region at multiple scales;

[0133] Synergy effect quantification module: used to manage the time profile of carbon emissions in the entire life cycle of energy with multi-scale integration of management technology, industry and region, operate the energy pollution reduction and carbon reduction synergy factor decomposition model with multi-scale integration of operation technology, industry and region, and quantify the pollution reduction and carbon reduction synergy of macro- and meso-scale energy systems and the pollution reduction and carbon reduction synergy of micro-scale energy technologies;

[0134] Collaborative path analysis module: used to analyze the positive and negative effects of the synergistic effects of pollution reduction and carbon reduction in macro- and meso-scale energy systems and the synergistic effects of pollution reduction and carbon reduction in micro-scale energy technologies, determine the positive synergistic effects of pollution reduction and carbon reduction in macro- and meso-scale energy systems and the positive synergistic effects of pollution reduction and carbon reduction in micro-scale energy technologies, and obtain the energy pollution reduction and carbon reduction synergistic path that integrates multiple scales of technology, industry and region.

[0135] The above are only embodiments of the present invention, and the common knowledge such as the known specific structures and / or characteristics in the scheme are not described in detail here. It should be pointed out that for those skilled in the art, several deformations and improvements can be made without departing from the structure of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A multi-scale fusion energy pollution reduction and carbon reduction collaborative path analysis method, characterized by: The steps include: S1. Integrate the carbon emission intensity accounting model and learning curve of energy technology throughout its life cycle to obtain a prediction model for carbon emission intensity throughout its life cycle; S2. Establish an energy technology cross-impact matrix to obtain a set of micro-scale energy technology development scenarios with systematic correlations; S3. Establish a system dynamics model of the energy system to obtain a set of macro- and meso-scale energy system development scenarios with systematic correlations; S4. Integrate the micro-scale energy technology development scenario set with the macro- and meso-scale energy system development scenario set to obtain the time profile of carbon emissions over the entire energy life cycle that integrates technology, industry, and region at multiple scales; S5. Establish a technology-industry-region multi-scale integrated energy pollution reduction and carbon reduction synergistic factor decomposition model. According to the obtained technology-industry-region multi-scale integrated energy life cycle carbon emission time profile, substitute it into the established technology-industry-region multi-scale integrated energy pollution reduction and carbon reduction synergistic factor decomposition model to quantify the macro- and meso-scale energy system pollution reduction and carbon reduction synergistic effect and the micro-scale energy technology pollution reduction and carbon reduction synergistic effect; analyze the positive and negative effects in the quantified macro- and meso-scale energy system pollution reduction and carbon reduction synergistic effect and the micro-scale energy technology pollution reduction and carbon reduction synergistic effect, determine the positive synergistic effect of pollution reduction and carbon reduction in the macro- and meso-scale energy system and the positive synergistic effect of pollution reduction and carbon reduction in the micro-scale energy technology, and obtain the technology-industry-region multi-scale integrated energy pollution reduction and carbon reduction synergistic path; The method of step S2 is as follows: S2.

1. Determine the qualitative logical relationship between various energy technologies based on their characteristics; S2.

2. Determine the quantitative logical relationship between various energy technologies based on the qualitative logical relationship between various energy technologies determined in S2.1 and combined with expert research and judgment; S2.

3. Based on the quantitative logical relationship between various energy technologies determined in S2.2, a correction model for the input rates of various energy technologies is established. The initial input rates of various energy technologies given by relevant policy planning or expert research are substituted, and the corrected input rates of various energy technologies are calculated and a time profile is formed to obtain a set of micro-scale energy technology development scenarios with systematic correlations.

2. According to the multi-scale fusion energy pollution reduction and carbon reduction collaborative path analysis method of claim 1, it is characterized by: The method of step S1 is as follows: S1.

1. Determine the boundaries of the carbon emission intensity accounting system for the entire life cycle based on the characteristics of energy technology, covering energy technology and its upstream materials, energy supply stage and downstream waste treatment stage involving air pollutants, wastewater pollutants, solid waste, and greenhouse gas emissions, and establish a carbon emission intensity accounting model for the entire life cycle of energy technology; S1.

2. Collect dynamic data on energy technology material consumption, energy consumption, and environmental emissions, combine them with the localized carbon emission life cycle background database, and substitute them into the energy technology life cycle carbon emission intensity accounting model established in S1.1 to obtain the dynamic energy technology life cycle carbon emission intensity; S1.

3. Establish a learning curve for the carbon emission intensity of energy technology throughout its life cycle, collect dynamic data on the production scale of energy technology, combine it with the dynamic carbon emission intensity of energy technology throughout its life cycle obtained in S1.2, calculate the "learning by doing" coefficient of the carbon emission intensity of energy technology throughout its life cycle and extrapolate its changing trend to obtain a prediction model for the carbon emission intensity of energy technology throughout its life cycle.

3. According to the multi-scale fusion energy pollution reduction and carbon reduction collaborative path analysis method of claim 1, it is characterized by: The method of step S3 is as follows: S3.

1. Determine key indicators based on the characteristics of the energy system, combine expert research and judgment, identify the causal relationship between key indicators, and establish a causal relationship diagram; S3.

2. Based on the cause-effect relationship diagram established in S3.1, determine the flow and stock types of key indicators, supplement auxiliary indicators, determine industry structure, energy production, and energy consumption as dependent variables, and other indicators as independent variables, and establish a flow-stock diagram; S3.

3. According to the flow and stock diagram established in S3.2, establish the correlation calculation equations between various indicators, form the system dynamics model of the energy system, and carry out intuitive testing, operation testing, and historical testing; S3.

4. Based on the system dynamics model of the energy system established in S3.3, substitute the independent variable time profile given by relevant policy planning or expert research and judgment to form the dependent variable time profile, and obtain a set of macro- and meso-scale energy system development scenarios with systematic correlation.

4. According to claim 3, a multi-scale fusion energy pollution reduction and carbon reduction collaborative path analysis method is characterized by: The method of step S4 is as follows: S4.

1. By multiplying the time profiles of the modified input rates of various energy technologies formed by S2.3 by the time profiles of the industry structure, energy production, and energy consumption formed by S3.4, the micro-scale energy technology development scenario set and the macro- and meso-scale energy system development scenario set are integrated to obtain the time profiles of the production scales of various energy technologies; S4.

2. Based on the production scale time profiles of various energy technologies obtained in S4.1, calculate the cumulative production scale time profiles of various energy technologies, substitute them into the energy technology life cycle carbon emission intensity prediction model established in S1.3, and obtain the carbon emission intensity time profile of the micro-scale energy technology life cycle; S4.

3. Based on the time profile of carbon emission intensity over the entire life cycle of micro-scale energy technologies obtained in S4.2, the time profiles of production scales of various energy technologies obtained in S4.1 are aggregated from bottom to top at the meso- and macro-scales to obtain a time profile of carbon emission over the entire life cycle of energy that integrates multiple scales of technology, industry, and region.

5. According to the multi-scale fusion energy pollution reduction and carbon reduction collaborative path analysis method of claim 4, it is characterized by: The method of step S5 is as follows: S5.

1. Based on the characteristics of energy system and energy technology, decompose the synergistic driving factors of energy pollution reduction and carbon reduction step by step from macroscopic, mesoscopic to microscopic scales, and establish a decomposition model of energy pollution reduction and carbon reduction synergistic factors integrating technology, industry and region at multiple scales; S5.

2. Substitute the time profile of carbon emissions in the energy life cycle obtained by the technology-industry-region multi-scale integration in S4.3 into the energy pollution reduction and carbon reduction synergy factor decomposition model established in S5.1 by the technology-industry-region multi-scale integration to quantify the pollution reduction and carbon reduction synergy effect of the macro- and meso-scale energy system and the pollution reduction and carbon reduction synergy effect of the micro-scale energy technology; S5.

3. Analyze the positive and negative effects of the synergistic effects of pollution reduction and carbon reduction of macro- and meso-scale energy systems and the synergistic effects of pollution reduction and carbon reduction of micro-scale energy technologies quantified in S5.2, determine the positive synergistic effects of pollution reduction and carbon reduction of macro- and meso-scale energy systems and the positive synergistic effects of pollution reduction and carbon reduction of micro-scale energy technologies, and obtain the synergistic path of energy pollution reduction and carbon reduction that integrates multiple scales of technology, industry and region.

6. A multi-scale integrated energy pollution reduction and carbon reduction collaborative path analysis system, characterized by: include: Carbon emission prediction model module: used to integrate the carbon emission intensity accounting model and learning curve of the energy technology throughout its life cycle to obtain the carbon emission intensity prediction model of the energy technology throughout its life cycle; Energy technology development scenario module: used to establish the energy technology cross-impact matrix and obtain a set of micro-scale energy technology development scenarios with systematic correlations; Energy system development scenario module: used to establish a system dynamics model of the energy system and obtain a set of macro- and meso-scale energy system development scenarios with systematic correlations; Energy development scenario integration module: used to integrate the micro-scale energy technology development scenario set with the macro- and meso-scale energy system development scenario set to obtain the time profile of carbon emissions over the entire energy life cycle with the technology-industry-region multi-scale integration; Pollution reduction and carbon reduction collaborative path module: used to establish a technology-industry-region multi-scale integrated energy pollution reduction and carbon reduction collaborative factor decomposition model, and obtain a technology-industry-region multi-scale integrated energy pollution reduction and carbon reduction collaborative path; The energy technology development scenario module includes: Qualitative logic identification module: used to determine the qualitative logic relationship between various energy technologies based on energy technology characteristics; Quantitative logic identification module: used to determine the quantitative logic relationship between various energy technologies based on the qualitative logic relationship between them and combined with expert research and judgment; Investment probability correction module: It is used to establish a correction model for the investment rate of various energy technologies based on the quantitative logical relationship between various energy technologies, manage the initial investment rates of various energy technologies given by relevant policy planning or expert research and judgment, calculate the corrected investment rates of various energy technologies and form a time profile, and obtain a set of micro-scale energy technology development scenarios with systematic correlation; The pollution reduction and carbon reduction collaborative pathway module includes: Synergy effect quantification module: used to manage the time profile of carbon emissions in the entire life cycle of energy with multi-scale integration of management technology, industry and region, operate the energy pollution reduction and carbon reduction synergy factor decomposition model with multi-scale integration of operation technology, industry and region, and quantify the pollution reduction and carbon reduction synergy of macro- and meso-scale energy systems and the pollution reduction and carbon reduction synergy of micro-scale energy technologies; Collaborative path analysis module: used to analyze the positive and negative effects of the synergistic effects of pollution reduction and carbon reduction in macro- and meso-scale energy systems and the synergistic effects of pollution reduction and carbon reduction in micro-scale energy technologies, determine the positive synergistic effects of pollution reduction and carbon reduction in macro- and meso-scale energy systems and the positive synergistic effects of pollution reduction and carbon reduction in micro-scale energy technologies, and obtain the energy pollution reduction and carbon reduction synergistic path that integrates multiple scales of technology, industry and region.

7. The multi-scale fusion energy pollution reduction and carbon reduction collaborative path analysis system according to claim 6 is characterized by: The carbon emission prediction model module includes: Carbon emission modeling module: used to determine the boundary of the carbon emission intensity accounting system for the entire life cycle according to the characteristics of energy technology, and to establish a carbon emission intensity accounting model for the entire life cycle of energy technology; Carbon emission accounting module: used to manage the dynamic data of energy technology material consumption, energy consumption, and environmental emissions, as well as the localized carbon emission life cycle background database, run the energy technology life cycle carbon emission intensity accounting model, and obtain the dynamic energy technology life cycle carbon emission intensity; Learning curve quantification module: used to establish the learning curve of carbon emission intensity over the entire life cycle of energy technology, manage the dynamic data of energy technology production scale and dynamic data of carbon emission intensity over the entire life cycle of energy technology, calculate the "learning by doing" coefficient of carbon emission intensity over the entire life cycle of energy technology and extrapolate its changing trend, and obtain a prediction model for carbon emission intensity over the entire life cycle of energy technology.

8. The multi-scale fusion energy pollution reduction and carbon reduction collaborative path analysis system according to claim 7 is characterized by: The energy system development scenario module includes: Causal relationship quantification module: used to determine key indicators based on energy system characteristics, identify the causal relationship between key indicators in combination with expert research and judgment, and establish a causal relationship diagram; Flow and stock quantification module: used to determine the flow and stock types of key indicators based on the cause-effect relationship diagram, supplement auxiliary indicators, determine industry structure, energy production, and energy consumption as dependent variables, and other indicators as independent variables, and establish a flow and stock diagram; System modeling and inspection module: used to establish the correlation calculation equations between various indicators based on the flow and stock diagram, form the system dynamics model of the energy system, and carry out intuitive inspection, operation inspection, and historical inspection; System model prediction module: used to manage the time profile of independent variables given by relevant policy planning or expert research and judgment, run the system dynamics model of the energy system, form the time profile of dependent variables, and obtain a set of macro- and meso-scale energy system development scenarios with systematic correlation.

9. The multi-scale fusion energy pollution reduction and carbon reduction collaborative path analysis system according to claim 8 is characterized by: The energy development scenario integration module includes: Development scenario integration module: used to integrate the micro-scale energy technology development scenario set with the macro- and meso-scale energy system development scenario set to obtain the time profile of the production scale of various energy technologies; Micro emission prediction module: used to manage the production scale time profiles of various energy technologies, calculate the cumulative production scale time profiles of various energy technologies, run the energy technology life cycle carbon emission intensity prediction model, and obtain the micro-scale energy technology life cycle carbon emission intensity time profile; Macro emission aggregation module: It is used to manage the time profile of carbon emission intensity throughout the life cycle of energy technology at the micro scale, and aggregate it from bottom to top at the meso and macro scales to obtain the time profile of carbon emission throughout the life cycle of energy that integrates technology, industry and region at multiple scales.

10. The multi-scale fusion energy pollution reduction and carbon reduction collaborative path analysis system according to claim 9 is characterized by: The pollution reduction and carbon reduction collaborative pathway module also includes: Factor decomposition modeling module: used to decompose the synergistic driving factors of energy pollution reduction and carbon reduction step by step from macroscopic, mesoscopic to microscopic scales according to the characteristics of energy system and energy technology, and establish a synergistic factor decomposition model of energy pollution reduction and carbon reduction that integrates technology, industry and region at multiple scales.

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