WO2022193703A1

WO2022193703A1 - Evaluation system and method for regional atmospheric pollution regulation and control scheme

Info

Publication number: WO2022193703A1
Application number: PCT/CN2021/131457
Authority: WO
Inventors: 贾宁; 凌帅; 杜慧滨; 邢璇雯
Original assignee: 天津大学
Priority date: 2021-03-18
Filing date: 2021-11-18
Publication date: 2022-09-22
Also published as: NL2030102A; NL2030102B1; CN113051744A

Abstract

Disclosed in the present invention are an evaluation system and method for a regional atmospheric pollution regulation and control scheme. The evaluation system comprises: a simulation system structure module, a causal relationship module, a system dynamics model module, a calculation module, and a simulation analysis module. Disclosed in the present invention is an evaluation method for a regional atmospheric pollution regulation and control scheme. According to the present invention, the system dynamics model is established by using Vensim software; and the mutual relationship among economy, population, energy, and atmospheric environment is simulated, the internal structure and dynamic behaviors of the system are analyzed, and the authenticity and effectiveness of the model are verified by comparing with historical data, such that the problem that the economic society is difficult to directly test is solved, and reference is provided for government decision making.

Description

An evaluation system and evaluation method for regional air pollution control scheme

This application claims the priority of the Chinese patent application filed on March 18, 2021 with the application number of 202110293191.5 and the invention titled "An Evaluation System for Regional Air Pollution Control Scheme and Its Evaluation Method", the entire contents of which are Incorporated herein by reference.

technical field

The invention belongs to the field of air pollution prevention and control, and in particular relates to an evaluation system and an evaluation method for a regional air pollution regulation scheme.

Background technique

In order to promote the smooth and effective progress of air pollution prevention and control, experts and scholars have carried out research on collaborative governance, legal regulation, energy conservation and emission reduction, performance evaluation and other aspects, and put forward corresponding suggestions on this basis. Most of the research perspectives focus on unilateral influencing factors, and it is impossible to simulate the feedback influence and dynamic interaction between factors. and overall impact on the environment.

The implementation of the comprehensive air pollution control policy will have multiple and complex impacts on the economy, society, energy, and the environment, forming an extremely complex large system. It is difficult to make policies based on intuition and subjective judgment to ensure the implementation effect, and the actual implementation test cost is too high. To this end, it is necessary to use computer simulation to simulate and analyze the effects of different policy implementations, try different policy combinations, and put forward corresponding suggestions based on this.

SUMMARY OF THE INVENTION

The purpose of the present invention is to overcome the defects of the prior art, and to provide an evaluation system and an evaluation method for a regional air pollution control scheme based on a system dynamics model. , the relationship between population, energy and atmospheric environment, analyze the internal structure of the system and its dynamic behavior, and compare it with historical data to verify the authenticity and effectiveness of the model, thus solving the problem that it is difficult to directly test the economy and society, and providing government decision-making. refer to.

The present invention is achieved through the following technical solutions:

An evaluation system for a regional air pollution control scheme, including:

Simulation system structure module: According to atmospheric environment theory and ecological economics theory, establish simulation system structure module, including economic subsystem, population subsystem, energy subsystem, environmental subsystem and government subsystem;

Causal relationship module: According to the structure and function of the evaluation system, manually select the program variables, determine the causal relationship between the variables, and draw a causal circuit diagram;

System dynamics model module: According to the causal relationship obtained by the causal relationship module, use Vensim software to establish a system dynamics model, and determine the simulation variables in the system dynamics model;

Calculation module: According to the causal relationship obtained by the causal relationship module and the model obtained by the system dynamics model module, determine the mathematical equations and coefficients of the simulation variables;

Simulation analysis module: The equations and coefficients obtained by the calculation module are added to the model obtained by the system dynamics model module, and a reference scenario and a simulation scenario are set for simulation analysis.

A method for evaluating a regional air pollution control scheme, including:

Step 1: Create the simulation system structure of the regional air pollution control scheme

According to the theory of atmospheric environment and ecological economics, it is clarified that the simulation system of air pollution control scheme includes five components: economy, population, energy, environment and government. Each component affects and restricts each other, and each component constitutes a subsystem From this, the simulation system structure of the regional air pollution control scheme is created;

The structure includes an economic subsystem, a population subsystem, an energy subsystem, an environmental subsystem and a government subsystem;

The economic subsystem is used to analyze the economic aggregate, economic structure and economic cost in the process of policy implementation;

The population subsystem is used to analyze the total population and population structure changes;

The energy subsystem is used to calculate energy consumption and analyze changes in energy structure;

The environmental subsystem is used to analyze the generation, treatment and emission of air pollutants;

The government subsystem is used to analyze the implementation effects of various policies according to the selected historical policy measures;

Step 2: Identify scenario variables and causality

According to the structure and function of the system in step 1, the program variables are manually selected, the causal relationship between the variables is determined, and a causal loop diagram is drawn; the start and end times of the historical data are determined, and the history of the variables is obtained from the existing statistical data. data.

The program variables include: economic data, population data, energy types, energy structure, types of air pollutants, sources of air pollutants, economic policies, production capacity policies, energy policies, and technological progress;

The causal relationship between the program variables is qualitatively described through the causal relationship loop diagram, and a feedback loop is formed by combining two or more causal relationships, reflecting the dynamic changes of the system;

Step 3: Use Vensim software to establish a system dynamics model;

According to the scheme variables and causal relationship diagram determined in step 2, manually select the simulation variables in the system dynamics model, and use Vensim software to build the system dynamics model,

The simulation variables include state variables, rate variables and auxiliary variables, the state variables reflect the cumulative effect and reflect the system state, the rate variables reflect state changes, and the auxiliary variables help to form a complete feedback loop;

Step 4: Determine the mathematical equations for the simulation variables and the coefficients in the equations

Step 401: Determine the mathematical equation of the simulation variable according to the causal relationship obtained in step 2 and the model obtained in step 3;

Step 402: Determine the coefficients in some equations by means of expert evaluation and reference of existing data;

Step 403: Add the equations and partial coefficients obtained above to the model obtained in step 3;

Step 404: For the coefficients in the undetermined equations, randomly determine a set of initial values, add them to the model obtained in step 3, and simulate;

Step 405: If the relative error between the simulated value and the actual value is greater than 20%, return to step 404 to modify the coefficient value, and continue the simulation until the relative error between the simulated value and the actual value is less than 20%.

Step 5: Simulation Analysis and Prediction

Add the equations and coefficients obtained in step 4 to the model obtained in step 3, and set up a benchmark scenario and a simulated scenario for simulation analysis.

Further, the types of air pollutants include SO ₂ , NOx, VOCs and soot.

Compared with the prior art, the advantages and positive effects of the present invention are:

The evaluation system and method of the regional air pollution control scheme proposed by the present invention truly simulates the interrelationship among economy, population, energy and atmospheric environment, analyzes the internal structure of the system and its dynamic behavior, and verifies the model by comparing with historical data. authenticity and validity;

The established system dynamics model includes a variety of air pollution control policies. It is possible to control the implementation and intensity of each policy by changing the policy parameters, observe the changing trends of various variables in the system, analyze the implementation effect and implementation cost of the policy, and comprehensively. Consider the economic and environmental benefits brought by the policy; at the same time, a variety of policy combinations can be set up, so that various policies can complement each other, seek the optimal policy combination, maximize economic and environmental benefits as much as possible, and solve the economic and social difficulties. The question of direct experiment provides a reference for government decision-making.

Instruction drawings

Fig. 1 is the flow chart of the evaluation method of the regional air pollution control scheme of the present invention;

Fig. 2 is the structure diagram of the regional air pollution control scheme created in Example 1 of the present invention;

3 is a causal relationship loop diagram of Embodiment 1 of the present invention;

Fig. 4 is the comparison diagram of the real value of the coefficient determined in the embodiment of the present invention and the simulation value; wherein Fig. 4a shows the comparison between the real value and the simulation value of the GDP value, Fig. 4b shows the comparison between the real value and the simulation value of SO ₂ , FIG. 4c shows the comparison of the actual and simulated NOx values, and FIG. 4d shows the comparison of the actual and simulated soot emissions; and

Figure 5 is a comparison chart of the results of the benchmark scenario and the simulation scenario in Embodiment 1 of the present invention; wherein, Figure 5a shows the comparison chart of the results of the benchmark scenario and the simulation scenario of GDP value; Figure 5b shows the benchmark scenario and the simulation scenario of total energy consumption Result comparison chart; Figure 5c shows the comparison of the results of the baseline scenario and the simulation scenario for SO2 _; Figure 5d shows the comparison chart of the baseline scenario and the simulation scenario results of NOx emissions; Figure 5e shows the baseline scenario and simulation of soot emissions. Scenario result comparison chart; Figure 5f shows the comparison chart of the baseline scenario and simulated scenario results of VOCs emissions;

Fig. 6 is the simulation flow chart of the economic subsystem in the system dynamics model established by Vensim software according to Embodiment 1 of the present invention; wherein, Fig. 6a shows the simulation flow chart of the first part of the economic subsystem; Fig. 6b shows the simulation flow chart of the economic subsystem The simulation flow chart of the second part; Fig. 6c shows the simulation flow chart of the third part of the economic subsystem.

Detailed ways

In order to make the purposes, technical solutions, beneficial effects and significant progress of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings provided in the embodiments of the present invention, Obviously, all the described embodiments are only part of the embodiments of the present invention, not all of the embodiments; based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work The embodiments all belong to the protection scope of the present invention.

Example 1

As shown in Figure 1, an evaluation method for a regional air pollution control scheme includes:

The types of air pollutants consider four pollutants: SO ₂ , NOx, VOCs and soot dust. From the analysis of the sources of air pollutants, air pollutants are mainly generated by the consumption of fossil energy, and energy consumption mainly occurs in industrial production and residential life, while air pollution The control plan usually includes economic policies (such as environmental protection tax, etc.), production capacity policies (such as eliminating outdated production capacity, etc.), energy policies (such as coal-to-gas, coal-to-electricity, etc.), technological progress (such as pollutant end treatment, etc.), implementation The process will have multiple and complex impacts on the economy, society, energy, and the environment. The simulation system includes five subsystems of economy, population, energy, environment and government.

As shown in Figure 2, the structure includes five subsystems of economy, population, energy, environment and government. Among them, the environmental subsystem mainly analyzes the generation, treatment and emission of air pollutants; the energy subsystem considers five kinds of coal, oil, natural gas, electricity and renewable energy, calculates the consumption of various energy sources and analyzes the change of energy structure; the population subsystem Analyze the total population and changes in population structure, which affects economic development; the economic subsystem mainly analyzes the total economic volume, economic structure and economic costs in the process of policy implementation, except for the energy industry, metallurgical building materials industry, equipment manufacturing industry, light industry and In addition to the output value of various industrial sectors in the high-tech industry, it also includes the output value of the primary, tertiary and construction industries; the government subsystem mainly includes various policies and measures implemented by the government, and analyzes the implementation effects of various policies.

Step 2: Identify scenario variables and causality

The selected program variables are as follows:

Economic subsystem: output value of various industries, fixed assets of various industries, investment in fixed assets of various industries, GDP and per capita GDP;

Population subsystem: total population, birth population, death population, mechanical population and labor force of various industries;

Energy subsystem: coal, oil, natural gas, electricity consumption in various industries, domestic oil consumption, coal consumption, gas consumption, electricity consumption, clean energy consumption, local power generation, external power purchase, thermal power generation, clean energy power generation capacity, thermal power installed capacity, clean energy installed capacity and clean energy power generation ratio;

Environmental subsystem: the amount of pollutants produced by each industry, the amount of pollutants discharged by each industry, the removal rate of pollutants, the amount of domestic pollutants discharged and the degree of environmental pollution;

Policy subsystem: cost of pollution control, environmental protection tax, coal-to-gas, coal-to-electricity, variable energy costs in various industries, target of clean energy power generation, clean energy policy costs, rate of change in production capacity of various industries, energy consumption target per unit of GDP, energy consumption Control policy factors, energy equipment investment increments and policy implementation costs.

The causal relationship between the simulation variables is qualitatively described through the causal relationship loop diagram, and a feedback loop is formed by combining two or more causal relationships to reflect the dynamic changes of the system; as shown in Figure 3, the most important causal loop is the industry output value Increase in energy consumption, increase in pollutant emissions, decline in environmental quality, increase in environmental costs, and adversely affect the growth of output value; at the same time, the increase in the total population, the increase in the total labor force, the increase in output value and the increase in domestic energy consumption increase, and the increase in domestic pollutant emissions. , the environmental quality declines, the death population increases, and the total population decreases; the air pollution prevention and control policy reduces the total energy consumption to reduce the amount of pollutants produced, or directly reduce the amount of pollutant emissions.

The starting and ending time of the data is 2006-2017, and the statistical data of the variables can be obtained from the existing statistical data, and some data are shown in Table 1-1 and Table 1-2.

Table 1-1 Main data in the simulation system of regional air pollution control scheme

Table 1-2 Main data in the simulation system of regional air pollution control scheme

Step 3: Use Vensim software to establish a system dynamics model;

According to the causal relationship diagram drawn in step 2, the simulation variables in the system dynamics model are determined, and the Vensim software is used to establish the system dynamics model for quantitative calculation. The simulation variables include state variables, rate variables and auxiliary variables. The state variables reflect the cumulative effect and reflect the system state, the rate variables reflect state changes, and the auxiliary variables help form a complete feedback loop. FIG. 6 illustrates a simulation flow diagram of an economic subsystem in building a system dynamics model using Vensim software.

In the model, the total population, fixed assets, output value, energy consumption per unit output value, and installed capacity are set as state variables, and variables related to changes in state variables such as output value growth, investment in fixed assets, births, deaths, and installed capacity growth etc., set as rate variables, total energy consumption, pollutant emissions, policy parameters, etc. are set as auxiliary variables.

Step 4: According to the causal relationship obtained in step 2 and the model obtained in step 3, determine the mathematical equations of the simulation variables of each subsystem; determine the coefficients in some equations by means of expert evaluation and reference of existing data, such as: pollution Then add the equations and some coefficients obtained above to the model obtained in step 3; for the coefficients in the undetermined equations, randomly determine a set of initial If the relative error between the simulated value and the real value is greater than 20%, return to step 404 to modify the coefficient value, and continue to simulate until the relative error between the simulated value and the real value is not greater than 20% %Finish.

Collect and sort out the data required for the model, take Tianjin 2006-2017 statistical data as the standard, and establish the main equations and parameters by means of data fitting, expert evaluation, and data reference based on the above-mentioned causal relationship and system flow diagram, as follows: Show:

(1) Economic subsystem

The industries in the economic subsystem are divided into primary industry, tertiary industry, construction industry, energy industry, metallurgy and building materials industry, equipment manufacturing industry, light industry and high-tech industry. The calculation formula of each industry is the same, but only some coefficients are different.

Tertiary industry fixed assets = INTEG (tertiary industry asset investment - tertiary industry asset depreciation, 909.098)

Growth rate of output value of the tertiary industry = 0.427 · Growth rate of fixed assets in the tertiary industry + 0.304 · Growth rate of labor force in the tertiary industry

The growth rate of the output value of the tertiary industry=the growth rate of the output value of the tertiary industry*the total output value of the tertiary industry/(1+0.05*environmental pollution degree)

The gross output value of the tertiary industry = INTEG (the increase in the output value of the tertiary industry - the decrease in the output value of the tertiary industry, 1917.67)

(2) Population subsystem

Total population = INTEG (birth population + mechanical population - death population, 1075)

total labor force = total population * labor force ratio

Death population = total population * death rate * (1 + environmental pollution degree * 0.01)

(3) Energy subsystem

The energy subsystem includes five types of energy sources: coal, oil, natural gas, electricity and renewable energy. It can calculate the consumption of various energy sources in the production process of each industry in the economic subsystem. Renewable energy is mainly consumed by power plants.

Energy consumption of each industry = output value of each industry * energy consumption per unit of output value of each industry

Living energy consumption = energy consumption per capita * total population

Local power generation = total electricity demand - external power purchase

(4) Environmental subsystem

The environmental subsystem includes five pollutants, SO ₂ , NOx, VOCs and soot, and the calculation method of the emission of each pollutant is the same.

SO ₂ emissions = industrial SO ₂ emissions + thermal power generation SO ₂ emissions + domestic SO ₂ emissions

(5) Government Subsystem

The government subsystem includes air pollution prevention and control measures and economic costs in the implementation process. The policies that can be simulated by the system are shown in Table 2, and some of the equations are shown below.

Table 2 Mockable Policies

Coal reduction per unit output value of the tertiary industry = coal consumption per unit output value of the tertiary industry * (energy consumption control policy factor + coal to gas)

Growth of natural gas per unit output value of the tertiary industry = initial increase of natural gas per unit output value of the tertiary industry + coal to gas * coal consumption per unit output value of the tertiary industry * 0.7143/13.3

In the modeling process, in order to determine some parameters in the relational expression, the existing historical data is used for fitting and simulation analysis, and some parameters are shown in Table 3.

Table 3 Some parameters in the simulation system of the regional air pollution control scheme

Add the above equations and parameters to the system flow diagram, set the year as the unit, the simulation step size as 1, the start year as 2006, and the end year as 2017, and use Vensim software to construct the "Regional Air Pollution Control Scheme". Simulation system” for simulation, in which the real and simulated values of GDP, SO ₂ , NOx and soot emissions are shown in Figure 4.

It can be seen from Figure 4 that the simulation value is in good agreement with the real value, the change trend is basically the same, and the total GDP has gradually increased. Since 2014, the pollutant emission has shown a significant downward trend. It is about one-third of that in 2014, which is basically the same as the actual situation. In general, the model can accurately describe the basic status of the research system, the system parameters are set reasonably, and it has a good prediction effect, and the model prediction results are credible.

Step 5: Simulation Analysis and Prediction

Add the method and parameters obtained in step 4 to the model obtained in step 3, and set the start year to 2006 and the end year to 2030. Obtain initial data for 2018, 2019 from existing statistics. For the variables determined by the table function, such as the labor force ratio and the fixed asset investment ratio, the forecast data for 2020-2030 is obtained by calculation based on the statistical data of the past years and the forecast results of experts. Input the above data into the model, keep the values of the policy variables unchanged, and run the model to obtain the benchmark scenario.

In the model, by modifying the start and end years, the initial value of the stock and the values of some auxiliary variables can be changed accordingly, so as to realize the prediction of future economic, social and energy environment development; at the same time, by adjusting the values of policy parameters, different policies and The implementation effect of the policy combination is obtained, and the economic cost of policy implementation is obtained at the same time. The simulation scenario is set here, and the policy parameters for 2018-2030 are modified. The parameter settings are shown in Table 4, and the results are shown in Figure 5.

Table 4 Policy parameter setting table for the baseline scenario and the simulated scenario

From the above results, it can be seen that when air pollution prevention and control measures are implemented, pollutant emissions are reduced, air quality is improved, and at the same time, it will also have a negative impact on GDP. Therefore, models can be used to analyze the emission reduction effect and economic impact of different policies, and constantly modify policy parameters. And simulate, screen out the optimal policy combination and policy implementation intensity, reduce economic losses while ensuring air quality compliance, and achieve a win-win situation for the economy and the environment.

The above embodiments and specific cases are only used to illustrate the technical solutions of the present invention, but not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should The technical solutions recorded in the foregoing embodiments may be modified, or some or all of the technical features thereof may be equivalently replaced, and these modifications or replacements do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention , the non-essential improvements, adjustments or replacements made by those skilled in the art according to the contents of this specification are all within the scope of the claimed protection of the present invention.

Claims

An evaluation system for a regional air pollution control scheme, characterized in that it includes:

Simulation system structure module: According to atmospheric environment theory and ecological economics theory, establish simulation system structure module, including economic subsystem, population subsystem, energy subsystem, environmental subsystem and government subsystem;

Causal relationship module: According to the structure and function of the evaluation system, manually select the program variables, determine the causal relationship between the variables, and draw a causal circuit diagram;

System dynamics model module: According to the causal relationship obtained by the causal relationship module, use Vensim software to establish a system dynamics model, and determine the simulation variables in the system dynamics model;

Calculation module: According to the causal relationship obtained by the causal relationship module and the model obtained by the system dynamics model module, the coefficients in some equations are determined by means of expert evaluation and reference of existing data; for the coefficients in the undetermined equations, random Determine a set of initial values and add them to the model for simulation; if the relative error between the simulated value and the real value is greater than 20%, modify the undetermined coefficient values, and continue to simulate until the relative error between the simulated value and the real value is not greater than 20% is over; all the determined coefficients are obtained after calculation;

Simulation analysis module: The equations and coefficients obtained by the calculation module are added to the model obtained by the system dynamics model module, and a reference scenario and a simulation scenario are set for simulation analysis.
A method for evaluating a regional air pollution control scheme, comprising:

Step 1: Create the simulation system structure of the regional air pollution control scheme

According to the theory of atmospheric environment and ecological economics, it is clarified that the simulation system of air pollution control scheme includes five components: economy, population, energy, environment and government. Each component affects and restricts each other, and each component constitutes a subsystem , thereby creating the simulation system structure of the regional air pollution control scheme;

The structure includes an economic subsystem, a population subsystem, an energy subsystem, an environmental subsystem and a government subsystem;

The economic subsystem is used to analyze the economic aggregate, economic structure and economic cost in the process of policy implementation;

The population subsystem is used to analyze the total population and population structure changes;

The energy subsystem is used to calculate energy consumption and analyze changes in energy structure;

The environmental subsystem is used to analyze the generation, treatment and emission of air pollutants;

The government subsystem is used to analyze the implementation effects of various policies according to the selected historical policy measures;

Step 2: Identify scenario variables and causality

According to the structure and function of the system in step 1, the program variables are manually selected, the causal relationship between the variables is determined, and a causal loop diagram is drawn; the start and end times of the historical data are determined, and the history of the variables is obtained from the existing statistical data. data;

The causal relationship between the program variables is qualitatively described through the causal relationship loop diagram, and a feedback loop is formed by combining two or more causal relationships, reflecting the dynamic changes of the system;

Step 3: Use Vensim software to establish a system dynamics model;

According to the scheme variables and the causal relationship diagram determined in step 2, manually select the simulation variables in the system dynamics model, and use Vensim software to establish the system dynamics model;

Step 4: Determine the mathematical equations for the simulation variables and the coefficients in the equations

Step 401: Determine the mathematical equation of the simulation variable according to the causal relationship obtained in step 2 and the model obtained in step 3;

Step 402: Determine the coefficients in some equations by means of expert evaluation and reference of existing data;

Step 403: Add the equations and partial coefficients obtained above to the model obtained in step 3;

Step 404: For the coefficients in the undetermined equations, randomly determine a set of initial values, add them to the model obtained in step 3, and simulate;

Step 405: If the relative error between the simulated value and the actual value is greater than 20%, return to step 404 to modify the coefficient value, and continue the simulation until the relative error between the simulated value and the actual value is not greater than 20% and end;

Step 5: Simulation Analysis and Prediction

Add the equations and coefficients obtained in step 4 to the model obtained in step 3, and set up a benchmark scenario and a simulated scenario for simulation analysis.
The method for evaluating a regional air pollution control plan according to claim 2, wherein the plan variables include: economic data, population data, energy type, energy structure, air pollutant type, air pollutant source, economic type Policy, capacity policy, energy policy and technological progress.
The evaluation method for a regional air pollution control scheme according to claim 2, wherein the simulation variable includes a state variable, a rate variable and an auxiliary variable, the state variable reflects the cumulative effect and reflects the state of the system, and the rate variable reflects the state change , auxiliary variables help form a complete feedback loop.
The method for evaluating a regional air pollution control scheme according to claim 2, wherein the types of air pollutants include SO 2 , NOx, VOCs and soot.