Method and system for analyzing and predicting influence factors of carbon emission in sewage treatment

Abstract

The invention discloses a sewage treatment carbon emission influence factor analysis and prediction method and system, and the method specifically comprises the steps: carrying out the accounting of the sewage treatment carbon emission of a region through the statistics of yearbook data in combination with a carbon emission accounting method, and obtaining all indexes affecting the sewage treatment carbon emission; the contribution value of each index is calculated through an LMDI model, important indexes are selected to construct an extended STIRPAT model, so that the model prediction performance is better, model parameters are obtained through a ridge regression analysis method, different development directions and control variables are considered in combination with a scene analysis method and development planning, and the prediction performance of the model is improved. And more accurate and more practical prediction can be made for carbon emission in sewage treatment.

Description

Technical Field

[0001] The present invention relates to the technical field of analysis and prediction of factors affecting carbon emissions from sewage treatment, and in particular to a method and system for analyzing and predicting factors affecting carbon emissions from sewage treatment based on a LMDI-STIRPAT model. Background Art

[0002] The sewage treatment industry is one of the top ten greenhouse gas emission industries in the world, and its total carbon emissions account for about 2% to 3% of the total global carbon emissions. With the rapid development of population and economy, wastewater discharge is also at a high growth level, and greenhouse gas emissions generated by wastewater treatment have become the main component of carbon emissions. Although sewage treatment can greatly reduce pollutants in the influent, obtain good effluent water quality and thus contribute to improving the water ecological environment. However, greenhouse gases and solid wastes such as sludge emitted during the treatment process, as well as energy and resources invested, will have a negative impact on the environment. Therefore, it is of great significance to the green development of the industry to study the influencing factors of carbon emissions in sewage treatment and quantify their contribution, and to analyze and predict future carbon emission trends based on influencing factors.

[0003] Current methods for calculating carbon emissions from wastewater treatment mainly rely on static models and short-term data, lacking consideration of dynamic changes. First, the wastewater treatment process is affected by many factors, such as technological advances, policy changes, and fluctuations in wastewater volume, which are difficult to accurately reflect in traditional models. Second, the lack of long-term monitoring data for regional specific conditions reduces the reliability of prediction models. Finally, existing methods generally ignore the interactions between ecosystems and socioeconomic factors, which makes it difficult to conduct comprehensive long-term assessments. Therefore, more comprehensive and dynamic models are needed to achieve more accurate long-term predictions. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a method and system for analyzing and predicting factors affecting carbon emissions from sewage treatment.

[0005] In a first aspect, the present invention provides a method for analyzing and predicting factors affecting carbon emissions from sewage treatment, comprising the following steps:

[0006] Step S1: Obtaining the environmental statistical yearbook data and city statistical yearbook data of the region over the years;

[0007] Step S2: Calculating greenhouse gas emissions of sewage treatment plants in the region to obtain the total regional carbon emissions;

[0008] Step S3: constructing a LMDI factor decomposition model based on the regional carbon emission total and regional activity level data to determine the key factors affecting carbon emissions from sewage treatment;

[0009] Step S4: Select the key factors as indicators and perform variance inflation factor test, and build the STIRPAT extended model based on the indicators that pass the test;

[0010] Step S5: Predict the carbon emissions under different development scenarios of the region.

[0011] In a second aspect, the present invention provides a system for analyzing and predicting factors affecting carbon emissions from sewage treatment, comprising:

[0012] Data acquisition module: used to obtain the environmental statistical yearbook data and city statistical yearbook data of the region over the years;

[0013] A carbon emission total amount calculation module is used to calculate the greenhouse gas emissions of the sewage treatment plants in the region to obtain the regional carbon emission total amount;

[0014] A carbon emission influencing factor analysis module is used to construct a LMDI factor decomposition model based on the regional carbon emission total amount and regional activity level data to determine the key factors affecting the carbon emission of sewage treatment;

[0015] A prediction model building module, used for selecting the key factors as indicators and performing variance inflation factor tests, and building a STIRPAT extended model based on the indicators that pass the tests;

[0016] The carbon emission prediction module is used to predict carbon emissions under different development scenarios in the region.

[0017] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0018] The present invention calculates the carbon emissions of regional sewage treatment through statistical yearbook data combined with the carbon emission accounting method and obtains various indicators that affect the carbon emissions of sewage treatment. The contribution value of each indicator is calculated through the LMDI model, and important indicators are selected to construct an extended STIRPAT model to make the model prediction performance better. The model parameters are obtained through the ridge regression analysis method, and different development directions and control variables are considered in combination with the scenario analysis method and development planning to make a more accurate and practical prediction of carbon emissions from sewage treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived based on the provided drawings without paying any creative work.

[0020] Figure 1This is a flow chart of the analysis and prediction method of factors affecting carbon emissions from wastewater treatment based on the LMDI-STIRPAT model;

[0021] Figure 2 This is a carbon accounting result diagram of the analysis of influencing factors and prediction method of carbon emissions from sewage treatment based on the LMDI-STIRPAT model;

[0022] Figure 3 This is an analysis chart of influencing factors and prediction methods of carbon emissions from wastewater treatment based on the LMDI-STIRPAT model;

[0023] Figure 4 This is a scenario prediction relationship diagram for the analysis of influencing factors and prediction methods of carbon emissions from wastewater treatment based on the LMDI-STIRPAT model. DETAILED DESCRIPTION

[0024] In order to make the present invention easy to understand and better implemented, the specific implementation methods of the present invention are further described below in conjunction with specific embodiments and the accompanying drawings.

[0025] Reference Figure 1 This embodiment proposes a method for analyzing and predicting factors affecting carbon emissions from sewage treatment based on the LMDI-STIRPAT model, which specifically includes the following steps:

[0026] S1: Obtain the data of the environmental statistical yearbooks and urban statistical yearbooks of the study area over the years. The data of the environmental statistical yearbooks include the amount of treated water, COD removal, TN removal, and electricity consumption. The data of the urban statistical yearbooks include the total population of the region, energy consumption, total GDP, and the proportion of the tertiary industry.

[0027] S2: Based on the emission factor method and pollutant removal method proposed in the IPCC Greenhouse Gas Emission Inventory Guidelines, the greenhouse gas emissions of sewage treatment plants in the study area were calculated to obtain the total regional carbon emissions.

[0028] S3: Construct a LMDI factor decomposition model based on regional carbon emissions and regional activity level data to determine the key factors affecting carbon emissions from wastewater treatment.

[0029] S4: Select key factors as model indicators and conduct variance inflation factor test, and build the STIRPAT extended model based on the indicators that pass the test.

[0030] S5: Use scenario analysis methods to predict carbon emissions under different regional development scenarios, and propose a reasonable carbon neutrality path based on the prediction results.

[0031] Specifically:

[0032] Step S1, obtaining the environmental statistical yearbook data and urban statistical yearbook data of the study area over the years, the environmental statistical yearbook data includes the treated water volume, COD removal volume, TN removal volume, and power consumption, and the urban statistical yearbook data includes the total population of the region, energy consumption, total GDP, and the proportion of the tertiary industry;

[0033] In this embodiment, relevant data of 30 provinces and autonomous regions in a country from 2006 to 2019 are obtained, and the collected data are sorted, and unified unit conversion is performed as needed to facilitate subsequent calculation processes.

[0034] Step S2: Calculate the greenhouse gas emissions of sewage treatment plants in the study area based on the emission factor method and pollutant removal method proposed by the IPCC Greenhouse Gas Emission Inventory Guidelines. Figure 2 Carbon accounting result diagram of the analysis of influencing factors and prediction method of carbon emissions from sewage treatment based on the LMDI-STIRPAT model. The specific expression is:

[0035] (1) The CH4 calculation formula for sewage treatment plants is as follows:

[0036]

[0037] is the CH4 emission from sewage treatment, kgCH4 / m 3 ; Q is the daily water treatment volume of the sewage treatment plant, m 3 ; B in B is the BOD mass concentration of the sewage treatment plant influent, mg / l; eff is the effluent BOD of the sewage treatment plant, mg / l; is the CH4 emission factor, kgCH4 / kgBOD.

[0038] (2) The calculation formula of N2O in sewage treatment plants is as follows:

[0039]

[0040] is the N2O emission from sewage treatment, kgN2O / m 3 ; Q is the daily water treatment volume of the sewage treatment plant, m 3 TN in is the total nitrogen mass concentration of the sewage treatment plant influent, mg / l; TN eff is the total nitrogen concentration of sewage treatment effluent, mg / l; is the N2O emission factor, kgN2O / kgCOD; is the molecular mass ratio of N2O to N2.

[0041] (3) Electricity consumption refers to the amount of electricity purchased during the production and operation of the sewage treatment plant, excluding electricity used in the office and living areas. The formula for calculating electricity consumption of the sewage treatment plant is as follows:

[0042] m e =Q×fe×W

[0043] m e is the indirect CO2 emission from electricity consumption in sewage treatment, kgCO2 / m 3 ; Q is the daily water treatment volume of the sewage treatment plant, m 3 ; fe is the carbon emission factor of electricity consumption, kgCO2 / kwh; W is the amount of electricity purchased for production operation, kwh.

[0044] (4) The calculation formula for the total carbon emissions of a sewage treatment plant is as follows:

[0045]

[0046] M t is the total carbon emission generated during the operation and maintenance of the sewage treatment plant, kgCO2; 28 is the global warming potential constant of CH4, kgCO2 / kgCH4; 265 is the global warming potential constant of N2O, kgCO2 / kgN2O.

[0047] Step S3, based on the regional carbon emission total and regional activity level data, a LMDI factor decomposition model is constructed to determine the key factors affecting carbon emissions from sewage treatment, and obtain Figure 3 Analysis of influencing factors and prediction methods of carbon emissions from sewage treatment based on the LMDI-STIRPAT model. The specific steps are:

[0048] Step S301: Get the Kaya identity, the specific expression is as follows:

[0049]

[0050] Where C is the greenhouse gas emission equivalent, t; Energy is the total regional energy consumption, 10,000 tons of standard coal; GDP is the per capita gross domestic product, yuan; POP is the total population.

[0051] Step S302: Expand the variables in step S301 to obtain the expanded Kaya identity, the specific expression of which is as follows:

[0052]

[0053] Among them, C t represents the total carbon emissions in year t, tCO2; represents the total carbon emission of the ith type of sewage treatment in year t, tCO2; represents the total amount of sewage of type i treated in year t, t; The total energy consumption for the treatment of the i-th type of sewage in year t is expressed in terms of electricity consumption, kWh; GDP t represents the per capita GDP in year t, in yuan; POP t represents the total population in year t.

[0054] Step S303: constructing the LMDI addition decomposition model, the specific expression is as follows:

[0055] ΔC t =C t -C0=ΔTL t +ΔEI t +ΔRU t +ΔEL t +ΔPOP t

[0056]

[0057] Where C0 is the base period carbon emission; C t is the carbon emission in the target year t; TL is the technology level; EI is the product intensity; RU is the energy intensity; EL is the economic level; POP is the population size.

[0058] Step S4, select key factors and perform variance inflation factor test, and build the STIRPAT extended model based on the indicators that pass the test.

[0059] The initial STIRPAT model is established, and the specific expression is as follows:

[0060] I=a×P b ×A c ×T d ×e

[0061] Where I, P, A, and T represent environmental impact, population size, affluence, and technological level; a is the model coefficient; b, c, and d are elasticity indices; and e is the random error.

[0062] On the basis of the initial STIRPAT model, other influencing factors of carbon emissions from sewage treatment are introduced. The specific expressions are as follows:

[0063]

[0064] Where P, A, D, I, and U are population size, affluence, industrial structure, energy intensity, and urbanization rate; a is the model coefficient; α1, α2, α3, α4, and α5 are elasticity indexes; and e is the random error.

[0065] Furthermore, in order to eliminate the influence of heteroscedasticity on the results, the original data is logarithmically processed. The specific expression is as follows:

[0066] lnC=lna+α1lnP+α2lnA+α3lnD+α4lnI+α5lnU+lne

[0067] Furthermore, the ridge regression analysis method was used to obtain the elastic coefficients and random errors of the STIRPAT model.

[0068] Step S5, use scenario analysis methods to predict carbon emissions under different regional development scenarios, and propose a reasonable carbon neutrality path based on the prediction results.

[0069] Specifically, the scenario analysis method predicts and analyzes carbon emissions under different development scenarios of the region based on the development data of various indicators combined with the LMDI-STIRPAT model;

[0070] The various indicators mentioned are based on historical trends and are set and adjusted in combination with a country's policies; the different development scenarios mentioned include the baseline scenario, the technological innovation scenario, the high-speed development scenario, the environment priority scenario, and the low-speed development scenario. Figure 4 .

[0071] This embodiment also provides a system for analyzing and predicting factors affecting carbon emissions from sewage treatment, including:

[0072] Data acquisition module: used to obtain the environmental statistical yearbook data and city statistical yearbook data of the region over the years;

[0073] A carbon emission total amount calculation module is used to calculate the greenhouse gas emissions of the sewage treatment plants in the region to obtain the regional carbon emission total amount;

[0074] A carbon emission influencing factor analysis module is used to construct a LMDI factor decomposition model based on the regional carbon emission total amount and regional activity level data to determine the key factors affecting the carbon emission of sewage treatment;

[0075] A prediction model building module, used for selecting the key factors as indicators and performing variance inflation factor tests, and building a STIRPAT extended model based on the indicators that pass the tests;

[0076] The carbon emission prediction module is used to predict carbon emissions under different development scenarios in the region.

[0077] As can be seen from the above, the present invention calculates the carbon emissions of regional sewage treatment by combining the statistical yearbook data with the carbon emission accounting method and obtains various indicators that affect the carbon emissions of sewage treatment. The contribution value of each indicator is calculated by the LMDI model and the important indicators are selected to construct an extended STIRPAT model, so that the model prediction performance is better, and the model parameters are obtained by the ridge regression analysis method. Combined with the scenario analysis method and the development plan, different development directions and control variables are considered to make a more accurate and more realistic prediction of carbon emissions from sewage treatment.

Claims

1. A method for analyzing and predicting factors affecting carbon emissions from sewage treatment, characterized in that: The following steps are involved: Step S1: Obtaining the environmental statistical yearbook data and city statistical yearbook data of the region over the years; Step S2: Calculating greenhouse gas emissions of sewage treatment plants in the region to obtain the total regional carbon emissions; Step S3: constructing a LMDI factor decomposition model based on the regional carbon emission total and regional activity level data to determine the key factors affecting carbon emissions from sewage treatment; Step S4: Select the key factors as indicators and perform variance inflation factor test, and build the STIRPAT extended model based on the indicators that pass the test; Step S5: Predict the carbon emissions under different development scenarios of the region.

2. The method according to claim 1, characterized in that: The environmental statistical yearbook data described in step S1 include the amount of treated water, COD removal, TN removal, and power consumption; the urban statistical yearbook data include the total population of the region, energy consumption, total GDP, and the proportion of the tertiary industry.

3. The method according to claim 1 or 2, characterized in that: In step S2, the greenhouse gas emissions of the sewage treatment plants in the region are calculated based on the emission factor method and the pollutant removal method proposed in the IPCC Greenhouse Gas Emission Inventory Guidelines.

4. The method according to claim 1, characterized in that: Step S3, constructing the LMDI factor decomposition model, comprises the following steps: Step S31: Obtain the Kaya identity; Step S32: Expand the variables in the Kaya identity to obtain the expanded Kaya identity; Step S33: Obtain the LMDI factor decomposition model from the expanded Kaya identity and the base period carbon emissions.

5. The method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Establishing the STIRPAT initial model; Step S42: Based on the initial STIRPAT model, the indicators that have passed the test are introduced to obtain the STIRPAT extended model.

6. The method according to claim 5, characterized in that: It also includes the use of ridge regression analysis method to obtain the elasticity coefficients and random errors of the STIRPAT extended model.

7. The method according to claim 1, characterized in that: The scenario analysis method is used to predict the carbon emissions of the region under different development scenarios.

8. The method according to claim 7, characterized in that: The different development scenarios include a baseline scenario, a technological innovation scenario, a high-speed development scenario, an environment-first scenario and a low-speed development scenario.

9. The method according to claim 7 or 8, characterized in that: It also includes providing a reasonable carbon neutrality path based on the forecast results.

10. A system for analyzing and predicting factors affecting carbon emissions from sewage treatment, characterized in that: The following steps are involved: Data acquisition module: used to obtain the regional environmental statistical yearbook data and city statistical yearbook data; A carbon emission total amount calculation module is used to calculate the greenhouse gas emissions of the sewage treatment plants in the region to obtain the regional carbon emission total amount; A carbon emission influencing factor analysis module is used to construct a LMDI factor decomposition model based on the regional carbon emission total amount and regional activity level data to determine the key factors affecting the carbon emission of sewage treatment; A prediction model building module is used to select the key factors as indicators and perform variance inflation factor tests, and build a STIRPAT extended model based on the indicators that pass the test; The carbon emission prediction module is used to predict carbon emissions under different development scenarios in the region.

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