Power big data-based pollution reduction and carbon reduction collaborative analysis method

Abstract

The invention relates to a pollution reduction and carbon reduction collaborative analysis method based on electric power big data. Constructing a pollution reduction and carbon reduction monitoring analysis model based on electric power big data according to the collected data, and calculating unit tax pollution emission intensity and unit tax carbon intensity of the enterprise; performing comprehensive performance calculation according to the calculated unit tax pollution emission intensity and unit tax carbon intensity; and ranking the enterprises according to the obtained comprehensive performance, and performing level-to-level management on the enterprises according to the ranking. The method breaks through emission reduction management and control means and measures of original key areas and key industries, innovatively introduces multi-industry and multi-dimensional enterprise production information and energy consumption information, breaks through inherent management thinking on the basis of original evaluation purely depending on pollutant emission, deeply associates enterprise economic benefits with pollution discharge and carbon discharge, and improves enterprise economic benefit evaluation efficiency. Pollutant emission intensity and electric carbon emission intensity indexes are newly added and constructed, brand new dimension classification rating is carried out on key emission reduction enterprises, and total amount and intensity double evaluation and double control are firstly achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power big data mining and application, and in particular to a collaborative analysis method for pollution reduction and carbon reduction based on power big data. Background Art

[0002] As the dual carbon goals are gradually incorporated into the overall layout of economic and social development and ecological civilization construction, green and low-carbon development has become a general consensus. Air pollution prevention and control has entered a critical period with pollution reduction and carbon reduction as the key strategic direction, promoting the comprehensive green transformation of economic and social development, and achieving a qualitative change in the improvement of ecological environment quality from quantitative change.

[0003] There are differences in problems, location, time and objects among industries and enterprises in the current environmental protection management and control work. The existing work on reducing air pollution emissions mainly revolves around total reduction. The formulation of relevant measures is mainly based on the production management level and emission level of enterprises. There is a lack of control over pollution emission intensity and carbon emission intensity, and there is an even greater lack of overall consideration of related areas such as resource and energy utilization, output value and tax growth. Summary of the invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and propose a collaborative analysis method for pollution reduction and carbon reduction based on power big data, which can objectively analyze which enterprises need to be governed and which enterprises need to be transformed, thereby ensuring high-quality economic development, achieving precise pollution control and improving environmental governance benefits.

[0005] The present invention solves the technical problem by adopting the following technical solutions:

[0006] A collaborative analysis method for pollution reduction and carbon reduction based on power big data, comprising the following steps:

[0007] Step 1: Collect data;

[0008] Step 2: Build a pollution reduction and carbon reduction monitoring and analysis model based on power big data according to the collected data, and calculate the pollution emission intensity and carbon intensity per unit tax of the enterprise;

[0009] Step 3: Calculate the comprehensive performance based on the calculated pollution emission intensity per unit tax and carbon intensity per unit tax;

[0010] Step 4: Rank the enterprises according to the comprehensive performance obtained in step 3, and manage the enterprises in different levels according to the ranking.

[0011] Moreover, the data in step 1 includes economic data, environmental protection data, tax data and electric carbon data.

[0012] Moreover, the specific implementation method of step 2 is: the pollution reduction and carbon reduction monitoring and analysis model based on power big data includes a unit tax pollution emission intensity model and a unit tax carbon intensity model.

[0013] Moreover, the unit tax pollution emission intensity model is:

[0014] Pollution emission intensity per unit of tax = air pollutant emission / tax payment

[0015] The emission of atmospheric pollutants is the weighted sum of eight pollutant indicators of nitrogen oxides, volatile organic compounds, particulate matter, sulfur dioxide, hydrogen chloride, sulfuric acid mist, ammonia and oil fume of an enterprise in the previous year.

[0016] Furthermore, the unit tax carbon intensity model is:

[0017] Carbon intensity per unit tax = carbon dioxide emissions ÷ tax amount paid

[0018] Carbon dioxide emissions refer to the carbon dioxide emissions generated by all production sites and facilities of the company in the previous year. They mainly include statistics on the carbon dioxide emissions from the combustion of fossil fuels and the implied carbon dioxide emissions from net purchased electricity. At the same time, the company's fossil fuel activity level data is verified through an electricity-carbon calculation model.

[0019] Furthermore, the calculation method for the CO2 emissions implied by the net purchased electricity is:

[0020]

[0021] Among them, E CO2净电 The CO2 emissions implied by the company’s net purchase of electricity; AD 电力 EF is the net electricity consumption purchased by enterprises; 电力 is the CO2 emission factor for electricity supply.

[0022] Furthermore, the enterprise electricity-carbon calculation model is:

[0023]

[0024] Among them, E CO2 E is the total amount of carbon dioxide emissions calculated based on the energy consumption data reported by the enterprise; CO2-elec It is the total amount of carbon dioxide emissions calculated based on the electricity data collected by the enterprise and the enterprise's electricity-carbon calculation model; δ is the deviation rate between the carbon dioxide measured by the enterprise's reported energy consumption data and the data calculated by the enterprise's electricity-carbon calculation model; if the error between the two exceeds 20%, the energy consumption data reported by the enterprise needs to be verified to ensure the quality of the enterprise's carbon emission data.

[0025] Moreover, the specific implementation method of step 3 is:

[0026] Comprehensive performance = Pollution intensity ranking per unit tax*70% + Carbon intensity ranking per unit tax*30%

[0027] According to the performance level of enterprises, they are divided into three levels: C1, C2 and C3 from high to low, and are graded based on the comprehensive ranking of enterprises, regional emission reduction targets and industry performance evaluation.

[0028] The advantages and positive effects of the present invention are:

[0029] 1. The present invention collects data; constructs a pollution reduction and carbon reduction monitoring and analysis model based on power big data based on the collected data, and calculates the pollution emission intensity and carbon intensity of the unit tax of the enterprise; performs comprehensive performance calculation based on the calculated pollution emission intensity and carbon intensity of the unit tax; ranks the enterprises according to the obtained comprehensive performance, and manages the enterprises in a graded manner according to the ranking. The present invention breaks through the original emission reduction control means and measures in key areas and key industries, and innovatively introduces multi-industry and multi-dimensional enterprise production information and energy consumption information. On the basis of the original evaluation based solely on pollutant emissions, it breaks through the inherent management thinking, deeply associates the economic benefits of enterprises with pollution and carbon emissions, and newly constructs pollutant emission intensity and electricity carbon emission intensity indicators, and conducts new dimensional classification and rating of key emission reduction enterprises, first realizing the dual evaluation and dual control of total amount and intensity.

[0030] 2. The present invention conducts in-depth mining of economic, environmental, energy consumption, and basic information data from multiple dimensions of industries and enterprises, and scientifically constructs an industry and enterprise pollutant emission intensity and carbon emission intensity assessment system to ensure that related areas such as resource and energy utilization, output value and tax growth are coordinated and balanced. In the case of heavy pollution weather, priority is given to the production of low-pollution, low-carbon, and high-tax enterprises to achieve a balance between economic benefits and green ecological benefits.

[0031] 3. The present invention constructs an enterprise electricity-carbon calculation model, which calculates the emission factor based on the electricity production of various energy sources, the standard coal conversion coefficients of various energy sources, the carbon emission coefficients of various energy sources, and the standard coal conversion coefficients of electricity (equivalent value), fully considering the changes in the production structure and realizing enterprise carbon emission accounting on a monthly basis. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Flowchart of the present invention

[0033] Figure 2 This is a schematic diagram of the electricity-carbon calculation model based on power big data of the present invention. DETAILED DESCRIPTION

[0034] The present invention is further described in detail below with reference to the accompanying drawings.

[0035] A collaborative analysis method for pollution reduction and carbon reduction based on power big data, such as Figure 1 As shown, the following steps are included

[0036] Step 1: Collect data.

[0037] The data includes economic data, environmental data, tax data and electricity carbon data.

[0038] Step 2: Build a pollution reduction and carbon reduction monitoring and analysis model based on power big data according to the collected data, and calculate the pollution emission intensity and carbon intensity per unit tax of the enterprise.

[0039] The pollution reduction and carbon reduction monitoring and analysis models based on power big data include the pollution emission intensity per unit tax model and the carbon intensity per unit tax model.

[0040] The unit tax pollution emission intensity model is:

[0041] Pollution emission intensity per unit of tax = air pollutant emission / tax payment

[0042] The emission of atmospheric pollutants is the weighted sum of the eight pollutant indicators of nitrogen oxides, volatile organic compounds, particulate matter, sulfur dioxide, hydrogen chloride, sulfuric acid mist, ammonia, and oil smoke of the enterprise in the previous year, and the unit of measurement is tons. Among them, hydrogen chloride, sulfuric acid mist, and ammonia are weighted 3 times, and the remaining pollutants are weighted 1 time. The basic data of atmospheric pollutant emissions shall be filled in and provided by the enterprise.

[0043] The unit tax carbon intensity model is:

[0044] Carbon intensity per unit tax = carbon dioxide emissions ÷ tax amount paid

[0045] Carbon dioxide emissions refer to the carbon dioxide emissions generated by all production sites and facilities of the company in the previous year. They mainly include statistics on the carbon dioxide emissions from the combustion of fossil fuels and the implied carbon dioxide emissions from net purchased electricity. At the same time, the company's fossil fuel activity level data is verified through an electricity-carbon calculation model.

[0046] The amount of tax paid refers to the total amount of taxes and fees paid by each enterprise to the district and town tax authorities in the previous year, and the unit of measurement is ten thousand yuan.

[0047] The calculation method for the carbon dioxide emissions implied by net purchased electricity is:

[0048]

[0049] Among them, E CO2净电 The CO2 emissions implied by the company’s net purchase of electricity; AD 电力 EF is the net electricity consumption purchased by enterprises; 电力 is the CO2 emission factor for electricity supply.

[0050] like Figure 2 As shown, the enterprise electricity-carbon calculation model is:

[0051]

[0052] Among them, E CO2 E is the total amount of carbon dioxide emissions calculated based on the energy consumption data reported by the enterprise; CO2-elec It is the total amount of carbon dioxide emissions calculated based on the electricity data collected by the enterprise and the enterprise's electricity-carbon calculation model; δ is the deviation rate between the carbon dioxide measured by the enterprise's reported energy consumption data and the data calculated by the enterprise's electricity-carbon calculation model; if the error between the two exceeds 20%, the energy consumption data reported by the enterprise needs to be verified to ensure the quality of the enterprise's carbon emission data.

[0053] The electricity-carbon calculation model prepares electricity consumption data for gas-related enterprises during the demand period, sorts out data demand fields, obtains enterprise electricity consumption data of various dimensions based on the power data center, performs data preprocessing, cleans invalid data, and removes empty data for electricity consumption. At the same time, the model fully considers the changes in the power production structure, and calculates emission factors from various energy power production, various energy conversion coefficients for standard coal, various energy carbon emission coefficients, and electricity (equivalent value) conversion coefficients for standard coal, to support timely and accurate grasp of the implicit CO2 emissions of enterprises' net purchased electricity, and uses the same method to verify the fossil fuel activity level data reported by enterprises themselves, so as to improve the level of coordinated management of precise pollution reduction and carbon reduction.

[0054] Step 3: Calculate the comprehensive performance based on the calculated pollution emission intensity per unit tax and carbon intensity per unit tax.

[0055] Comprehensive performance = Pollution intensity ranking per unit tax*70% + Carbon intensity ranking per unit tax*30%

[0056] According to the performance level of enterprises, they are divided into three levels: C1, C2 and C3 from high to low, and are graded based on the comprehensive ranking of enterprises, regional emission reduction targets and industry performance evaluation.

[0057] Step 4: Rank the enterprises according to the comprehensive performance obtained in step 3, and manage the enterprises in different levels according to the ranking.

[0058] Based on the comprehensive performance ranking of enterprises, enterprises rated as C1 will be granted priority emission rights and priority energy allocation rights, while enterprises rated as C3 will be subject to relatively strict pollution reduction and carbon reduction control measures, especially during periods of heavy pollution control.

[0059] It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention includes but is not limited to the embodiments described in the specific implementation manner. Any other implementation manners derived by those skilled in the art based on the technical solution of the present invention also fall within the scope of protection of the present invention.

Claims

1. A collaborative analysis method for pollution reduction and carbon reduction based on power big data, characterized by: The following steps are included Step 1: Collect data; Step 2: Build a pollution reduction and carbon reduction monitoring and analysis model based on power big data according to the collected data, and calculate the pollution emission intensity and carbon intensity per unit tax of the enterprise; Step 3: Calculate the comprehensive performance based on the calculated pollution emission intensity per unit tax and carbon intensity per unit tax; Step 4: Rank the enterprises according to the comprehensive performance obtained in step 3, and manage the enterprises in different levels according to the ranking.

2. According to claim 1, a collaborative analysis method for pollution reduction and carbon reduction based on power big data is characterized by: The data in step 1 include economic data, environmental protection data, tax data and electricity carbon data.

3. According to claim 1, a collaborative analysis method for pollution reduction and carbon reduction based on power big data is characterized by: The specific implementation method of step 2 is: the pollution reduction and carbon reduction monitoring and analysis model based on power big data includes a unit tax pollution emission intensity model and a unit tax carbon intensity model.

4. The method for collaborative analysis of pollution reduction and carbon reduction based on power big data according to claim 3 is characterized by: The unit tax pollution emission intensity model is: Pollution emission intensity per unit of tax = air pollutant emission / tax payment The emission of atmospheric pollutants is the weighted sum of eight pollutant indicators of nitrogen oxides, volatile organic compounds, particulate matter, sulfur dioxide, hydrogen chloride, sulfuric acid mist, ammonia and oil fume of an enterprise in the previous year.

5. The method for collaborative analysis of pollution reduction and carbon reduction based on power big data according to claim 3 is characterized by: The unit tax carbon intensity model is: Carbon intensity per unit tax = carbon dioxide emissions ÷ tax amount paid Carbon dioxide emissions refer to the carbon dioxide emissions generated by all production sites and facilities of the company in the previous year. They mainly include statistics on the carbon dioxide emissions from the combustion of fossil fuels and the implied carbon dioxide emissions from net purchased electricity. At the same time, the company's fossil fuel activity level data is verified through an electricity-carbon calculation model.

6. The method for collaborative analysis of pollution reduction and carbon reduction based on power big data according to claim 5 is characterized by: The calculation method for the carbon dioxide emissions implied by the net purchased electricity is: Among them, E CO2净电 The CO2 emissions implied by the company’s net purchase of electricity; AD 电力 EF is the net electricity consumption purchased by enterprises; 电力 is the CO2 emission factor for electricity supply.

7. The method for collaborative analysis of pollution reduction and carbon reduction based on power big data according to claim 5 is characterized by: The enterprise electricity-carbon calculation model is: Among them, E CO2 E is the total amount of carbon dioxide emissions calculated based on the energy consumption data reported by the enterprise; CO2-elec It is the total amount of carbon dioxide emissions calculated based on the electricity data collected by the enterprise and the enterprise's electricity-carbon calculation model; δ is the deviation rate between the carbon dioxide measured by the enterprise's reported energy consumption data and the data calculated by the enterprise's electricity-carbon calculation model; if the error between the two exceeds 20%, the energy consumption data reported by the enterprise needs to be verified to ensure the quality of the enterprise's carbon emission data.

8. The method for collaborative analysis of pollution reduction and carbon reduction based on power big data according to claim 1 is characterized by: The specific implementation method of step 3 is: Comprehensive performance = Pollution intensity ranking per unit tax*70% + Carbon intensity ranking per unit tax*30% According to the performance level of enterprises, they are divided into three levels: C1, C2 and C3 from high to low, and are graded based on the comprehensive ranking of enterprises, regional emission reduction targets and industry performance evaluation.

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