A method for evaluating industry carbon emission quotas under dual carbon goals

Abstract

The present invention relates to a method for conducting industry carbon emission quota assessment under the dual carbon target. It includes: constructing decision variables, objective functions and constraints of a carbon emission quota assessment model, wherein the decision variables are the carbon emission quota of each energy structure; the objective function is to minimize the average annual cost of each energy structure transformation, which includes the average annual investment, operation and maintenance, fuel consumption and equipment retirement cost; the constraints consider the future service / product demand, energy demand, carbon emissions and total equipment operation constraints of each industry. Further set the future social and economic conditions or product / service demand assumptions under the dual carbon target, and explore the changes in the future corresponding product / service demand under the conditions of low, medium and high-speed development of future GDP growth. Finally, set the future carbon emission reduction implementation path scenario, focusing on the impact of five main paths, namely ecological carbon sink, artificial carbon sink, energy conservation and consumption reduction, energy substitution and policy encouragement, on the future carbon emission quota of the entire industry.

Description

Technical Field

[0001] The present invention relates to a method for carrying out industry carbon emission quota assessment under dual carbon targets. Background Art

[0002] Based on the current situation in Fujian Province, from the perspective of total carbon emissions, Fujian Province's total social carbon emissions in 2019 were 236 million tons, carbon emission intensity was 0.65 tons / 10,000 yuan, and per capita carbon emissions were 5.95 tons. From the source point of view, carbon emissions mainly come from the combustion of fossil fuels such as coal, oil, and natural gas, of which carbon emissions from coal account for 74% of total carbon emissions, followed by oil (22%) and natural gas (4%). From the perspective of industry share, the carbon emissions of various industries are ranked from high to low as follows: electricity (32.6%), transportation (17.8%), residents' life (9.7%), building materials (8.3), steel (8.1%), service industry (6.5%), chemical industry (5.3%), petrochemical (4.2%) and agricultural activities (1.7%), among which the power industry has the highest proportion of carbon emissions in the entire industry.

[0003] In order to achieve the carbon reduction target by 2060, it is necessary to build an industry-wide carbon emission quota assessment model, consider future changes in GDP growth rate and product / service demand, explore the carbon emission reduction amounts and costs under different carbon emission reduction paths in various industries in the future, and achieve carbon emission quota planning under the optimal conditions for future industry transformation costs. Summary of the invention

[0004] The purpose of this invention is to overcome the fact that most existing models focus on the optimal path for carbon emission reduction in a specific industry under specific goals, while few literatures plan carbon emission quotas for the entire industry from a system-wide perspective to achieve optimal cost conditions. Therefore, a method for conducting industry carbon emission quota assessment under the dual carbon goals is provided, which can provide a reference for future government policy formulation on carbon emission quotas.

[0005] To achieve the above purpose, the technical solution of the present invention is: a method for conducting industry carbon emission quota assessment under the dual carbon goals, comprising the following steps:

[0006] Step S1, constructing decision variables and objective functions of a carbon emission quota assessment model;

[0007] Step S2, constructing constraint conditions for the carbon emission quota assessment model;

[0008] Step S3: Set assumptions about future GDP growth rate and product / service demand under the dual carbon goals;

[0009] Step S4, setting a future carbon emission reduction implementation scenario path;

[0010] Step S5: Evaluate the carbon emission reduction of the entire industry in 2060.

[0011] In one embodiment of the present invention, in step S1, the decision variables x1, x2, x3...x n The carbon emission quota of each energy structure; the objective function is set to minimize the average annual cost of each energy structure transformation within a predetermined period. This cost includes the average annual investment cost, the average annual operation and maintenance cost, the average annual fuel consumption cost, and the average annual equipment retirement cost, which can be expressed in the formula:

[0012] MinTC t =IC t +OC t +FC t +RC t (1)

[0013] Among them: TC t is the average annual total cost of each energy structure transformation; IC t is the average annual investment cost of each energy structure transformation; OC t is the average annual operation and maintenance cost of each energy structure transformation; FC t is the average annual fuel consumption cost of each energy structure transformation; RC t is the average annual equipment retirement cost of each energy structure transformation; t is the year;

[0014] The average annual investment cost IC is affected by factors including government subsidy rate, internal rate of return and equipment life, and can be expressed as:

[0015]

[0016] Among them: ic m,n,t,x is the investment cost per unit of product / service produced by machinery and equipment driven by energy structure n in industry m in year t; 1,2。。。n is the carbon emission quota of each energy structure; n,t is the unit carbon emission coefficient of energy structure n in year t; ux m,n,t is the energy consumption per unit of product / service produced by machinery and equipment driven by energy structure n in industry m in year t; AR m,n,t is the government subsidy rate for energy structure n in industry m in year t; IRR is the internal rate of return for energy structure transformation; M is the industry classification category, including mining, manufacturing, electricity and heat, construction, transportation, service industry, residents' life and others, a total of 8 categories; N is the energy structure category, including coal, coke, crude oil, gasoline, kerosene, diesel, fuel oil and natural gas;

[0017] Operation and maintenance costs OC t Affected by factors including maintenance cost, management cost, labor cost and government subsidy rate, it can be expressed as

[0018]

[0019] Among them: oc m,n,t mc is the maintenance cost per unit of product / service produced by machinery and equipment driven by energy structure n in industry m in year t; m,n,t hc is the management cost per unit of product / service produced by machinery and equipment driven by energy structure n in industry m in year t; m,n,t is the labor cost per unit of product / service produced by machinery and equipment driven by energy structure n in industry m in year t;

[0020] Fuel consumption cost FC t Affected by the total fuel consumption and fuel unit price, the formula is:

[0021]

[0022] Where: P m,n,t is the unit price of energy structure n in industry m in year t; FE m,n,t is the energy efficiency of energy structure n in industry m in year t;

[0023] Equipment decommissioning cost RC t Affected by factors including equipment processing costs and government subsidy rates, the formula is expressed as

[0024]

[0025] Among them: rc m,n,t is the retirement cost per unit of product / service produced by machinery and equipment driven by energy structure n in industry m in year t.

[0026] In one embodiment of the present invention, in step S2, based on the decision variables and objective function established in step S1, future constraints including product / service demand, energy demand, emission restrictions, and total equipment operation volume of each industry are set, which are respectively expressed as formulas:

[0027] In the product / service demand services of each industry, the product / service production volume under the carbon emission quota of each industry in the future needs to meet the basic needs of future society, which can be expressed as

[0028]

[0029] Where: x 1,2。。。n is the carbon emission quota of each energy structure; n,t is the carbon emission coefficient of energy structure n in year t; ux m,n,tFE is the energy consumption per unit of product / service produced by machinery and equipment driven by energy structure n in industry m in year t; m,n,t is the energy efficiency of energy structure n in industry m in year t; GDP is the GDP growth rate in year t; D m,n,t is the product / service output demand of machinery and equipment driven by energy structure n in industry m in year t; t is the year; M is the industry category, including mining, manufacturing, electricity and heat, construction, transportation, service industry, residents' life and others, a total of 8 categories; N is the energy structure category, including coal, coke, crude oil, gasoline, kerosene, diesel, fuel oil and natural gas;

[0030] In the energy demand of each industry, the total energy consumption must meet the constraints of the government's energy policy goals, which can be expressed as

[0031]

[0032]

[0033] …

[0034]

[0035] Among them: MaxTE t is the upper limit of the government’s policy target for energy structure n in year t;

[0036] Among the greenhouse gas emission constraints of various industries, the carbon emissions of energy structure n must meet the dual carbon policy, which can be expressed as

[0037] x 1，2…n ≤MaxTS n，t (10)

[0038] Among them: MaxTS n,t is the government’s carbon emission policy target for energy structure n in year t;

[0039] The number of equipment in each industry must meet the total equipment constraint, which can be expressed as

[0040]

[0041] Among them: SN m,n,t-1 is the inventory quantity of energy structure n in industry m in year (t-1); SR m,n,t is the operating rate of energy structure n in industry m in year t.

[0042] In one embodiment of the present invention, in step S3, based on the carbon emission quota assessment model set in step S1, future socio-economic assumptions and industry demand service assumptions are set respectively; wherein the future socio-economic assumptions include three types: low-speed GDP development, medium-speed GDP development, and high-speed GDP development. Under these three socio-economic assumptions, combined with the current consumption status of each product / service, the consumer demand in 2060 is predicted.

[0043] In one embodiment of the present invention, in step S4, the impact of five future carbon emission reduction paths on the carbon emission reduction of the entire industry in 2060 is discussed.

[0044] Compared with the prior art, the present invention has the following beneficial effects: the method of the present invention can provide a reference for the government to formulate carbon emission quota policies in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The figure is a schematic flow chart of the steps of the method of the present invention. DETAILED DESCRIPTION

[0046] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings.

[0047] The present invention provides a method for carrying out industry carbon emission quota assessment under the dual carbon goals, comprising the following steps:

[0048] Step S1, constructing decision variables and objective functions of a carbon emission quota assessment model;

[0049] Step S2, constructing constraint conditions for the carbon emission quota assessment model;

[0050] Step S3: Set assumptions about future GDP growth rate and product / service demand under the dual carbon goals;

[0051] Step S4, setting a future carbon emission reduction implementation scenario path;

[0052] Step S5: Evaluate the carbon emission reduction of the entire industry in 2060.

[0053] The following are specific examples of the present invention.

[0054] Embodiment 1:

[0055] This paper analyzes the carbon emission quota planning for the entire industry under the carbon emission reduction target in Fujian Province - taking the power industry as an example.

[0056] 1. The cost of energy structure transformation for the entire industry.

[0057] (1) Unit cost and discount rate of energy structure transformation in various industries

[0058] The cost per unit of product / service after the energy structure transformation varies from industry to industry. Take the power industry as an example (Table 1). After the existing non-renewable energy such as coal, coke, and gasoline is transformed into clean energy such as nuclear energy, hydropower, and solar energy, the initial cost per unit of electricity produced by clean energy-driven machinery and equipment, the average annual operation and maintenance cost, the average annual fuel consumption cost, and the average annual equipment retirement cost are calculated.

[0059] Table 1 Cost and discount rate per unit of product / service after energy structure transformation in various industries

[0060]

[0061]

[0062] Note: The product / service output of the mining industry is steel output and aluminum output; the product / service output of the manufacturing industry is ethylene output; the product / service output of the electricity, heat, gas and water production and supply industry / residential life / wholesale and retail industry, accommodation and catering industry is the demand for heating, cooling and other needs; the product / service output of the construction industry is cement output; the unit product / service output of the transportation, warehousing and postal industry is passenger turnover.

[0063] (2) Carbon emission coefficient of energy structure of each industry, energy consumption per unit product / service, government subsidy rate and energy operation efficiency

[0064] Each energy category has its own carbon emission factor, energy consumption per unit of product / service, government incentive policies, and energy efficiency. Table 2 lists the carbon content, average lower calorific value, and carbon emission coefficient ue of non-renewable energy sources such as coal, coke, and crude oil. n,t , the unit product energy consumption ux of machinery and equipment driven by different energy structures in each industry in year t m,n,t As well as the operating efficiency of each energy category; in addition, it is assumed that the government subsidy rate for renewable energy is 5% of the total cost.

[0065] Table 2 Carbon emission coefficient, unit product energy consumption and operating efficiency of each energy category

[0066]

[0067]

[0068] 2. Constraints on the transformation of the energy structure of the entire industry.

[0069] In terms of future demand constraints on products / services across the industry, this study assumes that the future product / service demand limits of each industry are closely related to the GDP growth rate and the current product / service demand. Under the conditions of low, medium and high GDP growth in the future, the representative product / service demand of each industry will be listed in Table 5.

[0070] In terms of the total energy consumption constraint for the whole industry, according to the China Energy and Power Development Outlook, the proportion of non-fossil energy is expected to reach 80% in 2060. Based on this goal, the proportion of fossil energy should not exceed 20%. According to the energy structure ratio in 2020, the output of each energy category in 2060 is predicted as the total energy consumption constraint for the whole industry (Table 3). It can be obtained that the future allowable carbon emissions of each energy category in 2060, provided that the proportion of fossil energy does not exceed 20%, are coal (850,600 tons), coke (452,500 tons), crude oil (5,656,000 tons), gasoline (697,300 tons), kerosene (866,700 tons), diesel (892,600 tons), fuel oil (394,900 tons) and natural gas (1,566,600 tons).

[0071] Table 3 Production of each energy category in 2020, current and future share, and production forecast in 2060

[0072]

[0073] In terms of the carbon emission constraints for the entire industry, based on the previous calculation experience of ecological carbon sinks in this study (Table 4), the impact of future terrestrial and marine ecosystems on the carbon emission quota for the entire industry will be discussed in subsequent scenarios: (1) low carbon emissions, historical trajectory of forest management, and conservative estimates of marine carbon sinks; (2) medium carbon emissions, moderate forest management, and average changes in marine carbon sinks; and (3) high carbon emissions, high carbon fixation levels of forest management, and a growing trend of marine carbon sinks.

[0074] Table 4 Assessment of future ecological carbon sink and artificial carbon fixation capacity

[0075]

[0076] 3. Forecast of future GDP growth rate and product / service demand.

[0077] Considering that China's economic development is vulnerable to the international situation and the global economic and political landscape in the future, it is assumed that China may have three GDP growth rates in 2060: 1.5% in the case of low-speed growth, 2.0% in the case of medium-speed growth, and 4.0% in the case of high-speed growth. Based on the GDP growth rate assumption and the actual product / service demand of various industries in 2020, the estimated product / service demand in 2060 is shown in Table 5.

[0078] Table 5 Demand for representative products / services in various industries in Fujian Province under different GDP growth rates in 2060

[0079]

[0080]

[0081] 4. Main implementation paths for carbon emission reduction.

[0082] If the future demand constraints of the industry's products / services, the total energy consumption constraints of the industry, and the carbon emissions constraints of the industry are to be met at the same time, if the proportion of non-renewable energy in the power industry is only set at 20% (10% for coal, 8% for oil, and 2% for natural gas) in the future, it will not be able to meet future electricity demand; if the proportion of non-renewable energy in the power industry is set at 25% (10% for coal, 10% for oil, and 5% for natural gas), the expected electricity production can reach the GDP slow development demand of 55.866 billion tons of standard coal, and the expected carbon emissions are 15.8538 million tons; if the proportion of non-renewable energy in the power industry is set at 25% (10% for coal, 10% for oil, and 5% for natural gas), the expected electricity production can reach the GDP slow development demand of 55.866 billion tons of standard coal, and the expected carbon emissions are 15.8538 million tons. Assuming that the proportion of non-renewable energy in the power industry is 29% (10% for coal, 13% for oil, and 6% for natural gas), the expected power production can reach 67.863 billion tons of standard coal, which is the demand for medium-speed GDP development, and the expected carbon emissions are 19.8274 million tons; assuming that the proportion of non-renewable energy in the power industry is 29% (10% for coal, 14% for oil, and 15% for natural gas), the expected power production can reach 147.289 billion tons of standard coal, which is the demand for high-speed GDP development, and the expected carbon emissions are 27.9403 million tons. The output of each energy category in 2060 under different settings is shown in Table 6.

[0083] Table 6 Output of each energy category in 2060 under different assumptions

[0084]

[0085]

[0086] (1) Scenario 1: Ecological Carbon Sink

[0087] Based on previous research on ecological carbon sinks, this study divides ecological carbon sink scenarios into (1) low carbon sinks in terrestrial ecosystems, historical trajectory scenarios of forest management, and conservative estimates of marine carbon sinks; (2) medium carbon sinks in terrestrial ecosystems, moderate forest management scenarios, and average changes in marine carbon sinks; and (3) high carbon sinks in terrestrial ecosystems, high carbon fixation levels in forest management, and a growing trend in marine carbon sinks. In the future, the power industry can be completely absorbed by the ecological carbon sink under the conditions of low, medium and high GDP development. In (1), the ecological carbon sink can provide 49.55 million tons of carbon absorption in 2060, and the carbon emission quota ratio of the power industry under the future low, medium and high GDP conditions is 32%, 40% and 56% respectively; in (2), the ecological carbon sink can provide 69.81 million tons of carbon absorption in 2060, and the carbon emission quota ratio of the power industry under the future low, medium and high GDP conditions is 23%, 28% and 40% respectively; in (3), the ecological carbon sink can provide 88.34 million tons of carbon absorption in 2060, and the carbon emission quota ratio of the power industry under the future low, medium and high GDP conditions is 18%, 22% and 32% respectively.

[0088] (2) Scenario 2: Artificial carbon sink

[0089] Based on past research on ecological carbon sinks, this study divides artificial carbon sink scenarios into (1) basic scenario; (2) medium scenario; and (3) ideal scenario, which can provide upper and lower limits of carbon absorption of 42.1 to 532.4 million tons. Under the condition that the energy structure under the condition of low-medium-speed GDP development meets the CCUS technology level and high penetration rate, in (1), artificial carbon sinks can provide a maximum of 47.64 million tons of carbon absorption in 2060, and the carbon emission quota ratios of the power industry under the conditions of low, medium and high GDP in the future are 32%, 42% and 59% respectively; in (2), ecological carbon sinks can provide 302.62 million tons of carbon absorption in 2060, and the carbon emission quota ratios of the power industry under the conditions of low, medium and high GDP in the future are 5%, 7% and 9% respectively; in (3), ecological carbon sinks can provide 532.4 million tons of carbon absorption in 2060, and the carbon emission quota ratios of the power industry under the conditions of low, medium and high GDP in the future are 3%, 4% and 5% respectively.

[0090] (3) Scenario 3: Energy saving and consumption reduction

[0091] Considering that the energy efficiency of various industries will be greatly improved in the future due to factors such as technological progress and industrial structure adjustment, it is assumed here that energy efficiency will increase to 80% from 60% in 2020. In this case, the main impact will be on the fuel cost of the transformation of various energy categories related to energy efficiency to renewable energy.

[0092] (4) Scenario 4: Energy Substitution

[0093] Under the constraints, the possibility of a substantial increase in the proportion of renewable energy in 2060 is discussed. When renewable energy accounts for 80% of the power industry, it cannot meet future electricity demand; when renewable energy accounts for a maximum of 75% of the power industry, it can meet future electricity demand under the condition of low GDP growth; when renewable energy accounts for a maximum of 71% of the power industry, it can meet future electricity demand under the condition of medium-to-high GDP growth. In addition, in the cost comparison of the transformation of various energy types to renewable energy, the cost of replacing non-renewable energy with hydropower is the lowest, followed by wind power, biomass, solar energy, nuclear energy and geothermal energy.

[0094] (5) Scenario 4: Policy encouragement

[0095] In order to encourage the whole industry to transform its energy structure, the government provides a certain amount of subsidies for renewable energy. Therefore, it is assumed that the government subsidy rate is increased from the original 5% to 20%. In this case, it will mainly affect the various costs of transforming various energy categories related to energy efficiency to renewable energy.

[0096] 5. Carbon emission quotas for various industries under different implementation paths in the future.

[0097] From the above research results, it can be seen that in order to meet the electricity demand under the low, medium and high GDP growth scenario in 2060, various energy types will still need to release 15.8538 million to 27.9403 million tons of carbon emissions, which will need to be absorbed by forest carbon sinks, ocean carbon sinks and CCUS technology.

[0098] The carbon emission quotas of various energy types within the power industry under the conditions of low, medium and high GDP growth in 2060 to meet future electricity demand and the carbon emission quotas of various energy categories within the power industry are shown in Table 6. In the share of different emission reduction paths: considering ecological carbon sinks alone, the future carbon emission quota of the power industry is about 18-56%; further considering artificial carbon sinks, the future carbon emission quota of the power industry is about 3-59%. In terms of energy substitution, when renewable energy accounts for 71-75% of the power industry, it can meet the electricity demand under the future social and economic development scenario. In the future, if the carbon emission quotas of various energy types under the optimal cost of each industry are further calculated, the direct CO2 emission reduction responsibility of each industry can be finally obtained.

[0099] The above are preferred embodiments of the present invention. Any changes made according to the technical solution of the present invention, as long as the resulting functions do not exceed the scope of the technical solution of the present invention, belong to the protection scope of the present invention.

Claims

1. A method for conducting industry carbon emission quota assessment under the dual carbon goals, characterized in that: The steps include: Step S1, constructing decision variables and objective functions of a carbon emission quota assessment model; Step S2, constructing constraint conditions for the carbon emission quota assessment model; Step S3: Set assumptions about future GDP growth rate and product / service demand under the dual carbon goals; Step S4, setting a future carbon emission reduction implementation scenario path; Step S5: Evaluate the carbon emission reduction of the entire industry in 2060; in step S1, the decision variables x1, x2, x3…x n The carbon emission quota of each energy structure; the objective function is set to minimize the average annual cost of each energy structure transformation within a predetermined period. This cost includes the average annual investment cost, the average annual operation and maintenance cost, the average annual fuel consumption cost, and the average annual equipment retirement cost, which can be expressed in the formula: MinTC t =IC t +OC t +FC t +RC t (1) Among them: TC t is the average annual total cost of each energy structure transformation; IC t is the average annual investment cost of each energy structure transformation; OC t is the average annual operation and maintenance cost of each energy structure transformation; FC t is the average annual fuel consumption cost of each energy structure transformation; RC t is the average annual equipment retirement cost of each energy structure transformation; t is the year; The average annual investment cost IC is affected by factors including government subsidy rate, internal rate of return and equipment life, and can be expressed as: Among them: ic m,n,t is the investment cost per unit of product / service produced by machinery and equipment driven by energy structure n in industry m in year t; 1,2...n is the carbon emission quota of each energy structure; n,t is the unit carbon emission coefficient of energy structure n in year t; ux m,n,t is the energy consumption per unit of product / service produced by machinery and equipment driven by energy structure n in industry m in year t; AR m,n,t is the government subsidy rate for energy structure n in industry m in year t; IRR is the internal rate of return for energy structure transformation; M is the industry classification category, including mining, manufacturing, electricity and heat, construction, transportation, service industry, residents' life and others, a total of 8 categories; N is the energy structure category, including coal, coke, crude oil, gasoline, kerosene, diesel, fuel oil and natural gas; Operation and maintenance costs OC t Affected by factors including maintenance cost, management cost, labor cost and government subsidy rate, it can be expressed as Among them: oc m,n,t mc is the maintenance cost per unit of product / service produced by machinery and equipment driven by energy structure n in industry m in year t; m,n,t hc is the management cost per unit of product / service produced by machinery and equipment driven by energy structure n in industry m in year t; m,n,t is the labor cost per unit of product / service produced by machinery and equipment driven by energy structure n in industry m in year t; Fuel consumption cost FC t Affected by the total fuel consumption and fuel unit price, the formula is: Where: P m,n,t is the unit price of energy structure n in industry m in year t; FE m,n,t is the energy efficiency of energy structure n in industry m in year t; Equipment decommissioning cost RC t Affected by factors including equipment processing costs and government subsidy rates, the formula is expressed as Among them: rc m,n,t is the retirement cost per unit of product / service produced by machinery and equipment driven by energy structure n in industry m in year t.

2. A method for conducting industry carbon emission quota assessment under the dual carbon goals according to claim 1, characterized in that: In step S2, based on the decision variables and objective function established in step S1, future constraints including product / service demand, energy demand, emission restrictions, and total equipment operation volume of each industry are set, which are respectively expressed as formulas: In the product / service demand services of each industry, the product / service production volume under the carbon emission quota of each industry in the future needs to meet the basic needs of future society, which can be expressed as Where: x 1,2...n is the carbon emission quota of each energy structure; n,t is the carbon emission coefficient of energy structure n in year t; ux m,n,t FE is the energy consumption per unit of product / service produced by machinery and equipment driven by energy structure n in industry m in year t; m,n,t is the energy efficiency of energy structure n in industry m in year t; GDP is the GDP growth rate in year t; D m,n,t is the product / service output demand of machinery and equipment driven by energy structure n in industry m in year t; t is the year; M is the industry category, including mining, manufacturing, electricity and heat, construction, transportation, service industry, residents' life and others, a total of 8 categories; N is the energy structure category, including coal, coke, crude oil, gasoline, kerosene, diesel, fuel oil and natural gas; In the energy demand of each industry, the total energy consumption must meet the constraints of the government's energy policy goals, which can be expressed as … Among them: MaxTE n,t is the upper limit of the government’s policy target for energy structure n in year t; Among the greenhouse gas emission constraints of various industries, the carbon emissions of energy structure n must meet the dual carbon policy, which can be expressed as x 1,2…n ≤MaxTS n,t (10) Among them: MaxTS n,t is the government’s carbon emission policy target for energy structure n in year t; The number of equipment in each industry must meet the total equipment constraint, which can be expressed as Among them: SN m,n,t-1 is the inventory quantity of energy structure n in industry m in year (t-1); SR m,n,t is the operating rate of energy structure n in industry m in year t.

3. A method for conducting industry carbon emission quota assessment under the dual carbon goals according to claim 1, characterized in that: In step S3, based on the carbon emission quota assessment model set in step S1, future socio-economic assumptions and industry demand service assumptions are set respectively; the future socio-economic assumptions include three types: low-speed GDP development, medium-speed GDP development, and high-speed GDP development. Under these three socio-economic assumptions, combined with the current consumption status of each product / service, the consumer demand in 2060 is predicted.

4. A method for conducting industry carbon emission quota assessment under the dual carbon goals according to claim 1, characterized in that: In step S4, the impact of five future carbon emission reduction pathways on the carbon emission reduction of the entire industry in 2060 is discussed.

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