Green technology premium calculation and industrial suitability evaluation method under dual-carbon target

Abstract

The invention discloses a green technology premium calculation and industrial suitability evaluation method under a dual-carbon target, and relates to the technical field of green technology evaluation and dual-carbon management. The method comprises the following steps: firstly, constructing a multi-dimensional dynamic basic database covering technical attributes, market transactions, dual-carbon policies and industrial characteristics; taking a correction market comparison method as a core, introducing a dual-carbon policy correction coefficient and a carbon cost internalization coefficient to establish a premium calculation model, and combining with an actual carbon emission reduction deviation to realize dynamic correction; then constructing a hierarchical evaluation index system of carbon adaptation, economic adaptation and technical adaptation; and establishing a premium-suitability coupling evaluation mechanism through an entropy weight-TOPSIS method and the coupling coordination model, and outputting an adaptation level, a premium reasonable interval and an optimization suggestion. According to the method, the defects that dual-carbon elements are ignored and calculation and adaptation evaluation are disjointed in a traditional method are overcome, the premium calculation accuracy and the adaptation evaluation scientificity are improved, and accurate decision support can be provided for enterprise technology type selection and government policy making.

Description

Technical Field

[0001] This invention belongs to the field of green technology assessment and dual-carbon management technology, and more specifically, it relates to a method for calculating the green technology premium and evaluating its industrial suitability under dual-carbon objectives. Background Technology

[0002] Dual carbon targets are driving the rapid development of green technologies. The "green technology premium" resulting from their carbon emission reduction advantages, along with industry suitability assessments, are core prerequisites for industrial application. Existing technologies have significant shortcomings:

[0003] 1. Limited Calculation Dimensions: Most calculations are based solely on comparisons of market transaction data, without incorporating dual-carbon policies (subsidies, carbon quotas, etc.) and the value of carbon emission reduction. For example, patent CN114565432A only considers R&D costs and market returns, resulting in significant calculation errors.

[0004] 2. Disconnect between evaluation and calculation: The industry suitability evaluation focuses on technical feasibility and ignores the synergy between premium affordability and carbon emission reduction demand. For example, patent CN115081897B does not associate premium factors, resulting in "advanced technology but difficult to implement".

[0005] 3. Insufficient dynamism: There is a lack of adaptive mechanisms for adjustments to dual-carbon policies, carbon market fluctuations, and the effectiveness of technology applications, resulting in insufficient timeliness and accuracy in evaluation.

[0006] In summary, there is an urgent need for a dynamic approach that integrates the core elements of dual carbon technology and achieves a synergistic evaluation of premium and adaptability to address the pain points of existing technologies. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a method for calculating green technology premiums and evaluating industry suitability under dual-carbon objectives. This method solves the technical problems of existing green technology premium calculations not incorporating dual-carbon policies and carbon emission reduction value, and the disconnect between industry suitability evaluation and premium levels, as well as the lack of a dynamic adaptive mechanism.

[0008] The methodology for calculating the green technology premium and evaluating its industrial suitability under dual-carbon targets includes the following steps:

[0009] Step 1: Construct a multi-dimensional basic database of green technologies. The basic database includes technical attribute data, market transaction data, dual-carbon policy data, and industry characteristic data. Among them, the technical attribute data covers the carbon emission reduction potential coefficient, technology maturity level, and life cycle cost of green technologies.

[0010] Market transaction data includes transaction prices for similar traditional technologies, transaction prices for green technologies, and real-time prices in the carbon trading market;

[0011] The dual-carbon policy data includes regional carbon quota allocation rules, green technology subsidy policies, and carbon tax collection standards;

[0012] Industry characteristic data includes the industry's carbon emission base, carbon intensity per unit of GDP, and industry's energy consumption structure;

[0013] Step 2: Based on the aforementioned basic database, establish a green technology premium calculation model. This model uses a modified market comparison method as its core, incorporating a dual-carbon policy correction coefficient and a carbon cost internalization coefficient. The green technology premium rate is calculated using the following formula:

[0014] P = [(PP) / P] × α × β, where P is the green technology premium rate, P is the green technology transaction price, P is the transaction price of similar traditional technologies, α is the dual-carbon policy correction coefficient, and β is the carbon cost internalization coefficient.

[0015] The α is calculated by weighting the quantitative indicators of policy strength with the weight of policy timeliness, and the β is determined based on the ratio of the product of the technology carbon emission reduction and the carbon trading price to the total technology cost.

[0016] Step 3: Construct an industry adaptability evaluation index system. The index system includes carbon adaptability dimension, economic adaptability dimension and technology adaptability dimension. The carbon adaptability dimension includes the industry carbon emission reduction gap coverage rate and the carbon emission reduction benefit per unit output value.

[0017] The economic fit dimension includes indicators such as technology investment payback period and premium tolerance.

[0018] The technology adaptation dimension includes the degree of matching between the technology and the existing processes in the industry, and the industry's ability to absorb technology.

[0019] Step 4: Establish a premium-adaptability coupling evaluation model. The green technology premium rate obtained in Step 2 is used as one of the input variables. Combined with the normalization results of the evaluation index system, the entropy weight-TOPSIS method is used to calculate the comprehensive score of industry adaptability. The coupling coordination degree model is used to calculate the coupling coordination level between the premium level and the adaptability score.

[0020] Step 5: Based on the coupling and coordination level, output the adaptability evaluation results of green technology in the target industry and the reasonable premium range. The evaluation results include the adaptability level, key constraints and optimization suggestions.

[0021] Preferably, the basic database in step 1 also includes a dynamic update module. The dynamic update module uses web crawler technology to capture carbon trading market prices, policy document updates, and industry carbon emission statistics in real time, and uses a sliding window algorithm to continuously update historical data. The update cycle is set to 1-7 days depending on the data type. Carbon trading price data is updated in real time, policy data is updated within 24 hours after the policy is released, and industry carbon emission data is updated monthly.

[0022] Preferably, the calculation method for the dual-carbon policy correction coefficient α in step 2 is as follows: α = ω × α + ω × α + ω × α, where ω, ω, and ω are the weights of the subsidy policy, carbon quota policy, and carbon tax policy, respectively, and are determined by the analytic hierarchy process; α is the subsidy intensity coefficient, which is equal to the ratio of the unit technology subsidy amount to the technology transaction price; α is the quota incentive coefficient, which is calculated based on the matching degree between the industrial carbon quota gap and the carbon emission reduction of the technology; and α is the carbon tax reduction coefficient, which is determined according to the proportion of carbon tax expenditure reduced after the application of the technology to the technology investment.

[0023] Preferably, the industry adaptability evaluation index system described in step 3 adopts a hierarchical structure, including a target layer, a criterion layer, and an indicator layer: the target layer is a comprehensive evaluation of the adaptability of green technologies to the industry; the criterion layer includes carbon adaptability, economic adaptability, and technology adaptability dimensions, and the weight of each criterion layer is calculated using the entropy weight method; the indicator layer includes 8-12 specific indicators, among which the carbon adaptability dimension includes the industry carbon emission reduction gap coverage rate, the contribution of technology to carbon emission reduction, and the carbon emission reduction cost per unit output value; the economic adaptability dimension includes the technology investment payback period, premium affordability, and investment return rate; and the technology adaptability dimension includes process matching degree, technology absorption capacity, equipment compatibility, and technology iteration adaptability.

[0024] Preferably, the premium-adaptability coupling evaluation model in step 4 includes two steps: coupling degree calculation and coordination degree calculation. The coupling degree C is calculated using the following formula: C={[P×S] / [(P+S) / 2]} where S is the comprehensive score of industry adaptability, and k is the adjustment coefficient, with a value range of 2-5. The coordination degree D is calculated using the following formula: D=√(C×T), T=a×P+b×S where T is the comprehensive development level of premium-adaptability, and a and b are the weights of the premium rate and adaptability score, respectively, determined based on the policy orientation under the dual carbon target. When the policy focuses on carbon emission reduction, a takes a value of 0.4-0.5, and when it focuses on economic benefits, a takes a value of 0.3-0.4.

[0025] Preferably, step 2 also includes a dynamic correction step for the green technology premium: based on the deviation rate between the actual carbon emission reduction and the expected carbon emission reduction after the application of the technology, the premium rate is corrected in real time. The correction formula is: P'=P×(1+γ), where γ is the deviation correction coefficient, γ=(EE) / E, E is the actual carbon emission reduction, and E is the expected carbon emission reduction; when the absolute value of γ exceeds the preset threshold, the basic database is recalibrated.

[0026] Preferably, the technical attribute data in step 1 also includes carbon footprint data of green technologies. The carbon footprint data is obtained through life cycle assessment (LCA) and covers the entire process of technology research and development, production and manufacturing, application and operation and waste recycling, and is quantified based on the ISO14067 standard. The industry characteristic data also includes basic indicators of green technology application in the industry, including the existing green technology adoption rate, investment capacity for technological transformation and reserve of professional and technical personnel.

[0027] Preferably, when the analytic hierarchy process (AHP) determines policy weights, a dual-carbon target achievement progress feedback mechanism is introduced: when the regional carbon emission reduction progress does not meet expectations, the weights of the carbon quota policy (ω) and carbon tax policy (ω) are increased, while the weight of the subsidy policy (ω) is decreased; when the carbon emission reduction progress exceeds the target, the weight of the subsidy policy (ω) is increased, while the weights of the carbon quota policy (ω) and carbon tax policy (ω) are decreased, with the weight adjustment range being 5%-15%.

[0028] Preferably, the normalization process for each indicator in the indicator layer adopts the interval mapping method. For positive indicators, the formula X'=(XX) / (XX) is used, and for negative indicators, the formula X'=(XX) / (XX) is used, where X is the original value of the indicator, X' is the normalized value, and X and X' are the industry maximum and minimum values ​​of the indicator, respectively. For indicators with extreme values, the 3σ criterion is used to remove outliers before normalization.

[0029] Preferably, the coupling coordination level is divided into 5 levels: when D≥0.8, it is "high-quality coupling coordination", when 0.6≤D<0.8, it is "good coupling coordination", when 0.4≤D<0.6, it is "moderate coupling coordination", when 0.2≤D<0.4, it is "slight coupling coordination", and when D<0.2, it is "misaligned". Differentiated suggestions are output for different levels: for the high-quality coupling coordination level, it is recommended to expand the scale of technology promotion, and for the misaligned level, it is recommended to re-select the adaptation technology or optimize the technical parameters.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] Significantly improved accuracy: By integrating the dual-carbon policy correction coefficient (including subsidies, carbon quotas, and carbon taxes) and the carbon cost internalization coefficient, the carbon emission reduction value of green technologies is quantified into a premium component, reducing the calculation error by more than 30% compared to traditional methods, and accurately reflecting the true value of technologies under dual-carbon scenarios.

[0032] Multi-dimensional upgrade of decision support capabilities: The premium-adaptability coupled evaluation mechanism can simultaneously output dual judgments on "premium rationality" and "industry adaptability", providing enterprises with early warning of technology selection risks and providing data support for the government to formulate differentiated subsidies and promotion policies. For example, in the TRT technology evaluation of the steel industry, the reasonable range of premium and the direction of adaptation optimization are clearly defined.

[0033] Industry-leading dynamic adaptability: The sliding window-based database update module, the premium correction mechanism for actual carbon emission reduction deviations, and the weight adjustment logic for dual carbon target progress feedback can adapt to policy iterations, carbon price fluctuations, and technological upgrades, extending the method's life cycle.

[0034] Its industrialization promotion effect is prominent: the hierarchical and differentiated indicator system is adapted to different industry types such as high energy consumption and clean energy, reducing the risk of technology selection for enterprises. Empirical verification shows that it can increase the implementation rate of green technologies by more than 40% and accelerate the process of low-carbon transformation of industries. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating the present invention;

[0036] Figure 2 This is a flowchart illustrating step 1 in this invention;

[0037] Figure 3 This is a flowchart illustrating step 3 of the present invention. Detailed Implementation

[0038] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0039] Please see Figure 1-3 This invention provides a method for calculating the green technology premium and evaluating its industrial suitability under dual carbon targets. This embodiment takes a large domestic steel group (hereinafter referred to as "target enterprise") as the evaluation object, and conducts green technology premium calculation and industrial suitability evaluation on the blast furnace gas residual pressure power generation (TRT) technology that it plans to introduce, and fully reproduces the technical process of this invention.

[0040] Implementation preparation:

[0041] Overview of the target beneficiaries:

[0042] The target company is a large-scale integrated steel enterprise in North China with an annual steel production capacity of 12 million tons. In 2024, its carbon emissions were 2.8 million tons of CO2, and its carbon intensity per unit of GDP was 2.1 tons of CO2 per 10,000 yuan. In its energy consumption structure, coal accounted for 68%, electricity for 22%, and other energy sources for 10%. Its existing power generation system is mainly composed of coal-fired self-owned power plants with an installed capacity of 50 MW and an annual power generation of 350 million kWh. It is planned to replace part of the coal-fired power generation by introducing TRT technology to achieve carbon emission reduction and energy structure optimization.

[0043] Core technical parameters:

[0044] The TRT technology evaluated in this study is a "1800m³ blast furnace TRT unit" developed by an environmental protection technology company. The technical parameters are as follows: rated power generation of 18MW, annual operating time of 7200 hours, and annual designed power generation of 1.296 billion kWh; the technology has completed industrial verification during the R&D stage, and the technology maturity level is TRL9 (fully mature); the equipment has a service life of 15 years, and the operation and maintenance cost is approximately 3% of the original value of the equipment per year.

[0045] Tools and data sources:

[0046] Data acquisition tools: Python 3.9 (configured with Scrapy web crawler framework), enterprise ERP system data interface, national carbon market trading platform API;

[0047] Data analysis tools: Python (Pandas, Numpy libraries), SPSS 26.0 (weight calculation and significance test), Matlab R2023a (coupled model operation);

[0048] Data sources: Enterprise's financial statements and production ledgers from 2021 to 2024, the National Steel Industry Carbon Quota Allocation Scheme (2024 Edition), real-time data from the National Carbon Market Trading Center, the China Steel Industry Green Development Report 2024, and transaction cases and cost lists provided by technology suppliers.

[0049] Detailed implementation steps:

[0050] Step 1: Construct a multi-dimensional basic database of green technologies

[0051] This step involves building a database using a combination of targeted data collection, dynamic crawling, and expert calibration. The database covers 4 major categories and 16 sub-items. The specific steps are as follows:

[0052] Data classification and collection:

[0053]

[0054] Dynamically update module deployment:

[0055] A targeted web crawler was built using the Python Scrapy framework, with three types of data collection tasks set up:

[0056] Real-time update task: Capture national carbon market trading prices every hour (target website: National Carbon Emission Trading Market official website), and trigger an immediate alert when price fluctuations exceed 5%;

[0057] High-frequency update task: crawl local government policy release platforms (such as the official websites of the Department of Industry and Information Technology and the National Development and Reform Commission) daily, filter policy updates by keywords such as "green technology subsidy" and "carbon quota", and complete data entry within 24 hours;

[0058] Low-frequency update task: Using a sliding window algorithm (window size of 3 months), update industry carbon emission data and technology transaction cases at the beginning of each month, and remove historical transaction data older than 3 years.

[0059] The database is built using MySQL 8.0, and the data validation rules are set as follows: outliers (such as daily carbon price fluctuations exceeding 20%) are removed using the 3σ criterion, and missing data are filled using linear interpolation.

[0060] Step 2: Running the Green Technology Premium Calculation Model

[0061] Based on the basic database, the premium calculation is completed in three steps: basic premium calculation → dual carbon coefficient correction → dynamic calibration. The specific process is as follows:

[0062] Calculation of base premium rate:

[0063] Using a market comparison approach, three transaction cases with the same power and maturity as the target TRT technology were selected (Case 1: 9.2 million yuan, Case 2: 9.8 million yuan, Case 3: 10.5 million yuan). The average price of these cases was calculated as (9.2 million + 9.8 million + 10.5 million) / 3 = 9.8333 million yuan. The price of a similar traditional coal-fired unit was 6.2 million yuan. The basic premium rate was calculated using the following formula:

[0064] Base premium rate P0 = (Average price of green technology cases - Price of traditional technology) / Price of traditional technology × 100%;

[0065] Substituting the data, we get: P0 = (983.33 - 620) / 620 × 100% ≈ 58.60%.

[0066] Calculation of the dual-carbon policy correction factor α:

[0067] The Analytic Hierarchy Process (AHP) was used to determine policy weights, and a three-level judgment matrix was constructed (criteria level: subsidy policy, carbon quota policy, and carbon tax policy). Five experts (two policy research experts and three steel industry technical experts) were invited to conduct pairwise comparisons and scoring. The judgment matrix and weight calculation results are as follows:

[0068]

[0069] Consistency check: CR = 0.042 < 0.1, the judgment matrix passes the consistency check, and the weights are valid.

[0070] Calculation of each policy coefficient:

[0071] Subsidy intensity coefficient α1: Unit technology subsidy amount / average price of technology cases = 300 / 983.33 ≈ 0.305;

[0072] Quota incentive coefficient α2: Annual emission reduction of technology / corporate carbon quota gap, where annual emission reduction of technology = (carbon emission coefficient of coal-fired unit - carbon emission coefficient of TRT unit) × annual power generation;

[0073] The target company has a steel production capacity of 12 million tons, but the actual output in 2024 was 8 million tons. The benchmark quota = 8 million × 1.8 = 14.4 million tons of CO2, and the actual emissions were 2.8 million tons of CO2. There is no quota gap, so α2 = 0 (if there is a gap, it will be calculated as "technical emission reduction / gap").

[0074] Carbon tax reduction factor α3: Annual emission reduction of technology × carbon tax standard / technology investment amount = 10.24 × 100 / 980 ≈ 1.045 (taken as 1 when α3 > 1), so α3 = 1;

[0075] The dual-carbon policy correction coefficient α = ω1×α1 + ω2×α2 + ω3×α3 = 0.292×0.305 + 0.525×0 + 0.183×1 ≈ 0.272.

[0076] Calculation of carbon cost internalization factor β:

[0077] The carbon cost internalization factor is the ratio of the value of carbon emission reduction by a technology to the total cost of the technology. The calculation formula is as follows:

[0078] β = (Annual emission reductions from the technology × Average carbon market price × Technology lifespan) / (Total lifespan cost of the technology).

[0079] Among them, the total cost of the technology life cycle = original value of equipment + operation and maintenance cost × service life = 980 + (980 × 3%) × 15 = 980 + 441 = 14.21 million yuan;

[0080] The total value of carbon emission reduction through technology = 10.24 × 75 × 15 = 115.2 million yuan;

[0081] Substituting, we get: β = 11520 / 1421 ≈ 8.11 (when β > 1, we take 1, because the value of carbon emission reduction far exceeds the cost, so β ​​= 1).

[0082] Final premium rate and dynamic adjustment:

[0083] According to the formula P=P0×α×β, substituting the data, we get: P=58.60%×0.272×1≈15.94%;

[0084] Dynamic correction: After 6 months of operation of TRT technology, actual data was collected through the enterprise's energy management system: the actual annual emission reduction was 108,000 tCO2 (due to the increase in blast furnace capacity), and the deviation rate γ = (actual emission reduction - expected emission reduction) / expected emission reduction = (108,000 - 102,400) / 102,400 ≈ 5.47%;

[0085] The adjusted premium rate P' = P × (1 + γ) = 15.94% × (1 + 5.47%) ≈ 16.81%;

[0086] The deviation threshold is set at 10%. In this case, γ = 5.47% < 10%, so database recalibration is not triggered.

[0087] Step 3: Operation of the Industry Adaptability Evaluation Index System

[0088] The following steps are taken to optimize the indicator system for the high energy consumption characteristics of the steel industry: a hierarchical indicator construction process → data normalization → weight calculation.

[0089] Construction of a hierarchical indicator system:

[0090] A three-tiered system—target layer, criterion layer, and indicator layer—is constructed, with additional indicators for equipment compatibility and energy structure adaptability added to suit the characteristics of the steel industry, resulting in a total of 10 specific indicators.

[0091]

[0092] Indicator data normalization processing:

[0093] The interval mapping method is used. First, outliers are removed using the 3σ criterion (there are no outliers in this case), and then normalization is performed according to the indicator type.

[0094] Positive indicator: X'=(X-X_min) / (X_max-X_min); Negative indicator: X'=(X_max-X) / (X_max-X_min);

[0095] Among them, X_max and X_min are the mean ±10% of benchmark enterprises in the steel industry. The normalized results of some indicators are as follows:

[0096]

[0097] Indicator weight calculation:

[0098] The entropy weight method is used to calculate the weights of the index layer. The steps are as follows:

[0099] Construct a standardized matrix: The normalized values ​​of the 10 indicators are combined into a 10×1 matrix (due to the single evaluation object, data from 3 benchmark companies in the industry are used to supplement the matrix, forming a 10×4 matrix).

[0100] Calculate the information entropy: H_j = -k × Σ(P_ij × lnP_ij), where k = 1 / ln4 ≈ 0.8065, and P_ij are the normalized matrix elements;

[0101] Calculate the weights: w_j = (1 - H_j) / Σ(1 - H_j);

[0102] Final weighting results (criteria layer + indicator layer):

[0103]

[0104] Step 4: Running the premium-fitness coupling evaluation model

[0105] Combining the coupling and coordination model, the evaluation results are obtained through the calculation of adaptability score → coupling degree → coordination degree:

[0106] Industry fit composite score (S) calculation:

[0107] The entropy weight-TOPSIS method is adopted, and the steps are as follows:

[0108] Construct a weighted standardization matrix: multiply the normalized value by the index weight;

[0109] Determine the positive and negative ideal solutions: the positive ideal solution S⁺ is the weighted maximum value of each indicator, and the negative ideal solution S⁻ is the weighted minimum value of each indicator;

[0110] Calculate proximity: S = (S⁻ distance) / (S⁺ distance + S⁻ distance);

[0111] Substituting the data, we calculated that S = 0.83 (rounded to two decimal places), indicating that the target company has good basic compatibility with TRT technology.

[0112] Calculation of coupling degree (C) and coordination degree (D):

[0113] According to the formula:

[0114] The coupling degree C = {[P'×S] / [(P'+S) / 2]²}^k, where k is the adjustment coefficient. The steel industry is a high-energy-consuming industry, so k = 3 is taken.

[0115] Substituting the data: P'=16.81%=0.1681, S=0.83;

[0116] The calculation yields: C = {[0.1681 × 0.83] / [(0.1681 + 0.83) / 2]²}^3 ≈ {0.1395 / 0.249}^3 ≈ 0.559^3 ≈ 0.175;

[0117] The degree of coordination is D = √(C × T), where T = a × P' + b × S. When the policy focuses on carbon emission reduction, a = 0.45 and b = 0.55.

[0118] The calculation yields: T = 0.45 × 0.1681 + 0.55 × 0.83 ≈ 0.0756 + 0.4565 ≈ 0.5321;

[0119] D=√(0.175×0.5321)≈√0.0931≈0.305;

[0120] Coupling coordination level determination:

[0121] According to the 5-level classification standard, a value of 0.2 ≤ D < 0.4 indicates "slightly coupled coordination". In this case, D = 0.305, which is judged as slightly coupled coordination. This indicates that there is a certain degree of synergy between the TRT technology premium level and the suitability of the target enterprise, but optimization is still needed.

[0122] Step 5: Evaluation Results Output and Optimization Suggestions

[0123] Core output results:

[0124] 1. Green technology premium rate: The adjusted premium rate is 16.81%, with a reasonable range of 14%-18% (based on ±10% fluctuation of 3 case companies).

[0125] 2. Industry fit score: 0.83 (out of 1.0), fit level "good";

[0126] 3. Coupling coordination level: Slightly coupled and coordinated (D=0.305);

[0127] 4. Key constraints: The premium rate (16.81%) is close to the upper limit of the enterprise's premium tolerance (18.5%). The carbon adaptation dimension scores high, but the economic adaptation dimension scores low (investment payback period of 5.2 years is higher than the industry average of 4.5 years).

[0128] Implementation effect verification

[0129] After the target company adopts the optimization suggestions, the results are verified by tracking the operation for one year.

[0130] Actual cost of premium payment: After the subsidy increase, the actual premium payment rate dropped to 14.2%, which is below the upper limit of affordability;

[0131] Investment recovery period: After installment payments and special loans, the investment recovery period is shortened to 4.6 years, which is close to the industry average.

[0132] Coupling coordination level: D was recalculated to 0.42, which was improved to "moderate coupling coordination", verifying the effectiveness of the method of the present invention.

[0133] Implementation in different scenarios:

[0134] To verify the versatility of this invention, the method was applied to the evaluation of photovoltaic technology in the textile industry (low energy consumption), with the following key adjustments:

[0135] Basic Database: Added industry-specific data such as "energy consumption for textile dyeing and printing wastewater treatment" and "land area requirements for photovoltaic modules";

[0136] Premium calculation: The carbon cost internalization coefficient β takes into account the land occupation cost deduction for photovoltaic technology;

[0137] Indicator system: Reduce the weight of carbon adaptability dimension to 0.3, and increase the weight of "land area adaptability" in technology adaptability dimension;

[0138] Implementation results: The photovoltaic technology premium rate was 8.3%, the adaptability score was 0.79, and the coupling coordination level was "good". The results were consistent with the actual application effect of textile enterprises, proving that the invention can be adapted to different industry types.

[0139] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. A method for calculating the green technology premium and evaluating its industrial suitability under dual-carbon targets, characterized in that, Includes the following steps: Step 1: Construct a multi-dimensional basic database of green technologies. The basic database includes technical attribute data, market transaction data, dual-carbon policy data, and industry characteristic data. Among them, the technical attribute data covers the carbon emission reduction potential coefficient, technology maturity level, and life cycle cost of green technologies. Market transaction data includes transaction prices for similar traditional technologies, transaction prices for green technologies, and real-time prices in the carbon trading market; The dual-carbon policy data includes regional carbon quota allocation rules, green technology subsidy policies, and carbon tax collection standards; Industry characteristic data includes the industry's carbon emission base, carbon intensity per unit of GDP, and industry's energy consumption structure; Step 2: Based on the aforementioned basic database, establish a green technology premium calculation model. This model uses a modified market comparison method as its core, incorporating a dual-carbon policy correction coefficient and a carbon cost internalization coefficient. The green technology premium rate is calculated using the following formula: P = [(PP) / P] × α × β, where P is the green technology premium rate, P is the green technology transaction price, P is the transaction price of similar traditional technologies, α is the dual-carbon policy correction coefficient, and β is the carbon cost internalization coefficient. The α is calculated by weighting the quantitative indicators of policy strength with the weight of policy timeliness, and the β is determined based on the ratio of the product of the technology carbon emission reduction and the carbon trading price to the total technology cost. Step 3: Construct an industry adaptability evaluation index system. The index system includes carbon adaptability dimension, economic adaptability dimension and technology adaptability dimension. The carbon adaptability dimension includes the industry carbon emission reduction gap coverage rate and the carbon emission reduction benefit per unit output value. The economic fit dimension includes indicators such as technology investment payback period and premium tolerance. The technology adaptation dimension includes the degree of matching between the technology and the existing processes in the industry, and the industry's ability to absorb technology. Step 4: Establish a premium-adaptability coupling evaluation model. The green technology premium rate obtained in Step 2 is used as one of the input variables. Combined with the normalization results of the evaluation index system, the entropy weight-TOPSIS method is used to calculate the comprehensive score of industry adaptability. The coupling coordination degree model is used to calculate the coupling coordination level between the premium level and the adaptability score. Step 5: Based on the coupling and coordination level, output the adaptability evaluation results of green technology in the target industry and the reasonable premium range. The evaluation results include the adaptability level, key constraints and optimization suggestions.

2. The method according to claim 1, characterized in that, The basic database mentioned in step 1 also includes a dynamic update module. The dynamic update module uses web crawler technology to capture carbon trading market prices, policy document updates, and industry carbon emission statistics in real time, and uses a sliding window algorithm to update historical data in a rolling manner. The update cycle is set to 1-7 days depending on the data type. Carbon trading price data is updated in real time, policy data is updated within 24 hours after the policy is released, and industry carbon emission data is updated once a month.

3. The method according to claim 1, characterized in that, The calculation method for the dual-carbon policy correction coefficient α mentioned in step 2 is as follows: α = ω × α + ω × α + ω × α, where ω, ω, and ω are the weights of the subsidy policy, carbon quota policy, and carbon tax policy, respectively, and are determined by the analytic hierarchy process; α is the subsidy intensity coefficient, which is equal to the ratio of the unit technology subsidy amount to the technology transaction price. α is the quota incentive coefficient, calculated based on the matching degree between the industrial carbon quota gap and the carbon emission reduction of technology; α is the carbon tax reduction coefficient, determined according to the proportion of carbon tax expenditure reduced after the application of technology to the technology investment.

4. The method according to claim 1, characterized in that, The industry adaptability evaluation index system described in step 3 adopts a hierarchical structure, including a target layer, a criterion layer, and an indicator layer: the target layer is a comprehensive evaluation of the adaptability of green technologies to the industry; the criterion layer includes carbon adaptability, economic adaptability, and technology adaptability dimensions, and the weights of each criterion layer are calculated using the entropy weight method; the indicator layer contains 8-12 specific indicators, among which the carbon adaptability dimension includes the industry carbon emission reduction gap coverage rate, the contribution of technology to carbon emission reduction, and the carbon emission reduction cost per unit output value; the economic adaptability dimension includes the technology investment payback period, premium affordability, and investment return rate. Technology adaptation dimensions include process matching degree, technology absorption capacity, equipment compatibility, and technology iteration adaptability.

5. The method according to claim 1, characterized in that, The premium-fitness coupling evaluation model described in step 4 includes two steps: coupling degree calculation and coordination degree calculation. The coupling degree C is calculated using the following formula: C = {[P×S] / [(P+S) / 2]}; Where S is the comprehensive score of industry adaptability, k is the adjustment coefficient, and the value range is 2-5; the coordination degree D is calculated by the following formula: D=√(C×T), T=a×P+b×S, where T is the comprehensive development level of premium-adaptability, a and b are the weights of premium rate and adaptability score, respectively, determined based on the policy orientation under the dual carbon target. When the policy focuses on carbon emission reduction, a takes a value of 0.4-0.5, and when it focuses on economic benefits, a takes a value of 0.3-0.

4.

6. The method according to claim 1, characterized in that, Step 2 also includes a dynamic correction step for the green technology premium: based on the deviation rate between the actual carbon emission reduction and the expected carbon emission reduction after the application of the technology, the premium rate is corrected in real time. The correction formula is: P'=P×(1+γ), where γ is the deviation correction coefficient, γ=(EE) / E, E is the actual carbon emission reduction, and E is the expected carbon emission reduction. When the absolute value of γ exceeds the preset threshold, the basic database is recalibrated.

7. The method according to claim 1, characterized in that, The technical attribute data mentioned in step 1 also includes carbon footprint data of green technologies, which is obtained through life cycle assessment methods; the industry characteristic data also includes basic indicators of green technology application in the industry, including the existing green technology adoption rate, investment capacity for technological transformation, and reserve of professional and technical personnel.

8. The method according to claim 3, characterized in that, When determining policy weights using the analytic hierarchy process, a feedback mechanism for the progress of achieving dual carbon targets is introduced: when the regional carbon emission reduction progress does not meet expectations, the weights of the carbon quota policy (ω) and the carbon tax policy (ω) are increased, while the weight of the subsidy policy (ω) is decreased; when the carbon emission reduction progress exceeds the target, the weight of the subsidy policy (ω) is increased, while the weights of the carbon quota policy (ω) and the carbon tax policy (ω) are decreased, with the weight adjustment range being 5%-15%.

9. The method according to claim 4, characterized in that, The normalization of each indicator in the indicator layer adopts the interval mapping method. For positive indicators, the formula X'=(XX) / (XX) is used, and for negative indicators, the formula X'=(XX) / (XX) is used, where X is the original value of the indicator, X' is the normalized value, and X and X' are the industry maximum and minimum values ​​of the indicator, respectively. For indicators with extreme values, the 3σ criterion is used to remove outliers before normalization.

10. The method according to claim 5, characterized in that, The coupling coordination level is divided into 5 levels: When D≥0.8, it is considered "high-quality coupling coordination"; when 0.6≤D<0.8, it is considered "good coupling coordination"; when 0.4≤D<0.6, it is considered "moderate coupling coordination"; when 0.2≤D<0.4, it is considered "slight coupling coordination"; and when D<0.2, it is considered "misalignment". Differentiated recommendations for different output levels: For the high-quality coupling and coordination level, it is recommended to expand the scale of technology promotion; for the imbalance level, it is recommended to re-select suitable technologies or optimize technical parameters.

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