An ESG-based comprehensive management evaluation method for a shipbuilding enterprise, an electronic device, a storage medium, and a product

Abstract

This invention proposes an ESG-based comprehensive governance assessment method, electronic device, storage medium, and product for shipbuilding enterprises. The method includes: collecting multi-source ESG data and operational data from the shipbuilding enterprise during the assessment period; calculating state baseline parameters based on the operational data according to preset time slices; converting the multi-source ESG data into standard evaluation units and forming ESG vector data corresponding to the preset time slices; matching corresponding ESG assessment parameters from a preset ESG assessment parameter library based on the enterprise size, ship type, or production organization method; if no match is found, adaptively calibrating the ESG assessment parameters to obtain calibrated ESG assessment parameters; calculating governance efficiency indicators for each time slice based on the ESG vector data, state baseline parameters, and ESG assessment parameters; calculating a comprehensive ESG governance efficiency coefficient based on the governance efficiency indicators for each time slice; and obtaining the ESG governance assessment result based on the comprehensive ESG governance efficiency coefficient.

Description

Technical Field

[0001] This invention relates to the field of assessment, and in particular to an ESG-based comprehensive governance assessment method, electronic device, storage medium, and product for shipbuilding enterprises. Background Technology

[0002] Currently, conducting comprehensive ESG governance assessments for complex manufacturing industries like shipbuilding—characterized by heavy assets, long cycles, and high energy consumption—faces the following limitations:

[0003] 1. The assessment is static and disconnected from dynamic operations. Existing assessment methods mostly rely on annual or quarterly summary report data. This static snapshot cannot reflect the huge operational changes in shipbuilding production as project stages and order fluctuations occur.

[0004] 2. Data silos and lack of correlation analysis: Environmental, safety, and governance data are usually managed independently by different departments and lack effective integration. During the assessment, only isolated scores or simple weighting of each dimension are applied, failing to establish ESG performance and core production drivers.

[0005] 3. Standardized parameters lack enterprise adaptability. Many evaluation systems use industry-standard or fixed evaluation parameters and thresholds. However, different shipbuilding companies vary significantly in terms of scale, main ship types, process flow, and level of automation.

[0006] 4. The results are too general and lack guidance. The final evaluation often only gives an overall score or grade, lacking insight into efficiency fluctuations over time and analysis of the production-related factors that cause efficiency fluctuations. This makes it impossible for companies to translate macro-level ESG ratings into specific, actionable improvement measures for specific production processes or time periods. Summary of the Invention

[0007] Purpose of the invention: To address the shortcomings of existing ESG integrated governance assessment methods, this invention proposes an ESG-based integrated governance assessment method for shipbuilding enterprises, electronic equipment, storage media, and products.

[0008] Technical Solution: In a first aspect, embodiments of the present invention propose a comprehensive governance assessment method for shipbuilding enterprises based on ESG, comprising the following steps:

[0009] Step 1: Collect multi-source ESG data and operational data from shipbuilding companies during the assessment period. The multi-source ESG data includes environmental data reflecting the company's environmental protection intensity, social responsibility data reflecting the company's employee work safety, and corporate governance data reflecting the company's governance intensity. The operational data is data reflecting the company's operational intensity.

[0010] Step 2: Based on the operational data, calculate the comprehensive production load index according to the preset time slice, which serves as a state benchmark parameter reflecting the operational intensity of the enterprise within the preset time slice;

[0011] Step 3: Convert the environmental data, social responsibility data, and corporate governance data into standard evaluation units and form ESG vector data corresponding to the preset time slice;

[0012] Step 4: Based on the enterprise size, ship type structure, or production organization method, match the corresponding ESG assessment parameters in the preset ESG assessment parameter library; if no match is found, generate initial ESG assessment parameters based on the statistical characteristics of the state reference parameters, and adaptively calibrate the ESG assessment parameters based on the state reference parameters and ESG vector data through an optimization algorithm to obtain calibrated ESG assessment parameters.

[0013] Step 5: Based on ESG vector data, state baseline parameters, and ESG assessment parameters, calculate the governance efficiency index for each time slice, and calculate the comprehensive ESG governance efficiency coefficient based on the governance efficiency index for each time slice.

[0014] Step 6: Obtain the ESG governance assessment results based on the comprehensive ESG governance efficiency coefficient.

[0015] Furthermore, the environmental data includes: electricity consumption for production, fuel consumption, equivalent carbon emissions from operational processes, and solid waste generation;

[0016] The social responsibility data includes: workplace accident rate, safety training coverage rate, and employment compliance information;

[0017] The corporate governance data includes: the implementation status of ESG-related governance systems, and records of internal audits or compliance incidents;

[0018] The operational data includes: the occupancy status of the dock or slipway, the working hours of key processes, and the amount of major materials handled.

[0019] Furthermore, based on the aforementioned operational data, a comprehensive production load index is calculated according to preset time slices, including:

[0020] The comprehensive production load index is obtained by normalizing the occupancy status of the dock or slipway, the working hours of key processes, and the handling volume of major materials, and then performing a weighted calculation.

[0021] Furthermore, the process of converting the environmental data, social responsibility data, and corporate governance data into standard evaluation units and forming ESG vector data corresponding to preset time slices includes:

[0022] Environmental data is converted into a unified environmental load assessment unit using a carbon emission factor conversion method, forming an environmental load vector.

[0023] Social responsibility data is converted into social performance evaluation units through a target benchmark normalization method to form a social performance vector.

[0024] Corporate governance data is converted into governance performance evaluation units using a system implementation rate conversion method, forming a governance performance vector.

[0025] The environmental load vector, social performance vector, and governance performance vector are matched and integrated according to a preset time slice to form ESG vector data.

[0026] Furthermore, based on the aforementioned state baseline parameters and ESG vector data, the ESG evaluation parameters are adaptively calibrated using an optimization algorithm to obtain calibrated ESG evaluation parameters, specifically including:

[0027] Based on the state baseline parameters and corresponding ESG vector data within the historical time slice, a theoretical expectation model is constructed with the state baseline parameters as input, the theoretical expected value of the ESG vector data as output, and the initial ESG evaluation parameters as model coefficients.

[0028] Using the deviation between the expected value of the ESG vector data output by the theoretical expected model and the actual value of the ESG vector data as the optimization objective, the initial ESG evaluation parameters are iteratively adjusted to determine the calibrated ESG evaluation parameters.

[0029] Furthermore, the process of calculating governance efficiency indicators for each time slice based on ESG vector data, state baseline parameters, and ESG assessment parameters, and then calculating a comprehensive ESG governance efficiency coefficient based on these indicators, specifically includes:

[0030] Using ESG assessment parameters or calibrated ESG assessment parameters as model coefficients of the theoretical expectation model, the state benchmark parameters in each preset time slice are input into the theoretical expectation model, and the theoretical expected value of ESG vector data in each preset time slice is output; the theoretical expected value of ESG vector data is compared with the actual value of ESG vector data in the corresponding time slice to obtain a governance efficiency index characterizing the ESG governance effect in that time slice.

[0031] The governance efficiency indicators within multiple preset time slices are time-weighted to calculate the comprehensive ESG governance efficiency coefficient.

[0032] Secondly, the present invention provides an electronic device, the electronic device comprising:

[0033] At least one processor;

[0034] and a memory communicatively connected to the at least one processor;

[0035] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the disclosed ESG-based integrated governance assessment method for shipbuilding enterprises.

[0036] Thirdly, the present invention proposes a computer-readable storage medium storing computer instructions that are used to cause a processor to execute the above-disclosed ESG-based shipbuilding enterprise integrated governance assessment method.

[0037] Fourthly, the present invention proposes a computer program product, characterized in that the computer program product includes a computer program, which, when executed by a processor, implements the ESG-based comprehensive governance assessment method for shipbuilding enterprises as described in any one of claims 1-6.

[0038] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0039] (1) This invention introduces a comprehensive production load index calculated by time slices, which dynamically binds ESG performance with the real-time operational intensity of enterprises, thereby realizing the dynamic and refined nature of ESG assessment.

[0040] (2) This invention breaks down data silos by synchronously collecting and processing multi-source ESG data and operational data, and by using adaptively calibrated ESG assessment parameters to construct a theoretical expectation model.

[0041] (3) Through parameter matching and adaptive calibration mechanism, the evaluation model can adjust itself according to the size, ship type and production mode of different enterprises, thus avoiding the bias of one-size-fits-all evaluation.

[0042] (4) The comprehensive ESG governance efficiency coefficient and its derived governance efficiency indicators, which are finally output by this invention, combined with the trend and correlation analysis in the visualization report, can directly and specifically point out the time period and production process where the management shortcomings are located, and transform the abstract ESG level into a locationable, executable and verifiable improvement instruction, which greatly enhances the guiding role and practical value of the evaluation results in improving the actual governance level of enterprises. Attached Figure Description

[0043] Figure 1 The flowchart illustrates an ESG-based comprehensive governance assessment method for shipbuilding enterprises, as provided in Embodiment 1 of the present invention. Detailed Implementation

[0044] The technical solution of this embodiment will now be further described in conjunction with the accompanying drawings and examples.

[0045] Example 1:

[0046] This invention proposes an ESG-based comprehensive governance assessment method for shipbuilding enterprises, such as... Figure 1 As shown, the main steps include:

[0047] Step 1: Collect multi-source ESG data and operational data of shipbuilding enterprises during the assessment period. In this embodiment of the invention, the multi-source ESG data includes environmental data, social responsibility data, and corporate governance data.

[0048] The environmental data includes at least electricity consumption, fuel consumption, equivalent carbon emissions from operations, and solid waste generation, collected through the enterprise's energy management system and / or environmental monitoring system.

[0049] The social responsibility data includes at least the workplace accident rate, safety training coverage, and employment compliance information, collected through the safety production system and / or human resource management system.

[0050] Corporate governance data includes at least the implementation status of ESG-related governance systems and records of internal audits or compliance incidents, collected through the enterprise management information system.

[0051] The operational data includes at least the occupancy status of the dock or slipway, the man-hour input of key processes, and the amount of major materials handled, which are collected through the manufacturing execution system.

[0052] For example, the evaluation period is 30 days, and data is collected through the company's existing information system:

[0053] Environmental data: Daily electricity consumption and natural gas consumption of each workshop are collected through the Energy Management System (EMS); the operation data of the VOCs treatment device in the painting workshop are collected through the Environmental Monitoring System and converted into equivalent carbon emissions; and the daily amount of hazardous waste generated is collected through solid waste management records.

[0054] Social responsibility data: The number of work-related accidents (including near misses) recorded daily is collected through the safety production management system, and the incidence rate is calculated; the proportion of employees who have completed the required safety training this month is collected through the human resources management system (coverage rate); and compliance records of outsourced team qualification review and on-site inspection are collected.

[0055] Corporate governance data: Collect meeting minutes and resolution implementation tracking of ESG-related committees (such as the Safety Production Committee and the Environmental Protection Committee) within the month through enterprise management information systems (such as OA and ERP); collect the issues found and rectification records regarding compliance operations in internal audit reports.

[0056] Operational data: The daily dock / slippage occupancy, daily total man-hours for each key process (such as section welding and equipment installation), and daily processing volume of steel pretreatment line are collected through the Manufacturing Execution System (MES).

[0057] Step 2: Based on operational data, calculate the comprehensive production load index according to preset time slices, serving as a benchmark parameter reflecting the operational intensity of the enterprise within the preset time slices. As one implementation method, the comprehensive production load index is obtained by weighting the normalized data on dock or slipway occupancy, key process time input, and main material handling volume.

[0058] For example, a time slice of 1 day is set to process the daily operational data:

[0059] Normalization: Divide the daily dock occupancy, total working hours, and steel processing volume by the maximum value within the evaluation period to obtain three dimensionless values ​​between 0 and 1.

[0060] Weighted calculation: Based on the production characteristics of Company A, the three indicators are assigned weights of 0.4, 0.4, and 0.2 respectively, and then the weighted sum is calculated to obtain the comprehensive production load index.

[0061] Step 3: Convert environmental data, social responsibility data, and corporate governance data into standard evaluation units and generate ESG vector data corresponding to preset time slices. Specific operations include:

[0062] Environmental data is converted into a unified environmental load assessment unit using a carbon emission factor conversion method, forming an environmental load vector.

[0063] Social responsibility data is converted into social performance evaluation units through a target benchmark normalization method to form a social performance vector.

[0064] Corporate governance data is converted into governance performance evaluation units using a system implementation rate conversion method, forming a governance performance vector.

[0065] The environmental load vector, social performance vector, and governance performance vector are matched and integrated according to a preset time slice to form ESG vector data.

[0066] For example, the transformation of environmental data is performed as follows:

[0067] For electricity consumption and fuel consumption in production, the equivalent carbon emissions are first calculated based on the carbon emission factors of the corresponding energy types. For example:

[0068] Equivalent carbon emissions = Electricity consumption × Electricity carbon emission factor + Material consumption × Fuel carbon emission factor

[0069] The carbon emission factor may be a standard factor published by the state or industry.

[0070] For the amount of solid waste generated, corresponding environmental impact coefficients can be assigned according to the treatment methods of different types of waste, and these coefficients can be converted into equivalent environmental load values.

[0071] Then, the equivalent carbon emissions and equivalent environmental load values ​​obtained above are normalized according to the unit production load, for example:

[0072] Unit load environmental intensity = Equivalent environmental load value / Daily comprehensive production load index

[0073] Then, by comparing it with the preset benchmark value, it is converted into a dimensionless environmental load evaluation value in the range of 0 to 1 using a proportional mapping method. Multiple evaluation values ​​constitute an environmental load vector.

[0074] For example, the transformation of social responsibility data is carried out as follows:

[0075] Regarding the incidence of workplace accidents:

[0076] Work injury safety score = 1 − (actual work injury incidence rate / industry warning threshold)

[0077] When the actual value is below the industry warning threshold, the score is close to 1; when it exceeds the threshold, the score drops.

[0078] For safety training coverage, it can be directly converted into a dimensionless value as a percentage:

[0079] Training score = Actual training coverage rate / 100

[0080] For employment compliance information, it can be calculated based on the ratio of the number of compliance inspections to the total number of inspections:

[0081] Compliance score = Number of compliance visits / Total number of inspections

[0082] The above scores are combined to form a social performance vector.

[0083] For example, the transformation of corporate governance data is carried out as follows:

[0084] Regarding the implementation of ESG-related policies, the implementation rate can be calculated based on the number of times the policy has been implemented and the number of planned implementations.

[0085] System implementation score = Number of actual implementations / Number of planned implementations

[0086] For internal audit or compliance incident rectification status, calculations can be made based on the rectification completion rate:

[0087] Rectification score = Number of rectified issues / Total number of issues found

[0088] The above indicators are converted into dimensionless governance performance evaluation values ​​in the range of 0 to 1 through normalization or proportional mapping. Multiple evaluation values ​​constitute a governance performance vector.

[0089] Step 4: Match the corresponding ESG assessment parameters in the preset ESG assessment parameter library according to the shipbuilding company's scale, ship type structure or production organization method.

[0090] If no match is found, initial ESG evaluation parameters are generated based on the statistical characteristics of the state baseline parameters. Then, based on the state baseline parameters and ESG vector data, an optimization algorithm is used to adaptively calibrate the ESG evaluation parameters. Specifically, this involves: constructing a theoretical expectation model based on the state baseline parameters and corresponding ESG vector data within historical time slices, using the state baseline parameters as input, the theoretical expected value of the ESG vector data as output, and the initial ESG evaluation parameters as model coefficients; using the initial ESG evaluation parameters as the starting point for iteratively adjusting the model coefficients, with the deviation between the expected value of the ESG vector data output by the theoretical expectation model and the actual value of the ESG vector data as the optimization objective, to determine the calibrated ESG evaluation parameters.

[0091] If a match is found, the matched ESG evaluation parameters will be used directly.

[0092] For example, initial ESG evaluation parameters can be generated using the following method:

[0093] (1) Adopt the industry average parameters of enterprises of similar size and similar ship type;

[0094] (2) Based on the company’s own historical data, the mean relationship between the state baseline parameters and the ESG vector data is fitted by linear regression, and the regression coefficient is used as the initial ESG assessment parameter.

[0095] (3) Set a preset default parameter set (such as α=1.0, β=1.0, γ=0.1) as the starting point for iterative optimization.

[0096] Step 5: Based on ESG vector data, state baseline parameters, and calibrated ESG assessment parameters, calculate the governance efficiency index for each time slice, and calculate the comprehensive ESG governance efficiency coefficient based on the governance efficiency index. Specific operations include:

[0097] Using ESG assessment parameters or calibrated ESG assessment parameters as model coefficients for the theoretical expectation model, the state baseline parameters within each preset time slice are input into the theoretical expectation model, and the theoretical expected values ​​of ESG vector data within each preset time slice are output. The theoretical expected values ​​of ESG vector data are compared with the actual ESG vector data within the corresponding time slice to obtain a governance efficiency index that characterizes the ESG governance effect within that time slice.

[0098] The governance efficiency indicators within multiple preset time slices are time-weighted to calculate the comprehensive ESG governance efficiency coefficient.

[0099] Step 6: Determine the ESG governance level based on the comprehensive ESG governance efficiency coefficient and generate an assessment report.

[0100] For example, after obtaining the comprehensive ESG governance efficiency coefficient in step S5, the ESG governance level is determined according to the following rules: D-ESG≥1.2 is Grade A (Excellent), 1.0≤D-ESG<1.2 is Grade B (Good), 0.8≤D-ESG<1.0 is Grade C (Compliant), and D-ESG<0.8 is Grade D (Needs Improvement), where D-ESG represents the comprehensive ESG governance efficiency coefficient.

[0101] In some embodiments, generating an evaluation report includes:

[0102] Trend Analysis Chart: The chart displays the curves of changes in the comprehensive production load index and governance efficiency indicators (including environmental efficiency indicators, social efficiency indicators, and governance efficiency indicators) over 30 days, visually revealing whether peak production days are accompanied by a decline in ESG efficiency.

[0103] Relevant summary: The text states that "during the assessment period from day 12 to day 15 (the peak docking period), production load increased by 35%, and the environmental efficiency index dropped to 0.85. It is recommended to check the operation status of energy dispatch and emission reduction facilities during this period."

[0104] Weakness suggestion: List the five time slices with the lowest scores across the three dimensions and their corresponding major production activities.

[0105] Conclusions and Recommendations: A final rating is given, and targeted management improvement suggestions are proposed based on the analysis.

[0106] This invention, through the introduction of a comprehensive production load index calculated on a time-slice basis, dynamically links ESG performance with the real-time operational intensity of enterprises, achieving dynamic and refined ESG assessment. By synchronously collecting and processing multi-source ESG data and operational data, and constructing a theoretical expectation model using adaptively calibrated ESG assessment parameters, data silos are broken down. Through parameter matching and adaptive calibration mechanisms, this invention can self-adjust according to the size, ship type, and production mode of different enterprises, avoiding the bias of a one-size-fits-all assessment. The final output comprehensive ESG governance efficiency coefficient and its derived governance efficiency indicators, combined with trend and correlation analysis in the visualization report, can directly and specifically point out the time period and production link where management shortcomings are located, transforming abstract ESG levels into locatable, executable, and verifiable improvement instructions, greatly enhancing the guiding role and practical value of the assessment results in improving the actual governance level of enterprises.

Claims

1. An ESG-based shipbuilding enterprise comprehensive management evaluation method, characterized in that: Includes the following steps: Step 1: Collect multi-source ESG data and operational data from shipbuilding companies during the assessment period. The multi-source ESG data includes environmental data reflecting the company's environmental protection intensity, social responsibility data reflecting the company's employee work safety, and corporate governance data reflecting the company's governance intensity. The operational data is data reflecting the company's operational intensity. Step 2: Based on the operational data, calculate the comprehensive production load index according to the preset time slice, which serves as a state benchmark parameter reflecting the operational intensity of the enterprise within the preset time slice; Step 3: Convert the environmental data, social responsibility data, and corporate governance data into standard evaluation units and form ESG vector data corresponding to the preset time slice; Step 4: Based on the enterprise size, ship type structure, or production organization method, match the corresponding ESG assessment parameters in the preset ESG assessment parameter library; if no match is found, generate initial ESG assessment parameters based on the statistical characteristics of the state baseline parameters, and adaptively calibrate the initial ESG assessment parameters based on the state baseline parameters and ESG vector data through an optimization algorithm to obtain calibrated ESG assessment parameters. Step 5: Based on ESG vector data, state baseline parameters, and ESG assessment parameters / calibrated ESG assessment parameters, calculate the governance efficiency index for each time slice, and calculate the comprehensive ESG governance efficiency coefficient based on the governance efficiency index for each time slice. Step 6: Obtain the ESG governance assessment results based on the comprehensive ESG governance efficiency coefficient.

2. The ESG-based shipbuilding enterprise integrated management evaluation method according to claim 1, characterized in that: The environmental data includes: electricity consumption, fuel consumption, equivalent carbon emissions from operations, and solid waste generation. The social responsibility data includes: workplace accident rate, safety training coverage rate, and employment compliance information; The corporate governance data includes: the implementation status of ESG-related governance systems, and records of internal audits or compliance incidents; The operational data includes: the occupancy status of the dock or slipway, the working hours of key processes, and the amount of major materials handled.

3. The ESG-based shipbuilding enterprise integrated management evaluation method according to claim 2, characterized in that: Based on the aforementioned operational data, a comprehensive production load index is calculated according to preset time slices, including: The comprehensive production load index is obtained by normalizing the occupancy status of the dock or slipway, the working hours of key processes, and the handling volume of major materials, and then performing a weighted calculation.

4. The ESG-based shipbuilding enterprise integrated management evaluation method according to claim 1, characterized in that: The process of converting the environmental data, social responsibility data, and corporate governance data into standard evaluation units and forming ESG vector data corresponding to preset time slices includes: Environmental data is converted into a unified environmental load assessment unit using a carbon emission factor conversion method, forming an environmental load vector. Social responsibility data is converted into social performance evaluation units through a target benchmark normalization method to form a social performance vector. Corporate governance data is converted into governance performance evaluation units using a system implementation rate conversion method, forming a governance performance vector. The environmental load vector, social performance vector, and governance performance vector are matched and integrated according to a preset time slice to form ESG vector data.

5. The ESG-based shipbuilding enterprise integrated management evaluation method according to claim 1, characterized in that: Initial ESG evaluation parameters are generated based on the statistical characteristics of the state baseline parameters. Based on the state baseline parameters and ESG vector data, the initial ESG evaluation parameters are adaptively calibrated using an optimization algorithm to obtain calibrated ESG evaluation parameters. Specifically, this includes: constructing a theoretical expectation model based on the state baseline parameters and corresponding ESG vector data within historical time slices, with the state baseline parameters as input, the theoretical expected value of the ESG vector data as output, and the initial ESG evaluation parameters as model coefficients. The initial ESG evaluation parameters are used as the starting point for iterative model coefficients. The deviation between the expected value of the ESG vector data output by the theoretically expected model and the actual value of the ESG vector data is used as the optimization objective. The initial ESG evaluation parameters are iteratively adjusted to determine the calibrated ESG evaluation parameters.

6. The ESG-based shipbuilding enterprise integrated management evaluation method according to claim 1, characterized in that: The process involves calculating governance efficiency indices for each time slice based on ESG vector data, state baseline parameters, and ESG assessment parameters. A comprehensive ESG governance efficiency coefficient is then calculated based on these indices. Specifically, this includes: Using ESG assessment parameters or calibrated ESG assessment parameters as model coefficients of the theoretical expectation model, the state reference parameters in each preset time slice are input into the theoretical expectation model, and the theoretical expectation value of ESG vector data in each preset time slice is output. By comparing the theoretical expected value of ESG vector data with the actual value of ESG vector data in the corresponding time slice, a governance efficiency index characterizing the ESG governance effect in that time slice is obtained. The governance efficiency indicators within multiple preset time slices are time-weighted to calculate the comprehensive ESG governance efficiency coefficient.

7. An electronic device, comprising: The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the ESG-based shipbuilding enterprise integrated governance assessment method as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the ESG-based shipbuilding enterprise integrated governance assessment method as described in any one of claims 1-6.

9. A computer program product, characterised in that, The computer program product includes a computer program that, when executed by a processor, implements the ESG-based comprehensive governance assessment method for shipbuilding enterprises as described in any one of claims 1-6.

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