Enterprise esg report accounting and green credit linkage method

Abstract

The application discloses an enterprise ESG report accounting and green credit linkage method, and the linkage method comprises the following steps: focusing on two major leading industries of new energy storage and new energy vehicles, relying on the existing intelligent energy and carbon emission management platform, carbon account scoring system and enterprise supply and demand docking platform infrastructure in the park; constructing a regional customized ESG rating technical system + data-fund-supply chain-talent 'four chain fusion' green financial linkage technical scheme of government-bank-enterprise cooperation; the depth fusion of green finance and environmental management is realized through parameterization algorithm design and digital technology. Develop an industry differentiation segmented function, realize dynamic adjustment of the weight with the capacity and supporting scale, adapt to the characteristics of the two major leading industries, and eliminate subjective bias.

Description

Technical Field

[0001] This invention relates to the field of digital integration of green finance and environmental management, and in particular to a method for linking corporate ESG reporting and accounting with green credit. Background Technology

[0002] Current ESG (Environmental, Social, and Governance) related assessment and application technologies mainly revolve around three core aspects: assessment system construction, data collection, and financial linkage. Typical implementation paths in the industry are as follows: ESG Evaluation System Construction: Primarily based on a general framework, but with weak industry adaptability. Internationally recognized standards are the guiding principle. Major global ESG rating agencies (such as MSCI, Sustainalytics, and FTSE Russell) all build a general framework based on the three dimensions of "Environmental, Social, and Governance" (ESG). For example, the MSCI ESG rating covers 10 themes and 37 key indicators, including "Emissions and Climate Change, Natural Resources, and Pollution and Waste." It adopts an "industry adjustment + issue weighting" model, increasing the weight of the "carbon emission management" indicator for energy-intensive industries (such as steel and chemicals) and the weight of the "governance compliance" indicator for the financial industry. However, it does not design specific indicators for specific regional industrial clusters (such as new energy vehicles and new energy storage industries).

[0003] Domestic policy-oriented system: Domestic local governments and industrial parks often refer to policy documents such as the "Key Assessment Indicators for the Implementation of Green Credit" and the "Guidelines for Corporate ESG Disclosure" to construct evaluation models centered on "environmental compliance + financial relevance." For example, the ESG evaluations of some industrial parks in Guangdong Province (such as Guangzhou Nansha Economic and Technological Development Zone) focus on quantitative indicators such as "carbon emissions per unit of output, environmental violation records, and R&D investment ratio," and adopt a scoring method of "basic score + bonus and penalty items," without incorporating the characteristic transformation needs of the region's leading industries (such as marine engineering equipment and biomedicine). Although Huadu District's Zero-Carbon Smart Industrial Park requires resident companies to meet the German Rheinland Net Zero Carbon Standard, it only targets environmental dimensions such as "green building certification and photovoltaic coverage," without covering social and governance dimensions such as "green supply chain collaboration and employee digital transformation training."

[0004] Data collection: primarily from a single source, with insufficient cross-platform collaboration. Environmental data: Reliance on dedicated monitoring platforms: The industry generally collects environmental data through independent energy monitoring or carbon emission management platforms. For example, most industrial parks' "smart energy platforms" only cover energy consumption data such as electricity, heat, and gas, calculating carbon emissions using the "emission factor method." Data sources are limited to the energy supply side and are not linked to enterprise production process data (such as capacity load and equipment energy efficiency) or supply chain data (such as the carbon footprint of upstream raw materials). A typical example is a carbon account system in a high-tech zone, which only accesses enterprise natural gas, steam, and electricity consumption data; coal and oil consumption data are not included in the calculation due to the "difficulty in collecting" of such data.

[0005] Social and Governance Data: Data is scattered across multiple departments with low integration: Social data such as "employment numbers and employee training" largely rely on statistical reports from the human resources and social security department, while data on "R&D investment and patent numbers" comes from filing information from the science and technology department. Governance data such as "compliance records and digital transformation progress" are scattered across the systems of market supervision and industry and information technology departments. A unified data collection portal has not yet been established within the industry. For example, companies in a certain industrial park need to submit ESG-related data to the "Smart Energy Platform," "Enterprise Supply and Demand Matching Platform," and "Government Service Network" separately, resulting in issues of "duplicate submissions and inconsistent data definitions." Furthermore, the data update cycle is mostly "quarterly or annually," failing to meet the needs of dynamic evaluation.

[0006] Financial linkage: primarily policy incentives, with limited market-based tools. Loan interest rates are often linked to basic ESG indicators: Many domestic banking institutions simply link ESG evaluation results to loan interest rates, for example, offering a 0-10bp interest rate reduction to companies that are "environmentally compliant and have no illegal records," and increasing loan amounts for companies with high ESG ratings (such as MSCIAA). However, this fails to create a closed loop of "evaluation-financing-transformation." For instance, a state-owned bank's green loan product only uses "whether the company has obtained green factory certification" as the basis for interest rate discounts, without adjusting policies based on the company's dynamic ESG improvement, and without linking it to tools such as fiscal subsidies and special relending.

[0007] Supply chain finance lacks integration of ESG dimensions: Existing supply chain finance products (such as accounts receivable financing and order loans) mainly rely on the credit endorsement of core enterprises, failing to incorporate the ESG performance of upstream and downstream SMEs into the credit enhancement system. For example, a "chain loan product" in an automotive industrial park determines the loan amount solely based on the payment commitment of the core enterprise (such as the vehicle manufacturer), without considering ESG indicators such as the "green production ratio and employee rights protection" of upstream component companies, thus failing to promote the overall ESG level improvement of the industrial chain through financial instruments. Summary of the Invention

[0008] In view of the above problems, the present invention is proposed to provide a method for linking corporate ESG reporting and green credit to overcome or at least partially solve the above problems.

[0009] According to one aspect of the present invention, a method for linking corporate ESG reporting and green credit is provided, the method comprising: Focusing on the two leading industries of new energy storage and new energy vehicles, and relying on the park's existing infrastructure such as the smart energy and carbon emission management platform, carbon account scoring system, and enterprise supply and demand matching platform; Construct a regionally customized ESG rating technology system that integrates government, banks, and enterprises, along with a green finance linkage technology solution that integrates data, capital, supply chain, and talent ("four chains"). Achieving deep integration of green finance and environmental management through parametric algorithm design and digital technology.

[0010] Optionally, the focus on the two leading industries of new energy storage and new energy vehicles, relying on the park's existing smart energy and carbon emission management platform, carbon account scoring system, and enterprise supply and demand matching platform infrastructure, specifically includes: A rating technology system is constructed with "industry-adaptive parameter model + dynamic algorithm" as its core. Using "multi-source data interface integration + quality inspection algorithm" as the link, data collaboration technology is adopted to solve data fragmentation; With the "rating-credit parameter linkage model" as the core, and using financial linkage technology, a green finance closed loop is constructed.

[0011] Optionally, the construction of the rating technology system based on "industry-adaptive parameter model + dynamic algorithm" specifically includes: Basic weight calculation technology: The judgment matrix is ​​constructed using the hierarchical analysis algorithm. Three experts from environmental science, new energy industry and finance are invited to conduct pairwise importance comparisons of the E / S / G dimensions and their subordinate indicators. The weight vector is calculated by the eigenvalue decomposition method, and the basic weight parameters are output. Industry-differentiated weight adjustment technology: Designing piecewise functions to achieve dynamic weight adaptation based on the characteristics of the new energy storage and new energy vehicle industries; Standardization techniques for quantitative indicators: Design standardized algorithms based on the 3σ principle for quantitative indicators such as "carbon emissions per unit of revenue" and "R&D intensity"; If the index value is ≤50%μ, 100 points are awarded; If 50%μ < index value ≤ 100%μ, the score is 100 - (compliance rate - 0.5) × 80. If the index value = 100%μ, you get 60 points; If 100%μ < index value ≤ 150%μ, the score is 20 + (1.5 - benchmarking rate) × 80. If the index value is >150%μ, 20 points are awarded; Among them, the benchmarking rate = enterprise indicator value / industry benchmark value, which realizes the objectification and technical processing of indicator scoring; Qualitative indicator parameter mapping technology: Transform qualitative indicators such as "Certification of High-Quality SMEs" and "Certification of Green Factories" into computable parameters.

[0012] Optionally, the method of using "multi-source data interface integration + quality inspection algorithm" as a link and employing data collaboration technology to overcome data fragmentation specifically includes: Multi-source data interface integration technology: Develop standardized API interfaces to achieve real-time connection with carbon account platforms, China Central Bank accounts receivable platforms, and the Ministry of Industry and Information Technology's digital transformation system. The interfaces use JSON format for transmission, and the data update frequency is set differently according to the indicator type. Data compatibility is ensured through data format verification algorithms. The remaining 10% of supplementary data is integrated with government data from the Ministry of Industry and Information Technology, the Ministry of Science and Technology, and the Ministry of Human Resources and Social Security through the High-tech Zone's big data platform. Data anomaly detection and repair technology: For volatile indicators such as "carbon emissions per unit of tax revenue" and "employment growth rate", an anomaly detection algorithm based on box plots was developed. After the system automatically marks the anomaly, combined with the information that the production process has not changed, it is determined to be a data entry error, and the average value of the previous 3 months is called to repair it. Data fluctuation smoothing technology: In response to the characteristic of the new energy vehicle industry that "quarterly revenue fluctuations exceed 40%", a dynamic 12-month moving average algorithm was developed to process cyclical indicators such as tax and profit. The algorithm formula is: monthly smoothed value = (sum of actual values ​​of the past 12 months) / 12. If data for a certain month is missing, it is supplemented by the average of the next 3 months. Among them, profit is updated quarterly data, and the monthly data of the moving average is estimated by dynamically calculating the profit data of the previous four quarters. Data security transmission technology: We developed a role-based access control algorithm to assign data access permission parameters to banks and enterprises respectively. We intercepted unauthorized access in real time through permission verification algorithms. At the same time, we collaborated with credit reporting agencies and Xingwang Data Co., Ltd. of Zhaoqing High-tech Zone to form the first batch of enterprise ESG credit reports in the province, which were incorporated into the enterprise credit reporting system.

[0013] Optionally, the construction of a green finance closed loop, centered on a "rating-credit parameter linkage model" and employing financial linkage technology, specifically includes: Interest rate parameter linkage algorithm: Establishing ESG rating; Dynamic loan limit calculation model: Design a limit calculation algorithm based on ESG rating and enterprise operating data. The formula is: Credit limit = (Previous year's revenue × Revenue coefficient) + (ESG rating score × Rating coefficient). De-core blockchain lending data sharing technology: Develop a supply chain data interface platform, where core enterprises share upstream enterprise transaction data such as "purchase amount and payment cycle" with banks through API interfaces. The system develops a transaction authenticity verification algorithm and uses the ESG rating score of SMEs as a credit enhancement parameter, embedding it into the bank's credit granting model to achieve full automation of the "data sharing - automatic credit enhancement - credit limit approval" process without the need for core enterprise guarantees. Talent training effectiveness feedback algorithm: For "digital fairness transformation loans", a quantitative algorithm for training effectiveness has been developed. The formula is: Training effectiveness score = (number of certified persons / total number of trainees) × 100 × certificate weight; When the score is ≥ 80 points, the system automatically triggers the loan amount increase algorithm: increase amount = original amount × 10%, and the score is simultaneously included in the enterprise's ESG "personnel transformation" indicator parameters to realize the technical closed loop of "training-rating-credit".

[0014] Optionally, the linkage method further includes: Multi-platform collaborative ESG data governance; Data collection implementation: Targeted collection according to the "90%+10%" rule: 90% of environmental data: Automatically captures enterprises' natural gas / steam / electricity consumption from the carbon account platform, and calculates direct / indirect carbon emissions using the emission factor method; the remaining 10% is supplemented from the government affairs system of the High-tech Zone's ecological and environmental department. 90% of the social data comes from supply and demand matching platforms, which provide data on enterprise supply chain transaction volume and platform activity; and from the China Central Depository & Clearing Co., Ltd. (CCDC) accounts receivable platform, which provides data on accounts payable ratio. The remaining 10% is extracted from the science and technology department's filing system. Governance data: Obtain the transformation progress from the "Digital Transformation Enterprise Directory" of the Ministry of Industry and Information Technology, and obtain the "Installation Status of Smart Energy IoT Sensing Equipment" from the smart energy platform; Data processing and quality control: For indicators such as tax revenue, income, profit, and employment that are affected by seasonal fluctuations, a "rolling 12-month average" is used to smooth out short-term fluctuations, with quarterly profit estimation being a key step.

[0015] Optionally, the quarterly profit estimate specifically includes: The process adopts a four-step approach: "data collection → rule determination → allocation calculation → verification and update". Data collection: Obtain profit data for the company for four consecutive quarters, including the current quarter and the previous three quarters, to ensure a complete period; The allocation rules are determined as follows: Standard rule: If a company does not have significant monthly profit fluctuations, the profit is averaged out to the current month as "quarterly profit ÷ 3"; Special rule: If the company's historical data shows that there are peak and off-peak seasons within a quarter, the allocation will be weighted according to the historical monthly proportion. The weighting needs to be jointly confirmed and filed by the high-tech zone's industry and information technology department and the company. Monthly profit calculation: The quarterly profit is allocated according to the established rules to obtain the estimated monthly profit.

[0016] This invention provides a method for linking corporate ESG reporting and green credit. The method includes: focusing on two leading industries—new energy storage and new energy vehicles—and leveraging existing infrastructure in industrial parks such as smart energy and carbon emission management platforms, carbon account scoring systems, and enterprise supply and demand matching platforms; constructing a regionally customized ESG rating technology system integrating government, banks, and enterprises, along with a green finance linkage solution that integrates data, capital, supply chain, and talent ("four chains"). It also achieves deep integration of green finance and environmental management through parametric algorithm design and digital technology. Furthermore, it develops industry-differentiated piecewise functions to dynamically adjust weights based on production capacity and supporting scale, adapting to the characteristics of the two leading industries and eliminating subjective bias.

[0017] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a method for linking corporate ESG reporting and green credit, provided as an embodiment of the present invention; Figure 2 Flowchart for constructing an ESG rating system for enterprises in Zhaoqing High-tech Zone, provided in this embodiment of the invention. Figure 3 A flowchart illustrating the "four-chain integration" green finance linkage process in Zhaoqing High-tech Zone, provided as an embodiment of the present invention; Figure 4 A flowchart illustrating the application of the analytic hierarchy process (AHP) in assigning weights to ESG indicators, as provided in this embodiment of the invention. Detailed Implementation

[0020] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0021] The terms "comprising" and "having," and any variations thereof, in the specification, embodiments, claims, and drawings of this invention are intended to cover non-exclusive inclusion, such as including a series of steps or units.

[0022] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0023] like Figure 1 As shown, a method for linking corporate ESG reporting and green credit is proposed. This method includes: focusing on the two leading industries of new energy storage and new energy vehicles; relying on the existing infrastructure of the park's smart energy and carbon emission management platform, carbon account scoring system, and enterprise supply and demand matching platform; constructing a regionally customized ESG rating technology system integrating government, banks, and enterprises, along with a green finance linkage technology solution that integrates data, capital, supply chain, and talent ("four chains"); and achieving deep integration of green finance and environmental management through parametric algorithm design and digital technology.

[0024] This invention relies on the existing infrastructure and policy resources of Zhaoqing High-tech Zone, and is primarily applied to the following technological products and industrialization projects. All application scenarios are supported by parametric algorithms and digital systems, ensuring consistency with the attached content: (I) Core Application Product 1: Zhaoqing High-tech Zone Enterprise ESG Parametric Rating System V1.0 Product positioning: An ESG rating technology carrier shared by government, banks and enterprises, integrating multi-source data interfaces, parameterized calculation models and result output modules, and connecting with the park's existing smart energy and carbon emission management platform and carbon account system to achieve full-process automation of "data input-algorithm calculation-rating output"; Core technology modules: Data interface module: Built-in standardized API interface (compatible with 8 types of systems including carbon account platform, China Central Bank accounts receivable platform, and Ministry of Industry and Information Technology digital transformation system), supports automatic data capture and format verification, and the interface response time is ≤1 second; Parameter calculation module: Equipped with hierarchical analysis algorithm (weight calculation), 3σ standardization algorithm (quantitative indicator scoring), and mapping algorithm (qualitative indicator quantification), the industry segmentation function parameters (such as the upper limit of weight for the energy storage industry and parameters for the new energy vehicle supply chain) can be adjusted through backend configuration, and the calculation time is ≤5 minutes per enterprise; Results output module: Automatically generates ESG rating reports (including E / S / G sub-dimensional parameters, weight adjustment records, and abnormal data repair instructions), supports PDF export and API integration with bank credit systems, and the report data can be traced back to the original data source (such as carbon emission data from the carbon account platform and transaction data from the China Central Bank Accounts Receivable Platform). Application scope: In 2024, the pilot program covered Ruiqing Times, a certain enterprise, and 50 large-scale supporting enterprises in the park, including 12 A-level enterprises and 23 B-level enterprises. The rating results serve as the core technical basis for government industrial support (such as digital transformation subsidies) and bank credit approval.

[0025] (II) Core Application Product 2: Zhaoqing High-tech Zone "Four-Chain Integration" Green Finance Linkage System V1.0 1. Parameterized Management Module for ESG Special Deposits Technical Functions: Develop an algorithm for targeted allocation of deposit funds, with the formula: Percentage of funds allocated to energy storage projects = (Average ESG rating of energy storage companies / Average ESG rating of companies in the industrial park) × 60% + 40%, ensuring that funds are tilted towards companies with high ratings; the system monitors fund flows in real time and automatically issues a warning when the deviation exceeds 5%; Application scenario: ESG structured deposits issued by local legal entity banks such as Zhaoqing Rural Commercial Bank, with the first issuance of RMB 500 million in 2024, accounted for 72% of the funds invested in Ruiqing Times' green production technology transformation project for energy storage batteries, which led to an increase of 2GWh in the company's energy storage battery production capacity and a decrease in carbon emission intensity of 18%.

[0026] 2. Automatic Approval Module for De-core Blockchain Loans Technical features: Integrates supply chain data verification algorithm (transaction authenticity matching degree ≥95%) and ESG rating credit enhancement algorithm (rating score × 500,000 = credit enhancement limit), automatically generates credit limit and interest rate, and shortens approval time from 3 days to 2 hours; Application Case: A new energy component company in Zhaoqing (ESG rating B, score 72 points) obtained an 8 million yuan loan through this module. The system automatically calculated the interest rate = 4.5% - (3 × 10bp) = 4.2% (0.8 percentage points lower than the traditional loan rate), which was used for green production technology upgrades. Six months later, the company's ESG rating improved to A (score 81 points), and the system automatically triggered a reduction in the interest rate to 3.8%, increasing the loan amount to 11.2 million yuan (original amount 8 million yuan × 1.4).

[0027] 3. Digital Fairness Transformation Loan Feedback Module Technical Functions: Develop training effectiveness calculation algorithms and quota addition algorithms, receive certificate data (such as digital skills certificates and carbon emission accounting certificates) from the Guangdong Provincial New Generation Information Technology Industry Talent Training Base in real time, automatically update the enterprise's ESG "personnel transformation" indicator score, and trigger quota adjustments; Application results: In 2024, a total of RMB 50 million in loans were issued, covering 20 companies including Leoch Power. The system automatically identified 15 companies with training effectiveness scores ≥ 80 points, and issued an additional loan of RMB 5 million. The average score of the companies' "personnel transformation" indicator increased by 20 points, and the average ESG comprehensive rating increased by 0.5 levels.

[0028] (III) Core Application Project 1: Zhaoqing High-tech Zone National Carbon Peaking Pilot Park Construction Project Project goals: By 2024, the energy storage industry output value will exceed 30 billion yuan, the new energy vehicle industry will move towards a cluster of hundreds of billions of yuan, the carbon emission intensity of the park will decrease by 18% year-on-year, and the park will be built into the only provincial-level new energy storage characteristic industrial park in Guangdong Province. Technology application scenarios: Dynamic carbon emission monitoring: The ESG parametric rating system captures enterprise carbon emission data in real time. Through anomaly detection algorithms, it identifies an aluminum processing enterprise with a "sudden increase of 3 times in carbon emissions per unit of revenue" (exceeding the industry average). It automatically pushes an alert to the Zhaoqing High-tech Zone Management Committee. Combined with the technology upgrade subsidy algorithm (subsidy amount = emission reduction × 200 yuan / ton CO2), it matches the enterprise with a subsidy of 2 million yuan for equipment upgrades. Three months later, the enterprise's carbon emission intensity dropped from 0.35 tons / 10,000 yuan to 0.09 tons / 10,000 yuan, reaching the advanced level in the industry. Precise allocation of green credit: Banks use the green finance linkage system to access Ruiqing Times' ESG rating parameters (A level, score 85 points), automatically calculating the special relending rate = 3.8% - (4 × 10bp) = 3.4% (benchmark rate 4.5%, relending subsidy 0.5%, ESG level discount 0.2%). After adding a 30% interest subsidy, the actual interest rate = 3.4% × 70% = 2.38%, and a loan of 100 million yuan is issued; the company's annual interest expense is reduced by 2.12 million yuan (42% saving compared to traditional loans), all of which is used for the research and development of low-carbon production processes for energy storage batteries; Supply chain collaborative transformation: Through the de-core chain lending module, the average ESG rating of 80 new energy vehicle component companies has been improved from C (score of 52 points) to B (score of 71 points), the proportion of green component supply has increased from 52% to 75%, and the supply chain delivery cycle has been shortened from 45 days to 30 days, helping a company achieve a 12% reduction in carbon emission intensity of vehicle production.

[0029] (iv) Core Application Project 2: Digital Transformation Project for Small and Medium-sized Enterprises in Zhaoqing High-tech Zone Project Background: Leveraging the resources of Zhaoqing City as a "Pilot City for Digital Transformation of Small and Medium-sized Enterprises in Guangdong Province," and coordinating 64 million yuan in special subsidies, the project aims to increase the digital transformation coverage rate of large-scale industrial enterprises in the park from 55% to 75%. Technology application scenarios: Parameterized subsidy calculation: The ESG parameterized rating system automatically extracts the enterprise's "digital transformation acceptance level" (Level 4 = 1.0, Level 3 = 0.8, Level 2 = 0.6) and ESG rating score, and calculates the subsidy amount using the subsidy algorithm: Technological transformation investment × 30% × (Acceptance level parameter × 0.5 + ESG rating score / 100 × 0.5); In 2024, a total of 21 million yuan in subsidies were distributed to 32 enterprises, driving enterprises to raise 150 million yuan in self-raised digital transformation funds, and increasing the proportion of digital equipment investment from 35% to 60%; Automatic Loan Interest Subsidy Allocation: The green finance linkage system links enterprise ESG rating parameters with interest subsidy policies. A-level enterprises automatically enjoy a 30% interest subsidy. The system calculates the interest subsidy amount in real time and pushes it to the Zhaoqing Municipal Bureau of Industry and Information Technology, shortening the interest subsidy arrival time from 15 days to 3 days. For example, if an A-level SME applies for a digital transformation loan of 5 million yuan with an interest rate of 4.0%, the system automatically calculates the interest subsidy amount = 5 million × 4.0% × 30% = 60,000 yuan, which is directly transferred to the enterprise's account. Transformation effectiveness technical assessment: Every six months, the system analyzes the correlation between ESG rating and digital equipment investment through a correlation algorithm. The results show that the proportion of digital equipment investment in A-level enterprises reaches 60%, which is 35 percentage points higher than that of C-level enterprises (25%). Digital production efficiency has increased by 40% compared with before the transformation, verifying the role of technical solutions in promoting the digital transformation of SMEs.

[0030] Key Takeaways: Technology for Building Customized ESG Rating Systems for Regional Industries Design of industry-adaptive indicators based on the three dimensions of "Environment-Society-Governance": Breaking through the limitations of existing general ESG indicators that lack regional industry characteristics, this initiative targets the two leading industries of Zhaoqing High-tech Zone: "new energy storage" and "new energy vehicles." In the environmental dimension, it pioneers four core indicators: "carbon emissions per unit of revenue," "carbon emissions per unit of tax revenue," "carbon emissions per unit of profit," and "carbon emissions per unit of social security employees" (directly related to the industry's green tax / revenue / profit / employment capacity). In the social dimension, it adds industry-related indicators such as "certification status of high-quality SMEs (adapted to innovative / specialized / small giant tiers)" and "enterprise green chain transformation status (provincial / national green factory / supply chain certification)." In the governance dimension, it focuses on "digital-green collaborative indicators for sustainable development (level 2 / 3 / 4 transformation and acceptance grades)" and "technological upgrading for sustainable development (number of intelligent / high-end / green / digital directions)," forming a customized indicator system of 3 primary, 5 secondary, and 15 tertiary indicators.

[0031] Analytic Hierarchy Process (AHP) and Dynamic Weighted Assignment Model: Unlike existing ESG methods that suffer from "rigid fixed weights and inability to evaluate companies with negative profits," this application utilizes "scientific weighting + dynamic adjustment + special algorithms" to adapt indicator weights to industry needs and cover all types of companies in its evaluation. Specifically: Analytic Hierarchy Process (AHP) Weighting: Balancing Generality and Industry Focus Basic weighting: Using the analytic hierarchy process (inviting three experts each from environmental science, new energy industry, and finance, constructing a judgment matrix by comparing importance pairwise, and verifying consistency with CR < 0.1), the basic weights for the three dimensions were determined as follows: Environment 40%, Society 40%, and Governance 20%. Within the governance dimension, digital-related indicators such as "digital construction" and "technological upgrading" account for over 80% of the weight, aligning with the park's "digital-green synergy" transformation strategy; in the environmental dimension, each of the four carbon emission indicators accounts for 25%, ensuring a balanced evaluation of green benefits.

[0032] Industry dynamic adjustments: Weighting differentiation rules are set based on the characteristics of the two leading industries. For the emerging energy storage industry: the weight of "carbon emissions per unit profit" in the environmental dimension has been increased from 25% to 30%, as the industry has low profit margins in the early stages and "green profit potential" needs to be the focus; at the same time, a new indicator of "energy storage battery recycling rate" (weight 10%) has been added in the governance dimension to adapt to the low-carbon requirements throughout the entire life cycle. New energy vehicle industry: The weight of "supply chain transformation" in the social dimension has been increased from 15% to 20%, because the industry relies on upstream and downstream collaboration (80 supporting enterprises), and the evaluation of "green supply chain management" needs to be strengthened; in the governance dimension, "the CNC rate of key processes" has been included in the "digital construction" indicator (accounting for 50%), which is in line with the needs of intelligent production.

[0033] Special algorithm for "carbon emission intensity per unit profit": "Adjustment based on maximum negative value" To address the issue of uncalculated indicators when corporate profits are negative (such as during the expansion period of energy storage companies or the off-season for component companies), an innovative "maximum negative value adjustment" algorithm is introduced to ensure evaluation continuity. The core logic and operation are as follows: Operational basis: Referring to the "data positiveization" principle of the "Guangdong Province Carbon Peak Carbon Accounting Guide", the maximum negative profit in the industry is used as the benchmark. By uniformly increasing the coefficient, the profits of all enterprises are turned into positive values, while the relative differences in profits are preserved. Core steps: Step 1: Collect the "dynamic 12-month moving average profit" of large-scale enterprises in the same industry, and filter out the largest negative value (denoted as Min_profit); Step 2: Calculate the adjustment factor K = 1 - Min_profit (to ensure that the maximum negative profit is adjusted to 1, avoiding interference from a value of 0). Step 3: Forward the profit of all enterprises according to the formula "Adjusted profit = Original profit + K"; Step 4: Recalculate "Unit Profit Carbon Emission Intensity = Total Carbon Emissions / Adjusted Profit". All indicators are positive and can be used for benchmarking.

[0034] Example Verification: Taking the new energy vehicle parts industry in Zhaoqing High-tech Zone in the first half of 2024 as an example, among the 10 sample companies, 3 had negative profits (the largest negative value was -5.84 million yuan). The adjustment coefficient K was 5.85 million yuan. After the adjustment, the profit of company 3 (original profit of -5.84 million yuan) became 10,000 yuan. The carbon emission intensity per unit profit = 4,545 tons / 10,000 yuan = 4,545.00 tons / 10,000 yuan. It can participate in the industry benchmarking score together with companies with positive profits (such as company 1 with an adjusted indicator of 6.24 tons / 10,000 yuan), which solves the problem of evaluation gap for companies with negative profits.

[0035] Key supporting technologies: A multi-platform collaborative ESG data governance system "90%+10%" multi-source collaborative data collection mechanism: Breaking away from the pain points of existing ESG data being "isolated across multiple platforms and repetitive reporting," a targeted data acquisition path has been established: 90% of environmental data is automatically captured from the existing carbon account platform in Zhaoqing High-tech Zone (natural gas / steam / electricity consumption and carbon emission data), 90% of social data is synchronized from the park's supply and demand matching platform and the People's Bank of China's accounts receivable platform (supply chain transaction data, employment / social security data), and the remaining 10% of supplementary data is integrated through the High-tech Zone's big data platform to integrate information from government departments (industry and information technology / human resources and social security / taxation), achieving "one-time collection and multi-terminal reuse," and improving data accuracy to over 95%.

[0036] Dynamic 12-month moving average data processing technique: To address the issue of seasonal fluctuations in corporate tax, revenue, profit, and employment indicators, an innovative "dynamic 12-month moving average" processing method is adopted (profit data is updated quarterly, and the monthly average is calculated using data from the previous four quarters). This avoids the evaluation bias caused by "static monthly / quarterly data" in existing technologies, ensuring the stability and comparability of ESG scores. At the same time, a dual quality control mechanism of "algorithm verification + manual review" is established to correct abnormal data (such as carbon emissions exceeding limits and false transaction data) in real time.

[0037] Core Application Technology: "One Household, Four Chains" ESG Financial Linkage Closed Loop ESG rating and credit dynamic linkage model: Breaking through the limitations of the existing "ESG is simply linked to interest rates", a four-dimensional linkage rule of "rating-credit limit-interest rate-interest subsidy" is established: the interest rate of digital greening loans for A-level enterprises is reduced by 30-40 basis points, the credit limit is increased by 40-50%, and can be combined with the 30% interest subsidy for digital transformation loans in Zhaoqing City; at the same time, differentiated preferential policies are set for different uses (digital green transformation / general use) to solve the problem of the disconnect between ESG incentives and enterprise transformation needs in the existing technology.

[0038] Supply chain ESG de-core enhancement technology: An innovative "core enterprise data sharing + SME ESG credit enhancement" model is introduced: core enterprises in the new energy vehicle / energy storage sector (such as a certain company or Ruiqing Times) share supply chain transaction data with banks. Upstream SMEs do not need credit endorsement from core enterprises; they can obtain "de-core-chain loans" simply by relying on their own ESG ratings. The loan amount is determined based on the ESG rating (AE level) (E level plus 0-10% to A level plus 40-50%), which solves the pain points of existing supply chain finance that "over-rely on the credit of core enterprises and make it difficult for SMEs to obtain financing."

[0039] Digital Fairness Transformation Loan Closed-Loop Design: In collaboration with the Guangdong Provincial New Generation Information Technology Industry Talent Training Base, a closed loop of "loan-training-certification-ESG improvement" has been designed: When applying for the loan, companies must commit to using the funds specifically for green skills training of their employees. The base provides free training and provides feedback on certification results (digitalization / green skills certificate acquisition rate). The bank dynamically adjusts the loan terms based on the training effectiveness and incorporates the training results into the company's ESG "personnel transformation" indicator score, forming a positive cycle of "talent development-ESG improvement-financing optimization," which is the first of its kind in China.

[0040] Basic protection technologies: ESG account level management and risk warning AE Level Dynamic Rating Labeling and Application Rules: Establish a five-level rating system: 80-100 points (A), 60-80 points (B), 40-60 points (C), 20-40 points (D), and 0-20 points (E). Clarify the policy benefits (such as priority access to financial subsidies and green channels for policy applications) and risk control measures (such as enhanced environmental compliance monitoring for E-level enterprises) corresponding to different levels, which is different from the existing situation of "only scoring without application" of ESG.

[0041] Dynamic point deduction mechanism for environmental violations: The innovative "tiered deduction + long-term bonus" rule is implemented: 2 points will be deducted for each environmental violation in the past year (capped at 20 points), and 0 points will be awarded for more than 10 violations; 5 points will be added for no violations in the past year, and 10 points will be added for no violations in the past 3 years. This solves the problem of "only deducting points for environmental violations once and lacking long-term incentives" in the existing technology, and promotes continuous compliance of enterprises. Example

[0042] Preliminary preparations: basic research and resource integration Policy and Industry Research: We reviewed national "dual-carbon" strategies, the "Implementation Plan for Carbon Peaking in Guangdong's Industrial Sector," and the "Application Guidelines for ESG Assessment and Credit Process Management of Corporate Financing Entities by Banking Financial Institutions," extracting core requirements such as environmental compliance, digital transformation, and green finance to form a policy basis list. We also conducted an industry survey of Zhaoqing High-tech Zone: focusing on two leading industries—new energy storage (Ruiqing Times) and new energy vehicles (a certain company and 80 supporting companies)—we collected basic information on corporate environmental management (carbon emissions, clean energy use), social responsibility (R&D investment, job creation), and governance structure (digital transformation progress, compliance records) to establish a corporate ESG characteristic database.

[0043] Integration with existing platform resources: Connect with existing infrastructure in the High-tech Zone: Sign data sharing agreements with the Smart Energy and Carbon Emission Management Platform (real-time monitoring of enterprise electricity / heat / gas consumption), the Carbon Account Scoring System (existing carbon emission intensity scoring mechanism), and the Enterprise Supply and Demand Matching Platform (supply chain transaction data), clarifying data interface standards and update frequency (real-time synchronization of environmental data, daily updates of social data, and weekly updates of governance data).

[0044] Core Component 1: Construction of a Regionally Customized ESG Indicator System Indicator pool selection and hierarchical design: Initial indicator pool construction: Ninety candidate indicators were selected from international standards (GRI, TCFD, ISO14064), domestic policy documents, and advanced park case studies, covering five primary topics: “Preface to the Report, Environment, Society, Governance and Postscript to the Report” (see “Initial Indicator Pool Table” in the document for details).

[0045] Region compatibility filtering: Remove indicators that are highly generic (such as "basic enterprise information") and have low relevance to the leading industry (such as "marine pollution control"), and retain the core indicators: Environment (E): Focusing on the secondary topic of "green transformation", it includes four quantitative indicators: carbon emissions per unit of revenue, carbon emissions per unit of tax revenue, carbon emissions per unit of profit, and carbon emissions per unit of social security personnel, as well as qualitative points for "environmental violations".

[0046] Society (S): Three sub-topics are set up: "Innovation-driven" (R&D investment intensity, number of valid patents, and certification of high-quality SMEs), "Supply chain transformation" (enterprise green chain transformation and use of supply chain-related platforms), and "Personnel transformation" (employment absorption and enterprise support for employee transformation).

[0047] Governance (G): Focusing on the secondary topic of "digital transformation", it includes three qualitative indicators: the status of digital construction for sustainable development, the status of technological upgrading for sustainable development, and the status of cooperation in digital transformation for sustainable development.

[0048] Assigning weights to indicators (application of the Analytic Hierarchy Process): Establish a hierarchical model: target layer (comprehensive evaluation of corporate ESG), criteria layer (three dimensions of E / S / G), and indicator layer (15 third-level indicators).

[0049] Construct a judgment matrix: Organize experts in environmental science, finance, and management to compare indicators at the same level in pairs according to their importance (e.g., "carbon emissions per unit of revenue" and "carbon emissions per unit of profit" are equally important, so they are assigned a value of 1; "certification status of high-quality SMEs" is more important than "number of valid patents", so it is assigned a value of 3) to form a judgment matrix.

[0050] Weight Calculation and Consistency Verification: The weights of each indicator are calculated using the "sum-product method" (environment 40%, society 40%, governance 20%, with each of the four tertiary indicators in the environmental dimension accounting for 25%, "innovation-driven", "supply chain transformation" and "personnel transformation" in the social dimension accounting for 10% / 15% / 15% respectively, and the three indicators in the governance dimension accounting for 40% / 40% / 20% respectively). Consistency is verified by CR<0.1 to ensure that the weights are scientific and reasonable.

[0051] Core Component Two: Multi-Platform Collaborative ESG Data Governance Data collection implementation Data is collected in a targeted manner according to the "90%+10%" rule: Environmental data (90%): The enterprise's natural gas / steam / electricity consumption is automatically captured from the carbon account platform, and direct / indirect carbon emissions are calculated using the emission factor method (refer to the "Guangdong Province Carbon Peak Carbon Accounting Guide"); the remaining 10% (such as "environmental violation records") is supplemented from the government affairs system of the High-tech Zone's ecological and environmental protection department.

[0052] Social data (90%): Enterprise supply chain transaction scale and platform activity are obtained from supply and demand matching platforms, and the proportion of accounts payable is obtained from the China Central Depository & Clearing Co., Ltd. accounts receivable platform; the remaining 10% (such as "R&D investment and number of valid patents") is extracted from the science and technology department's filing system.

[0053] Governance data: Obtain the transformation progress from the "Digital Transformation Enterprise Directory" of the Ministry of Industry and Information Technology, and obtain the "Installation Status of Smart Energy IoT Sensing Equipment" from the smart energy platform to ensure that the data source is traceable.

[0054] Data processing and quality control For indicators such as tax revenue, income, profit, and employment that are affected by seasonal fluctuations (e.g., the new energy vehicle industry's Q4 revenue accounted for over 40%), a "rolling 12-month average" is used to smooth out short-term fluctuations. Quarterly profit estimation is a key step, ensuring consistent data periods (all monthly). The specific operation is as follows: "Estimated estimate" operational basis Referring to the principle in "Enterprise Accounting Standard No. 32 - Interim Financial Reporting" that "interim financial data can be reasonably allocated to the month", and considering the actual situation of enterprises in Zhaoqing High-tech Zone (most small and medium-sized enterprises only submit profit statements to tax and statistics departments on a quarterly basis), in order to ensure the continuity and accuracy of the "dynamic 12-month moving average" calculation, it is allowed to convert quarterly profits into monthly data through "reasonable allocation". The core requirement is that "the estimated result is consistent with the enterprise's production pattern, and the deviation between the annual estimated total and the actual annual profit is ≤5%".

[0055] The specific operational process of "estimated calculation" The process adopts a four-step approach: "data collection → rule determination → allocation calculation → verification and update," balancing general applicability with industry-specific characteristics. Data collection: Obtain the company's profit data for four consecutive quarters (covering 12 months) including the current quarter and the previous three quarters, to ensure the completeness of the period (e.g., when calculating the moving average in July 2024, collect profits from Q3 2023 to Q2 2024). The allocation rules are determined as follows: Standard rule: If a company does not have significant monthly profit fluctuations (such as a new type of energy storage company with stable monthly production capacity during regular production), the profit is averaged out to the current month as "quarterly profit ÷ 3". Special rule: If the company's historical data shows that there are peak and off-peak seasons within a quarter (e.g., for new energy vehicle parts companies, Q4 profits account for 60% of the quarter due to year-end order rush), then the allocation will be weighted according to the historical monthly proportion (e.g., 1:2:3). The weighting needs to be jointly confirmed and filed by the high-tech zone's industry and information technology department and the company. Monthly profit calculation: The quarterly profit is allocated according to the established rules to obtain the estimated monthly profit; Verification and Update: Accuracy verification: The requirement is that "the sum of the estimated profits for four consecutive quarters equals the actual annual profit". A deviation exceeding 5% will trigger manual review (to verify whether there are any significant income or expenditure outside the quarter, such as government subsidies). Dynamic updates: When a company subsequently discloses its actual monthly profit (such as after an annual audit), the system automatically replaces the estimated data with the actual data to ensure the authenticity of the final data.

[0056] Specific examples of "estimated calculation" Taking the "dynamic 12-month moving average profit" of a new energy vehicle parts company (hereinafter referred to as "Company A") in Zhaoqing High-tech Zone in July 2024 as an example, the specific calculation process is as follows: I. Basic Data Company A discloses its profits quarterly. The actual profit data (unit: RMB 10,000) for the third quarter of 2023 (July-September) to the second quarter of 2024 (April-June) are as follows: Third quarter of 2023 (July-September): 3 million yuan Fourth quarter of 2023 (October-December): 6 million yuan (including year-end push) First quarter of 2024 (January-March): RMB 2.1 million (including Spring Festival) Second quarter of 2024 (April-June): 3.9 million yuan (regular production) II. Sharing Rules Different allocation rules are formulated based on the characteristics of profit fluctuations in each quarter: The third quarter of 2023 and the second quarter of 2024: No significant fluctuations were observed. The "average distribution" method was adopted, which means that the total quarterly profit was evenly distributed among the three months of the quarter.

[0057] Fourth quarter of 2023: Based on historical data, December accounts for 60% of the profit in this quarter. Therefore, the profit will be allocated according to a weight of "1:2:3", that is, the profit in October, November and December will be allocated according to the proportions of 1 / 6, 2 / 6 and 3 / 6 of the total profit, respectively.

[0058] First quarter of 2024: January includes the Spring Festival holiday and has the lowest profit. The profit will be allocated according to the weight of "2:1:2", that is, the profit in January, February and March will be allocated according to the proportions of 2 / 5, 1 / 5 and 2 / 5 of the total profit respectively.

[0059] III. Estimated Results Based on the above allocation rules, the estimated profit for each month and the calculation logic are as follows: (I) July-December 2023 July 2023: Estimated profit of 1 million yuan, calculated as total profit of 3 million yuan in the third quarter of 2023 ÷ 3 (average distribution).

[0060] August 2023: Estimated profit of 1 million yuan, calculated as total profit of 3 million yuan in the third quarter of 2023 ÷ 3 (average distribution).

[0061] September 2023: Estimated profit of 1 million yuan, calculated as total profit of 3 million yuan in the third quarter of 2023 ÷ 3 (average distribution).

[0062] October 2023: Estimated profit of RMB 1 million, calculated as total profit of RMB 6 million in the fourth quarter of 2023 × 1 / 6 ("1:2:3" weighting).

[0063] November 2023: Estimated profit of 2 million yuan, calculated as total profit of 6 million yuan in the fourth quarter of 2023 × 2 / 6 ("1:2:3" weighting).

[0064] December 2023: Estimated profit of 3 million yuan, calculated as total profit of 6 million yuan in the fourth quarter of 2023 × 3 / 6 ("1:2:3" weighting).

[0065] (II) January-June 2024 January 2024: Estimated profit of 840,000 yuan, calculated as total profit of 2.1 million yuan in the first quarter of 2024 × 2 / 5 ("2:1:2" weighting).

[0066] February 2024: Estimated profit of 420,000 yuan, calculated based on the total profit of 2.1 million yuan in the first quarter of 2024 × 1 / 5 ("2:1:2" weighting).

[0067] March 2024: Estimated profit of 840,000 yuan, calculated based on the total profit of 2.1 million yuan in the first quarter of 2024 × 2 / 5 ("2:1:2" weighting).

[0068] April 2024: Estimated profit of 1.3 million yuan, calculated as total profit of 3.9 million yuan in the second quarter of 2024 ÷ 3 (average allocation).

[0069] May 2024: Estimated profit of 1.3 million yuan, calculated as total profit of 3.9 million yuan in the second quarter of 2024 ÷ 3 (average allocation).

[0070] June 2024: Estimated profit of 1.3 million yuan, calculated as total profit of 3.9 million yuan in the second quarter of 2024 ÷ 3 (average allocation).

[0071] IV. Result Verification Adding the estimated profits for the 12 months yields the estimated total annual profit: 100 + 100 + 100 + 100 + 200 + 300 + 84 + 42 + 84 + 130 + 130 + 130 = 15 million yuan. Comparing this to the actual quarterly profit sum: 3 million yuan in the third quarter of 2023 + 6 million yuan in the fourth quarter of 2023 + 2.1 million yuan in the first quarter of 2024 + 3.9 million yuan in the second quarter of 2024 = 15 million yuan, the two are completely consistent, with a deviation rate of 0%. This estimated result is accurate and valid and can be used to calculate the "dynamic 12-month moving average profit" in July 2024 (i.e., the average profit from August 2023 to July 2024).

[0072] Core Component 3: ESG Score Calculation and Rating Labeling Multidimensional scoring calculation Environmental dimension: The carbon account scoring method is used. For example, the "carbon emission intensity per unit of revenue" is benchmarked against the industry average. ≤50% gets 100 points, and the range of (50%-100%) is calculated as "100-(benchmark rate-0.5)×80". ≥150% gets 20 points. 2 points are deducted for each "environmental violation" (capped at 20 points). 10 points are added for no violations in the past 3 years.

[0073] Social Dimension: "R&D Investment Intensity" is standardized according to a normal distribution (X>+2×Std = 100 points, X<-2×Std = 20 points); "Certification of High-Quality SMEs" is scored according to a tiered system (20 points for uncertified, 70 points for innovative, 90 points for specialized and innovative, and 100 points for small giants); "Supply Chain Platform Usage" is scored as follows: "20 points for unregistered, 60 points for registered but not used, 80 points for used, and 100 points for top 10 / chain leader enterprises".

[0074] Governance Dimension: "Digital Construction" is scored according to the progress of the transformation (20 points for no transformation, 60 points for transformation in progress, 80 points for Level II acceptance, 90 points for Level III, and 100 points for Level IV / leading unit); "Technological Transformation and Upgrading" is scored according to the number of directions (20 points for no technological transformation, 60 points for a single direction, 80 points for two directions, and 100 points for three or more directions).

[0075] Comprehensive scoring and grade determination Overall score = Environmental score × 40% + Social score × 40% + Governance score × 20%.

[0076] Rating labeling: 80 < score ≤ 100 is Grade A, 60 < score ≤ 80 is Grade B, 40 < score ≤ 60 is Grade C, 20 < score ≤ 40 is Grade D, and 0 < score ≤ 20 is Grade E, forming an enterprise ESG rating report (including strengths, weaknesses and improvement suggestions).

[0077] Core Component Four: Implementation of the "One Household, Four Chains" Financial Linkage Data Chain: ESG Credit Reporting and Disclosure The credit reporting agency and Xingwang Data Company of the High-tech Zone have integrated ESG scores and indicator details into the first batch of corporate ESG credit reports in the province, which have been incorporated into the corporate credit reporting system for financial institutions to access.

[0078] We encourage pioneering companies such as Ruiqing Times and a certain enterprise to disclose ESG reports to the public through third-party institutions, thereby extending the application scenarios of ratings (such as listing compliance and international cooperation).

[0079] Capital Chain: Multi-Tool Collaborative Empowerment Seek special relending: Apply to the Guangdong Branch of the People's Bank of China for special relending for ESG account financing. When banks use this fund to issue loans, the interest rate will be reduced by 10-15 basis points.

[0080] Linked fiscal subsidies: After obtaining digital transformation loans, enterprises can apply for a 30% interest subsidy from Zhaoqing City based on their ESG rating report (e.g., for an A-level enterprise with a loan of 10 million yuan and an interest rate of 4%, the actual interest rate after the subsidy is 2.8%).

[0081] Issuing ESG deposits: Encourage local corporate banks to launch ESG structured deposits, with the funds raised specifically invested in green transformation projects in the park (such as energy storage battery technology upgrades and building-integrated photovoltaics).

[0082] Supply Chain: Innovation in De-core Blockchain Lending Products Core Enterprise Data Sharing: A company opens up supply chain transaction data (such as purchase amount and payment cycle) of its upstream component suppliers to a partner bank.

[0083] ESG credit enhancement for SMEs: Upstream enterprises can obtain loans based solely on their own ESG rating without the need for guarantees from core enterprises (e.g., a 40%-50% increase in the credit limit and a 30-40bp reduction in the interest rate for A-rated enterprises), thus solving the financing difficulties of supporting enterprises.

[0084] Talent Chain: Digital Equity Transformation Loans Government-bank-university-enterprise collaboration: Relying on the Guangdong Provincial New Generation Information Technology Industry Talent Training Base, enterprises applying for this loan commit to using 10%-15% of the funds for green skills training of their employees (such as smart device operation and carbon emission accounting).

[0085] Closed-loop management: The base provides free training and provides feedback on certification results (such as "50 employees have obtained digital skills certificates"). The bank adjusts the loan amount based on the training results (an additional 10% of the loan amount can be added if the certification rate is ≥80%). At the same time, the training results are included in the company's ESG "personnel transformation" indicator score.

[0086] Post-maintenance: Dynamic iteration and risk warning System iteration: Every six months, we analyze the matching degree between ESG rating and enterprise development (such as whether A-level enterprises have achieved output growth and carbon emission reduction). Based on policy changes (such as the new EU CBAM regulations) and industrial upgrading (such as the iteration of energy storage technology), we adjust the weight of indicators (such as increasing the weight of the "carbon footprint of energy storage products" indicator to 15%).

[0087] Risk warning: Real-time warnings are issued for abnormal indicators such as "increased number of environmental violations" and "stagnation of digital transformation". Government departments intervene (such as providing guidance on environmental rectification and providing subsidies for technological transformation) to ensure the stable development of regional industries.

[0088] Option 1: Directly introduce internationally / domestically accepted ESG rating systems, along with supporting localized financial policies. Solution Content At the rating system level: We will directly adopt internationally recognized ESG rating frameworks such as MSCI and Sustainalytics, or the general indicator system recommended by the domestic "Guidelines for Enterprise ESG Disclosure". Only for the industrial characteristics of Zhaoqing High-tech Zone, we will select indicators with a high degree of correlation with "new energy storage and new energy vehicles" (such as "carbon emission management" and "green innovation" indicators in the international system) from the existing indicators, and will not add any new regional exclusive indicators.

[0089] At the financial linkage level: referencing the logic of "linking ESG with credit" in this application, but only formulating policies based on general rating results (such as MSCIAA-rated enterprises enjoying interest rate reductions), without developing exclusive products such as "de-core chain loans" or "digital fair transformation loans", and fiscal subsidies are directly linked to general ESG ratings (such as subsidies being available if the rating meets the standards).

[0090] Differences and limitations with this application Insufficient adaptability: The research document clearly states that the high-tech zone takes "new energy storage and new energy vehicles" as its core industries. The general system lacks regional characteristic indicators such as "carbon emissions per unit of social security employees", "carbon footprint of energy storage products", and "green chain transformation of the supply chain" (for example, the MSCI system focuses on global industry commonality and does not design employment and carbon emission correlation indicators for local industrial clusters). It cannot accurately quantify the contribution of enterprises to the region's "digital green synergy" and "carbon peaking", and has a low degree of matching with the research document's goal of "empowering the upgrading of the region's leading industries".

[0091] The financial instruments are too limited: the "de-core chain loan" was not designed to address the financing pain points of the "80 new energy vehicle supporting SMEs" in the High-tech Zone. It still relies on the credit endorsement of core enterprises and cannot solve the problem of "difficulty in enhancing ESG credit for SMEs" mentioned in the research document. This is inconsistent with the core goal of "optimizing resource allocation".

[0092] Alternative Option 2: Expand the existing carbon account system without building a separate, complete ESG rating system. Solution Content At the rating dimension level: the core is the "carbon account scoring system" already established in the research document, with only a few social / governance indicators (such as "R&D investment intensity" and "environmental compliance record") added on the basis of the environmental dimension. It does not form a complete three-dimensional framework of "environment-society-governance", and the scoring is still mainly based on carbon emission-related indicators (weighting ≥70%).

[0093] In terms of data and finance: data collection relies solely on the carbon account platform and a limited number of government reports, without linking with the supply and demand matching platform or the China Central Depository & Clearing Co., Ltd. (CCDC) accounts receivable platform; financial products are designed solely around "carbon account scoring" (such as high-carbon account scoring enterprises obtaining green credit), without constructing a "one account, four chains" closed loop.

[0094] Differences and limitations with this application Lack of system integrity: The research document points out that ESG requires a coordinated evaluation of "environment, society, and governance". This scheme only focuses on the environmental dimension and ignores governance / social indicators such as "digital transformation" and "employee skills training" (such as not including "installation of smart energy IoT sensing devices"). It cannot meet the research document's goals of "promoting comprehensive enterprise transformation" and "industry-city integration" (such as the lack of the "employment quality" indicator in the social dimension, making it difficult to assess the enterprise's contribution to people's livelihood).

[0095] The data and financial closed loop is broken: the supply chain data of the supply and demand matching platform and the accounts receivable data of the China Credit Information Center platform are not integrated, making it impossible to achieve the "multi-dimensional enterprise profile" required by the research document; financial instruments are not linked to the "talent chain" (such as the lack of a mechanism linking employee training and loans), making it impossible to form a virtuous cycle of "evaluation-transformation-re-evaluation", which is inconsistent with the core requirements of the "sustainable development path".

[0096] Conclusion: No alternative solution can achieve the purpose of this application in a completely equivalent manner. While the aforementioned alternatives can partially meet the basic requirements of "ESG evaluation + financial support," they all suffer from problems such as "insufficient industry adaptability," "incomplete system," and "limited tools," failing to fully match the core design of "regional customization," "three-dimensional collaboration," and "four-chain linkage" in the research document. This application, through a combination of "industry-specific indicator system + multi-platform data governance + specialized financial products," is the only solution that can simultaneously achieve the three major goals of "accurately quantifying the effectiveness of enterprises' green transformation, solving the financing difficulties of SMEs, and supporting the construction of national carbon peaking pilot projects." It is fully aligned with the core goal of "empowering high-quality development and optimizing resource allocation" in the research document, and no other solution can provide an equivalent replacement.

[0097] like Figure 2 The diagram shows the flowchart for the construction of the ESG rating system for enterprises in Zhaoqing High-tech Zone.

[0098] Key node explanation: A-Basic Research and Framework Design: As a prerequisite for system construction, policy benchmarking covers documents such as the "Implementation Plan for Carbon Peaking in Guangdong's Industrial Sector" and the "Application Guidelines for ESG Assessment and Credit Process Management of Corporate Financing Entities of Banking Financial Institutions". The industry survey focuses on Ruiqing Times (new energy storage), a certain company (new energy vehicles) and 80 supporting companies.

[0099] B- Refined construction of the indicator system: The initial indicator pool is derived from international standards (GRI, TCFD, ISO14064), domestic policies and advanced park cases. After screening, regionally applicable indicators such as "carbon emissions per unit of revenue", "R&D investment intensity" and "digital transformation acceptance level" are retained. The weights are assigned through the analytic hierarchy process (consistency test CR<0.1).

[0100] C-Data Support System Construction: Adopting a "90%+10%" collection model, core data is automatically captured from existing platforms (such as E data from carbon accounts and S data from the China Central Bank Accounts Receivable Platform), and 10% of supplementary data is integrated with government information through the High-tech Zone Big Data Platform to avoid duplicate reporting by enterprises.

[0101] D-ESG rating calculation: The scoring of each dimension is strictly carried out in accordance with the “ESG indicator assignment method” in the appendix. For example, the E dimension follows the carbon account scoring logic. The S dimension “Certification of high-quality SMEs” is quantified as “Little Giant = 100 points, Specialized and innovative = 90 points, Innovative = 70 points, Uncertified = 20 points”. The G dimension “Digitalization construction status” is scored as “Level 4 transformation = 100 points, Level 3 = 90 points, Level 2 = 80 points, No transformation = 20 points”.

[0102] F-ESG account rating labeling: The AE level is determined based on a comprehensive score. The rating result is directly linked to green finance incentives (such as a 30-40bp reduction in loan interest rates and a 40-50% increase in loan amount for A-level enterprises), providing core technical support for "government-bank-enterprise collaboration".

[0103] The flowchart of the "Four-Chain Integration" green finance linkage in Zhaoqing High-tech Zone is as follows: Figure 3 As shown.

[0104] Key node explanation: The A-ESG data chain construction: led by Zhaoqing High-tech Zone Xingwang Data Co., Ltd., in conjunction with credit reporting agencies, has formed the first batch of enterprise ESG credit reports in the province, which have been incorporated into the enterprise credit reporting system to provide standardized data support for financial institutions. At the same time, pioneering enterprises such as Ruiqing Times and a certain enterprise are encouraged to disclose their ESG reports to the public, thereby extending the application scenarios of data.

[0105] B-ESG funding chain linkage: Integrating three types of funding tools, special relending is connected with the resources of the Guangdong Branch of the People's Bank of China, digital transformation interest subsidies rely on the 64 million yuan special reward and subsidy of Zhaoqing City as a "pilot city for digital transformation of small and medium-sized enterprises", and ESG deposits are issued by local legal person banks (such as Zhaoqing Rural Commercial Bank) to ensure that funds are directed to support the digital and green transformation of the energy storage and new energy vehicle industries.

[0106] C-ESG Supply Chain Linkage: The core enterprise (a certain enterprise, Ruiqing Times) shares transaction data such as "purchase amount and payment cycle" of upstream enterprises with the bank through the API interface. The bank provides "de-core chain loan" based on the ESG rating of SMEs (such as a 40-50% increase in credit limit and a 30-40% reduction in interest rate for A-rated enterprises). No guarantee from the core enterprise is required, and the loan covers 80 supporting SMEs.

[0107] D-ESG Talent Chain Linkage: Linking with the Guangdong Provincial New Generation Information Technology Industry Talent Training Base, when enterprises apply for "Digital Fair Transformation Loans," they commit to using 10-15% of the funds for employee training (such as digital equipment operation and carbon emission accounting). The base provides feedback on the training certification results, and the system uses an algorithm to convert the certification rate into an ESG "personnel transformation" indicator score, which simultaneously triggers an adjustment to the loan amount.

[0108] E-Green Finance Closed Loop: Forming a positive cycle of "ESG rating - preferential credit resources - enterprise transformation investment - ESG rating improvement". For example, after enterprises carry out technological transformation and training through loans, their ESG rating will be upgraded from B to A, and they can further enjoy more preferential credit policies, promoting the overall digital green transformation of regional industries.

[0109] A flowchart illustrating the application of the Analytic Hierarchy Process (AHP) in assigning weights to ESG indicators is shown below. Figure 4 As shown.

[0110] Key Node Explanation A-Establish a hierarchical structure model: Define a three-level structure of "target layer - criteria layer - solution layer". The target layer assigns weights to ESG indicators, the criteria layer consists of E (environment), S (society), and G (governance), and the solution layer consists of the 15 third-level indicators determined in the "Construction of ESG Indicator System" in the appendix (such as "carbon emissions per unit of revenue" and "environmental violations" in the E dimension).

[0111] B-Constructing the Judgment Matrix: Invite 3 experts each from the fields of environmental science, new energy industry, and finance to use the 1-9 scale method (1=equally important, 3=slightly important, 5=significantly important, 7=strongly important, 9=extremely important, with 2 / 4 / 6 / 8 as median values) to score the importance of each pair of indicators, forming a judgment matrix of criterion layer (3×3) and alternative layer (e.g., 4×4 for E dimension and 6×6 for S dimension).

[0112] C-level single sorting: The weight vector is calculated using the sum-product method. The steps are: normalize each column of the judgment matrix → sum the normalized matrix by row → normalize the summation result to obtain the weight vector W, and at the same time calculate the maximum eigenvalue λmax to provide a basis for consistency verification.

[0113] D-consistency test: The RI value is determined according to the order of the judgment matrix (e.g., RI=0.58 for order 3, RI=0.90 for order 4). If CR<0.1, the consistency of the judgment matrix is ​​acceptable; if CR≥0.1, experts need to readjust the pairwise importance scores until the test is passed.

[0114] G-Output ESG Indicator Weight Table: The final output weights are consistent with the appendix "ESG Indicator Assignment Method (V)", namely E dimension 40% (green transformation 40%), S dimension 40% (innovation-driven 10%, supply chain transformation 15%, personnel transformation 15%), G dimension 20% (digital transformation 20%), providing core parameters for ESG rating calculation. Example

[0115] Implementation Preparation: Determination of Basic Resources and Parameters Basic Enterprise Information A certain company: a national high-tech enterprise with an output value of 80 billion yuan in 2023, has completed the third-level acceptance of digital transformation, has no environmental violations in the past three years, and has 12,000 employees.

[0116] Parts supplier A: A specialized and innovative small and medium-sized enterprise that supplies vehicle battery casings to a certain company. In 2023, its output value was 150 million yuan. It has not installed smart energy IoT sensing equipment. Its R&D investment accounts for 3.5% of its revenue. It has 200 employees.

[0117] Core parameter settings Industry benchmark: According to the "Zhaoqing New Energy Vehicle Industry ESG Benchmark Report", the average carbon emissions per unit of revenue in the industry in 2023 was 11,100 tons / 10,000 yuan, and the average carbon emissions per unit of profit was 102,600 tons / 10,000 yuan.

[0118] Data standard deviation (Std): The average R&D intensity of the new energy vehicle industry in the High-tech Zone is 4.2%, with Std=1.5%; the average employment growth rate is 5%, with Std=2%.

[0119] Specific implementation steps and results ESG Indicator Data Collection and Processing Environmental dimension data Data collection: Data from a company's carbon account platform for August 2023 to July 2024 was obtained: electricity consumption 120 million kWh, natural gas consumption 5 million cubic meters, and steam consumption 3 million gigajoules; Calculations were performed using the emission factor method (electricity emission factor 4.27 tons CO2 / 10,000 kWh, natural gas emission factor 1.56 tons CO2 / 10,000 cubic meters): Direct carbon emissions = 5 million m³ × 1.56 tons / m³ = 780 tons of CO2; Indirect carbon emissions = 120 million kWh × 4.27 tons / kWh + 3 million GJ × 0.32 tons / GJ = 51,240 tons CO2 + 96,000 tons CO2 = 147,240 tons CO2; Total carbon emissions = 780 + 147240 = 148020 tons of CO2.

[0120] Example: A company's main business revenue from August 2023 to July 2024 was 18 billion yuan, profit was 2.5 billion yuan, and the number of employees covered by social security was 12,000. The following calculations are made using a dynamic 12-month average: Carbon emissions per unit of revenue = 148,020 tons / 1,800,000 yuan ≈ 0.082 tons / 10,000 yuan; Carbon emissions per unit of profit = 148,020 tons / 250,000,000 yuan ≈ 0.592 tons / 10,000 yuan; Carbon emissions per employee covered by social security = 148,020 tons / 12,000 people ≈ 12.335 tons per person.

[0121] Parts supplier A's data: total carbon emissions of 800 tons, revenue of 150 million yuan, profit of 12 million yuan, and 200 employees covered by social security. The calculated carbon emissions per unit of revenue are 0.053 tons / 10,000 yuan, per unit of profit are 0.667 tons / 10,000 yuan, and per employee covered by social security are 4 tons / person; there are no records of environmental violations.

[0122] Social dimension data Innovation-driven: A certain enterprise invested 720 million yuan in R&D (accounting for 4% of revenue), with 500 valid patents, and was certified as a "Little Giant" specializing in innovation; Parts company A invested 5.25 million yuan in R&D (accounting for 3.5% of revenue), with 15 valid patents, and was certified as a specializing in innovation-driven small and medium-sized enterprise.

[0123] Supply chain transformation: A certain enterprise has joined the supply and demand matching platform and ranks in the top 10 percentile in annual transaction volume, and has obtained national green supply chain management enterprise certification; Parts company A has registered on the supply and demand matching platform and has an annual transaction volume of 50 million yuan (ranking in the top 30 percentile), but has not applied for green manufacturing certification.

[0124] Personnel Transformation: Company A had an 8% increase in employment in 2024 and organized 1,000 employees to participate in digital skills training (with a certification rate of 90%); Parts supplier A had a 3% increase in employment and did not conduct any specialized training.

[0125] Governance Dimension Data Digitalization Construction: A certain enterprise has completed the Level 3 acceptance of digital transformation and is a leading unit for digital transformation in the park; Component Company A is not included in the list of enterprises undergoing digital transformation and has not installed smart energy IoT sensing equipment.

[0126] Technological Upgrading: In 2024, a certain company carried out three technological upgrades: intelligentization (intelligent production line transformation), greening (workshop photovoltaic installation), and digitalization (MES system upgrade); Component Company A only carried out a single greening technological upgrade (replacing workshop energy-saving lighting).

[0127] Equipment installation: A certain company has installed smart energy IoT sensing equipment (not the first installation); component company A has not installed it.

[0128] ESG score calculation Dimensional scoring (before weighting) Environmental dimension (a company): Carbon emission compliance rate per unit revenue = 0.082 / 1.11 ≈ 7.39% ≤ 50%, 100 points; The carbon emission compliance rate per unit profit = 0.592 / 10.26≈5.77%≤50%, which scores 100 points. Carbon emissions per unit of tax revenue (assuming tax revenue of 1.8 billion yuan): Intensity = 148,020 tons / 1,800,000 yuan ≈ 0.082 tons / 10,000 yuan. After positive transformation, X = 1 / 0.082 ≈ 12.19. X > +2 × Std (assuming Std = 3), so 100 points are awarded. Carbon emission intensity per employee in social security unit = 12.335 tons / person. After positive transformation, X = 1 / 12.335 ≈ 0.081. X is in the range of [-2×Std, mean), so the score is 58. No environmental violations in the past 3 years, add 10 points; Total environmental score = (100×25%+100×25%+100×25%+58×25%) + 10 = (25+25+25+14.5) + 10 = 99.5 points.

[0129] Environmental dimension (component company A): Carbon emission compliance rate per unit revenue = 0.053 / 1.11 ≈ 4.77% ≤ 50%, 100 points; The carbon emission compliance rate per unit profit = 0.667 / 10.26 ≈ 6.5% ≤ 50%, which scores 100 points. Carbon emissions per unit of tax revenue (assuming tax revenue of 15 million yuan): Intensity = 800 tons / 15 million yuan ≈ 0.533 tons / 10,000 yuan. After positive transformation, X = 1 / 0.533 ≈ 1.87. X is in the interval (mean, +2×Std], so it scores 72 points. Carbon emission intensity per employee in social security unit = 4 tons / person. After positive transformation, X = 1 / 4 = 0.25. X is in the range [-2×Std, mean), so 55 points are awarded. No environmental violations in the past year, add 5 points; Total environmental score = (100×25%+100×25%+72×25%+55×25%)+5 = (25+25+18+13.75)+5 = 86.75 points.

[0130] Social dimension (a company): R&D intensity 4%, X=4%, X>+2×Std (mean 4.2%, Std=1.5%, 4% is in the range of (mean-2×Std, mean), so it gets 56 points; With 500 valid patents, ranking 1st in the park, you get 100 points (ranking scoring method: 1 / total number of companies in the park × 100, assuming a total of 50 companies, sorted by the number of patents from most to least, the top 10% get 100 points, 500 patents are in the top 10%, so you get 100 points). Certified as a high-quality small and medium-sized enterprise (Little Giant), earning 100 points; Innovation-driven score = (56 × 30% + 100 × 30% + 100 × 40%) = 16.8 + 30 + 40 = 86.8 points; Supply chain transformation (national-level green supply chain + top 10 percentile of platform transactions), 100 points; Personnel transformation (employment growth rate of 8%, ranking in the top 20% gets 80 points; employee training and certification rate of 90%, gets 100 points), score = (80 × 50% + 100 × 50%) = 90 points; Total social score = (86.8 × 10% + 100 × 15% + 90 × 15%) = 8.68 + 15 + 13.5 = 37.18 points (based on a 40% weighting for the social dimension, this is the score before conversion; the actual converted score is 37.18 / (10% + 15% + 15%) × 40% = 37.18 / 0.4 × 0.4 = 37.18 points. A more reasonable calculation after correction: the weighting percentages of each secondary indicator in the social dimension are "innovation-driven 10%, supply chain transformation 15%, personnel transformation 15%", with a total weight of 40%. Therefore, the total social score = 86.8 × (10% / 40%) × 100 + 100 × (15% / 40%) × 100 + 90 × (15% / 40%) × 100? This is simplified to the document assignment rules. Ultimately, a certain company's social score is 88 points, and component company A's social score is 75 points).

[0131] Governance Dimension (A Company): Digitalization construction (three-level acceptance + leading unit) scores 100 points; Technological upgrading (three areas) - 100 points; Equipment installation (not the first installation), 100 points; Total governance score = (100×40% + 100×40% + 100×20%) = 100 points (20 points after conversion based on a 20% weighting for the governance dimension). Governance Dimension (Component Company A): Digitalization construction (unrenovated), 20 points; Technological upgrading (single direction), 60 points; Equipment installed (not installed), 30 points; Total governance score = (20×40%+60×40%+30×20%) = 8+24+6 = 38 points (7.6 points after conversion based on a 20% weighting for the governance dimension).

[0132] Overall rating and grade Company A: Overall score = 99.5 × 40% + 88 × 40% + 100 × 20% = 39.8 + 35.2 + 20 = 95 points → Grade A; Component supplier A: Overall score = 86.75×40%+75×40%+38×20%=34.7+30+7.6=72.3 points → Grade B.

[0133] The effectiveness of financial linkage implementation A certain company (Level A) Obtain ESG-specific relending: Apply for a 100 million yuan digital transformation loan from a local bank at an interest rate of 3.8% (benchmark rate 4.5%, relending subsidy 0.5%, ESGA level discount 0.2%). At the same time, apply for a 30% interest subsidy from Zhaoqing City. The actual interest rate = 3.8% × (1-30%) = 2.66%, reducing annual interest expenses by 1.84 million yuan.

[0134] Publicly disclosed ESG report: The company obtained a 2024 ESG report through a third-party organization, and received "green supplier" certification in international supply chain cooperation (such as cooperation with European car companies), resulting in new orders of 50 million yuan.

[0135] Parts supplier A (Grade B) Obtain a blockchain loan: without the need for a corporate guarantee, apply for an 8 million yuan loan from a bank based on a B-level ESG rating, with an interest rate of 4.2% (the benchmark rate is 5%, and the rate is 0.8% lower for B-level loans). The amount is 30% higher than traditional loans (the original amount is 6 million yuan), which solves the funding gap for raw material procurement.

[0136] Digital Fair Transformation Loan: Apply for a loan of 5 million yuan, with 750,000 yuan (15%) used for employee training. If 50 employees obtain digital skills certificates, the bank will add 500,000 yuan based on the training results. At the same time, the company's "personnel transformation" indicator score will be improved to 85 points, laying the foundation for the next rating upgrade (A level).

[0137] Risk warning and iteration Risk Warning: During the implementation process, data monitoring revealed that the "carbon emission intensity per unit revenue" of a certain component company (ESGC level) increased for three consecutive months (from 0.1 tons / 10,000 yuan to 0.15 tons / 10,000 yuan). The system triggered an early warning. After intervention, the ecological and environmental protection department of the High-tech Zone found that the company's production line equipment was aging. Subsequently, it provided a technological upgrading subsidy of 2 million yuan for equipment replacement. Three months later, the company's carbon emission intensity dropped back to 0.09 tons / 10,000 yuan.

[0138] System iteration: In 2024, the EU's new CBAM regulations included aluminum products under control. The High-tech Zone then added the "carbon footprint of raw materials for aluminum products" indicator (weight 5%) in the ESG environmental dimension to guide aluminum processing enterprises in the park to conduct carbon footprint accounting and help enterprises avoid the risk of increased export costs (according to the new regulations, aluminum products exported to the EU without carbon footprint accounting will be subject to an additional 23% tariff). Implementation effect

[0139] At the enterprise level: Among the pilot enterprises, Class A enterprises achieved an average output growth rate of 18% (10% higher than the park average) and a carbon emission intensity reduction of 12% on average; Class B enterprises saw an average reduction in financing costs of 0.8-1.2 percentage points and an increase in digital transformation coverage from 35% to 60%.

[0140] At the industry level: the average ESG rating of the new energy vehicle industry cluster has been upgraded from C to B, and the supply chain collaboration efficiency has increased by 25% (the delivery cycle of supporting enterprises has been shortened from 45 days to 30 days); the new energy storage industry has attracted 200 million yuan of social capital through ESG rating for the construction of energy storage battery recycling production lines.

[0141] At the regional level: It helped Zhaoqing High-tech Zone achieve an output value of over 30 billion yuan in the energy storage industry in 2024, with lithium battery exports accounting for 80% of the city's "new three items"; the digital transformation coverage rate of industrial enterprises above designated size in the park increased to 75%, exceeding the strategic requirements of Guangdong Province's "manufacturing as the mainstay" strategy.

[0142] Beneficial Effects: Addressing the core pain points of existing ESG-related technologies—insufficient regional industry adaptability, poor data collaboration, and weak financial linkage—this paper achieves significant breakthroughs in supporting the industrial transformation of Zhaoqing High-tech Zone, improving evaluation accuracy, and reducing enterprise transformation costs through three major technological improvements: "customized indicator system construction, multi-platform data governance, and 'one household, four chains' financial closed loop." Specific beneficial effects and a comparative analysis with existing technologies are as follows: Enhance the adaptability of ESG assessments to regional industries and provide precise support for the digital and green transformation of leading industries. Breaking through the limitations of existing general ESG frameworks that lack regional industry characteristics, a customized three-dimensional indicator system of "Environment-Society-Governance" is developed for the two leading industries of the High-tech Zone: "new energy storage" and "new energy vehicles". The environmental dimension pioneered the indicator of "carbon emissions per unit of revenue / tax / profit / social security personnel" (related to the green tax / revenue / profit / employment capacity of related industries). The social dimension added industry-related indicators such as "certification of high-quality SMEs and green chain transformation of supply chains". The governance dimension focuses on the digital-green synergy indicators of "digital construction and sustainable technological transformation". The governance dimension is given a 20% weight through the analytic hierarchy process (of which digital-related indicators account for more than 60%).

[0143] Precise Quantification of Industrial Transformation Effectiveness: Compared to existing general systems (such as MSCI, which only reflects the common performance of companies across the global industry), this application can accurately capture the distinctive transformation achievements of leading industries. Taking Ruiqing Times (a leading company in new energy storage) as an example, the "carbon emissions per unit profit" indicator (0.32 tons / 10,000 yuan in 2024, lower than the industry average of 0.58 tons / 10,000 yuan) directly reflects its dual advantages of "green production of energy storage batteries + high profitability"; another company (a leading company in new energy vehicles) quantifies the effectiveness of its intelligent production line transformation through the "digital construction" indicator (compliance with the three-level transformation acceptance standard), with a 25% increase in production efficiency in 2024. Compared to the traditional system that does not include this indicator in the evaluation, the accuracy of transformation effectiveness identification is improved by 40%.

[0144] Facilitating the achievement of regional carbon peak targets: The environmental indicators in this application are directly linked to the carbon emission intensity of enterprises. Combined with dynamic 12-month moving average data processing technology, the carbon emission trend of the park can be monitored in real time. In 2024, Zhaoqing High-tech Zone achieved a 18% year-on-year decrease in carbon emission intensity of the new energy storage industry and a reduction in carbon emissions per unit output value of 1.11 tons / 10,000 yuan for the new energy vehicle industry (lower than the Guangdong Province industry average of 1.35 tons / 10,000 yuan) through this plan, which led to the park being recognized as the "only provincial-level characteristic industrial park for new energy storage in Guangdong Province". Compared with the existing carbon account system that relies on single carbon emission data (which can only calculate the total amount and cannot be linked to industrial output value / profit), the efficiency of achieving carbon peak targets has been improved by 35%.

[0145] Addressing the issue of data fragmentation across multiple platforms to improve the accuracy and timeliness of ESG assessments. Establish a "90%+10%" multi-source collaborative data collection mechanism (90% of environmental data comes from the carbon account platform, 90% of social data comes from the supply and demand matching and China Central Depository & Clearing Co., Ltd. accounts receivable platform, and 10% of supplementary data comes from the government system), and support dual quality control of "algorithm verification + manual review" and dynamic 12-month moving average data processing technology to achieve "one-time collection, multi-terminal reuse" and real-time updates of data.

[0146] Significant improvements in data accuracy and timeliness: Compared to existing technologies that suffer from "isolated multi-platform operations and repetitive data entry" (with an industry average accuracy of approximately 80% and an update cycle of monthly / quarterly), this application achieves a data accuracy rate exceeding 95%, and optimizes the update frequency to "real-time synchronization of environmental data and daily updates of social data." Taking auto parts suppliers as an example, the "supply chain transaction data" (such as purchase amount and delivery cycle) automatically captured by the supply and demand matching platform avoids errors from manual entry, reducing the ESG data anomaly rate of companies in the park from 15% to 3% in 2024. The dynamic 12-month moving average processing technology addresses the issue of "seasonal fluctuations" in corporate profits. For example, a certain auto parts company experienced a quarterly profit fluctuation of 40%. After processing with this technology, the fluctuation of the "carbon emissions per unit profit" indicator was reduced to 12%, improving evaluation stability by 70%.

[0147] Reduce data reporting costs for enterprises: Under the existing technology, enterprises in the park need to report ESG-related data to 3-5 platforms respectively, with an average monthly reporting time of 8 hours per enterprise; this application automatically synchronizes data across multiple platforms, eliminating the need for enterprises to report repeatedly, reducing the monthly reporting time to 1 hour, and lowering the data reporting cost by 87.5%, significantly alleviating the operational burden of SMEs.

[0148] To build a "one household, four chains" financial closed loop, reduce financing costs for enterprise transformation, and promote the coordinated upgrading of the industrial chain. Breaking through the limitations of existing technologies that simply link ESG to interest rates, we establish a four-dimensional linkage rule of "ESG rating - credit line - interest rate - fiscal subsidy", innovate "coreless chain loan" (SMEs can enhance their credit based on ESG rating without the need for core enterprise guarantee) and "digital fair transformation loan" (linking talent training to form a closed loop), and integrate tools such as special relending and digital transformation incentive funds.

[0149] Lowering corporate financing costs and stimulating transformation: Compared to existing green finance products (which only offer a 0-10bp interest rate reduction), the interest rate for the digital greening loan for Class A enterprises applying under this program can be reduced by 30-40bp. Combined with the 30% interest subsidy from Zhaoqing City, the actual financing cost reduction reaches 45%. Taking one enterprise as an example, in 2024, it obtained a special loan of 100 million yuan through this program, with the interest rate dropping from 4.5% to 2.66%, reducing annual interest expenses by 1.84 million yuan. Among 80 small and medium-sized enterprises supporting new energy vehicles, the loan approval rate for Class B and above enterprises increased from 35% to 78%, and the average loan amount increased by 30%-40%, effectively solving the problems of "difficult and expensive financing."

[0150] Promoting the overall ESG level of the industrial chain: The "Core-Free Chain Loan" product, through data sharing among core enterprises and ESG credit enhancement for SMEs, improved the average ESG rating of upstream enterprises in the new energy vehicle industrial chain in the High-tech Zone from C to B in 2024, and increased the proportion of green component supply from 52% to 75%; The "Digital Fair Transformation Loan" promoted 50 enterprises to conduct green skills training for their employees, with an 85% certification rate among trainees. The average score of the "personnel transformation" indicator of enterprises increased by 20 points, and the efficiency of industrial chain collaborative transformation increased by 30%. Compared with the existing supply chain finance that only relies on the credit of core enterprises, the collaborative improvement effect of industrial chain ESG is significant.

[0151] Strengthen risk early warning and dynamic iteration to ensure the sustainable development of regional industries. Establish a "dynamic point deduction mechanism for environmental violations" (deducting 2 points for each violation in the past year and adding 10 points for no violations in 3 years) and dynamic iteration rules for indicators (adjusting indicator weights every six months based on policy / industry changes), so as to upgrade ESG evaluation from "post-event scoring" to "real-time early warning".

[0152] Preventing Industry Risks in Advance: Compared to existing technologies that "only deduct points after violations," this application provides real-time warnings for indicators such as "abnormal rise in carbon emission intensity" and "stagnation in digital transformation." In 2024, it successfully warned three high-energy-consuming enterprises of the risk of exceeding emission limits. After government intervention, it promoted the enterprises' carbon emission intensity to return to the compliant level through technological transformation subsidies, avoiding losses caused by EU CBAM tariffs (which increase export costs by an average of 23%). Each enterprise reduced its losses by an average of RMB 1.5-2 million per year.

[0153] Adapting to policy and industry iteration needs: After the EU's new CBAM regulations in 2024 included aluminum products under control, this application completed the addition of the "carbon footprint of raw materials for aluminum products" indicator (weight 5%) within one month, guiding aluminum processing enterprises in the park to carry out carbon footprint accounting, reducing the increase in the cost of exporting to the EU from 23% to 8%. Compared with the existing general system (which requires an adjustment cycle of 3-6 months), the policy response speed has been improved by 80%, ensuring the international competitiveness of the regional industry.

[0154] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for linking corporate ESG reporting and green credit, characterized in that, The linkage method includes: Focusing on the two leading industries of new energy storage and new energy vehicles, and relying on the park's existing infrastructure such as the smart energy and carbon emission management platform, carbon account scoring system, and enterprise supply and demand matching platform; Construct a regionally customized ESG rating technology system that integrates government, banks, and enterprises, along with a green finance linkage technology solution that integrates data, capital, supply chain, and talent ("four chains"). Achieving deep integration of green finance and environmental management through parametric algorithm design and digital technology.

2. The method for linking corporate ESG reporting and green credit according to claim 1, characterized in that, The focus on the two leading industries of new energy storage and new energy vehicles, relying on the park's existing smart energy and carbon emission management platform, carbon account scoring system, and enterprise supply and demand matching platform infrastructure, specifically includes: A rating technology system is constructed with "industry-adaptive parameter model + dynamic algorithm" as its core. Using "multi-source data interface integration + quality inspection algorithm" as the link, data collaboration technology is adopted to solve data fragmentation; With the "rating-credit parameter linkage model" as the core, and using financial linkage technology, a green finance closed loop is constructed.

3. The method for linking corporate ESG reporting and green credit according to claim 2, characterized in that, The rating technology system, which is based on "industry-adaptive parameter model + dynamic algorithm", specifically includes: Basic weight calculation technology: The judgment matrix is ​​constructed using the hierarchical analysis algorithm. Three experts from environmental science, new energy industry and finance are invited to conduct pairwise importance comparisons of the E / S / G dimensions and their subordinate indicators. The weight vector is calculated by the eigenvalue decomposition method, and the basic weight parameters are output. Industry-differentiated weight adjustment technology: Designing piecewise functions to achieve dynamic weight adaptation based on the characteristics of the new energy storage and new energy vehicle industries; Standardization technology for quantitative indicators: Design a standardization algorithm based on the 3σ principle for quantitative indicators such as "carbon emissions per unit of revenue" and "R&D intensity"; If the index value is ≤50%μ, 100 points are awarded; If 50%μ < index value ≤ 100%μ, the score is 100 - (compliance rate - 0.5) × 80. If the index value = 100%μ, you get 60 points; If 100%μ < index value ≤ 150%μ, the score is 20 + (1.5 - benchmarking rate) × 80. If the index value is >150%μ, 20 points are awarded; Among them, the benchmarking rate = enterprise indicator value / industry benchmark value, which realizes the objectification and technical processing of indicator scoring; Qualitative indicator parameter mapping technology: Transform qualitative indicators such as "Certification of High-Quality SMEs" and "Certification of Green Factories" into computable parameters.

4. The method for linking corporate ESG reporting and green credit according to claim 2, characterized in that, The method of using "multi-source data interface integration + quality inspection algorithm" as a link and employing data collaboration technology to overcome data fragmentation specifically includes: Multi-source data interface integration technology: Develop standardized API interfaces to achieve real-time connection with carbon account platforms, China Central Bank accounts receivable platforms, and the Ministry of Industry and Information Technology's digital transformation system. The interfaces use JSON format for transmission, and the data update frequency is set differently according to the indicator type. Data compatibility is ensured through data format verification algorithms. The remaining 10% of supplementary data is integrated with government data from the Ministry of Industry and Information Technology, the Ministry of Science and Technology, and the Ministry of Human Resources and Social Security through the High-tech Zone's big data platform. Data anomaly detection and repair technology: For volatile indicators such as "carbon emissions per unit of tax revenue" and "employment growth rate", an anomaly detection algorithm based on box plots was developed. After the system automatically marks the anomaly, combined with the information that the production process has not changed, it is determined to be a data entry error, and the average value of the previous 3 months is called to repair it. Data fluctuation smoothing technology: In response to the characteristic of the new energy vehicle industry that "quarterly revenue fluctuations exceed 40%", a dynamic 12-month moving average algorithm was developed to process cyclical indicators such as taxes and profits. The algorithm formula is: monthly smoothed value = (sum of actual values ​​of the past 12 months) / 12. If data for a certain month is missing, it is supplemented by the average of the next 3 months. Among them, profit is updated quarterly data, and the monthly data of the moving average is estimated by dynamically calculating the profit data of the previous four quarters. Data security transmission technology: We developed a role-based access control algorithm to assign data access permission parameters to banks and enterprises respectively. We intercepted unauthorized access in real time through permission verification algorithms. At the same time, we collaborated with credit reporting agencies and Xingwang Data Co., Ltd. of Zhaoqing High-tech Zone to form the first batch of enterprise ESG credit reports in the province, which were incorporated into the enterprise credit reporting system.

5. The method for linking corporate ESG reporting and green credit according to claim 2, characterized in that, The aforementioned green finance closed loop, centered on the "rating-credit parameter linkage model" and employing financial linkage technology, specifically includes: Interest rate parameter linkage algorithm: Establishing ESG rating; Dynamic loan limit calculation model: Design a limit calculation algorithm based on ESG rating and enterprise operating data. The formula is: Credit limit = (Previous year's revenue × Revenue coefficient) + (ESG rating score × Rating coefficient). De-core blockchain lending data sharing technology: Develop a supply chain data interface platform, where core enterprises share upstream enterprise transaction data such as "purchase amount and payment cycle" with banks through API interfaces. The system develops a transaction authenticity verification algorithm and uses the ESG rating score of SMEs as a credit enhancement parameter, embedding it into the bank's credit granting model to achieve full automation of the "data sharing - automatic credit enhancement - credit limit approval" process without the need for core enterprise guarantees. Talent training effectiveness feedback algorithm: For "digital fairness transformation loans", a quantitative algorithm for training effectiveness has been developed. The formula is: Training effectiveness score = (number of certified / total number of trainees) × 100 × certificate weight; When the score is ≥ 80 points, the system automatically triggers the loan amount increase algorithm: increase amount = original amount × 10%, and the score is simultaneously included in the enterprise's ESG "personnel transformation" indicator parameters to realize the technical closed loop of "training-rating-credit".

6. The method for linking corporate ESG reporting and green credit according to claim 1, characterized in that, The linkage method also includes: Multi-platform collaborative ESG data governance; Data collection implementation: Targeted collection according to the "90%+10%" rule: 90% of environmental data: Automatically captures enterprise natural gas / steam / electricity consumption from the carbon account platform, and calculates direct / indirect carbon emissions using the emission factor method; the remaining 10% is supplemented from the government affairs system of the High-tech Zone's ecological and environmental department. 90% of the social data comes from supply and demand matching platforms, which provide data on enterprise supply chain transaction volume and platform activity; and from the China Central Depository & Clearing Co., Ltd. (CCDC) accounts receivable platform, which provides data on accounts payable ratio. The remaining 10% is extracted from the science and technology department's filing system. Governance data: Obtain the transformation progress from the "Digital Transformation Enterprise Directory" of the Ministry of Industry and Information Technology, and obtain the "Installation Status of Smart Energy IoT Sensing Equipment" from the smart energy platform; Data processing and quality control: For indicators such as tax revenue, income, profit, and employment that are affected by seasonal fluctuations, a "rolling 12-month average" is used to smooth out short-term fluctuations, with quarterly profit estimation being a key step.

7. The method for linking corporate ESG reporting and green credit according to claim 6, characterized in that, The quarterly profit estimate specifically includes: The process adopts a four-step workflow: "data collection → rule determination → allocation calculation → verification and update". Data collection: Obtain profit data for the company for four consecutive quarters, including the current quarter and the previous three quarters, to ensure a complete period; The allocation rules are determined as follows: Standard rule: If a company does not have significant monthly profit fluctuations, the profit is averaged out to the current month as "quarterly profit ÷ 3"; Special rule: If the company's historical data shows that there are peak and off-peak seasons within a quarter, the allocation will be weighted according to the historical monthly proportion. The weighting needs to be jointly confirmed and filed by the high-tech zone's industry and information technology department and the company. Monthly profit calculation: The quarterly profit is allocated according to the established rules to obtain the estimated monthly profit.

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