A carbon credit conversion method and application system driven by consumer behavior

Abstract

This invention discloses a consumer behavior-driven carbon credit conversion method and application system. By collecting purchase data of near-expiry food, the system calculates emission reductions by category carbon reduction coefficient multiplied by measurement, and maps these reductions to carbon credits in real time (1kg CO₂e = 10 credits, error ≤5%), and connects to a third-party carbon trading platform. This solves the problems of coarse calculations and poor real-time performance in existing methods, and can accurately incentivize users to engage in green consumption. It is suitable for carbon credit scenarios on community O2O platforms.

Description

Technical Field

[0001] This invention relates to the field of carbon credit technology applications, specifically an application system and method that calculates carbon emission reductions driven by consumer behavior (such as purchasing near-expiry food) and converts the emission reductions into carbon credits in real time. It is applicable to scenarios such as community O2O e-commerce platforms and local life service platforms, promoting the implementation of a closed loop of "green consumption - carbon credit incentives". Background Technology

[0002] 2.1 Deficiencies of Existing Carbon Credit Conversion Methods Current carbon credit conversion methods largely rely on extensive calculations (such as uniformly converting emission reductions based on consumption amounts), which have the following problems: - Inaccurate emission reduction calculations: Failure to differentiate between consumption behavior types (such as purchasing near-expiry food versus regular food), ignoring the core emission reduction contribution of "reducing food waste"; - Poor real-time performance: Carbon credit conversion is delayed (e.g., monthly settlement), which cannot immediately incentivize users to engage in green behaviors; - Lack of public credibility: Without connection to a third-party carbon trading platform, the value of carbon credits is difficult to quantify.

[0003] 2.2 Problems to be Solved by the Invention To address the above-mentioned deficiencies, the present invention needs to achieve: - Accurate calculation of emission reductions: Differentiated carbon emission reduction coefficients are set based on near-expiry food categories, and the calculation is based on "category coefficient × measurement"; - Real-time carbon credit mapping: Real-time conversion between emission reductions and carbon credits (1kg CO2e = 10 carbon credits), with an error of ≤5%; - Connect with third-party platforms: Enhance the credibility of carbon credits and support the conversion of credits into cash or charitable donations. Summary of the Invention

[0004] 3.1 Technical Issues This paper provides a carbon credit conversion method and application system driven by consumer behavior, which solves the problems of crude emission reduction calculation, poor real-time performance and insufficient credibility in existing methods.

[0005] 3.2 Technical Solution Core logic: Consumption behavior (purchasing near-expiry food) → Emission reduction calculated by category carbon reduction coefficient × metering → 1kg CO2e = 10 carbon credits, real-time mapping of carbon credits → Connecting to third-party carbon trading platforms (such as Shanghai Environment Exchange).

[0006] Method steps (as shown in Figure 1): 1. Consumer behavior data collection: Obtain information on the categories (such as bread, milk, and snacks), quantities, and net contents of near-expiry food purchased by users through the platform's transaction system; 2. Setting Carbon Emission Reduction Factors by Category: Based on the "Guidelines for Carbon Emission Reduction Accounting of Near-Expiry Foods," carbon emission reduction factors (unit: kg CO2 / g / ml) are preset for each category of near-expiry foods, for example: - Near-expiry bread: 0.1256kg CO2e / 100g (emission reduction corresponding to reducing food waste); - Milk nearing its expiration date: 0.63 kg CO2e / 200 ml; - Near-expiry snacks: 0.5kg CO2e / 100g; 3. Emission Reduction Calculation: The total emission reduction R is calculated using the formula R = \sum_{i=1}^{n} (k_i \times q_i), where k_i is the carbon emission reduction coefficient of the i-th type of food and q_i is the quantity purchased; 4. Real-time carbon integral mapping: Converting carbon integral P = R × 10 based on 1kg CO2e = 10, with error control ≤5% (through calibration mechanism: weekly comparison with third-party platform data to correct coefficients). 5. Connect to third-party carbon trading platforms: Upload carbon credits to platforms such as the Shanghai Environment Exchange via API interface, supporting the redemption of credits for benefits (such as public tree planting and commodity deduction).

[0007] System architecture (as shown in Figure 2): - Data acquisition module: Connects to the platform's transaction system to obtain purchase data of near-expiry food products; - Coefficient Management Module: Stores and updates category carbon emission reduction coefficients (supports manual adjustment or automatic synchronization with national standards); - Emission reduction calculation module: Execute the formula R = \sum (k_i \times q_i); - Integral mapping module: Real-time conversion of emission reductions to carbon credits (error ≤ 5%); - Third-party integration module: Connects to the Shanghai Environment Exchange carbon trading platform via HTTPS API to upload credit data.

[0008] 3.3 Beneficial Effects - Accuracy: Emission reduction is calculated based on category coefficients with an error of ≤5% (compared to >20% for traditional methods); - Real-time performance: Points conversion delay < 1 second (traditional monthly settlement); - Credibility: Connected to third-party platforms, carbon credits are traceable and convertible to cash; - Incentives: Users earn points instantly for green consumption, resulting in a 35% increase in repurchase rate in actual tests. Attached Figure Description - Figure 1: Flowchart of the carbon credit conversion method (including 5 core steps); - Figure 2: System architecture diagram (data acquisition → coefficient management → emission reduction calculation → integral mapping → third-party integration). Detailed Implementation Example 1: Carbon credit conversion of bread purchased by users nearing its expiration date 1. Data collection: The user purchases 2 near-expiry breads (Category: Bread, Quantity = 2, Net weight 100g / piece) through the "Enjoy Points Platform". 2. Coefficient Matching: Retrieve the coefficient k=0.1256 for "near-expiry bread" from the coefficient management module; 3. Emission reduction calculation: R = 0.1256 × 2 = 0.2512; 4. Integral mapping: P = 0.2512 × 10 = 2.512, carbon integral, error verification (compared with similar data from the Environmental Exchange, error 0.002 kg → 0.2%, which meets the requirement of ≤5%). 5. Third-party integration: Upload 1 carbon credit to the Environmental Exchange via API, and the user's carbon account will be updated in real time.

[0009] Example 2: Error Control Mechanism - Weekly calibration: The platform's points data is compared with the feedback data from the Environmental Exchange every week. If the deviation of a certain category coefficient is >5% (e.g., the actual coefficient of near-expiry milk is 0.07 vs. the preset coefficient of 0.08), the coefficient is automatically corrected to 0.075.

Claims

1. A consumer behavior-driven method for converting carbon credits into inclusive benefits, characterized in that, Includes the following steps: (1) Collect consumer behavior data on users purchasing food nearing its expiration date, including food category and quantity; (2) Match a preset carbon emission reduction coefficient according to the food category. The coefficient is the CO2 emission reduction corresponding to the reduction of food waste per unit quantity of near-expiry food (unit: kgCO2 / piece / item). (3) Calculate the total emission reduction R according to the formula R=\sum_{i=1}^{n}(k_i\timesq_i), where k_i is the carbon emission reduction coefficient of the i-th type of food and q_i is the quantity purchased; (4) The total emission reduction R is mapped in real time to carbon integral P = R × 10 at a ratio of 1 kg CO2 = 10, and the mapping error is controlled to be ≤5%; (5) Connect the carbon credits P to a third-party carbon trading platform via the API interface.

2. The method according to claim 1, characterized in that, The carbon emission reduction coefficient mentioned in step (2) is set based on the "Guideline for Carbon Emission Reduction Accounting of Near-Expiry Foods", including: 0.1256kg CO2e / 100g for near-expiry bread, 0.63kg CO2e / 200ml for near-expiry milk, and 0.5kg CO2e / 100g for near-expiry snacks.

3. The method according to claim 1, characterized in that, The error control described in step (4) is achieved through weekly calibration: weekly comparison of platform integral data with feedback data from third-party platforms, correcting category coefficients with deviations >5%.

4. The method according to claim 1, characterized in that, The third-party carbon trading platform mentioned in step (5) is the Shanghai Environment and Energy Exchange, which supports the exchange of carbon credits for public tree planting or commodity deduction.

5. A carbon inclusive credit conversion system implementing the method of claims 1-4, characterized in that, include: - Data acquisition module, which connects to the platform's transaction system to obtain purchase data of near-expiry food products; - The coefficient management module stores and updates category carbon emission reduction coefficients; - The emission reduction calculation module executes the formula R = \sum (k_i \times q_i); - Integral mapping module, converts emission reductions into carbon credits in real time (error ≤5%). - Third-party integration module, which connects to third-party carbon trading platforms via HTTPS API.

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