Enterprise-level carbon popularity behavior quantitative excitation and management system

Abstract

The invention provides an enterprise-level carbon popularity behavior quantitative excitation and management system, and relates to the field of enterprise-level carbon popularity behaviors. The enterprise-level carbon popularity behavior quantitative excitation and management system comprises a system overall architecture, a low-carbon scene recognition module, a behavior quantification module, a carbon point management module, an excitation measure module and a data statistics and report module, and the system overall architecture adopts a cloud-based service architecture. The low-carbon scene recognition module defines and recognizes low-carbon behaviors through user behavior data. The data processing efficiency of the whole system is improved through automatic data acquisition of the Internet of Things technology, so that resource allocation and strategy making are optimized, a user can immediately see a direct result of a low-carbon behavior of the user, and instant feedback is a key factor for stimulating continuous participation and behavior change, so that a single behavior is awarded, and the user experience is improved. And continuous environmental protection behaviors are encouraged, and long-term environmental responsibility is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise-level carbon inclusive behavior, and specifically to an enterprise-level carbon inclusive behavior quantitative incentive and management system. Background Art

[0002] As global attention to environmental protection and climate change increases, various companies are seeking ways to reduce carbon emissions. Carbon management and emission reduction have become an important part of corporate social responsibility and sustainable development strategies. Although there are a variety of carbon management tools and solutions on the market, many still rely on manual data entry and processing, lacking automation and personalized incentive mechanisms to truly stimulate employee participation.

[0003] Existing carbon management systems mainly focus on carbon emission monitoring and reporting of corporate production, and rarely involve carbon emission data processing and dynamic integral feedback systems at the employee level. Existing systems usually lack data collection and analysis functions directly related to employees' daily behaviors, and fail to provide effective incentives to encourage employees to adopt low-carbon behaviors. In addition, these systems often require complex configuration and high-cost maintenance, which limits their popularity and efficiency. Specifically, defects and shortcomings of existing technologies Many carbon management systems rely on manual data input, which is not only prone to data errors, but also greatly affects the efficiency and reliability of data processing. Existing systems usually cannot provide users with instant updated feedback, which makes it difficult for employees to understand their carbon emission reduction contributions in a timely manner, reducing participation and motivation. In addition, traditional carbon management tools usually provide very limited incentives, which are often insufficient to attract and maintain long-term employee participation, especially in stimulating sustained behavioral changes. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides an enterprise-level carbon inclusive behavior quantification incentive and management system, which solves the problems of reliance on manual data input, lack of real-time dynamic feedback, and the singleness and lack of innovation of incentive measures.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an enterprise-level carbon inclusive behavior quantification incentive management system, including a system overall architecture, a low-carbon scenario recognition module, a behavior quantification module, a carbon credit management module, an incentive module and a data statistics and reporting module. The system overall architecture adopts a cloud-based service architecture. The low-carbon scenario recognition module defines and identifies low-carbon behaviors through user behavior data. The behavior quantification module implements quantitative processing of identified low-carbon behaviors. The carbon credit management module is used to manage the carbon credits obtained by users. The incentive module provides corresponding incentives based on the user's points. The data statistics and reporting module provides data reports required by management.

[0006] Preferably, the overall architecture of the system includes a front-end user interface and a back-end data processing module, wherein the front-end provides an interactive interface for users and the back-end processes data collection, analysis and feedback.

[0007] Preferably, the low-carbon scenario recognition module identifies low-carbon behavior patterns through user behavior data, such as commuting methods, vehicle energy consumption and other information.

[0008] Preferably, the behavior quantification module calculates the corresponding carbon emission reduction and converts the data on the use of new energy vehicles and public transportation into carbon credits.

[0009] Preferably, the carbon credit management module realizes the recording, accumulation and use of user credits.

[0010] Preferably, the incentive module is used to enhance the user's motivation for low-carbon behavior.

[0011] Preferably, the data statistics and reporting module is used to help management formulate more effective carbon emission reduction strategies through carbon emission reduction statistical reports and employee participation analysis.

[0012] Preferably, the working mode of the module can be summarized into the following steps:

[0013] S1. Define low-carbon activity parameters: The administrator defines the low-carbon activity parameters of the enterprise, including but not limited to:

[0014] User information maintenance: automatically import basic employee information, such as name, work number, department, commuting method, boarding and alighting stations, etc.;

[0015] Route vehicle management: input and update information of shuttle buses and new energy vehicles, such as license plate number, route, and driving distance;

[0016] Algorithm model parameters for low-carbon activities: set carbon emission calculation models for different commuting modes, such as shuttle bus commuting and new energy vehicle commuting;

[0017] S2. Collect low-carbon activity data of target enterprises and identify specific application scenarios of carbon credit for enterprises:

[0018] S2.1 Collect low-carbon activity data of target enterprises: Use Internet of Things (IoT) technology to install sensors on shuttle buses and new energy vehicles to automatically record key parameters such as vehicle driving distance, energy consumption, and number of starts. Through enterprise resource planning system (ERP) integration, automatically collect employee commuting patterns, new energy vehicle-related data, etc.;

[0019] S2.2 Identify the application scenarios of enterprise carbon credits: Determine the specific application scenarios based on the source of the low-carbon activity data collected from the target enterprise. For example, if it involves new energy private car data, it is determined to be a new energy private car commuting scenario; if it involves shuttle bus data, it is determined to be a public shuttle bus commuting scenario;

[0020] S3. Based on the identified specific scenarios, design a suitable algorithm model to calculate the carbon emission reduction and carbon credits of the corresponding low-carbon activities:

[0021] S3.1 Calculate the corresponding carbon emission reduction based on the specific scenarios identified;

[0022] (1) Calculate the corresponding carbon emission reduction in the scenario where corporate employees take public buses to work;

[0023] (2) Calculate the corresponding carbon emission reductions in scenarios where corporate employees drive new energy vehicles to work;

[0024] (3) Calculate the corresponding carbon emission reductions for enterprise employees walking or biking to work;

[0025] S3.2 Convert the calculated carbon emission reduction into carbon credits;

[0026] Convert reduced carbon dioxide emissions (usually in metric tons) into carbon coins, and implement different point redemption strategies to better reflect the different contributions of low-carbon and zero-carbon activities; divided into low-carbon scenario point redemption, zero-carbon scenario point redemption and additional incentives;

[0027] S4. Statistical analysis and report generation: The system classifies and counts all collected and calculated data, including generating detailed carbon credit lists and reports by individual employee, department, and commuting mode, showing the overall and individual carbon reduction results, as well as the carbon accumulation obtained by employees;

[0028] S5. Incentive measures based on the system: According to the carbon credits of employees, the system automatically provides corresponding incentives, including material rewards, such as coupons, health insurance discounts, etc., which can be directly redeemed with credits through the system, honorary rewards, such as issuing the title of "Green Pioneer", public recognition in company newsletters or bulletin boards, real-time updates and feedback on employees' credit status and incentive results, and enhanced transparency and participation.

[0029] The present invention provides an enterprise-level carbon benefit behavior quantitative incentive and management system, which has the following beneficial effects:

[0030] 1. The present invention uses the Internet of Things technology to automate data collection and integrate the real-time operation data of shuttle buses and new energy vehicles. Automated collection reduces human errors and improves data accuracy. This not only makes carbon emission calculations more accurate, but also improves the data processing efficiency of the entire system, thereby optimizing resource allocation and strategy formulation.

[0031] 2. The present invention allows employees to instantly view their carbon credits and carbon reduction contributions through real-time data processing and credit update systems. This function enhances employee participation and motivation because users can immediately see the direct results of their low-carbon behaviors. This instant feedback is a key factor in stimulating continued participation and changing behavior.

[0032] 3. The present invention has designed differentiated carbon credit exchange ratios for low-carbon and zero-carbon scenarios, and introduced an additional incentive mechanism based on continuous compliance. Different credit exchange ratios are designed for low-carbon and zero-carbon scenarios to reflect the difference in the contribution of different behaviors to carbon reduction. In low-carbon scenarios (such as using new energy buses), 10 carbon coins are obtained for every ton of CO2 emissions reduced, while in zero-carbon scenarios (such as walking or cycling), 15 carbon coins are obtained for every ton of CO2 emissions reduced. This method encourages employees to adopt more environmentally friendly travel methods, further improves the emission reduction effect, and introduces additional incentives based on continuous compliance. For employees who meet or exceed personal carbon reduction targets for three consecutive months (low-carbon) or six months (zero-carbon), additional carbon coin rewards are provided. This design not only rewards single behaviors, but also encourages continuous environmental protection behaviors and promotes long-term environmental responsibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A service method flow chart of a corporate carbon credit platform provided for this application;

[0034] Figure 2 A flow chart of a quantitative calculation method for carbon inclusive low-carbon activities of enterprises provided for this application;

[0035] Figure 3 A structural diagram of the service system of an enterprise carbon credit platform provided for this application. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] Embodiment 1:

[0038] like Figure 1-3As shown, an embodiment of the present invention provides an enterprise-level carbon inclusive behavior quantification incentive management system, including a system overall architecture, a low-carbon scenario recognition module, a behavior quantification module, a carbon credit management module, an incentive measure module and a data statistics and reporting module. The system overall architecture system adopts a cloud-based service architecture. The low-carbon scenario recognition module defines and identifies low-carbon behaviors through user behavior data. The behavior quantification module implements quantification processing of the identified low-carbon behaviors. The carbon credit management module is used to manage the carbon credits obtained by the user. The incentive measure module provides corresponding incentive measures according to the user's credit situation. The data statistics and reporting module provides data reports required by the management. The architecture includes a front-end user interface and a back-end data processing module. The front-end provides an interactive interface for users, and the back-end processes data collection, analysis and feedback. The low-carbon scenario recognition module identifies low-carbon behavior patterns through user behavior data, such as commuting methods, vehicle energy consumption and other information. The behavior quantification module calculates the corresponding carbon emission reduction and converts the data on the use of new energy vehicles and public transportation into carbon credits. The carbon credit management module records, accumulates and uses user credits. The incentive module enhances the user's motivation for low-carbon behavior. The data statistics and reporting module uses carbon emission reduction statistical reports and employee participation analysis to help management formulate more effective carbon emission reduction strategies.

[0039] Specific IoT technology automates data collection and integrates real-time operation data of shuttle buses and new energy vehicles. Automated collection reduces human errors and improves data accuracy. This not only makes carbon emission calculations more accurate, but also improves the data processing efficiency of the entire system, thereby optimizing resource allocation and strategy formulation. Through real-time data processing and point update systems, employees are allowed to instantly view their carbon points and carbon reduction contributions. This feature enhances employee participation and motivation because users can immediately see the direct results of their low-carbon behavior. This instant feedback is a key factor in stimulating continued participation and changing behavior. Different carbon point exchange ratios are designed for low-carbon and zero-carbon scenarios, and additional incentive mechanisms based on continuous compliance are introduced. Different point exchange ratios are designed for low-carbon and zero-carbon scenarios to reflect the difference in the contribution of different behaviors to carbon reduction. Low-carbon scenarios (such as using new energy shuttles) receive 10 carbon coins for every ton of CO2 emissions reduced, while zero-carbon scenarios (such as walking or cycling) receive 15 carbon coins for every ton of CO2 emissions reduced. This approach encourages employees to adopt more environmentally friendly travel methods, further improves the emission reduction effect and introduces additional incentives based on continuous achievement of targets. Employees who meet or exceed their personal carbon reduction targets for three consecutive months (low carbon) or six consecutive months (zero carbon) are provided with additional carbon coin rewards. This design not only rewards single behaviors, but also encourages continuous environmentally friendly behaviors and promotes long-term environmental responsibility.

[0040] Embodiment 1:

[0041] The embodiment of the present invention provides an enterprise-level carbon inclusive behavior quantitative incentive method, using an enterprise-level carbon inclusive behavior quantitative incentive management system as described in any one of claims 1 to 7, characterized in that it includes the following steps:

[0042] S1. Define low-carbon activity parameters: The administrator defines the low-carbon activity parameters of the enterprise, including but not limited to:

[0043] User information maintenance: automatically import basic employee information, such as name, work number, department, commuting method, boarding and alighting stations, etc.;

[0044] Route vehicle management: input and update information of shuttle buses and new energy vehicles, such as license plate number, route, and driving distance;

[0045] Algorithm model parameters for low-carbon activities: set carbon emission calculation models for different commuting modes, such as shuttle bus commuting and new energy vehicle commuting;

[0046] S2. Collect low-carbon activity data of target enterprises and identify specific application scenarios of carbon credit for enterprises:

[0047] S2.1 Collect low-carbon activity data of target enterprises: Use Internet of Things (IoT) technology to install sensors on shuttle buses and new energy vehicles to automatically record key parameters such as vehicle driving distance, energy consumption, and number of starts. Through enterprise resource planning system (ERP) integration, automatically collect employee commuting patterns, new energy vehicle-related data, etc., including:

[0048] ① Shuttle bus data: including daily operating hours, number of passengers, actual driving distance and time, and fuel consumption or electricity consumption;

[0049] ② New energy private car data: record daily driving distance, battery consumption, charging times, etc.;

[0050] ③ Employee commuting pattern data: commuting time and entry and exit data collected through the employee ID card punching system to calculate commuting distance and mode;

[0051] S2.2 Identify the application scenarios of enterprise carbon credits: Determine the specific application scenarios based on the source of the low-carbon activity data collected from the target enterprise. For example, if it involves new energy private car data, it is determined to be a new energy private car commuting scenario; if it involves shuttle bus data, it is determined to be a public shuttle bus commuting scenario;

[0052] S3. Based on the identified specific scenarios, design a suitable algorithm model to calculate the carbon emission reduction and carbon credits of the corresponding low-carbon activities:

[0053] S3.1 Calculate the corresponding carbon emission reduction based on the specific scenarios identified;

[0054] (1) Calculate the corresponding carbon emission reduction in the scenario where corporate employees take public buses to work;

[0055] The baseline scenario is a realistic scenario before the implementation of the project activities, that is, employees drive traditional fuel vehicles to work. Employees take the company shuttle bus to work instead of driving traditional fuel vehicles, reducing carbon dioxide emissions.

[0056] ① Baseline emissions;

[0057] Baseline emissions (BE c，t ) is the carbon dioxide emissions generated under the baseline scenario, BE c，t The calculation formula is as follows:

[0058]

[0059] In the formula,

[0060] BE c，t : Total emissions of the baseline scenario in year t, t CO2;

[0061] EF km，i，m : Emission factor for baseline vehicle type m and fuel type i, g CO2 / km;

[0062] DD b，t : driving distance of project vehicle type m in year t, km;

[0063] DD b，t =D b，i ×N b，i

[0064] D b，i : The distance traveled by an employee on a single bus trip, km / trip;

[0065] N b，i : The number of times an employee takes the shuttle bus in year t;

[0066] EF km，i，m =BSFC km，i,m ×NCV i ×EF i

[0067]

[0068] In the formula,

[0069] EF km，i，m : Emission factor for baseline vehicle type m and fuel type i, g CO2 / km;

[0070] BSFC km，i，m: Baseline vehicle type m fuel type i consumption rate, g / km;

[0071] NCV i : The lower calorific value of fuel type i consumed by the baseline vehicle, GJ / t;

[0072] EF i : Emission factor of fuel type i consumed by the baseline vehicle, t CO2 / GJ;

[0073] CC i : Carbon content per unit calorific value of fuel type i of the baseline vehicle, t C / GJ;

[0074] OF i : Carbon oxidation rate of fuel type i of the baseline vehicle, %;

[0075] The ratio of the molecular weight of carbon dioxide to carbon;

[0076] ②Emissions from taking shuttle buses;

[0077] Emissions from shuttle bus rides (PE b，t ) is the carbon dioxide emissions generated by fuel consumption in vehicle operation, PE b，t The calculation is as follows:

[0078] PE b,t =DD b，t ×EF e，t

[0079] In the formula,

[0080] PE b，t : total emissions in year t, t CO2;

[0081] DD b，t : The distance traveled by an employee by bus in year t, km;

[0082] EF e，t : CO2 emission factor of the energy consumed by the shuttle bus in year t, t CO2 / km.

[0083] EF e，t =E m,t / L t / N

[0084] E m，t : The total emissions generated by a bus in year t, t CO2

[0085] L t : The total mileage of a bus in year t, km

[0086] N: the total number of passengers taking the bus in year t;

[0087] ③ Emission reduction;

[0088] Emission reduction from taking a shuttle bus instead of driving a traditional fuel vehicle (ER b，t ) is calculated as follows:

[0089] ER b,t =BE c，t -PE b,t

[0090] in:

[0091] ER b，t : Emission reduction caused by an employee taking the shuttle bus in year t, t CO2;

[0092] BE c，t : baseline emissions in year t, t CO2;

[0093] PE b，t : Emissions of a certain employee taking the shuttle bus in year t, t CO2;

[0094] (2) Calculate the corresponding carbon emission reductions in scenarios where corporate employees drive new energy vehicles to work;

[0095] The baseline scenario is a realistic scenario before the implementation of the project activities, that is, employees drive traditional fuel vehicles to work. Employees drive new energy vehicles to work instead of traditional fuel vehicles, reducing carbon dioxide emissions. New energy vehicles are assumed to consume electricity.

[0096] ① Baseline emissions;

[0097] Baseline emissions (BE c，t ) is the carbon dioxide emissions generated under the baseline scenario, BE c，t The calculation formula is as follows:

[0098]

[0099] In the formula,

[0100] BE c，t : Total emissions of the baseline scenario in year t, t CO2;

[0101] EF km，i，m : Emission factor for baseline vehicle type m and fuel type i, g CO2 / km;

[0102] DD x，t : The driving distance of an employee’s self-driving new energy vehicle in year t, km;

[0103] DD x,t =D x,i ×N x，i

[0104] D x，i : The distance traveled by an employee in a single self-driving new energy vehicle, km;

[0105] N x，i : The number of times an employee drives a new energy vehicle in year t;

[0106] EF km，i，m =BSFC km,i,m ×NCV i ×EF i

[0107]

[0108] In the formula,

[0109] EF km，i，m : Emission factor for baseline vehicle type m and fuel type i, g CO2 / km;

[0110] BSFC km，i，m : Baseline vehicle type m fuel type i consumption rate, g / km;

[0111] NCV i : The lower calorific value of fuel type i consumed by the baseline vehicle, GJ / t;

[0112] EF i : Emission factor of fuel type i consumed by the baseline vehicle, t CO2 / GJ;

[0113] CC i : Carbon content per unit calorific value of fuel type i of the baseline vehicle, t C / GJ;

[0114] OF i : Carbon oxidation rate of fuel type i of the baseline vehicle, %;

[0115] The ratio of the molecular weight of carbon dioxide to carbon;

[0116] ②Emissions from an employee's self-driving new energy vehicle;

[0117] Emissions of a certain employee's self-driving new energy vehicle (PE x，t ) is the carbon dioxide emissions generated by the electricity consumption of vehicle operation, PE x，t The calculation is as follows:

[0118] PE x，t =DD x，t ×EF x,t

[0119] In the formula,

[0120] PE x，t : The total emissions of a certain employee's self-driving new energy vehicle in year t, t CO2;

[0121] DD x，t : The distance traveled by an employee in the tth year by using a new energy vehicle, km;

[0122] EFx ，t : CO2 emission factor of energy consumption of self-driving new energy vehicles in year t, tCO2 / km;

[0123] ③ Emission reduction;

[0124] Emission reduction brought about by self-driving new energy vehicles instead of self-driving traditional fuel vehicles (ER x，t ) is calculated as follows:

[0125] ER x，t =BE c，t -PE x，t

[0126] in:

[0127] ER x，t : Emission reduction brought about by an employee driving a new energy vehicle in year t, t CO2;

[0128] BE c，t : baseline emissions in year t, t CO2;

[0129] PE x，t : The total emissions of a certain employee's self-driving new energy vehicle in year t, t CO2;

[0130] (3) Calculate the corresponding carbon emission reductions for enterprise employees walking or biking to work;

[0131] The baseline scenario is a realistic scenario before the implementation of the project activities, that is, employees take public transportation to work. Employees travel green (walking, cycling), reducing carbon dioxide emissions.

[0132] ① Baseline emissions;

[0133] Baseline emissions (PE g，t ) is the carbon dioxide emissions generated by employees taking public transportation, PE c，t The calculation is as follows:

[0134] PE g，t =DD g，t ×EF g，t

[0135] In the formula,

[0136] PEg，t : Total emissions of an employee taking public transportation in year t, t CO2;

[0137] DD g，t : The distance travelled by an employee by public transport in year t, km;

[0138] EF g，t : CO2 emission factor of energy consumed by public transport in year t, t CO2 / km.

[0139] ②Emissions from an employee’s green travel;

[0140] Project Emissions (PE z，t ) is the carbon dioxide emissions generated by employees walking in a certain enterprise under the carbon inclusive scenario, PE z，t The calculation formula is as follows:

[0141] PE z，t =0

[0142] PE z，t : Total green travel emissions of an employee in year t, t CO2.

[0143] ③ Emission reduction;

[0144] The corresponding reduction in carbon dioxide emissions (PE g，t ) is calculated as follows:

[0145] ER z，t =PE g，t -PE z，t

[0146] In the formula,

[0147] ER z，t : The carbon dioxide emission reduction corresponding to the green travel of an employee in the tth year, t CO2;

[0148] BE c，t : Total emissions of the baseline scenario in year t, t CO2;

[0149] PE z，t : Total green travel emissions of an employee in year t, t CO2;

[0150] S3.2 Convert the calculated carbon emission reduction into carbon credits;

[0151] Convert reduced carbon dioxide emissions (usually in metric tons) into carbon coins, and implement different point redemption strategies to better reflect the different contributions of low-carbon and zero-carbon activities; divided into low-carbon scenario point redemption, zero-carbon scenario point redemption and additional incentives;

[0152] Low-carbon scenario points redemption: Low-carbon scenarios refer to activities that reduce but do not completely eliminate carbon emissions, such as taking the company's new energy shuttle. For such activities, 10 carbon coins will be awarded for every ton of CO2 emissions reduced. In addition, to encourage continuous improvement, employees who meet or exceed their personal carbon reduction targets for three consecutive months will receive an additional 5% carbon coin reward;

[0153] Zero-carbon scenario points redemption: Zero-carbon scenarios refer to activities that do not generate carbon emissions at all, such as employees walking or cycling to work. For such activities, 15 carbon coins will be awarded for every ton of CO2 emissions reduced (compared to baseline emissions). Since zero-carbon activities contribute more to carbon reduction targets, higher rewards are provided as encouragement;

[0154] Additional incentives: Employees who achieve their carbon reduction targets for six consecutive months, whether low-carbon or zero-carbon scenarios, will receive an additional 10 carbon coins, as well as possible non-material rewards such as priority parking spaces or special recognition within the company.

[0155] The specific calculation steps are as follows:

[0156] (1) Basic carbon coin calculation

[0157] ① Calculation of carbon coins for low-carbon activities

[0158] The number of carbon coins obtained by an employee from low-carbon activities in year t (Clow,t) = the total carbon emission reduction of an employee in year t (ERt) * the basic carbon coin reward ratio of low-carbon activities (Plow)

[0159] ② Calculation of carbon coins for zero-carbon activities

[0160] The number of carbon coins obtained by an employee from zero-carbon activities in year t (Czero,t) = the total carbon emission reduction of an employee in year t (ERt) * the basic carbon coin reward ratio of low-carbon activities (Pzero)

[0161] (2) Calculation of additional carbon coins

[0162] ① Extra rewards for low-carbon activities (if the target is met for three consecutive months):

[0163] Extra bonus for low-carbon activities (Clow, bonus) = the number of carbon coins an employee receives from low-carbon activities in year t (Clow, t) * the extra bonus ratio for low-carbon activities for three consecutive months (Ilow)

[0164] ② Additional rewards for zero-carbon activities (if the target is met for six consecutive months):

[0165] Extra bonus for zero-carbon activities (Czero, bonus) = the number of carbon coins an employee received from zero-carbon activities in year t (Czero, t) * the extra bonus ratio for zero-carbon activities for six consecutive months (Izero)

[0166] (3) Calculation of total carbon coins

[0167] ①Calculation of total carbon coins for low-carbon activities

[0168] Low-carbon activity carbon coins total carbon coins (Clow, total, t) = low-carbon activity basic carbon coins (Clow, t) + low-carbon activity additional bonus carbon coins (Clow, bonus)

[0169] ②Calculation of total carbon coins for zero-carbon activity carbon coins

[0170] Zero-carbon activity carbon coins total carbon coins (Czero, total, t) = zero-carbon activity basic carbon coins (Czero, t) + zero-carbon activity additional bonus carbon coins (Czero, bonus)

[0171] ③Total carbon coins calculation

[0172] Total carbon coins (Ctotal, t) = low carbon activity carbon coins total carbon coins (Clow, total, t) + zero carbon activity carbon coins total carbon coins (Czero, total, t);

[0173] S4. Statistical analysis and report generation: The system classifies and counts all collected and calculated data, including generating detailed carbon credit lists and reports by individual employee, department, and commuting mode, showing the overall and individual carbon reduction results, as well as the carbon accumulation obtained by employees;

[0174] S5. Incentive measures based on the system: According to the carbon credits of employees, the system automatically provides corresponding incentives, including material rewards, such as coupons, health insurance discounts, etc., which can be directly redeemed with credits through the system, honorary rewards, such as issuing the title of "Green Pioneer", public recognition in company newsletters or bulletin boards, real-time updates and feedback on employees' credit status and incentive results, and enhanced transparency and participation.

[0175] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An enterprise-level carbon inclusive behavior quantification incentive management system, including a system overall architecture, a low-carbon scenario recognition module, a behavior quantification module, a carbon credit management module, an incentive measure module, and a data statistics and reporting module, characterized in that: The overall system architecture adopts a cloud-based service architecture. The low-carbon scenario recognition module defines and identifies low-carbon behaviors through user behavior data. The behavior quantification module quantifies the identified low-carbon behaviors. The carbon credit management module is used to manage the carbon credits obtained by users. The incentive module provides corresponding incentives based on the user's credit situation. The data statistics and reporting module provides data reports required by management.

2. According to claim 1, an enterprise-level carbon benefit behavior quantitative incentive management system is characterized by: The overall architecture of the system includes a front-end user interface and a back-end data processing module. The front-end provides an interactive interface for users, and the back-end handles data collection, analysis and feedback.

3. According to claim 1, an enterprise-level carbon benefit behavior quantitative incentive management system is characterized by: The low-carbon scenario recognition module identifies low-carbon behavior patterns through user behavior data, such as commuting methods, vehicle energy consumption and other information.

4. According to claim 1, an enterprise-level carbon benefit behavior quantitative incentive management system is characterized by: The behavior quantification module calculates the corresponding carbon emission reduction and converts the data of new energy vehicle use and public transportation travel into carbon credits.

5. According to claim 1, an enterprise-level carbon benefit behavior quantitative incentive management system is characterized by: The carbon credit management module realizes the recording, accumulation and use of user credits.

6. According to claim 1, an enterprise-level carbon benefit behavior quantitative incentive management system is characterized by: The incentive module is used to enhance the user's motivation for low-carbon behavior.

7. According to claim 1, an enterprise-level carbon benefit behavior quantitative incentive management system is characterized by: The data statistics and reporting module is used to help management formulate more effective carbon emission reduction strategies through carbon emission reduction statistical reports and employee participation analysis.

8. A method for quantifying incentives for enterprise-level carbon inclusive behavior, using an enterprise-level carbon inclusive behavior quantification incentive management system as described in any one of claims 1 to 7, characterized in that: It includes the following steps: S1. Define low-carbon activity parameters: The administrator defines the low-carbon activity parameters of the enterprise, including but not limited to: User information maintenance: automatically import basic employee information, such as name, work number, department, commuting method, boarding and alighting stations, etc.; Route vehicle management: input and update information of shuttle buses and new energy vehicles, such as license plate number, route, and driving distance; Algorithm model parameters for low-carbon activities: set carbon emission calculation models for different commuting modes, such as shuttle bus commuting and new energy vehicle commuting; S2. Collect low-carbon activity data of target enterprises and identify specific application scenarios of carbon inclusiveness of enterprises: S2.1 Collect low-carbon activity data of target enterprises: Use Internet of Things (IoT) technology to install sensors on shuttle buses and new energy vehicles to automatically record key parameters such as vehicle driving distance, energy consumption, and number of starts. Through enterprise resource planning system (ERP) integration, automatically collect employee commuting patterns, new energy vehicle-related data, etc.; S2.2 Identify the application scenarios of enterprise carbon credits: Determine the specific application scenarios based on the source of the low-carbon activity data collected from the target enterprise. For example, if it involves new energy private car data, it is determined to be a new energy private car commuting scenario; if it involves shuttle bus data, it is determined to be a public shuttle bus commuting scenario; S3. Based on the identified specific scenarios, design a suitable algorithm model to calculate the carbon emission reduction and carbon credits of the corresponding low-carbon activities: S3.1 Calculate the corresponding carbon emission reduction based on the specific scenarios identified; (1) Calculate the corresponding carbon emission reduction in the scenario where corporate employees take public buses to work; (2) Calculate the corresponding carbon emission reductions in scenarios where corporate employees drive new energy vehicles to work; (3) Calculate the corresponding carbon emission reductions for enterprise employees walking or biking to work; S3.2 Convert the calculated carbon emission reduction into carbon credits; Convert reduced CO2 emissions (usually in metric tons) into carbon coins and implement different redemption strategies to better reflect the different contributions of low-carbon and zero-carbon activities; It is divided into low-carbon scenario points redemption, zero-carbon scenario points redemption and additional incentives; S4. Statistical analysis and report generation: The system classifies and counts all collected and calculated data, including generating detailed carbon credit lists and reports by individual employee, department, and commuting mode, showing the overall and individual carbon reduction results, as well as the carbon accumulation obtained by employees; S5. Incentive measures based on the system: According to the carbon credits of employees, the system automatically provides corresponding incentives, including material rewards, such as coupons, health insurance discounts, etc., which can be directly redeemed with credits through the system, honorary rewards, such as issuing the title of "Green Pioneer", public recognition in company newsletters or bulletin boards, real-time updates and feedback on employees' credit status and incentive results, and enhanced transparency and participation.

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