## 3 Materials and Methods
### Applicability of the TAO Model  
The Three-Way Decision models are an effective problem-solving method that enables rapid, low-cost, or high-yield decision-making while tolerating general errors to a certain extent [8]. Positive region values are obtained from the positive region to represent acceptance of something; negative region values are obtained from the negative region to represent rejection of something; and the third type of region value that falls in the boundary region is used to indicate deferred decision-making on something. This decision-making process aligns with human cognitive behavior. In actual decision-making processes, people can make immediate judgments on things they are confident in accepting or rejecting, while they often adopt a deferred decision-making strategy for things they cannot immediately decide on. The strategies and actions adopted vary for different decision problems.  
The research on China's CGSP primarily focuses on the certification of verified emission reductions (VERs) and the implementation of energy-saving measures. Regarding VERs, China has registered over 200 methodologies across 16 sectors, encompassing most types of emission reductions [30] These methodologies are continually optimized as industries develop. In terms of energy-saving measures, various local governments and enterprises in China have recently launched corresponding carbon generalized platforms. However, the implementation still faces challenges such as incomplete coverage of low-carbon emission reduction scenarios and insufficient penetration rates. Additionally, a lack of systematic research on user incentives affects public participation in emission reduction activities.  
The TAO model offers a more systematic and comprehensive decision-making framework for the CGSP. First, by utilizing the Three-Way Decision mechanism, this model addresses complex decision-making scenarios, enhancing both the accuracy and effectiveness of decisions, making it well-suited to handle the uncertainty and variability inherent in carbon reduction behaviors within the CGSP. Second, the TAO model facilitates the recording and quantification of low-carbon behaviors while enabling incentive rewards and timely adjustments based on data feedback from these behaviors. Additionally, the TAO model can assess the targets of behavioral incentives, meeting the need for continuous strategy optimization within the CGSP.  
Implementing the Three-Way Decision TAO model in the operational design of the CGSP digital platform effectively addresses the complexity and uncertainty challenges present in the platform's operation. First, based on the trisection method of the TAO model, a list of low-carbon behaviors within the community is established, addressing the issue of incomplete coverage of low-carbon scenarios in the CGSP. This requires categorizing common low-carbon behaviors within the community and constructing a comprehensive and practical low-carbon behavior list. Second, participants in low-carbon behaviors are categorized into three groups: low-engagement, medium-engagement, and high-engagement groups. For each group, scoring and reward mechanisms are introduced in the decision-making process, with corresponding incentive strategies developed for each category. This design aims to enrich the existing reward mechanisms of the CGSP and further enhance public motivation to participate. Finally, an outcome evaluation mechanism is designed. By monitoring and analyzing various low-carbon behavior indicators within the community, the actual impact of these behaviors is accurately reflected, leading to adjustments in the reward mechanisms accordingly. This mechanism addresses the current limitations of single-dimensional approaches in the CGSP, ensuring the effectiveness and sustainabilityof incentive measures. Through a scientifically sound incentive mechanism, the public can be guided to voluntarily participate in low-carbon green actions, encouraging active fulfillment of low-carbon environmental responsibilities.