## 3 Materials and Methods
### Carbon Generalized System of Preferences Communities Design
#### 3.4.2 Triscretion Operational Mechanism

Table 1: _Cont._For Energy Conservation and Emission Reduction (Section A), the carbon footprint data from the 2022 China Major Cities Commuting Monitoring Report serves as a benchmark for setting monthly targets in water, electricity, and gas savings among community members [40]. In contrast, the metrics for Green Travel (Section B), Active Advocacy (Section C), and Digital Lifestyle (Section D) are not directly quantifiable through usage alone, necessitating a detailed explanation of their respective assessment methods. In the Green Travel segment (Section B) of the platform, eco-friendly commuting options such as shared bicycles, public buses, and subways are examined. The 2022 China Major Cities Commuting Monitoring Report informs this section by providing key data: the average one-way commute time in major Chinese cities is 36.25 min, covering a distance of 8.05 km [40]. The commuting metrics are found for determining the carbon footprints in the Green Travel domain, as per the China Product Life Cycle Greenhouse Gas Emission Coefficient Database [36], with carbon emissions quantified at 0.070 kgCO2e for bicycles and 0.513 kgCO2e for taxis. This data is instrumental in defining the positive, negative, and boundary domains for green commuting within the digital platform. In promoting Initiative C, the plastic recycling and kitchen waste aspects primarily reference the China Product Life Cycle Greenhouse Gas Emission Coefficient Database. The carbon footprint for plastic recycling is 6.80 kgCO2e, and for kitchen waste, it is 2.620 kgCO2e. Valuation is based on per capita generation, establishing their positive domain, negative domain, and boundary domain. Dietary recommendations are mainly guided by the Chinese Resident Dietary Guidelines (2022), specifying the proportional intake of vegetables to meat for a normal adult [41]. According to the China Product Life Cycle Greenhouse Gas Emission Coefficient Database, the carbon footprints for vegetables and meat are 0.540 kgCO2e and 2.464 kgCO2e, respectively, forming the positive domain, negative domain, and boundary domain within dietary advocacy [36]. In the digital lifestyle D, concerning online shopping, according to the Comparison Study of Greenhouse Gas Emissions between Online and Offline Shopping in China, the carbon emissions per transaction for online shopping are lower than those for equivalent-value offline shopping. Specifically, they are 1094.920 gCO2e and 1360.240 kgCO2e per transaction, respectively [42]. Using the data on per capita express delivery usage from the National Postal Administration for the year 2021, which increased from 4.2 items to 59 items [37], as a reference, and considering an average of 5 online shopping transactions per person per month, the positive domain, negative domain, and boundary domain in online shopping are determined. This process establishes the positive domain, negative domain, and boundary domain for online work in the digital lifestyle. Online meetings establish the positive domain, negative domain, and boundary domain based on the carbon reduction quantification derived from the Research Report on Online Meetings Facilitating Carbon Emission Reduction and considering the average number of meetings per person [39]. The classification into positive, negative, and boundary domains for community low-carbon activities is based on the cumulative carbon footprints of each activity. The Z-value represents the aggregate of the products of each activity's weights within the community behavior inventory and the residents' actual carbon emissions. Notably, actions like plastic recycling (c1) and kitchen waste reduction (c2) under the Active Advocacy initiative (Section C) are categorized as positive carbon mitigation activities. Therefore, their contributions are deducted from the total Z-value. In contrast, activities designated as negative in terms of carbon mitigation are added to the overall Z-value. Consequently, the formula for calculating the Z-value is equation as follows:  
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