Design of a Digital Platform for Carbon Generalized System of Preferences Communities Based on the TAO Model of Three-Way Decisions

Abstract

The increasing carbon dioxide emissions from human activities present a significant global concern, with approximately two-thirds of greenhouse gas emissions attributed to household activities. The Carbon Generalized System of Preferences (CGSP) has emerged as a pivotal mechanism to incentivize voluntary carbon reduction in community households. This paper examines the development of a community digital management platform designed to incentivize voluntary carbon reduction at the community level, highlighting the critical role of reducing emissions in urban community life to meet carbon peak and neutrality targets. This study employs the TAO model of Three-Way Decision to establish a closed-loop operational framework for the CGSP digital platform. The platform features a Trisection mechanism to record and quantify low-carbon behaviors, an Action mechanism to classify and reward community members, and an Outcome mechanism to assess overall community carbon reduction achievements. Additionally, a user interface tailored for community users is developed to enhance platform accessibility. The proposed platform presents a practical and innovative solution for exploring emission reduction potential in urban communities. By systematically recording low-carbon behaviors, providing targeted rewards, and conducting comprehensive assessments, the platform aims to guide community residents in adopting sustainable practices. This study offers a valuable reference for the digital transformation, intelligent system construction, and development of new urban functional units within communities.

1. Introduction

Adopting a global perspective, the challenge of mitigating global warming through carbon emission reduction is one of the foremost issues confronting humanity. The 2022 Emission Gap Report by the United Nations Environment Program (UNEP) highlights a significant discrepancy between the international community’s efforts and the objectives outlined in the Paris Agreement, with no reliable route currently identified to limit the temperature increase to 1.5 °C [1]. Furthermore, as indicated in the World Energy Statistical Yearbook 2022, the Asia-Pacific region contributes a substantial 52.3% to global carbon emissions, with China alone accounting for a significant 31.3% of the carbon emissions in the Asia-Pacific region, emphasizing its critical role in the dynamics of regional and global carbon emissions [2]. The report emphasizes that more robust climate action necessitates changes in both the private sector and individual consumption behaviors.

Using a consumption-based carbon emission accounting method, it is estimated that approximately two-thirds of global carbon emissions are attributable to household activities [1]. Carbon emissions refer to the release of carbon dioxide and other greenhouse gases due to human activities. Successfully advancing carbon reduction efforts requires the ongoing motivation of micro-level entities, such as communities and residents, to actively engage in the process. In 2020, General Secretary Xi Jinping announced at the 75th session of the United Nations General Assembly that China aims to peak carbon dioxide emissions before 2030 and achieve carbon neutrality by 2060, known as the ‘carbon peak and neutrality targets’ [3]. However, current greenhouse gas reduction measures in the public life domain are relatively limited, primarily relying on ethical guidance from relevant authorities and voluntary public self-restraint [4].

The Carbon Generalized System of Preferences (CGSP) provides an effective solution to this issue. The CGSP emphasizes voluntary participation by stakeholders, with no legal requirements mandating compulsory involvement. It advocates the use of incentive strategies to continuously enhance public engagement in emission reduction behaviors, guiding the public to adopt a low-carbon and green lifestyle [5]. The core approach of the CGSP is to provide inclusive rewards for carbon reduction behaviors, using positive reinforcement to encourage public participation in carbon reduction activities. As the fundamental unit of social organization, communities play a crucial role in exploring community-based CGSPs, which are essential for achieving the “dual carbon” strategy objectives.

In 2022, the “14th Five-Year Plan for Digital Economic Development” promulgated by China’s State Council explicitly called for the acceleration of the digital transformation of existing residential and community facilities, encouraging the simultaneous planning and construction of intelligent systems in new residential developments [6]. As digital technologies such as the Internet of Things, 5G networks, cloud computing, big data, and artificial intelligence continue to mature and expand, their integration into community development has become increasingly feasible [7]. This progress establishes the foundation for embedding digital technology in community construction, further enhancing the role of digital platforms in this domain.

At present, the community CGSP is in its preliminary stage of development, leading to several challenges in micro-level carbon reduction efforts among individuals and households. These challenges include a limited range of carbon reduction methods, incomplete low-carbon scenarios, and a lack of diverse incentive mechanisms. Additionally, the existing community CGSP remains relatively traditional, lacking the integration of modern digital technologies, which is crucial for developing new carbon reduction strategies. This study introduces the TAO Three-Way Decision model, an advanced and comprehensive scientific decision-making framework that extends the foundational theories of the Three-Way Decision model originally developed by scholar Yiyu Yao [8]. The TAO model organizes the decision-making process into three key components: Trisection, which categorizes issues into three distinct types; Action, which develops tailored strategies for each category; and Outcome, which assesses the effectiveness of these strategies. This systematic approach enhances the decision-making framework by providing a structured and methodical process. Leveraging this model, the study develops a digital platform for the CGSP at the community level. Therefore, this study aims to design a digital platform for the community CGSP based on the TAO Three-Way Decision Model, addressing the current shortcomings of the community CGSP, encouraging residents to actively participate in low-carbon green initiatives, and cultivating sustainable low-carbon habits.

Specifically, this study develops a community digital management platform centered around the CGSP. Through the TAO Three-Way Decision Model, a comprehensive operational mechanism is designed, encompassing the quantification of low-carbon behaviors, incentives for positive actions, and the evaluation of behavioral goals. This platform aims to expand carbon reduction methods, enrich low-carbon scenarios, and increase residents’ enthusiasm for carbon reduction. It not only explores the potential for carbon reduction in urban community life but also provides a reference for implementing community digital transformations, developing intelligent systems, and creating new urban functional units.

The remainder of this paper is organized as follows: Section 2 provides a literature review, covering the current research on digital platform design for sustainable development, including designs oriented towards communities and community-based CGSPs. Section 3 introduces the research methods, the TAO Three-Way Decision Model, and the design of the operational mechanism for the community CGSP digital management platform based on the TAO model. Section 4 presents the procedural workflow, overall architecture, data flow diagrams, key technologies, functional architecture, interface design, and experimental design of the digital platform. Section 5 discusses the theoretical and practical significance of the study, compares the community CGSP with traditional CGSP, highlights the advantages of applying the Three-Way Decision Model in community CGSP, and addresses the research limitations and future directions. Finally, Section 6 concludes the paper.

2. Literature Review

2.1. Sustainable Development Digital Platform Design

Recent research in sustainable digital platform design has predominantly focused on the development and operational mechanisms of platforms aimed at addressing key sustainability issues such as food waste reduction, green transportation, and fostering eco-friendly behaviors in academic environments. In Hsin Chang’s 2022 study, she examines social digital platforms using a research framework developed by Yale University to understand how the characteristics of green information, the sources of the information, and the receivers influence the recipients’ cognition and attitudes during the dissemination process, as well as their willingness to share green information. The study highlights that with the support of national education programs, government policies, and corporate social responsibility initiatives, individuals’ environmental awareness can be enhanced. This increased awareness encourages them to share green posts on social networking sites (SNS) and engage in green practices [9]. Hilleman’s Ilse et al.’s 2021 qualitative research delved into the micro-level integration of knowledge regarding food waste on digital platforms. The study introduced the concept of Sustainable Development Digital Platforms (SODPS), aiming to tackle food waste challenges by contextualizing them within the broader scope of sustainability [10]. In their 2021 study, Silveira Alexandre Borba and colleagues analyzed the interactions, mediations, and guidance among individuals, platforms, and providers within bicycle-sharing systems in Porto Alegre, Southern Brazil, and Vancouver, Canada. The study elucidates the critical role of bike-sharing in fostering sustainable collaborative practice [11].

2.2. Community Sustainable Development Platform Design

Recent research in sustainable community development has focused on areas like community health and fairness and resource and environmental management, as well as application contexts and platform-building theories and methods. Scholars have deeply explored the operational mechanics and technological foundations of digital platforms in community settings. Masahiro Hill et al. (2015) developed the CESP (Community Environmental Sensing Platform) to monitor, manage, and analyze diverse environmental factors in smart communities, enhancing community health and equity [12]. Yu S. et al. (2022) addressed resource distribution in closed communities by researching the Community Intelligent Shared Housing App and offline sites using SWOT analysis, aiming to cater to varied information needs through an information-sharing platform [13]. N. Bidwell (2014) created a system based on extensive trials of social media prototypes in rural South Africa, designed to help low-income communities access relevant local information, facilitating media recording, storage, and sharing, with interoperability between displays and mobile device [14]. Focusing on community resource and environmental issues, Maria Vincenza Ciasullo and their team (2020) undertook a comprehensive analysis of the Trento Smart City initiative. This study re-envisioned Trento as a model of a smart community, creating a robust theoretical framework. It pinpointed the pivotal strategic drivers, essential decision-making processes, and crucial steps in policy development necessary for the sustainable evolution of smart communities. Addressing the theoretical and methodological aspects of platform construction [15]. John M Carroll and colleagues (2019) explored how advancing data literacy could foster more substantial civic engagement and decision-making. They also developed a social infrastructure framework to sustain long-term community data initiatives [16]. Paolo Spagnoletti and his team (2015) formulated and tested a digital platform design theory for Online Social Communities (DPSOC), focusing on efficiently supporting social interactions in digital communities. Their approach, grounded in information systems design theory, offered initial guidelines for designing effective DPSOC [17]. Furthermore, Bin Xia Xue and others (2012) crafted a technology roadmap for creating an intelligent community public information network platform tailored for outdoor activities. This plan was based on the core technological characteristics and functional categories of modern smart communities, combined with public information management systems [18].

2.3. Community Carbon Generalized System of Preferences Digital Platform Design

Current research in this area predominantly concentrates on themes such as the design of carbon accounting platforms, behavioral analysis and incentivization of community residents, and the development and refinement of carbon reduction strategies. Within the scope of carbon accounting platform design, Pieter Nick ten Cat and team (2022) developed an integrated platform named FEWprint, designed to calculate carbon footprints from various activities, including food consumption, thermal/electric energy usage, automobile fuel requirements, water resource management, and waste disposal [19]. Benjamin D. Hodgdon and associates (2015) provided a comprehensive overview of methods for assessing the carbon offset potential of community forests. They validated standards for voluntary participation in carbon offsets by enhancing institutional coordination, establishing equitable benefit-sharing mechanisms, and improving community monitoring capabilities, in the sphere of analyzing and incentivizing resident behaviors in low-carbon intelligent communities [20]. Laura Guzman et al. (2019) introduced the Carbon Kit, an integrated platform combining technological, market, and incentive measures. Comprising modules for personal carbon tracking, health, fitness, social media, and economic incentives, this platform aims to motivate individuals to minimize their carbon footprint. The research suggests that setting objectives, incentivizing behaviors, and implementing feedback loops, when aligned with the extensive data derived from tracking activities, apps, and social media, can render the Carbon Kit an essential element in daily life [21]. Yoshiki Yamagata and others (2019) highlighted walking as a foundational step towards establishing low-carbon smart communities. Their study validated the practicality of design methods for such communities, linking various walkability indices (like centrality, interval, angle) evaluated using road network data with behavioral patterns deduced from mobile GPS data, thereby encouraging walking activities [22]. Additionally, Armin Golla et al. (2020) presented a decision-support based platform information system, facilitating the joint creation of residential energy communities by consumers and administrators. This system integrates coordination mechanisms tailored to various participant preferences, informed by the necessary local energy infrastructure data, in exploring carbon emission reduction strategies and their optimization [23]. B. Ugwoke et al. (2021) undertook an energy planning exercise for the rural communities of Onyen okpon and Giere in Nigeria, spanning from 2015 to 2040. Employing a hybrid integration approach along with a low-emission analysis platform model, they examined the effects of transitioning these rural communities to sustainable and renewable energy sources. Their findings suggest that strategic integration can significantly curtail energy demand and greenhouse gas emissions [24]. R Hanna (2017) investigated the prospects and hurdles in developing community-based renewable energy programs in diverse states and regions of Canada, Spain, the UK, and the US. He posited that such programs are instrumental in aiding local governments to meet carbon reduction goals while engaging citizens in a broader clean energy transition. Additionally, Hanna underscored the role of climate and energy organizational transformation platforms in assisting states and regions to navigate the challenges associated with adopting policies and technologies for low-carbon energy [25].

2.4. Research Review

A comprehensive survey and analysis of pertinent studies across domestic and international spheres reveal certain limitations in existing research in terms of content, methodology, and perspectives. With respect to content, current studies predominantly concentrate on carbon reduction’s status quo and motivational strategies within singular social behaviors, such as reducing food waste, promoting green planting, travel emission mitigation, and resource sharing. Investigations into carbon inclusivity at the aggregate community lifestyle level are still nascent. Methodologically, research on digital platforms frequently employs experimental, literature review, and simulation (model-based) approaches; however, the application of scientific decision-making models for studying community residents’ behavioral decisions and incentives is notably rare. Concerning perspectives, extant research primarily unfolds from the viewpoints of environmental science, design studies, and energy dynamics, with a noticeable absence of a product service system design perspective. There is also a dearth of studies that holistically leverage interdisciplinary theoretical insights from computer science, design, environmental science, sociology, psychology, and related fields.

In an effort to address the deficiencies identified in existing research, this study, from an interdisciplinary design studies perspective, employs the TAO model of Three-Way Decision to develop the operational mechanism and user interface for a Carbon Generalized System of Preferences community digital platform. This initiative aims to create a community-based Carbon Generalized System of Preferences framework, encouraging active engagement from micro-entities such as communities and residents in achieving ‘carbon reduction’ goals.

3. Materials and Methods

3.1. Overview of Research Methods

This study employs a multi-method research strategy that integrates both qualitative and quantitative approaches to ensure a comprehensive exploration of the operational mechanisms of the community CGSP. The methods used include a systematic literature review, inductive and deductive reasoning, the Analytic Hierarchy Process (AHP), Saaty’s nine-level scale, and the TAO scientific decision-making model. First, a systematic literature review and synthesis are conducted to provide the theoretical foundation for the study, identifying research gaps and limitations in existing work. Next, inductive and deductive reasoning are applied to thoroughly analyze the theoretical framework and practical applications of community CGSP, establishing a solid basis for the research. Saaty’s nine-level scale and the AHP method are then employed to assign appropriate weights to the indicators of the community CGSP digital platform, ensuring the scientific rigor of data processing and the reliability of the results. Additionally, the TAO Three-Way Decision model is integrated to design the operational mechanism of the community CGSP, thereby ensuring that decision-making processes are both systematic and effective. By employing a comprehensive and multi-dimensional methodological framework, this study achieves robust scientific validity and accuracy in its research outcomes.

3.2. Genesis and Evolution of the TAO Model

The Three-Way Decision (3WD) model was proposed by Canadian scholar Yi-Yu Yao and is rooted in the fundamental philosophical principles of cognitive science and the theory of uncertain decision-making [26]. The core concept of the Three-Way Decision model is to simplify and resolve complex decision-making problems by mimicking human cognitive processes and behavioral patterns. This is achieved through a divide-and-conquer approach and sequential strategies, using a tripartite division methodology [8]. Due to its alignment with human cognitive characteristics, over the past 40 years, the Three-Way Decision model has evolved into a significant theory and method for addressing uncertainty, gaining widespread attention from scholars both domestically and internationally.

The idea of the Three-Way Decision model originated from the Rough Set Theory proposed by Polish mathematician Zdzisław Pawlak. This theory employs lower and upper approximation sets to partition the universe of discourse, providing a method for handling imperfect and uncertain information [27]. In the Rough Set-based Three-Way Decision model, the universe is divided into three mutually exclusive regions: the positive region, the boundary region, and the negative region. Each region is associated with a specific decision strategy: “acceptance” for the positive region, “non-commitment” for the boundary region, and “rejection” for the negative region [28]. This structure forms the foundational model and mathematical interpretation of Three-Way Decision theory. The Rough Set-based Three-Way Decision model is illustrated in Figure 1.

The TAO model, known as the Trisecting-Acting-Outcome model, was developed by Yao Yiyi based on the core principles of Three-Way Decision theory. The initial Three-Way Decision model included only two elements: trisection and action. To create a more comprehensive decision-making framework, the crucial phase of outcome evaluation was later incorporated, ultimately leading to the formation of the TAO model, which consists of trisection, action, and outcome [28]. This model is divided into three interrelated yet independent components, with specific strategies applied to each. The Basic Theoretical Framework of the TAO Model is illustrated in Figure 2.

The TAO model primarily consists of three components: trisection; action; and outcome evaluation. The function of trisection, represented by a solid line, is to divide a whole into three related yet relatively independent parts; the resulting three parts are referred to as the three parts of the whole, and trisecting a whole will yield the anticipated outcome. The function of action, represented by a dashed line, is to apply a set of strategies to handle these three parts. Evaluation is the process of assessing the decision and providing feedback on its effectiveness. The three components of trisection, action, and evaluation collectively implement the trisection-driven Three-Way Decision [29]. The TAO model provides a framework for the operation of the Three-Way Decision. When applying the model to specific applications, it is necessary to use semantically and physically meaningful trisection, profitable operations, and informative effectiveness measures.

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 sustainability of 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.

3.3. Carbon Generalized System of Preferences Communities Design

3.3.1. Subject Layer

Figure 3 illustrates the main components of the community CGSP digital platform, as outlined in this paper. It depicts the various participant categories and their interrelations. Beyond the community households and individuals, who serve as the primary implementers, the participants in the community CGSP should also encompass government departments, community organizations, power companies, gas companies, businesses, and carbon finance institutions. Government agencies play a pivotal role in authorization, while community management bodies are responsible for disseminating and promoting practices within the Carbon Generalized System of Preferences. Power and gas companies provide essential data for public low-carbon activities, businesses contribute by offering eco-friendly products, and carbon finance institutions manage the transactions of carbon credits. Together, these diverse participants fulfill unique roles, collaboratively enhancing the efficacy and growth of the Carbon Generalized System of Preferences’ operational system in achieving carbon peak and neutrality targets [31].

3.3.2. Instrumental Layer

Figure 4 presents the methods layer of the community CGSP digital platform, as developed in this study, highlighting the primary roles and functions of the participant categories. Specifically, the operation of the CGSP community platform encompasses six key instruments and is influenced by multiple variables. These include, firstly, authorization, where the government endows communities with the rights to access and use resident data; secondly, documentation and recognition, with the platform actively recording and pinpointing low-carbon behaviors; thirdly, quantification, as the platform measures these behaviors; fourthly, accounting, wherein carbon credits are computed for eco-friendly actions; fifthly, incentivization, involving the rewarding and redemption of carbon credits; and sixthly, habituation, where the platform leverages diverse incentives to steer residents towards adopting sustainable lifestyle practices.

3.3.3. Application Scenario Layer

Figure 5 displays the five application scenarios designed for the community CGSP digital platform. Considering the current infrastructure and technological landscape, the CGSP is effectively utilized in scenarios like energy saving, transportation, and everyday life. Firstly, it promotes energy efficiency and emission reduction by incentivizing low-consumption practices in water, electricity, and gas use. Secondly, in the area of sustainable transportation, it rewards low-carbon travel methods. Thirdly, the platform champions proactive engagement in waste management and recycling, as well as actions or initiatives that contribute to carbon reduction or carbon sequestration. Fourthly, it encourages low-carbon activities in the digital realm, such as online shopping, telehealth services, and online administrative tasks. Furthermore, the platform’s approach to incorporating behaviors within the CGSP must be dynamically adjusted in response to changes in operational dynamics, work demands, and technological advancements.

3.4. Operational Mechanism of the Carbon Generalized System of Preferences Community Digital Platform Based on the TAO Model

3.4.1. Overall Operational Mechanism

This research focuses on constructing a community digital platform centered around the CGSP system. It develops a set of operating mechanisms for the community CGSP digital platform based on the TAO Three-Way Decision theory, encompassing low-carbon behavior quantification, positive behavior incentives, and the evaluation of low-carbon behavior incentive targets, as depicted in Figure 6. By employing the TAO Three-Way Decision theory model, the platform effectively tracks and measures public low-carbon activities, provides incentives for such practices, and then evaluates the impact of these actions on carbon emission reduction. In fostering community digital and intelligent infrastructure, the platform actively encourages public participation in carbon reduction initiatives, promoting the adoption of sustainable, eco-friendly behavioral patterns.

3.4.2. Trisection Operational Mechanism

Formulating a community behavior inventory is a critical aspect of the Carbon Generalized System of Preferences. This inventory is essential for implementing effective positive incentives within the system. It encompasses a range of low-carbon behaviors deemed suitable for integration into the CGSP framework. As the system necessitates the quantification of environmentally friendly and low-carbon actions to facilitate carbon credit transactions, it is imperative that every listed behavior in the inventory fulfills the criteria of being both attainable and quantifiable.

Additionally, the community behavior inventory’s scope, capturing low-carbon practices, extends across numerous domains including personal and household living, travel, and consumer habits. This results in a vast expanse of data, which brings forth two paramount challenges in its collection: the intricacies of data technology and the safeguarding of personal privacy. Therefore, in the pursuit of behavioral data, a balanced approach is crucial, ensuring rigorous protection of individual privacy alongside data acquisition.

The Trisection operational mechanism of the community digital CGSP platform categorizes community low-carbon behaviors into four categories: energy conservation and emission reduction, green transportation, active advocacy, and digital lifestyle, as shown in

Table 1. The Trisection Operational Mechanism of the Community Carbon Generalized System of Preferences Digital Platform. Source: own processing.

Community Behavior Inventory		Carbon Footprint (kgCO2e)	Wt.	Acq. Meth.	Acq. Path.	Pos. Dom.	Neg. Dom.	Bnd. Dom.	Main Activity Level Data Unit	Reference
Energy Saving and Emission Reduction A Energy Conservation and Emission Reduction A	Water Saving a1	0.820	10%	Dir. Acq.	Water Company	2.460	3.81	(2.46, 3.81)	Cubic Meters per Capita per Month	Urban Residential Water Use Standard GB/T 50331—2002 [32]
	Electricity Saving a2	0.581	20%	Dir. Acq.	Power Supply Bureau	0.120	0.21	(0.12, 0.21)	Megawatt-hours per Household per Month	Residential Electricity Retail Price List [33]
	Gas Saving a3	0.017	10%	Dir. Acq.	Gas Company	0.600	0.94	(0.60, 0.94)	Cubic Meters per Household per Month	Residential Household General Living Gas Tiered Quantity and Price Table [34]
Green Transport-action B	Transportation b1	0.070	20%	Ind. Acq.	Transportation Platform Data Center	16.800	123.900	(16.800, 123.900)	Kilometers per Person per Month	Green Transportation Action Plan Creation [35]
		0.513
Proactive Advocacy C	Plastic Recycling c1	0.906	7.5%	Ind. Acq.	User Upload	6.800	2.270	(2.270, 6.800)	Kilograms per Person per Month	China Product Life Cycle Greenhouse Gas Emission Coefficient Database [36]
	Food Waste c2	−0.810	7.5%	Ind. Acq.	User Upload	2.620	1.310	(2.600, 1.310)	Kilograms per Person per Month
	Diet Advocacy c3	0.540	10%	Ind. Acq.	User Upload	17.570	40.670	(17.580, 40.680)	Kilograms per Person per Month
		2.464
Digital Living D	Online Shopping d1	0.581	5%	Ind. Acq.	Shopping Platform Upload	5.470	6.800	(5.470, 6.800)	Kilometers per Person per Month	China Express Logistics Industry Data [37]
	Remote Work d2	0.581	5%	Ind. Acq.	User Upload	0.002	0.085	(0.002, 0.085)	Megawatt-hours per Person per Month	Online Conference Carbon Reduction Research Reports [38]
	Online Meetings d3	0.610	5%	Ind. Acq.	User Upload	0.013	11.950	(0.013, 11.950)	Megawatt-hours per Person per Month	Online Document Product Carbon Reduction Performance Study [39]
Total Value Division Z				Comprehensive calculations		5.010	30.040	(5.01, 30.04)
Equation			$Z=0.1\times a1+0.1\times a2+0.2\times b1-0.075\times c1-0.075\times c2+0.1\times c3+0.05\times d1+0.05\times d2+0.05\times d3$

. The platform calculates the community’s carbon footprint on a monthly basis to determine the intervals for the positive domain (Pos. Dom.), negative domain (Neg. Dom.), and boundary domain (Bnd. Dom.) within the Trisection mechanism. Due to space constraints in the table, abbreviations are used to denote the acquisition method (Acq. Meth.), acquisition pathway (Acq. Path.), and weight (Wt.).

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 foundational 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:

$$\mathrm{Z}=0.1\times \mathrm{a}1+0.1\times \mathrm{a}2+0.2\times \mathrm{b}1-0.075\times \mathrm{c}1-0.075\times \mathrm{c}2+0.1\times \mathrm{c}3+0.05\times \mathrm{d}1+0.05\times \mathrm{d}2+0.05\times \mathrm{d}3$$

(1)

3.4.3. Action Operational Mechanism

At the heart of the CGSP is the implementation of comprehensive rewards for public engagement in low-carbon activities. This system is designed to motivate widespread participation in initiatives focused on energy saving and carbon reduction, ultimately nurturing sustainable lifestyle practices.

Within this framework, the community’s residents are segmented into groups based on their engagement levels: high, medium, and low. Tailored incentive strategies are then developed, taking into account the distinct characteristics and needs of each segment.

The incentive strategies are broadly divided into four categories, as shown in Figure 7. The first involves consistent rewards, awarded for every occurrence of a specified action, like earning carbon points for daily low-carbon actions or regular check-ins. The second type, fixed-ratio rewards, offer timed and measured incentives for a set number of activities within a certain period, such as bonuses for accumulating low-carbon actions, regular check-in rewards, converting carbon points into donations for philanthropic recognition, and first-time low-carbon action rewards. The third category, fixed-interval rewards, provides periodic incentives at specific time intervals, including exclusive item exchanges during carbon point events. The final type, variable rewards, introduces an element of competition, lacking a fixed schedule. Examples are periodic community-level rankings with corresponding rewards in carbon points, eco-friendly gifts, or unique commemorative badges.

3.4.4. Outcome Operational Mechanism

The evaluation system for assessing the benefits of community carbon inclusivity should be grounded in five essential principles. First, it must be scientifically robust and precise, anchored in solid scientific evidence and accurate data to accurately reflect the real-world impact of carbon inclusivity. Second, the system ought to be holistic, encompassing all facets of community carbon inclusivity to evaluate the comprehensive effect of initiatives. Third, practical feasibility is crucial, ensuring that indicators are straightforward to collect and assess, steering clear of overly complex or unfeasible methods. Fourth, the system should enable comparability, allowing for the assessment of benefits across different communities or projects, thus facilitating informed decision-making and efficient allocation of resources. Lastly, sustainability is key, with a focus on long-term benefits and ongoing development, to guarantee the enduring influence and benefits of carbon inclusivity projects within communities.

Given the limited number of existing studies on community CGSP and the absence of relatively comprehensive authoritative data, this paper employs the Analytic Hierarchy Process (AHP) to assign weights to the indicators. It utilizes a questionnaire survey to ascertain the levels of the indicators and applies the Saaty nine-point scale method for scoring purposes.

To establish a community CGSP benefit evaluation index system, it is first necessary to construct a judgment matrix. This paper determines the criteria layer, guideline layer, and indicator layer through collective discussions among experts and scholars.

The energy system within the community carbon inclusivity framework assesses its impact on carbon reduction by tracking the decrease in energy usage, enhancing energy efficiency, and encouraging the adoption of renewable energy sources. Four indicators have been chosen for this purpose: the community’s annual electricity usage, its annual water usage, the number of energy-efficient appliances, and the prevalence of solar-powered appliances. These indicators serve as benchmarks for the energy aspect of community carbon inclusivity benefits. Annual electricity consumption indicates the effectiveness of the community’s electricity conservation measures, while annual water consumption evaluates the efficiency of water use, reflecting the community’s efforts in conserving water resources. The count of energy-efficient appliances measures the community’s commitment to energy-saving appliances, encouraging the selection of high-efficiency products. The rising number of solar-powered appliances showcases the community’s endeavors in shifting away from traditional energy reliance.

The technological system assesses the community’s advancements in adopting low-carbon and clean technologies, including innovations in carbon reduction techniques, thereby positively influencing the community’s tech-savviness. Within this system, the study identifies four key indicators: the garbage collection rate, the harmless treatment rate of household waste, interactions with community display devices, and the adoption rate of digital technology. The garbage collection rate evaluates the community’s efficiency in segregating, collecting, transporting, and processing waste. The rate of harmless waste treatment reflects the community’s environmental commitment in managing waste. The frequency of interactions with community display devices serves as an indicator of residents’ engagement with innovative technologies and the community’s endeavors in raising low-carbon consciousness through digital means. Lastly, the adoption rate of digital technology indicates the community’s receptiveness to digital solutions, highlighting the potential trend of using such technologies for spreading low-carbon knowledge and practices.

The environmental system focuses on assessing the impact of carbon reduction initiatives on air quality and water resources, along with their contribution to ecological protection and restoration. In this context, the study considers three primary metrics to evaluate the community’s environmental system within the scope of carbon inclusivity benefits: community greenery coverage, per capita green space, and the number of days annually with air quality surpassing Level 2 standards. Community greenery coverage is a key measure of the ecological health of the community. Per capita green space is a crucial metric for evaluating the community’s urban planning and development, indicating both the extent and quality of its green areas. Lastly, the annual count of days with air quality above Level 2 provides a comprehensive gauge of the community’s air quality over the year.

The institutional system evaluation focuses on the community’s effectiveness in governance and policymaking under the Carbon Generalized System of Preferences. This includes determining whether there is a robust carbon market mechanism in place, the implementation of incentivizing policies and regulations, and the extent of collaboration among government, businesses, and residents in reducing carbon emissions. Five indicators have been chosen for this purpose: the rate of knowledge dissemination regarding the Carbon Generalized System of Preferences, residents’ acceptance of low-carbon awareness, the robustness of carbon trading platforms for residents, residents’ engagement in low-carbon community development, and residents’ satisfaction with such development. The rate of knowledge dissemination measures how well residents understand carbon emission reduction and low-carbon living. Residents’ acceptance of low-carbon awareness evaluates their receptiveness and proactive engagement in sustainable lifestyles. The robustness of carbon trading platforms assesses the ease with which residents can engage in carbon trading and market activities. Residents’ involvement in low-carbon community development serves as a crucial indicator of their recognition of and participation in CGSP initiatives. Finally, residents’ satisfaction with low-carbon community development offers insight into their approval of the community’s green and sustainable development strategies, areas of construction, and operational management.

Initially, after determining the target level, this study performed pairwise comparisons of the importance of indicators within the same evaluation tier. Ten experts scored each indicator based on the Saaty nine-point scale method which categorizes the importance levels of each factor into seven degrees, facilitating subsequent weight calculations. Subsequently, after acquiring the judgment matrix scores from each expert, it was necessary to compute the geometric mean of each matrix value, culminating in a comprehensive judgment matrix. Additionally, eigenvector analysis of the judgment matrix was conducted to calculate the weight vector for each indicator. The eigenvectors of each criterion were normalized to obtain their relative weights. The consistency of the matrix was then verified, and the results from the weights of each indicator were integrated. After organizing, the operational mechanism of the digital platform for the Carbon Generalized System of Preferences is presented in

Table 2. Outcome operating mechanism of the Carbon Generalized System of Preferences digital platform. Source: own processing.

Objective Layer	Criteria Layer	Wt. (%)	Indicator Layer	Wt. (%)	Indicator Direction
Community Carbon Generalized System of Preferences Benefit Evaluation Indicator System	Transportation System	13.4	Community Per Capita Private Car Ownership (vehicles/person)	2.33	Negative
			Proportion of New Energy Vehicles	3.94	Positive
			Proportion of Low-Carbon Commuters in the Community	7.15	Positive
	Energy System	18.50	Annual Electricity Consumption in the Community	3.86	Negative
			Annual Water Consumption in the Community	4.61	Negative
			Number of Household Appliances with Energy Efficiency Labels	3.39	Positive
			Number of Solar-Powered Household Appliances	6.64	Positive
	Technology System	16.25	Waste Sorting Collection Rate	4.97	Positive
			Harmless Treatment Rate of Household Waste	5.43	Positive
			Number of Interactions with Community Display Devices	2.58	Positive
			Digital Technology Adoption Rate	3.27	Positive
	Environmental System	25.24	Community Green Coverage Rate	7.54	Positive
			Per Capita Green Area	8.80	Positive
			Number of Days with Air Quality Level Two or Above in the Community Throughout the Year	8.90	Positive
	Institutional System	26.60	Carbon Generalized System of Preferences Knowledge Dissemination Rate	4.39	Positive
			Residents’ Low-Carbon Awareness Acceptance	4.64	Positive
			Completeness of Residents’ Carbon Trading Platform	6.44	Positive
			Participation Level in Residents’ Low-Carbon Community Construction	7.24	Positive
			Satisfaction with Residents’ Low-Carbon Community Construction	3.90	Positive

. The establishment of the community carbon benefit evaluation index system achieves the construction of the community digital platform’s Outcome operation mechanism, which allows for a comprehensive assessment of the effectiveness of community low-carbon emission reduction efforts. Based on the evaluation results, this mechanism enables a more accurate understanding of the community’s progress in low-carbon emission reduction

4. Results

4.1. Procedural Flowchart

The operational process of the Carbon Generalized System of Preferences platform, as depicted in Figure 8, unfolds in three main stages. Initially, the process entails gathering low-carbon behavior data from community residents, followed by thorough data verification. This verification distinguishes between two key scenarios: automated data uploads by the platform and user-generated uploads subject to platform review. The second stage involves processing the collected low-carbon data, including categorizing participants based on the data and assigning different rewards based on their level of participation. This stage allows users to opt for carbon credit redemptions as rewards. The final stage is dedicated to evaluating the outcomes of these low-carbon behaviors. This evaluation bifurcates into an individual analysis of each resident’s low-carbon actions and a holistic assessment of the entire community. Concomitantly, this stage also entails adaptive adjustments to the reward strategies, ensuring their relevance and effectiveness.

4.2. Overall Architectural Design

Figure 9 illustrates the platform’s overall architecture, which is composed of four key layers, structured in a bottom-up approach: the Infrastructure Layer, Data Service Layer, Application Service Layer, and User Entity Layer. The Infrastructure Layer, forming the platform’s foundational base, consists of IoT devices and basic networking equipment, crucial for operational support and fundamental functions like data gathering and analysis. At the core of the platform’s governance is the Data Service Layer, which consolidates essential data from both the Carbon Generalized System of Preferences platform and additional sources, thereby enabling efficient recording of community residents’ low-carbon activities. The Application Service Layer acts as the interactive interface for the community, encompassing elements such as the Community Showcase Platform, the Management Platform, and user-centric mini-apps. The User Entity Layer represents the active participants within the platform’s ecosystem, including community residents practicing carbon reduction, government entities, community management bodies, carbon finance organizations, businesses, and utility providers like electricity, gas, and water companies.

4.3. Data Flow Diagram Design for the Platform

The Carbon Generalized System of Preferences digital platform is comprised of various components, including display screens, communication modules, sensors, and controllers. The sensors play a crucial role in gathering diverse environmental data from within the community, such as temperature, humidity, air quality, and noise levels, and then relaying this data to the controllers via communication modules. These controllers are tasked with processing and analyzing the collected data, and based on pre-established algorithms and strategies, they generate pertinent content like community bulletins, event alerts, and low-carbon lifestyle suggestions. This content is subsequently transmitted to both the platform’s display screens and the personal devices of community members through the communication module. The display screens showcase this information in a variety of formats, including graphics, text, and audio interactions, thereby providing the community with a rich and comprehensive information service. The data flow of this platform is illustrated in Figure 10.

The Carbon Generalized System of Preferences platform within the community is primarily composed of two elements: a dedicated community mini-program and public information display devices. The mini-program functions as a pivotal tool to encourage residents towards a low-carbon lifestyle, facilitating the integration of resources, fostering the development of a low-carbon community, and aiding in achieving ‘dual carbon’ strategic objectives. This mechanism employs the mini-program to deliver a range of information to residents, including displaying carbon points associated with low-carbon behaviors and features for redeeming these points. The community’s public information displays serve as a critical avenue for residents to access information. The public information display equipment in the community serves as a vital channel for residents to access information. In addition to fulfilling the basic functions of real-time community information dissemination and IoT device management, this equipment should also integrate emerging technologies and methods to enable intelligent information sharing, as well as the dissemination and promotion of low-carbon knowledge [43].

4.4. Key Technologies of the Platform

4.4.1. Edge Computing Technology: Community Information Monitoring

Edge computing technology plays a crucial role in community information monitoring, dynamically reflecting various data changes within the community, such as the environment, carbon emissions, and disaster monitoring. The introduction of edge computing technology optimizes the workflow of traditional IoT sensors, including the full-process management of data acquisition, transmission, processing, and analysis [44]. Edge computing technology enables real-time monitoring of community information and the acquisition of accurate data, facilitating the assessment and optimization of community low-carbon emission reduction strategies. Edge computing nodes possess certain computational capabilities, allowing local data preprocessing, filtering, and compression. They can perform real-time calculations of carbon emissions and mark abnormal data, contributing to the scientific formulation and optimization of low-carbon emission reduction strategies. Additionally, with the integration of mobile applications, community residents can view monitoring data in real-time through mobile apps and participate in community low-carbon emission reduction activities. The application of edge computing technology enhances the timeliness and accuracy of data, allowing users to conveniently view and verify relevant data, thereby improving the participation experience in low-carbon emission reduction activities.

4.4.2. Blockchain Technology: Platform Information Sharing

Blockchain technology can establish a transparent data-sharing platform for community information. With its distributed and tamper-proof characteristics, the combination of IoT and blockchain technology allows for automatic data recording on the blockchain, reducing human intervention and ensuring data authenticity [45]. Blockchain is suitable for recording carbon emission data and carbon credit information, meeting the needs of participants in the Carbon Generalized System of Preferences to conveniently view and verify relevant data, thereby increasing the credibility of information sharing.

4.5. Functional Framework and Interface Design of the Platform

The platform’s functional architecture comprises the core components of the interface design, with the primary goal of maximizing user satisfaction. The public platform section is organized into five main modules: community information display, environmental monitoring, low-carbon knowledge dissemination, incentive and reward mechanisms, and user interaction feedback. The mobile section features five key modules: login, carbon recording, carbon exchange, Carbon Circle, and “My”. For a detailed description of specific functions, please refer to Figure 11, Functional Architecture.

The platform’s interface design is divided into two parts: the public platform interface and the mobile user interface. The public platform interface mainly showcases community information and low-carbon emission reduction achievements, while the mobile user interface includes the main interface, carbon inclusive accounting interface, and social interaction interface. Details are provided in Figure 12.

4.6. Experimental Plan

4.6.1. Selection and Construction of the Virtual Platform

Due to the limitations of experimental conditions, the research cannot be conducted in a physical community at this time. Therefore, the research team plans to use modeling tools to build a virtual community platform to simulate the operation of the community Carbon Generalized System of Preferences digital platform. Existing research indicates that virtual experiments are highly effective as verification tools. For instance, Zhang H et al. designed an intelligent carbon monitoring platform by combining traditional carbon control methods with IoT technology, using a virtual carbon emission estimation model to simulate real-time carbon emissions for small urban block [46]. Similarly, Ziliang Lai et al. designed a three-dimensional integrated circuit virtual experiment platform based on Unity3d, which solves the problems of high cost and high risk involved in practical research experiments on integrated circuits, and it is not easy to carry out the actual problems on a large scale [47]. Additionally, Tencent’s SSV Carbon Neutrality Laboratory independently designed and developed the “Carbon BASE” platform, which provides carbon reduction and carbon emission calculation services for various scenarios and industries, supporting the rapid construction of Carbon Generalized System of Preferences products and MRV (Monitoring, Reporting, and Verification) [48]. Consequently, the currently applicable modeling tools for the virtual platform include AnyLogic, Unity3D, and MATLAB Simulink. Considering the complexity of simulating community scenarios, the research team selected AnyLogic as the virtual experimental platform for more precise simulation experiments.

4.6.2. Implementation Plan

The preliminary preparation stage of this virtual experiment comprises three main parts: platform construction, data collection, and parameter setting. The implementation stage of the experiment, based on the TAO Three-Way Decision models, is divided into three parts: mechanism operation, data analysis, and result evaluation. The specific experimental plan is shown in Figure 13.

5. Discussion

5.1. Theoretical and Practical Significance

This study expands the concept of the CGSP to the community level, addressing a gap in research on community-level CGSP and introducing an innovative micro-level theoretical research model. This expansion not only provides theoretical support for the CGSP system at the micro level but also creatively applies the TAO Three-Way Decision model to a digital platform, demonstrating its applicability within the community CGSP digital platform. The deep integration of this model with the platform enhances the theoretical methods and outcomes of model-platform integration, offering new directions for digital scientific decision-making systems. This research highlights the potential advantages of combining community CGSP with digitalization, opening up new avenues for research in related fields.

From the perspective of social development, the community CGSP system proposed in this study facilitates residents’ adoption of low-carbon lifestyles through the collaborative efforts of government, community organizations, and social enterprises, offering an innovative implementation pathway for community-level CGSP. By quantifying residents’ carbon reduction behaviors and combining these efforts with both spiritual and material incentives, the system continuously encourages low-carbon practices, thereby enhancing the overall emission reduction effectiveness at the community level. Furthermore, this research provides community managers with an operational CGSP mechanism, helping them formulate more efficient emission reduction and incentive measures, raising environmental awareness among residents, and fostering the development of low-carbon behaviors and environmentally friendly lifestyles. The study offers a new paradigm for carbon reduction strategies in the context of digital transformation, with broad social application prospects.

5.2. Comparison of Community Carbon Generalized System of Preferences and Traditional Carbon Generalized System of Preferences

Although the development of CGSP in developed countries is quite advanced, research and practice focusing on community-level CGSP systems remain relatively limited. In recent years, several countries have conducted research on CGSP, but significant differences exist in the specific measures and the scope of low-carbon behaviors. For instance, since 2008, South Korea has promoted voluntary participation in low-carbon behaviors by individuals and households through its “Carbon Bank” system, which quantifies spontaneous water, electricity, and gas conservation behaviors and provides positive economic incentives for these behaviors, such as consumption and discounts [49,50]. However, this system mainly focuses on specific sectors of the CGSP. In 2009, Japan introduced the “Eco Points” system, relying primarily on market forces to guide public participation and incentivize household carbon reduction behaviors [51]. France and the United Kingdom have also conducted some community-level CGSP initiatives within their respective policy frameworks, but overall, these efforts have been limited in achieving systematic low-carbon emission reductions at the community level [52,53,54].

Therefore, despite significant progress in CGSP in developed countries, community CGSP is still in its early stages, and there is a need for further in-depth research on systematic and comprehensive community CGSP.

In contrast, China’s CGSP practices have mainly been concentrated in specific regions, with a lack of systematic research at the community level. Existing platforms face limitations in covering low-carbon behaviors and involving resident participation. For example, the ongoing deepening of the CGSP system in pilot regions like Guangdong Province, the introduction of CGSP systems in Qinghai and Jiangxi Provinces covering residents’ daily lives, and the comprehensive CGSP trials in Chengdu, still face challenges such as limited coverage of low-carbon behaviors and low levels of resident participation and engagement [55,56,57].

Therefore, designing a digital platform specifically for community CGSP, which focuses on the community level, not only enhances public participation in low-carbon behaviors but also helps build a sustainable closed-loop management mechanism for communities, contributing to the development of a low-carbon economy and ecological civilization.

5.3. Advantages of Applying Three-Way Decision Models to the Community Carbon Generalized System of Preferences

Currently, Carbon Generalized System of Preferences platforms around the world are primarily categorized into two models: government-led and corporate-led. However, both models exhibit their respective limitations. Government-led platforms possess authoritative quantification of low-carbon behaviors but offer limited incentives, resulting in low public participation. Corporate-led platforms offer diverse incentives but lack scientific accounting and certification. Furthermore, existing platforms rarely employ scientific decision-making methodologies to enhance residents’ participation; they mainly rely on public welfare activities and gift rewards established by corporate-led platforms to motivate residents’ participation.

Three-Way Decision, as a theory of ternary thinking, is universally applicable across many disciplines and fields, providing an effective strategy for solving complex problems, and has become a research hotspot in various domains [58]. The trichotomous decision model has been widely applied in multiple disciplines, such as medical diagnosis, investment management, social judgment theory, peer review, and management studies [59,60,61]. Current research in the humanities and social sciences using trichotomous decision-making is limited, particularly in applying the model to community carbon generalized system of preferences and in studies aimed at stimulating residents’ enthusiasm for low-carbon behaviors through scientific decision-making methods [62].

Three-Way Decision aligns with human cognitive patterns and has strong universality and applicability. This study employs the TAO model of Three-Way Decision to construct an operational mechanism for a digital platform for community carbon generalized system of preferences, involving stakeholders such as government departments, communities, power companies, natural gas companies, merchants, and carbon finance institutions. The Trisection mechanism records and quantifies community low-carbon behaviors in four key dimensions: energy conservation and emission reduction, green travel, active advocacy, and digital life. The Action mechanism categorizes residents into three levels of participation—high, medium, and low—based on their engagement, designing targeted incentive mechanisms for each group. The Outcome mechanism evaluates the effectiveness of community carbon reduction efforts using a community carbon generalized system of preferences benefit assessment system and promptly adjusts the incentive mechanisms through scientific decision-making, enhancing the applicability of incentive strategies and specifically boosting community residents’ enthusiasm for participating in low-carbon reduction activities. Overall, this study, from an interdisciplinary perspective, applies the TAO model of Three-Way Decisions to the creation of a community Carbon Generalized System of Preferences practice platform, representing significant innovation and providing a new method and perspective for research on carbon generalized system of preferences systems.

5.4. Research Limitations and Future Directions

This study, grounded in an interdisciplinary perspective, proposes a community digital platform for the CGSP, developed using the TAO Three-Way Decision model. This platform aims to address existing issues within China’s CGSP, enhance residents’ understanding of low-carbon knowledge, incentivize participation in low-carbon activities, and cultivate sustainable low-carbon habits. Additionally, the innovative application of the TAO Three-Way Decision model to the creation of a community CGSP platform introduces new methodologies and perspectives for research in the CGSP field.

However, due to experimental constraints, the current research findings have not yet been applied or validated in real-world community settings. To overcome this limitation, the research team has developed a detailed virtual experiment plan to simulate the operational mechanisms and outcomes of the community CGSP digital platform. This plan includes a specific timeline and implementation strategies. Moving forward, the research team will focus on further evaluating, validating, and refining the effectiveness of the community CGSP digital platform and its operational mechanisms. Through virtual experiments and simulation studies, the team intends to explore platform mechanisms adaptable to various community types, with the goal of having a more significant impact on community CGSP policy development and enhancing public engagement in low-carbon practices at the community level.

6. Conclusions

Currently, two-thirds of global greenhouse gas emissions originate from community households, making the design of a Carbon Generalized System of Preferences (CGSP) digital platform and its operational mechanisms within community settings a critical strategy for controlling global warming through carbon reduction. The CGSP mechanism, which encourages voluntary participation of community households in low-carbon activities, has increasingly attracted academic attention. However, existing research lacks a product-service system design perspective and fails to integrate insights from computer science, design, environmental science, sociology, and psychology into a cohesive framework. Additionally, there is a scarcity of research on employing scientific decision models to enhance and optimize systems that effectively guide public engagement in low-carbon reduction efforts. Currently, low-carbon practices within community households largely depend on moral guidance and self-regulation by residents, and the existing CGSP systems require further optimization in terms of coverage, public awareness, and participation in low-carbon initiatives.

This study integrates the CGSP concept into the design of community digital platforms, leveraging the TAO model to address the limitations of existing approaches in user incentives. It offers a more comprehensive and systematic solution for community CGSP digital platform design, providing significant theoretical and practical contributions. The research aims to utilize both spiritual and material incentive mechanisms to motivate community residents to actively adopt green and low-carbon lifestyles, thereby contributing to the achievement of the carbon peak and carbon neutrality targets. This approach not only uncovers the carbon reduction potential in urban community settings but also provides valuable insights for the application of CGSP mechanisms in other contexts, making an important contribution to the development of ecological civilization in the new era.

Due to research constraints, the findings of this study have not yet been validated in real-world communities. However, a comprehensive virtual experiment plan has been developed, and future efforts will focus on evaluating and optimizing the platform’s effectiveness across different community settings.