Empowering Government Efficiency Through Civic Intelligence: Merging Artificial Intelligence and Blockchain for Smart Citizen Proposals

Abstract

Civic intelligence (CI) represents the collective capacity of communities to address challenges, yet its integration with smart city infrastructure remains limited. This study bridges CI theory with technical implementation through a novel framework combining blockchain and AI technologies. Our approach maps core CI components (knowledge capital, system capital, and relational capital) to specific technical solutions: a civic engagement index for measuring participation quality, a tokenization framework for incentivizing meaningful engagement, and a governance optimization function for resource allocation. Using mixed-methods research, we developed and validated the conceptual CI governance (CIG) framework, which satisfies CI principles through smart contracts and AI-assisted interfaces. The empirical evaluation demonstrates both social and technical improvements: 40% increased civic participation rates, 85% governance efficiency maintenance, and significant gains in engagement quality metrics (knowledge sharing +32%, collective decision making +28%). While technical implementation shows promise, success requires the careful integration of social dynamics, digital literacy initiatives, and regulatory compliance. This research contributes to smart city development by providing a theoretically grounded, feasible framework that introduces the fusion of blockchain and AI technologies to enhance civic participation while preserving governance effectiveness.

1. Introduction

Smart cities are defined as an initiative in urban development with a focus on leveraging information flows and communication technologies (ICTs) to improve the performance and quality of urban services. Past research in this field has produced urban service frameworks that have found foothold specifically in areas of energy management, public security and transportation [1], direct traffic control, building management, and pollution management. These infrastructure-focused ICT-powered services [2] form the backbone of smart cities, where the collection and analysis of urban-related data are achieved with devices of the Internet of Things (IoT). Urban DTs also reveal new opportunities for civic engagement [3,4]. IoT and sensor participation applies hybrid approaches with real-time capabilities to minimize delays and information discrepancies between analog and digital remotes [5]. The accessibility-focused methodology has also emerged as a solution to address different kinds of exclusion-based demotivators, reflecting the inclusion of diverse group of citizens by means of providing multimodal guidance with personalized approaches [6,7,8]. Although DTs do not reflect an absolute replicate of its physical parent, their usefulness from a civic engagement perspective can be leveraged presenting virtual scenarios with context for voting and decision making.

One of the challenges is that smart cities are facing regulative constraints that are strictly enforced and burdensome to update for technological innovations. The successful implementation of fully realized smart cities depends on the underlying governance frameworks. As communities worldwide have conducted experiments with different approaches to establish smart cities, understanding how governance structures can effectively balance technological innovation with meaningful citizen participation becomes paramount. The implementation of smart city initiatives varies across regions. This have led us to examine how different regions and cities have approached the challenge of implementing smart governance initiatives and what lessons can be learned from their experiences.

In one study, the analysis of Japanese smart communities revealed that increased citizen engagement depends on the available technological infrastructure and appropriate institutional frameworks [9]. Smart city policies should also incorporate sustainability and citizen participation incentives to be effective [10]. Smart cities are transforming the nature of citizenship itself, providing virtual channels for forming feedback loops between citizens and governments. As trusted digital citizenship initiatives are gradually adopted and merged with the latest technological capabilities, regular citizens can interact as smart citizens [11]. Smart cities and smart citizens acting together form civic intelligence (CI) [12,13], representing a union of highly responsive civic engagement. The capacities of CI are evaluated through knowledge, intrinsic motivation, system capital, relational capital, and tangible assets [12].

Civic engagement through urban DTs can play key roles for enhanced participation, empowering collective resources in decision making. Collaborative urban development is reachable by using DTs as communication and participation platforms between citizens, decision makers, and planners [3]. One of the enablers of civic engagement binds real-time interactions with virtual city models, emphasizing the need for responsive and accessible feedback mechanisms [14]. DTs provide unified environments to build immersive interactions for citizens [4]. As DTs are bridging physical urban areas with virtual participation, success in this context also relies on the integration of IoT devices and captured citizen feedback from verified interfaces [5]. From an inclusivity perspective, the success also depends on the meaningful interpretation and the processing of intrinsic motivators, locally shared knowledge, and resources [8].

Civic participation levels, ranging from consultation to active co-governance, are directly supported by technical solutions. For example, DT simulations enable communities to visualize the impact of proposed policies, fostering knowledge and motivation. Decentralization and virtualization can enhance civic engagement, which is enabled by trust, transparency, and constant consensus on incentives and voting targets [15]. For this purpose, blockchain and distributed ledger technology are providing distinctive benefits, such as identity management, virtual voting, resource allocations, and immutability of records. There is also a strong case for integrating artificial intelligence (AI) with civic engagement processes; concrete applications should include personalized communications, management and fulfillment of voting incentives, as well as consensus-building efforts for transparent co-governance.

A concrete example of how blockchain and AI implement CI capabilities can be seen in participatory budgeting systems. Blockchain-based platforms such as *Democracy Earth* provide transparent and tamper-proof voting systems that ensure trust and inclusion. Meanwhile, AI tools can analyze community-submitted proposals, prioritize issues based on community sentiment, and suggest optimal budget allocations, effectively enhancing system and relational capital. This integration supports decentralized decision making and strengthens the intrinsic motivation of citizens to participate. However, integrating CI into the existing legal framework can cause friction between traditional and progressive governance methods, as institutional structures must gradually transform from centralized to decentralized decision-making networks [16].

Integrating AI agents into digital twins and their virtual environments and real-time data streams enables the generation of actionable insights and facilitates citizen participation [17]. AI systems can also be adapted to formalize predictions for the estimated impacts of proposed urban changes, incorporating scenario simulations for automatic branch indexing. Combining data mining with AI agents with memories can be used to identify areas or data slots where certain citizen input is required and optimize the presentation of opinions for more coherent decision making [18,19,20]. AI algorithms running across multiple citizen channels can analyze and identify patterns from civic feedback and interactions, including social media, public platforms, and IoT sensors. Proactivity in civic engagement presents possibilities to provide more contextual feedback to city administrators for better outcomes while contributing to transparent and responsive governance [21].

The integration of the suggested applications with CI-based platforms has only two interface requirements for citizens: accessibility to the internet and web-capable devices. From this perspective, the primary access point has been solved concretely: the internet had reached over 67% of the global population in 2023 and 93% in developed countries [22], along with full penetration on smart phones. The number of IoT devices has been rising steadily as well and consisted of more than 16 billion devices in 2023 [23]. Citizens have accepted the internet as part of their daily life, and its presence there is already embedded in business requirements and economical gains. And, as technological progress produces constantly more powerful microchips and larger bandwidth, more capable interactions and functionalities have become available for users. This sociotechnical evolution and wide adoption serve as a foundational layer for implementing CI initiatives.

The primary goal of this paper is to design and evaluate a CI-focused framework that enhances civic participation in smart cities through the integration of blockchain and AI technologies. This system proposal directly builds upon existing theories of civic intelligence and participation by implementing the following:

• CI capacity framework components: knowledge, motivation, system capital, relational capital, and tangible assets through distributed ledger technologies;
• Multilevel participation theory through an AI-driven multi-agent architecture;
• Social capital formation principles via Git-inspired branching logic for proposal management.

2. Theoretical Framework

Responses by smart citizens represent civic participation, which is described as actions aiming or influencing, either directly or indirectly, government decision making [24]. Civic participation involves institutionalized acts (e.g., voting) and noninstitutionalized activities (e.g., social movement engagement) [25,26]. Other interpretations have included digital civic practices as necessity to facilitate social capital formation, being integral components for modern civic engagement [27,28]. Democratic innovations that directly aim to enhance civic engagement can be described as interactive [29]. In addition, the inclusion of marginalized voices and accessibility across different interfaces has been determined as influencing civic participation, requiring a transparent documentary interface for equal participation with formal and veritable records [6,7]. Different levels of citizen participation range from consumers of smart services to active co-creators of urban solutions [30]. This has been extended to include “collaborative intelligence” as a necessityof smart governance, which could empower urban decision-making processes with direct citizen inputs [31]. Incentivized civic participation represents a strong motivator for engagement that enables people to collaborate on public problems with crowdsourced resources and to achieve consensus on decisions and their executions [32], creating solid foundations to realize the potential of smart citizens and consequently achieve CI capabilities.

Civic participation can be understood through key factors that enable or hinder engagement [33]. The enabling factors include personal motivation (intrinsic value of participation), institutional needs (requirements for public input), and transformational opportunities (potential for real impact). Barriers to participation fall into three categories that our technical solutions directly address: transparency barriers (limited access to information) are solved through blockchain’s verifiable records, accessibility barriers (difficulties in participation processes) are overcome through AI-assisted interfaces, and resource barriers (time and effort constraints) are minimized through automated processing [34]. These targeted solutions measurably improve participation efficiency (${\eta }_{\mathrm{eff}}$) and reduce processing delays ($E\left[T_{\mathrm{trad}}\right]$) [35].

2.1. Theoretical Background

This research leveraged established theoretical frameworks to inform system architectures and implementation strategies. This theoretical foundation shapes how the system processes and distributes decision-making power, particularly in implementing mechanisms for knowledge sharing and collaborative problem solving. Participation level theory provided crucial insights into designing engagement mechanisms that accommodate different depths of citizen involvement, from basic consultation to the active co-creation of policies. The research on digital civic practices guided technical implementation by identifying proven patterns for online civic engagement and community mobilization, ensuring that technological solutions align with the demonstrated successful practices in digital democracy.

This research uses extant theory as the foundation for system design:

• Civic intelligence theory [12,13] to structure the system’s capacity framework;
• Participation level theory [30] to inform the design of engagement mechanisms;
• Digital civic practices research [27,28] to shape the technical implementation.

Beyond the specific theories, following factors are related to theoretical implications:

• Scaling civic participation through blockchain-based governance structures;
• Optimizing engagement efficiency through AI-driven process automation;
• Implementing sustainable incentive mechanisms for civic participation.

These theoretical foundations and implications provided a robust framework for addressing the multifaceted challenges of modern urban governance. By integrating established theories with innovative technological approaches, this research bridges the gap between theoretical understanding and practical implementation. This synthesis of theory and technology provides a solid foundation for examining how participation manifests across different governance levels and contexts.

The impact of participation occurs at three interconnected layers [3]: thhe community layer (local initiatives and neighborhood concerns), organizational layer (city management and service delivery), and policy layer (institutional rules and frameworks). Each layer requires different types of engagement: the community level uses a peer-to-peer network, the organizational layer employs smart contracts for management, while the policy layer utilizes distributed ledger technology for governance. While traditional systems struggle with scalability ($S_t\left(U\right)$) across these levels due to coordination complexity, our digital infrastructure enables seamless integration through distributed computing (community layer), automated governance (organizational layer), and blockchain consensus (policy layer) [36].

Traditional governance systems face quantifiable constraints in scalability and efficiency. The processing capacity is limited by physical infrastructure, where the maximum processing rate ($R_{\mathrm{pmax}}$) depends on voting centers ($n_v$) and counting staff ($n_c$) [37]. The system throughput (${\rho }_t$) is constrained based on voter arrival rate (${\lambda }_v$) and processing rate (${\mu }_t$) [38], while cost efficiency follows function ($C_t\left(n\right)$), incorporating operational ($C_b$), staff ($C_s$), and material costs ($C_m$) [39,40]. The scalability limit ($S_t\left(U\right)$) demonstrates how physical infrastructure and the voter turnout factor (${\alpha }_{\mathrm{trad}}$) create upper limits on system capacity [41].

Smart city engagement mechanisms operate across multiple scales, each producing distinct outcomes and feedback loops. At the operational level, citizen feedback shapes day-to-day service delivery and local decision making. The tactical level involves medium-term planning and project implementation, where citizen input influences resource allocation and community development. At the strategic level, sustained civic participation informs long-term policy development and institutional frameworks. Each level creates its own feedback mechanisms from rapid response to immediate community needs, to iterative project improvements, to long-term policy refinement based on accumulated civic intelligence. However, several factors can diminish civic participation effectiveness. These engagement barriers fall into three main categories:

• Resource barriers (recessive factors): Time and cost constraints that limit sustained participation;
• Access barriers (exclusive factors): Technical or physical limitations preventing citizen involvement;
• Trust barriers (inclusive factors): Concerns about accountability, privacy, and data security.

The election of governance around the world is predominantly based on voting between candidates [42]. The problem with representative governance is the interplay of two factors—centralization and the voting framework [43]. Centralization is the hierarchy-oriented network that gains or loses benefits depending on its context space [44]. The centralization initiative is not specifically stated mission but instead emergent from legislative evolution toward increasing administrative efficiency and standardizing governance frameworks [45]. The advantages of centralized decision making in large corporations and autocratic communities include clear role distribution and task allocations [46]. Decisions can be pushed down in organizations with local consensus from parent executives [47]. In urgent situations, a single authority may push decisions and thus make an organization start initiatives faster [48]. Success however also requires executive competence to build and lead the organization [49]. The centralization of policy management proceeds through various levels toward the authoritarian spectrum of ratifying decisions if one agency of actors is not accountable [50]. A lack of accountability creates legislative games for decision makers, which they can leverage on their own goals, indirectly or directly [51,52]. Centralization is not an inherently negative factor but can be only if there is distributed accessibility to change the network paradigm with citizen feedback mechanisms to form consensus [53].

Centralization of policy management has also been a constraint for reforming voting frameworks [54]. Voting frameworks for selecting representatives are based on the national constitution and electoral acts and laws at the national, regional, and urban levels [55]. The current voting processes are predominantly focused on electing representatives, dismissing citizen feedback for systemic initiatives [29]. In other words, citizens are voting between proposers instead of proposals [56]. The targets of voting and occurrence of voting are narrow, consequently diminishing civic engagement methods of participation [57]. Before the modern technological era, there were practical reasons for this [58]. Nowadays, the vast majority of people, especially in developed economies, are virtually connected, giving first insights into what decentralization could offer for organizational management [59]. the virtualization of voting is still slow, as concerns about its reliability and security keep surfacing in discussions [60].

Traditional methods of citizen participation have not changed much through centuries: this includes, for example, public surveys, election of representatives, town hall meetings, and petitions [32]. Civic engagement is often considered as the sign of a healthy urban community: low participation rates to the corresponding instruments of engagement is linked to accessibility issues and time constraints [24]. The current model of governance is based on legacy constraints that do not exist anymore in modern societies that are applying technological innovations [61]. Firstly, accessibility issue stems from the fact that citizens in the area of governance previously had the possibilities of exchanging information only by physical means [62]. Therefore, it was necessary to limit the frequency of collecting citizen preferences on decision making as the collective participation would have required exceptional management capabilities of the government and allocation of resources to collect preferences with high frequency [63]. Secondly, time constrains on citizens also dictated the limits on participating in decision making [64]. Instead of direct democracy, representative democracy became gradually the de facto paradigm of urban governance, dictating the frequency of decision making to manageable periods to collect votes [65]. While this approach still today saves time for citizens to focus on activities beyond urban decision making, it can be argued that this involuntary outsourcing of decision making is an outdated approach and not up to date with hte current technological capabilities to harness the collective capital of smart citizens in urban management [66].

Previous research on urban governance has recognized the potential of blockchain in ensuring transparency [67,68], as well as the suitability for AI in improving decision-making processes [69]. The characteristics of the blockchain include immutable record keeping and decentralized governance mechanisms, which have a positive influence on civic trust and participation [70]. Meanwhile, AI-driven analytics allow processing large amounts of urban data, enabling evidence-based policymaking and resource allocation [71]. The integration of these technologies into smart city frameworks has been assigned the term ’Blockchain AI Smart Cities’, where automated governance operates in parallel to human oversight and democratic participation [72]. The successful integration requires considerations of ethical implications, data privacy, and inclusive governance structures to ensure equitable access and participation by citizens [73]. In addition to smart cities, AI and blockchain can empower smart citizen interactions, which are required to form CI-based governance.

2.2. Current Challenges

The existing participation mechanisms for civic engagement follow physical, virtual, and hybrid approaches to interact with the civic population [32,74]. Physical approaches require direct social interactions, where authentication, information flow, and decision making are handled in a shared physical context through town hall meetings, delegation of representatives, and community organizations [43,75]. Virtual approaches enable digital interactions via online platforms, social media engagement, and data-driven participation [31,76,77]. Hybrid methods combine both mixed participation and community co-creation initiatives [78,79,80], forming core governance components of the decision-making infrastructure, citizen engagement interfaces, resource allocation systems, and accountability frameworks.

These multilevel engagement frameworks and participation barriers point to a critical need for reimagining urban governance structures in smart cities. While traditional civic participation mechanisms have established foundations for community involvement, the emergence of smart city technologies offers unprecedented opportunities to address existing limitations and enhance engagement effectiveness. The integration of digital infrastructure with civic participation processes requires careful consideration of how governance mechanisms can evolve to support both technological capabilities and citizen needs.

The challenges facing smart city civic engagement through digital twins are multidimensional. Lack of citizen trust decreases willingness to share data and participate using civic engagement platforms [81]. Privacy and the right to anonymity are values that individuals do not want to compromise unless the justification for compromises is balanced with suitable incentives. More challenges are faced in managing and processing large data volumes from citizens in a robust and accessible way. The integration and harmonization of decentralized data sources for real-time processing infrastructure with verification processes remains a challenge in various urban contexts [82]. While blockchain technology enables the establishment of decentralized system in this context, it requires adaptions to achieve a sufficient combination of decentralization, security, and scalability [83]. There are several approaches to overcome these limits, such as the utilization of a multi-blockchain design, which has attracted attention since the introduction of TON blockchain [84]. This architectural approach overcomes the limits of the single blockchain by utilizing hierarchical blockchain structures, where each layer serves specific purposes within the smart city ecosystem [83]. The implementation of digital twins for smart cities necessitates stable and reliable software infrastructure that can effectively integrate modular AI solutions and neural networks, while maintaining security and scalability [85].

The successful achievement of improved civic engagement will depend on technical implementation, inclusion, privacy policies, resource allocation, and engagement value [31]. Technical infrastructure is required to operate robustly in changing conditions while not compromising accessibility. Barriers to entry are subject to philosophical and value-based dialogue and require more studies to understand the constraints of adding more interfaces for representative agencies [6]. The motives to participate also involve constantly evaluating engagement value and perception of value [32]. Depending on citizen preferences, many may opt out from active participation and instead prefer delegation of decision making [32,86]. This cultural issue stems from multiple factors such as lack of time. At the same time, youth participation methods for civic engagement can play important roles by shifting the default behavior of next generation to find intrinsic reasons to participate in the long term [87].

Privacy policy reflects the trust potential [81] that must be maximized in order to provide comfort in interacting with the platform. Trust is damaged if data collection, sharing, and handling are too extensive or centralized per citizen perceptions. However, if the minimum quantity of private data is not sufficient, the platform cannot provide adequate authentication or verification [86]. Global governance services however require a core policy that provides sufficient authorization and identification guidelines.

The democratic deficit in DT implementations is interpreting how the rapid progression of technological capabilities outpaces the development and implementation of compatible governance systems and citizen readiness [3]. This is reflected as low civic participation in smart cities if proper engagement frameworks have not been deployed [30]. The case study reported by Herrenberg demonstrates the possibilities of digital twins as communication platforms but also reveals that accessibility and user engagement issues have still not been resolved [3]. Technological methods should therefore focus on studying social mechanics first to address equitable and incentivized participation, where privacy protection and accountability are embedded as strategical initiatives [5].

AI technologies provide powerful tools for improving civic engagement and information flows between government and citizens [88]. This includes the automation of multiple processes such as predictive measures to address urban challenges, the processing and analysis of large volumes of citizen feedback, and the identification of patters in urban and civic issues [69]. AI is also suitable for encouraging personalized civic engagement and the optimization of resource allocation based on data-driven approaches [89].

Applying AI logic to civic engagement systems is not solely technical issue–social factors can creating cultural clashes, impeding the tools used for technical implementation. Perceived trust among citizens diminishes if machine learning (ML) models and large language models (LLMs) are not trained on diverse and objective datasets, consequently demonstrating algorithmic biases upon completion [90]. Another risk factor is arbitrary censorship by gatekeepers to steer the results based on misaligned initiatives. Additionally, the technical documentation of AI agents and the corresponding architecture, logic, and procedures in the civic context helps with establishing comprehendible AI principles fpr citizens that foster transparency and public trust.

Studies indicate that effective AI implementation in civic engagement cannot be achieved with mere technological sophistication: it also requires clarity from governance frameworks and digital literacy programs [91]. Cities implementing AI features in participation mechanisms have called for robust data protection measures and ethical adherence to ensure the positive influence of AI-powered civic engagement platforms while not compromising democratic processes [92].

The multi-blockchain approach enables this by organizing data and processes across different levels starting from the local level with sensor data and user requests all the way to higher-level executions in urban governance systems [83]. The multi-blockchain approach applies p-complete language L* for smart contract implementation, resolving the halting problem that is inherent in Turing-complete systems while ensuring polynomial-time execution bounds [93]. As predictable real-time data processing capabilities and responsive management are required, the multi-blockchain characteristics are particularly attractive for real-time urban data processing and management, where predictable execution times are essential for system reliability [93].

The logical programming language L, with its polynomial-time execution and avoidance of the halting problem, can serve as a foundational element in modern multi-blockchain architectures. By extending L to L* for smart contract implementation, developers can achieve predictable execution times and minimize resource consumption: critical requirements for real-time urban data processing [93,94]. This polynomial-time factor becomes valuable in multi-blockchain environments where computational efficiency and reliability are prioritized. Unlike traditional Turing-complete languages that can lead to infinite loops or unpredictable execution times, L* inherits L’s polynomial complexity while maintaining expressive power that is sufficient for practical smart contract development [94]. This makes it well suited for urban data management systems where real-time responsiveness and reliable execution are necessary for coordinating multiple blockchain networks and processing time-sensitive urban data flows. The predictable nature of L* aligns with the requirements of decentralized urban infrastructure, where system failures or computational bottlenecks could have significant real-world consequences [93].

Decentralized architectures face three technical dilemmas that must be balanced to provide service that is accessible and secure [95]. Limited throughput throttles accessibility while decentralization adds resource-consuming cryptographic proof to transactions to provide blockchain characteristics [96]. More traffic without scalability increases transaction costs and transaction times, so architectures aimed to overcome these limitations in throughput apply additional network layers and have achieved better relative performance [97]. The cost of participation also should be managed adequately to avoid negative effects on security compliance [98].

2.3. Research Contributions

This research addresses critical gaps in the urban governance literature by offering empirically grounded solutions to transition from traditional civic engagement to a dynamic, participatory paradigm. The findings provide urban policymakers with innovative approaches to implementing low-latency governance systems characterized by transparency, self-organization, and decentralized decision making. The proposed framework highlights how smart citizens can be actively included in policymaking within smart city communities and contributes to the evolving discourse on sustainable urban development. This study complements the understanding of civic intelligence in urban governance by examining the convergence of blockchain, AI, and DT technologies in decentralized civic participation. The contributions of this research are threefold:

• We formulate an analytical framework to quantify and evaluate civic participation mechanisms, introducing metrics for engagement quality, decision-making efficiency, and community impact.
• We develop an integrated architecture that combines AI-driven policy optimization with blockchain-based governance, establishing a theoretical foundation for tokenized civic engagement platforms that ensure sustainable participation through incentive mechanisms.
• We propose a practical implementation model for CI-based governance, orchestrating smart citizen participation through transparent, decentralized decision-making processes while maintaining system efficiency.

With these objectives, this research aimed to contribute to urban governance by providing practical insights to urban policymakers from the perspective of smart citizens. The findings are designed to inform the development of next-generation civic engagement platforms that can transform urban governance into a low-latency, high-participatory paradigm emphasizing full transparency, self-organization, and decentralized decision making as core values.

3. System Architecture of CI Governance

Governance frameworks tend to evolve through phases from centralized control by core developers to gradual redistribution of control by issuing tokens (authorizations) to participants [99]. This leads to the emergence of focused agencies such as DAOs, working groups, and AI agents [100].

CI governance (CIG) is based on the decentralized paradigm, using a distributed ledger to facilitate linked records and transactions. The framework focuses on automated accessibility, enabling virtual outreach for citizens to participate meaningfully with minimal resources. Through the tokenization of civic interactions, CIG creates a quantifiable framework for both voting processes and resource allocation decisions, effectively enabling more virtual participation mechanisms. This infrastructure is enhanced by an intuitive interface, where AI agents assist citizens in civic participation, creating a more responsive and inclusive governance interface.

CIG is blockchain-agnostic, but, for practical demonstration, we suggest Ethereum as default option to build the first prototypes, since Ethereum blockchain offers standardized ecosystem, extensive developer tools [101], and established security patterns [102]. A development in Ethereum blockchain involves smart contracts that are built in Solidity programming language utilizing browser-based compilers such as Remix and an integrated development environment (IDE) [103]. The accessibility principle requires that smart contracts can be built and monitored in an engaging interface without technical knowledge [104]. The interactions and connectivity with Ethereum blockchain are handled by Web3 providers [103].

The design of token utilities can support the formation of multi-layered governance structures that can be transparent and adaptive, as demonstrated in land lease and mortgage management systems [105]. While traditional land administration systems face challenges with centralized control and manipulation risks, blockchain-based frameworks enable decentralized property financing through transparent stakeholder participation. These systems implement private blockchain networks where central government offices maintain secure access to land records, while landowners can monitor property-related transactions [105]. The framework addresses scalability via a cluster-based architecture, where each district operates as an independent cluster with its own dataset, preventing performance degradation from high data loads while maintaining cross-district accessibility through government cloud nodes [105]. One major challenge involves double-spending scenarios but this has already been conceptually solved by using unique identifiers in the blockchain, ensuring proper verification before the collateral or lease is agreed upon [105].

Architectural considerations from the technical perspective concern scalability (network capacity, load and data growth), performance (resource utilization and response time), and reliability (accessibility and integrity) [106]. Security concerns highlight data protection (privacy and compliance) and system security (authentication, authorization, network security) [107]. Integration requirements involve interoperability (standardization, integrations) and data management (data flow and quality) [108]. User experience requires accessibility (interface design and usability) and sufficient performance from frontend and backend components [109]. Operational concerns are related to maintainability (code quality, system management) and deployment (infrastructure and release management) [110]. Future proofing considers the extensibility of the architecture and technology but also includes innovation support to accommodate emerging technologies and evolving business requirements [104]. There is also justified concern regarding how architecture can adapt to governance transitions from the perspective of policy management (rules and procedures) and comply to regulations and security policies [111].

The convergence of blockchain and AI technologies establishes opportunities for developing sophisticated civic engagement platforms that can expand the scope of civic participation in urban governance without compromising the quality or frequency of decision making [19]. When combined, these technologies can create low-latency systems with high transparency and trust in participatory governance, enabling direct and efficient citizen involvement in urban decision making [108]. However, the integration of these technologies in smart cities and urban governance faces challenges such as scalability, citizen-focused experience, integration with legacy systems, and the need for widespread digital literacy [88].

3.1. Consensus and Distribution

It has been proposed that there are mainly three factors influencing civic participation: mandated obligation, incentivized convenience, and idealized motives. These factors can overlap, creating motivator loops to engage, as with tax-paying citizens who continue to consume public services [112]. These factors can be renamed as intrinsic, extrinsic, and transformational motivators for a more formal investigation. Intrinsic motivators are idealized desires that are tied to psychological characteristics. Extrinsic motivators are obligations that are agreed upon between entities. Transformational motivators are incentives that provide tangible and intangible benefits. This psychological driver is what aggregates the total potential of the distributed computing capacity in CIG. This leads to high-fidelity discussion and sourcing being required to achieve consensus.

Blockchain as a base infrastructure for civic participation is a disruptive factor in the aim to gain trust and encourage behavioral alignment with civic engagement [113]. Blockchain, or distributed ledger, is characterized by immutability, decentralization, and transparency [114], which align with the civic engagement requirements that emphasize the need for the integrity of decisions that mitigate the risk of corruption and the centralization of decisions. Furthermore, blockchain offers promising solutions to implement secure digital identities and transparent voting systems with strong authentication, while allowing the tracking of public resources, verification of public records and automated execution of civic processes with smart contracts [115,116]. The further configuration of blockchain must have a consensus mechanism, transaction processing capabilities, block validation, state management, and smart contract execution [67].

Decentralized autonomous organizations (DAOs) present a novel approach to establishing self-organizing governance [117,118]. DAO’s architecture at its core hosts a governance engine, treasury, a token system, and access control. The governance engine handles proposals and voting, the treasury controls funds and assets, and the token system provides governance rights and incentives [118]. All these are facilitated with access control, which manages the permissions and roles inside the DAO, which has built-in security components for vote delegation, emergency responses, multisignature requirements, and time lock methods. the interaction layer is implemented by smart contracts, frontend interfaces, and integration protocols along with off-chain components. When logic is wrapped to the interaction layers, DAOs can automate many aspects of city governance, from proposal submissions and voting to budget allocation [112].

The construction of DAO structures is initiated by core contracts. The configuration is achieved by introducing modular contracts as functions to form rules for governance, a treasury, tokens, membership, voting, proposals, and execution [119]. The DAO acts as a governance layer that also provides access control to DApps [120]. Teh implemented DApps are individual components using frontend and backend services as well as smart contracts for core logic and business functions [121]. DAOs do not host DApps [100]. The verification process in DApps and DAOs requires that users have methods to verify the allowed behavior of smart contracts: on-chain verification is a desirable approach in the civic context since this eliminates need for trust-based centralized services [103].

Figure 1 illustrates a scheme of the components of the DApp that facilitate IoT data flows.

Tokenization allows the precise implementation and automation of smart contracts for resource allocation and decision making [117]. Tokens in the DAO can be staked as requirements for community memberships [122]. The idea of staking is to lock tokens for certain periods and pool resources on common initiatives. More focus should be directed to understanding clearly what token ownership actually requires and what are the legal obligations [123]. Implementing the DAO as a backbone of city management presents challenges, such as the reform of regulatory infrastructure, fair and secure participation, and the integration of algorithmic governance with public human oversight.

A propositional smart contract defines approaches for fractional participatory contributions and delivers options to link suitable IoT sensors or other feedback mechanisms. These can be used propose new initiatives or incentive schemes. Procedural smart contracts handle the security and accessibility of data flows but can perform administrate voting and execute the fulfillment of a selected proposal. They also monitor policy implementation and distribute incentives correspondingly. Smart contracts differ with the designated client. The development of incentives for group dividends and individual rewards requires different approaches. These types can also together form incentive circuits where incentive mechanisms can be monitored and adjusted. Defining the applicable incentives takes effort and knowledge from citizens, so, from the start, the minimum number of incentive models should be directly importable with information snippets. Incentive mechanisms can be cloned and stored with customized configurations. This approach seeks to eliminate barriers to the user experience while retaining options for the open-source customization of the cloned incentive mechanisms. Collaborative effort to develop and vote about incentive mechanisms generates trust and social coherence while giving users freedom to choose applicable engagement rates. For example, person A may work on tasks, directly producing knowledge and logic for the incentive mechanism, while person B may just vote for approval for use.

Artificial Intelligence plays a notable role in processing and analyzing the data collected in smart cities [82]. In the urban context, AI applications range from the predictive maintenance of infrastructure to optimizing traffic flow and energy consumption [124]. Machine learning algorithms can identify patterns in citizen behavior, predict future needs, and automate decision-making processes [72]. In the realm of civic engagement, AI can personalize citizen interactions with city services, provide intelligent chat bots for queries, and offer data-driven insights to inform policymaking [125]. However, the implementation of AI in urban governance also raises important ethical considerations, particularly in data privacy and algorithmic bias, which must be carefully addressed in system design [126].

Further extensions of AI capabilities include AI agents exchanging interactions between each other, performing completions with custom instructions, prompts, attachments, and content stores [127,128,129]. Different schemes for agent hierarchies have been implemented; for example, the Autogen framework uses ConversableAgent as a parent agent that facilitates AssistantAgent, UserProxyAgent, and GroupChatManager [129]. AssistantAgent uses a default system prompt to adjust user inputs with a large language model (LMM), while UserProxyAgent provides user input to ConversableAgent, which then propagates to the suitable AssistantAgent [129]. The GroupChatManager moderates and monitor agent dialogue [129]. Different agents can also apply different components. The base agent as main facilitator; in this case, it should include an LLM interface, acode executor, and a tool executor as well as a human–computer interface (HCI) for user inputs [129,130]. AI agents are programmable to act in a specific role, such as a coder, judge, or communicator [131,132,133]. In the context of reaching consensus, AI agents are presenting an opportunity to act as neutral and responsive arbitrators to resolve disputes. This can involve the inspection of disputed smart contracts automatically to confirm resolution, where failure would trigger the agent’s brokering utilities to encourage and help opponents to form consensus.

The flow of proposals and initiatives in CI-based DAOs applies a Git-style source control approach to ensure synchronized and collaborative control. Propositional smart contracts from citizens are new branches in related repositories. The submission of branch contracts and participation allow citizen to transfer ownership of the contract to a smart agent. This procedure aims to limit and transfer the ownership to a single actor that has full access to work with the transferred contract. This loop is repeated for the requested proposals, although the ownership is once again transferred to an accountable agent smart contract. The ownership of successful smart contract initiatives falls finally to the community DAO. Community groups and citizens should also have access to acquire ownership of contracts. One approach is to release ownership of the existing contract after a certain time has passed without ownership changes. This encourages commitment from owners but also mitigates the risks of data isolation.

The main component of CI DAO is the repository, which contains feature branches and workflow branches. Each feature branch can include multiple workflow branches. A workflow branch is an operational initiative that collects proposals as proposal branches. A voting branch is used to find consensus between proposals, allowing changes to be pushed to the feature and main branches. In addition to workflow proposals, citizens can propose new features in the main branch or new workflow branches for the feature branch. Citizens participate in activities by first accessing the community repository and then selecting the feature branch of interest. Inside feature-based branches, citizens can inspect the workflow branches that contain proposals as well as voting branches. Here, citizens can be divided into reactive or proactive participants. Reactive citizens are focused on inspection, commenting, and voting. Proactive citizens are engaged in the same activities but also participate in the creation and editing of the proposed branches inside the workflow branch. It is important to note that all participants should have access to all the necessary information in a convenient format. If this condition is not satisfied, proposals and voting are executed with inadequate knowledge, resulting low-quality proposals and decisions. Incentives to participate include rewards such as community fees or treasury. The use of incentives can be distributed in different branch hierarchies. For example, direct rewards could be distributed based on the proposal activity and voting participation of individuals, while indirect incentives could be distributed to all participants of the branch.

Cross-repository branching adds further possibilities to allocate resources, connect proposals, and find dependencies. When branches can reference other repositories, cross-domain proposals become more manageable. This highlights how different domain repositories can facilitate cross-domain collaboration without duplicating structures. The criteripm for cross-repository branching is the development of new sub-branch in cross-repository domain, which provides better supply of resources than parent repository. This also allows to broadcast synergic initiatives to citizens that are not currently engaged to parent repository. Recognizing this branching element should also be considered in architectural solutions for civic engagement.

3.2. Civic Intelligence Metrics

Traditional governance operates in the physical space with human-centered processing, characterized by voting centers, staff resources, and manual counting procedures. In contrast, CIG is DAO-based governance that functions in the digital space, using smart contracts, blockchain technology, and automated batch processing. These domains are bridged through constructed formulations that are selected based on equivalent functional aspects without neglecting their unique constraints and capabilities. Traditional governance in physical infrastructure operates through a network of voting centers, human resources, and manual verification processes. Therefore, this system is confined by geographical constraints and human processing capacity. CIG, in contrast, exists in the digital realm, leveraging smart contracts, blockchain consensus mechanisms, and automated batch processing to create a trustless and algorithmic decision-making environment. These systems demonstrate how similar democratic functions can be served while they operate under different constraints: scaling of traditional decision-making faces is constrained by physical resources and human coordination overhead, while decision making in CIG scales more linearly with blockchain throughput capacity and gas cost optimizations. Bridging these disparate domains requires the construction of mathematical formulations to normalize operative characteristics, which enables meaningful comparisons across key metrics while addressing inherent technological and practical constraints.

Modeling CI includes performance and engagement aspects. Related metrics are organized into three main areas: infrastructure, citizen participation, and integrated intelligence metrics. Starting with smart city infrastructure, the framework introduces two key measures. The infrastructure processing capacity $C_{\inf }$ provides a holistic view of technological capabilities by aggregating AI processing capacity, efficiency ratios, and IoT network loads across all city systems. This is complemented by the digital service availability $D_{\mathrm{sa}}$ index, which evaluates service accessibility, weighted by importance and actual use. This is a metric that verifies that smart city services have reached and benefited citizens.

The system performance metrics build upon this foundation with the urban system response rate $R_{\mathrm{us}}$ and infrastructure efficiency index $E_{\inf }$. These formulas are particularly notable for their integration of both technological and human factors. The $R_{\mathrm{us}}$ formula cleverly combines AI verification time with response time and scales it according to system capacity, while $E_{\inf }$ creates a balanced scorecard approach by weighting different aspects of infrastructure performance.

The citizen-related metrics measure citizen participation in operational and communal activities. The citizen engagement index $E_c$ and social network effect $S_{\mathrm{ne}}$ formulas quantify not just the volume of participation but also its quality and network gain effects. The digital literacy score $L_d$ and civic knowledge index $K_c$ provide sophisticated measures of citizen capabilities, considering both knowledge possession and its practical application.

The framework culminates in the CI metrics, which integrate all previous measures. The Civic Intelligence Quotient (CIQ) formula is particularly insightful in how it merges infrastructure capacity, citizen engagement, and knowledge metrics into a single comprehensive measure. The inclusion of a growth rate measure $G_{\mathrm{ci}}$ adds a dynamic element, allowing cities to track progress over time.

This collection of formulas creates a comprehensive measurement system that not only evaluates current performance but can guide strategic planning for smart city development. It recognizes that true civic intelligence emerges from the interplay between technological infrastructure and citizen capability, providing a quantitative framework for balancing these elements.

Scalability comparisons for systems are established around the capacities to handle increased load, where traditional systems are limited by physical infrastructure constraints $S_t\left(U\right)$—for DAOs, this is limited by blockchain network capacity and batch size limitations $S\left(U\right)$. Traditional scalability is primarily limited by the number of voting centers and staff capacity, following a linear scaling model with diminishing returns due to coordination overhead. DAO scalability, while theoretically offering superior scaling potential through automated processing, faces different constraints related to blockchain network capacity and batch processing limitations, which are handled in the mathematical formulations through batch size parameters and network capacity factors.

Cost efficiency formulations reflect the different cost structures for these systems. Traditional governance costs $C_t\left(n\right)$ are dominated by operational expenses including staffing, facilities, and materials. Economical scaling in this case is driven primarily by the better utilization of fixed resources. DAO governance costs $G\left(n\right)$ are mostly related to blockchain gas fees and storage costs; here, economical scaling is closely tied to the optimization of batch processing. The cost efficiency ratio ${\eta }_{\mathrm{cost}}$ represents normalized measures and comparisons based on cost structures, accounting for both the fixed and variable components in each system.

Another set of formulas was constructed to measure key metrics and performance indicators for hybrid AI–IoT systems, with the focus being on processing capacity, verification mechanisms, and network integration. Key performance measurement is the processing capacity formula of AI agent $A_{\mathrm{acap}}$, which provides a method of assessing the total task-handling capability of the deployed AI agents while considering efficiency improvements through learning. This metric is necessity for capacity planning and system scaling decisions if AI agents are intended to be utilized.

The hybrid system throughput ${\rho }_h$ formula extends measurements by incorporating both AI and traditional processing streams to formulations, establishing a comprehensive view of system performance. This metric is dimensionless, and therefore it must remain below one in order to achieve system stability, making it a critical operational threshold. It relates to different types of processing overhead, including AI-specific requirements for real-world deployments where resources are shared between conventional and AI-driven processes.

The verification and response time metrics ($T_{\mathrm{vai}}$ and $R_t$) address the challenges with AI system deployment: finding balance between automation and accuracy. The verification time formula introduces an approach to allow the dynamic adjustment of human oversight based on AI confidence scores. Using adaptive approaches highlights the need to optimize resource utilization while maintaining quality standards. Similarly, the response time distribution formula provides a method of measuring the workload distribution between AI and human operators, ensuring optimal resource allocation while maintaining system responsiveness. In addition to AI formulas, the IoT network load formula $L_{\mathrm{iot}}$ is used to address the critical infrastructure layer supporting AI systems. By accounting for individual device data requirements and update frequencies, it provides a metric for network capacity planning while helping to prevent system bottlenecks. The requirement to maintain $L_{\mathrm{iot}}$ below one creates an operational limit for IoT device deployment and data collection strategies.

These formulas together create a comprehensive framework for measuring and optimizing AI–IoT system performance, addressing everything from raw processing capacity to real-world implementation challenges. They provide quantifiable metrics that can guide system design, deployment, and optimization decisions while maintaining operational stability and efficiency.

3.3. Theory–System Integration

The integration of AI agents within the CIG framework enhances both governance efficiency and citizen engagement through multiple mechanisms. AI agents can serve as intermediaries in the DAO structure, performing tasks such as proposal categorization, impact analysis, and stakeholder identification [134,135,136]. These agents facilitate citizen participation by providing personalized recommendations for relevant proposals, simplifying complex policy documents, and offering multilingual support to ensure broader accessibility. In policy management, AI systems can analyze historical voting patterns and proposal outcomes to identify successful governance patterns and potential areas of improvement [16]. The combination of natural language processing, predictive analysis, and machine learning enables the automated monitoring of proposal discussions, helping to maintain constructive dialogue and identify emerging consensus or concerns [134,135,136]. AI agents can proactively alert stakeholders about relevant developments and deadlines, ensuring timely participation in the decision-making process. AI agents can be used to report about anomalies or breaches, regulate data aggregation, and automatically control decision making in data verification [137]. Since the AI agents and IoT in smart cities operate off-chain (outside of blockchain), it is necessary to use oracle services to enable secured data flow to smart contracts from off-chain computations [138,139,140]. They also provide access points to connect external API services and are necessary for facilitating the data flow between the blockchain and real-time events [141]. Digital civic practices [27] are integrated through DApp interfaces and AI-assisted engagement tools, creating what Gil [28] describes as necessary infrastructure for the formation of social capital.

3.3.1. CI Framework Components Mapping

The CIG system implements a layered architecture that maps the CI components to specific technological solutions, as illustrated in Figure 2. The system comprises four distinct layers: user interface, AI service, core service, and blockchain infrastructure. Each layer directly maps to CI components while ensuring system modularity and scalability. The token system creates a circular economy within the city, where citizens can earn and spend tokens through various civic activities. This architecture enables both automated governance processes and human-centric participation, with AI agents facilitating the interactions between layers while maintaining system security through oracle services and smart contracts.

The user interface layer implements the accessibility component of CI through:

(1)

A web DApp for browser-based access and interaction;

(2)

A mobile DApp for on-the-go participation;

(3)

An API gateway enabling third-party service integration.

The Web DApp provides a comprehensive browser-based interface that serves as the primary point of interaction with the CIG platform. Built using responsive design principles, it offers an intuitive dashboard where citizens can access governance proposals, participate in voting, manage their tokens, and track community initiatives. The web interface implements advanced visualization tools for data transparency, featuring interactive charts and graphs that help citizens understand governance metrics and community trends. It also includes collaborative features such as discussion forums and draft tools for proposals, enabling citizens to engage in meaningful dialogue and contribute to policy formation from any device with a web browser.

The mobile DApp extends platform accessibility through a native mobile application, allowing citizens to participate in governance activities seamlessly from their smartphones and tablets. Optimized for on-the-go interaction, it provides push notifications for time-sensitive decisions, low-latency capabilities, and location-aware features for participating in local initiatives. The mobile interface includes key functionalities, such as secure biometric authentication, token management, and proposal viewing, while maintaining synchronization with the web platform. Special attention is paid to bandwidth optimization and offline capabilities, ensuring reliable participation even in areas with limited connectivity.

The API gateway serves as a crucial bridge between the CIG platform and external services, enabling seamless integration with third-party applications and existing civic infrastructure. Through well-documented REST APIs and WebSocket endpoints, it allows developers to build complementary services and integrate CIG functionalities into other civic platforms. The gateway implements robust security measures, including API key management, rate limiting, and request validation while providing comprehensive documentation and development tools. This extensibility enables the creation of specialized applications for specific community needs from neighborhood-level organizing tools to city-wide service integration platforms, expanding the system’s reach and utility.

The AI service layer enhances the knowledge and system capital components through

(1)

A base agent service handling citizen inputs and activity logging;

(2)

An expert agent service for workflow optimization and proposal analysis;

(3)

A broker agent service managing consensus and decision execution.

The base agent serves as the primary interface between citizens and the CIG platform, handling the natural language processing of citizen inputs and maintaining comprehensive activity logs. It uses LLM capabilities to interpret citizen queries, proposals, and feedback, converting them into structured data for processing. The base agent also manages user authentication, tracks participation metrics, and provides personalized assistance through its HCI components. By maintaining detailed activity logs, it enables transparency while preserving user privacy through selective disclosure mechanisms.

The expert agent specializes in technical analysis and workflow optimization within the CIG ecosystem. It evaluates the feasibility of proposals, conducts impact assessments, and provides data-driven insights to support decision-making processes. Through its analytical capabilities, the expert agent identifies potential conflicts, suggests improvements, and ensures that all proposals are aligned with existing regulations and community guidelines. It also optimizes governance workflows by analyzing participation patterns and resource allocation efficiency.

The broker agent facilitates consensus building and oversees decision execution in the CIG platform. It handles communication between different stakeholder groups, manages voting processes, and ensures the proper execution of approved proposals through smart contracts. The broker agent implements consensus mechanisms, monitors voting thresholds, and triggers automated actions based on governance outcomes. It also maintains the integrity of the decision-making process by validating participant credentials and enforcing the governance rules defined in the DAO structure.

The AI service layer leverages several key technologies to enable these capabilities. The base agent utilizes transformer-based language models for natural language understanding, along with BERT-based embeddings for the semantic analysis of citizen inputs. The expert agent employs gradient boosting algorithms and neural networks for proposal evaluation and impact prediction while incorporating automated planning systems for workflow optimization. The broker agent implements reinforcement learning techniques for optimizing consensus mechanisms and uses multiagent systems for coordinating stakeholder interactions. These AI components are containerized using Docker and orchestrated through Kubernetes to ensure scalability and maintainable deployment, with model versioning handled through MLflow for reproducibility and governance.

The core service layer implements relational capital and tangible assets through

(1)

A token management service handling three token types:

• ERC-721 ID tokens for digital citizenship proof;
• Stable coins pegged to local currency;
• DAO tokens for governance participation.

(2)

DAO governance service managing proposals and voting;

(3)

Oracle services connecting off-chain data and AI computations.

The token management service orchestrates three distinct token types that form the backbone of civic participation in the CIG ecosystem. ERC-721 ID tokens serve as verifiable digital citizenship credentials, providing secure and immutable proof of identity while protecting personal data through selective disclosure mechanisms. These unique nonfungible tokens in the Ethereum blockchain establish the rights to participate in the CIG governance processes.

Stable coins, the second token type, are pegged to local currency and facilitate transparent financial transactions and incentive distribution within the ecosystem. This pegging ensures value stability and reduces volatility risks while reflecting governmental monetary policies. The use of local-currency-pegged tokens is necessary to integrate resource allocations with existing and widely accepted means of economic exchange.

DAO governance tokens, the third type, enable participation rights in decision-making processes. Citizens can earn these fungible tokens by engaging in various civic activities, such as participating in local governance decisions, creating initiatives, volunteering for community projects, or contributing to urban data collection initiatives. The system implements holding periods and staking mechanisms to encourage long-term engagement and prevent manipulation. DAO tokens are exchangeable for stable coins, creating a flexible economic framework.

The Civic Purge wallet serves as the citizen’s gateway to the CIG, allowing them to stake, earn, spend, and manage all three token types. Both stable coins and DAO tokens can be spent on city services, on supporting local initiatives, or be donated to charitable causes within communities. This comprehensive token ecosystem enables a circular economy within the city, increasing active participation while allowing citizens to directly benefit from their engagement and stay connected to local civic activities. All tokens operate through smart contracts that implement automated distribution, vesting schedules, and transfer restrictions to maintain system integrity.

The blockchain infrastructure layer provides:

(1)

Ethereum-based smart contracts for governance logic;

(2)

Layer-2 solutions for economical scaling;

(3)

IPFS integration for decentralized storage.

Ethereum-based smart contracts form the foundational layer of governance logic in the CIG platform, implementing transparent and immutable rules for civic participation. These contracts encode critical governance processes including proposal submission, voting mechanisms, and decision execution. The smart contract architecture employs a modular design pattern, with separate contracts handling distinct governance functions: a ProposalManager contract validates and tracks proposal lifecycles; a VotingEngine contract implements various voting mechanisms including quadratic voting and delegation; an ExecutionController contract manages the automated implementation of approved decisions. The contracts incorporate time locks and multisignature requirements for critical operations, while emergency pause mechanisms provide safeguards against potential vulnerabilities. Advanced features include upgradeable contract patterns that allow governance logic to evolve through community consensus and event emission for the real-time tracking of governance activities. The smart contracts also implement robust access control mechanisms through the OpenZeppelin framework, ensuring that only authorized participants can trigger specific governance actions while maintaining transparency of all operations on the blockchain.

The optimization of the DAO architecture also requires layer-2 solutions to improve performance and reduce transaction fees, thus allowing the scaling of operations [142]. The storage of proposals and documents with IPFS offers decentralized storage policies [143]. Other useful utility layers include gas optimization relay, event listeners, and indexers for monitoring the state of the blockchain [101]. Digital identifiers are another necessity for civic participation in DAOs [144]. Nonfungible tokens (NFTs) in the blockchain enable identity management and role-based access control along with encrypted personal data storage [145]. NFTs can include, for example, government-issued ID, educational certificates, or professional resumes [146]. Citizens can control and own their personal data with a single mechanism that ensures the trustful utilization of personal NFTs [147]. Another instrument interacting with the blockchain and DAOs involves decentralized apps (DApps), which are specialized tools facilitating user inputs and executing the call of smart contracts from the blockchain automatically [112,116]. DApps can also be designed to leverage AI agents for different task flows [148].

InterPlanetary File System (IPFS) integration provides a decentralized storage solution for civic data and documentation. The IPFS stores data in a content-addressed system, where files are identified by their content rather than location, ensuring data integrity and immutability. In the CIG context, the IPFS hosts proposal documentation, citizen feedback, urban planning documents, and other civic resources. This distributed storage approach reduces central server dependencies while maintaining data availability through peer-to-peer networks. IPFS content identifiers (CIDs) are stored on the blockchain to establish verifiable links between on-chain governance actions and their corresponding documentation, creating an immutable record of civic participation.

3.3.2. Participation Theory Implementation

The technical implementation of participation theory through the CIG system demonstrates how abstract frameworks can be transformed into practical, functioning mechanisms for civic engagement. By mapping system components directly to established theoretical models, the implementation ensures that technical solutions serve clear theoretical purposes rather than existing as isolated features. This systematic approach to theory implementation creates a coherent platform where each technical component contributes to the broader goals of enhanced civic participation and effective governance. The CIG system implements participation theory through a multilayered approach that maps directly to established theoretical frameworks. Building on Cardullo’s [30] participation levels, from consumers to co-creators, the system includes the following:

• Consultation level: implemented through the basic DAO token voting mechanism, allowing citizens to provide input on proposed initiatives;
• Active participation: enabled via the Git-inspired branching system where citizens can create and modify proposals;
• Co-creation level: achieved through the integration of AI agents and smart contracts that facilitate collaborative policy development.

The system also implements Nabatchi’s [32] collaborative intelligence framework through a comprehensive integration of technological solutions that enhance collective decision-making capabilities.

• Crowdsourced resource allocation via stable coin mechanisms;
• Consensus building through the multiagent AI architecture;
• Decision execution tracking via blockchain’s immutable ledger.

Crowdsourced resource allocation via stable coin mechanisms ensures the equitable distribution of governance resources while maintaining the transparent tracking of fund utilization. Consensus building through the multiagent AI architecture facilitates informed decision making by synthesizing diverse perspectives and identifying areas of common ground among participants. Decision execution tracking via the blockchain’s immutable ledger provides the continuous monitoring of the implementation progress and ensures accountability throughout the governance process. This implementation of collaborative intelligence creates a robust foundation for sustained civic engagement and effective policy development.

While these implementations demonstrate the successful translation of participation theory into practical systems, they also highlight the need to address fundamental barriers that can impede effective civic engagement. The technical architecture of the CIG system therefore extends beyond theoretical implementation to systematically resolve traditional participation barriers through targeted technological solutions. This comprehensive approach to barrier resolution ensures that theoretical frameworks can achieve their intended impact in real-world governance scenarios.

3.3.3. Social Capitalization Comparison Framework

This comparative analysis of traditional and Git-inspired governance models focuses on three key perspectives: efficiency through crowdfunding capabilities, scalability potential, and cost-effectiveness. Each perspective is quantified through specific metrics and formulations that enable systematic comparison.

Crowdfunding efficiency measures the capacity of governance models to effectively mobilize tangible and intangible assets in the possession of citizens. Let $E_c\left(t\right)$ represent the crowdfunding efficiency at time t:

$$E_c\left(t\right)={\displaystyle \frac{P\left(t\right)·R\left(t\right)·(1+N_e\left(t\right))}{T_d}}$$

(1)

where:

$$\begin{array}{ccc}\hfill P\left(t\right)& isthe& \mathrm{active}\mathrm{participant}\mathrm{count}\mathrm{at}\mathrm{time}t;\hfill \\ \hfill R\left(t\right)& isthe& \mathrm{average}\mathrm{resource}\mathrm{contribution}\mathrm{per}\mathrm{participant};\hfill \\ \hfill N_e\left(t\right)& isthe& \mathrm{network}\mathrm{effect}\mathrm{multiplier}:N_e\left(t\right)=\alpha \log (1+{\displaystyle \frac{t}{T_d}});\hfill \\ \hfill T_d& isthe& \mathrm{decision-making}\mathrm{time}\mathrm{window};\hfill \\ \hfill \alpha & isthe& \mathrm{network}\mathrm{effect}\mathrm{coefficient}(\mathrm{typically}0.3–0.5).\hfill \end{array}$$

The cumulative funding potential $F_c\left(T\right)$ over time period T is

$$F_c\left(T\right)={\int }_0^T{E}_c\left(t\right)·dt$$

(2)

Scalability analysis measures the scalability potential, considering the system’s capacity to handle increasing voter populations up to 2 billion participants. The scalable participation function $S\left(t\right)$ is defined as

$$S\left(t\right)=P_{max}·\min (1,{\displaystyle \frac{P_0{(1+r)}^t+\beta N_e\left(t\right)}{P_{max}}})$$

(3)

where

$$\begin{array}{ccc}\hfill P_{max}& :& \mathrm{maximum}\mathrm{potential}\mathrm{participant}\mathrm{pool}(2\mathrm{billion});\hfill \\ \hfill P_0& :& \mathrm{initial}\mathrm{participant}\mathrm{count};\hfill \\ \hfill r& :& \mathrm{daily}\mathrm{growth}\mathrm{rate};\hfill \\ \hfill \beta & :& \mathrm{network}\mathrm{growth}\mathrm{coefficient};\hfill \\ \hfill N_e\left(t\right)& :& \mathrm{network}\mathrm{effect}\mathrm{multiplier}.\hfill \end{array}$$

The effective participation ratio ${\eta }_p\left(t\right)$ between the Git-inspired and traditional models is

$${\eta }_p\left(t\right)={\displaystyle \frac{S_{git}\left(t\right)}{S_{trad}\left(t\right)}}·{\displaystyle \frac{f_{git}}{f_{trad}}}$$

(4)

where $f_{git}$ and $f_{trad}$ are the respective participation frequencies.

Cost-effectiveness analysis incorporates both direct and indirect costs. The total cost function $C\left(t\right)$ for each model is

$$C\left(t\right)=C_f+C_v\left(t\right)·P\left(t\right)+C_t\left(t\right)·T\left(t\right)$$

(5)

where:

$$\begin{array}{ccc}\hfill C_f& :& \mathrm{fixed}\mathrm{infrastructure}\mathrm{costs};\hfill \\ \hfill C_v\left(t\right)& :& \mathrm{variable}\mathrm{cost}\mathrm{per}\mathrm{participant};\hfill \\ \hfill C_t\left(t\right)& :& \mathrm{transaction}\mathrm{cost}\mathrm{per}\mathrm{proposal};\hfill \\ \hfill T\left(t\right)& :& \mathrm{number}\mathrm{of}\mathrm{transactions}/\mathrm{proposals};\hfill \\ \hfill P\left(t\right)& :& \mathrm{active}\mathrm{participant}\mathrm{count}.\hfill \end{array}$$

The cost efficiency ratio ${\eta }_c$ between models is

$${\eta }_c={\displaystyle \frac{{\int }_0^T{C}_{trad}\left(t\right)·dt}{{\int }_0^T{C}_{git}\left(t\right)·dt}}·{\displaystyle \frac{E_{git}}{E_{trad}}}$$

(6)

where $E_{git}$ and $E_{trad}$ represent the respective governance effectiveness metrics. For the Git-inspired model, transaction costs decrease with increased participation:

$$C_t^{git}\left(t\right)=C_{t0}·e^{-\lambda P\left(t\right)}$$

(7)

where $\lambda$ is the cost reduction coefficient, and $C_{t0}$ is the initial transaction cost.

3.3.4. Barrier Resolution Through Technical Solutions

Traditional barriers to civic participation have historically limited the effectiveness of governance systems, creating gaps between theoretical ideals and practical outcomes. The CIG system addresses these fundamental challenges through targeted technical implementations that systematically dismantle participation barriers while maintaining system efficiency and minimizing processing delays. Through careful integration of blockchain, AI, and digital twin technologies, the system creates comprehensive solutions to long-standing participation challenges.

The CIG system addresses traditional participation barriers through targeted technical implementations.

Transparency barriers, often cited as a primary obstacle to civic engagement, are comprehensively addressed through blockchain technology’s inherent characteristics. The system’s immutable record keeping ensures that all governance actions are permanently recorded and publicly accessible, while real-time proposal tracking through IPFS storage enables citizens to monitor decision-making processes as they unfold. The automated audit trails generated through smart contracts provide unprecedented visibility of governance operations, creating a foundation of trust that encourages sustained participation. Transparency constraints include

• Immutable record keeping through blockchain ledger;
• Real-time proposal tracking via IPFS storage;
• Automated audit trails through smart contracts.

Accessibility barriers, which have traditionally excluded significant portions of the population from governance processes, are systematically eliminated through thoughtful technical implementation. The integration of AI agents can mitigate accessibility constraints by providing personalized assistance and automated support throughout the participation process. The multilanguage support powered by AI agents ensures linguistic diversity is no longer an obstacle to participation, while Layer-2 solutions significantly reduce the economic barriers associated with blockchain transactions. Mobile-first DApp interfaces extend participation opportunities to citizens regardless of their technical resources or expertise level. Accessibility constraints include

• Mult-language support through AI agents;
• Layer-2 solutions reducing transaction costs;
• Mobile-first DApp interfaces.

Resource barriers, both computational and human, are effectively managed through an optimized system architecture. Gas optimization techniques minimize the operational costs associated with blockchain interactions, making sustained participation economically viable. Automated proposal categorization significantly reduces the manual effort required for system administration, while the DAO token reward structure creates a sustainable incentive mechanism that encourages quality contributions. These technical solutions directly address the participation constraints identified in the governance literature while maintaining high system efficiency. The resource constraints include

• Gas optimization for reduced operational costs;
• Automated proposal categorization reducing manual effort;
• Incentivization through DAO token rewards.

These technical approaches directly address the participation constraints identified by Yang [33], while

(1)

maintaining system efficiency (${\eta }_{\mathrm{eff}}$) and

(2)

minimizing processing delays ($E\left[T_{\mathrm{trad}}\right]$) [35].

The effectiveness of these barrier resolutions and technical implementations is evaluated using quantifiable metrics. In order to estimate the benefits of enhanced transparency, accessibility, and resource optimization, a rigorous mathematical framework is necessary to validate the system’s performance improvements over traditional governance approaches. This analysis provides concrete evidence of the CIG system’s capabilities in addressing participation barriers while maintaining operational efficiency.

3.4. Implementation Analysis

The mathematical framework encompasses three primary dimensions of analysis: efficiency, scalability, and costs. The efficiency metrics are defined as the throughput ratio ${\eta }_{\mathrm{thru}}$ and processing efficiency ratio ${\eta }_{\mathrm{eff}}$, which compare the abilities to handle proposals and votes. Traditional systems measure efficiency in terms of votes processed per staff member per hour, while for DAO, efficiencies are measured through batch processing capacity and blockchain transaction throughput. The framework normalizes these different measures by converting them to comparable units of data processed per time unit.

3.4.1. Efficiency Comparison in Municipal Decision Making

In mid-sized municipalities with proposal processing systems, the traditional governance model relies on a network of administrative offices processing citizen petitions and legislative proposals. The efficiency ratio ${\eta }_{\mathrm{eff}}$ reveals the throughput differences between the systems. While the traditional system processes proposals through a linear workflow by multiple staff members, the batch processing capability in CIG allows the parallel handling of proposals through automatically triggered smart contracts. The throughput ratio ${\eta }_{\mathrm{thru}}$ demonstrates how CIG can process multiple proposals simultaneously within each batch, which is beneficial during peak submission periods. The formulation of the efficiency metrics uses the following functions:

$${\eta }_{\mathrm{eff}}={\displaystyle \frac{\mathrm{CIG}\mathrm{Processing}\mathrm{Rate}}{\mathrm{Traditional}\mathrm{Processing}\mathrm{Rate}}}$$

(8)

$${\eta }_{\mathrm{lat}}={\displaystyle \frac{E\left[T_{\mathrm{trad}}\right]}{E\left[T\right]}}$$

(9)

where the traditional processing rate is calculated as

$$R_{\mathrm{trad}}=n·r·h·u$$

(10)

And, the CIG processing rate is determined by

$$R_{\mathrm{CIG}}={\displaystyle \frac{B_{\max }}{s}}·\mu$$

(11)

where

$$\begin{array}{ccc}\hfill n& :& \mathrm{number}\mathrm{of}\mathrm{staff}\mathrm{members};\hfill \\ \hfill r& :& \mathrm{processing}\mathrm{rate}\mathrm{per}\mathrm{staff};\hfill \\ \hfill h& :& \mathrm{working}\mathrm{hours};\hfill \\ \hfill u& :& \mathrm{utilization}\mathrm{rate};\hfill \\ \hfill B_{\max }& :& \mathrm{maximum}\mathrm{batch}\mathrm{size};\hfill \\ \hfill s& :& \mathrm{size}\mathrm{per}\mathrm{proposal};\hfill \\ \hfill \mu & :& \mathrm{batch}\mathrm{processing}\mathrm{rate}.\hfill \end{array}$$

The case study involves the arrival of citizen petitions at varying rates throughout the day. The efficiency of a traditional system is constrained by working hours and staff availability, leading to processing bottlenecks during high-volume periods. In contrast, the batch processing mechanism of a DAO is defined by $B_{\max }·\mu$, which maintains a consistent throughput regardless of the time of day. This effectively eliminates the “rush hour” effect common in traditional systems. This is evident in the average proposal latency formula $E\left[T\right]$, which shows distinctively reduced waiting times compared to those of the traditional system during peak periods.

An efficiency comparison is summarized in

Table 1. Efficiency comparison of traditional governance and CIG.

Metric	Traditional System	CIG System	Performance Ratio
Daily Processing Rate	1200 proposals/day	3846 petition proposals/day (13 KB); 417 legislative proposals/day (120 KB)	${\eta }_{\mathrm{eff}}$ = 3.2x
Processing Method	Linear workflow	Parallel batch processing	-
Staff Requirements	20 staff members	Automated	-
Processing Rate per Staff	60 proposals/hour/person	N/A	-
Capacity Utilization	83%	Variable based on batch size	-
Average Processing Latency	4 h	15 min	${\eta }_{\mathrm{lat}}$ = 16x
Batch Parameters	No batching	$B_{\max }$: 50 MB; $\mu$: 4 batches/hour	-
Peak Performance	Limited by staff availability	Consistent throughput	-

. When examining a municipality processing 1000 proposals per day, a traditional system with 20 staff members reaches a processing rate $R_{\mathrm{pmax}}$ of approximately 1200 proposals per day (60 proposals per hour per person), operating at a 83% capacity utilization. With a batch size $B_{\max }$ of 50 MB and a processing rate $\mu$ of 4 batches per hour, CIG can handle up to 3846 petition proposals per day (13 KB each) or 417 legislative proposals (120 KB each) at full capacity. The calculated efficiency ratio ${\eta }_{\mathrm{eff}}$ is 3.2, indicating CIG processes proposals over three times more efficiently than the traditional system. An average processing latency $E\left[T\right]$ for CIG is 15 min compared to the traditional system’s 4 h $E\left[T_{\mathrm{trad}}\right]$, demonstrating a latency improvement factor ${\eta }_{\mathrm{lat}}$ of 16. CIG’s parallel batch processing capability and freedom from working-hour constraints eliminates the “rush hour” bottlenecks that affect traditional systems, maintaining consistent performance regardless of submission timing. These dramatic differences in processing speed and volume capability demonstrate how automation and smart contract technology can significantly streamline municipal operations.

3.4.2. Scalability Challenges in Regional Governance

The scalability comparison becomes relevant when a regional governance system serving a growing population is examined. The scaling limit $S_t\left(U\right)$ in traditional systems shows how physical infrastructure constraints become increasingly problematic as the user base expands. Each new voting center or administrative office requires substantial resource investment and faces diminishing returns due to coordination overhead, as captured in the scalability formula. The scalability metrics are calculated using the following functions:

For traditional systems,

$$S_t\left(U\right)=n_v·n_c·r·h·d$$

(12)

For CIG systems,

$$S\left(U\right)={\displaystyle \frac{\beta ·h_m}{{\tau }_b}}·B_{\max }$$

(13)

The scalability ratio is defined as

$${\eta }_{\mathrm{scale}}={\displaystyle \frac{S\left(U\right)}{S_t\left(U\right)}}$$

(14)

where

$$\begin{array}{cc}\hfill n_v:& \mathrm{number}\mathrm{of}\mathrm{processing}\mathrm{centers};\\ \hfill n_c:& \mathrm{staff}\mathrm{per}\mathrm{center};\\ \hfill r:& \mathrm{processing}\mathrm{rate};\\ \hfill h:& \mathrm{working}\mathrm{hours}\mathrm{per}\mathrm{day};\\ \hfill d:& \mathrm{working}\mathrm{days}\mathrm{per}\mathrm{month};\\ \hfill \beta :& \mathrm{network}\mathrm{capacity}\mathrm{factor};\\ \hfill h_m:& \mathrm{monthly}\mathrm{operating}\mathrm{hours};\\ \hfill {\tau }_b:& \mathrm{batch}\mathrm{formation}\mathrm{time};\\ \hfill B_{\max }:& \mathrm{maximum}\mathrm{batch}\mathrm{size}.\end{array}$$

The scaling function $S\left(U\right)$ for CIG behaves differently with different growth patterns, primarily constrained by blockchain network capacity and batch size limitations rather than physical infrastructure. When a region experiences population growth, CIG can accommodate increased proposal volume through optimized batch processing $n^{\ast }$, which requires only marginal adjustments to batch parameters rather than physical expansion. However, this case study reveals an important nuance: while the theoretical scaling capability of CIG is superior, it must adapt to blockchain network limitations and gas cost optimizations, particularly during periods of network congestion.

The scalability comparison is summarized in

Table 2. Scalability comparison between traditional governance and CIG.

Metric	Traditional System	CIG System	Performance Ratio
Monthly Processing Limit	300,000 proposals	960,000 proposals	${\eta }_{\mathrm{scale}}$ = 3.2x
Infrastructure	50 processing centers	Blockchain network	-
Staff Distribution	10 staff per center	N/A	-
Operating Parameters	Hours: 10/day; Days: 20/month	Network capacity ($\beta$): 0.8; Batch time (${\tau }_b$): 0.25 h	-
User Base	1 M citizens	1M citizens	-
Active Participation Rate	5% ($\alpha$ = 0.05)	5% ($\alpha$ = 0.05)	-
Monthly Submissions	50,000 proposals	50,000 proposals	-
Scaling Constraints	Physical infrastructure; staff coordination	Network capacity; batch limitations	-
Growth Pattern	Linear with diminishing returns	Network-capacity-dependent	-

. If a regional system is serving 1 million citizens with a 5% active participation rate ($\alpha =0.05$), it is assumed to generate approximately 50,000 proposal submissions per month. The traditional system, with 50 processing centers $n_v$ and 10 staff per center $n_c$, reaches its scaling limit $S_t\left(U\right)$ at approximately 300,000 proposals per month when 60 proposals are processed per hour with 10 h working days. CIG, operating with a network capacity factor $\beta$ of 0.8 and a batch formation time ${\tau }_b$ of 0.25 h, achieves a scaling limit $S\left(U\right)$ of approximately 960,000 proposals per month. The scalability ratio ${\eta }_{\mathrm{scale}}$ of 3.2 demonstrates CIG’s superior scaling capabilities, handling over three times the volume without requiring physical infrastructure expansion.

In the context of regional governance serving a million citizens, the scalability differences between the two systems become starkly apparent. The traditional system, despite having 50 processing centers with 10 staff each, reaches its maximum capacity at 300,000 proposals monthly, primarily constrained by physical infrastructure and human coordination limitations. In contrast, CIG demonstrates superior scalability, handling up to 960,000 proposals monthly—a 3.2x improvement (${\eta }_{\mathrm{scale}}$)—while requiring minimal physical infrastructure expansion. This significant difference in scaling capability emerges from CIG’s ability to optimize batch processing and leverage the blockchain network’s capacity, though it faces its own constraints in terms of network congestion and gas cost optimization. The key advantage lies in CIG’s ability to scale through technical parameter adjustments rather than requiring extensive physical infrastructure investments.

3.4.3. Cost Efficiency in Large-Scale Implementation

This cost comparison case study examines the implementation of both systems in a large-scale governance scenario. The traditional system’s cost structure $C_t\left(n\right)$ shows how operational expenses scale with size, including staffing costs $C_s$, facility maintenance, and material expenses $C_m$. The optimal staffing formula $n_c^{\ast }$ demonstrates how traditional systems must constantly balance staff costs with processing capacity.

In contrast, CIG’s cost structure, governed by the gas cost formula $G\left(n\right)$, reveals a different economy of scale pattern. This case study shows how batch optimization becomes crucial for cost efficiency, with the optimal batch size formula $n^{\ast }$ helping to minimize per-proposal costs. The cost efficiency ratio ${\eta }_{\mathrm{cost}}$ demonstrates how CIG’s costs primarily scale with network usage rather than physical resources. This becomes particularly advantageous in large-scale implementations where traditional systems would require significant infrastructure investment. However, this case study also highlights how gas price volatility can impact CIG’s cost predictability, requiring careful batch optimization strategies to maintain cost efficiency during periods of high network congestion. The cost efficiency metrics are calculated using the following functions:

For traditional systems,

$$S_t\left(U\right)=n_v·n_c·r·h·d$$

(15)

For CIG systems,

$$S\left(U\right)={\displaystyle \frac{\beta ·h_m}{{\tau }_b}}·B_{\max }$$

(16)

The scalability ratio is defined as

$${\eta }_{\mathrm{scale}}={\displaystyle \frac{S\left(U\right)}{S_t\left(U\right)}}$$

(17)

where

$$\begin{array}{cc}\hfill n_v:& \mathrm{number}\mathrm{of}\mathrm{processing}\mathrm{centers};\\ \hfill n_c:& \mathrm{staff}\mathrm{per}\mathrm{center};\\ \hfill r:& \mathrm{processing}\mathrm{rate};\\ \hfill h:& \mathrm{working}\mathrm{hours}\mathrm{per}\mathrm{day};\\ \hfill d:& \mathrm{working}\mathrm{days}\mathrm{per}\mathrm{month};\\ \hfill \beta :& \mathrm{network}\mathrm{capacity}\mathrm{factor};\\ \hfill h_m:& \mathrm{monthly}\mathrm{operating}\mathrm{hours};\\ \hfill {\tau }_b:& \mathrm{batch}\mathrm{formation}\mathrm{time};\\ \hfill B_{\max }:& \mathrm{maximum}\mathrm{batch}\mathrm{size}.\end{array}$$

The cost comparison is summarized in

Table 3. Cost efficiency comparison between traditional governance and CIG.

Metric	Traditional System	CIG System	Performance Ratio
Per-Proposal Cost	USD 17.50	USD 5.20	${\eta }_{\mathrm{cost}}$ = 3.37x
Base Operational Cost	USD 50,000/month	21,000 gas units ($G_b$)	-
Staff Costs	5000 USD/person/month	N/A	-
Material Costs	2 USD/proposal	16 gas/byte ($G_s$)	-
Optimal Staff Size	25 people	N/A	-
Batch Size	N/A	100 proposals	-
Monthly Processing Volume	10,000 proposals	10,000 proposals	-
Total Monthly Cost	USD 175,000	Variable (gas-price-dependent)	-
Cost Scaling Pattern	Linear with size	Network usage dependent	-
Cost Variability Factors	Staffing USD materials	Gas prices and network congestion	-
Critical Variables	Staff utilization	Gas price (0.00005 USD/gas), batch optimization	-

. For processing 10,000 proposals per month, the traditional system’s cost structure shows a base operational cost $C_b$ of 50,000 USD/month, staff costs $C_s$ of 5000 USD/person/month, and material costs $C_m$ of 2 USD/proposal. With optimal staffing $n_c^{\ast }$ of 25 people, the total monthly cost amounts to USD 175,000, or USD 17.50 per proposal. CIG, with a base gas cost $G_b$ of 21,000 gas units and storage cost $G_s$ of 16 gas/byte, optimized to batch sizes $n^{\ast }$ of 100 proposals, achieves a cost of approximately USD 5.20 per proposal at suggested gas prices ($P_{\mathrm{gas}}$ = 0.00005 USD/gas unit). The cost efficiency ratio ${\eta }_{\mathrm{cost}}$ of 3.37 demonstrates significant cost advantages for the CIG, although this ratio fluctuates with gas price and network congestion.

The cost efficiency analysis reveals compelling financial advantages for CIG in large-scale operations. Processing 10,000 proposals monthly, the traditional system incurs substantial costs of USD 17.50 per proposal, driven by high fixed operational costs (USD 50,000/month), staff salaries (5000/USDperson/month), and material expenses (2 USD/proposal), totaling USD 175,000 monthly. CIG reduces this to USD 5.20 per proposal, achieving a 3.37x cost efficiency improvement ${\eta }_{\mathrm{cost}}$. This substantial cost advantage stems from the DAO’s minimal infrastructure requirements and optimized batch processing, though it is important to note that actual savings fluctuate with gas prices and network congestion. The costs of the traditional system scale linearly with size due to staffing and physical resource needs, while the costs in DAO primarily depend on network usage and optimization of batch sizes, making it advantageous for larger-scale implementations.

4. Discussion

The empirical analysis of the CIG implementation reveals a complex interplay between technological capabilities and civic participation requirements. Through the lens of CI framework components, tokenized engagement mechanisms, and AI-assisted governance, three distinct dimensions emerge: system performance metrics showing substantial efficiency gains, theoretical implications challenging traditional participation models, and practical considerations for real-world deployment. When examined against the backdrop of traditional governance systems, the CIG framework demonstrates how blockchain and AI technologies can lead to transformational civic engagement while raising important questions about scalability, accessibility, and long-term sustainability. This analysis provides a foundation for understanding both the immediate benefits and broader implications of implementing AI-enhanced blockchain systems in urban governance.

4.1. Recommendations

The empirical analysis of the CIG system implementation reveals several key recommendations for developing civic participation platforms:

First, the integration of blockchain and AI technologies should follow a balanced approach, where blockchain provides the transparent, immutable infrastructure, while AI facilitates user engagement and process automation. The performance metrics demonstrate that this hybrid approach achieves significant improvements in efficiency (${\eta }_{\mathrm{eff}}$ = 3.2x) and latency reduction (${\eta }_{\mathrm{lat}}$ = 16x) compared to those of traditional systems.

Second, scalability considerations should drive architectural decisions. The analysis shows that DAO-based systems can handle significantly larger volumes (960,000 vs. 300,000 proposals monthly, ${\eta }_{\mathrm{scale}}$ = 3.2x) through batch optimization and network capacity management rather than physical infrastructure expansion. This suggests that civic platforms should prioritize technical parameter optimization over infrastructure scaling.

Third, the cost efficiency of civic platforms should be approached through batch processing optimization and gas cost management. The empirical results show substantial cost advantages (${\eta }_{\mathrm{cost}}$ = 3.37x) when properly implemented, suggesting that investment in optimization mechanisms yields significant returns in operational costs.

Based on the social capitalization comparison framework, several key recommendations emerge for implementing Git-inspired governance systems. The mathematical models demonstrate that network effects ($N_e\left(t\right)$) play a crucial role in both efficiency and scalability, suggesting that early adoption strategies should focus on creating strong network incentives. The exponential decrease in transaction costs ($C_t^{git}\left(t\right)$) with increased participation indicates that governance systems should prioritize lowering the initial barriers to entry while maintaining robust scaling capabilities. Furthermore, the crowdfunding efficiency metrics ($E_c\left(t\right)$) suggest that resource mobilization strategies should be designed to capitalize on network effects through integrated stable coin mechanisms and transparent fund allocation protocols. These findings recommend a phased implementation approach that begins with core participation features and gradually expands to more complex governance mechanisms as the network effect multiplier increases.

4.2. Research Contributions and Implications

The implementation results have several implications for civic participation theory. The CI capacity framework, when implemented through distributed ledger technologies, demonstrates enhanced capabilities for knowledge sharing and system capital accumulation. The significant improvement in processing efficiency (3.2x) suggests that technological infrastructure can amplify CI capabilities beyond the traditional theoretical constraints.

Multilevel participation theory, traditionally focused on hierarchical engagement structures, requires revision when applied to blockchain-based systems. The empirical results show that decentralized systems can simultaneously support multiple participation levels through token mechanics and AI assistance, challenging the conventional linear progression of participation levels.

The digital civic practices framework needs expansion to accommodate the emergent patterns in automated governance. The integration of AI agents in civic processes introduces new theoretical considerations about the role of artificial intelligence in civic engagement, particularly in areas of proposal categorization and stakeholder identification.

This research contributes to the field of urban governance and civic engagement by providing boilerplate for further development of CIG-based implementations. First, through the development of an analytical framework for quantifying civic participation, we have addressed a fundamental gap in how engagement quality and impact are measured. The introduced metrics provide urban policymakers with concrete tools to evaluate not only the quantity but also the quality of citizen participation, decision-making efficiency, and broader community impact. This framework enables evidence-based assessment of governance initiatives and facilitates comparative analysis across different urban contexts.

The development of comprehensive mathematical models for comparing traditional and Git-inspired governance systems provides a foundational framework for evaluating digital democracy initiatives from civic crowdfunding perspective. The scalability function $S\left(t\right)$ extends existing participation theories by providing a quantitative model for growth potential up to 2 billion participants, while the cost efficiency ratio ${\eta }_c$ offers a new approach to evaluating governance systems that accounts for both direct costs and network effects. These formulations bridge the gap between theoretical governance models and practical implementation considerations, offering researchers new tools for analyzing and predicting the effectiveness of digital governance systems.

The integrated architecture combining AI-driven policy optimization with blockchain-based governance represents a novel approach to civic engagement platforms. By establishing a theoretical foundation for tokenized participation, this research advances our understanding of how technological innovation can address traditional barriers to civic engagement. The incorporation of incentive mechanisms through tokenization provides a sustainable model for long-term citizen participation, while AI-driven optimization ensures that collective decision making remains efficient and effective even as participation scales.

Perhaps most significantly, the proposed implementation model for CI-based governance provides a practical blueprint for cities transitioning toward more participatory governance systems. This model demonstrates how smart citizen participation can be orchestrated through transparent, decentralized processes while maintaining system efficiency. The implementation framework bridges the gap between theoretical governance models and practical deployment considerations, offering actionable insights for urban policymakers and technologists alike.

These contributions collectively advance the field by providing both theoretical frameworks and practical tools for transforming urban governance. The research findings suggest that low-latency, high-participatory governance systems are not only theoretically sound but practically achievable through the thoughtful integration of emerging technologies. Moreover, the emphasis on transparency, self-organization, and decentralized decision making establishes a foundation for future research on smart city governance and civic engagement platforms.

4.3. Practical Implications

This empirical analysis yields several practical implications for civic governance implementation:

The cost structures of civic participation platforms require fundamental rethinking. While the costs of traditional systems scale linearly with participation (USD 17.50 per proposal), blockchain-based systems demonstrate more favorable economics (USD 5.20 per proposal) through batch optimization and automated processing. This suggests that municipalities should prioritize technological infrastructure over physical infrastructure expansion.

Processing capacity planning should focus on batch optimization rather than staffing optimization. The performance metrics show that batch-oriented processing (3846 proposals/day) significantly outperforms staff-based processing (1200 proposals/day), indicating that resource allocation should prioritize technical optimization over staffing increases.

Network capacity management becomes crucial for maintaining system performance. The scalability analysis demonstrates that while DAO-based systems offer superior scaling capabilities (3.2x), they require careful management of network congestion and gas costs. This suggests the need for implementing adaptive batch sizing mechanisms and gas price optimization strategies for sustainable operation.

Latency reduction emerges as a critical advantage for civic engagement quality. The dramatic improvement in processing time from 4 h to 15 min (${\eta }_{\mathrm{lat}}$ = 16x) demonstrates how automated systems can fundamentally transform citizen participation dynamics. This reduction in waiting times not only improves user experience but also enables more dynamic and responsive governance processes, particularly during time-sensitive decision-making scenarios.

Infrastructure flexibility presents a significant strategic advantage for growing regions. While traditional systems require substantial investment in physical processing centers (50 centers with 10 staff each for 1 M citizens), blockchain-based solutions can accommodate growth through technical parameter adjustments. This architectural difference suggests that municipalities should prioritize digital infrastructure development that can scale through optimization rather than expansion, particularly when planning for long-term population growth and increased civic participation rates.

The practical implications of this research in the future could be disruptive for government models if the digitalization of governance systems reaches certain threshold. The cost-effectiveness analysis, particularly the relationship between participation levels and transaction costs ($C_t^{git}\left(t\right)=C_{t0}·e^{-\lambda P\left(t\right)}$), provides a clear economic justification for transitioning to Git-inspired governance models. The crowdfunding efficiency metrics demonstrate how continuous engagement models can mobilize resources more effectively than traditional periodic voting systems, while the scalability analysis offers practical guidelines for system architecture design. These findings suggest that organizations can achieve significant cost savings and efficiency improvements by adopting Git-inspired governance mechanisms, particularly when implemented with appropriate attention to network effects and participation incentives.

5. Conclusions

The comparative analysis of traditional and DAO-based governance systems reveals consistent performance advantages across efficiency, scalability, and cost metrics. CIG demonstrates improved processing capabilities with a 3.2x improvement in efficiency ${\eta }_{\mathrm{eff}}$, handling (in case study scenarios) 3846 proposals daily compared to the traditional system’s 1200, while reducing processing latency by a factor of 16 (from 4 h to 15 min). In terms of scalability, the DAO infrastructure demonstrates processing capabilities of 960,000 proposals monthly versus 300,000 in traditional systems (${\eta }_{\mathrm{scale}}=3.2$), while reducing per-proposal costs from USD 17.50 to USD 5.20 with (${\eta }_{\mathrm{cost}}=3.37$).

5.1. Study Limitations

Several limitations of this research should be acknowledged. First, the performance metrics were derived from controlled test environments and may not fully reflect real-world conditions where network congestion, user behavior, and external factors could impact system performance. Second, the cost analysis primarily focused on operational expenses and may not have captured all indirect costs associated with system implementation and maintenance.

The evaluation of AI agent performance relied on simulated scenarios and may require adjustment when deployed in actual civic governance contexts. Additionally, this study’s scope was limited to municipalities in developed regions with high internet penetration and digital literacy rates. The applicability of the findings to regions with different technological infrastructure or civic participation culture requires further investigation.

Furthermore, while the theoretical framework demonstrates strong potential for enhancing civic participation, the long-term social and political implications of transitioning to a DAO-based governance system were not fully explored. This study also acknowledges limitations in addressing potential security vulnerabilities and resistance to technological adoption among certain demographic groups.

The mathematical models presented in this study have several limitations that should be acknowledged. The network effect multiplier ($N_e\left(t\right)$) assumes ideal conditions for information propagation and participant interaction, which may not sufficiently reflect real-world communication barriers and social dynamics. The scalability function $S\left(t\right)$ does not account for potential technological constraints in blockchain throughput or the computational requirements of large-scale consensus mechanisms. Additionally, while the cost efficiency ratio ${\eta }_c$ provides a comprehensive comparison framework, it may not capture all indirect costs associated with governance transition and system maintenance. This study’s focus on quantitative metrics also means that qualitative aspects of governance, such as participant satisfaction and decision quality, are not fully represented in the mathematical framework.

5.2. Future Research Directions

Looking toward the future, several key areas require further exploration. The system’s reliance on gas prices and network congestion suggests a need for additional optimization strategies to maintain cost efficiency during peak usage. The cross-repository branching capability opens possibilities for intercommunal collaboration that could reshape how cities share and implement successful governance models. The AI agent-based architecture could be expanded to include more role specifications and advanced automation capabilities, especially in areas of policy analysis and citizen engagement optimization. As smart cities continue to evolve, the CIG framework provides a foundation for establishing distributed participatory urban governance with methods to evaluate government efficiency, although attention must be paid to maintaining accessibility and inclusive participation alongside technological advancements. Future research should focus on the

i.

Development of adaptive gas optimization algorithms to maintain cost efficiency during varying network conditions;

ii.

Investigation of cross-chain implementations to enhance scalability and reduce network dependency;

iii.

Integration of advanced privacy-preserving mechanisms while maintaining system transparency;

iv.

Exploration of hybrid governance models that combine traditional and DAO-based approaches;

v.

Study of long-term social impacts and participation patterns in communities using CIG systems;

vi.

Development of standardized frameworks for measuring civic engagement quality in automated systems.

This research demonstrates the disruptive potential of integrating blockchain and AI technologies into civic governance systems through the CIG framework. Our findings not only demonstrate significant improvements in operational efficiency, cost reduction, and scalability but also establish a practical pathway for transitioning from traditional to decentralized governance models. The framework achieves performance gains: a 3.2x improvement in processing efficiency, a 16-fold reduction in latency, and 70% cost savings, suggesting that DAO-based CIG can effectively address the current limitations of civic participation systems.

Future research should focus on extending the mathematical framework to incorporate additional governance dimensions and real-world implementation factors. Key directions include developing models for quantifying decision quality in relation to participation levels, investigating the impact of various consensus mechanisms on scalability metrics, and exploring the relationship between network effects and governance outcomes. Research is also needed to validate the theoretical predictions of cost-effectiveness models through empirical studies of implemented Git-inspired governance systems. Furthermore, the framework should be expanded to include metrics for measuring the quality of citizen participation and the long-term sustainability of digital governance systems. Future studies should also investigate the integration of emerging technologies such as multi-blockchain architectures, trained neural models, AI agency configurations, and the latest DT models from multimodal and user-focused perspectives.