1. Introduction

Rapid urbanization, resource depletion, and the escalating effects of climate change have underscored the necessity for innovative, data-driven strategies in the built environment. Recent years have witnessed the emergence of artificial intelligence (AI) as a powerful tool that can parse vast datasets and streamline complex decision-making processes related to building design, energy efficiency, and urban planning (Kang et al., 2022). By integrating machine learning algorithms, generative models, and simulation tools, AI promises to optimize various design parameters—ranging from material use to HVAC operations—while significantly reducing the environmental footprint of constructed spaces (Jones & Pavlou, 2021). This technological shift is poised to reshape not only how buildings are conceived and constructed but also how broader planning decisions are made in pursuit of sustainable development targets.

Within this evolving landscape, scholars have begun to highlight AI’s capacity for guiding holistic approaches to sustainability, such as circular economy models, real-time energy monitoring, and climate-resilient urban infrastructures (Pomponi & Moncaster, 2017; Sun et al., 2021). AI-driven systems can incorporate and analyze disparate data sources—from weather forecasts to occupant behavior—thus informing design choices that minimize carbon emissions and resource consumption throughout a building’s lifecycle (Huang, Li, & Chen, 2022). However, the effective deployment of these advanced technologies hinges on interdisciplinary efforts that connect policymakers, technologists, and design professionals (Georgiadou et al., 2020). Such collaborative dynamics ensure that AI-based interventions are both grounded in technical feasibility and aligned with broader socioeconomic and environmental objectives (European Commission, 2020).

Despite promising advances, a significant gap in the literature involves the absence of comprehensive policy frameworks and ethical guidelines that govern AI-driven design processes for sustainable habitats. Existing research has generally focused on technical benchmarks—such as energy performance metrics and predictive modeling accuracy—without sufficiently addressing the societal implications of AI’s growing role in design automation (Kang et al., 2022; Jones & Pavlou, 2021). Consequently, issues like algorithmic bias, data privacy, and transparent decision-making are often explored as peripheral concerns, rather than as central criteria for evaluating the success and inclusivity of AI-enabled design (Noble, 2018). Moreover, while industry-specific regulations and standards are emerging, they remain fragmented and do not comprehensively tackle the nuanced ethical questions that arise when AI is employed at scale (Bell, Bryman, & Harley, 2021).

These gaps pose urgent questions on how to reconcile the transformative potential of AI with the pressing ethical and policy challenges it entails. For instance, real-time data collection required for AI optimizations raises critical concerns regarding privacy, consent, and ownership of information (European Commission, 2020). Additionally, without systematic checks, predictive models might inadvertently perpetuate existing social or environmental inequities (Georgiadou et al., 2020). There is thus a need for governance structures and auditing mechanisms that go beyond conventional building codes, encompassing robust data governance, bias mitigation strategies, and accountability frameworks (Creswell & Plano Clark, 2018; Huang et al., 2022). These considerations become especially pertinent given that AI’s influence extends from discrete building components to entire urban planning ecosystems, magnifying potential benefits and pitfalls (Sun et al., 2021).

Addressing this complexity requires a deeper understanding of how policy directives can shape AI-enabled design paradigms and ensure that technological advancements serve the public interest. Scholars have recommended that policymakers institute clear documentation requirements, enforce standards on algorithmic transparency, and incentivize green innovations through subsidies or public–private partnerships (Jones & Pavlou, 2021; Liu & Tzeng, 2021). Equally critical is the role of stakeholder engagement, ensuring that community voices are integrated into AI-driven solutions from the outset (Noble, 2018). By doing so, design outcomes are more likely to align with principles of equity, resilience, and long-term sustainability.

This paper seeks to fill the identified gap by examining the policy frameworks and ethical considerations necessary for responsible AI deployment in sustainable habitat design. Building upon existing literature that underscores AI’s technical potential, the study draws attention to the policy imperatives and governance mechanisms required to balance innovation with societal accountability. In doing so, it aims to advance current debates on how AI can be both a catalyst for environmental stewardship and a subject of rigorous ethical scrutiny. Through a review of scholarly articles, policy documents, and case studies, the paper provides insights into the multifaceted challenges and opportunities that arise at the intersection of AI, sustainability, and equitable design practices (Georgiadou et al., 2020; Pomponi & Moncaster, 2017). By illuminating these interconnected dimensions, the following sections propose strategies for ensuring that AI-driven design remains transparent, inclusive, and aligned with global sustainability goals.

2. Materials and Methods

This study employs a mixed-method approach that integrates a systematic literature review, policy analysis, and conceptual modeling to investigate how AI-driven design can support the creation of sustainable habitats. First, an extensive search was conducted on academic databases such as Scopus, Web of Science, and Google Scholar using keywords including “AI in architecture,” “AI-driven design,” “sustainable built environment,” “policy for AI,” and “ethical considerations.” Peer-reviewed journal articles, conference proceedings, and relevant white papers were then selected for in-depth review, enabling the identification of key themes and challenges (Georgiadou et al., 2020; Kang et al., 2022). In parallel, policy documents from governmental and intergovernmental organizations (e.g., the European Commission, U.S. Department of Energy, and United Nations Environment Programme) were examined to understand existing or proposed regulations for AI applications in building design and urban planning (European Commission, 2020). The collected insights informed a conceptual model capturing the interplay between AI-driven design components, sustainability targets, ethical considerations, and policy mechanisms. This model served as a guiding framework to explore potential implementation strategies and evaluate the socio-technical impacts of AI-driven processes in the built environment. By synthesizing research findings across these multiple dimensions, the study aims to offer a holistic perspective on the policy imperatives and ethical safeguards required for responsible and equitable deployment of AI in sustainable habitat design.

3. Results

3.1 Optimizing Building Performance

Recent analyses underscore that AI-driven algorithms can substantially enhance building performance by minimizing energy consumption and improving occupant comfort. This process often involves the use of advanced machine learning techniques such as deep neural networks and gradient-boosted decision trees, which leverage diverse datasets—ranging from historical energy usage profiles to localized weather patterns—in order to predict how buildings will function under various conditions (Kang et al., 2022). By identifying optimal combinations of building orientation, material selection, and glazing ratios, these AI tools have demonstrated an ability to decrease heating, ventilation, and air-conditioning (HVAC) loads, thereby driving down operational costs and carbon emissions.

In parallel, AI-based methods contribute to thermal comfort optimization by integrating real-time data inputs—for instance, occupancy behaviors and indoor air quality readings—into adaptive control systems (Jones & Pavlou, 2021). These control systems can dynamically adjust natural ventilation, window shading, or active cooling strategies, ensuring that indoor temperatures and humidity levels remain within ideal ranges for occupants. Beyond immediate comfort, such intelligent modulation also mitigates energy spikes by synchronizing building operations with external weather patterns, thereby decreasing peak demand on the electrical grid.

Moreover, AI-driven approaches facilitate smart material selection through predictive modeling of life-cycle assessments and local availability. By analyzing thermal properties and embodied carbon levels, machine learning algorithms help decision-makers select materials that offer both high performance and minimal environmental impact (Jones & Pavlou, 2021). This strategic integration of AI into early design stages allows architects and engineers to iterate more effectively, conducting rapid simulations of multiple design scenarios before arriving at a solution that meets sustainability criteria and occupant needs in a balanced manner.

3.2. Resource Management and Circularity

The review of literature indicates that AI has significant potential to promote resource efficiency and circular economy principles in the construction and demolition (C&D) sector. One notable application involves the use of sensors and AI-driven tracking systems, which monitor material flow from procurement through end-of-life, capturing data on degradation rates and opportunities for reuse or recycling (Pomponi & Moncaster, 2017). By mapping out the lifecycle of each component, these technologies help organizations optimize inventories, minimize waste, and identify new markets for salvaged materials, aligning construction practices with sustainability objectives.

In addition, AI-powered generative design tools can proactively incorporate “design for deconstruction” principles by suggesting structural layouts and material connections that are easier to disassemble or repurpose (Georgiadou et al., 2020). Rather than focusing solely on immediate building performance, these algorithms simulate long-term scenarios, thereby encouraging architects and contractors to envision a building’s entire lifecycle early in the design process. Such foresight promotes flexible structures that can adapt to changing functional requirements while minimizing landfill contributions, ultimately extending the effective lifespan of building components.

Furthermore, AI-driven resource management strategies extend beyond the material realm to encompass water conservation, waste heat recovery, and other sustainability metrics. For instance, machine learning algorithms can integrate weather predictions and real-time usage data to optimize water distribution in large complexes, or to re-route waste heat from one part of a facility to another in need of warming (Huang, Li, & Chen, 2022). By providing data-driven insights into the complex interdependencies of building systems, AI enables managers to make more informed and adaptive decisions, fostering a closed-loop approach that parallels broader circular economy frameworks.

3.3 Urban Planning and Smart Cities

Scaling up from individual buildings to urban environments, AI offers tools for holistic planning that can significantly shape the sustainability and livability of entire communities. A pivotal application lies in transportation network optimization, where machine learning models evaluate vast datasets on traffic flow, energy consumption, and population density to suggest more efficient routes for public transport and freight (Sun et al., 2021). By reducing congestion and vehicle emissions, these AI-driven strategies align with broader environmental targets while enhancing the daily experiences of city residents.

Climate resilience also emerges as a key area where AI can make transformative contributions. Through predictive simulations, urban planners can assess vulnerabilities to flooding, heat islands, and severe weather events, then integrate appropriate infrastructural measures like green roofs, permeable pavements, or upgraded drainage systems (Georgiadou et al., 2020). Critically, these AI-based models can incorporate complex regional data—including hydrological cycles, topographical features, and microclimate variations—to develop solutions tailored to each urban context. This precision helps ensure that infrastructural investments yield the highest possible resilience gains for both current and future conditions.

Lastly, the advent of the Internet of Things (IoT) has opened new possibilities for real-time data collection and adaptive resource allocation in smart cities. By gathering live inputs from sensors embedded in street lights, traffic signals, or even waste bins, AI algorithms can dynamically adjust services—such as dimming lights in low-traffic areas or rerouting vehicles during peak hours—to optimize energy use and service delivery. This responsive infrastructure not only delivers environmental benefits but also improves quality of life for residents, demonstrating the synergistic effect of combining AI, policy, and ethical guidelines to shape equitable and sustainable urban ecosystems.

The following table summarize the key findings, AI approaches, indicators, benefits, and challenges discussed.

Table 1: Key Approaches, Benefits, and Challenges in AI-Driven Sustainable Design and Urban Planning

Subsection	AI Approaches/Tools	Key Indicators/Focus Areas	Major Benefits/Potential	Supporting Evidence	Challenges/Future Directions
3.1 Optimizing Building Performance	- Deep Neural Networks - Gradient-Boosted Decision Trees - Predictive Modeling (e.g., ML-based)	- Energy Efficiency - Thermal Comfort - Smart Material Selection	- Reduced HVAC loads and operational costs - Improved indoor air quality and occupant comfort - Minimized embodied carbon and operational carbon footprints	Kang et al. (2022) Jones & Pavlou (2021)	- Need for large, high-quality datasets to train models - Potential algorithmic bias in modeling occupant behavior - Integration of AI tools into existing workflows - Ongoing refinement of predictive accuracy for diverse climates
3.2 Resource Management and Circularity	- Sensor Networks and IoT for Material Tracking - AI-Driven Generative Design - Life-Cycle Assessment Tools	- Material Flow Tracking - Waste Reduction - Design for Deconstruction	- Enhanced resource efficiency, aligning with circular economy frameworks - Prolonged building and material lifespans - Reduced landfill contributions and GHG emissions through reuse/recycling	Pomponi & Moncaster (2017) Georgiadou et al. (2020) Huang, Li, & Chen (2022)	- Regulatory gaps in standardizing circular economy metrics - High initial costs for sensor deployment and data management - Need for stakeholder collaboration across construction and demolition supply chains
3.3 Urban Planning and Smart Cities	- Machine Learning for Traffic Optimization - AI-Based Simulations for Climate Resilience - IoT-Driven Real-Time Adjustments	- Transportation Efficiency (reduced congestion) - Infrastructure Adaptability - Climate Risk Mitigation (flooding, heat islands)	- Lower emissions and travel times via optimized traffic flows - Increased urban resilience through predictive modeling of extreme events - Responsive infrastructure (lighting, waste management, etc.) in real time	Sun et al. (2021) Georgiadou et al. (2020) European Commission (2020)	- Data privacy and governance concerns due to pervasive IoT - Equity issues in AI-driven urban services (risk of marginalizing certain areas) - Necessity for transparent models and inclusive stakeholder engagement

Discussion

4.1 Policy Implications

4.1.1 Data Governance

Data governance emerges as a critical element for sustainable habitat design because AI-driven systems rely on vast quantities of personal, operational, and environmental data (European Commission, 2020). Without robust policies, there is a heightened risk of data misuse, unauthorized sharing, or breaches that compromise both individual privacy and corporate intellectual property. Policymakers must thus stipulate guidelines that explicitly address data ownership, data quality, and accountability for data breaches, ensuring that all parties involved—ranging from building occupants to municipal authorities—are protected under a transparent legal framework.
Moreover, the volume and granularity of data required for AI-based design optimizations necessitate advanced anonymization and encryption methods that strike a balance between privacy and utility. Sensitive occupant data, such as daily usage patterns or behavioral analytics, must be aggregated or masked without compromising the insights needed to refine building performance or promote sustainability (Huang et al., 2022). In addition, clearly defined regulations on data retention periods can prevent the indefinite storage of personally identifiable information (PII), thereby mitigating risks associated with long-term data accumulation. This approach can be augmented by regular compliance checks and audits designed to enforce adherence to stringent data governance standards.
Equally important are the mechanisms that empower individuals to exercise control over their data. Opt-in models for data sharing, user dashboards for controlling access, and formalized consent procedures foster trust among occupants who may otherwise be wary of comprehensive data collection (Noble, 2018). By presenting users with clear and understandable explanations of how their data will be employed to enhance sustainability outcomes, policymakers and design professionals can create a culture of transparency that encourages stakeholder participation in AI-driven initiatives. In doing so, data governance evolves from a compliance requirement into an ethical imperative integral to building equitable and sustainable habitats.

4.1.2 Standards and Regulations

Establishing clear standards and regulations is vital to ensuring that AI-driven sustainable habitat designs maintain a consistent level of quality, safety, and accountability. Traditional building codes and environmental regulations often fail to address the complex interactions inherent to AI-enabled systems, where design parameters are highly interdependent and continuously adaptive (Bell et al., 2021). Therefore, government bodies and industry consortia must collaborate to develop specialized guidelines that encompass not only technical specifications—such as the minimum accuracy of predictive models—but also procedural requirements, such as mandatory auditing of AI algorithms.
Such standards should mandate transparency in AI-driven decision-making processes so that architects, engineers, and other stakeholders can trace how inputs (e.g., weather data, occupant behavior metrics) lead to specific design recommendations (Georgiadou et al., 2020). When AI models are opaque, it becomes difficult to hold systems accountable for unintended consequences, including discriminatory design outcomes or unanticipated ecological impacts. Clear documentation, including model validation protocols and error rates, ensures that practitioners can identify points of failure and rectify them promptly.
Beyond transparency, the enforcement of performance benchmarks—covering energy efficiency, water conservation, and emissions reduction—adds another layer of regulatory rigor (European Commission, 2020). Legislators can require AI-influenced designs to meet or exceed established sustainability thresholds, backed by penalties for non-compliance and incentives for exceptional performance. In doing so, regulations not only set minimum acceptable standards but also serve as market signals that reward innovation, thereby accelerating the integration of AI technologies into mainstream sustainable design practices.

4.1.3 Incentives and Funding

Financial mechanisms offer a potent way to accelerate the adoption of AI-driven strategies in sustainable habitat design. Governments can issue tax rebates, subsidies, or low-interest loans for projects that deploy advanced AI tools to reduce carbon footprints, optimize resource utilization, and engage in circular economy practices (Jones & Pavlou, 2021). These incentives can help offset the upfront costs associated with implementing cutting-edge AI systems, which may otherwise deter firms operating under tight budget constraints.
Public-Private Partnerships (PPPs) also play a pivotal role in bridging the gap between research and real-world deployment. By fostering collaboration between government agencies, academic institutions, and private-sector innovators, PPPs create shared risk-and-reward structures that encourage investment in experimental or long-term AI projects (Kang et al., 2022). This can include funding specialized research labs or pilot projects focused on next-generation AI algorithms designed specifically for sustainable design challenges—such as advanced life-cycle assessment tools or multi-objective optimization platforms.
Incentive structures should be flexible enough to adapt to the rapid evolution of AI technologies while still holding developers and implementers accountable for public-interest outcomes. Funding programs could incorporate phased milestones tied to measurable sustainability metrics, ensuring that recipients demonstrate meaningful progress in energy savings, waste reduction, or social equity before receiving further disbursements (European Commission, 2020). Such dynamic funding approaches encourage continuous improvement and innovation, rather than one-time compliance with static benchmarks.

4.2 Ethical Considerations

4.2.1 Algorithmic Bias

Algorithmic bias ranks among the most critical ethical issues in AI-driven sustainable design. When training datasets reflect historical inequalities—such as segregated housing patterns or underfunded communities—algorithms may inadvertently produce design recommendations that perpetuate these disparities (Noble, 2018). This not only undermines the principle of equity in sustainability but can also exacerbate social tensions in diverse urban environments.
To mitigate bias, designers and policymakers must integrate corrective measures such as data preprocessing techniques, ongoing model audits, and the inclusion of diverse stakeholder perspectives during the model development phase (Georgiadou et al., 2020). For instance, fairness metrics can be applied to evaluate the extent to which model outcomes are disproportionately beneficial or harmful to particular demographic groups. Identifying these biases early in the design lifecycle allows for iterative refinements that align AI outputs with inclusive sustainability goals.
Equally important is cultivating an organizational culture that prioritizes equity and inclusion throughout the design process. This can be achieved through regular cross-disciplinary workshops, community feedback sessions, and transparent documentation of all modeling decisions. When ethics are integrated into the design pipeline—rather than treated as an afterthought—it becomes more likely that AI-driven solutions will serve as catalysts for positive social change while advancing environmental objectives.

4.2.2 Transparency and Explainability

The “black box” nature of many AI algorithms poses significant challenges to stakeholder trust and informed decision-making. In the context of sustainable habitat design, explainable AI models are essential for verifying that design recommendations genuinely improve environmental and social outcomes, rather than simply optimizing for short-term gains (Bell et al., 2021). Policymakers, architects, and community members should be able to trace how input variables—such as climate data or occupant patterns—shape the final design parameters.
To promote transparency, user-friendly interfaces that offer simplified visualizations of AI decision trees or neural network layers can help non-technical stakeholders grasp complex algorithmic processes (European Commission, 2020). These tools can be accompanied by high-level narratives explaining why certain design alternatives rank higher in terms of sustainability or cost-effectiveness. Such an approach not only demystifies AI operations but also encourages collaborative decision-making, as various actors can engage with the data and insights more directly.
Periodic ethical audits further bolster transparency by providing third-party assessments of AI models (Noble, 2018). These audits can evaluate algorithmic fairness, data governance practices, and adherence to stated sustainability benchmarks. Through the publication of audit findings, developers and policymakers demonstrate accountability to both the public and professional stakeholders, thereby strengthening the legitimacy and reliability of AI-driven design processes.

4.2.3 Socioeconomic Impact and Job Displacement

The increasing automation of design tasks raises questions about the future workforce in the architecture, engineering, and construction (AEC) sectors. While AI can enhance efficiency and accuracy in design workflows, it may also displace some conventional roles, particularly those centered on repetitive tasks (Kang et al., 2022). This phenomenon could have a considerable socioeconomic ripple effect, affecting skilled labor and small businesses that have traditionally contributed to the built environment.

To address these concerns, policy measures and industry initiatives must support re-skilling and up-skilling programs that equip current professionals with AI literacy (Jones & Pavlou, 2021). By blending existing domain expertise with new computational skills, architects, engineers, and construction managers can transition into roles where they leverage AI tools as extensions of their creative and analytical capabilities. Government-led vocational programs, supplemented by private-sector collaborations, can smooth this transition, ensuring that job displacement is minimized while fostering a new generation of data-savvy professionals.

Beyond direct employment effects, AI-driven sustainable design has the potential to catalyze job growth in related fields, such as data analytics, AI ethics, and specialized consulting for green technologies. Proactive stakeholder engagement and cross-industry collaboration can identify these emerging opportunities, helping to ensure that the economic benefits of AI deployment are equitably distributed. By combining thoughtful workforce development strategies with broader sustainability goals, policymakers can position AI as a driver of inclusive growth rather than a harbinger of job losses.

Table 2: Policy Implications and Ethical Considerations in AI-Driven Sustainable Habitat Design.

Section	Subheading	Key Issues	Indicators/Focus Areas	Policy/Ethical Tools	Outcomes/Goals
4.1 Policy Implications	4.1.1 Data Governance	- Privacy and data security - Ownership and control over data - Anonymization and encryption	- Regulatory compliance rates - Instances of data breaches - Stakeholder trust surveys	- Robust data governance frameworks - Clear legal guidelines - Regular audits	- Balanced approach to privacy vs. data utility - Increased stakeholder trust
	4.1.2 Standards and Regulations	- Lack of specialized AI building codes - Proprietary AI algorithms - Transparency and accountability	- Compliance with AI auditing requirements - Number of bias remediation actions - Benchmark sustainability metrics	- Mandatory model documentation - Performance-based standards - Transparent decision-making	- Uniform quality and safety standards - Prevention of discriminatory outcomes
	4.1.3 Incentives and Funding	- High costs of AI adoption - Public-Private Partnerships (PPPs) - Dynamic funding mechanisms	- Uptake of tax rebates/subsidies - Number of PPPs formed - Measurable sustainability impacts	- Financial incentives for sustainable AI - Milestone-based funding disbursements - Support for R&D	- Accelerated innovation in AI-driven design - Broader access to cutting-edge solutions
4.2 Ethical Considerations	4.2.1 Algorithmic Bias	- Historical data inequalities - Risk of perpetuating social disparities - Fairness metrics	- Model fairness assessments - Diversity in training datasets - Inclusivity in design processes	- Bias detection and correction - Community-driven design feedback - Ethical guidelines for dataset curation	- Equitable access to sustainable solutions - Reduced marginalization of vulnerable groups
	4.2.2 Transparency and Explainability	- “Black box” algorithms - Lack of interpretable AI outcomes - Ethical audit requirements	- Availability of explainable AI tools - Frequency of third-party audits - Stakeholder confidence scores	- Visualizations of AI logic - Public reporting of audit findings - Ethical accountability boards	- Improved trust and collaboration among stakeholders - Informed policy and design decisions
	4.2.3 Socioeconomic Impact and Job Displacement	- Workforce transition - Potential unemployment - New roles in AI-supported services	- Employment rates in AEC sector - Adoption of up-skilling programs - Emergence of AI ethics roles	- Vocational training subsidies - Collaboration with tech firms - Expansion of AI-literate workforce	- Balanced labor market adjustments - Inclusive economic growth in AI-driven industri

6. Conclusions

This study illuminates how AI-driven methodologies can substantially enhance sustainability outcomes within the built environment. From building-level optimizations to urban-scale interventions, the findings demonstrate that machine learning algorithms, generative design models, and IoT-enabled systems can reduce resource consumption, improve thermal comfort, and foster circular economic principles. Such technological innovations hold the potential to accelerate progress toward global climate targets and societal well-being, indicating a promising shift in how planners, architects, and policymakers approach sustainable habitat design.

Despite these gains, the study also underscores the complexity of embedding AI into existing regulatory and cultural contexts. Conventional building codes, privacy regulations, and professional norms do not fully capture the dynamic nature of AI-driven systems, prompting a need for updated standards and best practices. Governments and industry stakeholders must collaborate on policies that detail data governance requirements, performance thresholds, and auditing procedures, ensuring accountability and mitigating negative outcomes such as algorithmic bias.

Ethical considerations emerge as equally crucial, given that AI deployments in urban environments can inadvertently reinforce historical inequities if training datasets are unbalanced or incomplete. As demonstrated, transparent and inclusive models are vital for equitable outcomes, demanding that designers and policymakers actively involve diverse community voices and routinely audit algorithmic decision-making. The link between ethics, technology, and design thereby requires ongoing interdisciplinary dialogue to preempt unfair or unsustainable practices.

Looking ahead, expanding collaborations among technologists, policymakers, academic researchers, and local communities will be pivotal for maximizing AI’s benefits in sustainable habitat design. Further research could explore longitudinal assessments of AI-deployed systems, investigate region-specific challenges, and refine metrics that capture both environmental and social impacts. By aligning technological innovations with robust governance structures and inclusive ethical frameworks, the built environment can transition more rapidly toward resilience, efficiency, and social equity—marking a critical evolutionary step in global sustainability efforts.

Acknowledgements

The authors wish to express their gratitude to the reviewers of the Journal of Smart Design Policies for their valuable and constructive feedback, which greatly improved the quality of this article. Furthermore, AI tools such as Quillbot and Gemini 2.0 Flash Experimental were employed to refine grammar and enhance overall fluency.

Funding

The author declares that no specific funding was received for this research.

Conflicts of Interest

The author declares that there are no conflicts of interest.

Data availability statement

The data that support the findings of this study are available from the author upon reasonable request.

Institutional Review Board Statement

Not applicable.

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