Leveraging Digital Twin Technology for Sustainable Urban Development: Financial Implications, Risk Governance, and Alignment with SDG Reporting Frameworks

3. MATERIALS AND METHODS

3.1. Mixed-Methods Integration Strategy

This study used explanatory mixed-methods design where the first phase of qualitative component was used as an interpreting and a contextualising method for quantitative findings. Interviews helped identify the causes of the relationships between the Digital Twin Technology, Sustainable Urban Development, Financial Implications, Risk Governance, and SDG Reporting Frameworks that were identified through the survey data and PLS-SEM analysis. The challenges in implementing Digital Twin Technology, institutional readiness concerns, and resource constraints, as detailed in the negative direct relationship, were explained through the qualitative evidence. It also specified that the Financial Implications are not mediating and showed that there are concerns about Financial Implications such as unclear ROI, budget constraints, and funding problems in the long-term. On the other hand, the interviews highlighted the positive mediation role of Risk Governance and SDG Reporting Frameworks, emphasising the accountability and trust-building properties, governance and sustainability reporting mechanisms. The qualitative findings, thus, played a supportive and explanatory role in enhancing the understanding results from the quantitative roles, so that the results of the qualitative were interpreted and explained with each other, which was carried out with methodological triangulation.

3.2. Data Collection and Sampling

To collect quantitative data survey questionnaire was used (Appendix A). The questionnaire was distributed to 600 potential respondents, primarily focusing on professionals, managers, urban planners, IT experts and policy makers operating in the diverse areas of the UK urban development. These professionals were appropriate as they possess high expertise and relevant experience to provide valuable insights into the implementation the governance of digital twin technology in urban development.

All constructs were operationalised through the use of a structured survey instrument that was established through the themes found in digital twin literature and within urban sustainability. Three reflective indicators were developed for each of the constructs. Digital Twin Technology, Sustainable Urban Development (Patel et al., 2024), Financial Implications (Karunaratne et al., 2025), Risk Governance (Ghaffarian, 2025) and SDG Reporting Frameworks (Stefanescu, 2022) and modified to suit the UK urban development landscape. As suggested by (Russo et al., 2021) the Likert scale was used to measure the perceptions and attitudes of the respondents in a standardised and comparable manner with a 5-point rating scale from 1 (Strongly Agree) to 5 (Strongly Disagree).

Digital Twin Technology was operationalised using items on efficiency perceptions, implementation of real-time data and potential of sustainability. Sustainable Urban Development perceived sustainable improvements in sustainability, decision making processes and alignment with sustainability objectives. Financial implications in this study refer to perceived financial feasibility, investment burden, and long-term economic implications of DTT implementation. The evaluation of cybersecurity risks and the governance’s effectiveness were conducted by Risk Governance, and SDG Reporting Frameworks was used to capture level of alignment to SDG 11 and the usefulness of sustainability reporting mechanisms. This systematic operationalisation ensured a construct validity, conceptual clarity and empirical consistency of the measurement model.

A purposive sampling strategy ensured the sample reflected persons that had the necessary background with regard to the implementation and governance of smart city technologies as suggested by (Campbell et al., 2020). Therefore, purposive sampling method was applied in this study to reach the researcher with the cross-cutting mentioned knowledge and experience in urban planning, digital infrastructure, governance and sustainability projects. The participants were selected based on their role within UK urban development projects and included managers, urban planners and IT professionals who were actively involved in urban development projects, which are relevant to the content and research aims of the study. The sample does not represent the broader professional population, but was analytically representative, because they were selected to provide experiences-based informed opinions to inform the institutional and operational aspects of DTT implementation.

Survey was distributed through various channels, including professionals’ networks (LinkedIn, etc.), email lists and direct contact with respective professionals working in urban development organisations and departments in the government of UK. 350 responses were received with a 60% response rate. 340 valid questionnaires were selected for analysis following data screening and incomplete/invalid responses were removed. The sample size is deemed adequate for PLS-SEM because it is a medium to large sample size that generally does not require normality conditions. Further, the final data set scores above the minimum recommended thresholds for statistical power, thus providing robustness, stability of path estimates and generalisability of the results of the structural model within the framework of the study. Moreover, attempts were made to involve the professionals of varying areas of urban planning, governance, and technology to minimise data bias and ensure a representative sample. The strategy reduced the selection bias, and the variety of the sample promoted the quality of the outcomes (Smith, 2020). Non-response bias was tested using an independent sample t-test by comparing the proportion of respondents between early and late respondents. The comparison revealed no significant differences between the two groups (P-value > 0.1) , indicating the absence of non-response bias and supporting the representativeness of the sample.

In addition, the qualitative data in this study was collected by using semi-structured interview questionnaire from 10 professionals who are actively involved in the process of urban development in the UK, including managers, urban planners, and IT experts. Thus, for qualitative component, the research instrument was comprised of 6 open-ended and semi structured questions (Appendix B). A purposive sampling technique was applied to guarantee representation from different professional backgrounds and geographical contexts, which allowed a detailed analysis and understanding of an application of digital twin applications in sustainable urban planning. The participants were contacted through the professional networks, LinkedIn, and email invitation, which stated the aims of the research and guaranteed confidentiality. The interviewing took place between July and August 2025, with the main platform such as MS Teams and Zoom, according to the schedules of the participants. The sessions remained 30 to 45 minutes long and were audio-taped, following permission of participants and transcribed precisely to be analysed. This strategy was necessary to guarantee the gathering of practical and rich insights to supplement the financial, governance, and sustainability aspects of the research.

3.3. Econometric Models

In the following equations, ‘i’ is representing the cross-sections and variable acronyms are the same as used in the conceptual model.

SUD_i=β₀+β₁DTT_i+β₂FI_i+β₃RG_i+β₄SDGRF_i+ϵ_i

FI_i=α₀+α₁DTT_i+ϵ_i

RG_i=γ₀+γ₁DTT_i+ϵ_i

SDGRF_i=δ₀+δ₁DTT_i+ϵ_i

3.4. Data Analysis

To analyse quantitative data, Partial Least Squares Structural Equation Modelling (PLS-SEM) was used, due to the appropriateness of the method to analyse complicated dependency between several variables and process a small-sized sample (Hair et al., 2017). The study was conducted and analysed within an exploratory mixed methods research framework and PLS-SEM was chosen for the analysis because of the number of mediated relationships to be explored and the modelling of complex predictive relationships involving multiple constructs (Dash & Paul, 2021). While as per (Hair & Alamer, 2022) covariance-based SEM has an implication of theoretical confirmation and involves strict assumption of normality, PLS-SEM is more suitable to prediction-oriented SEM models, with less strict normality assumption. Other methods like multiple regression were not able to simultaneously evaluate the interconnected mediation effects and latent constructs indicated by (Appah et al., 2024) as well. Thus, PLS-SEM was applied as suitable analysis to investigate the multi-dimensional structural relationships of the study and predictive goals. Therefore, PLS-SEM approach allowed to explore both direct and indirect impact of the key variables, which include financial implication of DTT, risk governance frameworks and SDG alignment. To achieve the validity and reliability of measurement model, Confirmatory Factor Analysis (CFA) was performed by evaluations of factor loadings, composite reliability and Average Variance Extracted (AVE) to confirm the validity and reliability of the measurement model.

In addition, the qualitative interview data were analysed using a method recommended by (Braun & Clarke, 2021), six-step thematic analysis. The transcripts of interviews were repeatedly analysed to become familiar with the data and look for possible patterns that are relevant to a digital twin implementation, digital twin governance, financial aspects of a digital twin and sustainability alignment of a digital twin. The second phase involved open coding that was done followed by conceptual labelling of statements and shared views were assigned to every participant. Codes that occurred in similar fashion were then categorically grouped according to thematic themes that represent common institutional and operational issues. The emerging themes were then critically examined and refined based on the findings in the dataset, for coherence and consistency, and to distinguish them conceptually. Lastly, the themes were triangulated with other qualitative findings using qualitative results and existing views in literature, respectively, enhancing the credibility of the results, analytical depth, and reliability of the mixed methods overall.

4. RESULTS

4.1. Demographic Analysis

As per Table 1, the gender analysis also reveals that males dominated the study and formed 64.7% whereas, females make up 35.3%. The gender imbalance reflects the broader demographics of larger areas of consideration such as urban development, technology and governance. The female representation is lower reflecting possible inclusion issues of the future research that might target a more unified perspective on the matter. Moreover, in terms of age, 35.3% respondents fell between the 26 -35 years making up the largest group followed by those between the ages 36-45 years, which was 26.5%. The 18 – 25 years age group constituted 23.5% and the age bracket, 45 years and above recorded 14.7%. Regarding educational level, postgraduate participants were found higher as compare to the undergraduate.

Table 1. Demographics.

4.2. Measurement Model Using Confirmatory Factor Analysis

As shown Table 2, the reliability and convergent validity of the constructs in the study were assessed using several developed metrics to ensure the robustness of the measurement model. Measurements of the internal consistency of latent constructs with Cronbach alpha and composite reliability are well-known measures. According to (Kline, 2023) a construct can be considered reliable when the value of the Cronbach alpha, and composite reliability is higher than 0.7. In the case of this study, Cronbach alpha values of Digital Twin Technology (0.852), Sustainable Urban Development (0.901), Financial Implication (0.815), Risk Governance (0.885) and SDG reporting frameworks (0.901) are all above acceptable threshold indicating that these items have a good internal consistency in measuring the study variables. Moreover, the constructs have composite reliability value that ranges between 0.826 and 0.905, which further indicates that the measurement tool that is used in this study is stable and reliable.

Table 2. Reliability and convergent validity testing.

The proportion of variance of each construct in comparison to the measurement error was presented through the Average Variance Extracted (AVE) to measure the convergent validity. According to (Hair et al., 2017; and Henseler et al., 2015), convergent validity is considered effective with an AVE of 0.5 or more. In the current study, the AVE values that are greater than 0.5, Digital Twin Technology (0.772), Sustainable Urban Development (0.825), Financial Implication (0.731), Risk Governance (0.812), SDG reporting frameworks (0.835) further support the sufficiency of the measurement model.

As per Table 3, it shows that discriminant validity of the constructs was assessed with the Heterotrait-Monotrait (HTMT) ratio, ensuring that each construct is empirically unique, separable and distinctive. As per (Rasoolimanesh, 2022; and Yusoff et al., 2020) HTMT values must not exceed 0.85 to prove that there are enough discriminant validity and no conceptual overlapping. From the results observed in Table 3, the HTMT values in the case of all constructs are found to be lower than the threshold value of 0.85. This shows that there is adequate discriminant validity with no conceptual overlapping between the variables and that all constructs are distinct from each other.

Table 3. Discriminant validity.

4.3. Path Analysis

The coefficients presented in Table 4 indicate strong direct relationships between Digital Twin Technology and the key constructs, underscoring its importance in facilitating sustainable urban development. Digital Twin Technology shows a negative, though, statistically significant direct relationship (β = -0.158, p = 0.037), proving that while it is an important area of urban development, its proper application and regard for accompanying matters might be necessary. The negative direct relationship between Digital Twin Technology and sustainable urban development suggests that the uptake of new technologies does not directly connect to sustainable urban development unveiled by (Perisic et al., 2025) also. This indicates that, if governance bodies, financial preparedness, and institutional arrangements supporting sustainability cannot be established, there could be additional challenges in the beginning, such as operational complexity, implementation expenses, technological uncertainty, and institutional adjustments when implementing digital twin systems. This implies that short-term pressures can arise that cancel out the supposed benefits of sustainability provided by DTT. This indicates that technological innovation is not as such sustainable, nonetheless, it relies on the larger socio-institutional context in which it is put into practice. Furthermore, it has a positive indirect impact (B=0.400, P-value = 0.000). Furthermore, SDG regulatory framework (B=0.144, P-value = 0.002) shows positive mediation between digital twin technology and sustainable urban development. Risk governance (B= 0.195, P-value= 0.000) shows positive mediation between digital twin technology and sustainable urban development. It shows that these variables partially mediate the relationship. Furthermore, financial implication (B= 0.061, P-value= 0.107) shows no mediation on the relationship. Although the direct effect of DTT on SUD was negative, the total indirect effect was positive and significant, indicating that sustainability benefits emerge primarily through governance and SDG alignment mechanisms. Implementing the digital twin within a risk management framework and reporting in an SDG reporting framework triggers positive mediating effects, which relate to sustainability outcomes. The governance mechanisms seem to help alleviate uncertainty, increase accountability and boosts trust among stakeholders, and SDG reporting systems integrate technological innovation with long-term sustainability goals. This suggests governance and sustainability alignment are enabling mechanisms through which digital capabilities create sustainable urban outcomes.

Table 4. Structural model.

Note: *: Significance at 10%; **: Significance at 5%; ***: Significance at 1%

4.4. Model Explanatory Power

In Table 5, the explanatory power of the model is given in terms of R-square values indicating the percent of variance explained by the constructs. The R-square of Financial Implication is 0.268 meaning that, the model can explain 26.8% of the variance in the change of financial outcomes of the Digital Twin Technology. The R-square is comparatively high at 0.394 indicating that 39.4% of the variation of outcomes of governance is surpassed by the model. The SDG reporting frameworks R-square is 0.285 and this indicates that 28.5% of SDG alignment variance is explained. The R-square results in Sustainable Urban Development which is 0.251 meaning that the variance in the outcome of sustainable urban development is explained as 25.1%.

Table 5. Model explanatory power.

Although the explanatory power of the model is moderate, such R² levels values indicate that the model’s explanations are acceptable for exploratory governance research which includes complex institutional and sustainability related phenomena. As noted in previous methodological literature such as (Lu et al., 2025) there are several interacting technologies, organisations, regulations and contexts that influence the outcomes of urban sustainability; moderate explanatory variances are prevalent in studies that look into how emerging digital governance systems and institutional transformation processes impact on urban sustainability

4.5. Qualitative-Thematic Analysis

4.5.1. Demographic Profile

The demographic profile results exhibited in Table 7 show that among 10 interview responses (n=10), 60% were males and 40% were females. Further, 30% of the participants were in the 26-35 age bracket, 50% were in the 36-45 age bracket, and 30% were 45 years and above. In addition, considering the occupation of the respondents, 30% were managers, 40% were urban planners, and 30% were IT experts.

4.5.2. Theme 1: Digital Twin Technology as a Transformational Tool for Urban Planning

In light of Table 6, this theme was derived from the recurrent references and arguments related to the role of digital twins in reforming urban planning procedures. Within the interviews, participants repeatedly signified simulation, collaboration, and proactive governance as the primary benefits of the adoption of digital twins. These insights adhere with arguments in the literature where (Nica et al., 2023) argue that digital technologies progressively function as living laboratories for urban areas. The interview responses related to this theme are analysed below.

R3 stated that,

“Digital twins allow us to simulate urban scenarios that were previously impossible to test at scale.”

The response points to digital twins being more than a technology, it is a planning infrastructure to decrease uncertainty prior to physical implementation. The focus on simulations has been due to a shift in governance from reacting to a management approach where policy interventions can be tested digitally upfront to and before their deployment in the city. This is partly due to the ‘living laboratory’ concept that (Omrany et al., 2024) put forward.

In contrast, R7 noted that,

“The technology creates a shared platform for planners, engineers, and policymakers to engage with real-time data.”

This makes digital twins more of an interface of collaboration rather than a simulation tool, as (Nica et al., 2023) believe that data platforms transform interactions of governance. Though, this also leads to the threat of excessive reliance on digital infrastructures, and this may downfall deliberative democratic procedures at the cost of unequal access of digital twins’ technology.

R4 reflected that,

“It pushes us to think proactively, rather than reactively, in city planning.”

This corroborates academic arguments that urban governance is regularly short-term which is reflected by (Malekloo et al., 2022) as well. These findings indicate that digital twins can turn planning into the forward-looking and collaborative process. Nevertheless, the transformative capacity can only be aspirational unless the institutions are ready and there is fair access. Together with the quantitative results, the responses indicate that the contribution to sustainability of digital twins is not just technological, but that they also have a potential to transform institutional coordination, planning behaviour and long-term decision-making processes.

4.5.3. Theme 2: Financial Implications and the Challenge of Justifying Investment

This theme developed from repeated concerns regarding the financial feasibility of digital twins. The participants frequently argued that high development costs and budgetary limitations are major mediating factors, which aligns with the critique of (Habib et al., 2025) that ambiguity between cost and benefit limits innovation despite the potential for sustainability.

R1 stated that,

“The cost of building and maintaining a digital twin often outweighs short-term project budgets.”

Instead of considering cost a matter to be addressed in the short-term, within the implementation phase, R1 considered financial pressure to be a structural problem that is set into public-sector budgeting processes. This argues that the concern is not just affordability but whether the models for fiscal planning and planning long-term investment in digital infrastructure are compatible. This argument echoes with (Jafari et al., 2023) who critiqued of smart city initiatives, where visionary pomposity outstrips fiscal feasibility. The stress on budgetary strain signifies the structural mismatch amid innovation cycles and annualised public finance systems.

Similarly, R5 responded that,

“Return on investment is difficult to quantify because benefits are long-term and diffuse.”

The lack of clarity in ROI is also linked to the fact that indirect sustainability-related return outcomes, like long-term efficiency gains, resilience and prevention, are more difficult to monetise. As a result, R5 considered the evaluation models used for finance to be poorly suited to technological innovation that is aimed at the sustainability agenda. This is in line with (Haraguchi et al., 2024), who argued that digitised innovations frequently introduce indirect efficiencies, lowered mistakes, and energy efficiency that do not follow traditional accounting systems. The problem, nevertheless, is methodological: how to value outcomes that are pre-emptive and systemic instead of immediate and tangible.

Adding another dimension, R9 explained that,

“Private investors remain sceptical without clear cost–benefit models, which delays adoption.”

In this case, there is an overlap between financial issues, governance, and trust. Investor confidence is depressed without clear models to slow down innovation. According to (Habib     et al., 2025), by reimagining digital twin financing based on sustainability-based bonds or hybrid public-private bonds, long-term investment could be justified. Overall, these arguments and findings imply the necessity of transitioning the evaluation from a narrow focus on cost-benefit assessment to more inclusive value models consistent with resilience and sustainability outcomes. These findings provide useful additions to the quantitative findings and provide an explanation for why financial implications did not emerge as a significant mediator. Currently, the financial systems seem to be more like implementation constraints than enablers of sustainability transformation, according to the interviews.

4.5.4. Theme 3: Risk Governance and Data Responsibility in Digital Twin Deployment

This theme originated from the concerns and responses provided by participants regarding accountability, governance, and trust in implementing digital twins. Respondents made references related to cybersecurity, ethical safeguards, threats and citizens’ confidence. It highlighted governance as a decisive mediator. The responses of participants associated with this theme are analysed below;

R8 argued that,

“Without clear protocols, digital twins could become vulnerable to misuse or cyber threats.”

R8 did not observed cybersecurity as a purely technical problem but as a governance one that could have an impact on institutional legitimacy and public acceptance. This highlights the ongoing need for city governance to rely on data trustworthiness, and digital governance. This highlights the intersection of technological innovation and governance gaps. According to (Botín-Sanabria et al., 2022), anticipatory governance is vital to reducing the risks that emerge in advance, but in practice, risk management frequently is reactive.

R10 specified that,

“We need frameworks that balance innovation with accountability and ethical safeguards.”

This imitates the critical tension between boosting creativity and implementing safeguards. In this perspective, (Attaran & Celik, 2023) also reflected that the absence of strong governance provides a high risk that smart technologies might disintegrate urban systems and strip trust. The issue highlighted by R10 is that governance must not be a hindrance but they must be enablers that legitimise innovation by making them accountable.

R2 further stressed that,

“Trust is fragile—if citizens feel their data is unsafe, the entire initiative loses legitimacy.”

Often the focus is on trust, which highlights the fact that there are social influences on sustainable solutions, rather than technical ones. R2 stressed to the points of viability, citizen trust and transparency, and legitimacy as linked to the long-term viability of digital twin initiatives. This highlights the fact that risk governance is not a technical problem only, but a societal one. The correspondence to the literature on data justice, (Ariyachandra & Wedawatta, 2023), supports the idea that trust and accountability are preconditions of adoption. Combined, it can be argued that it is essential to incorporate governance early to prevent the pitfalls that can undermine the validity of digital twin projects and compromise their sustainability provisions. The qualitative results therefore complement the quantitative results by showing that risk governance is not only a regulatory process, but also a social legitimisation process impacting on acceptance and sustainability outcomes.

4.5.5. Theme 4: Alignment with SDG Reporting and Sustainable Urban Development Outcomes

The emergence of this theme was observed from the responses of participants where they connected digital twins to sustainability targets and international reporting standards. The responses related visualising impacts, standardisation challenges and efficiency risks signified SDG alignment as vital requirement for sustainable urban development outcomes. Hence, the responses related to this theme are discussed and analysed in detail below.

R6 stated that,

“Digital twins can directly show how urban decisions impact SDG indicators like energy use and emissions.”

R6 observed digital twins as tools that could make intangible sustainability targets tangible and measurable in the context of operations. This implies that DTT can contribute to enhance the implementation of practical sustainability governance, related to making the environmental phenomena in urban areas more visible and traced. This aligns with the (Ali et al., 2025) advocating data-driven monitoring that digital twins can render sustainability results real to the policy makers. This has its strength on visualisation, which transfers abstract objectives into viable insights.

In contrast, R9 noted that,

“The challenge is translating complex simulations into standardised reporting frameworks.”

A large difference was also noted between the technological capability and the institutional reporting sets of standards. This means that SDG integration cannot happen simply through the use of technology but must be performed alongside enabling governance and reporting mechanisms. This indicates the criticism of (Jafari et al., 2023), who also observed that cities embrace SDGs on paper but fail to operationalise them because they have a mixed metric. R9’s perspective explains that not only technological capacity but also methodological integration into global frameworks is necessary.

R4 added that,

“If SDGs are not integrated early, digital twins risk becoming efficiency tools rather than sustainability tools.”

This response as a warning against technocratic drift, in which the gains in efficiency are favoured over the overall sustainability. Importantly, the respondents also point out that adherence to SDGs is not spontaneous; it must be introduced intentionally during the design phase. Consistent with the insights of (Omrany et al., 2024), these standpoints indicate that digital twins have a powerful potential for developing sustainable urban development. Still, these developments must be built on normative alignment of SDGs, rather than on managerial efficiency, so that the dependent variable sustainability is indeed met. The results also add to the quantitative mediation findings by demonstrating that the SDG reporting frameworks are more than just reporting frameworks, but are also institutional structures and that influence the direction of digital twin systems, whether towards sustainability goals or simply towards operating efficiency. The qualitative results were, overall, able to support and build on the statistical results by providing insights into the institutional, financial and governance structures that shape the use of digital twin technologies and their impacts on sustainability outcomes in practice. This qualitative engagement with technological effectiveness in interviews further illuminated the quantitative model, giving us more explanation on the extent to which technological effectiveness is dependent on governance preparedness, financial flexibility, stakeholder support, and integration with the Sustainable Development Goals (SDG).

Table 6. Thematic analysis table.

Table 7. Demographic profile of participants.

5. DISCUSSION

The qualitative analysis was integrated into the discussion, which helped to interpret the quantitative findings. The results of the qualitative analysis complemented the quantitative analysis results by providing a better understanding of why governance and SDG alignment had a stronger relationship between the DTT and SUD outcomes likened to financial implications. The institutional readiness, governance integration, and financial adaptability are highlighted as key factors that influence the potential of digital twin technology to support sustainable urban development, which can be seen as a conditional relationship. At one hand, the findings highlight that digital twin technology ensure the effectiveness of operations and the creation of long-term value through the possibility to perform predictive maintenance, optimal resource distribution, and minimise waste. These results show that the technology can enhance financial implications in case it is integrated successfully. Conversely, both quantitative and qualitative data indicate that none of these benefits are immediately realised, given that high initial costs, unpredictable returns to investment and the lack of proven funding models can keep up the adoption of these.

One of the more significant aspects of this research is that negative direct relationships between Digital Twin Technology and Sustainable Urban Development are found in conjunction with positive indirect relationships through Risk Governance and SDG Reporting Frameworks. This indicates that pressures from institutions and operations could lead to the implementation of digital twin technologies, but this could only be followed by sustainability benefits. (Ehwi et al., 2022) highlights the fact that technological innovations alone do not yield positive results, but do need underlying governance arrangements, organisational capacities, stakeholder buy-in and regulatory congruence. Thus, the negative direct effect indicates the implementation burden, transition costs, technological disruptions and institutional immaturity during the initial phase of digital twin implementation. This is also consistent with the work of (Fox & Macleod, 2023) who claims that urban organisations can experience significant resource demands, skill gaps, organisational integration and accountability and data management issues, all of which could temporarily limit capabilities for sustainability performance. In the meantime, these negative pressures can be mediated when adequate governance arrangements and SDG reporting are put in place as evidenced by the positive mediation role. In this context, the findings suggest that technology adoption is not the only factor that can contribute towards sustainable urban development but its extent of embedding into the systems that reflect accountability and institutional support. The findings thus underlined governance dependency as a key factor to link the technological capacity to sustainability.

These findings can be compared and contrasted with the arguments of (Habib et al., 2025) where the it was argued that the provision of digital twins through strong centralised funding and integrated urban governance to reach the level of delivering quantifiable financial efficiencies is achievable in a relatively short timeframe. Conversely, local authorities in the UK frequently exist under austerity regimes, devolved roles and cultures that are risk averse towards investments, which make the financial effect of digital innovations difficult to deliver (Grace et al., 2023). Moreover, the weak mediating role of financial implications further suggests that financial investment alone does not automatically translate technological innovation into sustainability outcomes. This is because the sustainability returns are hard to measure in existing public-sector accounting policy frameworks, where fiscal responsibility is more of an immediate priority and resilience or preventive investments not necessarily obvious. Financial systems in the UK urban context could therefore now be may currently act as a constraint than a facilitator to digital transformation.

The outcomes of H2 indicate that the mediating effects of risk governance and SDG reporting models are highly supported with weaker results of financial influence. Quantitative data suggests that the positive relationship between the adoption of digital twins and sustainable development results is significantly improved by governance and reporting frameworks. On the other hand, the qualitative also complements the findings by unveiling that accountability, transparency, and SDG alignment being our critical factors that ensure that the technology is valuable in achieving the sustainability goals.

A systems-theoretical view of governance and SDG reporting frameworks seems to imply that they are coordination systems that impinge on institutional accountability, regulatory oversight, and sustainability goals, alongside coordinating technological innovation. These mechanisms are not only about supporting implementation, but also about stabilisation of the interactions, by lowering uncertainties, by legitimising data-driven decision-making and by increasing the trust in digital governance systems among stakeholders indicated by (Omrany et al., 2024) as well. This is also reflected by (Nica et al., 2023) that governance regimes define whether smart technologies are of benefit to society, and (Malekloo et al., 2022) opinion that sustainability reporting is the key to integrating innovation into the long-term urban sustainable development strategy. The qualitative results extend this interpretation, though, by concretising the perception of actors that governance is not just about the regulatory control over digital twins, but also about the process of building legitimacy which impacts on the trust and uptake of digital twins and their compatibility with public sustainability objectives.

The lack of statistical significance for the mediating role of Financial Implications on the relationship between DT and Sustainable Urban Development outcomes is of theoretical interest because it indicates that the assumption that a digital twin can be adopted purely for economic reasons and yield sustainable urban development outcomes is not supported. While the results show that Digital Twin Technology plays a role in the financial aspects, financial mechanisms are only seen as an effective pathway to achieve sustainability benefits, as also noted by (Ehwi et al., 2024; Kim & Kim, 2021) also. This could be due to a number of factors, for instance, the long-term horizon of digital twin investments; return on investment uncertainty; budget limitations; and the lack of developed funding models in the urban governance chain. This means that financial resources alone do not have the capacity to create sustainability outcomes, but require complementary governance and institutional arrangements. Taking this into account, the proposed conceptual framework is also refined by pointing to the fact that the governance and accountability frameworks seem to be a more significant mediator, rather than the financial aspects, in supporting sustainable urban transformation with the aid of digital twin technologies.

One of the valuable contributions of this study is the identification of governance and SDG alignment as institutional enablers which can turn DT technologies into sustainability driven urban governance. In contrast to earlier research, which in the main focuses on the technical optimisation, the current results show that outcomes regarding sustainability occur when technological systems, governance structures and institutional legitimacy are combined in the context of urban development in the UK highlighted in (Fox & Macleod, 2023) also. Overall, the study findings show that ability of digital twin technology to move towards sustainability cannot be assessed by technology alone. Rather, sustainable urban outcomes can be achieved by combinations of digital innovation, governance legitimacy, institution coordination, and procedures and policies oriented towards sustainability. This further confirms the institutional legitimacy approach as it is an enterprise and institutional readiness that is essential to the digital transformation of cities and not just the technical.