Developing Social Simulations to Aid Scenario-Based Planning for Urban Regeneration Projects

Abstract

Developing sustainable and circular urban precincts requires the buy-in and participation of users of the infrastructure in an optimal manner. The most well-designed and developed infrastructure will achieve its objectives only if they are used in the intended manner. To achieve this, planners need to consider social behaviour and expectations of users, and design precincts to facilitate sustainable behaviour. This paper presents research on developing a social simulation tool to aid decision-making in an urban regeneration project. Findings from a community survey of typical users of the precinct were used to understand sustainability behaviours and challenges. Outcome-based recommendations were assessed by the team to explore how these relationships could translate into tangible, built environment outcomes. Alternative options for different elements within the precinct were identified and prioritised based on the optimal environmental impacts for each option. These options were then used to develop a social simulation model, which was validated through focus group sessions with stakeholders within the precinct. The major contribution of this research is the development of a realistic simulation model to visualise and assess sustainability impacts of potential built environment interventions. Further research in this area will focus on disseminating the tool for use by different stakeholders and understanding the preferences of options for different groups of stakeholders and their related environmental impacts.

1. Introduction

Urban precincts play a major role in human lives, with an increasing proportion of the population living, working, and playing in urban areas. Human wellbeing and urban spaces are interconnected in various ways with the urban spaces influencing health and quality of life aspects, while human activities affect the ecological impacts of such areas. The built environment contributes to most of the resource consumption and energy use and can alter microclimates, leading to positive feedback effects such as urban heat islands. Therefore, urban planners and developers are taking vital steps for precincts to be more inclusive and sustainable by design. Including sustainability aspects at the outset of a development project has the potential to include more sustainability considerations, whilst contributing to a more lasting impact rather than trying to include such additions after the completion of a project.

Urban precincts significantly influence human wellbeing and ecological conditions. Increasing urbanisation and associated human activities, such as extensive use of impermeable materials and energy-intensive infrastructure, contribute markedly to ecological impacts including the urban heat island effect, biodiversity loss, and increased flooding risk [1]. Therefore, effective sustainable urban regeneration requires addressing these human-driven ecological challenges through proactive design strategies.

This paper adopts two complementary theoretical frameworks—circular economy (CE) and Regenerative Design Theory. CE principles focus on eliminating waste through resource loops, sustainable product use, and material recovery [2], while Regenerative Design moves beyond sustainability to actively restores ecological integrity and enhances community wellbeing [3]. Integrating these frameworks highlights the need for regeneration strategies that consider both ecological improvements and the critical role user behaviour plays in achieving lasting sustainability outcomes. Despite robust research in urban sustainability and circular economy implementation, the literature consistently identifies significant gaps regarding the effective integration of behavioural dimensions into sustainable planning and interventions [4,5]. The behavioural dimension plays a significant role in the entire life cycle of urban precincts, from design and development to the use phase. We use the concept of stakeholders to analyse the behavioural dimension in a built environment setting as different individuals can have competing interests in a given project [6]. For the purpose of this study, we characterise a stakeholder to be any individual or group to have an interest in the urban regeneration project and includes architects, developers, project proponents, and end-users of the precinct.

Although, the built environment sector focuses on designing infrastructure to help urban spaces to be more socially and environmentally sustainable, such outcomes rely heavily on user behaviour. If user behaviour is not accounted for, infrastructure may be designed for sustainability but utilised ineffectively, leading to suboptimal outcomes [7]. This illustrates that users of infrastructure play an important role, in achieving the intended sustainability outcomes of a project. In addition, pedestrian bridges also make cities less walkable by prioritising motorised traffic over pedestrian access, which can have a further detrimental effect on pedestrian behaviour. Therefore, it is vital to consider potential user behaviour when designing infrastructure, as the main aim of infrastructure is to aid better outcomes for those using it. Current practices often overlook how human perceptions and behaviours influence the successful use and effectiveness of sustainable infrastructure [8,9].

Addressing these gaps, this research develops and validates an innovative social simulation tool that integrates empirical community behavioural data, stakeholder-driven scenario planning, and real-time visualisation of sustainability interventions. The tool enables planners to realistically evaluate potential user acceptance and the environmental impacts of various regenerative and circular strategies, significantly improving decision-making effectiveness in urban regeneration projects.

2. Literature Review

Research on sustainable urban regeneration increasingly recognises the necessity of addressing human behavioural factors alongside technological interventions. The recent literature indicates a significant shift from focusing purely on infrastructural or technical solutions towards integrated frameworks that emphasise user behaviours and social participation to achieve sustainable urban development outcomes.

Prior studies have extensively discussed principles of CE within the built environment context, focusing on design strategies that minimise waste and facilitate resource recovery. Merli et al. [10] and Serrano-Bedia and Perez-Perez [11] provided comprehensive overviews highlighting how CE principles have been conceptualised within academic and institutional frameworks. Vergani [12] further examined higher education institutions as microcosms for CE practices, yet noted a prevalent focus on waste management and material reuse, often omitting user behaviour and social acceptance factors critical for real-world implementation success.

Parallelly, the emerging Regenerative Design Theory has expanded the sustainability discourse beyond resource management to active ecological restoration and social revitalisation [3]. Bakos and Schiano-Phan [13] demonstrated potential benefits of regenerative design through bioclimatic strategies; however, their research primarily emphasised architectural and climatic outcomes without sufficiently integrating how user perceptions and behaviours influence these outcomes.

Agent-based social simulations have emerged as promising tools to bridge this behavioural gap by explicitly modelling human–environment interactions and user decision-making [8,9]. Gerst et al. [4] and Aguirre and Nyerges [5] have illustrated the effectiveness of such simulations in policy formulation, yet highlighted ongoing limitations in adequately capturing diverse behavioural dynamics, especially in relation to circular economy strategies and urban regeneration scenarios.

Notably, previous applications of social simulations often rely on abstract or simplified assumptions regarding user behaviours [8]. Gaube and Remesch [14] highlighted this issue, indicating that oversimplification reduces the simulations’ predictive accuracy and decision-making utility in urban contexts. Similarly, Katoshevski-Cavari et al. [15] identified that realistic visualisation and interaction capabilities significantly enhance simulation usefulness, yet these elements remain underdeveloped in most current models.

Despite advances, the literature consistently underscores several persistent gaps such as limited behavioural integration and insufficient realism in scenario visualisations. Existing models inadequately capture complex user preferences, perceptions, and behavioural responses critical for implementing sustainable design interventions effectively. Most simulation tools often lack realistic visualisation, diminishing their applicability for stakeholder engagement and practical urban planning. Social simulations also exhibit underdeveloped methodologies for integrating circular economy principles explicitly within behavioural models and stakeholder-driven scenario planning.

Addressing these critical gaps, this research introduces an advanced, stakeholder-validated social simulation tool integrating realistic visualisation methods, empirical behavioural data, and explicit circular economy and regenerative design principles. Through detailed user interaction modelling and comprehensive scenario simulations, this study significantly advances the methodological rigour and practical applicability of social simulations for sustainable urban regeneration planning.

2.1. Urban Regeneration Within University Precincts

Urban regeneration is the development of inner-city areas through efficient land reutilisation, the revival of cultural heritage, and the restoration of the natural environment, which leads to new, vibrant inner-city communities that are both sustainable and inclusive [16]. From an ecological point of view, the term regeneration is considered going above and beyond the commonly used term of sustainability. While sustainability is understood as how to maintain the current way of life and environment, regeneration refers to the improvement of living systems through regrowth and flourishing of nature [3]. Regenerative urban precincts thus aim to create more social revival for communities, while regenerating nature.

Circular economy (CE) is a relatively recent concept within the broader sustainability discipline and has gained interest in academic, business, and policy sectors due to major crises caused by waste and pollution. CE is based on three principles driven by design: eliminating waste and pollution, circulating products and materials (at their highest value), and regenerating nature [2]. Therefore, designing urban precincts based on CE principles can culminate in regenerative precincts, which are not only environmentally sustainable but help regenerate ecologically degraded areas, relatively common in urban regions.

Higher educational institutions play an influential role in advancing CE and broader environmental practices by providing the necessary knowledge and the development of tools, instilling ethical and sustainable values [10], and by encouraging policymakers and corporate decision makers to learn, think, and act differently [11]. Within this climate, universities also need to exemplify these values and methods that they teach, through their actions. Universities are increasingly looking inward to improve their sustainability impacts, with university rankings considering institutions’ efforts towards achieving sustainable development goals [17]. From a built environment perspective, universities could design and implement the regenerative principles, which form their circular economy education, research, and corporate strategies.

Urban regeneration can play an important role in enhancing sustainability within revitalising projects at universities as most university buildings were constructed long before the principles of circular economy and sustainability became prevalent. Consequently, these structures must be adapted to meet contemporary needs, as well as updated to standards and building codes that address environmental sustainability [12]. Older buildings could also have heritage value and repurposing such buildings needs to consider how such values can be maintained while catering to current educational needs [18]. Such adaptive reuse strategies of older buildings can be used as learning tools to understand the efficacy of circular strategies, which are instilled in educational courses [19]. Refurbishing buildings instead of knock-down rebuilds typically generates more jobs, comparable energy consumption, and less use of water and new materials [20].

As universities grow, they also build new facilities and infrastructure, providing an opportunity for such new builds to act as living labs. Some strategies that could be used in such instances are replacing conventional, energy-intensive building materials with more circular alternatives [21]. However, it is vital that such substitution of materials considers broader environmental impacts from an entire life cycle approach. Circular and regenerative buildings needs to consider not just the structural elements but also the internal fit outs, with the reuse and repurposing of products for fixtures and fittings [22].

Regenerative buildings go further than reducing the environmental impacts of the buildings by sequestering carbon dioxide, clean the air, treat waste water, and turn sewage back into soil nutrients during the operational phase of the building [23]. These buildings should be integrated into a precinct that is also regenerative. Such precincts may feature liveable green spaces that harmonise with the built environment, thoughtfully designed areas that enhance thermal, visual and auditory comfort, bioclimatic design strategies, and fossil-free transportation within the campus [13], wildlife corridors, and urban agriculture [23].

Within city campuses, a more holistic approach to urban regeneration is required, as spaces are used by the public, who are not involved in academic activities. Therefore, repurposed facilities need to be open and welcoming to the public and be multifunctional spaces [24]. City campuses could also showcase how circular design could be implemented not only to learners but also to the wider public, catalysing the mind set changes that are needed for a systemic transition. As city campuses are open to the public, understanding the behaviour and mind set of typical users is a vital aspect in designing sustainable and circular campus precincts.

2.2. Social Simulations for Sustainability Assessments

Sustainability is a very broad but complex concept and can be interpreted in a multitude of different ways. Assessing sustainability impacts of a specific activity can be challenging due to the multiplicity of factors contributing to sustainability [8]. Some of these aspects include the broad temporal and spatial dimensions of consequences, as decisions related to sustainability often unfold over extended periods of time and may manifest far from their origin. Sustainability typically encompasses economic, environmental, and social factors, which can have competing interests and intricate interdependencies between them, complicating efforts to understand the full range of impacts and outcomes. The dynamic, non-monotonic, and non-deterministic nature of these interactions also make it difficult to predict and analyse the long-term effects of specific activities or decisions. Assessing sustainability-related interventions requires an ability to integrate different levels of granularity, such as tracing the connections between individual human activities and their broader, long-term effects on the planet. Integrating the behavioural aspects of individuals to sustainability assessments typically takes an interdisciplinary approach by combining the behavioural sciences with technical, engineering-related interventions.

Social simulations is one method that can be used to bridge this gap between the subjective individual decision-making and the objective technical solutions to find optimal interventions. Social simulations is a type of simulation modelling that aims to incorporate vital human factors into agent architectures and simulated environments [9]. Social simulations aim to account for the cognitive and cultural complexity of individuals, as well as for the role individuals take in the decision-making process that influences societal and ecological outcomes. Social simulations help in understanding and providing insights into system dynamics, evaluating and comparing different options prior to implementation, supporting decision-making processes, developing new investigative tools, and facilitating training and education at the individual level [8].

Social simulations allow for researchers and policymakers to identify and model plausible and desirable futures based on individual decision makers [25]. In addition to simulating future scenarios, they also facilitate diverse decision-making strategies where cooperation and information exchange are important [26]. Social simulations can be used to understand agents’ preferences in cases where conflicts and contradictions occur. This is an important area within sustainability as actions can have competing demands on the different pillars of sustainability: social, environmental, and economic. Research has used social simulations to model how policies impact economic growth and environmental impacts [4]. The complexity of sustainability interventions also needs to consider the different types of environmental impacts that can be created. With increased attention to global warming and resultant climate change impacts, there has been an increase in attention to carbon emissions, with reduced attention to other environmental impacts in life cycle analysis [27]. Social simulations can thus help draw attention to these contradictions and be used to understand the mental models that underlie decision-making.

Social simulations also help in understanding how public participation can be used to develop collective sustainable management practices [5] and identify emerging patterns based on individual differences in actors [28]. Such studies play a critical role in understanding optimal solutions for urban and community planning, where individuals’ behaviours and preferences have a high influence on the operations of the plans [14,15,29]. Although understanding human interactions and decision-making is essential in considering human-centred design approaches in the built environment, systematically incorporating human experience in social simulation research is limited in scope [8]. Therefore, it has been recommended that refining currently available software as well as creating new software for sustainability-related social simulations are necessary and desirable. This project, therefore, takes an action research approach where a social simulation model was developed, in order to increase user awareness of the sustainability aspects within an urban precinct and to understand preferences of specific sustainable options. The paper presents the methods adopted in developing the model and the outcomes of it.

3. Methods

This paper took an action research approach to understand how user behaviour and practices could be integrated into an urban regeneration project within a university precinct. The research used information on the community behaviour of individuals who will be using the precinct and business practices of firms located within and abutting the precinct from two questionnaire surveys distributed previously [30]. The responses received through these surveys were analysed and translated to potential actions that could be implemented in an urban planning setting. The different strategies were presented to the university community through a temporary exhibition and a focus group session, which included researchers and staff members from across the university and broader industry partners including community development organisations. This presented an opportunity to validate the potential solutions and identify areas for improvement. Based on feedback received through the focus group sessions and the student programmes, place-based strategies for specific areas within the precinct were developed.

Areas with high foot traffic within the precinct were identified to design optimal solutions based on the initial data collected. These areas were photographed and the most relevant CE strategies that could be adopted were identified based on accepted CE techniques and the behavioural surveys conducted. Each proposed element was selected for its potential to enhance sustainability, community engagement, and alignment with circular economy principles, ensuring a comprehensive approach to urban development.

Different intervention options were selected for elements within the precinct. These interventions are designed to address key areas such as waste management, energy efficiency, stormwater management, and public health, while also fostering community participation and social benefits. The selection of intervention strategies for urban renewal follows scientifically validated sustainability principles, focusing on environmental, economic, and social impacts. Multi-criteria decision analysis (MCDA) was employed to integrate both quantitative and qualitative assessments from peer-reviewed studies. Cost-effectiveness was evaluated through life cycle cost analysis (LCCA), ensuring long-term material value [31] while balancing initial installation costs against maintenance and operational savings [32].

Environmental impacts of strategies were evaluated based on various environmental factors such as water management [33], urban heat island effect [34], and incorporation of recycled content [35]. Social impact considerations include public health benefits, such as reduced waterborne disease risks due to improved flood management (WHO, 2018), and enhanced walkability and safety through smooth, permeable surfaces [36]. These criteria ensured that interventions were selected based on robust scientific evidence, optimising sustainability outcomes. To prioritise interventions, a weighted composite index approach was applied, ensuring an evidence-based selection of strategies. The evaluation process involves defining criteria, assigning weights, normalising values, and computing a final score for ranking interventions.

3.1. Scoring Formula

The composite score for each intervention is calculated as follows:

$$FinalScore={\omega }_1.C+{\omega }_2.Q_1+{\omega }_3.Q_2+\cdots +{\omega }_n.Q_n$$

where

$C$ (Cost-effectiveness) = Negative weighting applied to penalise higher costs.

$Q$ (Quantified Benefits) = Includes carbon sequestration, energy savings, stormwater management, urban heat reduction, biodiversity, and social/health impact.

3.2. Weight Assignments

Weights for each criterion were selected based on sustainability principles and findings from previous research [37,38,39,40] and are presented in

Table 1. Weights allocated for each criterion.

Criterion	Weight	Rationale
Cost (AUD/m2)	−0.2	Lower costs are preferred, so a negative weight is used to penalise expensive interventions.
Carbon Sequestration (kg CO2/m2/year)	0.2	High priority due to its role in mitigating climate change.
Energy Savings (kWh/m2/year)	0.2	Reduces operational costs and environmental impact [39].
Stormwater Management (litres/m2/year)	0.2	Helps mitigate urban flooding and infrastructure strain [38].
Urban Heat Reduction (°C)	0.1	Lowers local temperature and reduces cooling demand.
Biodiversity Score	0.15	Supports ecosystem health and urban sustainability.
Social/Health Benefits	0.15	Enhances community wellbeing and liveability.

.

The weights assigned to the MCDA criteria were explicitly informed by the current literature to accurately reflect the priorities essential for urban precinct regeneration. The highest weights allocated to carbon sequestration (0.2), energy savings (0.2), and stormwater management (0.2) directly align with their critical roles in mitigating climate change and enhancing urban environmental resilience [33,35]. Moderate weighting for urban heat reduction (0.1) and biodiversity (0.15) is justified based on evidence showing these factors as influential yet secondary compared to direct carbon management and energy efficiency impacts within urban regeneration strategies [38,41]. Finally, Social/Health Benefits (0.15) received substantial emphasis, acknowledging the literature underscoring the critical need to explicitly include social dimensions to secure sustained community acceptance, participation, and overall effectiveness of sustainable urban development initiatives [36,42]. This literature-grounded rationale ensures transparency, academic rigour, and enhanced applicability of the MCDA framework presented in this study.

3.3. Normalisation and Ranking

All values were normalised to a [0, 1] scale to ensure comparability across different measurement units. The final weighted composite score determines the ranking, with higher scores indicating superior overall performance. This approach ensured flexibility, quantitative objectivity, and a balanced evaluation incorporating economic, environmental, and social factors, allowing for informed decision-making in urban renewal planning. Five interventions for each element within the precinct were chosen based on the sustainability ranking and the community feedback obtained through the surveys and focus group sessions.

An Agent-Based Simulation Model was developed, where users could interact with their environment. And agent-based model allows for the simulation of complex systems using a bottom-up approach that begins individual agents. An Agent-Based Simulation Model was used to capture the complexity of human–environment interactions in the precinct. This method was chosen for its ability to model individual decision-making and simulate emergent collective behaviours based on diverse user preferences, knowledge, and values. By allowing users to explore various intervention options and observe outcomes, the model supports the identification of strategies with broad user acceptance and systemic impact, aligning with bottom-up, participatory planning approaches.

The model provides users with the ability to select different intervention options for different elements within the precinct. It is assumed that each user (agent) is an individual entity possessing its own intelligence, values, and beliefs and that they make decisions based on what they perceive from their inherent knowledge and assumptions and any additional information provided through the model. The additional information provided for the model included the financial cost and environmental impacts of each intervention. As the number of users of the model increase, emerging behaviours, patterns, and structures could be understood. These patterns and structures could then be used to identify optimal interventions that will have broad scale buy-in from users of the actual precinct.

4. Findings and Development of the Social Simulation Model

The social simulations are based on four elements within the precinct that were identified as the most appropriate for interventions to be simulated. These elements are as follows: building facades, road pavements, internal building spaces, and outdoor spaces. These elements were selected as incorporating interventions at different stages of the life cycle of the project was possible. Three specific locations within the precinct were selected as case study locations, where interventions for the elements could be simulated.

The major insight from the survey responses was that individuals were lacking the supporting infrastructure to act in a more sustainable manner. The four key areas where infrastructure could aid circularity were identified as public transport, waste management, repair and reuse solutions, and more sustainable dining options. The feedback received through focus group sessions highlighted the lack of focus on the social benefit aspect, especially for the broader community who would frequent this area. Possible interventions proposed in the focus group session were setting up community gardens for food waste recycling and refund collection points for used containers. Further engagement with students through a micro-internship programme revealed that student participation will be higher if strategies can be visually experienced, increased social media engagement, and incentivised to take sustainability related actions.

Table 2. Ranked road pavement intervention strategies.

Intervention Strategy	Cost (AUD/m2)	Normalised Cost	Carbon Sequestration (kg CO2/m2/Year)	Energy Savings (kWh/m2/Year)	Normalised Stormwater Management	Urban Heat Reduction (°C)	Biodiversity Score	Social/Health Benefits	Composite Score	Rank
Grass/Gravel Pavers (Turf Reinforcement Systems)	87–142	0.90	1	1	1	0.95	1	1	1	1
Pervious Concrete	173–283	0.29	0	1	0.9	0.71	0.7	0.7	0.73	2
Permeable Interlocking Concrete Pavers (PICP)	142–212	0.56	0	0.75	0.75	1	0.7	0.9	0.64	3
Porous Asphalt	118–189	0.69	0	0.5	0.9	0.48	0.7	0.7	0.5	4
Resin-Bound Permeable Pavements	63–126	1	0	0.5	1	0.24	0.7	0.7	0.43	5
Plastic Grid Pavers	71–126	0.98	0	0.5	0.6	0.81	0.5	0.7	0.37	6
Pervious Concrete with Titanium Dioxide	Unknown	0	0	0	0	0	0	0	0	7

,

Table 3. Ranked building façade intervention strategies.

Intervention Strategy	Estimated Cost (AUD/m2)	Normalised Cost	Carbon Sequestration (kg CO2/m2/Year)	Energy Savings (kWh/m2/Year)	Stormwater Management (litres/m2/Year)	Urban Heat Reduction (°C)	Biodiversity Score	Social/Health Benefits	Composite Score	Rank
Green Facade (Living Walls)	800–1500	0.57	1	0.75	0.83	1	1	1	0.80	1
Facade with Water Management Systems	1000–1500	0.52	0	0	1	0.33	0.25	1	0.32	2
Permeable (Breathable) Facade	120–170	0.18	0	0.25	0	0.67	0.5	1	0.31	3
Reflective Cool Facade	2.5–32.5	0	0	0.2	0	1	0	1	0.29	4
Recycled or Low-Embodied Carbon Material Facade	63–100	0.07	0	0.1	0	0.33	0.25	1	0.23	5
Photovoltaic (PV) Facade	1000–1500	0.30	0	1	0	0	0.25	0	0.18	6
Double-Skin Facade	1200–1800	0.71	0	0.5	0	0.67	0	1	0.17	7
Kinetic Facade	1500–2000	1	0	0.4	0	0.33	0	1	0.06	8

,

Table 4. Ranked free building space strategies.

Intervention Strategy	Cost (AUD)	Carbon Sequestration (kg CO2/Unit/Year)	Energy Savings (kWh/Unit/Year)	Stormwater Management (Litres/Unit/Year)	Social/Health Benefits	Composite Score	Rank
Tool Libraries	47,500	0.5	0.5	0	1	0.15	3
Urban Materials Banks	87,500	1	1	0	1	0.15	3
Library of Things	50,000	0.4	0.5	0	1	0.15	3
Upcycling Studios	32,500	0.5	0.3	0	1	0.15	3
Food Rescue and Redistribution Hubs	105,000	1	1	0	1	0.15	3
Zero-Waste Markets	110,000	0.5	0.5	0	1	0.15	3
Community Fix-it Stations (Repair Shops and Cafes)	37,500	0.2	0.2	0	0.5	0.08	8
Maker Spaces and Fab Labs	180,000	0.3	0.4	0	0.5	0.08	8
E-Waste Repair and Recycling Centres	115,000	0.2	0.2	0	0.5	0.08	8
Swap Shops or Free Stores	11,000	0.3	0.3	0	0	0	10
Op-shop	37,500	0.4	0.4	0	0	0	10

and

Table 5. Ranked free public area strategies.

Intervention Strategy	Cost (AUD/Unit)	Carbon Sequestration (kg CO2/Unit/Year)	Stormwater Management (Litres/Unit/year)	Urban Heat Reduction (°C)	Biodiversity Score	Social/Health Benefits	Composite Score	Rank
Urban Forests or Pocket Parks	10,000	10	2000	1	1	1	0.4	1
Native Vegetation	5000	5	500	0.67	0.67	0.71	0.27	2
Urban Wildlife Habitats	2500	3.5	500	0.33	0.83	0.71	0.26	3
Green Walls and Vertical Gardens	1400	1	500	0.67	0.4	0.71	0.23	4
Rain Gardens	7500	3	1000	0.5	0.5	0.53	0.20	5
Public Edible Gardens	2000	2	200	0.33	0.33	0.41	0.15	6
Composting Area	1000	1	100	0.17	0.27	0.41	0.12	7
Planter Boxes Using Reused Wood	250	0.2	0	0.07	0.17	0.29	0.08	8
Planter Boxes Using Recycled Plastic	300	0.2	0	0.07	0.17	0.29	0.08	8
Solar-Powered Charging Stations	5000	0	0	0	0.1	0.12	0.03	10
Smart Waste Bins	1500	0	0	0	0	0.12	0.02	11
Bicycle Racks and Parking Hubs	1500	0	0	0	0.07	0	0.01	12

present the results of the MCDA, clearly ranking interventions based on environmental, economic, and social sustainability criteria. The different intervention strategies for the four elements within the precinct are ranked according to the composite sustainability score. This ranking reflects a holistic assessment of cost-effectiveness, environmental benefits, and social impact, ensuring a data-driven prioritisation of interventions for urban renewal.

Table 2, demonstrates that Grass/Gravel Pavers are optimal due to their excellent stormwater management and heat reduction capabilities, significantly addressing urban heat island effects and flood risks, crucial considerations for the region the case is located in [33,34]. Table 3 highlights the superior overall environmental performance of Green Facades, supported by their capacity to enhance biodiversity and mitigate urban temperatures through carbon sequestration and improved air quality, addressing critical environmental sustainability goals [43]. Table 4 and Table 5 show community-driven strategies, such as Urban Materials Banks and Urban Forests, effectively aligning circular economy principles with social wellbeing, promoting community engagement and resource efficiency, and thus highlighting critical intersections between environmental sustainability and social inclusion [23,44]. Collectively, these explanations explicitly link MCDA data to broader implications, illustrating how targeted sustainable interventions tangibly address urban sustainability challenges.

The ranking of facade interventions highlights the dominance of Green Facades (Living Walls), which achieve the highest composite score due to their strong performance across carbon sequestration, stormwater management, urban heat reduction, biodiversity, and social benefits. Other strategies, such as Facade with Water Management Systems and Permeable Facades, provide targeted benefits but with lower overall impact. Reflective and photovoltaic facades offer specific advantages, particularly in energy efficiency and heat mitigation, while kinetic and double-skin facades rank lower due to their limited contribution across multiple criteria.

“Free Building Space” strategies, such as Tool Libraries, Swap Shops, and Maker Spaces, primarily contribute to social benefits, resource-sharing, and waste reduction. However, as these interventions do not involve green infrastructure or heat-mitigating materials, they have little to no direct impact on urban heat reduction or biodiversity, resulting in empty columns for these criteria.

Additionally, since specific impacts on urban heat reduction and biodiversity were not quantified in the dataset, they were excluded from the composite score calculation to ensure fairness and accuracy in ranking.

The free public area strategies generally show zero values for energy savings (kWh/unit/year) because their primary focus is on environmental sustainability, biodiversity, and social wellbeing, rather than direct energy efficiency. Interventions such as Native Vegetation, Rain Gardens, and Urban Forests or Pocket Parks emphasise stormwater management, urban cooling, and ecological restoration. While they may contribute to energy reductions indirectly, their impacts are not readily quantifiable in terms of kilowatt/hour savings. The absence of direct energy-related data in the document accurately reflects the purpose of these interventions, which contrasts with infrastructure projects like Solar-Powered Charging Stations, explicitly designed to enhance energy efficiency and generate renewable energy.

Five intervention options were selected to be displayed on the user interface of the social simulation model. It was revealed in the focus group sessions that providing a large number of interventions would lead to users being overwhelmed and only selecting the options that were presented at the top. The specific five intervention strategies were selected based on the sustainability ranking as well as the findings from the surveys and focus group sessions. Therefore, some interventions that were included in the social simulation model were lower ranked on the composite score but were preferred by users. For example, although second hand shops and repair cafes did not score well on the composite score they were included in the simulations as there was high demand for such locations in the surveys. This was similar to the building integrated PV, where users wanted to visualise the renewable energy solutions.

The simulation dashboard presented financial and environmental impacts of each intervention, so that users can make a decision on which intervention to use based on different financial and environmental outcomes of the strategy. The different interventions were also given star ratings on three environmental categories: carbon reduction potential, biodiversity and ecosystem services, and overall environmental benefits. This star rating was included based on the validation feedback received on the proof of concept. The final intervention strategies included in the social simulation model and the relevant star ratings for each are detailed in

Table 6. Star ratings for road pavement interventions.

Road Pavement		Cost (AUD/m2)	Reduction in Carbon	Biodiversity and Ecosystem Services	Overall Environmental Benefits
1	Grass/Gravel Pavers (Turf Reinforcement Systems)	87–142	2.5	5.0	4.5
2	Pervious Concrete	142–212	2.0	3.0	3.5
3	Permeable Interlocking Concrete Pavers (PICP)	118–189	2.0	3.5	3.5
4	Porous asphalt	71–126	1.0	2.5	3.0
5	Resin-Bound Permeable Pavements	173–283	1.5	2.5	3.0

,

Table 7. Star ratings for building façade interventions.

Building Facade		Cost (AUD/m2)	Reduction in Carbon	Biodiversity and Ecosystem Services	Overall Environmental Benefits
1	Green Facade (Living Walls)	1000–1800	3.5	5.0	4.5
2	Facade with Water Management Systems	1000–1600	1.0	3.0	3.0
3	Reflective Cool Facade	150–300	2.5	1.0	2.5
4	Recycled or Low-Embodied Carbon Material Facade	250–500	3.5	2.0	3.5
5	Photovoltaic (PV) Facade	600–1100	4.5	1.5	3.5

,

Table 8. Star ratings for free building area interventions.

Free Building Space		Cost (AUD)	Reduction in Carbon	Biodiversity and Ecosystem Services	Overall Environmental Benefits
1	Tool Libraries	20,000 to 75,000	2.5	1.5	3.0
2	Urban Materials Banks	45,000 to 130,000	3.5	3.5	4.0
3	Food Rescue and Redistribution Hubs	70,000 to 140,000	4.0	3.0	4.0
4	Repair Café	20,000 to 55,000	2.0	1.0	2.5
5	Op-Shop	25,000 to 50,000	2.5	1.5	3.0

and

Table 9. Star ratings for free open area interventions.

Free Open Area		Cost (AUD)	Reduction in Carbon	Biodiversity and Ecosystem Services	Overall Environmental Benefits
1	Urban Forests or Pocket Parks	150–250 per m2	5.0	5.0	5.0
2	Public Edible Gardens	200–350 per m2	2.0	4.0	3.5
3	Composting Area	200–400 per m2	3.5	3.5	4.0
4	Smart Waste Bins	1800–3700 per unit	1.5	0.5	2.0
5	Bicycle Racks and Parking Hubs	350–700 per rack for 2 bikes	1.0	1.0	2.0

.

Developing Social Simulations

A 3D visualisation system was developed using Three.js within a React framework. This increases interactivity and provides the ability to modify urban elements, such as building facades and road pavements. The core environment of the precinct was structured as a 360-degree panoramic sphere, with an equirectangular texture mapped onto a spherical geometry to simulate a realistic scene.

Rather than employing a drag-and-drop interface, the system utilises a click-based selection method for modifying materials. When a user clicks on an adjustable surface (e.g., a wall or pavement), a sidebar panel is displayed, offering a range of material options. Upon selecting a material, it is dynamically applied to the chosen surface. To ensure seamless application, overlay images are rendered as separate layers of SphereGeometry, thus preserving the integrity of the base panorama.

For navigation, the system integrates OrbitControls, enabling intuitive camera movement while enforcing constraints on azimuth and polar angles. This prevents unnatural motions, such as excessive horizontal panning or downward tilting, and ensures a controlled and realistic viewing experience. Zooming functionality is also provided, with restrictions in place to maintain an optimal field of view.

To ensure accurate interaction, alpha-based texture filtering was implemented, enabling only non-transparent sections of textures (i.e., clickable areas) to respond to user input. Raycasting was used to process mouse click events and determine the validity of interactions. Additionally, a calculation model was integrated for users to visualise the environmental and financial impacts of the different intervention options. These values are dynamically updated and presented in an analytics panel, providing real-time insights.

The proof of concept (POC) of the social simulations were uploaded on an online platform. The social simulations were validated by obtaining feedback on the proof of concept from potential end-users. A visual representation and a live link to the PoC was shared with stakeholders at university showcase events to obtain feedback. Detailed feedback was also obtained through a focus group session of internal and external researchers. The live link was accessed by 13 individuals allowing them to provide detailed feedback on the look and feel of the tool. The main improvements incorporated following the validation sessions were adding a description for each of the elements for it to be understood by non-experts, increasing compatibility with the Green Star Communities tool, and providing a cost-range instead of an absolute value.

Figure 1 illustrates the user interface where a user can select the different intervention options based on a brief description of it, while Figure 2 illustrates the simulation showing what the intervention would look like in real time on the ground and the relevant cost and environmental implications of it.

5. Discussion

The results of this research show that ecocentric interventions such as Grass/Gravel Pavers for paved footpaths, Green Facades and facades with water management systems for building facades, and Urban Forests and Native Vegetation in public areas can have better environmental impacts as opposed to more technocentric interventions. However, this can be in contradiction to user perceptions, which seem to align more with technocentric interventions such as PV facades and recycled plastic products. Further research into understanding user perceptions on technocentric vs. ecocenteric interventions can be conducted through the social simulation model developed through this project.

A major advantage of the model is the real-life simulation of how the interventions would look when adopted. This is similar to previous research, which found that using realistic features rather than abstract features in simulations help better decision-making in planning activities [15]. However, the use of realistic visualisations come with a cost, which is that only a limited number of locations or interventions could be modelled due to computational limitations.

A limitation of the model at this stage is the simplifying assumptions that have been used to interpret the environmental and financial impacts of the interventions. This limitation can have implications on user decision-making as there is a risk of users assuming that the specified impacts are definitive. The set of decision-making rules available to users of the model has been streamlined for greater simplicity, which aid users to select specific strategies without the ability to combine them. Such simplifications were considered a necessity as this model is an initial version that allows for modifications in future versions meant to answer more complicated questions [4]. User feedback will be collected from users through formal feedback forms and quick questionnaires when using the model. This feedback will be used to improve user experience in future iterations of the model.

As the focus of the research was to identify circular and environmentally sustainable interventions, a limitation of this model is the lack of focus on the social dimensions of these interventions. As the lack of consideration of social aspects within social simulations was identified [8], we strived to incorporate elements which have social impacts in the intervention strategies. However, the evaluation metrics of the strategies did not include a scoring for the social impacts, as quantifying them was challenging. Further research needs to include some level of quantification of the social impacts so that all three pillars of sustainability are evaluated.

To strengthen the integration of social dimensions into simulation tools, future iterations must embed quantified social impact metrics alongside environmental indicators. This includes indices for inclusiveness, accessibility, and behavioural responsiveness, which can be modelled through participatory agent-based frameworks that reflect diverse stakeholder profiles [5,42]. Incorporating structured stakeholder input through co-design workshops and value-sensitive design principles can ensure social priorities are not externally imposed but contextually embedded [8]. Additionally, adapting existing sustainability assessment tools such as the Social Life Cycle Assessment (SLCA) framework [45] could enable consistent and transparent representation of social trade-offs. Doing so would support the development of holistic, multi-dimensional models that better reflect the realities of equitable urban regeneration.

6. Conclusions

This paper presents the process followed for the design and development of a social simulation model to identify specific intervention strategies that aim to increase the sustainability of an urban regeneration project. Five key built environment elements where interventions could be adopted and three locations were identified as cases where the simulations could be modelled. The intervention strategies were identified and prioritised based on previous stakeholder engagement sessions carried out. The model was developed using Three.js system within a React framework, which enables 3D visualisation of the precinct. The model included financial and environmental impacts of each intervention strategy allowing for users to make informed decisions on how the selection of strategies impacts sustainability outcomes. The model was validated through a focus group session.

Future research in this area will focus on the use of the model by decision makers to understand their preferences for different sustainability-related interventions. The use of the model by relevant stakeholders and modifying the model based on feedback and user data will not only increase the reliability of the model but will also increase the use of such models for decision-making [46]. User data on the model will help researchers and policymakers design and develop more catered solutions for future urban renewal projects.