Call for contributions

Smart Buildings and Territories 2026: designing trustworthy and sustainable futures.

Introduction and research issues

From the first stones raised by the ancients to today’s sensor-woven cityscapes, progress in building has always reshaped our civilizations and mirrored the deepest expressions of our cultures. The richness and technicality of crafts in this field, unquestionably recognized as central to the organization of our individual and collective lives, has long been a differentiating factor for entire nations.

Today again, the rapid development of digital technologies is reshaping the way we design, construct, and inhabit our buildings and territories. The arrival of Digital Twins (DT) and simulation models, on the scale of buildings as well as territories, combining physical models with social, economic, energy or environmental data (Batty 2018; Iranshahi et al. 2025), promises upheavals both in the design phases and in the life of our living and working environments. Amplified by real-time data and the Big Data phenomenon, full of technical and analytical promise and tinged with persistent myths (Boyd and Crawford 2012), they are part of the lightning evolution of computer technologies, in particular through Building Information Modeling (BIM) solutions, impacting our efficiency and daily lives (Kitchin and Dodge 2014). Doubled with Artificial Intelligence (AI) assembling predictive Machine Learning models and the most recent generative AI, these platforms enable more informed decisions, increasingly automated processes and real-time optimization across the planning, construction, and management of living environments and sustainability. These technologies, increasingly shaping our behaviors (Voordijk and Dorrestijn, 2019), are also prompting entirely new questions about algorithmic agencies (Gillespie 2013), governance and control (Russell 2021), safety (Varshney 2016) and infrastructure dependence (Verma et al. 2019), ethical and ecological tensions (Du and Xie 2021), urban stakeholder participation and collaboration (Wan et al. 2023), liberty and democratic participation (Alizadeh and Sharifi 2023).

The conference “Smart Buildings and Territories 2026: Designing Trustworthy and Sustainable Futures” positions itself at this crossroads, fostering interdisciplinary dialogue among researchers, but also with professionals and policymakers.  It seeks to identify pathways for trustworthy digital infrastructures that would support genuine progress of our communities marked by heterogeneous and shifting sets of values. Core areas of exploration include the most innovative applications in the sector, lifecycle management of data, the design of participatory and transparent governance and decision-making processes, and the ethical implications of pervasive AI and monitoring systems. These themes highlight the need to rethink technological design, the institutional, cultural, and human dimensions of digital transformation. Anticipating such impacts, whether through empirical case studies or theoretical foresight, requires us to actively bridge disciplinary boundaries, professional expertise, and stakeholder perspectives, ensuring that both sectoral workers and citizens living in new environments are at the heart of our collective progress choices and adoptions.

This conference seeks to engage with multi- and interdisciplinary research challenges. First, the technical challenges of developing reliable and interoperable data, DT and AI applications must be addressed alongside the organizational and cultural hurdles of adoption, change management, and the building of data cultures. Second, risks and ethical dimensions, including surveillance, algorithmic accountability, sovereignty, safety and security, require continuous scrutiny through the transparency of the design and operating cycle and robust governance and human oversight so that the orientation toward public good remains clear and responsive in both space and time. Finally, the broader societal impacts on work practice and skills demand nuanced foresight, capturing both operational changes in the sector and shifts in everyday experience. These questions frame the call for contributions and guide the collective effort to imagine trustworthy and sustainable digital futures.

Track 1: Designing Smart Buildings and Territories

BIM and DT create live, virtual replicas of buildings and cities that simulate or mirror in real-time their structure, life and usage, including energy use or behavioral patterns of human activity (Nechesov et al., 2025). By fusing data from a dense and interoperable sensor network, such as air quality, lighting, traffic and power flows, with robust Machine and Deep learning models, the DT becomes a decision engine that tests scenarios, forecast performance, and optimize comfort and sustainability before interventions, reducing risks, costs and down times (Mihai et al., 2022). These decisions can be translated into actionable plans that can be validated and executed on the field. Furthermore, on site, matching sensor networks, robotics, and autonomous systems keep the Twin aligned with reality (Lee et al., 2022): robots execute precise tasks and feed results back for quality checks and scheduling, while drones and autonomous vehicles verify layout, move materials, and monitor progress of the work with fewer errors. This closes the loop between planning, construction, and operations so each phase continuously informs the next, shortening timelines, lowering risk and cost, and accelerating the transition to smarter, more resilient, and sustainable cities.

Beyond their technical sophistication, these systems demand trustworthy data governance, interoperability, and ethical oversight. Ensuring transparency and accountability in AI-driven processes requires shared standards, open data models, documentation and auditability mechanisms that make algorithmic decisions explainable and verifiable. Co-design processes that include ethical deliberation within various stakeholders, including designers, engineers, policymakers, and citizens, help anticipate social impacts, address biases, and promote participatory and inclusive innovation in the development of smart structures.

At the human level, AI and DT technologies are reshaping how professionals work, collaborate, and define their roles in architecture, engineering, and construction. Algorithmic management and data-driven processes enhance creativity, efficiency and coordination but also raise concerns about transparency, autonomy, accountability and even declining critical thinking (Lee et al. 2025). As digital collaboration becomes central to practice, new competencies in data literacy, adaptability, and ethical awareness are increasingly required. Ensuring that these transformations empower worker well-being and job security seems essential to achieving a sustainable and human-centred digital transition, which questions professional roles, human resource strategies, workplace dynamics, continuous learning and comprehension of increasingly digitalized environments. These issues call for interdisciplinary approaches at the intersection of management, information systems, organizational studies, and spatial design.

Track 2: Managing and Using living spaces

This track focuses on multi-stakeholder value creation and sharing throughout the spectrum of Public, Private actors, Partners, and People (the 4Ps) (Marana and al., 2018) implied in building management. It highlights management and governance models that involve property developers, Facility and Asset Managers, local authorities, and citizens or professionals at their workplace. This perspective opens discussion on decision support and contribution balance, especially when resource allocation, predictive maintenance or behavioural guidance are mediated by algorithms (Wei and al., 2025). Examples include simulation and AI for lifecycle assessment (Mahlan and al., 2024), augmented living environments, sustainability and energy optimisation or monitoring (Hwang and al., 2025). Ethical deliberation around AI usage thus requires interoperable and robust frameworks for data governance, transparency, and accountability. This includes addressing data ownership, privacy protection, cybersecurity vulnerabilities, or the mitigation of algorithmic bias. Addressing these issues seems to be a prerequisite for embedding a so-called “responsible AI” into the governance of the built environment, and constitutes a prerequisite for trust, resilience, and long-term value creation across stakeholders.

In this context, Augmented Living Environments also integrate DT simulations with AI-driven control architectures to achieve high-performance sustainability, comfort and energy efficiency. Building-scale simulations model thermodynamic behaviour, equipment performance, and stochastic occupancy to predict load profiles and evaluate control strategies under variable conditions. Real-time data acquisition through Internet of Things (IoT) sensor networks enables continuous monitoring of temperature, air quality, lighting, and power consumption at high temporal resolution. These data streams feed into Machine Learning pipelines that perform anomaly detection, predictive maintenance, and adaptive optimisation of devices, lighting, and distributed energy resources (Fährmann et al., 2024). Advanced control methods, such as model predictive control and reinforcement learning, dynamically adjust temperatures, ventilation, and shading to minimize energy consumption while maintaining thermal comfort and indoor air quality for a better quality of life. Integration with grid-interactive demand response further enhances flexibility by shifting energy loads and coordinating usage time. These capabilities create autonomous, self-optimising building ecosystems that reduce operational costs and carbon emissions while ensuring occupant well-being and system resilience (Guo et al., 2025).

But this massive digitalisation of the living and working spaces also gives rise to new challenges, balancing the protection of corporate information systems (cybersecurity, confidentiality, infrastructure resilience) with the preservation of users’ privacy, autonomy, and mental health (Asatiani & Norström, 2023). Research on workplace transformation (Bouchez, 2023), hybrid professional practices, living labs as participatory experimentation frameworks (Lehmann et al., 2015), and organizational innovation (Corbett-Etchevers, 2024) highlight these evolving dimensions. The emergence of algorithmic management in workplace and building monitoring (Jarrahi et al., 2021) also redefines organizational dynamics, well-being, and trust, while raising critical ethical questions about surveillance, autonomy, and fairness. Simultaneously, the reconfiguration of workplace models, from traditional offices to augmented living environments, opens new perspectives on inclusion, collaboration, and organizational resilience (Bergeaud et al., 2023). These works underscore the need to articulate building management and workplace design with broader reflections on the future of work, territorial ecosystems, digital governance, and the emergence of sustainable organizational models.

Track 3: Territorial ecosystems and dynamics

Smart Territories are increasingly defined by the complex movement patterns of residents, workers and families, whose daily activities generate diverse and dynamic flows across urban and regional spaces. These human mobilities are not only shaped by transportation networks but are also deeply influenced by digital infrastructures and the integration of realtime sensing and data technologies in the built environment (Batty, 2012; Sheller & Urry, 2006; Kitchin, 2014). The interactions between physical, social, and digital systems create new forms of connectivity and co-presence, which underpin both the opportunities and the challenges faced by contemporary Smart Cities (Batty, 2019; Sheller & Urry, 2006). Understanding and modeling these flows is thus essential for ensuring the adaptability, resilience, and inclusiveness of Smart Territories in the face of ongoing economic and societal transformations.

Human flows in Smart Territories also encompass tourism, which acts both as a driver of development and a potential source of disruption. As AI reshapes territorial systems, tourism must be understood as a dynamic ecosystem structured by mobility, infrastructures, and digital networks. AI-powered systems in smart buildings and territories now provide decision-makers with advanced tools to model and anticipate flows, manage environmental and urban pressures (Ivars-Baidal et al., 2021), and support the governance of visitor movements. Predictive analytics and real-time monitoring enhance the efficiency of mobility management and help prevent overtourism (Gretzel and Koo, 2021; Gretzel et al., 2015), particularly in sensitive ecosystems. Yet, these advances also raise questions about privacy, surveillance, and algorithmic bias Gong and Schroeder, 2022; Zuboff, 2023). At the same time, the rise of digital nomadism and platform-based economies is redefining the spatial and social organization of destinations, challenging existing urban balances and sustainability goals (Lacárcel, 2025).

Urban flows are not limited to people; they also encompass the diverse and dynamic circulation of materials, energy, water, waste, goods, and even non-human organisms throughout the city. The concept of urban metabolism (Wolman, 1965; Kennedy et al., 2007), offers a framework to analyze how cities process resources through interconnected flows and stocks, shaping both sustainability and resilience.

In these transformations of urban territories, AI is becoming a key driver, shaping how cities function, evolve, and connect with their communities. Besides technological optimization, it raises new questions about digital dependency, AI safety and security in these critical applications, as well as the transposability and tensions, in time and space, between the ethical values (freedom vs. surveillance, equity vs. justice, sustainability vs. individual choices...) of the communities sharing these territories, requiring a solid articulation with regulatory and policy mechanisms. Current research highlights both the importance and the prudence of integrating AI into the social fabric of urban life. This involves moving beyond top-down, data-driven models toward participatory approaches in which local communities play an active role in co-designing and co-governing digital services. Together with a widening range of CivilTechs, AI can become a tool for facilitating dialogue between institutions, citizens, and spatial infrastructures, translating technical capacities into forms of collective territorial intelligence. In this perspective, Smart Territories evolve not just through networks and algorithms, but also through the continuous negotiation between technological innovation, community practices, and governance processes. Such a shift reframes territories as dynamic, relational ecosystems where AI supports collaborative decision-making, strengthens social cohesion, and fosters more resilient and context-sensitive futures.

Bibliography

Asatiani, A., & Norström, L. (2023). Information systems for sustainable remote workplaces. The Journal of Strategic Information Systems, 32(3), 101789.

Bergeaud, A., Eyméoud, J. B., Garcia, T., & Henricot, D. (2023). Working from home and corporate real estate. Regional Science and Urban Economics, 99, 103878.

Bouchez J.-P., (2023), Le travail et ses espaces, Méthodes et recherches Management, De Boeck.

Corbett-Etchevers I., Carton S., Falcy S. & Farastier A., (2024) Communities of practice as hybrids: delving into the hybridization work of community leaders, European Management Journal, https://doi.org/10.1016/j.emj.2024.04.007.

Fährmann, D., Martín, L., Sánchez, L., and Damer, N., 2024. Anomaly Detection in Smart Environments: A Comprehensive Survey. IEEE Access, 12, 64006–64049.

Gong, Y., & Schroeder, A. (2022). A systematic literature review of data privacy and security research on smart tourism. Tourism Management Perspectives, 44, 101019. doi:https://doi.org/10.1016/j.tmp.2022.101019

Gretzel, U., & Koo, C. (2021). Smart tourism cities: a duality of place where technology supports the convergence of touristic and residential experiences. Asia Pacific Journal of Tourism Research, 26(4), 352-364. doi:https://doi.org/10.1080/10941665.2021.1897636 

Gretzel, U., Werthner, H., Koo, C., & Lamsfus, C. (2015). Conceptual foundations for understanding smart tourism ecosystems. CHB Computers in Human Behavior, 50, 558-563. https://doi.org/10.1016/j.chb.2015.03.043 

Guo, F., Ham, S., Kim, D., and Moon, H.J., 2025. Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building. Applied Energy, 377, 124467.

Hwang J et al. (2025), « DT-BEMS: Digital Twin-enabled building energy management system », Energy (Elsevier). https://doi.org/10.1016/j.energy.2025.136162  

Ivars-Baidal, J. A., Celdrán-Bernabeu, M. A., Femenia-Serra, F., Perles-Ribes, J. F., & Giner-Sánchez, D. (2021). Measuring the progress of smart destinations: The use of indicators as a management tool. Journal of Destination Marketing & Management, 19, 100531. doi:https://doi.org/10.1016/j.jdmm.2020.100531

Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021). Algorithmic management in a work context. Big data & society, 8(2), 20539517211020332.

Lacárcel, F. J. S. (2025). Digital technologies, sustainable lifestyle, and tourism: How digital nomads navigate global mobility? Sustainable Technology and Entrepreneurship, 4(2), 100096. doi:https://doi.org/10.1016/j.stae.2025.100096

Lee, D., Lee, S., Masoud, N., Krishnan, M.S., and Li, V.C., 2022. Digital twin-driven deep reinforcement learning for adaptive task allocation in robotic construction. Advanced Engineering Informatics, 53, 101710.

Lehmann, V., Frangioni M., Dubé P., (2015). "Living Lab as knowledge system: an actual approach for managing urban service projects?" Journal of Knowledge Management, Vol. 19, n°5, 1087–1107.

Mahlan, S., Francis, A., Thumuganti, V., Thomas, A., Sadick, A. M., & Tokede, O. (2024). An integrated life cycle assessment and energy simulation framework for residential building walling systems. Building and Environment, 257, 111542. 

Mihai, S., Yaqoob, M., Hung, D.V., Davis, W., Towakel, P., Raza, M., Karamanoglu, M., Barn, B., Shetve, D., Prasad, R.V., Venkataraman, H., Trestian, R., and Nguyen, H.X., 2022. Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects. IEEE Communications Surveys & Tutorials, 24(4), 2255–2291. 

Nechesov, A., Dorokhov, I., and Ruponen, J., 2025. Virtual Cities: From Digital Twins to Autonomous AI Societies. IEEE Access, 13, 13866–13903.

Wan, Li, Timea Nochta, Junqing Tang, Jennifer Schooling, Kwadwo Oti-Sarpong, and Viviana Angely Bastidas Melo. 2023. “Conceptualising City Digital Twins (CDTs).” In Digital Twins for Smart Cities: Conceptualisation, Challenges and Practices. ICE Publishing. 

Zuboff, S. (2023). The age of surveillance capitalism. In Social theory re-wired (pp. 203-213): Routledge. https://doi.org/10.4324/9781003320609-27