Affiliation:
1. University of Reading, GB
Abstract
Digital twins have attracted much of the attention from the researchers and policy makers as a potent industry-agnostic concept to support ambitious decarbonization goals. Consequently, much of the latest research has focused on computational methods for building and connecting digital twins to monitor and measure energy consumption and resulting emissions from buildings. At the same time, it has been recognized that achieving a truly sustainable built environment goes beyond environmental sustainability and is much more complex, calling for approaches that transcend any single discipline. Initiatives such as the National Digital Twin in the UK and globally, begin to offer a long-term vision of interconnected, purpose-driven and outcome-focused digital twins, grounded in systems thinking. Such approaches recognize the economic, social and ecological layers as critical data components in these digital ecosystems for understanding the built environment as a whole. Yet, social and ecological sustainability will remain difficult to address without involving allied disciplines and those from the realms of sociology, ecology, or anthropology in a conversation about the critical data sitting at the intersections between human behavior and technological innovation. In this paper, we review and discuss the state of the art research on digital twins to identify the disciplines dominating the narrative in the context of a sustainable built environment. We unpack a techno-rationalist view that emphasizes the sole reliance on technology for problem-solving and argue that by going beyond energy consumption and carbon emissions, digital twins can facilitate a more nuanced assessment of sustainability challenges, encompassing social equity, cultural preservation, and ecological resilience
Reference41 articles.
1. Allam, Z., & Dhunny, Z. A. (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80–91.
2. Azar, C., Holmberg, J., & Lindgren, K. (1996). Socio-ecological indicators for sustainability. Ecological Economics, 18(2), 89–112.
3. Batty, M. (2018). Artificial intelligence and smart cities. Environment and Planning B: Urban Analytics and City Science, 45(1), 3–6.
4. Bonci, A., Carbonari, A., Cucchiarelli, A., Messi, L., Pirani, M., & Vaccarini, M. (2019). A cyber-physical system approach for building efficiency monitoring. Automation in Construction, 102, 68–85.
5. CDBB. (2019). National Digital Twin Programme. https://www.cdbb.cam.ac.uk/what-we-did/national-digital-twin-programme