A Framework for Scalability and Intelligent Integration in Multi-Level Smart Spatial Planning

TY - CONF

T1 - A Framework for Scalability and Intelligent Integration in Multi-Level Smart Spatial Planning

AU - Forghaniallahabadi, Maryam

PY - 2024/11/7

Y1 - 2024/11/7

N2 - This research introduces a novel framework for understanding scalability within smart spatial planning, focusing on how smart technologies, such as smart buildings and energy systems, are implemented across local, regional, and national scales. Current approaches often suffer from fragmentation, with little coherence between scales. By conceptualizing scale as a dynamic interaction, this study redefines scalability, illustrating how smart technologies' meaning, effectiveness, and metrics shift as they move across spatial levels. Using a multi-method approach, the research draws on case studies from the UK and US while also modeling Glasgow's smart buildings and energy systems using Grasshopper Python. This innovative methodology allows for visualizing how technologies are adapted and scaled, showing how local smart building projects can either support or conflict with larger sustainability goals. One key insight is that while local projects may prioritize immediate benefits like energy efficiency, regional and national initiatives must align these technologies with broader targets, such as reducing carbon emissions. Grounded in Semantic Consistency Theory, Scale Theory, and Policy Implementation Theory, the research emphasizes consistency in how smart technologies are applied across scales. The findings reveal that scalability requires more than just replicating technologies; it involves adapting and aligning them with each level's specific challenges and policies. This study contributes to the discourse on multi-scalar governance in smart spatial planning by introducing new thinking on how scale expands and contracts. It offers policymakers and planners a practical framework for ensuring coherence, scalability, and broader impact across all levels of governance, thereby informing their decision-making processes.

AB - This research introduces a novel framework for understanding scalability within smart spatial planning, focusing on how smart technologies, such as smart buildings and energy systems, are implemented across local, regional, and national scales. Current approaches often suffer from fragmentation, with little coherence between scales. By conceptualizing scale as a dynamic interaction, this study redefines scalability, illustrating how smart technologies' meaning, effectiveness, and metrics shift as they move across spatial levels. Using a multi-method approach, the research draws on case studies from the UK and US while also modeling Glasgow's smart buildings and energy systems using Grasshopper Python. This innovative methodology allows for visualizing how technologies are adapted and scaled, showing how local smart building projects can either support or conflict with larger sustainability goals. One key insight is that while local projects may prioritize immediate benefits like energy efficiency, regional and national initiatives must align these technologies with broader targets, such as reducing carbon emissions. Grounded in Semantic Consistency Theory, Scale Theory, and Policy Implementation Theory, the research emphasizes consistency in how smart technologies are applied across scales. The findings reveal that scalability requires more than just replicating technologies; it involves adapting and aligning them with each level's specific challenges and policies. This study contributes to the discourse on multi-scalar governance in smart spatial planning by introducing new thinking on how scale expands and contracts. It offers policymakers and planners a practical framework for ensuring coherence, scalability, and broader impact across all levels of governance, thereby informing their decision-making processes.

KW - Scalability

KW - Multi-Level Smart Spatial Planning

KW - Scale Theory

KW - Urban Sustainability

UR - https://virtual.oxfordabstracts.com/event/public/4582/program?session=115891&s=3000

UR - https://www.elsevier.com/events/conferences/all/urban-transitions/previous-events

U2 - 10.20933/100001474

DO - 10.20933/100001474

M3 - Poster

T2 - Urban Transitions 2024

Y2 - 5 November 2024 through 7 November 2024

ER -