| id |
caadria2024_30 |
| authors |
Alva, Pradeep, Mosteiro-Romero, Martín and Stouffs, Rudi |
| year |
2024 |
| title |
Estimating Operational Greenhouse Gas Emissions in the Built Environment Using an Urban Digital Twin: Sustainable City Management Tool for Decarbonisation of Cities |
| doi |
https://doi.org/10.52842/conf.caadria.2024.2.365
|
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 365–374 |
| summary |
With climate change strategies and action plan policies, many countries pledge to reduce greenhouse gas (GHG) emissions in their long-term vision for efficient urban processes and operations. An Urban Digital Twin (UDT) integrates multiple disciplines on a digital platform and assists with city management. However, UDTs that explore GHG emissions-related policy development or decarbonisation initiatives for cities are limited. To support decarbonisation policies, smart cities require UDTs with state-of-the-art control and management systems that demonstrate emissions accounting and administration. In response to these concerns, our paper introduces a web-based UDT application dedicated to estimating and managing GHG emissions in the built environment using a 3D city dataset created from open data sources. The 3D city dataset is combined with energy modelling results to calculate buildings’ operational GHG emissions. Forecasting is proposed to estimate energy use and GHG emissions along with alternative scenarios for the future. Additionally, we describe how we calculate energy demand and GHG emissions. We introduce user input parameters in the interactive dashboard to generate alternative scenario outputs different from the business-as-usual state. As a result, the UDT dashboard can assist decision-makers and stakeholders involved in carbon-neutral strategies, GHG emission reduction, and policy development. |
| keywords |
Decarbonisation of cities, Energy demand forecasting, City dataset, Urban analytics |
| series |
CAADRIA |
| email |
akipaa@nus.edu.sg |
| full text |
 file.pdf (839,082 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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