Hybrid AI and multiscale physical modelling for optimal urban decarbonisation combating climate change

The challenges articulated in this proposal are how to (1) accurately assess carbon emissions in urban areas; (2) help design and manage cities so that the carbon footprint is reduced; and (3) quantify the impact of urban carbon emissions on global climate change-towards the 1.5 degree climate goal.
Greenhouse gas emission reduction is key to tackling global warming. In 2020, 24% of net greenhouse gas emissions in the UK were estimated to be from the transport sector, 21% from energy supply, 18% from business, 16% from the residential sector and 11% from agriculture [1]. Accurate assessment of urban carbon emissions will help policy makers in their decision-making processes and managers of public and private spaces to optimise energy use, carbon reduction and economic benefit. Models are powerful tools in understanding carbon life cycle and atmospheric processes, making predictions, uncertainty quantification and optimal control/design for decarbonisation. However, integrated assessment of the environment and human development is arguably the most difficult and important "systems" problem faced [2]. The complex carbon cycle and atmospheric physical processes act over a wide range of spatial (from meters to degrees) and temporal (from hours, days to decades) scales. Currently, there is no integrated modelling across neighbourhood, city and global scales which can be used for exploring the complex relationship between carbon emissions associated with human activities and global climate change.
Here I aim to develop a hybrid AI (Artificial Intelligence)-multiscale physics-informed optimal management framework for accurate assessment and mitigation of CO2 in urban areas. Effective carbon assessment and management necessitate the implementation of multiscale carbon models that can capture adequate spatial and temporal variability of urban carbon emissions & dispersion patterns. Current models are either excessively computationally expensive, or fail to capture the detailed variability of such problems. The proposed work will advance the status of science by developing an advanced multiscale carbon model (based on our recently developed Fluidity-Urban model) where, the use of dynamically adapted meshes enables us to resolve complex urban turbulent flows and carbon dispersion processes. The effect of city infrastructures on carbon dispersion processes is considered at different scales. AI-based modelling will then be used for the optimal design of urban infrastructures and layout for mitigation of carbon emissions. Energy efficiency and carbon-based energy usage in cities are measured based on detailed datasets of existing infrastructures in the selected city-London. The modelling framework will include new carbon parameterisation schemes for urban infrastructures/layout, enabling more accurate assessment of urban carbon emissions, and their impact on climate change. Potential improvements to existing urban infrastructures, and optimal designs for new urban developments will be provided through the AI-based optimal control tool proposed here for carbon reduction and energy efficiency. Finally, an AI-based GHG parameterisation module will be developed for coupling the calculated CO2 fluxes at high resolution grids with existing Earth System modelling. The impact of carbon emissions in cities on global climate can then be evaluated accurately based on existing and improved city infrastructure and layouts.
This innovative framework will allow the critical assessment of existing and new policy options on decarbonisation to be carried out, thus improving local and global climate. The tool could potentially change the way in which city infrastructure design, GI and BI for decarbonisation are used in our future cities and pave the way for accurate quantification of the impact of urban carbon emissions on global warming.
[1] BEIS, N.. 2020 UK Greenhouse Gas Emissions, Final Figures.
[2] Navarro et al. 2018. Earth Syst. Dynam., 9, 1045