Climate Compatible Growth: Clean Energy Access Modelling
Geospatial, AI-driven least-cost modelling to identify how best to deliver clean energy to remote, low-access communities in Ghana and Zambia — and to chart energy-sector pathways within broader socio-economic development.
The challenge
Delivering clean energy to remote, sparsely populated and low-energy communities is one of the hardest problems in the energy transition. The Climate Compatible Growth programme — funded by the UK’s Foreign, Commonwealth & Development Office — sought to improve understanding of how best to reach these communities in Ghana and Zambia, and to frame energy-sector growth within wider socio-economic development.
Our approach
As modelling consultant, Dr Felix Amankwah Diawuo helped develop an artificial-intelligence tool to estimate electricity and clean-energy access, consumption levels and their determinants on a spatial basis.
An AI geo-spatial least-cost model integrated diverse datasets — the location of education and health facilities, existing generation and grid infrastructure, socio-economic data, renewable energy resources, agricultural land classifications, transport infrastructure, and water and protected areas — to identify least-cost routes to clean energy access.
Outcomes
The modelling produced spatially explicit insight into where and how clean energy can be delivered most cost-effectively, and presented pathways for developing the energy sector in step with socio-economic growth.
The approach demonstrates how data-driven, geospatial planning can target electrification investment where it delivers the greatest development impact.
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