A team of researchers at the International Institute for Applied Systems Analysis (IIASA) has developed an innovative machine-learning framework to estimate global rooftop area growth from 2020 to 2050.
This groundbreaking tool has significant implications for sustainable energy planning, urban development, and climate change mitigation efforts worldwide.
The framework, which utilises big data from approximately 700 million building footprints, global land cover, road networks, and population information, provides estimates for about 3.5 million small areas across the globe.
This high-resolution data set offers valuable insights into potential rooftop area growth under five different future scenarios.
Key findings from the research include:
- In 2020, the total global rooftop area was estimated at 0.25 million square kilometres, with Asia having the largest share at 0.12 million square kilometres.
- By 2050, the global rooftop area is projected to increase to between 0.3 and 0.38 million square kilometres, representing a 20-52 per cent growth from 2020 levels.
- Africa is expected to see the highest growth, potentially doubling its rooftop area by 2050.
The implications of this research are far-reaching, particularly for emerging economies.
With rapid rooftop area growth projected, these regions have the opportunity to leverage their manufacturing capabilities, high solar potential, cost-effective labour, and entrepreneurial spirit to achieve sustainable development and prosperity.
Lead author Siddharth Joshi, a research scholar in the Integrated Assessment and Climate Change Research Group at IIASA, emphasised the significance of this work for policy-making and public awareness.
“Our dataset can aid in more realistic planning of decentralised solar energy systems, promoting sustainable energy solutions.
“It can also help make climate policies more effective and affordable, aligning with the Sustainable Development Goals for clean energy, sustainable cities, climate action, and life on land,” Joshi stated.
This research represents a significant step forward in utilising large geospatial datasets and machine learning to support sustainable development and climate action.
By providing a comprehensive view of global rooftop growth, it enables more informed decision-making in urban planning, energy systems, and environmental policy.
The full dataset is now available to researchers and policymakers, offering a valuable resource for future studies and planning efforts in sustainable development and climate change mitigation.