Buildings account for 30% of global final energy consumption and 26% of global energy-related emissions, thereby contributing significantly to climate change. As the world’s population continues to grow, we will need more buildings, which will in turn increase demand for both energy and construction materials.

Global rooftop area refers to the total gross surface area of all the roofs on buildings around the world. This measurement is important for various purposes, such as installing roof-mounted solar panels for clean energy, planning cities, and studying environmental impacts. By understanding the global rooftop area and its growth in the next 30 years, we can better plan for sustainable energy systems, improve urban development, and reduce the impacts of buildings on issues such as climate change and biodiversity loss.

To help with this, an international team of researchers has developed a machine learning framework that uses big data from about 700 million building footprints, global land cover, as well as global road, and population information. Their framework, which has since been published in the journal Scientific Data, provides estimates of rooftop area growth from 2020 to 2050 under five different future scenarios. The data covers approximately 3.5 million small areas worldwide.

The team’s work provides the first high-resolution global estimate of rooftop area growth based on different socioeconomic pathway narratives and demonstrates how large geospatial datasets and machine learning can support sustainable development and climate action. The key takeaway is that rooftop solar power holds significant potential for emerging economies. With rapid rooftop area growth, these regions can leverage their manufacturing capabilities, high solar potential, cost-effective labor, and entrepreneurial spirit to achieve sustainable development and prosperity.

Behnam Zakeri, Assistant Professor at WU’s Institute for Data, Energy, and Sustainability (IDEaS) and IIASA-affiliated Senior Research Scholar and co-author adds “This work is a great example to show the power of artificial intelligence (AI) and emerging sources of data to potentially resolve global sustainability challenges facing human. This was not possible a few years ago or would be very costly using alternative methods. We at WU’s IDEaS continuously work on exploring such applications of data science in energy and climate-related research.”

“The implications of this research for policy and the public are significant. Our dataset can aid in more realistic planning of decentralized solar energy systems, thereby promoting sustainable energy solutions. Estimating the potential of rooftop solar technology in achieving climate policies, especially in emerging economies, can help these policies be more effective and affordable, in line with the Sustainable Development Goals for clean energy, sustainable cities, climate action, and life on land,” concludes lead author Siddharth Joshi, a research scholar in the Integrated Assessment and Climate Change Research Group of the IIASA Energy, Climate, and Environment Program.

Joshi started work on the conceptualization, development, and analysis of the framework while participating in the 2021 IIASA Young Scientists Summer Program and received the Mikhalevich Award for this work. The contribution of Behnam Zakeri, who supervised this work, was in part supported by the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation, and Technology (BMK) under the endowed professorship for "Data-Driven Knowledge Generation: Climate Action".

Details of the study and further information

Joshi, S., Zakeri, B., Mittal, S., Mastrucci, A., Holloway, P., Krey, V., Ramprasad Shukla, P., O’Gallachoir, B., & Glynn, J. (2024). Global high-resolution growth projections dataset for rooftop area consistent with the shared socioeconomic pathways, 2020–2050. Scientific Data 
Link to the study

The full dataset is available at: https://zenodo.org/records/11085013

This text is based on the original published on the website of IIASA.