A groundbreaking machine learning framework created by researchers at the International Institute for Applied Systems Analysis (IIASA) is revolutionizing how we estimate global rooftop area growth from 2020 to 2050. The innovative tool not only aids in planning sustainable energy systems and urban development but also plays a crucial role in mitigating climate change impacts, particularly in emerging economies.
Buildings worldwide accounted for 18% of total electricity consumption in 2019 and were responsible for emitting 21% of greenhouse gases into the atmosphere, making them a significant contributor to climate change. As the global population continues to expand, the demand for new buildings will increase, leading to higher electricity needs and more construction activities.
Understanding the growth of global rooftop area over the next three decades is vital for various reasons, such as installing solar panels for clean energy, developing cities, and analyzing environmental effects. With insights into rooftop area expansion, we can better plan for sustainable energy systems, enhance urban development practices, and minimize the environmental footprint of buildings.
The IIASA researchers harnessed a vast amount of data, including building footprints, global land cover information, and global population statistics, to develop their machine learning framework. Published in the journal Scientific Data, the framework offers predictions on rooftop area growth from 2020 to 2050 across five different scenarios, covering approximately 3.5 million small areas worldwide.
In 2020, the total global rooftop area was estimated at 0.25 million square kilometers, out of a total human-made built-up surface area of 1.46 million square kilometers. Asia led the pack with 0.12 million square kilometers, followed by Europe, North America, and Africa. By 2050, the global rooftop area is projected to increase to between 0.3 and 0.38 million square kilometers, marking a 20-52% rise from 2020 levels. Africa is expected to experience the most significant growth, potentially doubling its rooftop area.
The IIASA team’s work provides a detailed global outlook on rooftop area growth under different socioeconomic scenarios, showcasing how large datasets and machine learning technologies can support sustainable development and climate action. One key takeaway is the vast potential of rooftop solar power for emerging economies. With accelerated rooftop area expansion, these regions can utilize their manufacturing capabilities, abundant solar resources, cost-effective labor, and innovative spirit to achieve sustainable growth and prosperity.
Lead author Siddharth Joshi, a research scholar in the IIASA Energy, Climate, and Environment Program, underscores the research’s policy and public implications. Their dataset facilitates more precise planning of decentralized solar energy systems, promoting sustainable energy solutions and aligning with global goals for clean energy, sustainable cities, climate action, and biodiversity conservation.
Joshi, recipient of the Mikhalevich Award, initiated the framework development during the 2021 IIASA Young Scientists Summer Program, highlighting the significant contribution of young researchers in advancing climate-related research and sustainable solutions.
Overall, the IIASA’s machine learning framework offers a comprehensive view of global rooftop area growth, emphasizing the transformative potential of rooftop solar technology in driving climate policies, particularly in emerging economies. By leveraging technological innovation and robust data analysis, we can pave the way for a greener, more sustainable future for all.
