Global high-resolution growth projections dataset for rooftop area consistent with the shared socioeconomic pathways, 2020-2050.

Assessment of current and future growth in the global rooftop area is important for understanding and planning for a robust and sustainable decentralised energy system. These estimates are also important for urban planning studies and designing sustainable cities thereby forwarding the ethos of the Sustainable Development Goals 7 (clean energy), 11 (sustainable cities), 13 (climate action) and 15 (life on land). Here, we develop a machine learning framework that trains on big data containing ~700 million open-source building footprints, global land cover, road, and population datasets to generate globally harmonised estimates of growth in rooftop area for five different future growth narratives covered by Shared Socioeconomic Pathways.

RASMI: Global ranges of building material intensities differentiated by region, structure, and function.

The construction materials used in buildings have large and growing implications for global material flows and emissions. Material Intensity (MI) is a metric that measures the mass of construction materials per unit of a building's floor area. MIs are used to model buildings' materials and assess their resource use and environmental performance, critical to global climate commitments.

A Framework for Modelling Consumption-Based Energy Demand and Emission Pathways.

Energy demand in global climate scenarios is typically derived for sectors - such as buildings, transportation, and industry - rather than from underlying services that could drive energy use in all sectors. This limits the potential to model household consumption and lifestyles as mitigation options through their impact on economy-wide energy demand. We present a framework to estimate the economy-wide energy requirements and carbon emissions associated with future household consumption, by linking Industrial Ecology tools and Integrated Assessment Models (IAM).