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). We apply the framework to assess final energy and emission pathways for meeting three essential and energy-intensive dimensions of basic well-being in India: food, housing and mobility. We show, for example, that nutrition-enhancing dietary changes can reduce emissions by a similar amount as meeting future basic mobility in Indian cities with public transportation. The relative impact of energy demand reduction measures compared to decarbonization differs across these services, with housing having the lowest and food the highest. This framework provides complementary insights to those obtained from IAM by considering a broader set of consumption and well-being-related interventions, and illustrating trade-offs between demand and supply-side options in climate stabilization scenarios.

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. However, MI data availability and quality are inconsistent, incomparable, and limited, especially for regions in the Global South. To address these challenges, we present the Regional Assessment of buildings' Material Intensities (RASMI), a new dataset and accompanying method of comprehensive and consistent representative MI value ranges that embody the variability inherent in buildings. RASMI consists of 3072 MI ranges for 8 construction materials in 12 building structure and function types across 32 regions covering the entire world. The dataset is reproducible, traceable, and updatable, using synthetic data when required. It can be used for estimating historical and future material flows and emissions, assessing demolition waste and at-risk stocks, and evaluating urban mining potentials.

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. The dataset provides estimates for ~3.5 million fishnet tiles of 1/8 degree spatial resolution with data on gross rooftop area for five growth narratives covering years 2020-2050 in decadal time steps. This single harmonised global dataset can be used for climate change, energy transition, biodiversity, urban planning, and disaster risk management studies covering continental to conurbation geospatial levels.

Social media data shed light on air-conditioning interest of heat-vulnerable regions and sociodemographic groups.

Cooling homes with air conditioners is a vital adaptation approach, but the wider adoption of air conditioners can increase hydrofluorocarbon emissions that have high global warming potential and carbon emissions as a result of more fossil energy consumption. The scale and scope of future cooling demand worldwide are, however, uncertain because the extent and drivers of air-conditioning adoption remain unclear. Here, using 2021 and 2022 Facebook and Instagram data from 113 countries, we investigate the usability of social media advertising data to address these data gaps in relation to the drivers of air-conditioning adoption. We find that social media data might represent air-conditioning purchasing trends. Globally, parents of small children and middle-aged, highly educated married or cohabiting males tend to express greater interest in air-conditioning adoption. In regions with high heat vulnerability yet little empirical data on cooling demand (e.g., the Middle East and North Africa), these sociodemographic factors play a more prominent role. These findings can strengthen our understanding of future cooling demand for more sustainable cooling management.