Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

Climate models have been key for assessing the impact of climate change and simulating
future climate scenarios. The machine learning (ML) community has taken an increased …

As Earth-observing satellites become more plentiful and climate models more powerful,
researchers who study global warming are facing a deluge of data. Some are now turning to …

The special issue on “Machine Learning for Science and Society” showcases machine
learning work with influence on our current and future society. These papers address …

The part that artificial intelligence plays in climate change has come under scrutiny,
including from tech workers themselves who joined the global climate strike last year. Much …

Accurate reporting of energy and carbon usage is essential for understanding the potential
climate impacts of machine learning research. We introduce a framework that makes this …

Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …

The rapid increase in both the quantity and complexity of data that are being generated daily
in the field of environmental science and engineering (ESE) demands accompanied …

Machine learning (ML) workloads have rapidly grown, raising concerns about their carbon
footprint. We show four best practices to reduce ML training energy and carbon dioxide …

From an environmental standpoint, there are a few crucial aspects of training a neural
network that have a major impact on the quantity of carbon that it emits. These factors …