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 …

The challenges for sustainable cities to protect the environment, ensure economic growth,
and maintain social justice have been widely recognized. Along with the digitization …

Abstract Machine learning (ML) is generating new opportunities for innovative research in
energy economics and finance. We critically review the burgeoning literature dedicated to …

In an era of US energy abundance, the persistently high energy bills paid by low-income
households is troubling. After decades of weatherization and bill-payment programs, low …

Artificial intelligence and machine learning are transforming scientific disciplines, but their
full potential for climate change mitigation remains elusive. Here, we conduct a systematic …

Buildings contribute 40% of global greenhouse gas emissions; therefore, strategies that can
substantially reduce emissions from the building stock are key components of broader efforts …

Machine Learning (ML), a subset of Artificial Intelligence (AI), is gaining popularity in the
architectural, engineering, and construction (AEC) sector. This systematic study aims to …

Building energy forecasting methodologies utilized by municipal governments tend to be
geared heavily towards depicting broader qualitative representations of regional change …

The identification of factors that influence household carbon emissions (HCEs)—a key driver
of the national emissions, is an important step in achieving more accurate predictions, as …

Global warming has become a challenge and reducing carbon emissions is an urgent task.
Household energy consumption and carbon emissions are substantial and need to be …