Study Examines Potential Use of Machine Learning for Sustainable Development of Biomass

Published on March 13, 2023

A new study led by Yale School of the Environment’s Yuan Yao, assistant professor of industrial ecology and sustainable systems, and doctoral student Hannah Szu-Han Wang, analyzed current machine learning applications for biomass and biomass-derived materials (BDM) to determine if machine learning is advancing the research and development of biomass products. The study authors found that machine learning has not been applied across the entire life cycle of BDM, limiting its ability for development.

Other news

Timber House: Can Mass Timber Help Decarbonize Real Estate?

March 13, 2023
Real estate is the source of around 40% of global greenhouse gas emissions, including 11% that come just from making building materials. Mass timber panels, which consist of small pieces of softwood glued together, offer a lower-emissions alternative to steel and cement, as well storing carbon sequestered by trees. Driven by climate concerns, mass timber technology has landed in the United States'

Evaluating the Terms Americans Use to Refer to “Carbon Emissions”

March 13, 2023
The effectiveness of climate change communication depends in part on how people perceive common terms used to describe key climate concepts. In a mixed methods study (N = 2859), we examined affect, top-of-mind associations, beliefs, policy support, and behavioral intentions elicited by terms communicators colloquially use to refer to the gases responsible for climate change: greenhouse gas emissio