Embed systemic equity throughout industrial ecology applications: How to address machine learning unfairness and bias.

Recent calls have been made for equity tools and frameworks to be integrated throughout the research and design life cycle -from conception to implementation-with an emphasis on reducing inequity in artificial intelligence (AI) and machine learning (ML) applications. Simply stating that equity should be integrated throughout, however, leaves much to be desired as industrial ecology (IE) researchers, practitioners, and decision-makers attempt to employ equitable practices. In this forum piece, we use a critical review approach to explain how socioecological inequities emerge in ML applications across their life cycle stages by leveraging the food system.

A multi-scale lifecycle and technoeconomic framework for higher education fleet electrification.

Transportation accounts for one-quarter of all energy related greenhouse gas emissions. As it pertains to transport electrification, higher education institutions-such as universities-can model solutions that affect broader society. Despite this, higher education's role in fleet electrification adoption has been understudied.

A Path Toward Systemic Equity in Life Cycle Assessment and Decision-Making: Standardizing Sociodemographic Data Practices.

Social equity has been a concept of interest for many years, gaining increased focus from energy and environmental communities. The equitable development, collection, and reporting of sociodemographic data (e.g.

Meeting EAT-Lancet Food Consumption, Nutritional, and Environmental Health Standards: A U.S. Case Study across Racial and Ethnic Subgroups.

In 2019, The EAT-Lancet Commission developed criteria to assist policymakers and health care systems worldwide in sustaining natural resources to feed a forecasted 10 billion people through the year 2050. Although American dietary habits and underlying food production practices have a disproportionately negative impact on land, greenhouse gas (GHG), and water resources, there is limited information on how this population can meet the EAT-Lancet criteria. To address this, we measured adherence to an adapted version of the EAT-Lancet diet score criteria in United States (U.