RESUMEN
The dataset contains â¼1.1 million records of total greenhouse gases directly emitted annually by economic sectors and households in the US from 2012-2020. Data are given for 16 unique greenhouse gases by 118 aggregate sectors for each state, and as totals by these aggregate sectors as well as by 540 detailed sectors at the national level. The dataset is a product of updated sector attribution models that improve upon the National Greenhouse Gas Industry Attribution Model. This paper provides documentation of the methods used to produce these datasets and proof of validation of the dataset, along with relevant supporting tables, figures, and source code.
RESUMEN
USEEIO v2.0 is an environmental-economic model of US goods and services that can be used for life cycle assessment, footprinting, national prioritization, and related applications. This paper describes the development of the model and accompanies the release of a full model dataset as well as various supporting datasets of national environmental totals by US industry. Novel methodological elements since USEEIO v1 models include waste sector disaggregation, final demand vectors for US consumption and production, a domestic form of the model that can be used to separate domestic and foreign impacts, and price adjustment matrices for converting outputs to purchaser price and in various US dollar years. Improvements in modeling national totals of industry and environmental flows are described. The model is validated through reproduction of national totals from input data sources and through analysis of changes from the most recent complete USEEIO model that can be explained based on data updates or method changes. The model datasets can all be reproduced with open source software packages.
RESUMEN
useeior is an open-source R package that builds USEEIO models, a family of environmentally-extended input-output models of US goods and services used for life cycle assessment, environmental footprint estimation, and related applications. USEEIO models have gained a wide user base since their initial release in 2017, but users were often challenged to prepare required input data and undergo a complicated model building approach. To address these challenges, useeior was created. In useeior, economic and environmental data are conveniently retrievable for immediate use. Users can build models simply from given or user-specified model configuration and optional hybridization specifications. The assembly of economic and environmental data and matrix calculations are automatically performed. Users can export model results to desired formats. useeior is a core component of the USEEIO modeling framework. It improves transparency, efficiency, and flexibility in building USEEIO models, and was used to deliver the recent USEEIO model.
RESUMEN
Quantifying industry consumption or production of resources, wastes, emissions, and losses-collectively called flows-is a complex and evolving process. The attribution of flows to industries often requires allocating multiple data sources that span spatial and temporal scopes and contain varied levels of aggregation. Once calculated, datasets can quickly become outdated with new releases of source data. The US Environmental Protection Agency (USEPA) developed the open-source Flow Sector Attribution (FLOWSA) Python package to address the challenges surrounding attributing flows to US industrial and final-use sectors. Models capture flows drawn from or released to the environment by sectors, as well as flow transfers between sectors. Data on flow use and generation by source-defined activities are imported from providers and transformed into standardized tables but are otherwise numerically unchanged in preparation for modeling. FLOWSA sector attribution models allocate primary data sources to industries using secondary data sources and file mapping activities to sectors. Users can modify methodological, spatial, and temporal parameters to explore and compare the impact of sector attribution methodological changes on model results. The standardized data outputs from these models are used as the environmental data inputs into the latest version of USEPA's US Environmentally Extended Input-Output (USEEIO) models, life cycle models of US goods and services for ~400 categories. This communication demonstrates FLOWSA's capability by describing how to build models and providing select model results for US industry use of water, land, and employment. FLOWSA is available on GitHub, and many of the data outputs are available on the USEPA's Data Commons.
RESUMEN
Reducing food loss and waste (FLW) is critical for achieving healthy diets from sustainable food systems. Within the United States, 30% to 50% of food produced is lost or wasted. These losses occur throughout multiple stages of the food supply chain from production to consumption. Reducing FLW prevents the waste of land, water, energy, and other resources embedded in food and is therefore essential to improving the sustainability of food systems. Despite the increasing number of studies identifying FLW reduction as a societal imperative, we lack the information needed to assess fully the effectiveness of interventions along the supply chain. In this paper, we synthesize the available literature, data, and methods for estimating the volume of FLW and assessing the full environmental and economic effects of interventions to prevent or reduce FLW in the United States. We describe potential FLW interventions in detail, including policy changes, technological solutions, and changes in practices and behaviors at all stages of the food system from farms to consumers and approaches to conducting economic analyses of the effects of interventions. In summary, this paper comprehensively reviews available information on the causes and consequences of FLW in the United States and lays the groundwork for prioritizing FLW interventions to benefit the environment and stakeholders in the food system.