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How to Build a Standardized Country-Specific Environmental Food Database for Nutritional Epidemiology Studies

There is a lack of standardized country-specific environmental data to combine with nutritional and dietary data for assessing the environmental impact of individual diets in epidemiology surveys, which are consequently reliant on environmental food datasets based on values retrieved from a heteroge...

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Autores principales: Bertoluci, Gwenola, Masset, Gabriel, Gomy, Catherine, Mottet, Julien, Darmon, Nicole
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4824438/
https://www.ncbi.nlm.nih.gov/pubmed/27054565
http://dx.doi.org/10.1371/journal.pone.0150617
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author Bertoluci, Gwenola
Masset, Gabriel
Gomy, Catherine
Mottet, Julien
Darmon, Nicole
author_facet Bertoluci, Gwenola
Masset, Gabriel
Gomy, Catherine
Mottet, Julien
Darmon, Nicole
author_sort Bertoluci, Gwenola
collection PubMed
description There is a lack of standardized country-specific environmental data to combine with nutritional and dietary data for assessing the environmental impact of individual diets in epidemiology surveys, which are consequently reliant on environmental food datasets based on values retrieved from a heterogeneous literature. The aim of this study was to compare and assess the relative strengths and limits of a database of food greenhouse gas emissions (GHGE) values estimated with a hybrid method combining input/output and LCA approaches, with a dataset of GHGE values retrieved from the literature. France is the geographical perimeter considered in this study, but the methodology could be applied to other countries. The GHGE of 402 foodstuffs, representative of French diet, were estimated using the hybrid method. In parallel, the GHGE of individual foods were collected from existing literature. Median per-food-category GHGE values from the hybrid method and the reviewed literature were found to correlate strongly (Spearman correlation was 0.83), showing similar rankings of food categories. Median values were significantly different for only 5 (out of 29) food categories, including the ruminant meats category for which the hybrid method gave lower estimates than those from existing literature. Analysis also revealed that literature values came from heterogeneous studies that were not always sourced and that were conducted under different LCA modeling hypotheses. In contrast, the hybrid method helps build reliably-sourced, representative national standards for product-based datasets. We anticipate this hybrid method to be a starting point for better environmental impact assessments of diets.
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spelling pubmed-48244382016-04-22 How to Build a Standardized Country-Specific Environmental Food Database for Nutritional Epidemiology Studies Bertoluci, Gwenola Masset, Gabriel Gomy, Catherine Mottet, Julien Darmon, Nicole PLoS One Research Article There is a lack of standardized country-specific environmental data to combine with nutritional and dietary data for assessing the environmental impact of individual diets in epidemiology surveys, which are consequently reliant on environmental food datasets based on values retrieved from a heterogeneous literature. The aim of this study was to compare and assess the relative strengths and limits of a database of food greenhouse gas emissions (GHGE) values estimated with a hybrid method combining input/output and LCA approaches, with a dataset of GHGE values retrieved from the literature. France is the geographical perimeter considered in this study, but the methodology could be applied to other countries. The GHGE of 402 foodstuffs, representative of French diet, were estimated using the hybrid method. In parallel, the GHGE of individual foods were collected from existing literature. Median per-food-category GHGE values from the hybrid method and the reviewed literature were found to correlate strongly (Spearman correlation was 0.83), showing similar rankings of food categories. Median values were significantly different for only 5 (out of 29) food categories, including the ruminant meats category for which the hybrid method gave lower estimates than those from existing literature. Analysis also revealed that literature values came from heterogeneous studies that were not always sourced and that were conducted under different LCA modeling hypotheses. In contrast, the hybrid method helps build reliably-sourced, representative national standards for product-based datasets. We anticipate this hybrid method to be a starting point for better environmental impact assessments of diets. Public Library of Science 2016-04-07 /pmc/articles/PMC4824438/ /pubmed/27054565 http://dx.doi.org/10.1371/journal.pone.0150617 Text en © 2016 Bertoluci et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Bertoluci, Gwenola
Masset, Gabriel
Gomy, Catherine
Mottet, Julien
Darmon, Nicole
How to Build a Standardized Country-Specific Environmental Food Database for Nutritional Epidemiology Studies
title How to Build a Standardized Country-Specific Environmental Food Database for Nutritional Epidemiology Studies
title_full How to Build a Standardized Country-Specific Environmental Food Database for Nutritional Epidemiology Studies
title_fullStr How to Build a Standardized Country-Specific Environmental Food Database for Nutritional Epidemiology Studies
title_full_unstemmed How to Build a Standardized Country-Specific Environmental Food Database for Nutritional Epidemiology Studies
title_short How to Build a Standardized Country-Specific Environmental Food Database for Nutritional Epidemiology Studies
title_sort how to build a standardized country-specific environmental food database for nutritional epidemiology studies
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4824438/
https://www.ncbi.nlm.nih.gov/pubmed/27054565
http://dx.doi.org/10.1371/journal.pone.0150617
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