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A method for calculating a land‐use change carbon footprint (LUC‐CFP) for agricultural commodities – applications to Brazilian beef and soy, Indonesian palm oil

Overview of attention for article published in Global Change Biology, June 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

Mentioned by

blogs
1 blog
policy
4 policy sources
twitter
5 X users

Citations

dimensions_citation
60 Dimensions

Readers on

mendeley
233 Mendeley
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Title
A method for calculating a land‐use change carbon footprint (LUC‐CFP) for agricultural commodities – applications to Brazilian beef and soy, Indonesian palm oil
Published in
Global Change Biology, June 2014
DOI 10.1111/gcb.12635
Pubmed ID
Authors

U. Martin Persson, Sabine Henders, Christel Cederberg

Abstract

The world's agricultural system has come under increasing scrutiny recently as an important driver of global climate change, creating a demand for indicators that estimate the climatic impacts of agricultural commodities. Such carbon footprints, however, have in most cases excluded emissions from land-use change and the proposed methodologies for including this significant emissions source suffer from different shortcomings. Here, we propose a new methodology for calculating land-use change carbon footprints for agricultural commodities and illustrate this methodology by applying it to three of the most prominent agricultural commodities driving tropical deforestation: Brazilian beef and soybeans, and Indonesian palm oil. We estimate land-use change carbon footprints in 2010 to be 66 tCO2 /t meat (carcass weight) for Brazilian beef, 0.89 tCO2 /t for Brazilian soybeans, and 7.5 tCO2 /t for Indonesian palm oil, using a 10 year amortization period. The main advantage of the proposed methodology is its flexibility: it can be applied in a tiered approach, using detailed data where it is available while still allowing for estimation of footprints for a broad set of countries and agricultural commodities; it can be applied at different scales, estimating both national and subnational footprints; it can be adopted to account both for direct (proximate) and indirect drivers of land-use change. It is argued that with an increasing commercialization and globalization of the drivers of land-use change, the proposed carbon footprint methodology could help leverage the power needed to alter environmentally destructive land-use practices within the global agricultural system by providing a tool for assessing the environmental impacts of production, thereby informing consumers about the impacts of consumption and incentivizing producers to become more environmentally responsible.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 233 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 6 3%
Germany 2 <1%
Switzerland 1 <1%
France 1 <1%
Italy 1 <1%
Ghana 1 <1%
Australia 1 <1%
Norway 1 <1%
India 1 <1%
Other 3 1%
Unknown 215 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 47 20%
Student > Ph. D. Student 44 19%
Student > Master 40 17%
Student > Bachelor 15 6%
Student > Doctoral Student 11 5%
Other 37 16%
Unknown 39 17%
Readers by discipline Count As %
Environmental Science 68 29%
Agricultural and Biological Sciences 51 22%
Earth and Planetary Sciences 12 5%
Social Sciences 11 5%
Economics, Econometrics and Finance 9 4%
Other 32 14%
Unknown 50 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 24. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 July 2020.
All research outputs
#1,638,431
of 26,017,215 outputs
Outputs from Global Change Biology
#2,032
of 6,765 outputs
Outputs of similar age
#15,758
of 246,836 outputs
Outputs of similar age from Global Change Biology
#38
of 108 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,765 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 34.8. This one has gotten more attention than average, scoring higher than 68% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 246,836 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 108 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.