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Data

Carbon opportunity costs per kilogram of food

About this data

Source
Searchinger et al. (2018) – processed by Our World in Data
Last updated
January 28, 2021
Date range
2018–2018
Unit
kilograms CO₂e per kilogram

Sources and processing

This data is based on the following sources

Searchinger et al. – Assessing the efficiency of changes in land use for mitigating climate change

The Carbon Opportunity Cost (COC) of each crop is equal to the amount of carbon lost from native vegetation and soils in order to produce a given food product. If food products were not produced on a given plot of land, this land could be used to restore native vegetation and sequester carbon.

Because carbon storage is lost quickly but crop production can continue indefinitely, any system for evaluating the carbon costs of land use must in some way address the relative costs of emissions over time. Much of the discussion focuses on the value of up-front versus later mitigation. In general, this question can be thought as a question about what is the relative value of reducing emissions sooner rather than later. Searchinger et al. (2018) apply a 4% time "discount rate" over 100 years. The the choice of a discount rate and a carbon value trajectory is a question of policy; the authors selected a 4% discount rate as their central scenario because it is roughly consistent with US bioenergy policies.

Retrieved on
January 28, 2021
Retrieved from
https://www.nature.com/articles/s41586-018-0757-z
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Searchinger, T. D., Wirsenius, S., Beringer, T., and Dumas, P. (2018). Assessing the efficiency of changes in land use for mitigating climate change. Nature, 564(7735), 249-253.

The Carbon Opportunity Cost (COC) of each crop is equal to the amount of carbon lost from native vegetation and soils in order to produce a given food product. If food products were not produced on a given plot of land, this land could be used to restore native vegetation and sequester carbon.

Because carbon storage is lost quickly but crop production can continue indefinitely, any system for evaluating the carbon costs of land use must in some way address the relative costs of emissions over time. Much of the discussion focuses on the value of up-front versus later mitigation. In general, this question can be thought as a question about what is the relative value of reducing emissions sooner rather than later. Searchinger et al. (2018) apply a 4% time "discount rate" over 100 years. The the choice of a discount rate and a carbon value trajectory is a question of policy; the authors selected a 4% discount rate as their central scenario because it is roughly consistent with US bioenergy policies.

Retrieved on
January 28, 2021
Retrieved from
https://www.nature.com/articles/s41586-018-0757-z
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Searchinger, T. D., Wirsenius, S., Beringer, T., and Dumas, P. (2018). Assessing the efficiency of changes in land use for mitigating climate change. Nature, 564(7735), 249-253.

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Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Carbon opportunity costs per kilogram of food”. Our World in Data (2026). Data adapted from Searchinger et al.. Retrieved from https://archive.ourworldindata.org/20260512-085513/grapher/carbon-opportunity-costs-per-kilogram-of-food.html [online resource] (archived on May 12, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Searchinger et al. (2018) – processed by Our World in Data

Full citation

Searchinger et al. (2018) – processed by Our World in Data. “Carbon opportunity costs per kilogram of food” [dataset]. Searchinger et al., “Assessing the efficiency of changes in land use for mitigating climate change” [original data]. Retrieved May 17, 2026 from https://archive.ourworldindata.org/20260512-085513/grapher/carbon-opportunity-costs-per-kilogram-of-food.html (archived on May 12, 2026).

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Data API

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Data URL (CSV format)
https://ourworldindata.org/grapher/carbon-opportunity-costs-per-kilogram-of-food.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/carbon-opportunity-costs-per-kilogram-of-food.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/carbon-opportunity-costs-per-kilogram-of-food.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/carbon-opportunity-costs-per-kilogram-of-food.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/carbon-opportunity-costs-per-kilogram-of-food.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/carbon-opportunity-costs-per-kilogram-of-food.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/carbon-opportunity-costs-per-kilogram-of-food.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/carbon-opportunity-costs-per-kilogram-of-food.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear