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country_name
stringclasses
33 values
country_iso3
stringclasses
33 values
year
int64
2k
2.02k
Permanent agriculture
float64
0.05
5.62k
Albania
ALB
2,001
368.1249
Albania
ALB
2,002
66.244156
Albania
ALB
2,003
65.77771
Albania
ALB
2,004
425.5093
Albania
ALB
2,005
104.144104
Albania
ALB
2,006
160.88524
Albania
ALB
2,007
544.5482
Albania
ALB
2,008
286.7751
Albania
ALB
2,009
142.33798
Albania
ALB
2,010
54.86486
Albania
ALB
2,011
79.672935
Albania
ALB
2,012
633.16956
Albania
ALB
2,013
104.819534
Albania
ALB
2,014
88.71859
Albania
ALB
2,015
28.077856
Albania
ALB
2,016
38.07991
Albania
ALB
2,017
47.298332
Albania
ALB
2,018
48.491592
Albania
ALB
2,019
40.005287
Albania
ALB
2,020
42.349533
Albania
ALB
2,021
31.188559
Albania
ALB
2,022
29.475975
Albania
ALB
2,023
97.57598
Albania
ALB
2,024
28.728085
Andorra
AND
2,004
1.026591
Andorra
AND
2,009
0.114068
Austria
AUT
2,001
66.30005
Austria
AUT
2,002
67.20747
Austria
AUT
2,003
29.985281
Austria
AUT
2,004
73.6155
Austria
AUT
2,005
150.24515
Austria
AUT
2,006
65.764656
Austria
AUT
2,007
123.79403
Austria
AUT
2,008
100.34095
Austria
AUT
2,009
74.88386
Austria
AUT
2,010
125.54933
Austria
AUT
2,011
94.408066
Austria
AUT
2,012
70.19779
Austria
AUT
2,013
53.211548
Austria
AUT
2,014
58.026005
Austria
AUT
2,015
55.587486
Austria
AUT
2,016
66.373795
Austria
AUT
2,017
56.350952
Austria
AUT
2,018
49.356815
Austria
AUT
2,019
47.274223
Austria
AUT
2,020
19.414715
Austria
AUT
2,021
23.030346
Austria
AUT
2,022
23.04937
Austria
AUT
2,023
28.056595
Austria
AUT
2,024
19.896807
Belarus
BLR
2,001
127.9811
Belarus
BLR
2,002
95.348625
Belarus
BLR
2,003
37.5908
Belarus
BLR
2,004
84.73987
Belarus
BLR
2,005
135.04823
Belarus
BLR
2,006
256.0803
Belarus
BLR
2,007
156.30275
Belarus
BLR
2,008
133.69275
Belarus
BLR
2,009
202.62997
Belarus
BLR
2,010
346.7155
Belarus
BLR
2,011
397.59
Belarus
BLR
2,012
251.63934
Belarus
BLR
2,013
129.42499
Belarus
BLR
2,014
189.60828
Belarus
BLR
2,015
191.5351
Belarus
BLR
2,016
609.04266
Belarus
BLR
2,017
706.37787
Belarus
BLR
2,018
634.2574
Belarus
BLR
2,019
191.71704
Belarus
BLR
2,020
175.42636
Belarus
BLR
2,021
94.50914
Belarus
BLR
2,022
62.91289
Belarus
BLR
2,023
57.91018
Belarus
BLR
2,024
98.15883
Belgium
BEL
2,001
41.571793
Belgium
BEL
2,002
55.35198
Belgium
BEL
2,003
18.941713
Belgium
BEL
2,004
54.225548
Belgium
BEL
2,005
47.60873
Belgium
BEL
2,006
77.847176
Belgium
BEL
2,007
45.977722
Belgium
BEL
2,008
53.54243
Belgium
BEL
2,009
59.913578
Belgium
BEL
2,010
87.99561
Belgium
BEL
2,011
69.155365
Belgium
BEL
2,012
56.258827
Belgium
BEL
2,013
10.558744
Belgium
BEL
2,014
23.538254
Belgium
BEL
2,015
33.31622
Belgium
BEL
2,016
32.14066
Belgium
BEL
2,017
26.215055
Belgium
BEL
2,018
45.54372
Belgium
BEL
2,019
41.664204
Belgium
BEL
2,020
38.4551
Belgium
BEL
2,021
38.287262
Belgium
BEL
2,022
20.481148
Belgium
BEL
2,023
19.380867
Belgium
BEL
2,024
26.909655
Bosnia and Herzegovina
BIH
2,001
53.19555
Bosnia and Herzegovina
BIH
2,002
26.180199
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Commodity Driven Deforestation | Europe (Our World in Data)

🇪🇺 890 observations · 40 Europe countries · 2001–2024 · Repackaged by Electric Sheep Europe

rows countries years license

TL;DR

This dataset contains 890 observations of Commodity Driven Deforestation data across 40 Europe countries, spanning 2001–2024.

About the source

Geographic coverage

40 Europe countries · top rows shown below, sorted by row count:

Country Rows First year Last year
ALB 24 2001 2024
AUT 24 2001 2024
BEL 24 2001 2024
BGR 24 2001 2024
BLR 24 2001 2024
BIH 24 2001 2024
CHE 24 2001 2024
CZE 24 2001 2024
SWE 24 2001 2024
DEU 24 2001 2024
DNK 24 2001 2024
ESP 24 2001 2024
EST 24 2001 2024
FIN 24 2001 2024
FRA 24 2001 2024
... 25 more countries

Schema

Column Type Description Example
country_name string Albania
country_iso3 string ALB
year int64 2001
Permanent agriculture float64 368.1249

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepeurope/europe-owid-commodity-driven-deforestation")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

germany = df[df["country_iso3"] == "DEU"]

Time-series for a single indicator

sample = df.sort_values("year")
sample.plot(x="year", y="Permanent agriculture")

Citation

@misc{europe_owid_commodity_driven_deforestation_2024,
  title        = {Commodity Driven Deforestation | Europe (Our World in Data)},
  author       = {Our World in Data},
  year         = {2024},
  url          = {https://ourworldindata.org/grapher/commodity-driven-deforestation},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Europe},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-owid-commodity-driven-deforestation}}
}

License

Released under cc-by-4.0.

Original data © Our World in Data. When using this dataset, please cite both the original source above and the Electric Sheep Europe repackaging.

About Electric Sheep

Electric Sheep Europe is part of the Electric Sheep mission: a unified, ML-ready data layer for Europe on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepeurope


Provenance: ingested 2026-06-03 via the Electric Sheep pipeline. Source URL: https://ourworldindata.org/grapher/commodity-driven-deforestation

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