Evidence map›Paper›PMID 42209538›Full record

ArticleNature communications2026

Mapping global resource driven nature loss in the mining sector from 2001 to 2022.

Yu-Tong Cheng, Nguyen Tien Hoang, Yushin Shinoda, Kamrul Islam, Masaharu Motoshita, Taku Kadoya, Keiichiro Kanemoto

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yu-Tong ChengGraduate School of Environmental Studies, Tohoku University, Sendai, Japan.ORCID http://orcid.org/0000-0002-7144-2469
Nguyen Tien HoangGraduate School of Environmental Studies, Tohoku University, Sendai, Japan.ORCID http://orcid.org/0000-0002-0681-4932
Yushin ShinodaBiodiversity Division, National Institute for Environmental Studies (NIES), Ibaraki, Japan.ORCID http://orcid.org/0000-0002-1605-6303
Kamrul IslamIntegrated Research Center for Nature Positive Technology, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan.ORCID http://orcid.org/0000-0003-3443-9030
Masaharu MotoshitaIntegrated Research Center for Nature Positive Technology, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan.ORCID http://orcid.org/0000-0003-1576-0531
Taku KadoyaBiodiversity Division, National Institute for Environmental Studies (NIES), Ibaraki, Japan.ORCID http://orcid.org/0000-0002-7816-5484
Keiichiro KanemotoGraduate School of Environmental Studies, Tohoku University, Sendai, Japan. keiichiro.kanemoto@gmail.com.ORCID http://orcid.org/0000-0003-0230-9020

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Global mineral extraction is expected to surge due to the growing demand for clean energy. While mining is critical to modern society, its environmental impacts, though increasingly studied, remain undocumented for half of the world's mining areas and are rarely analysed at the commodity level. Here, we introduce a novel approach integrating remote sensing, machine learning, and cloud computing to classify approximately 70,000 mining sites by commodity. Using this newly detailed dataset, we quantify the nature loss associated with 20 extracted commodities, focusing on deforestation and habitat destruction. From 2001 to 2022, mining activities worldwide resulted in the removal of 16,268 km² of forest cover, with 65.64% occurring in tropical and subtropical regions. Notably, approximately half of this deforestation was attributed to the extraction of gold, coal, aluminium (bauxite), nickel-cobalt and copper, primarily in countries intersecting the Amazon, Southeast Asian, and Congo Basin rainforests. Our analysis also reveals that deforestation-to-mining area ratios and biodiversity risks vary by mining location, and conservation threats do not always scale with deforestation rates. By providing commodity-specific maps of mining-induced nature loss, our work equips companies and organisations with actionable insights to identify risks within their supply chains and implement targeted mitigation strategies.

Indexed as

Conservation of Natural ResourcesMiningBiodiversityEcosystemForestsMachine LearningRemote Sensing Technology

Identifiers

PMID42209538
PMCPMC13388966

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.