Evidence map›Paper›PMID 41526403›Full record

ArticleNature communications2026

Enhancing carbon sinks in China using a spatially-optimized forestation strategy.

Yanli Dong, Zhen Yu, Thomas Pugh, Evgenios Agathokleous, Fangmin Zhang, Stephen Sitch, Weibin You, Wangya Han, Stefan Olin, Shirong Liu and 3 more

Abstract read
PubMed Full text at PMC Publisher
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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

13 authors.

Yanli DongState Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, China.
Zhen YuState Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, China. zyu@nuist.edu.cn.ORCID http://orcid.org/0000-0002-7729-249X
Thomas PughDepartment of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden.ORCID http://orcid.org/0000-0002-6242-7371
Evgenios AgathokleousState Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, China.ORCID http://orcid.org/0000-0002-0058-4857
Fangmin ZhangState Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, China.
Stephen SitchFaculty of Environment, Science and Economics, University of Exeter, Exeter, UK.ORCID http://orcid.org/0000-0003-1821-8561
Weibin YouCollege of Forestry, Fujian Agriculture and Forestry University, Fuzhou, China.
Wangya HanState Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, China.ORCID http://orcid.org/0000-0002-1075-1540
Stefan OlinDepartment of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden.ORCID http://orcid.org/0000-0002-8621-3300
Shirong LiuKey Laboratory of Forest Ecology and Environment, China's National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Beijing, China.ORCID http://orcid.org/0000-0002-1103-9803
Guoyi ZhouState Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, China.
Pedro CabralState Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, China.
Pengsen SunKey Laboratory of Forest Ecology and Environment, China's National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

China plans expanding 49.5 million hectares of new forests by 2050 to strengthen carbon sequestration. However, estimates of the carbon benefits from this expansion rarely consider the effect of 'forest edge', where tree mortality increases under intensified stress from wind, drought, pests, and fire. Here we show that proximity to forest edges substantially reduces biomass carbon storage, and develop a spatial optimization strategy that prioritizes planting in areas that minimize edge effects. Our projections show that forestation optimized for edge effects results in a 51% increase in carbon gain (986 ± 22 Tg by 2060), with approximately half of the total gain driven by reduced edge effects. These findings demonstrate that ignoring edge effects can significantly overestimate carbon sink potential and highlight spatially optimized forestation as a pathway to maximize climate mitigation and ecological benefits.

Indexed as

Carbon SequestrationForestryForestsBiomassCarbonChinaClimate ChangeConservation of Natural ResourcesTreesCarbon

Identifiers

PMID41526403
PMCPMC12901047

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.