Evidence map›Paper›PMID 41098315›Full record

ArticleAnnual review of environment and resources2021

Forest Restoration in Low- and Middle-Income Countries.

Jeffrey R Vincent, Sara R Curran, Mark S Ashton

Abstract read
In one paragraph

Article in Annual review of environment and resources, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Urban tree canopy fragmentation and child mortality in low- and middle-income countries.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Abandoned land: Overestimated potential.Science (New York, N.Y.) · 2023
    Article
  10. Review
  11. Article
  12. 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

3 authors.

Jeffrey R VincentNicholas School of the Environment, Duke University, Durham, North Carolina 27708, USA.
Sara R CurranHenry M. Jackson School of International Studies, University of Washington, Seattle, Washington 98195, USA.
Mark S AshtonThe Forest School at the Yale School of the Environment, Yale University, New Haven, Connecticut 06511, USA.

Funding

Scientific/Technical CoreP2CHD042828 · NICHD · UNIVERSITY OF WASHINGTON · PI SARA R. CURRAN · 2017 to 2026
$6.3M
NICHD NIH HHS P2C HD042828
6 · The paper itself

Abstract

A series of international initiatives have set ambitious goals for restoring global forests. This review synthesizes natural and social science research on forest restoration (FR), with a focus on restoration on cleared land in low- and middle-income countries. We define restoration more broadly than reestablishing native forests, given that landholders might prefer other forest types. We organize the review loosely around ideas in the forest transition literature. We begin by examining recent trends in FR and forest transition indicators. We then investigate two primary parts of the forest transition explanation for forest recovery: wood scarcity, including its connection to restoration for climate change mitigation, and the dynamic relationships between migration and land use. Next, we review ecological and silvicultural aspects of restoration on cleared land. We conclude by discussing selected interventions to promote restoration and the challenge of scaling up restoration to achieve international initiatives' goals.

Indexed as

afforestationforest restorationforest transitionmigrationpayments for ecosystem servicesreforestation

Identifiers

PMID41098315
PMCPMC12520201

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.