Evidence map›Paper›PMID 42860530›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Optimized Land-Water Management Expands Afforestation Potential Under Land and Human Water-Use Constraints.

Binbin Huang, Fei Lu, Yuebo Su, Xuming Wang, Hua Zheng, Zhiyun Ouyang

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 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

6 authors.

Binbin HuangState Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China.ORCID https://orcid.org/0000-0003-1623-3063
Fei LuState Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China.ORCID https://orcid.org/0000-0003-0864-5957
Yuebo SuYunnan Key Laboratory of Forest Ecosystem Stability and Global Change, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, Yunnan, China.ORCID https://orcid.org/0009-0008-4137-1533
Xuming WangKey Laboratory for Humid Subtropical Eco-Geographical Processes of the Ministry of Education, College of Geographical Sciences, Fujian Normal University, Fuzhou, China.ORCID https://orcid.org/0000-0001-5291-9332
Hua ZhengState Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China.ORCID https://orcid.org/0000-0003-1196-7332
Zhiyun OuyangState Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China.ORCID https://orcid.org/0000-0003-0927-0499

Funding

Carbon Peak Carbon Neutrality Eco-Environmental Technology Project of Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences RCEES-TDZ-2021-8China Postdoctoral Science Foundation 2023M743704China Postdoctoral Science Foundation 2024T17097Ecological Society of China, Youth Talent Support Program 2023-2025Key Research and Development Program of Yunnan Province 202303AC100009National Natural Science Foundation of China 42301325National Natural Science Foundation of China 72174192National Natural Science Foundation of China 72374194
6 · The paper itself

Abstract

Afforestation is a low-cost and high-potential climate change mitigation strategy, but its feasibility remains highly uncertain because most existing assessments focus primarily on biophysical suitability and land resource constraints, while largely overlooking the critical limitation imposed by human water demand. Here, we developed an integrated framework combining machine learning, statistical modelling, the Biome-BGCMuSo model, and multi-scenario analysis to quantify China's afforestation potential under coupled land and water constraints and explore how optimized land-water management can enhance afforestation potential and associated carbon sink. We found human water demand reduced China's feasible afforestation potential from 78.13 to 40.57 Mha, with climate change causing a further 15.23%-15.91% decline. Notably, optimized land management and water-saving measures could individually increase feasible afforestation area by 6.04-7.05 Mha and 5.89-6.97 Mha, respectively. When combined, these measures would further increase afforestation potential by 14.95 and 13.66 Mha under SSP-245 and SSP-585, respectively, generating an additional carbon sink of 109.64 ± 12.08 and 125.92 ± 13.40 Tg C yr

Indexed as

afforestationcarbon sinkclimate changehuman water demandland constraintsoptimized land managementoptimized water management

Identifiers

PMID42860530
PMCPMC13655261

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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.