Evidence map›Paper›PMID 42519042›Full record

ArticleiScience2026

Spatial trade-offs between environmental sustainability and crop production in China under SDG-aligned scenarios.

Yetong Li, Shiliang Liu, Xutong Wu, Shuang Song, Yifei Zhao, Yuanze Li, Wanting Wang, Jingyang Fan, Ziang Tian, Jiayi Lin and 3 more

Abstract read
In one paragraph

Article in iScience, 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

13 authors.

Yetong LiSchool of Environment, Beijing Normal University, Beijing 100875, China.
Shiliang LiuSchool of Environment, Beijing Normal University, Beijing 100875, China.
Xutong WuState Key Laboratory of Earth Surface Processes and Hazards Risk Governance, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
Shuang SongState Key Laboratory of Earth Surface Processes and Hazards Risk Governance, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
Yifei ZhaoDepartment of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China.
Yuanze LiSchool of Earth Sciences and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China.
Wanting WangSchool of Environment, Beijing Normal University, Beijing 100875, China.
Jingyang FanSchool of Environment, Beijing Normal University, Beijing 100875, China.
Ziang TianSchool of Environment, Beijing Normal University, Beijing 100875, China.
Jiayi LinSchool of Environment, Beijing Normal University, Beijing 100875, China.
Zongyi JinState Key Laboratory of Earth Surface Processes and Hazards Risk Governance, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
Gang WuResearch Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
Lam-Son TranInstitute of Genomics for Crop Abiotic Stress Tolerance, Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reconciling lower environmental impacts from agriculture with food security is a core national objective in China. We develop spatially explicit projections for nine major crops to 2030 and 2060 under Business-as-Usual (BAU), BAU with carbon-efficiency constraints (BAU-CE), and Sustainable Development Goal (SDG)-aligned pathway (SDG-60). Despite improved input-use efficiency, national output declines modestly by 2030 relative to 2020 (-11.4% BAU and -11.5% BAU-CE). By 2060, efficiencies approach ∼90%, with substantial reductions in aggregate inputs (fertilizer -30%, pesticides -50%, and irrigation water -34.4%). Under SDG-60, cropping greenhouse-gas emissions are ∼72% lower than under BAU-CE, while aggregate production across the nine crops declines by ∼50.5%. Spatial heterogeneity reveals trade-off zones where large mitigation gains and reduced chemical and water use coincide with output and income losses, particularly in rice-intensive provinces. Provinces with below-median labor productivity and income but above-median mitigation burdens are identified as structurally disadvantaged, highlighting where differentiated targets, incentives, and transition support are most needed to balance mitigation, food security, and rural livelihoods.

Indexed as

China agriculturecrop productionGHG mitigationmachine learningspatial projectionsustainability trade offs

Identifiers

PMID42519042
PMCPMC13382589

What Socratic 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 Socratic graph.