Evidence map›Paper›PMID 38710695›Full record

ArticleScientific data2024

County-level intensity of carbon emissions from crop farming in China during 2000-2019.

Cheng Li, Junwen Jia, Fang Wu, Lijun Zuo, Xuefeng Cui

Abstract readDataset
In one paragraph

Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Cheng Li *Department of Ecology, School of Plant Protection / Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education, Yangzhou University, Yangzhou, 225009, China.
Junwen Jia *School of Systems Science, Beijing Normal University, Beijing, 100875, China.ORCID 0000-0001-6334-1150
Fang WuSchool of Systems Science, Beijing Normal University, Beijing, 100875, China.
Lijun ZuoAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100093, China.
Xuefeng CuiSchool of Systems Science, Beijing Normal University, Beijing, 100875, China. xuefeng.cui@bnu.edu.cn.

Funding

Chinese Academy of Sciences (CAS) XDA19090119National Natural Science Foundation of China (National Science Foundation of China) 41801013Yangzhou University (YZU) xjj2023-05
6 · The paper itself

Abstract

Agriculture is an important contributor to global carbon emissions. With the implementation of the Sustainable Development Goals of the United Nations and China's carbon neutral strategy, accurate estimation of carbon emissions from crop farming is essential to reduce agricultural carbon emissions and promote sustainable food production systems in China. However, previous long-term time series estimates in China have mainly focused on the national and provincial levels, which are insufficient to characterize regional heterogeneity. Here, we selected the county-level administrative district as the basic geographical unit and then generated a county-level dataset on the intensity of carbon emissions from crop farming in China during 2000-2019, using random forest regression with multi-source data. This dataset can be used to delineate spatio-temporal changes in carbon emissions from crop farming in China, providing an important basis for decision makers and researchers to design agricultural carbon reduction strategies in China.

Indexed as

CarbonAgricultureChinaCrops, AgriculturalCarbon

Identifiers

PMID38710695
PMCPMC11074320

What OpenQuestion holds

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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.