Evidence map›Paper›PMID 41171721›Full record

ArticlePloS one2025

Spatiotemporal evolution and influencing factors of agricultural carbon emissions in China.

Minghui Wei, Mengxue Cao, Dongmei Yin, Fei Li, Yan Lv, Laijun Lu

Abstract read
In one paragraph

Article in PloS one, 2025. 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.

Minghui WeiGeomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, China.
Mengxue CaoGeomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, China.ORCID https://orcid.org/0000-0002-8470-0500
Dongmei YinGeomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, China.
Fei LiSchool of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China.
Yan LvCollege of Information Technology, Jilin Agricultural University, Changchun, China.
Laijun LuGeomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clarifying the spatiotemporal characteristics of agricultural carbon emissions and influencing factors in China is crucial. A system for measuring agricultural carbon emissions was established, thus evaluating the level of carbon emissions in China and its provinces. Moreover, the dynamic evolution of agricultural carbon emissions in China and the regions on both sides of the Hu Line was analyzed, then investigated factors affecting agricultural carbon emissions by the LMDI model. The results indicate that the total amount and intensity of agricultural carbon emissions showed an upward and then a downward trend in China from 2001 to 2021. The peaks were 330.72 million tons and 1.98 tons\ha, respectively. Agricultural carbon intensity in provinces was mostly Low-Low Cluster and the range of High-High Cluster has decreased. Inter-provincial disparities in agricultural carbon emissions were also gradually narrowing. These show that the effect of agricultural carbon emissions reduction was obvious in China. It is important to note that carbon emissions from energy consumption in agriculture and agricultural material inputs were substantial, accounting for about 95% of the total. Agricultural carbon emissions were restricted by the agricultural production efficiency, changes in industrial structure, rural population size, and agricultural industrial structure, but were promoted by the level of economy and urbanization. Therefore, we recommend enhancing inter-provincial synergistic collaboration to create agricultural carbon emissions reduction pathways with unique features. It is also essential to maximize agricultural production efficiency and grasp the direction of green and low-carbon. We also suggest that the Chinese government should accelerate the in-depth adjustment and transformation and upgrading of the industrial structure, thereby reducing agricultural carbon emissions at source.

Indexed as

AgricultureCarbonChinaEnvironmental MonitoringSpatio-Temporal AnalysisUrbanizationCarbon

Identifiers

PMID41171721
PMCPMC12578253

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.