Evidence map›Paper›PMID 39013982›Full record

ArticleScientific reports2024

Exploring the dynamics and trends of carbon emission spatiotemporal patterns in the Chengdu-Chongqing Economic Zone, China, from 2000 to 2020.

Lu Che, Sidai Guo, Yangli Li, Yihao Zhu

Abstract read
In one paragraph

Article in Scientific reports, 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

4 authors.

Lu CheSchool of Environment and Resources, Southwest University of Science and Technology, Mianyang, 612000, China.
Sidai GuoSchool of Economics and Management, Southwest University of Science and Technology, Mianyang, 612000, China. guosidai@swust.edu.cn.
Yangli LiSchool of Civil Engineering and Architecture, Southwest University of Science and Technology, Mianyang, 612000, China.
Yihao ZhuSchool of Civil Engineering and Architecture, Southwest University of Science and Technology, Mianyang, 612000, China.

Funding

the National Natural Science Foundation of China: Mechanisms and optimization methods of district-neighborhood surface morphology on spatial carbon performance in cold region 52378066
6 · The paper itself

Abstract

Analysis of the spatial-temporal pattern and trend of carbon emissions provides an important scientific basis for the development of a low-carbon economy. Based on the corrected NPP-VIIRS and DMSP/OLS nighttime light data, a carbon emission model for the Chengdu-Chongqing Economic Zone (CCEZ) in China is constructed. Furthermore, the article establishes an integrated qualitative and quantitative research system. The qualitative results show that at the city and county scales, the high carbon emission areas and counties are mainly distributed in Chengdu and Chongqing, while the low carbon emission areas are concentrated in the marginal cities of the CCEZ and the counties with low levels of industrialization around the Sichuan Basin. The high-carbon emission zone tended to expand to the north, and the low-carbon emission zone tended to expand to the south. At the grid scale, the carbon emissions of the CCEZ fluctuated and increased from 2000 to 2020, forming a trend connected with those of the central city, with high carbon emissions at the core and radiating outward expansion. Quantitative analysis revealed that carbon emissions at the county and grid scales exhibited a significant positive global spatial correlation, and the overall correlation degree exhibited an increasing trend.

Indexed as

Carbon emissionsQualitative analysisQuantitative analysisSpatiotemporal variations

Identifiers

PMID39013982
PMCPMC11252400

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Registered trials

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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.