Evidence map›Paper›PMID 41298616›Full record

ArticleScientific reports2025

Coupled coordination study of inter-provincial carbon emission efficiency and ecological infrastructure in China.

Dandan Wang, Ping Guo

Abstract read
In one paragraph

Article in Scientific reports, 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

2 authors.

Dandan WangSchool of Management, Shijiazhuang Tiedao University, Shijiazhuang, Hebei, China.
Ping GuoSchool of Economics and Law, Shijiazhuang Tiedao University, Shijiazhuang, Hebei, China. guop2024@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the coupling relationship between ecological infrastructure (EI) and carbon emission efficiency (CEE) is critical for China to achieve its 2030 carbon peak target. This study develops a comprehensive evaluation index system to assess inter-provincial EI development and employs the Non-Radial Directional Distance Function (NDDF) method to quantify provincial-level CEE. Subsequently, the coupling coordination degree (CCD) between EI and CEE from 2004 to 2022 is calculated using a coupling coordination model. Based on these findings, the development trend of the CCD for the period 2023-2030 is projected using the Grey Prediction GM(1,1) model. The results indicate that both EI and CEE have exhibited steady improvements between 2004 and 2022, albeit with significant regional disparities. The eastern region demonstrates the highest levels of CEE, while the western region surpasses others in EI development. The CCD between EI and CEE has increased consistently over time, revealing distinct spatial autocorrelation and clustering effects. Notably, regions characterized by low CCD (Cold spots) have decreased, whereas those with high CCD (Hot spots) have become more pronounced, although no continuous geographical pattern has emerged. Projections for 2023-2030 suggest that maintaining the current trajectory will lead to slower progress. However, targeted policies could substantially enhance CCD and mitigate regional disparities. This study provides valuable insights into the dynamic interaction between ecological infrastructure and carbon efficiency, offering strategic implications for promoting regional sustainable development, advancing environmental justice, and enhancing long-term climate resilience.

Indexed as

Carbon emission efficiencyEcological infrastructureGM (1,1) modelNDDF

Identifiers

PMID41298616
PMCPMC12657881

What OpenQuestion 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 OpenQuestion graph.