Evidence map›Paper›PMID 40360576›Full record

ArticleScientific reports2025

Multiscale exploration of spatiotemporal dynamics and decoupling effects of carbon emissions in China.

Chong Liu, Xiaoman Wang, Haiyang Li

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.

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0citing papers in PubMed
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1 · What the graph read from it

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Chong LiuSchool of Public Policy & Management, Anhui Jianzhu University, Hefei, 230022, China.
Xiaoman WangSchool of Government, Sun Yat-sen University, Guangzhou, 510006, China. wangxm87@mail2.sysu.edu.cn.
Haiyang LiSchool of Public Policy and Management, Tsinghua University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Studying carbon emissions (CE) at different administrative scales will help facilitate crafting tailored emission reduction policies for China's regions, which is vital for achieving the dual carbon goals. However, previous studies focused on a single administrative scale, lacking multiscale research. This paper combined energy consumption data with nighttime light data and adopted a spatial autocorrelation, variation coefficient (VC), and decoupling model to study the spatiotemporal dynamics and decoupling effect of CE at the three administrative scales of provinces, prefecture-level cities, and counties in China from 2000 to 2020. The results were as follows: (1) The VC of CE showed different trends at different scales, with its coefficient size successively ranked at the county, prefecture, and province levels. (2) CE at different scales showed positive spatial autocorrelation and the significance was strongest at the county level. (3) The decoupling trend between CE and economic growth has generally shown a positive development across different spatial scales. The average elasticity decoupling index at the provincial, prefectural, and county levels has decreased overall, from 0.88, 1.82, and 3.74 to 0.19, 1.36, and 3.05, respectively. However, the characteristics of these changes differ. The CV of the elasticity decoupling index increased at the provincial and prefectural levels, rising from 1.161 to 1.563 to 1.419 and 2.669, respectively, while it decreased slightly at the county level, from 3.862 to 3.765. (4) The dominant type of decoupling at the provincial level had changed from expansive negative decoupling (ED) to strong decoupling (SD). Meanwhile, at the prefectural and county levels, the ED was still dominant, but the number of SD had increased significantly, rising from 75 to 138 at the prefectural level and from 748 to 1160 at the county level. This study demonstrates China's carbon emissions sensitivity to scale, emphasizing the importance of adapting emission reduction measures to local conditions.

Indexed as

Carbon emissionsChinaDecoupling effectsMultiscaleSpatiotemporal dynamics

Identifiers

PMID40360576
PMCPMC12075700

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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.