Evidence map›Paper›PMID 40779588›Full record

ArticlePloS one2025

Scenario-based Kaya identity analysis for city-level carbon dioxide emissions.

Junyou Liu, Bohong Zheng

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

2 authors.

Junyou LiuSchool of Architecture and Art, Central South University, Changsha, Hunan, China.ORCID https://orcid.org/0000-0002-8343-7536
Bohong ZhengSchool of Architecture and Art, Central South University, Changsha, Hunan, China.ORCID https://orcid.org/0000-0002-3163-9718

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many countries worldwide have committed to reduce their carbon dioxide emissions in response to climate change. In China, cities are striving to achieve their 2030 peak carbon emission targets. In this study, we developed a scenario-based Kaya identity analysis methodology to explore the notable uncertainty inherent in future carbon dioxide emissions. Using the case of Changning City, Hunan province, China, we found that under the business-as-usual scenario, the city's carbon dioxide emissions of 3,839.1 thousand tons in 2022 are projected to reach 4,674.3 thousand tons in 2031. Changning is unlikely to achieve its carbon peak target. In a more challenging future scenario of rapid economic growth, carbon dioxide emissions are expected to rise from 3,839.1 thousand tons in 2022-5,447 thousand tons in 2031. Under the environmentalist scenario, Changning could achieve its carbon peak target before 2030 (carbon dioxide emissions would peak at 3,922 thousand tons in 2028). Under a slowed economic development scenario, Changning could also achieve a carbon peak in 2028 (carbon dioxide emissions would peak at 3,980.2 thousand tons in 2028). However, without sufficient energy-saving and emissions-reduction measures, annual increases in carbon dioxide emissions are likely to resurge during periods of rapid economic development.

Indexed as

Carbon DioxideChinaCitiesClimate ChangeEconomic DevelopmentCarbon Dioxide

Identifiers

PMID40779588
PMCPMC12334010

What OpenQuestion holds

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LicenceCC BY
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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.