Evidence map›Paper›PMID 41339402›Full record

ArticleScientific reports2025

Carbon emission forecasting in the Yangtze river middle reaches under dual carbon goals with multiple drivers.

Jing Wu, Zaicheng Xu, Junyi Zhao, Dou Dou

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
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0citing papers in PubMed
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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

4 authors.

Jing WuSchool of Urban Design, Wuhan University, Wuhan, 430072, China.
Zaicheng XuSchool of Urban Design, Wuhan University, Wuhan, 430072, China.
Junyi ZhaoSchool of Urban Design, Wuhan University, Wuhan, 430072, China. J.zhao@whu.edu.cn.
Dou DouSchool of Urban Design, Wuhan University, Wuhan, 430072, China.

Funding

Hubei Provincial Natural Science Foundation of China No. 2025AFC008National Natural Science Foundation of China No. 42471124
6 · The paper itself

Abstract

Amidst the increasing urgency of global climate change, achieving carbon neutrality has become a critical objective for rapidly developing economies like China. This study presents an innovative carbon emission forecasting framework for the Yangtze River Middle Reaches-comprising Hubei, Hunan, and Jiangxi provinces-by integrating an extended STIRPAT model with partial least squares (PLS) regression. Distinct from existing provincial-level research, our approach incorporates a broader set of socio-economic and environmental drivers, utilizes variable importance analysis, and employs scenario-based projections to systematically compare emission trajectories and driving mechanisms across multiple provinces. By simulating carbon emission pathways from 2001 to 2021 and projecting future trends to 2080 under three differentiated scenarios, the study reveals pronounced regional heterogeneity in emission peaks, neutrality timelines, and driver effects. Results indicate that while all three provinces are likely to achieve peak emissions around 2030, the path to carbon neutrality by 2060 remains highly challenging due to persistent technological and structural constraints, particularly in provinces with slower industrial transformation. The findings underscore the necessity of region-specific, adaptive mitigation strategies-balancing economic growth, industrial upgrading, and energy structure optimization-to ensure practical progress toward China's dual-carbon goals. This work not only advances carbon forecasting methodology by quantifying the interactive effects of multiple drivers at a subnational scale, but also offers empirical evidence to inform targeted, differentiated policy interventions.

Indexed as

Carbon emission forecastingCarbon neutralityPLS regression analysisScenario analysisSTIRPAT model

Identifiers

PMID41339402
PMCPMC12675490

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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.