Evidence map›Paper›PMID 41688799›Full record

ArticleCarbon balance and management2026

Machine learning-based analysis of economic efficiency disparities and transition drivers between high- and low-carbon industries in China.

Zhilin Huang, Qianyi Zhang, Yayin Zheng, Enliang Tian

Abstract read
In one paragraph

Article in Carbon balance and management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
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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.

Zhilin HuangSchool of Business, Shantou University, Shantou, China. zhlhuang@stu.edu.cn.
Qianyi ZhangSchool of Business, Shantou University, Shantou, China. 22qyzhang@stu.edu.cn.
Yayin ZhengSchool of Mathematics and Computing, Shantou University, Shantou, China.
Enliang TianSchool of Mathematics and Computing, Shantou University, Shantou, China.

Funding

Shantou University STU Scientific Research Initiation Grant STF23011
6 · The paper itself

Abstract

In the context of global climate change, understanding economic efficiency disparities between high-carbon and low-carbon industries is crucial for advancing low-carbon transitions and improving carbon governance. This study examines heterogeneity in corporate carbon emission management and economic performance across Chinese industries and identifies key drivers of firms' transformation capacity. Using a panel dataset of 633 listed enterprises from eight industries in China over 2010-2021, we classify firms into high- and low-carbon groups based on their emissions profiles and benchmark four machine-learning models-Random Forest, XGBoost, LightGBM, and Decision Tree-to capture nonlinear relationships and evaluate the relative importance of environmental and financial indicators. Random Forest delivers the best performance, achieving a classification accuracy of 95.7% (rounded) and strong discriminatory ability (AUC = 0.989). Feature-importance results consistently show that carbon emissions are the most influential variable, followed by total liabilities and total assets, while profitability-related indicators (e.g., operating revenue and gross profit margin) also contribute to distinguishing firms' carbon profiles and performance differences. Overall, high-carbon enterprises appear to face greater transition barriers due to higher abatement cost exposure and tighter balance-sheet constraints, whereas low-carbon firms may be better positioned to benefit from policy incentives and market opportunities. These findings highlight the pivotal role of financial health in enabling low-carbon transformation and underscore the need for differentiated policy design. Policy implications include targeted transition finance and more flexible allowance allocation mechanisms for high-carbon enterprises, alongside continued incentives for technological innovation and market expansion in low-carbon sectors. JEL CLASSIFICATION: Q56; G30; C55; Q43; L60.

Indexed as

Carbon emission managementEconomic performanceHigh-carbon and low-carbon industriesMachine learning modelsPolicy optimization

Identifiers

PMID41688799
PMCPMC12918023

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.