ArticleCarbon balance and management2026
Machine learning-based analysis of economic efficiency disparities and transition drivers between high- and low-carbon industries in China.
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.
What it found
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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.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Artificial intelligence for carbon emissions management: advances, challenges, and future directions across monitoring, prediction, and reduction.Carbon balance and management · 2026Review
- Dynamic drivers of carbon emission reduction in Lithuania under the European Green Deal: a wavelet analysis of synergies in waste management, energy efficiency, R&D, and international legal compliance.Carbon balance and management · 2026Article
- Beyond emissions: a stochastic cusp-logit analysis of environmental cost regimes in BRICS.Carbon balance and management · 2026Article
- Assessing the environmental sustainability prospects of financial development, biocapacity, and renewable energy.Carbon balance and management · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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Registered trials
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.