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.
Huang et al. (2026) studied this question.