Modelling Corporate Transition Dynamics Using Markov Chains, Hidden Markov Models and CatBoost: Evidence from High-Emission Sectors

This study investigates how firms in high-emission sectors progress along the low-carbon transition by analysing the joint dynamics of Management Quality (MQ) and Carbon Performance (CP) using probabilistic modelling and explainable machine-learning methods. Digitalisation is conceptualised as the increasing use of data-driven and algorithmic tools in corporate governance, sustainability monitoring, and regulatory oversight, enabling a more granular assessment of corporate transition pathways across multiple time horizons. Using annual Transition Pathway Initiative data for 175 firms over the period 2018–2025, we apply discrete-time Markov chains to capture state persistence and directional mobility in MQ and CP, while Hidden Markov Models uncover latent performance regimes shaping firms’ transition trajectories across three decarbonisation horizons (2028, 2035, and 2050). To enhance interpretability and policy relevance, CatBoost-based feature importance analysis identifies governance, emissions-related, and sector-specific drivers of transitions between states. The results indicate a steady and highly persistent improvement in Management Quality, reflecting cumulative consolidation of governance structures, while Carbon Performance evolves more slowly and heterogeneously, with only moderate convergence emerging toward the 2050 horizon. Latent-regime estimates reveal a gradual shift from volatile, low-performance pathways toward more stable transition regimes over time. From a policy perspective, the findings suggest that governance improvements alone are insufficient to ensure timely emission reductions, highlighting the need for digitally enabled, sector-specific regulatory incentives and enforcement mechanisms targeting realised Carbon Performance.