ESG ratings are increasingly used to assess firms’ sustainability profiles and to support investment, risk-management, and disclosure decisions. This paper investigates whether ESG rating dynamics depend on firm-level carbon emission intensity. We propose an exogenous-regime Markov-modulated model in which ESG rating transition probabilities vary across regimes defined by a carbon-emission-intensity index. The index is based on total CO \(_2\) emissions normalized by revenue, which controls for firm size and allows environmental conditions to be compared across companies operating at different scales. This index is discretized through a likelihood-based change-point procedure, and the resulting regime process is modeled as a Markov chain. We apply the model to annual Refinitiv ESG rating categories for 100 firms over 2015-2024. The empirical results show that ESG ratings are highly persistent, but their transition dynamics differ across environmental regimes. The regime-dependent modeling is statistically supported and improves one-step-ahead forecasting accuracy relative to the homogeneous Markov-chain benchmark, with the two-threshold model providing the best overall performance.
