Beyond emissions: a stochastic cusp-logit analysis of environmental cost regimes in BRICS.

Emerging economies suffer a huge economic burdens from environmental damage because most of the time we measure environmental damage using conventional emissions-based metrics. This study addresses this gap by introducing environmental cost (EC) as a novel measure that quantifies the economic burden of environmental degradation. The need for the new measurement stems from the requirement to examine the non-linear and asymmetric behaviors of the relationship between the environment and the economy in rapidly developing economies such as the BRICS countries in their transition from fossil fuels, their drive for new types of innovation, and their increased susceptibility to sudden changes in the cost of degradation due to energy transition, innovation-related pressures, and vulnerability to sudden changes. Therefore, stabilizing mechanisms such as Energy Transition, Innovation, Environmental Adaptability, and Trade in Low-carbon Technologies (TLCT) will reduce the possibility of experiencing high levels of Environmental Cost, while Environmental Vulnerability (EV) will heighten the probability of experiencing catastrophic regime shifts to high-cost, unstable states with sudden and extreme changes in costs. To accomplish this objective of validating the Cusp-Logit framework, a balanced panel data consisting of the BRICS countries (Brazil, Russia, India and South Africa) for the years 2015 to 2024 is used. The hybrid model of the Cusp-Logit framework consists of the geometrical layout of the Cusp Catastrophe model and a stochastic Logit transition mechanism that will capture the nonlinear regimes of the Cusp Catastrophe model and the dynamic persistence of costs associated with environmental degradation. Although empirical evidence points to the stabilizing effects of energy transition, innovation, environmental adaptability and TLCT on flattening the potential surface and reducing the possible occurrence & duration of high-cost regimes; EV creates a systemic tilt which increases the likelihood of a shift to an unstable/high-EC state. The cross-national analysis demonstrates that while China & India have stable innovation-based environments; Brazil, Russia & South Africa are much more susceptible to instability due to a limited ability to adapt to & utilize technology. Given that there is currently no comprehensive framework for understanding environmental performance in terms of costs; this work develops a new methodology for modeling the dynamic behaviors of asymmetrical regimes as well as understanding the transition probabilities between them. This provides an early warning system, which will assist BRICS policymakers to manage their exposure to environmental risks while moving towards developing sustainable pathways with low cost. Unlike conventional emission-based and linear approaches, the proposed framework captures nonlinear regime shifts, transition probabilities, and persistence of environmental cost dynamics, offering a scalable tool for policy design under uncertainty.