Analyzing the dynamics of deforestation in the world: applying Markov models to identify convergence trends.

Deforestation poses a significant environmental challenge that demands global efforts to address, aiming to preserve forest ecosystems and promote sustainable development worldwide. This study investigates the global phenomenon of deforestation convergence from 1990 to 2020 across 149 countries, employing state transition probabilities assessed using Markov chains, dispersion indicators, and average deforestation rates. The analysis indicates a consistent decrease in dispersion indicators and average deforestation rates observed across the studied countries over three decades, coupled with high probabilities of maintaining current forest ecosystem states. These findings suggest the presence of sigma and beta convergence phenomena in the shift towards sustainable development, akin to patterns observed in economic growth. Furthermore, our research identifies the emergence of a Kuznets curve based on deforestation growth rates and dispersion indicators relative to GDP per capita since 2010. This also suggests a potential global convergence in sustainable forest-related activities. Hence, it is clear that coordinating environmental development policies among countries is crucial for achieving global ecological stability. This underscores the necessity for proactive measures to ensure worldwide forest conservation and sustainable management. [ABSTRACT FROM AUTHOR]