CO2 emissions intensity; Convergence; Spatial Markov chain; Iran
It is essential to study CO2 emissions intensity as the most critical factor affecting temperature increase and climate change in a country like Iran, which ranked seven regarding CO2 emissions intensity. Investigating the convergence of CO2 emissions intensity is essential in recognizing its dynamics in identifying the effectiveness of government environmental policies. In this paper, using the Markov chain and spatial Markov chain methods, the convergence of CO2 emissions intensity from fossil-fuel consumption has been investigated in 28 provinces of Iran from 2002 to 2016. The empirical results showed that convergence clubs and neighbors significantly influenced the transition probability of regions to clubs with high and low CO2 emissions. Therefore, if a province had a neighbor with low (high) CO2 emissions intensity, the transition probability of this province to the club with low (high) CO2 intensity increased. Therefore, in provinces that have neighbors with low (high) CO2 emissions intensity, the transition probability to the club with low (high) CO2 intensity increases.