The spatial distribution dynamic and convergence of CO(2) emissions in Iran’s provinces

It is essential to study CO(2) emissions intensity as the most critical factor affecting temperature increase and climate change in a country like Iran, which ranked seven regarding CO(2) emissions intensity. Investigating the convergence of CO(2) 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 CO(2) 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 CO(2) emissions. Therefore, if a province had a neighbor with low (high) CO(2) emissions intensity, the transition probability of this province to the club with low (high) CO(2) intensity increased. Therefore, in provinces that have neighbors with low (high) CO(2) emissions intensity, the transition probability to the club with low (high) CO(2) intensity increases.