Predicting the Impact of Climate Conditions on the Economic Production of Iranian Provinces with The Approach of Random Forest Algorithm

Climate change is among the vital phenomena related to human societies, and its consequences have been demonstrated, particularly in recent decades, with varying intensity in many regions of the world. The importance of climate as one of the fundamental aspects of human life has never been as significant as it is today in macroeconomic and social issues. Analyzing climate change's economic impacts necessitates precise national and local analyses. Although numerous global studies examine the economic effects of climate change, limited research has been conducted at local levels within countries, especially concerning Iran. This article attempts to provide a comprehensive analysis of the susceptibility of the country's provincial economic production to climate change by utilizing a new dataset of weather conditions in the provinces from 2000 to 2020 through the random forest algorithm of machine learning subsets. Studies indicate a significant relationship between weather and sectors beyond agriculture, forestry, food security, tourism, health, fisheries, livestock, mining, and energy. Weather conditions can directly or indirectly impact various economic sectors, and their effect on economic activities is inevitable. However, research results regarding the impact of weather fluctuations face varied findings. Drought, with a higher recurrence than floods, poses more significant challenges to the economy on scales beyond local levels.