The main purpose of this study is to predicting the effects of fiscal policies on greenhouse emissions in Iran from 1991 to 2021. To achieve this, bayesian model averaging (BMA) and Bayesian vector autoregression (BVAR) approaches were employed. The results indicate that out of 14 fiscal policy variables, the top five models with the highest posterior probabilities were identified using the aforementioned methods. The most effective models included variables such as financial asset acquisitions, oil revenues, corporate taxes, wealth taxes, current expenditures, and other revenues. Subsequently, the impact of these variables on CO2 emissions was analyzed over 10 periods using the BVAR method. The impulse response function results revealed that shocks to the financial asset acquisitions, oil revenues, wealth taxes, current expenditures, and other revenues had positive effects on CO2 emissions, with the most significant impact stemming from shocks to financial asset acquisitions. Conversely, only shocks to the corporate taxes demonstrated a negative effect. Additionally, the variance decomposition of CO2 emission forecast errors indicated that the oil revenues and wealth taxes played the most significant roles in explaining forecast errors, with their contributions increasing during intermediate periods.
