پیش‌بینی اثرات سیاست‌های مالی بر انتشار گازهای گلخانه‌ای (CO2) در ایران: رهیافت الگوی خودرگرسیون برداری بیزی (BVAR)

نویسندگان
ابراهیم قائد 1
محمد طاهر احمدی شادمهری 2
مهدی خداپرست مشهدی 2
نرگس صالح نیا 2
1 دانشکده علوم اداری و اقتصاد، دانشگاه فردوسی مشهد، مشهد، ایران
2 دانشکده علوم اداری و اقتصاد،دانشگاه فردوسی مشهد، مشهد ایران
چکیده
هدف اصلی این پژوهش، پیش‌بینی اثرات سیاست‌های مالی بر انتشار گازهای گلخانه‌ای (CO2) در ایران طی دوره زمانی 1370 تا 1400 است. د این راستا، از روش میانگین‌گیری بیزی (BMA) و الگوی خود رگرسیون برداری بیزی (BVAR) بهره گرفته شد. نتایج نشان می‌دهد که با استفاده از روش مذکور، از بین 14 متغیر سیاست‌های مالی، پنج مدل اول با بیشترین احتمال وقوع پسین استخراج شد. بهترین نتایج به مدل‌هایی تعلق داشت که شامل متغیرهای تملک دارایی‌های مالی، درآمد نفت، مالیات اشخاص حقوقی، مالیات بر ثروت، پرداخت­های جاری و‌ سایر درآمدها بودند. در ادامه، به‌کمک روش BVAR تأثیر این متغیرها بر انتشار گازهای گلخانه ای در 10 دوره بررسی شد. نتایج تابع واکنش آنی نشان داد که شوک‌های تملک دارایی‌های مالی، درآمد نفت، مالیات بر ثروت، پرداخت­های جاری و‌ سایر درآمدها، اثرات مثبتی بر انتشار داشته‌اند که بیشترین اثر مربوط شوک تملک دارایی‌های مالی است. در مقابل، تنها شوک‌ مالیات اشخاص حقوقی، اثر منفی را نشان داد. همچنین، تجزیه واریانس خطای پیش‌بینی انتشار'گازهای گلخانه ای نشان داد که متغیرهای درآمد نفت و مالیات بر ثروت بیشترین نقش را در توضیح خطای پیش‌بینی دارند و در دوره‌های میانی سهم این متغیرها افزایش می‌یابند.
کلیدواژه‌ها
سیاست‌های مالی
انتشارگازهای گلخانه‌ ای (CO2)
روش میانگین گیری بیزی (BMA)
رهیافت الگوی خودرگرسیون برداری بیزی (BVAR)

عنوان مقاله English

Predicting the Effects of Fiscal Policies on Greenhouse Emissions (CO2) in Iran: Bayesian Vector Auto regression (BVAR) Approach

نویسندگان English

Ebrahim Ghaed 1
Mohammad Taher Ahmadi Shadmehri 2
Mahdi Khodaparast Mashhadi 2
Narges Salehnia 2
1 Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
2 Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
چکیده English

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.

کلیدواژه‌ها English

Fiscal Policies
Greenhouse Emissions (CO2)
Bayesian Model Averaging (BMA)
Bayesian Vector Auto regression (BVAR) Approach
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