Türkiye’de Ekonomik Politika Belirsizliği ve Vergi Gelirleri Arasındaki İlişki: DOLS, FMOLS ve CCR Zaman Serisi Eşbütünleşme Yaklaşımı

Vergi gelirleri bir ülkede maliye politikalarının yürütülmesinde, kamu gelirleri içerisinde en önemli gelir kaynağı olarak gösterilmektedir. Vergi gelirlerinin pozitif ya da negatif yönde değişimi devletlerin ekonomik, sosyal, siyasi konjonktürlerinde farklı değişimler meydana getirebileceğinden vergi gelirleri devletler için büyük önem arz etmektedir. Öte yandan işletmeler açısından da önemli harcama kaynaklarından biri olan vergi, ekonomik ve politik etkenlerden önemli ölçüde etkilenebilmektedir. Bu etkenler dikkate alınarak yapılan bu çalışmada, Türkiye’de ekonomik politika belirsizliği ile toplam vergi gelirleri arasındaki ilişki araştırılmıştır. Belirsizlik ile vergi gelirleri arasındaki ilişki Johansen eşbütünleşme, DOLS, FMOLS ve CCR istatistiksel model analizleri ile test edilmiştir. Belirsizlik ve vergi gelirlerinin yanı sıra, oluşturulan ekonometrik modelde faiz, işsizlik oranı, döviz kuru ve sanayi üretim endeksi kontrol değişkeni olarak kullanılmıştır. Yapılan analizlerde değişkenlerin 2008Q1-2023Q4 dönemine ait çeyreklik zaman serisi verileri kullanılmıştır. Johansen testinde Türkiye’de toplam vergi gelirleri ile politik ve ekonomik belirsizlik arasında uzun dönemli bir eşbütünleşmenin olduğu ve DOLS, FMOLS ile CCR yöntemlerinden elde edilen regresyon katsayı sonuçlarında belirsizliğin toplam vergi gelirleri üzerinde önemli düzeyde azaltıcı bir etkiye sahip olduğu görülmüştür. Sonuç olarak, ekonomik politika belirsizliğinin, ekonomik sistemde oluşturduğu negatif etkiye bağlı olarak vergi gelirlerini azalttığı tespit edilmiştir.Tax revenues are considered the most significant source of public income when implementing fiscal policy within a country. Whether positive or negative, changes in tax revenue can lead to various shifts in states’ economic, social, and political dynamics, making tax revenues highly critical for governments. On the other hand, taxes, as a significant expenditure for businesses, can be substantially influenced by economic and political factors. Considering these factors, this study examines the relationship between economic policy uncertainty and total tax revenues in Türkiye. The relationship between uncertainty and tax revenues was tested using Johansen Cointegration, Dynamic Ordinary Least Squares (DOLS), Fully Modified Ordinary Least Squares (FMOLS), and Canonical Cointegrating Regression (CCR) statistical model analyses. In addition to uncertainty and tax revenue, the econometric model included interest rates, unemployment rates, exchange rates, and industrial production indices as control variables. The analysis utilised quarterly time series data spanning from 2008Q1 to 2023Q4. The Johansen test indicated a long-run cointegration relationship between total tax revenue and political and economic uncertainty in Türkiye. Furthermore, the regression coefficient results derived from DOLS, FMOLS and CCR methods revealed that uncertainty significantly reduces total tax revenues. In conclusion, the study found that economic policy uncertainty has a negative impact on tax revenues due to the adverse effects it creates within the economic system

Deep Learning-Based Rooftop PV Detection and Techno Economic Feasibility for Sustainable Urban Energy Planning

Accurate estimation of available rooftop areas for PV power generation at the city scale is critical for sustainable energy planning and policy development. In this study, using publicly available high-resolution satellite imagery, rooftop solar energy potential in urban, rural, and industrial areas is estimated using deep learning models. In order to identify roof areas, high-resolution open-source images were manually labeled, and the training dataset was trained with DeepLabv3+ architecture. The developed model performed roof area detection with high accuracy. Model outputs are integrated with a user-friendly interface for economic analysis such as cost, profitability, and amortization period. This interface automatically detects roof regions in the bird’s-eye -view images uploaded by users, calculates the total roof area, and classifies according to the potential of the area. The system, which is applied in 81 provinces of Turkey, provides sustainable energy projections such as PV installed capacity, installation cost, annual energy production, energy sales revenue, and amortization period depending on the panel type and region selection. This integrated system consists of a deep learning model that can extract the rooftop area with high accuracy and a user interface that automatically calculates all parameters related to PV installation for energy users. The results show that the DeepLabv3+ architecture and the Adam optimization algorithm provide superior performance in roof area estimation with accuracy between 67.21% and 99.27% and loss rates between 0.6% and 0.025%. Tests on 100 different regions yielded a maximum roof estimation accuracy IoU of 84.84% and an average of 77.11%. In the economic analysis, the amortization period reaches the lowest value of 4.5 years in high-density roof regions where polycrystalline panels are used, while this period increases up to 7.8 years for thin-film panels. In conclusion, this study presents an interactive user interface integrated with a deep learning model capable of high-accuracy rooftop area detection, enabling the assessment of sustainable PV energy potential at the city scale and easy economic analysis. This approach is a valuable tool for planning and decision support systems in the integration of renewable energy sources