Türkiye'de Yenilenebilir Enerji Tüketimini Etkileyen Faktörlerinin MARS Metodolojisi İle Belirlenmesi

Öz

Bu çalışmanın amacı Türkiye’deki yenilenebilir enerji tüketimini etkileyen faktörlerin belirlenmesidir. Bu bağlamda, ilk olarak, literatürdeki benzer çalışmalar incelenmiştir. Yapılan inceleme neticesinde, yenilenebilir enerji kullanımını etkileyebilecek olan 11 farklı değişken belirlenmiştir. Bahsi geçen değişkenlere ait 1990-2018 dönem aralığındaki yıllık veriler dikkate alınmıştır. Öte yandan, çalışmanın analiz sürecinde MARS yönteminden faydalanılmıştır. Netice itibarıyla, ülkedeki nüfusun arttığı durumda, yenilenebilir enerji kullanımının da arttığı belirlenmiştir. Buradan anlaşılabileceği üzere, artan nüfus ile birlikte enerjiye yönelik talepte de artış yaşanmıştır. Bunun sonucunda da yenilenebilir enerji de daha fazla kullanılmaya başlanmıştır. Ayrıca, doğalgaz fiyatlarındaki artışın da yenilenebilir enerji kullanımını arttırdığı tespit edilmiştir. Doğalgazın daha pahalı bir hale geldiği durumda, insanların başka alternatiflere yöneldiği anlaşılmaktadır. Ülkedeki kredi miktarı da yenilenebilir enerji tüketimi üzerinde etkili olan başka bir faktördür. Kredi miktarı belirli bir oranı aştığı durumda, bu kredilerin yenilenemez enerji kaynakları üzerinde yoğunlaştığı görülmektedir. Ek olarak, ülkedeki karbon emisyonu ile yenilenebilir enerji kullanımı arasında da negatif yönlü bir ilişki olduğu belirlenmiştir. Bu çalışmadan elde edilen sonuçlardan anlaşılabileceği üzere, Türkiye’deki yenilenebilir enerjinin talep artması ve doğalgaz fiyatlarının yükselmesi gibi mecburi nedenlerden dolayı arttığı tespit edilmiştir. Bu durum, yenilenebilir enerji kullanımına yönelik Türkiye’de yeterli bilincin oluşmadığını göstermektedir. Bu yüzden, yenilenebilir enerji kullanımının daha cazip hali gelebilmesi için devlet tarafından vergi avantajı gibi gerekli teşviklerin sağlanması önem arz etmektedir.

Anahtar Kelimeler

• Yenilenebilir Enerji;,Ekonomik Kalkınma,MARS

Identifying The Influencing Factors of Renewable Energy Consumption in Turkey With MARS Methodology

Öz

The aim of this study is to determine the factors affecting the renewable energy consumption in Turkey. In this context, firstly, similar studies in the literature have been examined. As a result of the investigation, 11 different variables have been identified that may affect the use of renewable energy. Annual data of the mentioned variables in the period of 1990-2018 are taken into consideration. On the other hand, MARS method is used in the analysis process of the study. As a result, it has been determined that renewable energy use increases when the population in the country goes up. As can be seen from here, with the increasing population, the demand for energy has also increased. As a result, renewable energy has started to be used more. In addition, it is also determined that the increase in natural gas prices leads to higher consumption of renewable energy. In the event that natural gas becomes more expensive, it is understood that people are turning to other alternatives. The loan amount in the country is another factor that has an impact on renewable energy consumption. In case the loan amount exceeds a certain rate, it is seen that these loans are concentrated on non-renewable energy sources. In addition, it has been determined that there is a negative relationship between carbon emissions in the country and renewable energy use. It can be understood that renewable energy usage can be increased mainly because of the obligatory reasons, such as higher demand for energy and natural gas prices increase. This indicates that no sufficient consciousness is formed in Turkey for renewable energy. Therefore, it is important to provide the necessary incentives such as tax advantage by the state to make renewable energy use more attractive.

Anahtar Kelimeler

• Renewable Energy,, Economic Development,MARS

Kaynakça

1. Adnan, R. M., Liang, Z., Heddam, S., Zounemat-Kermani, M., Kisi, O., & Li, B. (2019). Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. Journal of Hydrology, 124371.
2. Alvarez-Herranz, A., Balsalobre-Lorente, D., Shahbaz, M., & Cantos, J. M. (2017). Energy innovation and renewable energy consumption in the correction of air pollution levels. Energy Policy, 105, 386-397. Anton, S. G., & Nucu, A. E. A. (2020). The effect of financial development on renewable energy consumption. A panel data approach. Renewable Energy, 147, 330-338.
3. Apergis, N., & Payne, J. E. (2014). Renewable energy, output, CO2 emissions, and fossil fuel prices in Central America: Evidence from a nonlinear panel smooth transition vector error correction model. Energy Economics, 42, 226-232.
4. Apergis, N., & Payne, J. E. (2014). The causal dynamics between renewable energy, real GDP, emissions and oil prices: evidence from OECD countries. Applied Economics, 46(36), 4519-4525.
5. Bateni, S. M., Vosoughifar, H. R., Truce, B., & Jeng, D. S. (2019). Estimation of Clear-Water Local Scour at Pile Groups Using Genetic Expression Programming and Multivariate Adaptive Regression Splines. Journal of Waterway, Port, Coastal, and Ocean Engineering, 145(1), 04018029.
6. Bekhet, H. A., & Othman, N. S. (2018). The role of renewable energy to validate dynamic interaction between CO2 emissions and GDP toward sustainable development in Malaysia. Energy economics, 72, 47-61.
7. Brini, R., Amara, M., & Jemmali, H. (2017). Renewable energy consumption, International trade, oil price and economic growth inter-linkages: The case of Tunisia. Renewable and Sustainable Energy Reviews, 76, 620-627.
8. Bui, D. T., Hoang, N. D., & Samui, P. (2019). Spatial pattern analysis and prediction of forest fire using new machine learning approach of Multivariate Adaptive Regression Splines and Differential Flower Pollination optimization: A case study at Lao Cai province (Viet Nam). Journal of environmental management, 237, 476-487.

9. Bujang, A. S., Bern, C. J., & Brumm, T. J. (2016). Summary of energy demand and renewable energy policies in Malaysia. Renewable and Sustainable Energy Reviews, 53, 1459-1467.
10. Cole, P., & Banks, G. (2017). Renewable energy programmes in the South Pacific–Are these a solution to dependency?. Energy Policy, 110, 500-508.
11. Dinçer, H., Hacıoğlu, Ü., & Yüksel, S. (2018a). Determining influencing factors of currency exchange rate for decision making in global economy using MARS method. In Geopolitics and strategic management in the global economy (pp. 261-273). IGI Global.
12. Dinçer, H., Hacıoğlu, Ü., & Yüksel, S. (2018b). Evaluating the effects of economic imbalances on gold price in Turkey with MARS method and discussions on microfinance. In Microfinance and its impact on entrepreneurial development, sustainability, and inclusive growth (pp. 115-137). IGI Global.
13. Dong, K., Sun, R., & Hochman, G. (2017). Do natural gas and renewable energy consumption lead to less CO2 emission? Empirical evidence from a panel of BRICS countries. Energy, 141, 1466-1478.
14. Eren, B. M., Taspinar, N., & Gokmenoglu, K. K. (2019). The impact of financial development and economic growth on renewable energy consumption: Empirical analysis of India. Science of the Total Environment, 663, 189-197.
15. Ferreira, L. B., Duarte, A. B., Cunha, F. F. D., & Fernandes Filho, E. I. (2019). Multivariate adaptive regression splines (MARS) applied to daily reference evapotranspiration modeling with limited weather data. Acta Scientiarum. Agronomy, 41.
16. Friedman, J. H. (1991). Multivariate adaptive regression splines. The annals of statistics, 1-67.
17. Fu, F., Liu, H., Polenske, K. R., & Li, Z. (2013). Measuring the energy consumption of China’s domestic investment from 1992 to 2007. Applied energy, 102, 1267-1274.
18. Jin, T., & Kim, J. (2018). What is better for mitigating carbon emissions–Renewable energy or nuclear energy? A panel data analysis. Renewable and Sustainable Energy Reviews, 91, 464-471.
19. Jones, G. A., & Warner, K. J. (2016). The 21st century population-energy-climate nexus. Energy Policy, 93, 206-212.
20. Khan, M. I., Yasmeen, T., Shakoor, A., Khan, N. B., & Muhammad, R. (2017). 2014 oil plunge: Causes and impacts on renewable energy. Renewable and Sustainable Energy Reviews, 68, 609-622.
21. Khoshnevis Yazdi, S., & Shakouri, B. (2017). The globalization, financial development, renewable energy, and economic growth. Energy Sources, Part B: Economics, Planning, and Policy, 12(8), 707-714.
22. Kyophilavong, P., Shahbaz, M., Anwar, S., & Masood, S. (2015). The energy-growth nexus in Thailand: Does trade openness boost up energy consumption?. Renewable and Sustainable Energy Reviews, 46, 265-274.
23. Lin, B., & Moubarak, M. (2014). Renewable energy consumption–Economic growth nexus for China. Renewable and Sustainable Energy Reviews, 40, 111-117.
24. Lin, B., Omoju, O. E., & Okonkwo, J. U. (2016). Factors influencing renewable electricity consumption in China. Renewable and Sustainable Energy Reviews, 55, 687-696.
25. Long, X., Naminse, E. Y., Du, J., & Zhuang, J. (2015). Nonrenewable energy, renewable energy, carbon dioxide emissions and economic growth in China from 1952 to 2012. Renewable and Sustainable Energy Reviews, 52, 680-688.
26. Nguyen, K. H., & Kakinaka, M. (2019). Renewable energy consumption, carbon emissions, and development stages: Some evidence from panel cointegration analysis. Renewable Energy, 132, 1049-1057.
27. Ocal, O., & Aslan, A. (2013). Renewable energy consumption–economic growth nexus in Turkey. Renewable and sustainable energy reviews, 28, 494-499.
28. Oktar, S., & Yüksel, S. (2016). Bankalarin Türev Ürün Kullanimini Etkileyen Faktörler: Mars Yöntemi ile Bir Inceleme/Determinants of the Use Derivatives in Banking: An Analysis with MARS Model. Finans Politik & Ekonomik Yorumlar, 53(620), 31.
29. Sadorsky, P. (2009). Renewable energy consumption, CO2 emissions and oil prices in the G7 countries. Energy Economics, 31(3), 456-462.
30. Salim, R. A., & Rafiq, S. (2012). Why do some emerging economies proactively accelerate the adoption of renewable energy?. Energy Economics, 34(4), 1051-1057.
31. Schmidt, T. S., Matsuo, T., & Michaelowa, A. (2017). Renewable energy policy as an enabler of fossil fuel subsidy reform? Applying a socio-technical perspective to the cases of South Africa and Tunisia. Global Environmental Change, 45, 99-110.
32. Sebri, M., & Ben-Salha, O. (2014). On the causal dynamics between economic growth, renewable energy consumption, CO2 emissions and trade openness: Fresh evidence from BRICS countries. Renewable and Sustainable Energy Reviews, 39, 14-23.
33. Shukla, A. K., Sudhakar, K., & Baredar, P. (2017). Renewable energy resources in South Asian countries: Challenges, policy and recommendations. Resource-Efficient Technologies, 3(3), 342-346.
34. Steen, M., & Weaver, T. (2017). Incumbents’ diversification and cross-sectorial energy industry dynamics. Research Policy, 46(6), 1071-1086.
35. Troster, V., Shahbaz, M., & Uddin, G. S. (2018). Renewable energy, oil prices, and economic activity: A Granger-causality in quantiles analysis. Energy Economics, 70, 440-452.
36. Tsai, S. B., Xue, Y., Zhang, J., Chen, Q., Liu, Y., Zhou, J., & Dong, W. (2017). Models for forecasting growth trends in renewable energy. Renewable and Sustainable Energy Reviews, 77, 1169-1178.
37. Uzunkaya, S. Ş., Dinçer, H., & Yüksel, S. (2018). A Historical Analysis of The Economic Development of The USA (1947-2017). MANAS Sosyal Araştırmalar Dergisi, 8(1), 209-222.
38. Vaona, A. (2016). The effect of renewable energy generation on import demand. Renewable Energy, 86, 354-359.
39. York, R., & Bell, S. E. (2019). Energy transitions or additions?: Why a transition from fossil fuels requires more than the growth of renewable energy. Energy Research & Social Science, 51, 40-43.
40. Yüksel, S. (2016a). Bankaların Takipteki Krediler Oranını Belirleyen Faktörler: Türkiye İçin Bir Model Önerisi. Bankacılar Dergisi, 98, 41-56.
41. Yüksel, S. (2016b). Türkiye’de cari işlemler açığının belirleyicileri: Mars yöntemi ile bir inceleme. Bankacılar Dergisi, 96(27), 102-121.
42. Yüksel, S., & Adalı, Z. (2017). Determining influencing factors of unemployment in Turkey with MARS method. International Journal of Commerce and Finance, 3(2), 25-36.
43. Yüksel, S., & Özsarı, M. (2017). Türkiye’nin Kredi Notunu Etkileyen Faktörlerin MARS Yöntemi İle Belirlenmesi. Politik Ekonomik Kuram, 1(2), 16-31.
44. Yüksel, S., & Zengin, S. (2016). 2008 Küresel Krizinin Öncü Göstergeleri: Logit ve Mars Yöntemleri ile Bir İnceleme. Finansal Araştırmalar ve Çalışmalar Dergisi, 8(15), 495-518.
45. Yüksel, S., Canöz, İ., & Adalı, Z. (2017). Determination of the Variables Affecting the Price Earning Ratios of Deposit Banks in Turkey by Mars Method. Fiscaoeconomia, 1(3), 40-55.
46. Yüksel, S., Mukhtarov, S., Mahmudlu, C., Mikayilov, J. I., & Iskandarov, A. (2018). Measuring international migration in Azerbaijan. Sustainability, 10(1), 132.
47. Yüksel, S., Zengin, S., & Kartal, M. T. (2016). Identifying the macroeconomic factors influencing credit card usage in Turkey by using MARS method. China-USA Business Review, 15(12), 611-615.
48. Zengin, S., Yüksel, S., & Kartal, M. T. (2018). Understanding the Factors that aFFect Foreign direct investment in tUrkey by Using mars method. Finansal Araştırmalar ve Çalışmalar Dergisi, 10(18), 177-192.
49. Zhang, B., Wang, B., & Wang, Z. (2017). Role of renewable energy and non-renewable energy consumption on EKC: evidence from Pakistan. Journal of Cleaner Production, 156, 855-864.
50. Zhang, D., Wang, J., Lin, Y., Si, Y., Huang, C., Yang, J., ... & Li, W. (2017). Present situation and future prospect of renewable energy in China. Renewable and Sustainable Energy Reviews, 76, 865-871.
51. Zhang, W., Wu, C., Li, Y., Wang, L., & Samui, P. (2019). Assessment of pile drivability using random forest regression and multivariate adaptive regression splines. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 1-14.
52. Zheng, G., Yang, P., Zhou, H., Zeng, C., Yang, X., He, X., & Yu, X. (2019). Evaluation of the earthquake induced uplift displacement of tunnels using multivariate adaptive regression splines. Computers and Geotechnics, 113, 103099.