THE RELATIONSHIP BETWEEN EMISSIONS AND GROSS DOMESTIC PRODUCT IN THE G20 COUNTRIES

Yıl 2018, , 625 - 642, 01.06.2018

Oytun Meçik , Mustafa Karabacak

https://doi.org/10.17130/ijmeb.2018343114

Cited By: 1

Öz

This study investigates Granger causality between carbon dioxide emissions and gross domestic product GDP in G20 countries. Kónya 2006 panel causality testing approach is adopted to examine the existence of causality relationship for G20 countries for the period of 1971-2012. In this context, GDP and GDP per capita have been used in order to ascertain causality between emissions and economic development level of countries. The results implies one-way causality from carbon dioxide emissions to GDP in Japan and India, one-way causality running from GDP to carbon dioxide emissions in Saudi Arabia, Canada and Germany and two way causality in Great Britain

Anahtar Kelimeler

Carbon Dioxide Emissions, Gross Domestic Product, Panel Granger Causality, Sustainable Development

G20 ÜLKELERİNDE EMİSYONLAR VE GSYH ARASINDAKİ İLİŞKİ

Öz

Bu çalışma, G20 ülkelerinde karbondioksit emisyonları ve GSYH arasındaki Granger nedenselliğini araştırmaktadır. G20 ülkeleri için 1971-2012 döneminde nedensellik ilişkisini incelemek için Kónya 2006 panel nedensellik testi yaklaşımı benimsenmiştir. Bu bağlamda, ülkelerin emisyonları ve ekonomik gelişmişlik seviyeleri arasındaki nedenselliğin tespiti için GSYH ve kişi başına GSYH kullanılmıştır. Sonuçlar, Japonya ve Hindistan'da karbondioksit emisyonlarından GSYH'ye tek yönlü nedensellik, Suudi Arabistan, Kanada ve Almanya'da GSYH’den karbondioksit emisyonlarına tek yönlü nedensellik ve Birleşik Krallık'ta iki yönlü nedenselliğin söz konusu olduğunu göstermektedir.

Anahtar Kelimeler

Karbondioksit Emisyonları, Gayri Safi Yurtiçi Hâsıla, Panel Granger Nedensellik, Sürdürülebilir Kalkınma

Kaynakça

• AGEB. (2017). Energy consumption in Germany in 2016. Retrieved August 5, 2017, from https://mining-report.de/english/blog/energy-consumption-2016/
• Baltagi, B. H. (2005). Econometric analysis of panel data. West Sussex: John Wiley & Sons Ltd.
• Breitung, J. (2002). A parametric approach to the estimation of cointegration vectors in panel data. Retrieved August 2, 2017, from http://dx.doi.org/10.18452/3437
• Bildirici, M. (2017). CO2 emissions and militarization in G7 countries: Panel cointegration and trivariate causality approaches. Environment and Development Economics, 1-21, doi:10.1017/S1355770X1700016X
• Bildirici, E. M., & Bakirtas, T. (2016). The relationship among oil and coal consumption, carbon dioxide emissions, and economic growth in BRICTS countries. Journal of Renewable and Sustainable Energy, 8, 045903, 1-15.
• Brock, W. A. (2005). Chapter 28 - Economic growth and the environment: A review of theory and empirics. In Handbook of Economic Growth (Vol. 1, Part B, pp. 1749-1821). Available: Science Direct.
• Chen, J., & Huang, Y. (2013). The study of the relationship between carbon dioxide (CO2) emission and economic growth. Journal of International and Global Economic Studies, 6(2), 45-61.
• Choi, E., Heshmati, A., & Cho, Y. (2010). An empirical study of the relationships between CO2 emissions, economic growth and openness. IZA Discussion Paper, No. 5304, Bonn.
• Chudik, A., & Peseran, M. H. (2013). Large panel data models with cross-sectional dependence: A survey. Federal Reserve Bank of Dallas Globalization and Monetary Policy Institute Working Paper, No. 153.
• Coondoo, D., & Dinda, S. (2002). Causality between income and emission: A country group- specific econometric analysis. Ecological Economics, 40, 351–367.
• Cowan, W. N., Chang, T., Inglesi-Lotz, R., & Gupta, R. (2014). The nexus of electricity consumption, economic growth and CO2 emissions in the BRICS countries. Energy Policy, 66, 359-368.
• Dağdemir, Ö. (2003). Çevre sorunlarına ekonomik yaklaşımlar ve optimal politika arayışları. Ankara: Gazi Kitabevi.
• De Hoyos, R. E., & Sarafidis, V. (2006). Testing for cross–sectional dependence in panel–data models. The Stata Journal, 6(4), 482-296.
• Eurostat. (2017). Greenhouse gas emission statistics. Retrieved August 5, 2017, from http:// ec.europa.eu/eurostat/statistics-explained/index.php/Greenhouse_gas_emission_ statistics
• Frase, P. (2017). Dört gelecek: Kapitalizmden sonra hayat. (Çev. A. E. Pilgir). İstanbul: Koç Üniversitesi Yayınları.
• Ghosh, S. (2010). Examining carbon emissions economic growth nexus for India: A multivariate cointegration approach. Energy Policy, 38, 3008–3014.
• Grossman, G.M., & Krueger, A. B. (1991). Environmental impacts of a North American free trade agreement. NBER Working Paper, No. 3914.
• Grossman, G. M., & Krueger, A. B. (1995). Economic growth and the environment. The Quarterly Journal of Economics, 110(2), 353-377.
• Jha, A. K. (2014). The long run relationship between carbon and GDP in the G-20 countries. Retrieved August 23, 2017, from https://papers.ssrn.com/sol3/papers.cfm?abstract_ id=1836252
• Kar, M., Nazlıoğlu, Ş., & Ağır, H. (2011). Financial development and economic growth nexus in the MENA countries: Bootstrap panel granger causality analysis. Economic Modelling, 28, 685-693.
• Keleş, R., Hamamcı, C., & Çoban, A. (2009). Çevre politikası. Ankara: İmge Kitabevi.
• Kırışık, F. (2013). Çevre alanındaki faaliyetlerde yaklaşım değişimi: İdare ile sivil toplum kuruluşlarının ortak çalışması. Uluslararası Yönetim İktisat ve İşletme Dergisi, 9(19), 333-346.
• Kónya, L. (2006). Exports and growth: Granger causality analysis on OECD countries with a panel data approach. Economic Modelling, 23, 978–992.
• Kuo, K. C., Kanyasathaporn, P., & Lai, S. (2014). The causal relationship between GDP, energy consumption and CO2 emissions in Hong Kong. 産研論集, 46-47, 127-138.
• Lau, E., Chye, X., & Choong, C. (2011). Energy-growth causality: A panel analysis. Presented at the International Conference on Applied Economics 2011, TEI of Western Macedonia, 355-361.
• Meçik, O., Karabacak, M., & Aytun, U. (2016, February). Karbondioksit emisyonu ve GSYİH: G7 ülkeleri üzerine bootstrap panel granger nedensellik analizi. The Third International Conference on Economics EconWorld 2016, Barcelona.
• Öztürk, Z., & Yıldırım, E. (2015). Environmental Kuznets curve in the MINT countries: Evidence of long-run panel causality test. Ekonomik ve Sosyal Araştırmalar Dergisi, 11(1), 175-183.
• Panayotou, T. (1993). Empirical tests and policy analysis of environmental degradation at different stages of economic development. ILO Technology and Employment Programme Working Paper, WP238, ILO, Geneva.
• Pao, H., & Tsai, C. (2011). Multivariate Granger causality between CO2 emissions, energy consumption, FDI (foreign direct investment) and GDP (gross domestic product): Evidence from a panel of BRIC (Brazil, Russian Federation, India, and China) countries. Energy, 36, 685-693.
• PBO. (2017). Canada’s greenhouse gas emissions: Developments, prospects and reductions. Retrieved August 13, 2017, from http://www.pbo-dpb.gc.ca/web/default/files/ Documents/Reports/2016/ClimateChange/PBO_Climate_Change_EN.pdf
• Saidi, K., & Hammami, S. (2015). The impact of CO2 emissions and economic growth on energy consumption in 58 countries. Energy Reports, 1, 62-70.
• Sarafidis, V., & Wansbeek, T. (2010). Cross-sectional dependence in panel data analysis. MPRA Paper, No. 2081.
• Shafik, N. (1994). Economic development and environmental quality: An econometric analysis. Oxford Economic Papers New Series, 46, 757-773.
• Soytas, U., Sari, R., & Ewing, B. T. (2007). Energy consumption, income, and carbon emissions in the United States. Ecological Economics, 62, 482-489.
• Stern, D. I. (2003). The environmental Kuznets curve. In Internet Encyclopedia of Ecological Economics, International Society for Ecological Economics.
• Xu, Q., Cai, Z., & Fang, Y. (2016). Panel data models with cross-sectional dependence: A selective review. Applied Mathematics-A Journal of Chinese Universities, 31(2), 127- 147.
• Yeldan, A. E. (2015). +2 Derecenin ekonomisi ve Türkiye’nin iklim değişikliği ile mücadele yükümlülükleri. İktisat ve Toplum, 62, 4-7.
• Zellner, A. (1962). An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association, 57(298), 348-368.