Estimation of the Global-Scale Ecological Footprint within the Framework of STIRPAT Models: The Quantile Regression Approach

Öz

Despite the fundamental role of human-induced forces in global environment having changed, knowledge about the specific factors that cause these impacts is limited and uncertainties remain. In this respect, the ecological footprint emerges as a concept used to emphasize both the apparent unsustainability of current practices and the inequalities in resource consumption among countries. The ecological footprint provides a method for measuring how much land can support the consumption of natural resources and provides a precise measure of human impact on the world. In recent years, sustainable development and biological capacity debate has mainly revolved around factors affecting the ecological footprint and approaches to improving environmental quality. Therefore, it is important to determine which factors affect the global ecological footprint. For this aim, a cross-section analysis was carried out with the quantile regression approach applied within the framework of the STIRPAT model structure for 154 countries that were allocated according to their income levels in 2016, taking into account current data. According to the quantile regression findings, the coefficients of the welfare and financial development index are positive and statistically significant. It has been concluded that the population decreases the amount of ecological footprint per person, thus, increasing the total ecological footprint. In addition, it has been determined that the density of the service sector negatively affects the ecological footprint.

Anahtar Kelimeler

• Ecological footprint
• STIRPAT model
• Quantile regression

Küresel Ölçekte Ekolojik Ayak İzinin STIRPAT Modelleri Çerçevesinde Tahmini: Kantil Regresyon Yaklaşımı

Öz

İnsan kaynaklı itici güçlerin küresel çevresel değişimde oynadığı temel role rağmen, bu etkilere neden olan belirli etkenler hakkındaki bilgi sınırlıdır ve belirsizlikler devam etmektedir. Bu bağlamda, ekolojik ayak izi hem mevcut uygulamaların görünürdeki sürdürülemezliğini hem de ülkeler arasında kaynak tüketimindeki eşitsizlikleri vurgulamak için kullanılan bir kavram olarak ortaya çıkmaktadır. Ekolojik ayak izi, ne kadar arazinin doğal kaynakların tüketimini destekleyebileceğini ölçmek için bir yöntem sağlar ve insanın dünya üzerindeki etkisini açık bir biçimde ortaya koyan bir ölçü sağlamaktadır. Son yıllarda sürdürülebilir kalkınma ve biyolojik kapasite tartışmaları, esas olarak ekolojik ayak izini etkileyen faktörler ve çevresel kaliteyi iyileştirme yaklaşımları etrafında dönmektedir. Bu nedenle, küresel ekolojik ayak izini etkileyen faktörlerin belirlenmesi önemlidir. Bu amaçla 2016 yılında gelir düzeylerine göre tahsis edilen 154 ülke için STIRPAT model yapısı çerçevesinde uygulanan kantil regresyon yaklaşımı ile güncel veriler dikkate alınarak yatay kesit analizi yapılmıştır. Kantil regresyon bulgularına göre; refah ve mali gelişme endeksinin katsayıları pozitiftir ve istatistiksel olarak anlamlıdır. Nüfusun kişi başına düşen ekolojik ayak izi miktarını azalttığı, böylece toplam ekolojik ayak izini artırdığı sonucuna varılmıştır. Ayrıca hizmet sektörünün yoğunluğunun ekolojik ayak izini olumsuz etkilediği tespit edilmiştir.

Anahtar Kelimeler

• Kantil Regresyon
• Ekolojik ayak izi
• STIRPAT model
• Kantil regresyon

Kaynakça

1. Başoğlu, A. (2018). STIRPAT modeli kapsamında Türkiye’de ekolojik ayak izinin belirleyicileri. In Erdem, H.F. (Ed.), İktisat Seçme Yazılar (pp. 133-155). Trabzon: Celepler Matbaacılık.
2. Bello, M.O., Solarin, S.A., & Yen, Y.Y. (2018). The impact of electricity consumption on CO2 emission, carbon footprint, water footprint and ecological footprint: The role of hydropower in an emerging economy. Journal of environmental management, 219, 218-230.
3. Bera, A., & Jarque, C. (1981). Efficient tests for normality, heteroscedasticity, and serial independence of regression residuals: Monte carlo evidence. Econometrics Letters, 7, 313-318.
4. Breusch, T.S., & Pagan, A.R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47(5), 1287-1294.
5. Çamurlu, Seçkin, & Erilli, Necati, A. (2019). Kantil regresyon analizinde bootstrap tahmini, Erciyes Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 35(2), 16-25.
6. Davino,C., Furno,M., & Vistocco, D. (2014). Quantile Regression Theory and Applications. United Kingdom: John Wiley&Sons.
7. Dietz, T., & Rosa, E.A. (1997). Effect of population and affluence on CO2 emissions. Proc. Natl. Acad. Sci. Amerika Birleşik Devletleri, 94(1), 175-179.
8. Dietz, T., Rosa, E.A., & York, R. (2007). Driving the human ecological footprint. Frontiers in Ecology and the Environment, 5(1), 13-18.

9. Ehrlich, Paul R. & Holdren, J.P. (1971). Impact of population growth. American Association for the Advancement of Science, 171(3977), 1212-1217.
10. Global Footprint Network. (n.d.). How the footprint works. Retrieved September 26, 2020 from https://www.footprintnetwork.org/our-work/ecological-footprint/
11. Grooten, M., & Almond, R.E.A. (Eds.). (2018). Living planet report-2018: Aiming higher. WWF, Gland, Switzerland.
12. Hayden, A., & Shandra, J.M. (2009). Hours of work and the ecological footprint of nations: An exploratory analysis. Local Environment, 14(6), 575-600.
13. Jia, J., Deng, H., Duan, J., & Zhao, J. (2009). Analysis of the major drivers of the ecological footprint using the STIRPAT model and the PLS method - A case study in Henan Province, China. Ecological Economics, 68(11), 2818-2824.
14. McMillen, P. D. (2013). Quantile Regression for Spatial Data Springer. New York: Springer.
15. Ramsey, J. (1969). Tests for specification errors in classical linear least-squares regression analysis. Journal of the Royal Statistical Society. Series B (Methodological), 31(2), 350-371.
16. Rosa, E.A., York, R., & Dietz, T. (2004). Tracking the anthropogenic drivers of ecological impacts. AMBIO: A Journal of the Human Environment, 33(8), 509-512.
17. Tang, W., Zhong, X., & Liu, S. (2011). Analysis of major driving forces of ecological footprint based on the STRIPAT model and RR method: A case of Sichuan Province, Southwest China. Journal of Mountain Science, 8(4), 611-618.
18. University of Groningen. (2016). Penn world table. Retrieved September 27, 2020 from https://www.rug.nl/ggdc/productivity/pwt/
19. Wackernagel, M., Rees, W. (1996). Urban ecological footprints: Why cities cannot be sustainable - And why they are a key to sustainability. Environmental Impact Assessment Review, 16(4), 223-248.
20. Wang, S., Zhao, T., Zheng, H., Hu,Z. (2017). The STIRPAT analysis on carbon emission in Chinese cities: An asymmetric laplace distribution mixture model. Sustainability, 9(12), 1-13.
21. World Wide Fund for Nature-Duetschland. (2016). Living planet report 2016. Retrieved from https://www. wwf. de/fileadmin/fm-wwf/Publikationen-PDF/WWF-LivingPlanetReport-2016-Kurzfassung. pdf
22. York, R., Rosa, E.A., & Dietz, T. (2003a). STIRPAT, IPAT and IMPACT: Analytic tools for unpacking the driving forces of environmental impacts. Ecological economics, 46(3), 351-365.
23. York, R., Rosa, E.A., & Dietz, T. (2003b). A rift in modernity? Assessing the anthropogenic sources of global climate change with the STIRPAT model. International Journal of Sociology and Social Policy, 23(10), 31-51.
24. York, R., Rosa, E.A., Dietz, T. (2003c). Footprints on the earth: the environmental consequences of modernity. American Sociological Review, 68(2), 279-300.