Effects of Environmental Unsustainability on Income Inequality: A Panel Data Analysis

Öz

The main goal of this research is to empirically examine how environmental unsustainability influences income disparity. Utilizing globally aggregated data and panel data analysis methods, the relationship between environmental indicators such as carbon emissions, natural resource depletion, deforestation, and climate change and the Gini coefficient is examined. When the analyses are performed individually for developed and developing nations, the results indicate that environmental degradation intensifies income inequality to a greater extent in developing countries compared to developed ones. Based on the results obtained through the fixed effects model and the panel ARDL approach, it is determined that environmental degradation has a statistically significant and long-term exacerbating effect on income inequality. Granger causality tests indicate that environmental indicators have a unidirectional impact on income distribution inequity. However, the literature review suggests that this relationship is often examined as bidirectional. The findings of this research highlight the necessity of evaluating environmental and social policies in an integrated manner. In light of these results, it is recommended that environmental protection policies be shaped based on principles of fair and inclusive development to prevent the deepening of social inequalities.

Anahtar Kelimeler

• environmental unsustainability
• income inequality
• Gini coefficient
• carbon emissions
• panel data analysis
• climate vulnerability

Destekleyen Kurum

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The author extends sincere thanks to international organizations (e.g. the World Bank, ND-GAIN) for facilitating access to data. In addition, the author would like to express appreciation to colleagues who provided valuable insights and suggestions throughout the research process. Gratitude is also expressed to all sources and academic contributions that have, directly or indire

Etik Beyan

It is declared that scientific and ethical principles have been followed while carrying out and writing this study and that all the sources used have been properly cited.

Çevresel Sürdürülemezliğin Gelir Eşitsizliği Üzerindeki Etkileri: Panel Veri Analizi

Öz

Bu araştırmanın temel amacı, çevresel sürdürülemezliğin gelir eşitsizliği üzerindeki etkisini ampirik olarak incelemektir. Küresel olarak toplanmış veriler ve panel veri analizi yöntemleri kullanılarak, karbon emisyonları, doğal kaynakların tükenmesi, ormansızlaşma ve iklim değişikliği gibi çevresel göstergeler ile Gini katsayısı arasındaki ilişki araştırılmıştır. Analizler, gelişmiş ve gelişmekte olan ülkeler için ayrı ayrı yapıldığında, sonuçlar çevresel bozulmanın gelir eşitsizliğini özellikle gelişmekte olan ülkelerde daha fazla artırdığını göstermektedir. Sabit etkiler modeli ve panel ARDL yaklaşımlarıyla elde edilen bulgulara göre, çevresel bozulmanın istatistiksel olarak anlamlı ve uzun vadeli bir şekilde gelir eşitsizliğini artırıcı etkisi vardır. Granger nedensellik testleri, çevresel göstergelerin gelir dağılımı adaletsizliği üzerinde tek yönlü bir etkiye sahip olduğunu göstermektedir. Ancak, literatür incelemesi, bu ilişkinin sıklıkla iki yönlü olarak incelendiğine işaret etmektedir. Bu araştırmanın bulguları, çevresel ve sosyal politikaların bütünsel olarak değerlendirilmesi gerekliliğini vurgulamaktadır. Bu çerçevede, sonuçlar doğrultusunda, çevresel koruma politikalarının adil ve kapsayıcı kalkınma ilkelerine dayalı olarak şekillendirilmesi, sosyal eşitsizliklerin derinleşmesini önlemek açısından önemlidir.

Anahtar Kelimeler

• çevresel sürdürülemezlik
• gelir eşitsizliği
• Gini katsayısı
• karbon emisyonları
• panel veri analizi
• iklim kırılganlığı

Destekleyen Kurum

Çalışma, kamusal, özel, ticari nitelikte ya da kâr amacı gütmeyen herhangi bir kurumdan destek alınmadan hazırlanmıştır.

Etik Beyan

Bu çalışma sırasında ve yazılırken bilimsel ve etik ilklerin takip edildiği ve kullanılan tüm kaynakların düzgünce belirtildiği beyan edilmektedir.

Kaynakça

1. Acheampong, A. , Dzator, J. , Abunyewah, M. , Erdiaw-Kwasie, M. , and Opoku, E. (2023). Sub-Saharan Africa’s Tragedy: Resource Curse, Democracy and Income Inequality. Social Indicators Research, 168(2), 471-509. https://doi.org/10.1007/s11205-023-03030-2
2. Ali, I. M. A. (2022). Income inequality and environmental degradation in Egypt: evidence from dynamic ARDL approach. Environmental Science and Pollution Research, 29(6), 8408-8422. https://doi.org/10.1007/s11356-021-16275-2
3. Arellano, M. , and Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297. https://doi.org/10.2307/2297968
4. Barrett, C. B. and Swallow, B. M. (2006). Fractal poverty traps. World Development, 34(1), 1–15. https://doi.org/10.1016/j.worlddev.2005.06.008
5. Boyce, J. K. (1994). Inequality as a Cause of Environmental Degradation. Ecological Economics, 11(3), 169-178. https://doi.org/10.1016/0921-8009(94)90198-8
6. Ceddia, M. G. (2019). The impact of income, land, and wealth inequality on agricultural expansion in Latin America. PNAS, 116(7), 2527-2532. https://doi.org/10.1073/pnas.1814894116
7. Cevik, S. and Jalles, J. T. (2022). For Whom the Bell Tolls: Climate Change and Inequality. IMF Working Paper No. 22(103), 1-27. https://doi.org/10.5089/9798400208126.001
8. Diffenbaugh, N. S. , and Burke, M. (2019). Global warming has increased global economic inequality. PNAS, 116(20), 9808-9813. https://doi.org/10.1073/pnas.1816020116

9. Dumitrescu, E.-I. , and Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460. https://doi.org/10.1016/j.econmod.2012.02.014
10. Food an Agriculture Organization (FAO). (2022). The State of the World’s Forests. Food and Agriculture Organization of the United Nations Publications. https://www.fao.org/forest-resources-assessment/en/
11. Grossman, G.M. , and Krueger, A.B. (1995). Economic growth and the environment. The Quarterly Journal of Economics, 110(2), 353-377. https://doi.org/10.2307/2118443
12. Grunewald, N. , Klasen, S, Martínez-Zarzoso, I. , and Muris C. (2011). Income inequality and carbon emissions. Discussion Papers, No. 92, Georg-AugustUniversität Göttingen, Courant Research Centre - Poverty, Equity and Growth (CRC-PEG), Göttingen, 1-7. https://hdl.handle.net/10419/90461
13. Hadri, K. (2000). Testing for stationarity in heterogeneous panel data. The Econometrics Journal, 3(2), 148–161. https://doi.org/10.1111/1368-423X.00043
14. Im, K. S. , Pesaran, M. H. , and Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74. https://doi.org/10.1016/S0304-4076(03)00092-7
15. International Energy Agency (IEA). (2023). World Energy Outlook. https://www.iea.org/reports/world-energy-outlook-2023
16. International Energy Agency (IEA). (2023). CO2 Emissions in 2023. https://www.iea.org/reports/co2-emissions-in-2023
17. Kuznets, S. (1955). Economic Growth and Income Inequality. American Economic Review, 45(1), 1-28. https://assets.aeaweb.org/asset-server/files/9438.pdf
18. Levin, A. , Lin, C. F. , and Chu, C. S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1–24. https://doi.org/10.1016/S0304-4076(01)00098-7
19. Liu, Q. ,Wang, S. , Zhang, W. , Li, J. , and Kong, Y. (2019). Examining the effects of income inequality on CO2 emissions: Evidence from non-spatial and spatial perspectives. Applied Energy, 236, 163-171. https://doi. org/10.1016/j.apenergy.2018.11.082.
20. Notre Dame Global Adaptation Initiative (ND-GAIN). (2023). Country Index Data. https://gain.nd.edu/our-work/country-index/
21. Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61(S1), 653–670. https://doi.org/10.1111/1468-0084.0610s1653
22. Pedroni, P. (2004). Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory, 20(3), 597–625. https://doi.org/10.1017/S0266466604203073
23. Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. CESifo Working Paper Series, No. 1229. https://doi.org/10.2139/ssrn.572504
24. Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312. https://doi.org/10.1002/jae.951
25. Pesaran, M. H. , Shin, Y. , and Smith, R. P. (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, 94(446), 621–634. https://doi.org/10.1080/01621459.1999.10474156
26. Pesaran, M. H. , and Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142(1), 50–93. https://doi.org/10.1016/j.jeconom.2007.05.010
27. World Bank. (2020). Poverty and Shared Prosperity 2020: Reversals of Fortune. Washington, DC: World Bank Publications. https://www.worldbank.org/en/publication/poverty-and-shared-prosperity
28. United Nations Development Programme (UNDP). (2020). Human Development Report 2020: The Next Frontier – Human Development and the Anthropocene. http://hdr.undp.org/