MIKTA Ülkelerinde Çevresel Phillips Eğrisi Hipotezinin Test Edilmesi: CS-ARDL Testi Yaklaşımı

Öz

Ekonomik büyüme odaklı politikalar doğa üzerindeki baskıyı artırmakta ve çevresel kirliliğe yol açmaktadır. Bu nedenle ekonomik büyüme ve istihdam ile eş zamanlı olarak çevrenin korunması günümüzde ülkeler için önemli bir kalkınma önceliği haline gelmiştir. Bu bağlamda çalışmanın amacı, 1991–2018 döneminde MIKTA ülkelerinde (Meksika, Endonezya, Güney Kore, Türkiye ve Avustralya) çevresel kirlilik ve işsizlik arasında negatif bir ilişki olduğunu varsayan Çevresel Phillips Eğrisi (EPC) hipotezini Narayan ve Narayan (2010)’ın Çevresel Kuznets Eğrisi (EKC) hipotezi yaklaşımı bağlamında test etmektir. Bu amaçla yapılan çalışmada çevresel kirlilik göstergesi olarak ekolojik ayak izi (ECF) kullanılmıştır. Ayrıca Durbin–Hausman eşbütünleşme testi ile uzun dönemli ilişki, yatay kesit genişletilmiş ARDL (CS–ARDL) tahmincisi ile de uzun dönem katsayılar tahmin edilmiştir. Analiz sonucunda MIKTA ülkelerinde hem kısa hem de uzun dönemde EPC hipotezinin geçerli olduğu, diğer bir ifade ile işsizliğin çevresel kirliliği azalttığı tespit edilmiştir. Bu bulgudan hareketle, MIKTA ülkelerine daha az kirliliğe neden olan sektörlerde istihdamı teşvik etmeleri, yoğun kirlilik oluşturan sektörlerde ise çevresel standartları yükseltmeleri önerilmektedir.

Anahtar Kelimeler

• Sürdürülebilir Kalkınma
• Ekolojik Ayak İzi
• Çevresel Phillips Eğrisi Hipotezi
• Çevresel Kuznets Eğrisi Hipotezi
• CS–ARDL Tahmincisi.

Destekleyen Kurum

Çalışma herhangi bir kurum tarafından desteklenmemektedir.

Testing the Environmental Phillips Curve Hypothesis in MIKTA Countries: CS-ARDL Test Approach

Öz

Policies that focus on economic growth increase the pressure on nature and cause environmental pollution. Therefore, environmental protection simultaneously with economic growth and employment has become an important development priority for countries today. In this context, the aim of the study is to test the Environmental Phillips Curve (EPC) hypothesis, which assumes a negative relationship between environmental pollution and unemployment, for MIKTA countries (Mexico, Indonesia, South Korea, Turkey, and Australia) in the period of 1991–2018 by drawing on Environmental Kuznets Curve (EKC) hypothesis approach of Narayan and Narayan (2010). To this end, ecological footprint (ECF) is used as an indicator of environmental pollution. Moreover, the long-term relationship is estimated with the Durbin–Hausman cointegration test and the long-term coefficients are estimated with the cross-section augmented ARDL (CS–ARDL) estimator. As a result of the analysis, it is determined that the EPC hypothesis is valid both in the short and long term in MIKTA countries, in other words, unemployment reduces environmental pollution. Based on this finding, MIKTA countries are recommended to encourage employment in sectors that cause less pollution, and to raise environmental standards in sectors that cause heavy pollution.

Anahtar Kelimeler

• Sustainable Development
• Ecological Footprint
• Environmental Phillips Curve Hypothesis
• Environmental Kuznets Curve Hypothesis
• CS–ARDL Estimator.

Kaynakça

1. Al-Mulali, U., Solarin, S. A., & Ozturk, I. (2016). Investigating the presence of the environmental Kuznets curve (EKC) hypothesis in Kenya: An autoregressive distributed lag (ARDL) approach. Natural Hazards, 80(3), 1729-1747.
2. Andrée, B. P. J., Chamorro, A., Spencer, P., Koomen, E., & Dogo, H. (2019). Revisiting the relation between economic growth and the environment; a global assessment of deforestation, pollution and carbon emission. Renewable and Sustainable Energy Reviews, 114, 1-16.
3. Anser, M. K., Apergis, N., Syed, Q. R., & Alola, A. A. (2021). Exploring a new perspective of sustainable development drive through environmental Phillips curve in the case of the BRICST countries. Environmental Science and Pollution Research, 28(35), 48112-48122.
4. Bhowmik, R., Syed, Q. R., Apergis, N., Alola, A. A., & Gai, Z. (2022). Applying a dynamic ARDL approach to the Environmental Phillips Curve (EPC) hypothesis amid monetary, fiscal, and trade policy uncertainty in the USA. Environmental Science and Pollution Research, 29(10), 14914-14928.
5. Breusch, T. S., & Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. The Review of Economic Studies, 47(1), 239-253.
6. Chudik, A., & Pesaran, M. H. (2015). Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors. Journal of Econometrics, 188(2), 393-420.
7. Cole, M. A. (2004). Trade, the pollution haven hypothesis and the environmental Kuznets curve: Examining the linkages. Ecological Economics, 48(1), 71-81.
8. Cole, M. A., Rayner, A. J., & Bates, J. M. (1997). The environmental Kuznets curve: An empirical analysis. Environment and Development Economics, 2(4), 401-416.

9. Cooper, A. F. (2018). “Rising” states and global reach: Measuring “Globality” among BRICS/MIKTA countries. Global Summitry, 4(1), 64-80.
10. Dasgupta, S., Laplante, B., Wang, H., & Wheeler, D. (2002). Confronting the environmental Kuznets curve. Journal of Economic Perspectives, 16(1), 147-168.
11. Dinda, S. (2004). Environmental Kuznets curve hypothesis: A survey. Ecological Economics, 49(4), 431-455.
12. Ditzen, J. (2018). Estimating dynamic common-correlated effects in Stata. The Stata Journal, 18(3), 585-617.
13. GFN. (2022). Global Footprint Network. https://data.footprintnetwork.org.
14. Grossman, G. M., & Krueger, A. B. (1991). Environmental Impacts of a North American Free Trade Agreement. NBER Working Papers Series, 1-39.
15. Grossman, G. M., & Krueger, A. B. (1995). Economic growth and the environment. The Quarterly Journal of Economics, 110(2), 353-377.
16. IEA. (2022). International Energy Agency. https://www.iea.org/data-and-statistics. Jeong, M. S. (2019). Critical realism: A better way to think about middle powers. International Journal, 74(2), 240-257.
17. Kashem, M. A., & Rahman, M. M. (2020). Environmental Phillips curve: OECD and Asian NICs perspective. Environmental Science and Pollution Research, 27(25), 31153-31170.
18. Kearsley, A., & Riddel, M. (2010). A further inquiry into the pollution haven hypothesis and the environmental Kuznets curve. Ecological Economics, 69(4), 905-919.
19. Kijima, M., Nishide, K., & Ohyama, A. (2010). Economic models for the environmental Kuznets curve: A survey. Journal of Economic Dynamics and Control, 34(7), 1187-1201.
20. Kim, S. M., Haug, S., & Rimmer, S. H. (2018). Minilateralism revisited: MIKTA as slender diplomacy in a multiplex world. Global Governance: A Review of Multilateralism and International Organizations, 24(4), 475-489.
21. Mert, M., & Caglar, A. E. (2020). Testing pollution haven and pollution halo hypotheses for Turkey: A new perspective. Environmental Science and Pollution Research, 27(26), 32933-32943.
22. Narayan, P. K., & Narayan, S. (2010). Carbon dioxide emissions and economic growth: Panel data evidence from developing countries. Energy Policy, 38(1), 661-666.
23. Narayan, P. K., Saboori, B., & Soleymani, A. (2016). Economic growth and carbon emissions. Economic Modelling, 53, 388-397.
24. Ng, C. F., Yii, K. J., Lau, L. S., & Go, Y. H. (2022). Unemployment rate, clean energy, and ecological footprint in OECD countries. Environmental Science and Pollution Research, 1-10.
25. Panayotou, T. (1993). Empirical tests and policy analysis of environmental degradation at different stages of economic development. International Labour Organization, 1-42.
26. Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. Cambridge Working Papers in Economics, (0435), 1-39.
27. Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74(4), 967-1012.
28. 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.
29. Pesaran, M. H., Ullah, A., & Yamagata, T. (2008). A bias‐adjusted LM test of error cross‐section independence. The Econometrics Journal, 11(1), 105-127.
30. Pesaran, M. H., & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142(1), 50-93.
31. Rees, W. E. (1992). Ecological footprints and appropriated carrying capacity: What urban economics leaves out. Environment & Urbanization, 4(2), 121-130.
32. Solarin, S. A. (2019). Convergence in CO2 emissions, carbon footprint and ecological footprint: Evidence from OECD countries. Environmental Science and Pollution Research, 26(6), 6167–6181.
33. Solarin, S. A., Al-Mulali, U., Musah, I., & Ozturk, I. (2017). Investigating the pollution haven hypothesis in Ghana: An empirical investigation. Energy, 124, 706-719.
34. Stern, D. I. (2004). The rise and fall of the environmental Kuznets curve. World Development, 32(8), 1419-1439.
35. Tanveer, A., Song, H., Faheem, M., & Chaudhry, I. S. (2021). Validation of environmental Phillips curve in Pakistan: A fresh insight through ARDL technique. Environmental Science and Pollution Research, 1-18.
36. Tariq, S., Mehmood, U., & Mariam, A. (2022). Exploring the existence of environmental Phillips curve in South Asian countries. Environmental Science and Pollution Research, 1-12.
37. Wachernagel, M., & Rees, W. (1996). Our ecological footprint: Reducing human impact on the earth. New Society Publishers, 1-29.
38. Wang, K. H., Liu, L., Adebayo, T. S., Lobonț, O. R., & Claudia, M. N. (2021). Fiscal decentralization, political stability and resources curse hypothesis: A case of fiscal decentralized economies. Resources Policy, 72, 102071.
39. WDI. (2022). World Development Indicators. https://databank.worldbank.org.
40. Westerlund, J. (2008). Panel cointegration tests of the Fisher Effect. Journal of Applied Econometrics, 23(2), 193-233.
41. Yılancı, V., & Pata, U. K. (2020). Investigating the EKC hypothesis for China: The role of economic complexity on ecological footprint. Environmental Science and Pollution Research, 27(26), 32683-32694.