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Environmental dimension of innovation: time series evidence from Turkey

  • Caner DemirEmail author
  • Raif Cergibozan
  • Ali Ari
Article
  • 71 Downloads

Abstract

The study aims to investigate whether domestic innovation reduces environmental degradation in Turkey. Since the empirical literature on this subject is relatively poor and there is no empirical evidence for the Turkish case, the study attempts to bring a new perspective to the existing literature. To do this, the study estimates the relationship between innovation and CO2 emissions over the period of 1971–2013, via the ARDL bounds test and threshold cointegration test. Empirical results obtained from the ARDL approach indicates that the relationship between CO2 emission level and number of domestic patents depicts an inverted U-shape curve for Turkey. Moreover, estimation results show that urbanization, income level and financial development have positive effects on CO2 emissions, while alternative energy sources and human capital negatively affect the emission level. Since the linear econometric methods may yield inconsistent and biased results in the presence of a nonlinear relationship, the threshold cointegration method is employed as a robustness check. The findings obtained from threshold cointegration confirm the existence of a nonlinear relationship between CO2 emissions and domestic innovation. This suggests that for early stages of economic development, increases in domestic innovation raise the CO2 emission level in Turkey, but after achieving a certain development level, increases in domestic innovation lead to decreases in CO2 emissions. Thus, either developing or developed countries can eventually reduce CO2 emission levels by concentrating on innovation. Policy makers and institutions dealing with environmental issues should certainly pay attention to innovation and technological progress to assure a sustainable growth path.

Keywords

Innovation Environmental degradation CO2 emissions ARDL bounds test Threshold cointegration 

JEL Classifications

Q5 O44 C24 

Notes

References

  1. Aghion, P., & Howitt, P. (1992). A model of growth through creative destruction. Econometrica, 60(2), 323–351.CrossRefGoogle Scholar
  2. Agras, J., & Chapman, D. (1999). A dynamic approach to the Environmental Kuznets Curve hypothesis. Ecological Economics, 28(2), 267–277.  https://doi.org/10.1016/S0921-8009(98)00040-8.CrossRefGoogle Scholar
  3. Andrews, D. W. K. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica, 61, 821–856.CrossRefGoogle Scholar
  4. Andrews, D. W. K., & Ploberger, W. (1994). Optimal tests when a nuisance parameter is present only under the alternative. Econometrica, 62, 1383–1414.  https://doi.org/10.2307/2951753.CrossRefGoogle Scholar
  5. Arrow, K. J. (1962). The economic implications of learning by doing. Review of Economic Studies, 29, 155–173.CrossRefGoogle Scholar
  6. Baek, J., & Gweisah, G. (2011). Does income inequality harm the environment? Empirical evidence from the United States. Energy Policy, 62, 1434–1437.  https://doi.org/10.1016/j.enpol.2013.07.097.CrossRefGoogle Scholar
  7. Baek, J., & Kim, H. S. (2013). Is economic growth good or bad for the environment? Empirical Evidence from Korea, Energy Economics, 36, 744–749.  https://doi.org/10.1016/j.eneco.2012.11.020.Google Scholar
  8. Balke, N. S., & Fomby, T. B. (1997). Threshold cointegration. International Economic Review, 38, 627–645.  https://doi.org/10.2307/2527284.CrossRefGoogle Scholar
  9. Beckerman, W. (1992). Economic growth and the environment: whose growth? Whose environment? World Development, 20, 481–496.  https://doi.org/10.1016/0305-750X(92)90038-W.CrossRefGoogle Scholar
  10. Beghin, J., & Poitier, M. (1997). Effects of trade liberalization on the environment in the manufacturing sector. The World Economy, 20(4), 337–347.CrossRefGoogle Scholar
  11. Bhagawati, J. (1993). The case for free trade. Scientific American, 42–49.  https://doi.org/10.1038/scientificamerican1193-42.
  12. Chevallier, J. (2011). Evaluating the carbon-macroeconomy relationship: Evidence from threshold vector error-correction and Markov-switching VAR models. Economic Modelling, 28(6), 2634–2656.  https://doi.org/10.1016/j.econmod.2011.08.003.CrossRefGoogle Scholar
  13. Dasgupta, S., Laplante, B., Wang, H., & Wheeler, D. (2002). Confronting the Environmental Kuznets Curve. Journal of Economic Perspectives, 16(1), 147–168.  https://doi.org/10.1257/0895330027157.CrossRefGoogle Scholar
  14. Davies, R. B. (1987). Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika, 74, 33–43.Google Scholar
  15. DeLong, J.B. (1998). Estimates of world GDP, one million BC–Present. Working Paper, UC Berkeley.Google Scholar
  16. Dinda, S. (2004). Environmental Kuznets curve hypothesis: A survey. Ecological Economics, 49(4), 431–455.  https://doi.org/10.1016/j.ecolecon.2004.02.011.CrossRefGoogle Scholar
  17. Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55, 251–276.CrossRefGoogle Scholar
  18. Esteve, V., & Tamarit, C. (2012). Threshold cointegration and nonlinear adjustment between CO 2 and income: The Environmental Kuznets Curve in Spain, 1857–2007. Energy Economics, 34(6), 2148–2156.CrossRefGoogle Scholar
  19. Feenstra, R.C., Robert I., & Marcel P.T. (2015). The next generation of the Penn World Table. American Economic Review, 105(10), 3150–3182. Available for download at www.ggdc.net/pwt.
  20. Fernández, Y. F., López, M. F., & Blanco, B. O. (2018). Innovation for sustainability: The impact of R&D spending on CO2 emissions. Journal of Cleaner Production, 172, 3459–3467.CrossRefGoogle Scholar
  21. Grossman, G. H., & Helpman, E. (1991). Innovation and growth in the global economy. Cambridge: MIT Press.Google Scholar
  22. Grossman, G.M., & Krueger, A.B. (1991). Environmental impacts of the North American Free Trade Agreement. NBER. Working paper 3914.Google Scholar
  23. Hansen, B. E., & Seo, B. (2002). Testing for two-regime threshold cointegration in vector error-correction models. Journal of econometrics, 110(2), 293–318.  https://doi.org/10.1016/S0304-4076(02)00097-0.CrossRefGoogle Scholar
  24. Iwata, H., Okada, K., & Samreth, S. (2010). Empirical study on the Environmental Kuznets Curve for CO2 in France: The role of nuclear energy. Energy Policy, 38, 4057–4063.  https://doi.org/10.1016/j.enpol.2010.03.031.CrossRefGoogle Scholar
  25. Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2–3), 231–254.  https://doi.org/10.1016/0165-1889(88)90041-3.CrossRefGoogle Scholar
  26. Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration—With applications to the demand of money. Oxford Bulletin of Economics and Statistics, 52(2), 169–210.  https://doi.org/10.1111/j.1468-0084.1990.mp52002003.x.CrossRefGoogle Scholar
  27. Kim, H. S., & Baek, J. (2011). The environmental consequences of economic growth revisited. Economics Bulletin, 31, 1198–1211.Google Scholar
  28. Komen, R., Gerking, S., & Folmer, H. (1997). Income and environmental R&D: Empirical evidence from OECD countries. Environment and Development Economics, 2, 505–515.CrossRefGoogle Scholar
  29. Kuznets, S. (1955). Economic growth and income inequality. American Economic Review, 49, 1–28.Google Scholar
  30. Lopez, R., & Mitra, S. (2000). Corruption, pollution, and the Kuznets Environment Curve. Journal of Environmental Economics and Management, 40(2), 137–150.CrossRefGoogle Scholar
  31. Lucas, R. E. Jr., (1988). On the mechanics of economic development. Journal of Monetary Economics, 22, 3–42.  https://doi.org/10.1016/0304-3932(88)90168-7.CrossRefGoogle Scholar
  32. Martin, P., & Wheeler, D. (1992). Price, policies and the international diffusion of clean technology: The case of wood pulp production. In P. Low (Ed.), International trade and the environment (pp. 197–224). Washington: World Bank.Google Scholar
  33. Panopoulou, E., & Pittis, N. (2004). A composition of autoregressive distributed lag and dynamic OLS cointegration estimators in the case of serially correlated cointegration error. Econometrics, 7, 585–617.CrossRefGoogle Scholar
  34. Pesaran, M. H., & Shin, Y. (1999). An autoregressive distributed lag modelling approach to cointegration analysis. In S. Strom (Ed.), Econometrics and economic theory in the 20th century: The Ragnar Frisch centennial symposium (pp. 371–413). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  35. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16, 289–326.  https://doi.org/10.1002/jae.616.CrossRefGoogle Scholar
  36. Rafindadi, A. A. (2014). Econometric prediction on the effects of financial development and trade openness on the German energy consumption: A startling revelation from the data set. International Journal of Energy Economics and Policy, 5(1), 182–196.Google Scholar
  37. Rafindadi, A. A. (2016). Does the need for economic growth influence energy consumption and CO2 emissions in Nigeria? Evidence from the innovation accounting test. Renewable and Sustainable Energy Reviews, 62, 1209–1225.CrossRefGoogle Scholar
  38. Rafindadi, A. A., & Ozturk, I. (2016). Effects of financial development, economic growth and trade on electricity consumption: Evidence from post-Fukushima Japan. Renewable and Sustainable Energy Reviews, 54, 1073–1084.CrossRefGoogle Scholar
  39. Rafindadi, A. A., & Ozturk, I. (2017). Dynamic effects of financial development, trade openness and economic growth on energy consumption: Evidence from South Africa. International Journal of Energy Economics and Policy, 7(3), 74–85.Google Scholar
  40. Rebelo, S. (1991). Long-run policy analysis and long-run growth. Journal of Political Economy, 99(3), 500–521.CrossRefGoogle Scholar
  41. Romer, P. M. (1986). Increasing returns and long-run growth. Journal of Political Economy, 94(5), 1002–1037.  https://doi.org/10.1086/261420.CrossRefGoogle Scholar
  42. Romer, P. M. (1990). Endogenous technological change. Journal of political Economy, 98(5, Part 2), S71–S102.CrossRefGoogle Scholar
  43. Samargandi, N. (2017). Sector value addition, technology and CO2 emissions in Saudi Arabia. Renewable and Sustainable Energy Reviews, 78, 868–877.CrossRefGoogle Scholar
  44. Santra, S. (2017). The effect of technological innovation on production-based energy and CO2 emission productivity: Evidence from BRICS countries. African Journal of Science, Technology, Innovation and Development, 9(5), 503–512.CrossRefGoogle Scholar
  45. Sarkodie, S. A., & Adams, S. (2018). Renewable energy, nuclear energy, and environmental pollution: Accounting for political institutional quality in South Africa. Science of the Total Environment, 643, 1590–1601.CrossRefGoogle Scholar
  46. Sarkodie, S. A., & Strezov, V. (2019). Effect of foreign direct investments, economic development and energy consumption on greenhouse gas emissions in developing countries. Science of the Total Environment, 646, 862–871.CrossRefGoogle Scholar
  47. Selden, T., & Song, D. (1994). Environmental quality and development: is there a Kuznets Curve for air pollution emissions? Journal of Environmental Economics and management, 27, 147–162.CrossRefGoogle Scholar
  48. Shahbaz, M., Nasir, M. A., & Roubaud, D. (2018). Environmental degradation in France: The effects of FDI, financial development, and energy innovations. Energy Economics, 74, 843–857.CrossRefGoogle Scholar
  49. Uzawa, H. (1965). Optimum technical change in an aggregative model of economic growth. International economic review, 6(1), 18–31.CrossRefGoogle Scholar
  50. Vukina, T., Beghin, J. C., & Solakoglu, E. G. (1999). Transition to markets and the environment: Effects of the change in the composition of manufacturing output. Environment and Development Economics, 4(4), 582–598.  https://doi.org/10.1017/S1355770X99000340.CrossRefGoogle Scholar
  51. Wang, S., Li, G., & Fang, C. (2018). Urbanization, economic growth, energy consumption, and CO2 emissions: Empirical evidence from countries with different income levels. Renewable and Sustainable Energy Reviews, 81, 2144–2159.CrossRefGoogle Scholar
  52. Xiaoli, H., & Chatterjee, L. (1997). Impact of growth and structural change on CO2 emissions of developing countries. World Development, 25(3), 395–407.CrossRefGoogle Scholar
  53. Yii, K. J., & Geetha, C. (2017). The nexus between technology innovation and CO2 emissions in Malaysia: Evidence from granger causality test. Energy Procedia, 105, 3118–3124.CrossRefGoogle Scholar
  54. Zivot, E., & Andrews, D. W. K. (2002). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 20(1), 25–44.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of EconomicsManisa Celal Bayar UniversitySalihli, ManisaTurkey
  2. 2.Department of EconomicsKırklareli UniversityKırklareliTurkey
  3. 3.Department of Political Science and Public AdministrationMarmara UniversityIstanbulTurkey

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