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DO ENVIRONMENTAL AND ECONOMIC FACTORS MATTER FOR INNOVATION? EVIDENCE FROM OIL-IMPORTING AND OIL-EXPORTING COUNTRIES

Yıl 2022, Sayı: 36, 95 - 110, 07.08.2022
https://doi.org/10.18092/ulikidince.1014615

Öz

The purpose of this paper is to investigate the effect of gross domestic product per capita, trade openness, renewable energy, energy consumption, foreign direct investment, carbon emission, and oil prices on innovation for selected 11 oil-importing and 11 oil-exporting countries and to compare the results from both country groups to see the differences and similarities. For this purpose, we employ Poisson regression and negative binomial fixed effect techniques from 1990 to 2018. The empirical findings illustrate that all variables are significant except for renewable energy in oil-exporting countries. Trade openness and carbon emission have a significant and negative relationship with innovation, while gross domestic product per capita, energy consumption, foreign direct investment, and oil price have a significant and positive relationship with innovation in oil-exporting countries. Gross domestic product per capita, energy consumption, and carbon emission have a significant and positive relationship with innovation in oil-importing countries, while there is a significant and negative relationship between renewable energy and innovation.

Kaynakça

  • Ahmed, A., Uddin, G. S., & Sohag, K. (2016). Biomass energy, technological progress and the environmental Kuznets curve: Evidence from selected European countries. Biomass and Bioenergy, 90, 202–208. https://doi.org/10.1016/j.biombioe.2016.04.004
  • Awaworyi Churchill, S., Inekwe, J., Smyth, R., & Zhang, X. (2019). R&D intensity and carbon emissions in the G7: 1870–2014. Energy Economics, 80, 30–37. https://doi.org/10.1016/j.eneco.2018.12.020
  • Baltagi, B. H. (2013). Econometric Analysis of Panel Data - Fifth Edition. In John Wiley & Sons, 2013.
  • Bhattacharya, M., & Bloch, H. (2004). Determinants of innovation. Small Business Economics. https://doi.org/10.1023/B:SBEJ.0000014453.94445.de
  • 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. https://doi.org/10.2307/2297111
  • Cameron, A. C., & Trivedi, P. K. (2013). Regression Analysis of Count Data. Cambridge University Press.
  • Chen, Y., & Lee, C.-C. (2020). Does technological innovation reduce CO2 emissions?Cross-country evidence. Journal of Cleaner Production, 263, 121550. https://doi.org/10.1016/j.jclepro.2020.121550
  • Cheng, C., Ren, X., & Wang, Z. (2019). The impact of renewable energy and innovation on carbon emission: An empirical analysis for OECD countries. Energy Procedia, 158, 3506–3512. https://doi.org/10.1016/j.egypro.2019.01.919
  • de Jesus, D. P., Lenin Souza Bezerra, B. F., & da Nóbrega Besarria, C. (2020). The non-linear relationship between oil prices and stock prices: Evidence from oil-importing and oil-exporting countries. Research in International Business and Finance, 54, 101229. https://doi.org/10.1016/j.ribaf.2020.101229
  • de Rassenfosse, G., & van Pottelsberghe de la Potterie, B. (2009). A policy insight into the R&D–patent relationship. Research Policy, 38(5), 779–792. https://doi.org/10.1016/j.respol.2008.12.013
  • Dutta, S., Lanvin, B., & Wunsch-Vincent, S. (2019). The Global Innovation Index 2019: Creating Healthy Lives-The Future of Medical Innovation. https://www.wipo.int/edocs/pubdocs/en/wipo_pub_gii_2019.pdf
  • Fei, Q., Rasiah, R., & Shen, L. J. (2014). The Clean Energy-Growth Nexus with CO 2 Emissions and Technological Innovation in Norway and New Zealand. Energy & Environment, 25(8), 1323–1344. https://doi.org/10.1260/0958-305X.25.8.1323
  • Ghimire, S., & Paudel, N. S. (2019). R&D, FDI, and Innovation: Examination of the Patent Applications in the OECD Countries. Journal of Development Innovations, 3(2).
  • Gu, G., & Wang, Z. (2018). Research on global carbon abatement driven by R&D investment in the context of INDCs. Energy, 148, 662–675. https://doi.org/10.1016/j.energy.2018.01.142
  • Guillouzouic-Le Corff, A. (2018). Did oil prices trigger an innovation burst in biofuels? Energy Economics, 75, 547–559. https://doi.org/10.1016/j.eneco.2018.08.031
  • Guloglu, B., Tekin, R. B., & Saridogan, E. (2012). Economic determinants of technological progress in G7 countries: A re-examination. Economics Letters, 116(3), 604–608. https://doi.org/10.1016/j.econlet.2012.06.012
  • 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
  • Hadri, K., & Kurozumi, E. (2012). A simple panel stationarity test in the presence of serial correlation and a common factor. Economics Letters, 115, 31–34. https://doi.org/10.1016/j.econlet.2011.11.036
  • Hausman, J., Hall, B. H., & Griliches, Z. (1984). Econometric Models for Count Data with an Application to the Patents-R & D Relationship. Econometrica, 52(4), 909–938. https://doi.org/10.2307/1911191
  • Johnstone, N., Haščič, I., & Popp, D. (2010). Renewable Energy Policies and Technological Innovation: Evidence Based on Patent Counts. Environmental and Resource Economics, 45(1), 133–155. https://doi.org/10.1007/s10640-009-9309-1
  • Kirikkaleli, D., Ozun, A., & Sari, A. (2018). How can policy makers foster innovation? Observations from an analysis of OECD countries. Innovation: The European Journal of Social Science Research, 1–14. https://doi.org/10.1080/13511610.2018.1504674
  • Mokni, K. (2020). Time-varying effect of oil price shocks on the stock market returns: Evidence from oil-importing and oil-exporting countries. Energy Reports, 6, 605–619. https://doi.org/10.1016/j.egyr.2020.03.002
  • Palmgren, J. (1981). The fisher information matrix for log linear models arguing conditionally on observed explanatory variable. Biometrika, 68(2), 563–566. https://doi.org/10.1093/biomet/68.2.563
  • Pesaran, M. H. (2004). General diagnostic tests for cross-sectional dependence in panels. Discussion Paper Series, IZA Discussion Paper No.1240. https://doi.org/10.1007/s00181-020-01875-7
  • Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics. https://doi.org/10.1002/jae.951
  • Pesaran, M. H., Ullah, A., & Yamagata, T. (2008). A bias-adjusted LM test of error cross-section independence. Econometrics Journal. https://doi.org/10.1111/j.1368-423X.2007.00227.x
  • Phillips, P., & Perron, P. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75(2), 335–346.
  • Riti, J. S., Song, D., Shu, Y., & Kamah, M. (2017). Decoupling CO2 emission and economic growth in China: Is there consistency in estimation results in analyzing environmental Kuznets curve? Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2017.08.117
  • Song, M., Wang, S., & Zhang, H. (2020). Could environmental regulation and R&D tax incentives affect green product innovation? Journal of Cleaner Production, 258, 120849. https://doi.org/10.1016/j.jclepro.2020.120849
  • Su, H.-N., & Moaniba, I. M. (2017). Does innovation respond to climate change? Empirical evidence from patents and greenhouse gas emissions. Technological Forecasting and Social Change, 122, 49–62. https://doi.org/10.1016/j.techfore.2017.04.017
  • Wen, D., Liu, L., Ma, C., & Wang, Y. (2020). Extreme risk spillovers between crude oil prices and the U.S. exchange rate: Evidence from oil-exporting and oil-importing countries. Energy, 212, 118740. https://doi.org/10.1016/j.energy.2020.118740
  • Westerlund, J. (2008). Panel Cointegration Tests of the Fisher Effect. Journal of Applied Econometrics, 23(2), 193–233. https://doi.org/10.1002/jae
  • Zhang, Y.-J., Peng, Y.-L., Ma, C.-Q., & Shen, B. (2017). Can environmental innovation facilitate carbon emissions reduction? Evidence from China. Energy Policy, 100, 18–28. https://doi.org/10.1016/j.enpol.2016.10.005

İNOVASYON İÇİN ÇEVRESEL VE EKONOMİK FAKTÖRLER ÖNEMLİ MİDİR? PETROL İTHAL EDEN VE PETROL İHRAÇ EDEN ÜLKELER ÖRNEĞİ

Yıl 2022, Sayı: 36, 95 - 110, 07.08.2022
https://doi.org/10.18092/ulikidince.1014615

Öz

Bu çalışmanın amacı, seçilmiş 11 petrol ithal eden ve 11 petrol ihraç eden ülkeler için GDP, ticari açıklık, yenilenebilir enerji, enerji tüketimi, doğrudan yabancı yatırımı, karbon emisyonu ve petrol fiyatlarının inovasyon üzerindeki etkisini araştırmaktır. Elde edilen sonuçlara göre petrol ihraç eden ülkelerde yenilenebilir enerji dışındaki tüm değişkenlerin anlamlı olduğu görülmektedir. Petrol ihraç eden ülkelerde ise ticari açıklık ve karbon emisyonunun inovasyonla olan ilişkisi anlamlı ve negatifken, GDP, enerji tüketimi, doğrudan yabancı yatırımı ve petrol fiyatı inovasyonla anlamlı ve pozitif bir ilişkiye sahiptir. Petrol ithal eden ülkelerde GDP, enerji tüketimi ve karbon emisyonu inovasyonla anlamlı ve pozitif bir ilişkiye sahipken, yenilenebilir enerji ile inovasyon arasında anlamlı ve negatif bir ilişki vardır.

Kaynakça

  • Ahmed, A., Uddin, G. S., & Sohag, K. (2016). Biomass energy, technological progress and the environmental Kuznets curve: Evidence from selected European countries. Biomass and Bioenergy, 90, 202–208. https://doi.org/10.1016/j.biombioe.2016.04.004
  • Awaworyi Churchill, S., Inekwe, J., Smyth, R., & Zhang, X. (2019). R&D intensity and carbon emissions in the G7: 1870–2014. Energy Economics, 80, 30–37. https://doi.org/10.1016/j.eneco.2018.12.020
  • Baltagi, B. H. (2013). Econometric Analysis of Panel Data - Fifth Edition. In John Wiley & Sons, 2013.
  • Bhattacharya, M., & Bloch, H. (2004). Determinants of innovation. Small Business Economics. https://doi.org/10.1023/B:SBEJ.0000014453.94445.de
  • 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. https://doi.org/10.2307/2297111
  • Cameron, A. C., & Trivedi, P. K. (2013). Regression Analysis of Count Data. Cambridge University Press.
  • Chen, Y., & Lee, C.-C. (2020). Does technological innovation reduce CO2 emissions?Cross-country evidence. Journal of Cleaner Production, 263, 121550. https://doi.org/10.1016/j.jclepro.2020.121550
  • Cheng, C., Ren, X., & Wang, Z. (2019). The impact of renewable energy and innovation on carbon emission: An empirical analysis for OECD countries. Energy Procedia, 158, 3506–3512. https://doi.org/10.1016/j.egypro.2019.01.919
  • de Jesus, D. P., Lenin Souza Bezerra, B. F., & da Nóbrega Besarria, C. (2020). The non-linear relationship between oil prices and stock prices: Evidence from oil-importing and oil-exporting countries. Research in International Business and Finance, 54, 101229. https://doi.org/10.1016/j.ribaf.2020.101229
  • de Rassenfosse, G., & van Pottelsberghe de la Potterie, B. (2009). A policy insight into the R&D–patent relationship. Research Policy, 38(5), 779–792. https://doi.org/10.1016/j.respol.2008.12.013
  • Dutta, S., Lanvin, B., & Wunsch-Vincent, S. (2019). The Global Innovation Index 2019: Creating Healthy Lives-The Future of Medical Innovation. https://www.wipo.int/edocs/pubdocs/en/wipo_pub_gii_2019.pdf
  • Fei, Q., Rasiah, R., & Shen, L. J. (2014). The Clean Energy-Growth Nexus with CO 2 Emissions and Technological Innovation in Norway and New Zealand. Energy & Environment, 25(8), 1323–1344. https://doi.org/10.1260/0958-305X.25.8.1323
  • Ghimire, S., & Paudel, N. S. (2019). R&D, FDI, and Innovation: Examination of the Patent Applications in the OECD Countries. Journal of Development Innovations, 3(2).
  • Gu, G., & Wang, Z. (2018). Research on global carbon abatement driven by R&D investment in the context of INDCs. Energy, 148, 662–675. https://doi.org/10.1016/j.energy.2018.01.142
  • Guillouzouic-Le Corff, A. (2018). Did oil prices trigger an innovation burst in biofuels? Energy Economics, 75, 547–559. https://doi.org/10.1016/j.eneco.2018.08.031
  • Guloglu, B., Tekin, R. B., & Saridogan, E. (2012). Economic determinants of technological progress in G7 countries: A re-examination. Economics Letters, 116(3), 604–608. https://doi.org/10.1016/j.econlet.2012.06.012
  • 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
  • Hadri, K., & Kurozumi, E. (2012). A simple panel stationarity test in the presence of serial correlation and a common factor. Economics Letters, 115, 31–34. https://doi.org/10.1016/j.econlet.2011.11.036
  • Hausman, J., Hall, B. H., & Griliches, Z. (1984). Econometric Models for Count Data with an Application to the Patents-R & D Relationship. Econometrica, 52(4), 909–938. https://doi.org/10.2307/1911191
  • Johnstone, N., Haščič, I., & Popp, D. (2010). Renewable Energy Policies and Technological Innovation: Evidence Based on Patent Counts. Environmental and Resource Economics, 45(1), 133–155. https://doi.org/10.1007/s10640-009-9309-1
  • Kirikkaleli, D., Ozun, A., & Sari, A. (2018). How can policy makers foster innovation? Observations from an analysis of OECD countries. Innovation: The European Journal of Social Science Research, 1–14. https://doi.org/10.1080/13511610.2018.1504674
  • Mokni, K. (2020). Time-varying effect of oil price shocks on the stock market returns: Evidence from oil-importing and oil-exporting countries. Energy Reports, 6, 605–619. https://doi.org/10.1016/j.egyr.2020.03.002
  • Palmgren, J. (1981). The fisher information matrix for log linear models arguing conditionally on observed explanatory variable. Biometrika, 68(2), 563–566. https://doi.org/10.1093/biomet/68.2.563
  • Pesaran, M. H. (2004). General diagnostic tests for cross-sectional dependence in panels. Discussion Paper Series, IZA Discussion Paper No.1240. https://doi.org/10.1007/s00181-020-01875-7
  • Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics. https://doi.org/10.1002/jae.951
  • Pesaran, M. H., Ullah, A., & Yamagata, T. (2008). A bias-adjusted LM test of error cross-section independence. Econometrics Journal. https://doi.org/10.1111/j.1368-423X.2007.00227.x
  • Phillips, P., & Perron, P. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75(2), 335–346.
  • Riti, J. S., Song, D., Shu, Y., & Kamah, M. (2017). Decoupling CO2 emission and economic growth in China: Is there consistency in estimation results in analyzing environmental Kuznets curve? Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2017.08.117
  • Song, M., Wang, S., & Zhang, H. (2020). Could environmental regulation and R&D tax incentives affect green product innovation? Journal of Cleaner Production, 258, 120849. https://doi.org/10.1016/j.jclepro.2020.120849
  • Su, H.-N., & Moaniba, I. M. (2017). Does innovation respond to climate change? Empirical evidence from patents and greenhouse gas emissions. Technological Forecasting and Social Change, 122, 49–62. https://doi.org/10.1016/j.techfore.2017.04.017
  • Wen, D., Liu, L., Ma, C., & Wang, Y. (2020). Extreme risk spillovers between crude oil prices and the U.S. exchange rate: Evidence from oil-exporting and oil-importing countries. Energy, 212, 118740. https://doi.org/10.1016/j.energy.2020.118740
  • Westerlund, J. (2008). Panel Cointegration Tests of the Fisher Effect. Journal of Applied Econometrics, 23(2), 193–233. https://doi.org/10.1002/jae
  • Zhang, Y.-J., Peng, Y.-L., Ma, C.-Q., & Shen, B. (2017). Can environmental innovation facilitate carbon emissions reduction? Evidence from China. Energy Policy, 100, 18–28. https://doi.org/10.1016/j.enpol.2016.10.005
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ekonomi
Bölüm MAKALELER
Yazarlar

Mervan Selçuk 0000-0001-8384-373X

Şakir Görmüş 0000-0002-1857-8682

Murat Güven 0000-0001-5604-4369

Yayımlanma Tarihi 7 Ağustos 2022
Yayımlandığı Sayı Yıl 2022 Sayı: 36

Kaynak Göster

APA Selçuk, M., Görmüş, Ş., & Güven, M. (2022). DO ENVIRONMENTAL AND ECONOMIC FACTORS MATTER FOR INNOVATION? EVIDENCE FROM OIL-IMPORTING AND OIL-EXPORTING COUNTRIES. Uluslararası İktisadi Ve İdari İncelemeler Dergisi(36), 95-110. https://doi.org/10.18092/ulikidince.1014615


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