Ekolojik ayak izi-enerji ar-ge harcamaları ilişkisi: OECD ülkeleri örneği

Year 2021, , 527 - 541, 12.04.2021

Efe Can Kılınç

https://doi.org/10.25287/ohuiibf.723064

Cited By: 12

Abstract

Sanayileşme, hızlı kentleşme, yüksek düzeyde elektrik tüketimi ve küreselleşme gibi olgular insanlığın çevre üzerindeki talep baskısını giderek artırmış ve küresel ısınma, iklim değişikliği ve hava kirliliği gibi birçok çevresel soruna neden olmuştur. Öyle ki Dünya Ekonomik Forumu tarafından 2018 yılında yayınlanan Küresel Risk Raporunda, Dünya’yı bekleyen en önemli riskin iklim değişimi olduğu ifade edilmiştir. İklim değişikliği ile mücadelede küresel düzeyde birçok girişim yürütülmekte ve ekolojik ayak izi, eko-inovasyon, enerji Ar-Ge faaliyetleri, karbon yakalama-depolama, karbon vergileri gibi uygulamalar geliştirilmektedir. Bu uygulamalardan ekolojik ayak izi hesaplamaları, insanlığın ihtiyaçlarını karşılarken çevre üzerinde yarattığı baskıyı ölçmektedir. Ekolojik ayak izi ile gelecek nesillere sürdürülebilir bir çevre bırakabilme düşüncesi açığa çıkartılmakta ve bunu sağlamak için gerek çözüm yolları aranmaktadır. Gelecek nesillere yaşanabilir bir çevre bırakma noktasında, zehirli gaz salınımını azaltacak çevre dostu inovatif teknolojilere ve bu teknolojileri ortaya çıkartacak Ar-Ge faaliyetlerine ihtiyaç vardır. Enerji alanında yürütülecek Ar-Ge faaliyetleri sayesinde ekolojik ayak izinin azaltılması mümkün olabilecektir. OECD ülkelerinde, 2002-2016 döneminde, enerji Ar-Ge ve demonstrasyon harcamalarının ekolojik ayak izi üzerindeki etkilerinin panel veri yöntemleri kullanılarak incelendiği bu çalışmanın temel bulguları, enerji Ar-Ge ve demonstrasyon harcamaları arttıkça ekolojik ayak izinin azaldığını göstermiştir. Ayrıca, enerji kullanımı ve kişi başına düşen GSYH arttıkça ekolojik ayak izinin de arttığı görülmüştür.

Keywords

Ekolojik Ayak İzi, Enerji Ar-Ge Harcamaları, Panel Veri Analizi

Ecological footprint-energy r&d expenditures relationship: The case of OECD countries

Abstract

Phenomena such as industrialization, rapid urbanization, high level of electricity consumption and globalization have gradually increased the demand pressure of humanity on the environment and caused many environmental problems such as global warming, climate change and air pollution. In a Global Risk Report published by the World Economic Forum in 2018, it is stated that the most important risk waiting for the world is climate change. Many initiatives are being carried out at the global level in the struggle against climate change and practices such as ecological footprint, eco-innovation, energy R&D activities, carbon capture-storage technologies, and carbon taxes are developed. Ecological footprint calculations from these applications measure the pressure on the environment while meeting the needs of humanity. With the ecological footprint, the idea of leaving a sustainable environment for future generations is revealed and necessary solutions are sought to achieve this. At the point of leaving a livable environment for future generations, there is a need for environmentally friendly innovative technologies to reduce greenhouse gas emissions and R&D activities that will reveal these technologies. Through R&D activities will be conducted in the field of energy will be possible to reduce the ecological footprint. The main findings of this study, which investigated the effects of energy R&D and demonstration expenditures on the ecological footprint using panel data methods in OECD countries in the period of 2002-2016, showed that the ecological footprint decreased as the energy R&D and demonstration expenditures increased. At the same time, as the energy use and GDP per capita increased, the ecological footprint also increased.

Keywords

Ecological FootPrint, Energy R&D Expenditures, Panel Data Analysis

References

• Adedoyin, F. F., Alola, A. A., & Bekun, F. V. (2020). An assessment of environmental sustainability corridor: The role of economic expansion and research and development in EU countries. Science of The Total Environment, 136726.
• Anderson, T. W. & C. Hsiao (1981). Estimation of dynamic models with error components. Journal of the American Statistical Association, 76(375), 598–606.
• Arellano, M. (1987). Computing Robust Standart Errors For Within Group Estimators. Oxford Bulletin of Economics and Statistics, 49(4), 431-434.
• Arellano, M. & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58, 277-297.
• Ao, X., (2007), Arellano-Bond model, Retrieved February 05, 2020 (de indirildi) from the World Wide Web: https://training.rcs.hbs.org/files/hbstraining/files/arellano-bond.pdf
• Bel, G. & Joseph, S. (2018). Climate change mitigation and the role of technological change: Impact on selected headline targets of Europe's 2020 climate and energy package. Renewable and Sustainable Energy Reviews, 82, 3798-3807.
• Belčáková, I., Diviaková, A., & Belaňová, E. (2017). Ecological footprint in relation to climate change strategy in cities. In IOP Conference Series: Materials Science and Engineering, 245 (6), IOP Publishing.
• Brown, L. (2017). 20 Ways to reduce your carbon footprint this spring. Retrieved February 07, 2020 (de indirildi) from the World Wide Web: https://www.shredit.com/en-us/blog/sustainability/ways-to-reduce-your-carbon-footprint-this-spring
• BT (2016), The role of ICT in reducing carbon emissions in the EU, Retrieved February 04, 2020 (de indirildi) from the World Wide Web: https://www.btplc.com/Purposefulbusiness/Ourapproach/Ourpolicies/ICT_Carbon_Reduction_EU.pdf
• Dam, T. A., Pasche, M., & Werlich, N. (2017). Trade patterns and the ecological footprint a theory-based empirical approach (No. 2017-005). Jena Economic Research Papers.
• Dogan, E., Taspinar, N., & Gokmenoglu, K. K. (2019). Determinants of ecological footprint in MINT countries. Energy & Environment, 30(6), 1065-1086.
• Fakher, H. A. (2019). Investigating the determinant factors of environmental quality (based on ecological carbon footprint index). Environmental Science and Pollution Research, 26(10), 10276-10291.
• Froot, K. A. (1989). Consistent Covariance Matrix Estimation with cross-sectional dependence and heteroskedasticity in financial data. Journal of Financial and Quantitative Analysis, 24(3), 333-355.
• Galli, A., Lin, D., Wackernagel, M. Gressot, M. & Winkler, S. (2015). Humanity’s growing ecological footprint: sustainable development implications, Retrieved February 01, 2020 (de indirildi) from the World Wide Web: https://sustainabledevelopment.un.org/content/documents/5686humanitysgrowingecologicalfootprint.pdf
• Ghita, S. I., Saseanu, A. S., Gogonea, R. M., & Huidumac-Petrescu, C. E. (2018). Perspectives of ecological footprint in European context under the impact of information society and sustainable development. Sustainability, 10(9), 3224.
• Koide, R., Lettenmeier, M., Kojima, S., Toivio, V., Amellina, A., & Akenji, L. (2019). Carbon footprints and consumer lifestyles: an analysis of lifestyle factors and gap analysis by consumer segment in Japan. Sustainability, 11(21), 5983.
• Hofmann, J. & Werkheiser, C. (2014). Efficiency of fixed and random effects estimators: A Monte Carlo analysis. Retrieved February 03, 2020 (de indirildi) from the World Wide Web: https://www.reed.edu/economics/parker/s14/312/Fin_Reports/6.pdf
• Irshad, H., & Hussain, A. (2017). Analysis of ecological efficiency and its influencing factors in developing countries. Pakistan Institute of Development Economics, Department of Environmental Economics, Working Paper No. 11.
• Naveed, A., Prean, N., & Rabas, A. (2011). Dynamic panel data models. Retrieved February 05, 2020 (de indirildi) from the World Wide Web: https://homepage.univie.ac.at/robert.kunst/pan2011_pres_rabas.pdf
• OECD (2012), The Future oF Eco-Innovation: The Role of Business Models in Green Transformation. Retrieved February 03, 2020 (de indirildi) from the World Wide Web: https://www.oecd.org/innovation/inno/49537036.pdf
• Pesaran, H. M. (2015). Time series and panel data econometrics, Oxford: Oxford University Press.
• Polák, M. & Cernegova, M. (2016). Carbon footprint and the concept of green economy and social inclusion in Snina town. Zeszyt Naukowy Wyższej Szkoły Zarządzania i Bankowościw Krakowie, (39), 12-22.
• Rogers, W. H. (1993). Regression standard errors in clustered samples. Stata Technical Bulletin, 13, 19-23.
• Sarkar, A. N. (2013). Promoting eco-innovations to leverage sustainable development of eco-industry and green growth, European Journal of Sustainable Development, 2, (1): 171-224.
• Sul, D. (2019). Panel data econometrics: common factor analysis for empirical researchers, New York: Routledge.
• Tsionas, M., (2019), Panel data econometrics: theory, London: Academic Press. World Economic Forum (2018), Global risk report, Retrieved September 28, 2019 (da indirildi) from the World Wide Web: http://www3.weforum.org/docs/WEF_GRR18_Report.pdf
• Zarębska, J., ve Michalska, M., (2016), Ecological innovations as a chance for sustainable development - directions and obstacles in their implementation, Management, 20( 2), 49-64.
• Zhu, C., & Gao, D. (2019). A research on the factors influencing carbon emission of transportation industry in “the belt and road Initiative” countries based on panel data. Energies, 12(12), 2405.
• Global Footprint Network, Tools & resources, data downloads and licences, Retrieved September 28, 2019 (da indirildi) from the World Wide Web: http://data.footprintnetwork.org/?_ga=2.126881989.991414474.1581013564-1420927460.1581013564#/exploreData
• Global Footprint Network, Retrieved July 02, 2020 (de indirildi) from the World Wide Web: https://www.footprintnetwork.org/our-work/ecological-footprint/
• International Energy Services, Statistics, online data services, RD&D budgets, Retrieved September 28, 2019 (da indirildi) from the World Wide Web: http://wds.iea.org/WDS/Common/Login/login.aspx
• World Bank, World development indicators, Retrieved September 28, 2019 (da indirildi) from the World Wide Web: https://databank.worldbank.org/source/world-development-indicators