AN EVALUATION WITH WINDOW ANALYSIS TO DETERMINE THE ENVIRONMENTAL EFFICIENCIES OF THE COUNTRIES THAT POLLUTE THE WORLD

Yıl 2018, 18. EYİ Özel Sayısı, 855 - 870, 22.01.2018

İhsan Alp , Mihraç Küpeli

https://doi.org/10.18092/ulikidince.350090

Öz

As it is known, The Kyoto Protocol is an international agreement aims to
reduce global warming and man-made greenhouse gas emissions. Approximately 160
countries entered into framework of the Kyoto Protocol and our world is
polluted by about 50 countries mostly. The purpose of this study is to
investigate values and the trends of performances of environmental/greenhouse
gas emissions of these 50 most polluted countries in period of 2005-2015. At
the same time, it is examined that the trend of the performance of Turkey in
the concerned years. Window Analysis (WA) is a DEA and operational research
(OR) based technique. DEA captures a moment photograph, each application is a
cross-sectional analysis of data. In some applications, observations for DMUs
are available over multiple time periods, so to perform an analysis where
interest focuses on changes over time is important. WA gives us trend of
changes of performance over time, and also details of the stability of
performance.

Anahtar Kelimeler

data envelopment analysis, window analysis, greenhouse emission, performance

AN EVALUATION WITH WINDOW ANALYSIS TO DETERMINE THE ENVIRONMENTAL EFFICIENCIES OF THE COUNTRIES THAT POLLUTE THE WORLD

Öz

Bilindiği gibi, Kyoto Protokolü küresel ısınmayı ve insan yapımı sera gazı salımını
azaltmayı amaçlayan uluslararası bir anlaşmadır. Kyoto Protokolü çerçevesine
yaklaşık 160 ülke girmektedir ve dünya çoğunlukla 50 ülke tarafından
kirletilmektedir. Bu çalışmanın amacı, 2005-2015 döneminde ençok kirleten 50
ülkenin çevre ve sera gazı emisyonlarının değerlerini ve eğilimlerini
araştırmaktır. Aynı zamanda Türkiye’nin bu zaman dilimindeki performans
eğilimini ve kararlılığını incelemektir. Window Analizi bir tür Veri Zarflama
Analizi’dir ve yöneylem araştırması tabanlı bir tekniktir. Veri Zarflama
Analizinde veriler kesitsel olarak analiz edilirler. Bazı uygulamalarda ise
karar verme birimleri için gözlemler çoklu zaman dilimlerinde mevcuttur. Bu
nedenle farklı zaman içindeki değişimlere odaklı bir analiz yapmak önemlidir.
Window analizi zaman içindeki performans değişimlerinin eğilimini ve performans
kararlığının detaylarını sunmaktadır.

Anahtar Kelimeler

veri zarflama analizi, window analizi, sera gazı emisyonu, performans

Kaynakça

• Agrell, P.J., Bogetoft, P. (2005). Economic and environmental efficiency of district heating plants, Energy Policy, 33(10), 1351-1362.
• Aigner, D., Lovell C.K., Schmidt P. (1977). Formulation and estimation of stochastic frontier production function models, J Econ, 6 (1), 21-37.
• Alp İ., Sözen A.(2011). Efficiency assessment of Turkey's carbonization index, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects,33(18), 1678-1691.
• Chang, Y.-T., Zhang, N., Danao, D., Zhang, N. (2013). Environmental efficiency analysis of trans-portation system in China: a non-radial DEA approach, Energy Policy, 58, 277-283.
• Charnes, A., Cooper W.W., Rhodes E. (1978).Measuring the efficiency of decision making units, European Journal of Operational Research, 2 (6), 429-444.
• Chien, T. , Hu, J.L. (2007). Renewable energy and macroeconomic efficiency of OECD and non-OECD economies, Energy Policy, 35 (7), 3606-3615.
• Cook, W.D., Seiford, L. M. (2009). Data envelopment analysis (DEA) – Thirty years on, European Journal of Operational Research, 192, 1-17.
• Cooper, W.W., Seiford, L.M., Zhou, J. (2006). Handbook on data envelopment analysis.Boston: Kluwer Academic Publishers, 24.
• Demirbas, A. (2003). Energy and environmental issues relating to greenhouse gas emissions in Turkey, Energy Conversion and Management, 44, 203–213.
• Emrouznejad, A., Parker, B. R., Tavare,s G. (2008). Evaluation of research in efficiency and pro-ductivity: A survey and analysis of the first 30 years of scholarly literature in DEA, So-cio-Economic Planning Sciences, 42(3), 151-157.
• Farrell, M. J. (1957). The Measurement of Productive Efficiency, Journal of the Royal Statistical Society. Series A (General), 120(3), 253-290.
• Halkos, G.E., Tzeremes N.G. (2013). A conditional directional distance function approach for measuring regional environmental efficiency: evidence from UK regions, European Jo-urnal of Operational Research, 227 (1), 182-189.
• Honma, S., Hu, J. (2009) .Total-factor energy productivity growth of regions in Japan, Energy Policy, 4,34.
• Honma, S., Hu, J. (2008). Total-factor energy efficiency of regions in Japan, Energy Policy, 36, 821–833.
• Lee, T., Yeo, G.T., Thai, V.V. (2014). Environmental efficiency analysis of port cities: Slacks-based measure data envelopment analysis approach, Transport Policy, 33, 82-88.
• Li X.-G., Yang J., Liu X.-J. (2013). Analysis of Beijing's environmental efficiency and related fac-tors using a DEA model that considers undesirable outputs, Math Comput Model, 58 (5), 956-960.
• Lozano, S., Gutierrez, E. (2008). Non-parametric frontier approach to modeling therelationships among population, GDP, energy consumption and CO2 emissions, Ecological Econo-mics 66, 687–699.
• Mandal, K., Madheswaran, S. (2010). Environmental efficiency of the Indian cement industry: An interstate analysis, Energy Policy, 38(2), 1108-1118.
• Meeusen, W., Broeck, J. V.D. (1977). Efficiency estimation from Cobb-Douglas production func-tions with composed error, Int Econ Rev, 435-444.
• Ramakrishnan, R. (2006). A multi-factor efficiency perspective to the relationships among world GDP, energy consumption and carbon dioxide emissions. Technological Forecas-ting & Social Change, 73, 483–494.
• Ray, S.C. (2004). Data Envelopment Analysis: Theory and Techniques for Economics and Ope-rations Research, UK: Cambridge University Pres.
• Reinhard, S., Lovell, C.A.K., Thijssen, G.J. (2000). Environmental efficiency with multiple envi-ronmentally detrimental variables; estimated with SFA and DEA, European Journal of Operational Research, 121(2), 287-303.
• Song, M., Guan, Y. (2014). The environmental efficiency of Wanjiang demonstration area: a Ba-yesian estimation approach, Ecol Indic, 36, 59-67.
• Song, M., Song, Y., An, Q., Yu, H. (2013). Review of environmental efficiency and its influencing factors in China: 1998–2009, Renewable and Sustainable Energy Reviews, 20, 8-14.
• Sözen, A. and Alp, İ. (2009). Comparison of Turkey’s performance of greenhouse gas emissions and local/regional pollutants with EU countries, Energy Policy, 37,5007–5018.
• Tunc, G.I., Aşık, S.T., Akbostancı, E. (2007). CO2 emissions vs. CO2 responsibility: an input–output approach for the Turkish economy, Energy Policy, 35, 855–868.
• Wang, H. , Zhou, P., Zhou, D. (2013). Scenario-based energy efficiency and productivity in Chi-na: a non-radial directional distance function analysis, Energy Econ, 40, 795-803.
• Wang, K., Yu, S., Zhang, W. (2013). China's regional energy and environmental efficiency: a DEA window analysis based dynamic evaluation, Math Comput Model, 58 (5), 1117-1127.
• Woo, C., Chung, Y., Chun, D., Seo, H., Hong, S. (2015). The static and dynamic environmental efficiency of renewable energy: a Malmquist index analysis of OECD countries, Renew Sustain Energy Rev, 47, 367-376.
• U.S. Environmental Protection Agency (2017). Retrieved from https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data references 1-2-3.
• Yang, L. and Wang, K.-L.(2013).Regional differences of environmental efficiency of China’s energy utilization and environmental regulation cost based on provincial panel data and DEA method, Math Comput Model, 58 (5), 1074-1083.
• Zhou, P., Poh, K.L, Ang, B.W., (2007). A non-radial DEA approach to measuring environmental performance, European Journal of Operational Research, 178 (1), 1-9.
• Zhou, Y. , Xing, X., Fang, K., Liang, D., Xu, C. (2013). Environmental efficiency analysis of power industry in China based on an entropy SBM model, Energy Policy, 57,68-75.
• Zhou, Y., Liang, D., Xing, X., (2013). Environmental efficiency of industrial sectors in China: an improved weighted SBM model, Math Comput Model, 58(5),990-9.
• Zofio, J.L., Prieto, A.M. (2001). Environmental Efficiency and Regulatory Standards: the Case of CO2 Emissions from OECD Industries, Resource and Energy Economics, 23(1), 63-83.