Kentsel Enerji Verimliliğinin Deterministik ve Stokastik Yöntemlerle Kıyaslanması

Yıl 2022, Cilt: 34 Sayı: 1, 107 - 122, 30.03.2022

Zühre Aydın Yenioğlu Züleyha Sara Belge

https://doi.org/10.7240/jeps.1002152

Öz

Kentsel sürdürülebilirlik araştırmalarında, kentsel enerji verimliliğini ölçmek için en çok ihtiyaç duyulan yöntemler kıyaslama yöntemleri olmuştur. Kentsel enerji verimliliğinin parametrik ve parametrik olmayan yöntemlerle kıyaslanması, enerji alanında önemlidir. Veri Zarflama Analizi (DEA) ve Stokastik Sınır Analizi (SFA), çeşitli endüstrilerin performansını çoklu göstergelerle ölçmek için ideal yaklaşımlardır. Bu çalışma, kentsel enerji verimliliğini VZA ve SFA metodolojilerini kullanarak deterministik ve stokastik yollarla değerlendirmektedir. Stokastik yöntem, verilerdeki gürültüyü dikkate alır ve enerji verimliliğinin kritik başarı parametrelerini ölçer. Çalışmada, Türkiye İstatistik Kurumu (TÜİK) ve Enerji Piyasası Düzenleme Kurumu (EPDK) Kalkınma Raporlarından elde edilen veriler deterministik ve stokastik yaklaşımlara eklenerek Türkiye'nin 30 büyük şehri için kentsel enerji verimliliği kıyaslaması yapılmıştır. Çalışmanın amacı, deterministik ve stokastik yaklaşımların kentsel enerji verimliliği ölçümünde etkilerini ve sonuçlarını göstermektir.

Anahtar Kelimeler

Urban Energy Efficiency, Stochastic Frontier Analysis, Data Envelopment Analysis, Urban Sustainability

Benchmarking Urban Energy Efficiency with Deterministic and Stochastic Methods

Öz

In urban sustainability researches, benchmarking methods have become the most needed ways to measure urban energy efficiency. Benchmarking the efficiency of urban energy with parametric and non-parametric methods are important cases within the energy field. Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) are ideal approaches to measure performance of various industries with multiple indicators. Stochastic method considers the noise in data and evaluates the critical success parameters of energy efficiency by separating noise from efficiency scores. This study evaluates urban energy efficiency by deterministic and stochastic ways with deploying DEA and SFA methodologies. The aim of the study is to show the effects and results of deterministic and stochastic approaches in urban energy efficiency measurement and to evaluate how Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) can be used to derive measures of efficiency and productivity change over time in complex multi‐output multi‐input contexts in the production and consumption of energy services. Using data gathered from Turkish Statistical Institute (TURKSTAT) and Energy Market Regulatory Authority (EMRA) Development Reports. In the study, 30 cities, which are accepted as metropolitans of Turkey by government, are selected as Decision Making Units (DMUs) of both methods. As a result, different efficiency estimates are presented and evaluated within the scope of statistical noise, multiple inputs and outputs by DEA and SFA methods.
Keywords: Urban Energy Efficiency; Urban Sustainability; Stochastic Frontier Analysis; Data Envelopment Analysis

Kaynakça

• [1] International Energy Agency. World Energy Outlook 2008. Head of Communication and Information Office, France; 2008.
• [2] Li, M., Tao, W., 2017. Review of methodologies and polices for evaluation of energy efficiency in high energy-consuming industry. Applied Energy 187, 203–215.
• [3] Patterson MG. What is energy efficiency: concepts, indicators and methodological issues. Energy Policy 1996;24(5):377–90.
• [4] Hjalmarsson, L., Kumbhakar, S.C., Heshmati, A., 1996. DEA, DFA and SFA: A comparison. Journal of Productivity Analysis 7 (2/3), 303±328.
• [5] Charnes A, Cooper WW, Rhodes E. Evaluating program and managerial efficiency: an application of data envelopment analysis to program follow through. Manage Sci 1981;27:668–97.
• [6] Keirstead, J. (2007). Selecting sustainability indicators for urban energy systems. International Conference on Whole Life Urban Sustainability and its Assessment . Glasgow.
• [7] Keirstead, J. (2007). Towards Urban Energy System Indicators. London: Imperial College London.
• [8] Keirstead, J. (2013). Benchmarking Urban Energy Efficiency . Energy Policy , 575- 587.
• [9] Doherty, M., Nakanishi, H., Bai, X., & Meyers, J. (2013). Relationships between form, morphology, density and energy in urban environments. Canberra, Australia: CSIRO Sustainable Ecosystems.
• [10] WEC. (2010). Energy and Urban Innovation. London: World Energy Council.
• [11] Forsström, J., Lahti, P., Pursiheimo, E., Rämä, M., Shemeikka, J., Sipilä, K., et al. (2011). Measuring Energy Efficiency: Indicators and Potentials in Buildings, Communities and Energy Systems. Finland: VTT.
• [12] Dizdarevic, N. V., & Segota, A. (2012). Total-factor energy efficiency in the EU Countries. Zb. rad. Ekon. fak. Rij, 247-265.
• [13] Yang, Z., Roth, J., Jain, R. (2018). DUE-B: Data-driven urban energy benchmarking of buildings using recursive partitioning and stochastic frontier analysis. Energy and Buildings 163, 58-69.
• [14] Baycan, T., İlhan, C. (2015). Measuring Urban Energy Efficiency in Turkey. Thesis (M.Sc.), Istanbul Technical University -Institute of Science and Technology.
• [15] Kuosmanen, T., Saastamoinen, A., Spilainen, T. (2013). What is the best practice for benchmark regulation of electricity distribution? Comparison of DEA, SFA and StoNED methods. Energy Policy 61, 740-750.
• [16] Moutinho, V., Madaleno, M., Macedo, P. (2020). The effect of urban air pollutants in Germany: eco-efficiency analysis through fractional regression models applied after DEA and SFA efficiency predictions. Sustainable Cities and Society 59, 102204
• [17] Yetkin, O. (2020). The Structure and Future of Metropolitan Municipality in Turkey. Akademik Düşünce Dergisi 1.
• [18] Li MJ, Song CX, Tao WQ. A hybrid model for explaining Yetkin, O. (2020). The Structure and Future of Metropolitan Municipality in Turkey. Akademik Düşünce Dergisi 1.the short-term dynamics of energy efficiency of China’s thermal power plants. Applied Energy 2016. 169:738–47.
• [19] Farrell MJ., 1957. The measurement of productive efficiency. J R Stat Soc Ser A. Gen,120,253-90.
• [20] Charnes, A., Cooper, WW., Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2 , 429-444.
• [21] Banker, R.D., Charnes, A., Cooper, W.W., 1984. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30, 1078– 1092.
• [22] Gil, D. R. G., Costa, M. A., Lopes, A. L. M. , Mayrink V. D., 2017. Spatial statistical methods applied to the 2015 Brazilian energy distribution benchmarking model: Accounting for unobserved determinants of inefficiencies. Energy Economics, 64, 373-383.
• [23] Lopes, A.L.M., Mesquita, R.B., 2015. Tariff regulation of electricity distribution: A comparative analysis of regulatory benchmarking models. The 14th European Workshop on Efficiency and Productivity Analysis, Helsinki. Proceedings of the 14th European Workshop on Efficiency and Productivity Analysis
• [24] Farrell MJ. The measurement of productive efficiency. J Royal Statist Soc (A, General) 1957;120(3):253–81.
• [25] Aigner, D.J., and Chu, S.F. (1968), On Estimating the Industry Production Function, American Economic Review, 58(4), 826–39.
• [26] Aigner D, Lovell CK, Schmidt P. (1977) Formulation and estimation of stochastic frontier production function models. J Econ 1977;6(1):21–37.
• [27] Broeck, V., Meeusen, W. (1977). Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error. International Economic Review Vol. 18, No. 2 , 435-444
• [28] Coelli, T.J., Rao, D.S.P., O’Donnell, C.J., and Battese, G.E. (2005), An Introduction to Efficiency and Productivity Analysis, 2nd edition, Springer
• [29] G.E. Battese, G.S. Corra, Estimation of a production frontier model: with application to the pastoral zone of eastern Australia, Aust. J. Agric. Econ. 21 (1977) 169–179
• [30] G.E. Battese, T.J. Coelli, Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India, J. Product. Anal. 3 (1992) 153–169
• [31] Hu JL, Wang SC. Total-factor energy efficiency of regions in China. Energy Policy 2006;34(17):3206–17.
• [32] Coelli, T. J. (1995). Estimators and hypothesis tests for a stochastic frontier function: A monte carlo analysis. Journal of Productivity Analysis, 6, 247-268.
• [33] Lau, L. (1986). “Functional Forms of Econometric Model Building.” In Griliches, Z. And Intriligator, M.D. eds., Handbook of Econometrics, V.3, pp.1513-1566