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Efficiency evaluation of urban development in Yazd City, Central Iran using data envelopment analysis

  • Amanehalsadat Pouriyeh
  • Nematollah Khorasani
  • Farhad Hosseinzadeh Lotfi
  • Parvin Farshchi
Article

Abstract

Unplanned growth of cities is a matter of concern these days. Lack of attention to proper patterns of urban development has left so many harmful effects on human health and the environment. One of the most effective methods that can be used to measure the efficiency of urban development is data envelopment analysis (DEA). The present study is an attempt to evaluate the performance and efficiency of development of Yazd City using the DEA over the years 1983–2013. In this regard, the ecological factors, affecting the growth of the city of Yazd in the study period, were identified initially. The factors include elevation, slope, aspect, geology, morphology, soil, water quantity, climatic features, and land cover. Next, using variable returns to scale (BCC) based on the output-oriented approach, the efficiency of development of Yazd City was calculated by GAMS software to recognize efficient and inefficient units. Then, Anderson-Peterson (AP) ranking method was used to rank the most efficient units in the development of Yazd City over the study years. The obtained results indicated that the DMUs 2 (1984), 3 (1986), 12 (1994), 15 (1997), 21 (2004), up to 30 (2013) were efficient and introduced as units with proper performance in terms of ecological indicators affecting the urban growth. According to the Anderson-Peterson method, DMU 3 (1986) was recognized as the most efficient unit, ranked the highest (with a score of 1.20319) among the other 30 units in terms of ecological indicators affecting development of the urban growth. The research findings could clarify the strength and weak points of the ecological characteristics of the city. According to which, a comprehensive understanding of the performance of the city could be given to relevant authorities in order to amend inefficient units of urban development or direct the orientation of the city growth toward the most efficient directions.

Keywords

Evaluation Efficiency Urban development Data envelopment analysis Yazd city Iran 

References

  1. Adhvaryu, B. (2010). Enhancing urban planning using simplified models: SIMPLAN for Ahmedabad, India. Progress in Planning, 37, 113–207.CrossRefGoogle Scholar
  2. Andersen, P., & Peterson, N. C. (1993). A procedure for ranking efficient unit in DEA. Management Science, 39(10), 1261–1294.CrossRefGoogle Scholar
  3. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some methods for estimating technical and inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092.CrossRefGoogle Scholar
  4. Bhattarai, K., & Conway, D. (2010). Urban vulnerabilities in Kathmandu valley, Nepal: visualizations of human/hazard interactions. Journal of Geographic Information System, 2, 63–84.CrossRefGoogle Scholar
  5. Bobylev, A. V., Bubin, M. N., & Rasskazova, N. S. (2016). The geoecological modelling of small water reservoirs and river catchment areas as a procedure in urban development. Procedia Engineering, 150, 2067–2072.CrossRefGoogle Scholar
  6. Bowlin, W. F., Charnes, A., Cooper, W. W., & Sherman, H. D. (1985). Data envelopment analysis and regression approaches to efficiency estimation and evaluation. Annals of Operations Research, 2, 113–138.CrossRefGoogle Scholar
  7. Bray, S., Caggiani, L., & Ottomanelli, M. (2015). Measuring transport systems efficiency under uncertainty by fuzzy sets theory based data envelopment analysis: theoretical and practical comparison with traditional DEA model. Transportation Research Procedia, 5, 186–200.CrossRefGoogle Scholar
  8. Cascini, L., Bonnard, C., Corominas, J., Jibson, R., & Montero-olarte, J. (2005). Landslide hazard and risk zoning for urban planning and development. In Hungr, Fell, Couture, & Eberhardt (Eds.), Landslide risk management, proceeding of the international conference on landslide risk management, Vancouver, Canada (pp. 199–235). London: A.A. Balkema Publishers, Taylor & Francis Group.Google Scholar
  9. Charnes, A., Cooper, W., Golany, B., Seiford, L., & Stutz, J. (1985). Foundation of data envelopment analysis for Pareto-Koopmans efficient empirical production. Journal of Econometrics, 30(1–2), 91–107.CrossRefGoogle Scholar
  10. Cooper, W. W., Seiford, L. M., & Tone, K. (2000). Data envelopment analysis: a, Comprehensive Text with Models, Application, References and DEA-Solver Software. Boston/Dordrecht/London: Kluwer Academic Publishers.Google Scholar
  11. Davidoff, P., & Reiner, T. A. (1973). A choice theory of planning. ED.A. Faludi. In A reader in planning theory (pp. 11–39). Oxford: Pergamon Press.CrossRefGoogle Scholar
  12. Fare, R., Grosskopf, S., & Lovell, C. (1985). The measurement of efficiency of production. Boston: Kluwer Nijhoff.CrossRefGoogle Scholar
  13. Fu, K. H., Leng, C. H., & Wan Tsou, K. (2014). Analysis of farming environmental efficiency using a DEAModel with undesirable outputs. In APCBEE Procedia, 5th international conference on environmental science and development, ICESD 2014, 10 (pp. 154–158).Google Scholar
  14. Golany, B., & Storbeck, J. E. (1999). A data envelopment analysis of the operational efficiency of Bank branches. Interfaces, 29, 14–26.CrossRefGoogle Scholar
  15. Hassan, M. M., & Nazem, M. N. I. (2016). Examination of land use/land cover changes, urban growth dynamics, and environmental sustainability in Chittagong city, Bangladesh. Environment, Development and Sustainability, 18(3), 697–716.CrossRefGoogle Scholar
  16. Hegazy, I., & Kaloop, M. (2015). Monitoring urban growth land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, 4(1), 117–124.CrossRefGoogle Scholar
  17. Huang, J., & Liu, Y. (2011). The assessment of regional vulnerability to natural disasters in China. International Journal of Disaster Risk Science, 2(2), 41–48.CrossRefGoogle Scholar
  18. Jenny, A., & Ericson, A. (2006). A participatory approach to conservation in the Calakmul biosphere reserve, Campeche, Mexico. Landscape Urban Plan, 74(3–4), 242–266.Google Scholar
  19. Krishna Veni, K., Rajesh, R., & Pugazhendhi, S. (2012). Development of decision making model using integrated AHP and DEA for vendor selection. Procedia Engineering, 38, 3700–3708.CrossRefGoogle Scholar
  20. Li, F., Liu, X., Hu, D., Wang, R., Yang, W., Li, D., et al. (2009). Measurement indicators and an evaluation approach for assessing urban sustainable development: a case study for China’s Jining city. Landscape and Urban Planning, (90(3–4), 134–142.Google Scholar
  21. Li, C. H., Li, N., Wu, L. C., & Hu, A. J. (2013). A relative vulnerability estimation of flood using data envelopment analysis in the Dongting Lake region of human. Natural Hazards and Earth System Sciences, 13, 1723–1734.CrossRefGoogle Scholar
  22. Liu, J., Ding, F. Y., & Lall, V. (2000). Using data envelopment analysis to compare suppliers for supplier selection and performance improvement. Supply Chain Management: An International Journal, 5(3), 143–150.CrossRefGoogle Scholar
  23. Liu, Y., Song, Y., & Arp, H. (2012). Examination of relationship between urban form and urban eco-efficiency in China. Habitat International, 36(1), 171–177.CrossRefGoogle Scholar
  24. Markovits-Somogy, R. (2011). Ranking efficient and inefficient decision making units in data envelopment analysis. International Journal for Traffic and Transport Engineering, 1(4), 245–256.Google Scholar
  25. Merwe, V. D., & Hendrik, J. (1997). GIS-aided land evaluation and decision-making for regulating urban expansion: a south African case study. Geo Journal, 43(2), 135–151.Google Scholar
  26. Neto, S. (2016). Water governance in an urban age. Utilities Policy, In Press, Corrected Proof.Google Scholar
  27. Pardalos P. M., & Resende, M.G.C. (2002). Handbook of applied optimization. Oxford University Press.Google Scholar
  28. Pauleit, S., Ennos, R., & Golding, Y. (2005). Modeling the environmental impacts of urban land use and cover change—a study in Merseyside, UK. Landscape Urban Plane, 71(2–4), 295–310.CrossRefGoogle Scholar
  29. Rizk, H. I., & Rashed, K. M. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, 4(1, 117–124.Google Scholar
  30. Ryngnga, P. K., & Ryntathiang, B. B. L. (2013). Dynamics of land use land cover for sustainability: a case of Shillong, Meghalaya, India. International Journal of Scientific & Technology Research, 2(3), 235–239.Google Scholar
  31. Serrao-Neumann, S., Renouf M., Kenway, S.J., & Low Choy, D. (2017). Connecting land-use and water planning: prospects for an urban water metabolism approach. Cities, 60, Part B, 13–27Google Scholar
  32. Sueyoshi, T., & Yuan, Y. (2015). China’s regional sustainability and diversified resource allocation: DEA environmental assessment on economic development and air pollution. Energy Economics, 49, 239–256.CrossRefGoogle Scholar
  33. Yu, Y., & Wen, Z. (2010). Evaluating China’s urban environmental sustainability with data envelopment analysis. Ecological Economics, 69(9), 1748–1755.CrossRefGoogle Scholar
  34. Zhang, T. (2000). Land market forces and Governments role in sprawl. Cities, 17(2), 123–135.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Amanehalsadat Pouriyeh
    • 1
  • Nematollah Khorasani
    • 1
  • Farhad Hosseinzadeh Lotfi
    • 2
  • Parvin Farshchi
    • 1
  1. 1.Department of Environmental Science, Faculty of Environment and Energy, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Mathematics, Science and Research BranchIslamic Azad UniversityTehranIran

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