Since the “smart growth” was put forward in the late 90s, it has become an accepted design idea and concept in the field of urban design in the world, and has been deeply studied and applied. In order to better promote “smart grown”, we set up an evaluation system, which consists of eleven indicators. In this paper, Oxford City and Fengzhen City are used as the objects of the study. Then smart growth evaluation model is established. The weight of the index is calculated by the entropy method. We use the model to evaluate the development plans of the two cities, from which to calculate the contribution of the indicators on the level of smart growth. Finally, we use the super-efficient data envelopment analysis model (DEA) to rank the importance of the indicators to the smart growth. The results show that the level of smart growth in Oxford is higher than that in Fengzhen. And “Multifunctional Building Density in Central City”, “The Density of Public Area in Central City” two indicators account for more than 36% weight. The contribution of the two indicators is also located in the top two indicators. Two cities focus on the direction of smart growth is also different. In summary, the differences between China and Western countries in urban planning are mainly focused on housing and public resources.
References
[1]
Shannon, C.E. (1949) Communication Theory of Secrecy Systems. Bell System Technical Journal, 28, 656-715. http://sci-hub.cc/10.1002/j.1538-7305.1949.tb00928.x https://doi.org/10.1002/j.1538-7305.1949.tb00928.x
[2]
Shannon, C.E. (2001) A Mathematical Theory of Communication. ACM SIGMOBILE Mobile Computing and Communications Review, 5, 3-55. http://sci-hub.cc/10.1145/584091.584093 https://doi.org/10.1145/584091.584093
[3]
Downs, A. (2005) Smart Growth: Why We Discuss It More than We Do It. Journal of the American Planning Association, 71, 367-378. https://doi.org/10.1080/01944360508976707
[4]
Bennett, C.H. and DiVincenzo, D.P. (2000) Quantum Information and Computation. Nature, 404, 247-255. http://sci-hub.cc/10.1038/35005001 https://doi.org/10.1038/35005001
[5]
Danielsen, K.A., Lang, R.E. and Fulton, W. (1999) Retracting Suburbia: Smart Growth and the Future of Housing. Housing Policy Debate, 10, 513-540. https://doi.org/10.1080/10511482.1999.9521341
[6]
Daniels, T. (2001) Smart Growth: A New American Approach to Regional Planning. Planning Practice and Research, 16, 271-279. http://210.74.184.3:8080/international/case/case/1575.pdf https://doi.org/10.1080/02697450120107880
[7]
Geller, A.L. (2003) Smart Growth: A Prescription for Livable Cities. American Journal of Public Health, 93, 1410-1415. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1447984/ https://doi.org/10.2105/AJPH.93.9.1410
[8]
Cooper, W.W., Seiford, L.M. and Zhu, J. (2004) Data Envelopment Analysis. In: Handbook on Data Envelopment Analysis, Springer, Berlin, 1-39. http://sci-hub.cc/10.1007/1-4020-7798-X_1 https://doi.org/10.1007/1-4020-7798-X_1
[9]
Poza, E.J. (1989) Smart Growth: Critical Choices for Business Continuity and Prosperity. Jossey-Bass Incorporated Pub.
[10]
Biaynicki-Birula, I. and Mycielski, J. (1975) Uncertainty Relations for Information Entropy in Wave Mechanics. Communications in Mathematical Physics, 44, 129-132. http://www.cft.edu.pl/~birula/publ/Uncertainty.pdf https://doi.org/10.1007/BF01608825
[11]
Liang, J. and Shi, Z. (2004) The Information Entropy, Rough Entropy and Knowledge Granulation in Rough Set Theory. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 12, 37-46. https://doi.org/10.1142/S0218488504002631
[12]
Liang, J., Shi, Z., Li, D. and Wierman, M.J. (2006) Information Entropy, Rough Entropy and Knowledge Granulation in Incomplete Information Systems. International Journal of General Systems, 35, 641-654. https://doi.org/10.1080/03081070600687668
[13]
Wei, Q. (2001) Data Envelopment Analysis. Chinese Science Bulletin, 46, 1321-1332. http://sci-hub.cc/10.1007/BF03183382 https://doi.org/10.1007/BF03183382
[14]
Banker, R.D., Charnes, A. and Cooper, W.W. (1984) Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30, 1078-1092. https://doi.org/10.1287/mnsc.30.9.1078
[15]
Charnes, A., Cooper, W.W. and Rhodes, E. (1978) Measuring the Efficiency of Decision Making Units. European Journal of Operational Research, 2, 429-444.
[16]
Roll, Y. and Hayuth, Y.E.H.U.D.A. (1993) Port Performance Comparison Applying Data Envelopment Analysis (DEA). Maritime Policy and Management, 20, 153-161. https://doi.org/10.1080/03088839300000025
[17]
Andersen, P. and Niels, C.P. (1993) A Procedure for Ranking Efficient Units in Data Envelopment Analysis. Management Science, 39, 1261-1264. https://doi.org/10.1287/mnsc.39.10.1261
[18]
Banker, R.D., Charnes, A., Cooper, W.W., Swarts, J. and Thomas, D.A. (1989) An Introduction to Data Envelopment Analysis with Some of Its Models and Their Uses. Research in Governmental and Nonprofit Accounting, 5, 125-163.