Contents

Evaluation of Smart Low-carbon Development in Major Chinese Cities

Expand
  • 1. China Film Archive (China Film Art Research Center), Beijing 100082, China;
    2. Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, China;

Received date: 2016-03-22

  Revised date: 2016-09-20

  Online published: 2016-11-15

Supported by

Key program of the National Natural Science Foundation of China(71433008); General program of the National Natural Science Foundation of China(41571151).; Number of granted invention patents /1 million people (number/ 1 million people)

Abstract

This study chose major Chinese cities and used the creative model of Smart Low-carbon Strength Quotient (SLSQ) to investigate the level and state of urban smart low-carbon development. The results show that: (1) three main categories of smart low-carbon cities are found; namely, leaders with high SLSQ, steady ones with average SLSQ and lagging ones with low SLSQ; (2) the SLSQ level shows a spatial change trend of diminishing from the southeast to the northwest and differing within regions; (3) the SLSQ level indicates an urban scale change trend of decreasing from the big to the small and differing within each scale; (4) the SLSQ level suggests an administrative hierarchy change trend of descending from the high to the low and differing within each class; (5) based on the SLSQ, three dynamic patterns were identified: leading mode, steady mode and preparing mode, among which the steady mode accounts for the vast majority of smart low-carbon development in major Chinese cities.

Cite this article

PANG Bo, MA Haitao . Evaluation of Smart Low-carbon Development in Major Chinese Cities[J]. Journal of Resources and Ecology, 2016 , 7(6) : 407 -417 . DOI: 10.5814/j.issn.1674-764x.2016.06.001

References

1 Antrobus D. 2011. Smart Green Cities: From Modernization to Resilience?. Urban Research & Practice , 4(2): 207-214.
2 Chen Y, Yang W L. 2009. The Research on How to Measure the Level of Informatization. Sci-Tech Information Development & Economy , (6): 90-92. (in Chinese)
3 Cheng Q Y. 2010. Structure entropy weight method to confirm the weight of evaluating index, Systems Engineering—Theory & Practice , 30(7): 1225-1228. (in Chinese)
4 Fang C L, Wei Y D. 2001. Evaluation on the sustainable development capacity and regularity of its regional differentiation in Hexi region. Acta Geographica Sinica , 56(5): 561-569. (in Chinese)
5 Feng B, Guo C Z. 2011. Jiangsu sustainable development capability and its influencing factors: based on DEA Model and panel data . China Urban Economy, (30): 22-23. (in Chinese)
6 Gao Z Q, Liu J Y, Zhuang D F. 1999. The relations anslysis between the ecological environmental quality of Chinese land resources and the population distribution based on remote sensing and GIS. Journal of Remote Sensing , 3(1): 66-70. (in Chinese)
7 Giffinger R, Gudrun H. 2010. Smart cities ranking: an effective instrument for the positioning of the cities?. Architecture City & Environment , 2(12): 7-26.
8 Hua J, Ren J. 2011. Research on low-carbon city evaluation with ANP. Science & Technology and Economy , 24(6): 101-105. (in Chinese)
9 Kim S A, Shin D Y, Choe Y, et al . 2012. Integrated Energy Monitoring and Visualization System for Smart Green City Development: Designing a Spatial Information Integrated Energy Monitoring Model in the Context of Massive Data Management on a Web Based Platform. Automation in Construction , 22: 51-59.
10 Komninosn, Pallotm, Schaf-fersh. 2013. Special issue on smart cities and the future internet in Europe. Journal of the Knowledge Economy , (2): 119-134.
11 Li C S, Tang D C, Wang Y. 2015. Research on the Construction of Low-Carbon City Based on Scenario Analysis in Nanjing City. Areal Research and Development , 34(1): 71-75. (in Chinese)
12 Li J, Zhang C M, Li H H. 2012. Research on smart cities development and evaluation. Telecommunications network technology , (1): 1-5. (in Chinese)
13 Li S, Di Y B. 2006. Discussion on the Grey relation analysis used on Sustainable Development coordination. Journal of Liaoning Institute of Technology , 8(2): 76-80. (in Chinese)
14 Li T X. 2013. Research progress in sustainable development indicator systems both at home and abroad. Ecology and Environment Sciences , 22(6): 1085-1092. (in Chinese)
15 Li X Y, Deng L. 2010. Comprehensive Assessment of the urban low-carbon economy: A Case Study of municipalities. Modern Economic Research , (2): 82-85. (in Chinese)
16 Liu W, Chen C F, Huan H Q, et al . 2014. Research on low-carbon city development based on system dynamics model. Environmental Pollution & Control , 36(4): 86-91. (in Chinese)
17 Murakami S, Kawakubo S, Asami Y, et al. 2011. Development of a comprehensive city assessment tool: CASBEE-city. Building Research & Information , 39(3): 195-210.
18 Pang B, Fang C L. 2015. Smart Low-carbon City: Progress and Prospect. Progress in Geography , 34(9): 1135-1147. (in Chinese)
19 Rees W E, Wackernagel M. 1996. Ecological footprints and appropriated carrying capacity: measuring the natural capital requirements of the human economy. Focus , 6(1): 45-60.
20 Shao C F, Ju M T. 2010. Study of the index system of low-carbon cities based on DPSIR model. Ecological Economy , (10): 95-99. (in Chinese)
21 Shen Q J. 2013. A Study on Fundamentals of Planning and Building Smart-ecological City. Urban Planning Forum , (5): 14-22. (in Chinese)
22 Shimada K, Tanaka Y, Gomi K, et al . 2007. Developing a long-term local society design methodology towards a low-carbon economy: An application to Shiga Prefecture in Japan. Energy Policy , 35 (9): 4688-4703.
23 Su M R, Lu W W, Chen C, et al . 2013. Evaluation of a Low-Carbon City: Method and Application. Entropy , 15(4): 1171-1185. (in Chinese)
24 Wang L F. 2001. The Theory and Algorithm of Analytic Network Process. Systems Engineering—Theory & Practice , 21(3): 44-50. (in Chinese)
25 Winters J V. 2011. Why Are Smart Cities Growing? Who Moves And Who Stays. Journal of Regional Science , 51: 253-270.
26 Xiang Y, Ren H. 2014. The study of smart city evaluation based on the ANP-TOPSIS method. Journal of Industrial Technological Economics, (4): 131-136. (in Chinese)
27 Yi M Y, Zhang X N, Zeng J, et al . 2013. Research on designing of low-carbon city index system and path selection via PCA and AHP: taking Zhuzhou as an example. Ecological Economy , (1): 37-41. (in Chinese)
28 Zhu M F, Hong T Q, Ye Q. 2005. Sustainable development indicator forecasting for resource-based cities based on neural network. Journal of University of Science and Technology of China , 35(3): 423-428. (in Chinese)
29 Zygiaris S. 2013. Smart City Reference Model: Assisting Planners to Conceptualize the Building of Smart City Innovation Ecosystems. Journal of the Knowledge Economy , 4(2): 217-231.

Outlines

/