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Journal of Geographical Sciences

, Volume 29, Issue 8, pp 1315–1330 | Cite as

Evaluation of the eco-efficiency of four major urban agglomerations in coastal eastern China

  • Yufei Ren
  • Chuangling FangEmail author
  • Xueqin Lin
  • Siao Sun
  • Guangdong Li
  • Beili Fan
Article

Abstract

Urban agglomerations in China have become the strategic core of national economic development and the main component of the new type of urbanization. However, they are threatened by a series of eco-environmental problems and challenges, including the severe overexploitation of natural resources. Eco-efficiency, which is defined as accomplishing the greatest possible economic benefit with the least possible resource input and damage to the environment, is used as an indicator to quantify the sustainability of urban agglomerations. In this work, a traditional data envelopment analysis (DEA) model with a slack-based measurement (SBM) model of undesirable outputs, was used to assess and compare the economic efficiency and eco-efficiency of four major urban agglomerations in eastern China (UAECs) in 2005, 2011, and 2014. The spatio-temporal characteristics of the evolution of urban agglomerations were analyzed. Based on the results of a slack analysis, suggestions for improving the eco-efficiency of the four UAECs are provided. The overall economic efficiency of urban agglomerations located in the Shandong Peninsula, Yangtze River Delta, and Pearl River Delta displayed a V-shaped pattern (decreased and then increased). In contrast, the overall economic efficiency of the Beijing-Tianjin-Hebei urban agglomeration declined during the study period. The Beijing-Tianjin-Hebei urban agglomeration had a considerable loss of economic efficiency due to pollution, whereas the Shandong Peninsula urban agglomeration was less impacted. Overall, the eco-environmental efficiency of the four UAECs declined from 2005 to 2011 and then increased from 2011 to 2014. In addition, the urban eco-efficiency in the four coastal UAECs was characterized by different evolution patterns. The eco-efficiency was higher in the peri-urban areas of the core cities, riverside areas, and seaside areas and lower in the inland cities. The core cities of the Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta urban agglomerations were characterized by high resource consumption, economic benefit output, and eco-efficiency. In most of cities in the urban agglomerations, the emission of pollutants declined, leading to a reduction of pollutants and mitigation of environmental problems. In addition, a differential analysis, from the perspective of urban agglomeration, was performed, and concrete suggestions for improvement are proposed.

Keywords

eco-efficiency data envelopment analysis undesirable SBM spatio-temporal pattern slacks analysis four urban agglomerations of eastern China 

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References

  1. Bai Y P, Deng X Z, Jiang S J et al., 2018. Exploring the relationship between urbanization and urban eco-efficiency: Evidence from prefecture-level cities in China. Journal of Cleaner Production, 195: 1487–1496.CrossRefGoogle Scholar
  2. Bozoglu M, Ceyhan V, 2009. Energy conversion efficiency of trout and sea bass production in the Black Sea, Turkey. Energy, 34(2): 199–204.CrossRefGoogle Scholar
  3. Camarero M, Castillo J, Picazo-Tadeo A J el al., 2013. Eco-efficiency and convergence in OECD countries. Environmental and Resource Economics, 55(1): 87–106.CrossRefGoogle Scholar
  4. Charnes A, Cooper W W, Golany B el al., 1985. Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. Journal of Economelrics, 30(1/2): 91–107.CrossRefGoogle Scholar
  5. Cooper W W, Seiford L M, Tone K, 2007. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References, and DEA-solver Software. Springer.Google Scholar
  6. Cooper W W, Seiford L M, Zhu J, 2011. Data Envelopment Analysis: History, Models, and Interpretations. Springer.CrossRefGoogle Scholar
  7. EEA, 1999. Making Sustainability Accountable: Eco-Efficiency, Resource Productivity and Innovation. European Environment Agency Publication.Google Scholar
  8. EEA, 2000. Environmental Signals, Copenhagen. European Environment Agency Publication.Google Scholar
  9. Fang C L, 2014. Important research progress and development directions of China’s urban agglomeration. Acla Geographica Sinica, 69(8): 1130–1144. (in Chinese)Google Scholar
  10. Fang C L, Guan X L, 2011. Comprehensive measurement and spatial distinction of input-output efficiency of urban agglomerations in China. Acla Geographica Sinica, 66(8): 1011–1022. (in Chinese)Google Scholar
  11. Fang C L, Mao Q Z, Ni P F, 2015. Discussion on the scientific selection and development of China’s urban agglomerations. Acla Geographica Sinica, 70(4): 515–527. (in Chinese)Google Scholar
  12. Fang C L, Song J T, Zhang Q el al., 2005. The formation, development and spatial heterogeneity patterns for the structures system of urban agglomerations in China. Acla Geographica Sinica, 60(5): 827–840. (in Chinese)Google Scholar
  13. Fang C L, Yu D L, 2017. Urban agglomeration: An evolving concept of an emerging phenomenon. Landscape & Urban Planning, 162: 126–136.CrossRefGoogle Scholar
  14. Goto M, Otsuka A, Sueyoshi T, 2014. DEA (Data Envelopment Analysis) assessment of operational and environmental efficiencies on Japanese regional industries. Energy, 66: 535–549.CrossRefGoogle Scholar
  15. Huang Y, Li L, Yu Y T, 2018. Does urban cluster promote the increase of urban eco-efficiency? Evidence from Chinese cities. Journal of Cleaner Produclion, 197: 957–971.CrossRefGoogle Scholar
  16. Huppes G, Ishikawa M, 2005. A framework for quantified eco-efficiency analysis. Journal of Induslrial Ecology, 9(4): 25–41.CrossRefGoogle Scholar
  17. Lee T, Yeo G, Thai V, 2014. Environmental efficiency analysis of port cities: Slacks-based measure data envelopment analysis approach. Transport Policy, 33(4): 82–88.CrossRefGoogle Scholar
  18. Liu Yong, Wang W, Li X Q el al., 2010. Eco-efficiency of urban material metabolism: A case study in Xiamen, China. Inlernalional Journal of Suslainable Developmenl & World Ecology, 17(2): 142–148.CrossRefGoogle Scholar
  19. Lu B, Yang J X, 2006. Review of methodology and application of eco-efficiency. Acla Ecologica Sinica, 26(11): 3898–3906. (in Chinese)Google Scholar
  20. Mao N, Song M J, Deng S M, 2016. Application of TOPSIS method in evaluating the effects of supply vane angle of a task/ambient air conditioning system on energy utilization and thermal comfort. Applied Energy, 180: 536–545.CrossRefGoogle Scholar
  21. Rashidi K, Shabani A, Saen R F, 2015. Using data envelopment analysis for estimating energy saving and undesirable output abatement: A case study in the Organization for Economic Co-operation and Development (OECD) countries. Journal of Cleaner Produclion, 105: 241–252.CrossRefGoogle Scholar
  22. Schaltegger S, Synnestvedt T, 2002. The link between ‘green’ and economic success: environmental management as the crucial trigger between environmental and economic performance. Journal of Environmenlal Managemenl, 65(4): 339–346.Google Scholar
  23. Shabani A, Torabipour S M R, Farzipoor Saen R el al., 2015. Distinctive data envelopment analysis model for evaluating global environment performance. Applied Malhemalical Modelling, 39(15): 4385–4404.CrossRefGoogle Scholar
  24. UNESCAP, 2010. Eco-efficiency Indicators: Measuring Resource-use Efficiency and the Impact of Economic Activities on the Environment. United Nations Publication.Google Scholar
  25. United Nations Conference on Trade and Development, 2003. Integrating Environmental and Financial Performance at the Enterprise Level: A Methodology for Standardizing Eco-efficiency Indicators. United Nations Publication, 29–30.Google Scholar
  26. WBCSD, 2001. Eco-efficient Leadership for Improved Economic and Environmental Performance. World Business Council for Sustainable Publication.Google Scholar
  27. Wettemann P J C, Latacz-Lohmann U, 2017. An efficiency-based concept to assess potential cost and greenhouse gas savings on German dairy farms. Agricullural Syslems, 152: 27–37.CrossRefGoogle Scholar
  28. Wu Q, Wu C Y, 2009. Research on evaluation model of energy efficiency based on DEA. Journal of Managemenl Sciences, 22(1): 103–112. (in Chinese)Google Scholar
  29. Zhang B, Bi J, Fan Z Y el al., 2008. Eco-efficiency analysis of industrial system in China: A data envelopment analysis approach. Ecological Economics, 68(1/2): 306–316.CrossRefGoogle Scholar
  30. Zhang J R, Guo Y H, 2004. The relationship between the number of factors and DEA efficiency. Syslems Engineering: Theory Melhodology Applicalions, 13(6): 520–523. (in Chinese)Google Scholar
  31. Zhou C S, Shi C Y, Wang S J el al., 2018. Estimation of eco-efficiency and its influencing factors in Guangdong province based on super-SBM and panel regression models. Ecological Indicalors, 86: 67–80.CrossRefGoogle Scholar

Copyright information

© Science Press Springer-Verlag 2019

Authors and Affiliations

  • Yufei Ren
    • 1
    • 2
  • Chuangling Fang
    • 1
    • 2
    Email author
  • Xueqin Lin
    • 3
  • Siao Sun
    • 1
  • Guangdong Li
    • 1
  • Beili Fan
    • 1
  1. 1.Institute of Geographic Sciences and Natural Resources ResearchCASBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.College of Resource Environment and TourismCapital Normal UniversityBeijingChina

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