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

doi:10.1007/s11442-019-1661-7

Abstract

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.

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

REN Yufei, FANG Chuangling, LIN Xueqin, SUN Siao, LI Guangdong, FAN Beili. Evaluation of the eco-efficiency of four major urban agglomerations in coastal eastern China[J].Journal of Geographical Sciences, 2019, 29(8): 1315-1330.

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References 31

[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.
[2]	Bozoğlu M, Ceyhan V , 2009. Energy conversion efficiency of trout and sea bass production in the Black Sea, Turkey. Energy, 34(2):199-204.
[3]	Camarero M, Castillo J, Picazo-Tadeo A J et al., 2013. Eco-efficiency and convergence in OECD countries. Environmental and Resource Economics, 55(1):87-106.
[4]	Charnes A, Cooper W W, Golany B et al., 1985. Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. Journal of Econometrics, 30(1/2):91-107.
[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.
[6]	Cooper W W, Seiford L M, Zhu J , 2011. Data Envelopment Analysis: History, Models, and Interpretations. Springer.
[7]	EEA, 1999. Making Sustainability Accountable: Eco-Efficiency, Resource Productivity and Innovation. European Environment Agency Publication.
[8]	EEA, 2000. Environmental Signals, Copenhagen. European Environment Agency Publication.
[9]	Fang C L , 2014. Important research progress and development directions of China’s urban agglomeration. Acta Geographica Sinica, 69(8):1130-1144. (in Chinese)
[10]	Fang C L, Guan X L , 2011. Comprehensive measurement and spatial distinction of input-output efficiency of urban agglomerations in China. Acta Geographica Sinica, 66(8):1011-1022. (in Chinese)
[11]	Fang C L, Mao Q Z, Ni P F , 2015. Discussion on the scientific selection and development of China’s urban agglomerations. Acta Geographica Sinica, 70(4):515-527. (in Chinese)
[12]	Fang C L, Song J T, Zhang Q et al., 2005. The formation, development and spatial heterogeneity patterns for the structures system of urban agglomerations in China. Acta Geographica Sinica, 60(5):827-840. (in Chinese)
[13]	Fang C L, Yu D L , 2017. Urban agglomeration: An evolving concept of an emerging phenomenon. Landscape & Urban Planning, 162:126-136.
[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.
[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 Production, 197:957-971.
[16]	Huppes G, Ishikawa M , 2005. A framework for quantified eco-efficiency analysis. Journal of Industrial Ecology, 9(4):25-41.
[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.
[18]	Liu Yong, Wang W, Li X Q et al., 2010. Eco-efficiency of urban material metabolism: A case study in Xiamen, China. International Journal of Sustainable Development & World Ecology, 17(2):142-148.
[19]	Lu B, Yang J X , 2006. Review of methodology and application of eco-efficiency. Acta Ecologica Sinica, 26(11):3898-3906. (in Chinese)
[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.
[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 Production, 105:241-252.
[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 Environmental Management, 65(4):339-346.
[23]	Shabani A, Torabipour S M R, Farzipoor Saen R et al., 2015. Distinctive data envelopment analysis model for evaluating global environment performance. Applied Mathematical Modelling, 39(15):4385-4404.
[24]	UNESCAP, 2010. Eco-efficiency Indicators: Measuring Resource-use Efficiency and the Impact of Economic Activities on the Environment. United Nations Publication.
[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.
[26]	WBCSD, 2001. Eco-efficient Leadership for Improved Economic and Environmental Performance. World Business Council for Sustainable Publication.
[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. Agricultural Systems, 152:27-37.
[28]	Wu Q, Wu C Y , 2009. Research on evaluation model of energy efficiency based on DEA. Journal of Management Sciences, 22(1):103-112. (in Chinese)
[29]	Zhang B, Bi J, Fan Z Y et al., 2008. Eco-efficiency analysis of industrial system in China: A data envelopment analysis approach. Ecological Economics, 68(1/2):306-316.
[30]	Zhang J R, Guo Y H , 2004. The relationship between the number of factors and DEA efficiency. Systems Engineering: Theory Methodology Applications, 13(6):520-523. (in Chinese)
[31]	Zhou C S, Shi C Y, Wang S J et al., 2018. Estimation of eco-efficiency and its influencing factors in Guangdong province based on super-SBM and panel regression models. Ecological Indicators, 86:67-80.

Category	Item	Indicator
Input	Capital	Fixed assets investment, disbursement of foreign capital
	Natural resources	Construction land area, water consumption
	Labor	Employees
	Energy	Energy consumption
Output	Desirable output	GDP, gross industrial output value, local financial revenue, added value of tertiary industry
Output	Undesirable output	Total waste water discharge, industrial exhaust gas emission, and industrial smoke and dust emissions

Urban agglomeration	Improvement potential of slack variables (δ_ij / %)
Urban agglomeration	Fixed assets investment	Disbursement of foreign capital	Employees	Construction land area	Energy consumption	Water consumption
BHT	10.74	17.72	26.17	12.75	15.03	9.29
SP	4.23	17.64	12.22	3.93	9.93	1.28
YRD	5.43	13.60	8.49	4.84	5.58	3.70
PRD	2.85	26.47	22.55	18.98	4.144	12.74