Abstract
The question of how to generate maximum socio-economic benefits while at the same time minimizing input from urban land resources lies at the core of regional ecological civilization construction. We apply stochastic frontier analysis (SFA) in this study to municipal input-output data for the period between 2005 and 2014 to evaluate the urbanization efficiency of 110 cities within the Yangtze River Economic Belt (YREB) and then further assess the spatial association characteristics of these values. The results of this study initially reveal that the urbanization efficiency of the YREB increased from 0.34 to 0.53 between 2005 and 2014, a significant growth at a cumulative rate of 54.07%. Data show that the efficiency growth rate of cities within the upper reaches of the Yangtze River has been faster than that of their counterparts in the middle and lower reaches, and that there is also a great deal of additional potential for growth in urbanization efficiency across the whole area. Secondly, results show that urbanization efficiency conforms to a “bar-like” distribution across the whole area, gradually decreasing from the east to the west. This trend highlights great intra-provincial differences, but also striking inter-provincial variation within the upper, middle, and lower reaches of the Yangtze River. The total urbanization efficiency of cities within the lower reaches of the river has been the highest, followed successively by those within the middle and upper reaches. Finally, values for Moran’s I within this area remained higher than zero over the study period and have increased annually; this result indicates a positive spatial correlation between the urbanization efficiency of cities and annual increments in agglomeration level. Our use of the local indicators of spatial association (LISA) statistic has enabled us to quantify characteristics of “small agglomeration and large dispersion”. Thus, “high- high” (H-H) agglomeration areas can be seen to have spread outwards from around Zhejiang Province and the city of Shanghai, while areas characterized by “low-low” (L-L) patterns are mainly concentrated in the north of Anhui Province and in Sichuan Province. The framework and results of this research are of considerable significance to our understanding of both land use sustainability and balanced development.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aigner S L P, 1977. Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1): 21–37.
Anselin L, 1995. Local indicators of spatial association-LISA. Geographical Analysis, 27(2): 93–115.
Battese G E, Coelli T J, 1995. A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20(2): 325–332.
Chen Y, Chen Z, Xu G et al., 2016. Built-up land efficiency in urban China: Insights from the general land use plan (2006–2020). Habitat International, 51: 31–38.
Deilmann C, Hennersdorf J R, Lehmann I et al., 2018. Data envelopment analysis of urban efficiency: Interpretative methods to make DEA a heuristic tool. Ecological Indicators, 84: 607–618.
Deilmann C, Lehmann I, Rei Mann D et al., 2016. Data envelopment analysis of cities: Investigation of the ecological and economic efficiency of cities using a benchmarking concept from production management. Ecological Indicators, 67: 798–806.
Fang C, Zhou C, Gu C et al., 2017. A proposal for the theoretical analysis of the interactive coupled effects between urbanization and the eco-environment in mega-urban agglomerations. Journal of Geographical Sciences, 27(12): 1431–1499.
Fu B, Zhang L, 2014. Land-use change and ecosystem services: Concepts, methods and progress. Progress in Geography, 33(4): 441–446. (in Chinese)
Ghosh R, Kathuria V, 2016. The effect of regulatory governance on efficiency of thermal power generation in India: A stochastic frontier analysis. Energy Policy, 89: 11–24.
Guan W, Xu S, 2015. Spatial energy efficiency patterns and the coupling relationship with industrial structure: A study on Liaoning Province, China. Journal of Geographical Sciences, 25(3): 355–368.
Huang W, Bruemmer B, Huntsinger L, 2016. Incorporating measures of grassland productivity into efficiency estimates for livestock grazing on the Qinghai-Tibetan plateau in China. Ecological Economics, 122: 1–11.
Jia S, Wang C, Li Y et al., 2017. The urbanization efficiency in Chengdu City: An estimation based on a three-stage DEA model. Physics and Chemistry of the Earth, 101: 59–69.
Jin G, Deng X, Chen D et al., 2016. Trends and spatial patterns of land conversions in the North China Plain. Resources Science, 38(8): 1515–1524. (in Chinese)
Jin G, Wang P, Zhao T et al., 2015. Reviews on land use change induced effects on regional hydrological ecosystem services for integrated water resources management. Physics and Chemistry of the Earth, 89: 33–39.
Jin G, Wu F, Li Z et al., 2017. Estimation and analysis of land use and ecological efficiency in rapid urbanization area. Acta Ecologica Sinica, 37(23): 8048–8057. (in Chinese)
Li R, Liu Y, Xie D, 2017. Evolution of economic efficiency and its influencing factors in the industrial structure changes in China. Acta Geographica Sinica, 72(12): 2179–2198. (in Chinese)
Lin B, Chen Y, Zhang G, 2017. Technological progress and rebound effect in china's nonferrous metals industry: An empirical study. Energy Policy, 109: 520–529.
Lin B, Du K, 2013. The energy effect of factor market distortion in China. Economic Research Journal, (9): 125–136. (in Chinese)
Meeusen W, Broeck J V D, 1977. Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 18(2): 435–444.
Nguyen T T, Do T L, Parvathi P et al., 2017. Farm production efficiency and natural forest extraction: Evidence from Cambodia. Land Use Policy, 71: 480–493.
Rashidi K, Shabani A, Farzipoor Saen R, 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.
Reinhard S, Lovell C A K, Thijssen G, 1999. Econometric estimation of technical and environmental efficiency: An application to Dutch Dairy Farms. American Journal of Agricultural Economics, 81(1): 44–60.
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.
Wang L, Li H, 2014. Cultivated land use efficiency and the regional characteristics of its influencing factors in China by using a panel data of 281 prefectural cities and the stochastic frontier production function. Geographical Research, 33(11): 1995–2004. (in Chinese)
Wang L, Li H, Shi C, 2015. Urban land-use efficiency, spatial spillover, and determinants in China. Acta Geographica Sinica, 70(11): 1788–1799. (in Chinese)
Yang L, Zhang X, 2018. Assessing regional eco-efficiency from the perspective of resource, environmental and economic performance in China: A bootstrapping approach in global data envelopment analysis. Journal of Cleaner Production, 173: 100–111.
Zuo L, Wang X, Zhang Z et al., 2014. Developing grain production policy in terms of multiple cropping systems in China. Land Use Policy, 40: 140–146.
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation: National Natural Science Foundation of China, No.41501593, No.41601592; National Program on Key Research Project, No.2016YFA0602500
Rights and permissions
About this article
Cite this article
Jin, G., Deng, X., Zhao, X. et al. Spatiotemporal patterns in urbanization efficiency within the Yangtze River Economic Belt between 2005 and 2014. J. Geogr. Sci. 28, 1113–1126 (2018). https://doi.org/10.1007/s11442-018-1545-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11442-018-1545-2