Data envelopment analysis of urban efficiency—Interpretative methods to make DEA a heuristic tool

Deilmann, Clemens; Hennersdorf, Jörg; Lehmann, Ines; Reißmann, Daniel

doi:10.1016/j.ecolind.2017.09.017

Cited by 29 publications

(11 citation statements)

References 14 publications

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“…Most studies have emphasized the issue of land use efficiency from different perspectives, including the efficiency of construction land and farmland productivity. Associations between land use and urbanization efficiency have also been analyzed (Huang et al, 2016;Nguyen et al, 2017;Deilmann et al, 2018) at all spatial scales (e.g., national and provincial as well as at the level of urban agglomerations, watersheds and individual cities) Rashidi et al, 2015). Previous research has also highlighted the mechanisms underlying changes in urbanization efficiency by analyzing drivers and spillover effects at different spatial scales ; thus, both parametric and non-parametric methods have been utilized in this area (Battese et al, 1995;Jin et al, 2017), corresponding with the classic data envelopment analysis (DEA) and stochastic frontier analysis (SFA) models, respectively.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal patterns in urbanization efficiency within the Yangtze River Economic Belt between 2005 and 2014

et al. 2018

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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" 1114Journal of Geographical Sciences (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.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal patterns in urbanization efficiency within the Yangtze River Economic Belt between 2005 and 2014

et al. 2018

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“…. , t) by comparing the distance from each evaluation area to the frontier of effective grain production [14][15][16][17][18]. This method does not require data to be dimensionless and does not require estimation of the function form and weight, thereby it simplifies the interference of subjective factors.…”

Section: Dea Methodsmentioning

confidence: 99%

The System Evaluation of Grain Production Efficiency and Analysis of Driving Factors in Heilongjiang Province

Zhao

Jiang

Wang

2019

Water

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Considering that grain production in Heilongjiang province, northeast China, consumes a lot of water and land, it is necessary to explore the grain production level under restricted water and land resources. This study uses the data envelopment analysis and Malmquist index models of grain production efficiency at the province and country levels from the perspective of space during the period of grain-growing. Furthermore, a logarithmic mean Divisia index model was introduced to construct the system dynamics model. The driving factors of food production efficiency in Heilongjiang province were revealed from the time angle, and the evolution of driving factors was simulated. The results showed the following: (1) although the decrease in the area of the rural population and crop planting has inhibited the growth of grain output in Heilongjiang province, the grain output will increase slightly in the future and is estimated to reach 65.01 million tons by 2030. (2) In 2015, the average grain production efficiency in Heilongjiang Province was 92.3%, and the index of change in grain production efficiency during the grain-growing period was 0.986, which indicates that the overall grain production efficiency in the region was declining, and the input and output were lower than expected. In the future, regional grain production inputs should be allocated to the areas where the grain production efficiency is not as effective as is desired. (3) The cultivated area has the largest contribution of grain production during the period of grain-growing, and the cumulative effect is 21.6 million tons; however, it is estimated that grain yield per hectare will exceed the cultivated land in 2021 as the leading factor promoting grain production, which shows that technological progress is the most important factor moving forward to promote grain production efficiency in Heilongjiang province. Therefore, the subsequent technical measures should be taken to improve grain-production conditions, and the parameter of grain yield per hectare should be used to improve grain production efficiency. The research framework of grain production efficiency is constructed based on space and time. It is of great practical significance to explore food security and reduce the demand for soil and water resources.

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“…For example, Gudipudi et al [32] estimated the urban eco-efficiency of 88 European cities by using an advanced regression residual method and DEA model, suggesting that larger cities are usually more eco-efficient and that Stockholm, Munich, and Oslo have relatively better performance. Deilmann et al [33] investigated the urban economic and ecological efficiency respectively for 116 cities in Germany from the perspective of land consumption. Their study reveals that the medium-sized and industrialized cities in Germany make the most efficient use of land.…”

Section: Introductionmentioning

confidence: 99%

What Is the Efficiency of Fast Urbanization? A China Study

Ren

Shen

et al. 2018

Sustainability

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Urbanization is commonly described as the process of population flow from rural to urban areas. As the largest developing country, China has experienced an unprecedentedly fast and large urbanization process since 1980s, which will continue for the coming future. The immense scale of the process has brought multidimensional benefits across all sectors in the country, yet also consumed a vast amount of resources and caused various types of environmental problems. The conflict between limited resources and an unstoppable urbanization process has become a pressing issue, which presents the urgent need for efficiency pursuance in the process of urbanization in order to ensure sustainable urban development. It is considered that the improvement of urbanization efficiency in large developing countries such as China has great implications for global sustainability. There is little existing study conducted to understand what efficiency achieved in the current fast urban development era in China. This study investigates the urbanization efficiency and its changes in the contemporary China. A set of input-output indicators are employed for analyzing the efficiency, in which both desirable and undesirable outputs are considered. The Super-efficiency Slack-based Measure (SBM) model and DEA-based Malmquist Production Index (MPI) are adopted collectively for conducting data analysis. The research is conducted at provincial level in China and the data collected for analysis are from 30 provinces for the period of 2006–2015. The results from this study show that the overall urbanization efficiency in China during the surveyed period is low, although certain improvement has been achieved. The difference between good and poor performers is considerable. In general, those provinces with better social and economic background have better urbanization efficiency performance. East China is much better than the rest of China, whilst Southwest region has the poorest performance.

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Data envelopment analysis of urban efficiency—Interpretative methods to make DEA a heuristic tool

Cited by 29 publications

References 14 publications

Spatiotemporal patterns in urbanization efficiency within the Yangtze River Economic Belt between 2005 and 2014

Spatiotemporal patterns in urbanization efficiency within the Yangtze River Economic Belt between 2005 and 2014

The System Evaluation of Grain Production Efficiency and Analysis of Driving Factors in Heilongjiang Province

What Is the Efficiency of Fast Urbanization? A China Study

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