Abstract
China’s urbanization over the past thirty years has been the fastest in history and has led to significant challenges in balancing urbanization and energy use. As a result, this study proposed a comprehensive green urbanization assessment index system considering energy use and environment protection together. To be specific, principal component analysis was applied to eliminate redundant information, and a slacks-based measure model was used to evaluate urbanization efficiencies. Meanwhile, with super efficiency incorporated, the proposed model enabled to distinguish the DMU from DMUs with same efficiency value, further, a projection analysis was conducted to direct the improvement of the identified inefficient decision-making units (DMUs). Finally, the comprehensive green urbanization index system was applied to a western Chinese province (Sichuan Province) to demonstrate the effectiveness of the proposed models, from which it was found that the overall green urbanization efficiency in Sichuan province was 81.54%, due to an input distribution imbalance and low technical efficiency.
Highlights
In today’s more global and more urban world in which population, production and wealth are increasingly concentrated in cities, urbanization has been playing a vital role in economic and social development (Zhang et al, 2014)
Based on slacks-based measure (SBM) and principal component analysis (PCA), this paper introduces an integrated approach to green urbanization efficiency assessments and optimization
This study developed a series of SBM DEA models to evaluate green urbanization efficiencies, where PCA was used for indicator filtering
Summary
In today’s more global and more urban world in which population, production and wealth are increasingly concentrated in cities, urbanization has been playing a vital role in economic and social development (Zhang et al, 2014). Urbanization rate Ratio of rural employed labor Urban population density per capita GDP Proportion of primary industry in GDP Engel’s coefficient for urban residents Funds for urban residents under basic provision protection per capita High school enrollment rate Education spending per capita Hospital beds per 1,000 people Medical spending per capita The technology spending per capita Energy consumption of unit added industry value Urban daily water consumption per capita Proportion of environmental investment in GDP Waste water discharge rate Proportion of green area in developed areas Proportion of standard air quality The energy consumption of unit GDP. The input-oriented models were applied using Eq (1) and the DEA SOLVER 5.0 and MAXDEA basic 6.4 software to determine the final overall, technical, scale and super efficiency values for each city and their respective rankings
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