Evaluating efficiency and order of urban land use structure: An empirical study of cities in Jiangsu, China

The efficiency and order of urban land use structure (ULUS) are vital for the level of urban sustainable development. This paper evaluated the efficiency and order of ULUS of thirteen cities in Jiangsu province, China, using the data envelopment analysis (DEA) approach and information entropy method to characterize the spatial-temporal variation. Although literature using the DEA approach and information entropy method to measure the performance of ULUS exists, the main contributions of this study were considering different types of undesirable outputs and using long-term panel data in the process of urban land use. The Slacks-Based Measure (SBM)-Undesirable window analysis technique was used to measure the annual change of urban land use efficiency (ULUE) in Jiangsu during the period 2006-2017. In particular, urban land resource saving, undesirable output emission reduction potential and reducing size were constructed to comprehensively evaluate the improvement approaches of ULUE. Moreover, the grey relational grade between efficiency and order of ULUS, land use types and ULUE were evaluated using the grey relational model. The empirical results revealed that the ULUE in Jiangsu province was relatively high and the potential to improve ULUE was small. The change of information entropy of ULUS showed a low fluctuation trend of "decline-rise-decline". In addition, there was a distinct correlation between ULUE and information entropy of ULUS during the period examined. The analysis using DEA and information entropy approach elucidated that overall, the performance of ULUS presented a typical spatial and temporal distribution, which was closely related to the level of regional social and economic development and urbanization. Based on these results, several policy implications are proposed to improve urban land use performance within Jiangsu province. Furthermore, these results enable researchers and policymakers to understand the performance of ULUS and set goals for further improvement. (C) 2020 Elsevier Ltd. All rights reserved.