An Integrated Indicator System and Evaluation Model for Regional Sustainable Development

Yifei Shi, Xinghang Ge, Xueliang Yuan, Qingsong Wang, Jon Kellett, Fangqiu Li and Kaiming Ba
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Yifei Shi: School of Energy and Power Engineering, Shandong University, Jinan 250061, China
Xinghang Ge: School of Energy and Power Engineering, Shandong University, Jinan 250061, China
Xueliang Yuan: School of Energy and Power Engineering, Shandong University, Jinan 250061, China
Qingsong Wang: School of Energy and Power Engineering, Shandong University, Jinan 250061, China
Jon Kellett: School of Architecture and Built Environment, The University of Adelaide, Adelaide, SA 5005, Australia
Fangqiu Li: School of Energy and Power Engineering, Shandong University, Jinan 250061, China
Kaiming Ba: School of Energy and Power Engineering, Shandong University, Jinan 250061, China

Sustainability, 2019, vol. 11, issue 7, 1-23

Abstract: Regional sustainable development has become a worldwide issue in recent years, but there is no single and universally agreed method of choosing indicators for sustainable development assessment. The subjective selection of indicators will affect the results of assessment. Each evaluation method has its own advantages and disadvantages, and the methods used to determine indicator weight also differ. Regional sustainable development is a complex system, which is difficult to evaluate objectively and scientifically using a single method. Therefore, a new integrated indicator system and evaluation model is constructed here to more accurately reflect regional sustainable development level. The indicator system and evaluation model were constructed using a case study of 17 cities in Shandong Province, China. The indicator system includes 4 subsystems, i.e., economy, society, resource, and environment. These indicators were selected through correlation analysis and discrimination analysis. A back propagation neural network was applied to evaluate the respective scores of the 4 subsystems. The comprehensive score for regional sustainable development was evaluated using the analytic hierarchy process with entropy correction. The results show that sustainable development levels in these 17 cities show a gradually decreasing trend from east to west and from coast to inland. Cities with an underdeveloped economy usually display poor levels of social development and serious environmental pollution. Through the improvement of indicator screening, evaluation model, and result correction, the error caused by a single evaluation method can be reduced significantly. This new methodology for indicator selection and comprehensive evaluation provides a new perspective for the assessment of regional sustainable development.

Keywords: regional sustainable development; evaluation; indicator system; back propagation artificial neural network; entropy correction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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