Regional sustainable development efficiency assessment model with the future performance for OECD countries: based on dynamic ratio measure model with network

UoM administered thesis: Phd

  • Authors:
  • Wendi Ouyang

Abstract

The existing regional sustainable development measurement approaches have some limitations. (1) Regional sustainability performance is severely affected by the external environment (natural conditions, culture, religion, etc.) and the internal structure of the study region. (2) Environmental indicators or economic indicators are only one dimension of the description of the region development, and their utility is coordinated rather than independent. However, existing measurement models are variable independence. (3) The results of the analysis based on past-present data are outdated, and policymakers prefer to have forward-looking assessments. A ratio measure model was developed, which would liberate the traditional frontier efficiency measurement models from the limitation that decision making units (DMUs) must be in a similar external environment. The ratio measure model is an efficiency evaluation model based on the theory of elastic constant, so it can eliminate the influence of the external environment on efficiency. On the other hand, the ratio measure allows for a correlation between variables, which is more suitable for use in cases with variable dependencies such as regional development. In order to the depth study the regional sustainable development, the new model developed based on the total factor energy efficiency model and bearing in mind the UN's 17 sustainable development goals. The internal structure of regional sustainable development was divided into intertemporal conflict and inter-department conflict. A dynamic model and a network model were used to analyse these two internal conflicts. At the same time, the ratio measure provides a suitable tool for regional sustainable development measurement. Furthermore, this research considered the future performance of regional sustainable development when measuring efficiency. It provides forward-looking measurement because regional sustainability factors are always changing over time. It is achieved through two parts of the research. firstly, this study developed a neural network time series prediction model based on global integrated thinking to provide more accurate forecasts of future performance. Secondly, we add future performance to the dynamic network efficiency assessment model. It allows our analytical models to assess regional sustainability from a future development perspective.

Details

Original languageEnglish
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Award date1 Aug 2020