An assessment model for national sustainable development based on the hybrid DEA and modified TOPSIS techniques

Author:

Lin Sheng-WeiORCID,Lo Huai-WeiORCID,Gul MuhammetORCID

Abstract

AbstractAssessing and improving energy efficiency is crucial for addressing the problems of climate change and global warming. This study evaluates the performance of each Organisation for Economic Cooperation and Development (OECD) nation based on the weights obtained by Data Envelopment Analysis (DEA), combined with a modified Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method based on the concept of the aspiration level (called modified TOPSIS-AL). Objective weights are obtained for each country through DEA. This method offers an effective improvement over the previous methods which only use the same subjective weights for the calculation. In addition, our model is also able to account for negative environmental externalities generated during development and provides a comprehensive assessment system for the evaluation of national sustainable development. The findings indicate that the member nations are gradually adopting policies to reduce fossil fuel consumption. However, regional analysis showed that the overall performance of the G7 nations differed significantly from that of the non-G7 nations. The importance of this study lies in the fact that energy consumption not only creates environmental burdens but also affects resource production patterns and productivity. By evaluating the performance of nations in terms of energy efficiency and environmental impact, we aim to address the challenges posed by energy consumption for sustainable development. The proposed hybrid model offers several advantages, including a comprehensive assessment system, consideration of negative externalities, and practical policy recommendations.

Funder

Ministry of Science and Technology, Taiwan

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Engineering (miscellaneous),Information Systems,Artificial Intelligence

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