二十国集团国家生产能力绩效分析:基于熵的topsis方法的应用

F. Altintaş
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引用次数: 0

摘要

特别是经济规模大的国家可以通过提高生产能力来增加它们对全球经济的贡献。为此,衡量各国的生产能力绩效,以提高对各国生产能力绩效的认识,并根据目前的生产能力制定战略,具有重要意义。在此背景下,该研究的目的是利用基于entropi的TOPSIS方法,评估拥有世界最大经济体的20国集团中19个国家的生产能力表现,以及2000年至2018年期间联合国贸易和发展会议(UNCTAD)生产能力指数- PCI组成部分的价值,这是最近和最新的。用…测量根据调查结果,已确定在熵法范围内就国家而言最重要的生产能力组成部分是“运输”。之后,根据基于entropy的TOPSIS方法,观察到生产能力表现最高的前三个国家是德国、美国和韩国。此外,通过计算各国的平均生产绩效值,得出低于该值的国家应提高其生产能力绩效以对全球经济做出更大贡献的结论。此外,采用基于entropi的多准则决策方法(ÇKKV: ARAS、COPRAS、EDAS、WASPAS、ROV、灰色关联分析)测量了该方法范围内各国的生产能力绩效值,并利用Pearson相关系数测量了这些值之间的关系。根据这个测量,我们已经评估了PCI可以被其他基于entroppi的MCDM方法,特别是基于entroppi的TOPSIS方法解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PRODUCTIVE CAPACITY PERFORMANCE ANALYSIS OF G20 GROUP COUNTRIES: AN APPLICATION WITH ENTROPY BASED TOPSIS METHOD
Especially countries with large economies can increase their contribution to the global economy by improving their productive capacities. For this, it is of great importance to measure the productive capacity performance of the countries in order to raise awareness about the productive capacity performances of the countries and to create strategies according to the current productive capacity. In this context, the aim of the research is to evaluate the productive capacity performances of 19 countries in the G20 group, which has the world's largest economies, over the values of the United Nationals Conference on Trade and Development (UNCTAD) Productive Capacities Index – PCI components between the years 2000-2018, which is the most recent and current, using the ENTROPI-based TOPSIS method. to measure with. According to the findings, it has been determined that the most important productive capacity component within the scope of the ENTROPY method in terms of countries is "transportation". Afterwards, it was observed that the top three countries with the highest productive capacity performances according to the ENTROPY-based TOPSIS method were Germany, the USA and South Korea. In addition, by calculating the average productive performance value of the countries, it was concluded that the countries with a lower value than the said value should increase their productive capacity performance in order to contribute more to the global economy. Apart from these, the productive capacity performance values of the countries within the scope of the method were measured with some ENTROPI-based Multi-Criteria Decision Making methods (ÇKKV: ARAS, COPRAS, EDAS, WASPAS, ROV, Gray Relational Analysis) and the relations between these values were measured with the Pearson correlation coefficient. According to this measurement, it has been evaluated that PCI can be explained by other ENTROPI-based MCDM methods, especially the ENTROPY-based TOPSIS method.
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