Clustering Countries on Logistics Performance and Carbon Dioxide (CO2) Emission Efficiency: An Empirical Analysis

Polat, Mustafa; Kara, Karahan; Yalçın, Galip Cihan

doi:10.20409/berj.2022.370

Cited by 8 publications

(8 citation statements)

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“…Derived from the concept of low-carbon economy, low-carbon logistics represents a novel theoretical framework. Although there is no standardized definition of low-carbon logistics in domestic and international contexts, a comprehensive review of existing research [5][6][7][8] reveals that it embodies the characteristics of "three lows and one high", namely, low emissions, low pollution, low energy consumption, and high efficiency, aligning with the principles of a low-carbon economy. The ultimate objective of low-carbon logistics is to achieve a low-carbon economy and facilitate circular, benign, and sustainable economic development.…”

Section: Literature Review 21 the Relationship Between Low-carbon Log...mentioning

confidence: 99%

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Coupling Coordination and Spatiotemporal Evolution of Low-Carbon Logistics, Industrial Agglomeration, and Regional Economy in the Yangtze River Economic Belt

Huang,

Liu

2023

Sustainability

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The logistics industry plays a crucial role in the global economy, but also poses significant challenges to the economy, society, and environment due to increasing carbon emissions. Therefore, coordinated development between the logistics industry and regional economy has become a strategic choice for achieving sustainable development. Taking the Yangtze River Economic Belt as an example, this study constructs an evaluation index system of “low-carbon logistics–industrial agglomeration–regional economy” to explore the coupling coordination relationship and spatiotemporal distribution characteristics of the three systems from 2006 to 2020. Furthermore, it analyzes the spatial correlation features and evolutionary trends of the coordinated development among the three systems. The results indicate that during the study period, the coupling coordination degree among the three systems in the Yangtze River Economic Belt showed a fluctuating upward trend but with a relatively low level of coordination. There were significant regional differences, presenting a stepped distribution pattern of “high in the east and low in the west.” The coordinated development among the three systems exhibited a significant positive spatial correlation, with “H–H” and “L–L” agglomerations being dominant. The spatial distribution of coupling coordination degree remained relatively stable, with the overall center of gravity located in the southeast of Hubei Province. The spatial evolution pattern showed a distinct “northeast–southwest” direction. Finally, suggestions for the coordinated and sustainable development of the three systems are put forward.

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Section: Literature Review 21 the Relationship Between Low-carbon Log...mentioning

confidence: 99%

“…The global Moran's I reflects the overall spatial association of attribute values in the study area. However, to further explore the heterogeneity characteristics of local regions, local spatial autocorrelation analysis is required, and it is calculated using Equation (8).…”

Section: Spatial Autocorrelationmentioning

confidence: 99%

Coupling Coordination and Spatiotemporal Evolution of Low-Carbon Logistics, Industrial Agglomeration, and Regional Economy in the Yangtze River Economic Belt

Huang,

Liu

2023

Sustainability

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“…According to the K-means cluster analysis, a total of 5 clusters were obtained in the study. Polat et al (2022) classified 150 countries according to their carbon dioxide emissions and the efficiency levels of their logistics performances. In the research, countries are divided into 3 clusters.…”

Section: Literature Reviewmentioning

confidence: 99%

Clustering of Developing Countries in Terms of Logistics Market Development with Fuzzy Clustering and Discriminant Analysis

Kara

2023

Journal of Yaşar University

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Logistics performance indicators are important in determining the market development levels of countries. Especially the logistics markets of developing countries play an active role in increasing the country's economy and trade volumes. In this research, it is aimed to cluster the developing countries according to their level of logistics market development in 2022. For this reason, fuzzy clustering and discriminant analyzes have been applied in the research. The sample area of the study consists of 50 developing countries. The data of the research have been taken from The Agility Emerging Markets Logistics Index reports. The research has been carried out in two phases. In the first phase, developing countries are classified by fuzzy cluster analysis. According to the analysis findings, 2 clusters have been obtained as high and low logistics market development cluster. In the second phase, discriminant analysis has been conducted to test the cluster membership of clustered countries. According to the discriminant analysis findings, all cluster memberships have been confirmed. As a result of the research, the cluster membership status of the developing countries and cluster centers according to the variables have been determined and the obtained implications have been presented.

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“…In this study, Data Envelopment Analysis is used to calculate efficiency values based on inputs and outputs and output-oriented DEA was preferred. DEA is a non-parametric mathematical programming method that provides information about the relative efficiency of decision-making units (Polat et al, 2022). The main reason for this preference is to ensure maximum output in determining the digital intensity activities of countries.…”

Section: Data Envelopment Analysismentioning

confidence: 99%

Determination of Digital Density Efficiency by Data Envelopment Analysis: EU Member States

Kaygısız

2022

Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi

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This study aims to determine the digital intensity levels, which are an indicator of the digital transformation performance of the countries. In this direction, the digital intensity efficiency levels of the European Union member countries were calculated by data envelopment analysis. The input-oriented Charnes Cooper and Rhodes (CCR-O) Model has been used to determine the digital intensity of the countries. The input criteria of the DEA model were obtained from the digital skills data in The Digital Economy and Society Index-2021 (DESI), and the output criteria were obtained from the digital intensity data of the same index. Digital content creation communication and collaboration, online information and communication, information and data literacy, problem-solving, and safety skills are used as input criteria for digital skills. Very low, low, high, and very high data were used as output criteria of digital intensity. According to the findings, it was seen that Hungary, Spain, Slovakia, and Ireland are not at the full efficiency level. It has been determined which input variables should be directed to reach the full efficiency level of the countries that are not at the full efficiency level. In this direction, it can be said that although countries that are not at the full efficiency level have digital skills, their digital density is not high and these skills are not used enough.

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Clustering Countries on Logistics Performance and Carbon Dioxide (CO2) Emission Efficiency: An Empirical Analysis

Cited by 8 publications

References 0 publications

Coupling Coordination and Spatiotemporal Evolution of Low-Carbon Logistics, Industrial Agglomeration, and Regional Economy in the Yangtze River Economic Belt

Coupling Coordination and Spatiotemporal Evolution of Low-Carbon Logistics, Industrial Agglomeration, and Regional Economy in the Yangtze River Economic Belt

Clustering of Developing Countries in Terms of Logistics Market Development with Fuzzy Clustering and Discriminant Analysis

Determination of Digital Density Efficiency by Data Envelopment Analysis: EU Member States

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