PCA-DEA-tobit regression assessment with carbon emission constraints of China’s logistics industry

Deng, Fumin; Xu, Lin; Yuan, Fang; Gong, Qunxi; Li, Bingbing

doi:10.1016/j.jclepro.2020.122548

Cited by 86 publications

(36 citation statements)

References 19 publications

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“…The basic model and its modifications were proposed [29]. Furthermore, the hybrid approaches of the DEA and PCA are used in supplier selection, performance estimation, and score ranking problems [30][31][32][33][34].…”

Section: The Comparison Of the Proposed Approach And Dea-pca Methodsmentioning

confidence: 99%

Estimating the Artificial Intelligence Learning Efficiency for Civil Engineer Education: A Case Study in Taiwan

Chiang¹

2021

Sustainability

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The civil engineering educators focused on implementing interdisciplinary learning in artificial intelligence (AI) based on a more innovative application of construction requirements. However, only a few pieces of literature discussed the educational learning efficiency and feedback for this trend. Hence, this study surveyed the 237 data from eight universities that issued the interdisciplinary courses. The factors were modified from the scales in science, technology, engineering, and mathematics education. Further, the descriptive analysis was used to explain this situation in Taiwan. A novel approach based on data envelopment analysis and Mahalanobis distance approaches was proposed to solve this problem. The advantages of the proposed approach were discussed and compared with traditional method. Based on the student gains in the interdisciplinary courses, three groups were clustered and compared. The feedback of a high-input and low-efficiency student group was suggested for improving learning strategies. The sensitivity analysis of this special group showed that effective teaching practice is the key factor in the artificial intelligence courses for civil engineering students. These students may increase technical efficiency by 37% by paying 21% inputs. Therefore, this paper provided a useful and easy approach to make learning strategies for non-informatics students in AI learning.

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Section: The Comparison Of the Proposed Approach And Dea-pca Methodsmentioning

confidence: 99%

Estimating the Artificial Intelligence Learning Efficiency for Civil Engineer Education: A Case Study in Taiwan

Chiang¹

2021

Sustainability

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“…Although others drew the conclusion that there is positive influence between the degree of opening to the outside world and logistics efficiency [23,24], the conclusion is that the degree of opening to the outside world cannot positively influence logistics efficiency [22,25]. Deng et al [26] considered carbon emissions, used the DEA method to measure and evaluate the logistics performance of 30 provinces and cities in China, and analyzed the characteristics of the efficiency of China's logistics industry on the overall level and space.…”

Section: E Empirical Research On Chinese Logistics Industrymentioning

confidence: 99%

If There Appears a Path to Improve Chinese Logistics Industry Efficiency in Low-Carbon Perspective? A Qualitative Comparative Analysis of Provincial Data

Mei-li

Wei

Gao

et al. 2021

Mathematical Problems in Engineering

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Taking the data of 30 Chinese provinces as a sample in which CO2 emission is denoted by undesirable output, this paper calculated the efficiencies of the logistics industry by applying the Data Envelopment Analysis (DEA) method and analyzed the factors that affect logistics industry efficiency by applying the Qualitative Comparative Analysis (QCA) method based on configuration thinking. It is found that the efficiency of China’s low-carbon logistics industry has presented an increasing trend and the efficiency gaps among the regions have been enlarged in the last 10 years. Two highly efficient paths have been formed in the three years after 2015. The path of management opening type has a high coverage ratio; logistics management level and operation are the core factors that improve logistics efficiency. The path of economy driving type covers few cases and it mainly relies on relative priority to influence and drive the development of regional logistics.

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“…The extensive analysis of the differences in carbon emission performance and the main driving factors in different regions are helpful to formulate carbon emission reduction measures suitable for different regions and promote China’s overall carbon emission reduction targets. Among the research concern global environmental issues, there are more and more studies on carbon emission estimation [ 7 ], carbon emission influencing factors [ 8 – 10 ], carbon intensity attenuation rate [ 11 ], and carbon emission performance [ 12 , 13 ]. Ramanathan [ 14 ] used the DEA model to measure carbon emission performance differences among countries regarding carbon emission performance methods.…”

Section: Introductionmentioning

confidence: 99%

Analysis of carbon emission performance and regional differences in China’s eight economic regions: Based on the super-efficiency SBM model and the Theil index

Zhang

2021

PLoS ONE

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China’s carbon emission performance has significant regional heterogeneity. Identified the sources of carbon emission performance differences and the influence of various driving factors in China’s eight economic regions accurately is the premise for realizing China’s carbon emission reduction goals. Based on the provincial panel data from 2005 to 2017, the super-efficiency SBM model and Malmquist model are constructed in this paper to measure regional carbon emission performance’s static and dynamic changes. After that, the Theil index is used to distinguish the impact of inter-regional and intra-regional differences on different regions’ carbon emissions performance. Finally, by introducing the Tobit model, the effect of various driving factors on carbon emission performance differences is analyzed quantitatively. The results show that: (1) There are significant differences in different regions’ carbon emission performance, but the overall carbon emission performance presents an upward fluctuation trend. Malmquist index decomposition results show substantial differences in technology progress index and technology efficiency index in different regions, leading to significant carbon emission performance differences. (2) Overall, inter-regional differences contribute the most to the overall carbon emission performance, up to more than 80%. Among them, the inter-regional and intra-regional differences in ERMRYR contributed significantly. (3) Through Tobit regression analysis, it is found that residents’ living standards, urbanization level, ecological development degree, and industrial structure positively affect carbon emission performance. On the contrary, energy intensity presents an apparent negative correlation on carbon emission performance. Therefore, to improve the carbon emission performance, we should put forward targeted suggestions according to the characteristics of different regional development stages, regional carbon emission differences, and influencing driving factors.

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PCA-DEA-tobit regression assessment with carbon emission constraints of China’s logistics industry

Cited by 86 publications

References 19 publications

Estimating the Artificial Intelligence Learning Efficiency for Civil Engineer Education: A Case Study in Taiwan

Estimating the Artificial Intelligence Learning Efficiency for Civil Engineer Education: A Case Study in Taiwan

If There Appears a Path to Improve Chinese Logistics Industry Efficiency in Low-Carbon Perspective? A Qualitative Comparative Analysis of Provincial Data

Analysis of carbon emission performance and regional differences in China’s eight economic regions: Based on the super-efficiency SBM model and the Theil index

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