A DEA-based empirical analysis for dynamic performance of China's regional coke production chain

Xia, Panpan; Wu, Jie; Ji, Xiang; Xi, Pingfang

doi:10.1016/j.scitotenv.2020.136890

Cited by 13 publications

(5 citation statements)

References 48 publications

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“…DEA model is a typical non-parametric static efficiency analysis method [44,45]. Based on the classic DEA model, Fried et al proposed a new research method of static efficiency, which is the three-stage DEA model [46].…”

Section: Three-stage Dea Modelmentioning

confidence: 99%

Research on Green Innovation Performance of Manufacturing Industry and Its Improvement Path in China

Yang

Zhu

2022

Sustainability

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Green innovation, which combines “innovation-driven” and “green development,” is one of the most powerful ways to overcome resource and environmental constraints and enhance manufacturing industry sustainability. Based on the innovation value chain perspective, the green innovation process of manufacturing industry is decomposed into two stages: green scientific and technological R&D and achievement transformation. Then, using the three-stage DEA and Malmquist index model to measure the green innovation performance of China’s manufacturing industry, and compare its regional heterogeneity from the dual perspectives of static efficiency and dynamic productivity. In addition, this paper further discusses the improvement path of green innovation performance of China’s manufacturing industry. The findings are as follows: (1)The green innovation efficiency of manufacturing industry in China is at a comparatively low degree and has great potential for improvement. Moreover, it shows apparent regional heterogeneity: The green innovation efficiency in the eastern region is higher than that in the western region, and both are higher than that in the center region, confirming the phenomenon of “central collapse”. (2)The green innovation productivity of China’s manufacturing industry shows a “W-type” dynamic evolution tendency, with green technological progress as the key driving factor, while the green technical efficiency does not clearly exhibit a “catch-up effect”. Additionally, it shows significant regional heterogeneity: green innovation productivity in the western region is higher than that in the central and eastern regions, indicating a potential “backwardness advantage”. (3) The eastern region of China is located in combination IV, which indicates that it has a high rate of green innovation efficiency but a low rate of green innovation productivity; the central region is located in combination III, which indicates that it has a low rate of both green innovation efficiency and productivity; and the western region is located in combination II, which indicates that it has a low rate of green innovation efficiency but a high rate of green innovation productivity. Last but not least, this paper puts forward three kinds of paths for the improvement of the green innovation performance of China’s manufacturing industry: unilateral breakthrough, step-by-step and stimulating jumping type.

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Section: Three-stage Dea Modelmentioning

confidence: 99%

Research on Green Innovation Performance of Manufacturing Industry and Its Improvement Path in China

Yang

Zhu

2022

Sustainability

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“…Vigorously reducing industrial wastewater discharge is the top priority to improve the ecological level. The eastern region has superior water resource endowment, high water consumption, and sewage discharge, which proves that the sewage treatment efficiency hinders the improvement of water efficiency in the eastern region, indicating that the eastern region may not fully consider water environmental pollution in the process of using advanced technology to promote economic development in the future, resulting in the decline of the overall efficiency of the eastern region's water resources system [30]. Since the central government launched the rise strategy of the central region in 2006, the central region has begun to undertake large-scale industrial transfer from the eastern region, and the regional GDP has grown rapidly.…”

Section: Estimate Results For Water Resource Utilization Efficiencymentioning

confidence: 99%

The Effect of Water Rights Trading Policy on Water Resource Utilization Efficiency: Evidence from a Quasi-Natural Experiment in China

Chen

Cao

2021

Sustainability

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Water shortage has become a serious problem in the world, and low water efficiency is the key to industrial and agricultural production and sustainable economic development. Based on the data of 30 provinces (municipalities) in China from 2010 to 2017, this study builds a DEA model based on the hybrid network structure of water systems to measure water resource utilization efficiency and uses a difference-in-differences (DID) model to examine the effects of the water rights trading policy on water resource utilization efficiency. We find that the water rights trading policy can significantly promote the total water efficiency of the water resources system and the initial water use efficiency, and after a series of robustness tests, the result is still valid. Further analysis indicates that the policy effect changes with the adjustment of the industrial and agricultural water use structure. Moreover, the dynamic impact of water rights trading policy on water resource utilization efficiency is still significant. This study provides macroscopic evidence for evaluating the effects of China’s water rights trading policy and offers new ideas and experiences for improving China’s water resource utilization efficiency.

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“…(2019), Mahmoudi and Emrouznejad (2019), Goodarzi and Saen (2020), Fang and Zhao (2020), Chen and Zhu (2020), Xia et al. (2020), Yu et al. (2021), Kweh et al.…”

Section: Further Research Directionmentioning

confidence: 99%

A review of network DEA models based on slacks‐based measure: Evolution of literature, applications, and further research direction

Alves

Meza

2023

Int Trans Operational Res

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This paper presents a literature review on network models in data envelopment analysis (DEA) focused on the slack‐based network model called slacks‐based measure‐network data envelopment analysis (SBM‐NDEA). The search was carried out in an international database, resulting in 97 publications in the period from 2009 to 2021. From these publications, a descriptive analysis is performed, the evolution of the literature is mapped, the articles are organized into categories, and the main methodological references for the models and applications used are identified. Also, the most used models and their main characteristics are described, in addition to the analysis of the applications. Moreover, the sectors and countries with the highest number of applications are also identified. Through this review, it was possible to observe an increase in the number of network studies using the SBM‐NDEA in recent years. Finally, directions for future research in SBM‐NDEA are suggested.

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A DEA-based empirical analysis for dynamic performance of China's regional coke production chain

Cited by 13 publications

References 48 publications

Research on Green Innovation Performance of Manufacturing Industry and Its Improvement Path in China

Research on Green Innovation Performance of Manufacturing Industry and Its Improvement Path in China

The Effect of Water Rights Trading Policy on Water Resource Utilization Efficiency: Evidence from a Quasi-Natural Experiment in China

A review of network DEA models based on slacks‐based measure: Evolution of literature, applications, and further research direction

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