Addressing the efficiency of the core ecological industrial chain: A DEA approach

Wang, Qingsong; Sun, Yue; Yuan, Xueliang; Cao, Dayong; Zuo, Jian; Gao, Zhenlei

doi:10.1016/j.jclepro.2017.03.185

Cited by 16 publications

(7 citation statements)

References 31 publications

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“…Pai et al [52] designed an eco-efficiency measurement model for EIPs in Taiwan, as a tool to be used for the continuous improvement of sustainability. Wang et al [53] developed a methodology for the optimal design of the ecological industrial chain within EIPs based on the DEA method.…”

Section: Sustainable Industrial Sites: the Eco-industrial Parksmentioning

confidence: 99%

Renewable energy in eco-industrial parks and urban-industrial symbiosis: A literature review and a conceptual synthesis

et al. 2019

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Section: Sustainable Industrial Sites: the Eco-industrial Parksmentioning

confidence: 99%

Renewable energy in eco-industrial parks and urban-industrial symbiosis: A literature review and a conceptual synthesis

et al. 2019

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“…Prior studies on supply chain and enterprise green performance evaluation take energy consumption or GHG emissions into consideration (Badiezadeh et al, 2018; Bostian et al, 2018; Halog, 2009; Wang et al, 2017). Although many studies have examined the implementation effects of GSCM, there is a paucity of studies for the semiconductor industry.…”

Section: Literature Reviewmentioning

confidence: 99%

Semiconductor industry supply chain productivity changes: Incorporating corporate green performances

Lu,

Lo,

Hung

et al. 2023

Manage Decis Econ

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Green supply chain management (GSCM) has been applied by semiconductor firms to ameliorate environmental problems. This study aims to evaluate the green performance of semiconductor supply chains to examine the effect of GSCM. Using the meta‐frontier Malmquist productivity index as the research method, and a sample of global semiconductor firms consisting of three groups, design, manufacturing, and assembling, as research objects, this study evaluates variation in performance while considering environmental variables and disregarding environmental variables respectively, and compares the differences between those two conditions for the whole supply chain and each group in the supply chain. This study discovers no significant differences in both considering and disregarding environmental variables scenarios, productivity of the whole supply chain shows progress. This study found the investment in enhancing technology development as a more important factor in productivity improvement and identifies design as the global leading sub‐industry in the semiconductor supply chain.

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“…This article selected traditional production factors labor and capital as the input indicator and GDP as the output indicator of the economic efficiency in Mongolia. In the existing research of regional eco-efficiency, ecological environment input indicators can include energy, land, water resources, and so on [60,61], and output indicators can include wastewater, SO 2 , NO 2 , PM 2.5 , garbage, and so on. [62,63].…”

Section: Indicatorsmentioning

confidence: 99%

Evolution Characters and Influencing Factors of Regional Eco-Efficiency in a Developing Country: Evidence from Mongolia

Xia

Dong

et al. 2021

IJERPH

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The sandstorm in 2021 in East Asia demonstrated the ecological issues that culminated for decades in Mongolia. Mongolia is facing challenges to realize green and sustainable development. This article aims to increase the understanding of eco-efficiency and its influencing factors in Mongolia and to provide a reference for similar developing countries and regions to achieve green and sustainable development. This article used the Slacks-Based Measure of Efficiency (SBM) model with advantages of dimension freedom and unit variable to estimate the economic efficiency and eco-efficiency of 22 provinces in Mongolia from 2007 to 2016; energy consumption and undesirable environmental outputs were taken as ecological/environmental indicators in the input and output system of regional eco-efficiency in Mongolia, combining traditional indicators of economic efficiency to build Mongolia’s eco-efficiency input–output framework. This article applied hot spot analysis and gravity center analysis to reveal the temporal and spatial evolution characters of eco-efficiency in Mongolia. Finally, the article applied panel Tobit regression to analyze the influencing factors of eco-efficiency. We were found that Mongolia’s eco-efficiency slightly improved from 0.7379 in 2007 to 0.7673 in 2016, lower than the economic efficiency. The high eco-efficiency provinces appeared in the capital Ulaanbaatar and its surrounding areas, showing an obvious spatial spillover effect. The low eco-efficiency provinces were mainly in the undeveloped western region. The relationship between per capita GDP and eco-efficiency was U-shaped and consistent with environmental Kuznets theory. Accelerating economic growth, optimizing population distribution, and improving energy structure and green technology can improve Mongolia’s eco-efficiency.

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Addressing the efficiency of the core ecological industrial chain: A DEA approach

Cited by 16 publications

References 31 publications

Renewable energy in eco-industrial parks and urban-industrial symbiosis: A literature review and a conceptual synthesis

Renewable energy in eco-industrial parks and urban-industrial symbiosis: A literature review and a conceptual synthesis

Semiconductor industry supply chain productivity changes: Incorporating corporate green performances

Evolution Characters and Influencing Factors of Regional Eco-Efficiency in a Developing Country: Evidence from Mongolia

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