A second-order cone programming based robust data envelopment analysis model for the new-energy vehicle industry

Chao, Lu; Jie, Tao; An, Qiuxian; Liu, Xiaodong

doi:10.1007/s10479-019-03155-9

Cited by 22 publications

(7 citation statements)

References 36 publications

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“…At present, research on NEVs mainly focuses on the innovation ecosystem [34][35][36] and the effect of policy subsidies on the NEV industry [37][38][39], while research on NEV enterprises' innovation efficiency evaluation is still rare. Lu et al [40] applied a SOCPR-DEA (second-order cone based robust data envelopment analysis) model to analyze 13 NEV enterprises' R&D efficiency. Although this research studies NEV enterprises' R&D efficiency, it did not measure the efficiency of the economic transformation stage (the stage after R&D stage).…”

Section: Technological Innovation Efficiency Of Nevsmentioning

confidence: 99%

Evaluation and Improvement of Technological Innovation Efficiency of New Energy Vehicle Enterprises in China Based on DEA-Tobit Model

et al. 2020

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It is vital to promote and optimize the technological innovation efficiency of new energy vehicle (NEV) enterprises for the green transformation of China’s automobile industry. However, China’s NEV enterprises still have problems such as insufficient research of technology and unreasonable innovative resource allocation. To improve the technological innovation efficiency of China’s NEV enterprises, the NEVs’ technological innovation process is divided into two stages: the research and development (R&D) stage and the achievement transformation stage in this research. Combining Tobit regression with data envelopment analysis (DEA), an evaluation framework of technological innovation efficiency of the NEV enterprises is constructed. Then, the innovation efficiency of 23 NEV listed enterprises from 2013 to 2018 is analyzed. The result reveals three findings. First, the overall technological innovation efficiency of NEV enterprises is low. Second, enterprises’ R&D efficiency is generally higher than the achievement transformation efficiency. Third, according to two-stage efficiency, 23 NEV enterprises are divided into four categories. For different types of enterprises, targeted guidance to improve innovation efficiency is proposed. This research provides a theoretical and practical basis for improving the innovation efficiency of NEV enterprises.

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Section: Technological Innovation Efficiency Of Nevsmentioning

confidence: 99%

Evaluation and Improvement of Technological Innovation Efficiency of New Energy Vehicle Enterprises in China Based on DEA-Tobit Model

et al. 2020

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“…Some scholars have also discussed how the government should correctly guide the upgrading of technology, e.g., some scholars proposed that a combination of R&D subsidy policy and double integral policy could better improve the market mechanisms, promote technological innovation, and bring about the healthy development of industry [13,14,15,16]. Some scholars proposed that China should increase policy supply for basic R&D of the new energy automobile industry, increase funding, and encourage enterprises to independently develop and innovate technology, so as to enhance industrial competitiveness and occupy the new technology line [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, some scholars hypothesized that with the adjustment and decline of two typical policies, the state gradually releases market signals, showing that the form and content of policy support are changing, especially in the field of industrial technology innovation and business model innovation [19,20,21]. The literature [13,14,15,16,17,18] mainly gives policy recommendations for technological innovation based upon symmetrical information. The literature [19,20,21] qualitatively analyses which policies the government should use to stimulate technological innovation in enterprises.…”

Section: Introductionmentioning

confidence: 99%

Design of Incentive Contract for Technological Innovation of New Energy Vehicles with Asymmetric Information

Cai

Guo

Tan

2019

IJERPH

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The improvement of China’s new energy automobile technology is one of the most pressing issues for the government and manufacturers, given that the existing new energy automobile subsidy policy is about to be withdrawn completely. Considering that the manufacturer has the private information of the initial technology level of new energy vehicles, its technology can be improved by means of technological innovation. Using principal–agent and regulation theory, this paper studies how the government designs incentive contracts to motivate manufacturers to strive to upgrade new energy automotive technology. The study has obtained a quantitative incentive contract under full information and a quantitative screening contract with asymmetric information, which provides an effective reference for the design of government subsidy contracts. It was found that the existence of asymmetric information reduces the expected net utility of the government in incentive projects, and the technology upgrading of low-level manufacturers is insufficient, but will not affect the technology upgrading of high-level manufacturers who will get information rent. The conclusion has good reference value and guiding significance for government policy-making with asymmetric information.

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“…Recently, Emrouznejad et al (2020) assessed DMUs by putting forward benchmarks based on reducing greenhouse gas (GHG) emissions. A two-stage DEA model was proposed by Lu et al (2020) to evaluate DMUs in different periods. Tavassoli et al (2020) used fuzzy DEA to evaluate the reliability of efficiency results.…”

Section: Literature Reviewmentioning

confidence: 99%

How to improve the future efficiency of Covid-19 treatment centers? A hybrid framework combining artificial neural network and congestion approach of data envelopment analysis

Yousefi¹,

Shabanpour

Ghods

et al. 2023

Computers & Industrial Engineering

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A second-order cone programming based robust data envelopment analysis model for the new-energy vehicle industry

Cited by 22 publications

References 36 publications

Evaluation and Improvement of Technological Innovation Efficiency of New Energy Vehicle Enterprises in China Based on DEA-Tobit Model

Evaluation and Improvement of Technological Innovation Efficiency of New Energy Vehicle Enterprises in China Based on DEA-Tobit Model

Design of Incentive Contract for Technological Innovation of New Energy Vehicles with Asymmetric Information

How to improve the future efficiency of Covid-19 treatment centers? A hybrid framework combining artificial neural network and congestion approach of data envelopment analysis

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