Green innovation ability evaluation of manufacturing enterprises based on AHP–OVP model

Pan, Xiongfeng; Han, Cuicui; Lu, Xiaowei; Jiao, Zhiming; Yang, Ming

doi:10.1007/s10479-018-3094-6

Cited by 29 publications

(25 citation statements)

References 27 publications

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“…The first is the overall measurement of environmental innovation by constructing an evaluation system covering as many indicators as possible [ 36 , 37 ]. The second is to focus on environmental innovation input, usually measured by environmental R&D investment [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

Does Environmental Regulation Promote Environmental Innovation? An Empirical Study of Cities in China

Duan

Xia

2021

IJERPH

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Promoting environmental innovation through environmental regulation is a key measure for cities to reduce environmental pressure; however, the role of environmental regulation in environmental innovation is controversial. This study used the number of environmental patent applications to measure urban environmental innovation and analyzed the role of urban environmental regulation on urban environmental innovation with the help of the spatial Durbin model (SDM). The results showed that: (1) From 2007 to 2017, the number of environmental patent applications in China has grown rapidly, and technologies related to buildings dominated the development of China’s environmental innovation. (2) Although the number of cities participating in environmental innovation was increasing, China’s environmental innovation activities were highly concentrated in a few cities (Beijing, Shenzhen, and Shanghai), showing significant spatial correlation and spatial agglomeration characteristics. (3) Urban environmental regulation had a positive U-shaped relationship with urban environmental innovation capability, which was consistent with what the Porter hypothesis advocates.

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Section: Methodsmentioning

confidence: 99%

Does Environmental Regulation Promote Environmental Innovation? An Empirical Study of Cities in China

Duan

Xia

2021

IJERPH

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“…Therefore, with the manufacturing industry as the research object and to ensure a comprehensive evaluation of the high-level manufacturing industry developments in China, based on previous studies, this research developed an integrated index system that encompassed seven aspects: innovation, structural optimization, economic benefit, efficiency improvement, green development, international competition, and social benefit. Many evaluation methods have been used in previous studies, such as AHP (Huang, Sun, & Zhang, 2018;Pan, Han, Lu, Jiao, & Ming, 2020), TOPSIS (F. Jiang et al, 2020;Wang & Duan, 2019), neural networks (Lei, Chen, Xue, & Liu, 2019;Sun, Tang, & Bai, 2019) and gray correlation analysis (Ding, Wu, Zhao, Mu, & Yu, 2019;Ozcan & Tuysuz, 2016), of which the gray correlation-TOPSIS method (Liang et al, 2016;Yang & Wu, 2019) has often been used for multi-attribute evaluations because it has simple calculations and no special data requirements. In this study, therefore, an entropy weighting method was used with the traditional gray correlation-TOPSIS method, and the entropy weight, mean square deviation and maximum deviation combination weighting methods were employed to determine the weights of each index indicator, which together provided more objectivity than a single weighting method and were able to effectively reflect the objective information contained in the index indicator data.…”

Section: Measurement Of Manufacturing Industry Developmentmentioning

confidence: 99%

The Measurement of Sustainable Regional Manufacturing Industry: The Case of China

Meng

Wang

Deng

2022

Preprint

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Since the introduction of Made in China 2025 and its focus on sustainable development and manufacturing industry transformation, appropriate evaluation methods to accurately assess the development of China’s manufacturing industry have become essential. Therefore, this research constructed an innovative evaluation index system for manufacturing development based on seven dimensions: innovation, structural optimization, economic benefits, efficiency enhancements, green development, international competition, and social benefits. An objective combination weighted-gray correlation-TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) evaluation model was applied to the Sichuan Province manufacturing industry data from 2009 to 2018 to create a representative sample, in which the overall development level from 2009 to 2016 took on an inverted U-shaped curve that reached its maximum in 2013, fell to its lowest point in 2016, and then began a steady upward trend marked by innovation and efficiency improvements; however, sustainability fell. Based on these results, this research provides a scientific reference for policy-makers with recommendations for innovation-driven development strategies, green development promotion, and social benefit improvements with the aim of promoting more sustainable development of China's manufacturing industry.

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“…In recent years, green technology innovation has become a research focus: Fang et al [3] evaluated green innovation efficiency in China's heavy polluting industries; Pan et al [4] used the AHP-OVP model for the evaluation of green innovation ability of manufacturing enterprises; Peng et al [5] studied the cultivation mechanism of green technology innovation in manufacturing industry from the perspective of ecological niche; and Wang and Guo [6] built an inclusive green innovation environment evaluation index and Fujian province was taken as an example.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of the Green Innovation Efficiency of Chinese Industrial Enterprises: Research Based on the Three-Stage Chain Network SBM Model

Chen

Wang

Peng

et al. 2020

Mathematical Problems in Engineering

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Green innovation is an important capability for an enterprise’s sustainable development. Evaluating the green innovation efficiency of China’s industrial enterprises can greatly affect its improvement. Considering the existing research results of green innovation efficiency evaluation, this paper added the impact of environmental factors in the technology industrialization stage. Various negative environmental externality indicators produced by industrial enterprise activities were incorporated into the undesired output, and an index system and model were constructed for evaluating the green innovation efficiency of China’s industrial enterprises. Combined with the actual data, a three-stage chain network SBM (slack-based measure) model was used to dynamically evaluate and analyze the industrial enterprises in 29 Chinese provinces. The results show that the overall efficiency of green innovation in China’s industrial enterprises has an upward trend, and the rise fluctuates during the technological development, achievement transformation, and industrialization stages. In the three major regions of China, industrial enterprises in the eastern area have the highest green innovation efficiency, and the industrial enterprises in the west and central regions have lower efficiencies. Based on the evaluation results, the corresponding countermeasures are proposed.

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Green innovation ability evaluation of manufacturing enterprises based on AHP–OVP model

Cited by 29 publications

References 27 publications

Does Environmental Regulation Promote Environmental Innovation? An Empirical Study of Cities in China

Does Environmental Regulation Promote Environmental Innovation? An Empirical Study of Cities in China

The Measurement of Sustainable Regional Manufacturing Industry: The Case of China

Evaluation of the Green Innovation Efficiency of Chinese Industrial Enterprises: Research Based on the Three-Stage Chain Network SBM Model

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