Green competitiveness evaluation of provinces in China based on correlation analysis and fuzzy rough set

Cheng, Xiu; Long, Ruyin; Chen, Hong; Li, Wenbo

doi:10.1016/j.ecolind.2017.11.045

Cited by 45 publications

(19 citation statements)

References 46 publications

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“…Based on the resulting index, the GC spectrum was divided into three levels (light green, medium green, and dark green) to explore changes in 30 provinces of China during the period 2004 to 2014. Spatial differences in GC vary from high to low in a westerly direction [ 29 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation and spatial agglomeration analysis of the green competitiveness of China’s manufacturing industry at the provincial level

Yang

Lou

et al. 2021

PLoS ONE

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The pressures to maintain a low-carbon economy makes green competitiveness (GC) a significant issue in China. It has been found that the development of Internet and E-commerce contributes significantly to GC of regional economy, yet there are still lack of quantitative investigation on their effects, which can be used to further guide the economic development. Therefore, this study constructs a new evaluation index for the green competitiveness of the regional manufacturing industry in China by introducing Internet application indicators and E-commerce development indexes into its evaluation system. The results show Jiangxi and Gansu moved upward significantly in the GC ranking over the period. The development of the Internet and E-commerce has the most significant impact on GC of regional manufacturing. The lack of green manufacturing capabilities and green innovation drivers led to a decline in the GC ranking of Liaoning, Tianjin, Guangxi and Heilongjiang. Finally, this study uses Moran’s I index to investigate the spatial agglomeration effect of the green development of the manufacturing industry at the province level. The results show an increase in the GC of China’s regional manufacturing industry, and the GC of manufacturing industry shows a significant agglomeration effect. Based on the above conclusions, the proposal to promote the balanced development of the GC of the manufacturing industry is proposed.

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Section: Literature Reviewmentioning

confidence: 99%

Evaluation and spatial agglomeration analysis of the green competitiveness of China’s manufacturing industry at the provincial level

Yang

Lou

et al. 2021

PLoS ONE

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“…An information system usually contains multiple reductions. If E is independent, then E is the smallest set that maintains the classification ability of the universe U [25,26].…”

Section: Definitionmentioning

confidence: 99%

“…Shiau et al [25] successfully used rough set theory to screen factors affecting transportation sustainability in Taiwan. Cheng et al [26] used the rough set theory to select and analyse 21 indicators affecting regional green competitiveness and formulate regional green competitiveness indicators. Ge et al [27] extracted spatial relationship indicators using rough set theory to describe the complex relationship between geographical phenomena and the environment.…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Metro Shield Construction Cost Based on a Backpropagation Neural Network Improved by Quantum Particle Swarm Optimization

Liu

Liu²,

et al. 2020

Advances in Civil Engineering

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The prediction of construction cost of metro shield engineering is of great significance to project management. In this study, we used the rough set theory, a backpropagation (BP) neural network, and quantum particle swarm optimization (QPSO) to establish a prediction model for predicting the metro shield construction costs. The model accounts for the complexity of metro shield construction and the nonlinear relationship between the construction cost factors. First, the factors affecting the construction cost were determined by referring to the Chinese National Standards and analysing the engineering practice of typical metro shield projects. The rough set theory was used to simplify the system of influencing factors to extract the dominant influencing factors and reduce the number of input variables in the BP neural network. Since the BP neural network easily falls into a local minimum and has a slow convergence speed, QPSO was used to optimize the weights and thresholds of the BP neural network. This method combined the strong nonlinear analysis capabilities of the BP and the global search capabilities of the QPSO. Finally, we selected 50 projects in China for a case analysis. The results showed the dominant factors affecting the construction cost of these projects included ten indicators, such as the type of tunnelling machine and the geological characteristics. The determination coefficient, mean absolute percentage error, root mean square error, and mean absolute error, which are frequently used error analysis tools, were used to analyse the calculation errors of different models (the proposed model, a multiple regression method, a traditional BP model, a BP model optimized by the genetic algorithm, and the BP model optimized by the particle swarm optimization). The results showed that the proposed method had the highest prediction accuracy and stability, demonstrating the effectiveness and excellent performance of this proposed method.

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“…The calculation process of the objective weight is presented below [ 68 ]. (1) The correlation coefficient matrix can be established as follows:

where r ij ( i , j = 1, 2 … n ) is the correlation coefficient of u i ; u j and r ij can be represented by the following formula:

(2) The mean value of the correlation degree of u i is as follows:

(3) The greater the correlation of an index, the smaller its importance.…”

Section: Information Fusion and Risk Evaluationmentioning

confidence: 99%

Fuzzy risk assessment of a deeply buried tunnel under incomplete information

et al. 2018

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Risk assessment has always been an important part of safety risk research in tunnel and underground engineering. Owing to the characteristics of tunnel construction, to achieve an expected risk control effect, it is necessary to carry out accurate risk assessment research according to the risk assessment concept based on the entire tunnel construction process. At present, because of the frequent occurrences of safety accidents, a variety of risk assessment models have been proposed for different tunnel projects such as subways and railway tunnels, which can be roughly classified into two types: probability-based and fuzzy set theories. However, the existing models may be more suitable for the construction stage, and the design stage lacks a reliable and practical fuzzy risk assessment method. Therefore, based on fuzzy set theory and similarity measure theory, a risk assessment model is proposed to adapt to the characteristics that the risk information is difficult to quantify the fuzziness in the design phase. Firstly, new ideas of fuzzy risk analysis are proposed to overcome deficiencies in existing methods; secondly, a new similarity measure is constructed; then fusing multi-source fuzzy information based on evidence theory, the relationship between similarity measure and mass function is established. Finally, the new method is applied to the Yuelongmen tunnel. Results show that the concept of risk control and the risk assessment model are feasible.

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Green competitiveness evaluation of provinces in China based on correlation analysis and fuzzy rough set

Cited by 45 publications

References 46 publications

Evaluation and spatial agglomeration analysis of the green competitiveness of China’s manufacturing industry at the provincial level

Evaluation and spatial agglomeration analysis of the green competitiveness of China’s manufacturing industry at the provincial level

The Prediction of Metro Shield Construction Cost Based on a Backpropagation Neural Network Improved by Quantum Particle Swarm Optimization

Fuzzy risk assessment of a deeply buried tunnel under incomplete information

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