Evaluating the sustainability of China's power generation industry based on a matter-element extension model

Wang, Yongli; Yang, Jiale; Zhou, Minhan; Zhang, Danyang; Song, Fuhao; Dong, Fugui; Zhu, Jianqin; Liu, Lin

doi:10.1016/j.jup.2021.101166

Cited by 26 publications

(20 citation statements)

References 30 publications

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“…MEEM is a goodness evaluation approach that is widely employed in information processing, decisionmaking, prediction, identification, evaluation, management, control, etc. [26][27][28]. Wang et al [27] employed the MEEM to effectively evaluate the sustainability of the Chinese power generation industry, whereby the MEEM used the correlation function between matter-element characteristics to establish a matter-element evaluation model with multi-index parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

“…[26][27][28]. Wang et al [27] employed the MEEM to effectively evaluate the sustainability of the Chinese power generation industry, whereby the MEEM used the correlation function between matter-element characteristics to establish a matter-element evaluation model with multi-index parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(3) Multilevel extension evaluation: Table 9 reports the calculated weights and correlation results of each secondary index collected via equation (10). Based on the principle of maximum correlation degree [27,28], the construction risk level of the Donghu Deep Tunnel Project was determined as IV. us, the construction safety risk of the project is acceptable and the project management personnel are only required to check the implementation of safety management measures.…”

Section: R I �mentioning

confidence: 99%

“…Step 3: establish correlation function. Based on the definition of the correlation degree in the extension engineering method, the correlation degree of the object to be evaluated is determined as [26,27] where k j (c ik ) is the correlation degree of the k-th second-level index in the i-th first-level index with respect to risk grade j.…”

Section: Introduction Of the Meemmentioning

confidence: 99%

“…(2) Evaluation of first-class indicators: correlation matrix K(N) of the object to be evaluated for each risk grade is calculated by multiplying the weight vector of each primary index and the correlation matrix of each risk grade corresponding to the primary index [26,27]:…”

Section: Introduction Of the Meemmentioning

confidence: 99%

See 4 more Smart Citations

Construction Safety Risk Assessment of Large-Sized Deep Drainage Tunnel Projects

Wang

2021

Mathematical Problems in Engineering

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Frequent extreme climate events and rapid global urbanization have amplified the occurrence of accidents such as waterlogging or the overflow of pollution in big cities. This has increased the application scenarios of large-sized deep drainage tunnel projects (LSDDTPs). The scientific and accurate evaluation of the construction safety risks of LSDDTP can effectively reduce the corresponding economic losses and casualties. In this paper, we employed the hierarchical holographic model to construct the safety risk list of LSDDTPs in terms of the risk source and construction unit. Based on social network analysis, we then screened key indicators and calculated the weights of all secondary indicators from the correlation between risk factors. We subsequently developed a construction safety risk assessment model of LSDDTPs based on the matter-element extension method. The Donghu Deep Tunnel Project in Wuhan, China, was selected as a case study for the proposed method. The results of empirical research demonstrated that eight indicators (e.g., failure to effectively detect the change of the surrounding environment of the tunnel project) were key factors affecting the construction safety risk of IV, which is within the acceptable risk level. Our proposed model outperformed other methods (the fuzzy comprehensive evaluation, analytic hierarchy process, entropy weight method, and comprehensive weight method) in terms of scientific validity and research advancements.

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Section: Literature Reviewmentioning

confidence: 99%