Energy sustainability evaluation of 30 provinces in China using the improved entropy weight-cloud model

Hou, Xiaoran; Lv, Tao; Xu, Jie; Deng, Xu; Liang, Feng; Pi, Duyang

doi:10.1016/j.ecolind.2021.107657

Cited by 38 publications

(20 citation statements)

References 43 publications

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“…However, they have insufficient energy resources. Energy supply and demand should be balanced to prevent energy shortages (Hou et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

A new multivariate grey prediction model for forecasting China’s regional energy consumption

Chiu

et al. 2022

Environ Dev Sustain

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Predicting energy consumption is an essential part of energy planning and management. The reliable prediction of regional energy consumption is crucial for the authority in China to formulate policies by with respect to the dual control of its energy consumption and energy intensity. Given that energy consumption is affected by a number of factors, this study proposes a non-homogeneous, discrete, multivariate grey prediction model based on adjacent accumulation to predict the regional energy consumption in China. Interestingly regional GDP was selected by grey relational analysis as the independent variable in the proposed model. The results show that it can outperform the other multivariate grey models considered in terms of predicting regional energy consumption in China. Moreover, we found that economic development and energy consumption of each region in China remain closely related. In the post-COVID-19 period, regional economic development will continue to grow and increase energy consumption.

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“…However, they have insufficient energy resources. Energy supply and demand should be balanced to prevent energy shortages (Hou et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

A new multivariate grey prediction model for forecasting China’s regional energy consumption

Chiu

et al. 2022

Environ Dev Sustain

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“…There are three types of cloud generators: (1) Forward Cloud Generator (FCG), which converts qualitative to quantitative-the calculation flowchart of the FCG is shown in Figure 2; (2) Backward Cloud Generator (BCG), which converts qualitative to qualitative; (3) X cloud generator and Y cloud generator constitute a special conditional cloud generator, in which C (E x , E n , H e ) is known and a specific value X or Y is used to calculate the degree of certainty. The X condition cloud generator is used to calculate the cloud digital characteristics of each evaluation index, and the equations [30] are:…”

Section: Evaluation Methods Cloud Model (Cm)mentioning

confidence: 99%

“…x n E E 、 represent the expected value and entropy value, respectively, of the i-th index corresponding to the j-th serviceability grade; xij represents the membership value of the i-th index corresponding to the j-th serviceability grade; (4) Related calculations on CM When the CM theory is used to transform qualitative concepts into quantitative values, the following steps will be taken: let the middle cloud be C0 (Ex0, En0, He0), and the adjacent clouds on the left and right are C−1 (Ex−1, En−1, He−1), C+1 (Ex+1, En+1, He+1) and C−2 (Ex−2, En−2, He−2), C+2 (Ex+2, En+2, He+2). The cloud algorithms are as follows [31]: The X condition cloud generator is used to calculate the cloud digital characteristics of each evaluation index, and the equations [30] are:…”

Section: Evaluation Methods Cloud Model (Cm)mentioning

confidence: 99%

Evaluation of Serviceability of Canal Lining Based on AHP–Simple Correlation Function Method–Cloud Model: A Case Study in Henan Province, China

Zhou

Ma³

et al. 2021

Sustainability

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In the process of sustainable development within modern agriculture, in order to ensure that agricultural production has adequate water resources, canal lining (CL) is often used to transport water in order to reduce water seepage, thus promoting the sustainable utilization of water resources. However, due to the influence of the terrain, environment, human factors and other factors, the CL often suffers a certain degree of damage. Therefore, it is necessary to evaluate the serviceability of the CL, so to realize the sustainable use of the CL strategy. Aiming at the weight assignment of CL evaluation indices that are subjective and not combined with actual index data, a weight calculation method based on the Analytic Hierarchy Process (AHP)–simple correlation function (SCF) method was proposed, and game theory was used to achieve combination weighting. For the evaluation indices with the characteristics of fuzziness and randomness, the cloud model (CM) was used to comprehensively consider these characteristics in order to realize the evaluation. Finally, a method to measure serviceability of CL based on AHP–SCF–CM was proposed. Taking a CL project in China as an example, this method was used to evaluate the serviceability of the CL. The evaluation result showed that the serviceability of the CL was poor, and the qualitative evaluation result was consistent with the actual damage condition of the project; meanwhile, a comparative study was performed in combination with the AHP–Entropy Weight (EW)–unascertained measurement theory (UMT). The quantitative evaluation results of the two methods displayed the same grade of serviceability, which verifies that the method proposed in this paper is more reasonable, objective and feasible from both qualitative and quantitative perspectives. Furthermore, the evaluation results lay the foundation for subsequent maintenance and fault prevention of the canal.

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“…Combining the cloud model theory with other methods, it was improved, the measurement and algorithm of cloud model similarity were proposed. At the same time, the application scope was extended to other forms of data, such as S-type cloud model, asymmetric trapezoidal cloud model and Z-trapezoidal conventional cloud model 36 – 40 . The cloud model was applied to the power industry to evaluate the power development and load response of distribution network, and the obtained results were used to help decision-makers make decisions 41 – 43 .…”

Section: Introductionmentioning

confidence: 99%

“…In addition, more objective weights could be obtained by combining entropy weight method with AHP, and more accurate evaluation models could be obtained by blending cloud model theory 46 , 47 . Cloud model was widely used in multi-condition assessment problems such as energy sustainability assessment, system efficiency assessment and risk assessment 40 , 49 , 50 because of its good handling of randomness and fuzziness.…”

Section: Introductionmentioning

confidence: 99%

Fishing capacity evaluation of fishing vessel based on cloud model

Lyu

Zhang

Liu

et al. 2022

Sci Rep

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In the evaluation problem of fishing vessel fishing capacity, the imperfect evaluation index system and the methods of evaluation indexes are mostly artificial qualitative evaluation methods, which lead to strong subjectivity and fuzziness as well as low accuracy of evaluation results. Therefore, this study introduces cloud model theory on the basis of improving the evaluation index system, converts the artificial qualitative evaluation results into the digital characteristics of clouds, realizes the mutual transformation of qualitative evaluation and quantitative evaluation, and improves the accuracy of evaluation results. Taking the trawler as an example, the cloud model method is used to evaluate the fishing capacity, and the result obtained is (77.1408, 1.6897, 0.0), the result obtained by the fuzzy comprehensive evaluation method is 76.664785, and the result obtained by the cloud center of gravity evaluation method is 0.7919. Compared with the other two methods, the cloud model method uses three numerical characteristics to describe the results, and combining the different numerical characteristics meanings, the evaluation results can be judged to be accurate, and the influence of ambiguity on the results is greatly reduced. Meanwhile, the evaluation results can be presented in the form of pictures, and the results are more intuitive; in addition, the cloud model of the evaluation results is compared with the standard cloud model for similarity, which improves the credibility and authenticity of the results.

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Energy sustainability evaluation of 30 provinces in China using the improved entropy weight-cloud model

Cited by 38 publications

References 43 publications

A new multivariate grey prediction model for forecasting China’s regional energy consumption

A new multivariate grey prediction model for forecasting China’s regional energy consumption

Evaluation of Serviceability of Canal Lining Based on AHP–Simple Correlation Function Method–Cloud Model: A Case Study in Henan Province, China

Fishing capacity evaluation of fishing vessel based on cloud model

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