Measuring Carbon Market Transaction Efficiency in the Power Industry: An Entropy-Weighted TOPSIS Approach

Zhu, Jin; Sun, Huaping; Liu, Nanying; Zhou, Dequn; Taghizadeh–Hesary, Farhad

doi:10.3390/e22090973

Cited by 14 publications

(14 citation statements)

References 59 publications

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“…Step 2 : Determine the weight of the entropy method [ 75 ]. First of all, we calculate the information entropy

of the j -th index:

…”

Section: Resultsmentioning

confidence: 99%

Assessment and Prediction of Water Resources Vulnerability Based on a NRS-RF Model: A Case Study of the Song-Liao River Basin, China

Chen

Feng

2021

Entropy

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The vulnerability of water resources is an important criterion for evaluating the carrying capacity of water resources systems under the influence of climate change and human activities. Moreover, assessment and prediction of river basins’ water resources vulnerability are important means to assess the water resources security state of river basins and identify possible problems in future water resources systems. Based on the constructed indicator system of water resources vulnerability assessment in Song-Liao River Basin, this paper uses the neighborhood rough set (abbreviated as NRS) method to reduce the dimensionality of the original indicator system to remove redundant attributes. Then, assessment indicators’ standard values after dimensionality reduction are taken as the evaluation sample, and the random forest regression (abbreviated as RF) model is used to assess the water resources vulnerability of the river basin. Finally, based on data under three different future climate and socio-economic scenarios, scenario predictions are made on the vulnerability of future water resources. The results show that the overall water resources vulnerability of the Song-Liao River Basin has not improved significantly in the past 18 years, and the overall vulnerability of the Song-Liao River Basin is in the level V of moderate to high vulnerability. In the future scenario 1, the overall water resources vulnerability of the river basin will improve, and it is expected to achieve an improvement to the level III of moderate to low vulnerability. At the same time, the natural vulnerability and vulnerability of carrying capacity will increase significantly in the future, and the man-made vulnerability will increase slowly, which will deteriorate to the level V of moderate to high vulnerability under Scenario 3. Therefore, taking active measures can significantly reduce the vulnerability of nature and carrying capacity, but man-made vulnerability will become a bottleneck restricting the fragility of the overall water resources of the river basin in the future.

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“…Step 2 : Determine the weight of the entropy method [ 75 ]. First of all, we calculate the information entropy

of the j -th index:

…”

Section: Resultsmentioning

confidence: 99%

Assessment and Prediction of Water Resources Vulnerability Based on a NRS-RF Model: A Case Study of the Song-Liao River Basin, China

Chen

Feng

2021

Entropy

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“…Step 2. Calculate the positive and negative ideal solutions of the weighted normalized matrix S [45][46][47][48]:…”

Section: Gra-topsis Methodsmentioning

confidence: 99%

“…Step 4. Calculate the grey relational analysis coefficient matrix of each machinery and the positive and negative ideal solutions [45][46][47][48]:…”

Section: Gra-topsis Methodsmentioning

confidence: 99%

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Selection of Agricultural Machinery Based on Improved CRITIC-Entropy Weight and GRA-TOPSIS Method

Zhao

Zhou

et al. 2022

Processes

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In view of the problems existing in the process of agricultural machinery selection, such as inadequate decision-making information, strong subjectivity and quantification difficulty of the index weight assignment, and accuracy deficiency of the selection results, a model on the selection of agricultural machinery based on the improved CRITIC-entropy weight and GRA-TOPSIS method was established in this study. Through analysis, based on the construction of a comprehensive evaluation index system for the selection of agricultural machinery, the combined weight value was determined by combining the weights obtained using the improved CRITIC method and the weights obtained using the entropy weight method. The grey relational analysis method was also combined with the TOPSIS method. The power machinery combination with 88.2 and 73.5 kW of the 68th Regiment of the Fourth Division of Xinjiang Production and Construction Corps was used as an example for verification to determine the optimal power machinery combination. Results indicated that the ranking results were consistent, and the GRA-TOPSIS method was of the greatest degree of discrimination, which was conducive to the selection and evaluation of agricultural machinery equipment. Moreover, the equipment selection results were determined after the comprehensive ranking of machinery types under the different subjective preferences of decision makers was performed.

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“…Chinese scholars have focused on CO 2 quota allocation among provinces and industries in China [14][15][16][17][18][19]. Song et al considered the comprehensive distribution principle of three indicators: hereditary system, egalitarianism, and payment, to create the provincial environmental fixed cost allocation optimization model (FCAM), which will be able to determine the allocation of provincial CO 2 emission 2 Complexity rights for 2020, with a more balanced equity and efficiency [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Use of BP Neural Networks to Determine China’s Regional CO₂ Emission Quota

Peng

Yan

et al. 2021

Complexity

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China declared a long-term commitment at the United Nations General Assembly (UNGA) in 2020 to reduce CO2 emissions. This announcement has been described by Reuters as “the most important climate change commitment in years.” The allocation of China’s provincial CO2 emission quotas (hereafter referred to as quotas) is crucial for building a unified national carbon market, which is an important policy tool necessary to achieve carbon emissions reduction. In the present research, we used historical quota data of China’s carbon emission trading policy pilot areas from 2014 to 2017 to identify alternative features of corporate CO2 emissions and build a backpropagation neural network model (BP) to train the benchmark model. Later, we used the model to calculate the quotas for other regions, provided they implement the carbon emission trading policy. Finally, we added up the quotas to obtain the total national quota. Additionally, considering the perspective of carbon emission terminal, a new characteristic system of quota allocation was proposed in order to retrain BP including the following three aspects: enterprise production, household consumption, and regional environment. The results of the benchmark model and the new models were compared. This feature system not only builds a reasonable quota-related indicator framework but also perfectly matches China’s existing “bottom-up” total control quota approach. Compared with the previous literature, the present report proposes a quota allocation feature system closer to China’s policy and trains BP to obtain reasonable feature weights. The model is very important for the establishment of a unified national carbon emission trading market and the determination of regional quotas in China.

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Measuring Carbon Market Transaction Efficiency in the Power Industry: An Entropy-Weighted TOPSIS Approach

Cited by 14 publications

References 59 publications

Assessment and Prediction of Water Resources Vulnerability Based on a NRS-RF Model: A Case Study of the Song-Liao River Basin, China

Assessment and Prediction of Water Resources Vulnerability Based on a NRS-RF Model: A Case Study of the Song-Liao River Basin, China

Selection of Agricultural Machinery Based on Improved CRITIC-Entropy Weight and GRA-TOPSIS Method

Use of BP Neural Networks to Determine China’s Regional CO₂ Emission Quota

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Measuring Carbon Market Transaction Efficiency in the Power Industry: An Entropy-Weighted TOPSIS Approach

Cited by 14 publications

References 59 publications

Assessment and Prediction of Water Resources Vulnerability Based on a NRS-RF Model: A Case Study of the Song-Liao River Basin, China

Assessment and Prediction of Water Resources Vulnerability Based on a NRS-RF Model: A Case Study of the Song-Liao River Basin, China

Selection of Agricultural Machinery Based on Improved CRITIC-Entropy Weight and GRA-TOPSIS Method

Use of BP Neural Networks to Determine China’s Regional CO2 Emission Quota

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Product

Resources

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Use of BP Neural Networks to Determine China’s Regional CO₂ Emission Quota