Ecological-economic efficiency evaluation of green technology innovation in strategic emerging industries based on entropy weighted TOPSIS method

Sun, Li; Miao, Cheng-lin; Yang, Li

doi:10.1016/j.ecolind.2016.10.018

Cited by 264 publications

(125 citation statements)

References 16 publications

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“…This function reflects the comprehensive characteristics of the evaluation objective, which avoids limiting these indicators [34]. The principle of the entropy weighting method is that information for each evaluation unit will be qualified and synthesized, while every factor is weighted to simplify the evaluation process [35].…”

Section: The Description Of the Entropy Weighting Methodsmentioning

confidence: 99%

“…Therefore, choosing these traditional indicators would lead to information loss during the model evaluation. Conversely, the entropy weighting method considers the advantages and characteristics of each traditional indicators and assesses the simulation results comprehensively form different perspectives [34,35]. Thus, the K values are more accurate and easier to compare.…”

Section: Rainfall Eventsmentioning

confidence: 99%

See 1 more Smart Citation

Modeling Multi-Event Non-Point Source Pollution in a Data-Scarce Catchment Using ANN and Entropy Analysis

Chen

Cheng

Wang

et al. 2017

Entropy

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Event-based runoff-pollutant relationships have been the key for water quality management, but the scarcity of measured data results in poor model performance, especially for multiple rainfall events. In this study, a new framework was proposed for event-based non-point source (NPS) prediction and evaluation. The artificial neural network (ANN) was used to extend the runoff-pollutant relationship from complete data events to other data-scarce events. The interpolation method was then used to solve the problem of tail deviation in the simulated pollutographs. In addition, the entropy method was utilized to train the ANN for comprehensive evaluations. A case study was performed in the Three Gorges Reservoir Region, China. Results showed that the ANN performed well in the NPS simulation, especially for light rainfall events, and the phosphorus predictions were always more accurate than the nitrogen predictions under scarce data conditions. In addition, peak pollutant data scarcity had a significant impact on the model performance. Furthermore, these traditional indicators would lead to certain information loss during the model evaluation, but the entropy weighting method could provide a more accurate model evaluation. These results would be valuable for monitoring schemes and the quantitation of event-based NPS pollution, especially in data-poor catchments.

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Section: The Description Of the Entropy Weighting Methodsmentioning

confidence: 99%

Section: Rainfall Eventsmentioning

confidence: 99%

Modeling Multi-Event Non-Point Source Pollution in a Data-Scarce Catchment Using ANN and Entropy Analysis

Chen

Cheng

Wang

et al. 2017

Entropy

View full text Add to dashboard Cite

show abstract

“…The TOPSIS method was first proposed by Hwang and Yoon in 1981 [42], was further developed by Yoon in 1987 [43] and Hwang et al in 1993 [44], and has become a widely recognized analytical technique for multi-criteria decisions [45]. On the basis of this approach, the best alternative is the one which has the shortest distance from the ideal solution and the longest distance from the worst solutions, usually measured as a Euclidean norm.…”

Section: The Topsis Methods Supported By Entropy Weight Modelmentioning

confidence: 99%

Evaluating Performance and Elucidating the Mechanisms of Collaborative Development within the Beijing–Tianjin–Hebei Region, China

et al. 2018

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Elucidating the performance of collaborative development within the Beijing Tianjin Hebei (BTH) region and developing an understanding of mechanisms underlying this process are of paramount importance to regional sustainable development as well as for the realization of Chinese national strategy. Thus, utilizing socioeconomic data for 13 districts within the BTH region between 2000 and 2014, this study applies the Gini coefficient alongside the technique for order preference by similarity to an ideal solution (TOPSIS) method supported by the entropy weight model and impulse response functions in order to assess the performance of collaborative development in this region and elucidate underlying mechanisms. The results of this study reveal that collaborative development within the BTH region has tended to slowly increase over time, but with fluctuations. Although some progress has been made in promoting urbanization, constructing traffic networks, protecting the environment, and improving living standards, very significant expansion space nevertheless remains for further improvements. The collaborative development of this region has also been increasingly affected by globalization, with either the equalization of per capita fixed asset investment or fiscal expenditure exerting a definite impact. The results show that although the equalization of per capita fixed asset investment boosts collaborative development at the start of this process, it is likely to impede it over longer time scales, while the equalization of per capita fiscal expenditure will contribute to this process within the BTH region over both the short and long term. A number of policy suggestions are therefore proposed in this paper to promote smooth collaborative development of the BTH region, including optimizing investment structures and establishing an ecological compensation mechanism.

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“…Zhang et al [14] combine the entropy method with the Shapley value method to allocate the carbon quotas in China. Sun et al [48] adopt the entropy weighted method to evaluate the impact of green technology innovation on ecological-economic efficiency of strategic emerging industries.…”

Section: The Entropy Methods To Determine the Weightsmentioning

confidence: 99%

“…If all z ij are equal and the distribution is absolutely concentrated, indicator j is invalid. Thus, when there is no subjective preference for indicator j, its weight factor is obtained as Equation (6) [48].…”

Section: The Entropy Methods To Determine the Weightsmentioning

confidence: 99%

The Allocation of Carbon Intensity Reduction Target by 2020 among Industrial Sectors in China

Yang

Liu

et al. 2017

Sustainability

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Abstract:In order to realize the national carbon intensity reduction target, China has decided to establish a unified national carbon emissions trading market in 2017. At the initial stage, eight industrial sectors will be covered in the carbon market and the other industrial sectors will be included gradually. The aim of this paper is to study the issue of how to allocate the carbon emissions quotas among different industrial sectors fairly and effectively. We try to provide theoretical support for how to determine the coverage scope and access order of the carbon market. In this paper, we construct a comprehensive reduction index based on indicators of equity and efficiency principle. We adopt entropy method to get the objective weights of the three indicators. Then, an allocation model is developed to determine each sector's reduction target for the year of 2020. The result shows that our allocation scheme based on entropy method is more reasonable, and our allocation method will promote the equity of carbon quotas allocation and the efficiency of carbon emissions. With consideration of China's current economic situation and industrial background, we discuss some policy implications regarding the construction of carbon market.

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Ecological-economic efficiency evaluation of green technology innovation in strategic emerging industries based on entropy weighted TOPSIS method

Cited by 264 publications

References 16 publications

Modeling Multi-Event Non-Point Source Pollution in a Data-Scarce Catchment Using ANN and Entropy Analysis

Modeling Multi-Event Non-Point Source Pollution in a Data-Scarce Catchment Using ANN and Entropy Analysis

Evaluating Performance and Elucidating the Mechanisms of Collaborative Development within the Beijing–Tianjin–Hebei Region, China

The Allocation of Carbon Intensity Reduction Target by 2020 among Industrial Sectors in China

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