Forecasting of Turkey's greenhouse gas emissions using linear and nonlinear rolling metabolic grey model based on optimization

Şahin, Utkucan

doi:10.1016/j.jclepro.2019.118079

Cited by 70 publications

(20 citation statements)

References 30 publications

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“…By generating and developing some known information and extracting valuable information, the theory can accurately describe and monitor the system's operation behavior and evolution law. In the case of less sample data, the uncertainty of the system and the imperfection of sample data are overcome, and a relatively accurate prediction model is established [51]. Assuming that any random process is the grey quantity that varies within a certain range, the random process is called the grey process.…”

Section: Grey-dematel Modelmentioning

confidence: 99%

Identification of Influencing Factors for Sustainable Development: Evaluation and Management of Regional Innovation Performance in Heilongjiang, China

Xia

Yin

2020

Sustainability

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Although the innovation factor endowment of Heilongjiang province is relatively rich, many innovation resources cannot effectively promote the sustainable development of innovation-driven performance. The combined drift degree evaluation method and grey-DEMATEL model were used to study regional innovation performance in Heilongjiang province and the key factors influencing it. The results of this study show that the overall trend of regional innovation performance is slowly rising. Compared with other regions, the level of science and technology output is in the forefront, and the level of economic output and social effect is at the end. The innovation performance level of enterprises and research institutions is at the end position, showing a trend of slow rise and then slow decline. The innovation performance level of universities is in the middle position, showing a trend of rising first, then falling and then rising. The degree of marketization, the financial environment, the intellectual property system, the transformation of scientific and technological achievements and the level of human capital are the core factors that affect the regional innovation performance. The dominant position and leading role of regional enterprises and the cooperation between industry, university and research institutes should be strengthened through government policies.

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Section: Grey-dematel Modelmentioning

confidence: 99%

Identification of Influencing Factors for Sustainable Development: Evaluation and Management of Regional Innovation Performance in Heilongjiang, China

Xia

Yin

2020

Sustainability

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“…Obviously, Professor Deng [18], a pioneer on grey system theory, would like to solve this topic and gave an innovative theory often called the grey system theory. In particular, the grey forecasting model, a key branch of this theory, has been widely concerned and applied in many fields, including engineering, economy, and especially energy (see [19][20][21][22][23][24][25][26][27][28]). In addition, as a basic model in grey system model, which is abbreviated as GM (1,1).…”

Section: Research On the Grey System Modelmentioning

confidence: 99%

Forecasting Natural Gas Consumption of China Using a Novel Grey Model

Zheng

Jiang

et al. 2020

Complexity

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As is known, natural gas consumption has been acted as an extremely important role in energy market of China, and this paper is to present a novel grey model which is based on the optimized nonhomogeneous grey model (ONGM (1,1)) in order to accurately predict natural gas consumption. This study begins with proving that prediction results are independent of the first entry of original series using the product theory of determinant; on this basis, it is a reliable approach by inserting an arbitrary number in front of the first entry of original series to extract messages, which has been proved that it is an appreciable approach to increase prediction accuracy of the traditional grey model in the earlier literature. An empirical example often appeared in testing for prediction accuracy of the grey model is utilized to demonstrate the effectiveness of the proposed model; the numerical results indicate that the proposed model has a better prediction performance than other commonly used grey models. Finally, the proposed model is applied to predict China’s natural gas consumption from 2019 to 2023 in order to provide some valuable information for energy sectors and related enterprises.

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“…It can realize an accurate description and understanding of the real world by processing mining the information of existing data [34,35]. The Grey Time-Series Prediction Model has been used for hydrology time series prediction, meteorological sequence prediction and forecast of greenhouse gas emissions [36][37][38]. The Grey Time-Series Prediction Model was used to predict the trends of the Indicator 11.6.2, which represent the per capita environmental impact in the next few years.…”

Section: Grey Time-series Prediction Modelmentioning

confidence: 99%

A Novel Evaluation Approach of County-Level City Disaster Resilience and Urban Environmental Cleanliness Based on SDG11 and Deqing County’s Situation

Wang

Zhou

et al. 2019

Sustainability

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City disaster resilience and urban environmental cleanliness are two representative indicators used to assess the safety of human settlements in China's Sustainability Development Goals (SDGs). Traditional research on SDGs mainly concentrated at large-scale spatial level, such as global level or national level. It brings unclear significance to the implementation of SDGs in the county-level. The goal of this paper is to find a new calculation method to apply the index of urban disaster resilience and urban environmental cleanliness to the evaluation of county-level areas. A localization of county-level city disaster resilience and urban environmental cleanliness based on Deqing County's situation was carried out. With quantification and projections of local data, the assessments of city disaster resilience and urban environmental cleanliness have completed. The evaluation showed that city disaster resilience is maintained at a low level, while indicators of urban cleanliness are lower than standards. The prediction of urban per capita environmental impact index based on Grey Time-Series Prediction Model was finished. The forecast showed that the urban per capita impact indicators in the next three years have not exceeded the standard line. The two indicators used to assess the safety of human settlements were consistent with the sustainable development of urban settlement. Partial results of this research were reported as a "county sample" at the first UN Geographic Information Conference held in Deqing in 2018.

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Forecasting of Turkey's greenhouse gas emissions using linear and nonlinear rolling metabolic grey model based on optimization

Cited by 70 publications

References 30 publications

Identification of Influencing Factors for Sustainable Development: Evaluation and Management of Regional Innovation Performance in Heilongjiang, China

Identification of Influencing Factors for Sustainable Development: Evaluation and Management of Regional Innovation Performance in Heilongjiang, China

Forecasting Natural Gas Consumption of China Using a Novel Grey Model

A Novel Evaluation Approach of County-Level City Disaster Resilience and Urban Environmental Cleanliness Based on SDG11 and Deqing County’s Situation

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