Grey Forecasting Model and Its Application in Shanghai’s Middle Carbon Intensity Index Analysis

CO2 is the most frequently implicated in global warming among the various greenhouse gases associated with climate change. Chinese government has been taking serious measures to control energy consumption to reduce CO2 emissions. This study applies the grey forecasting model to estimate future CO2 emissions and carbon intensity in Shijiazhuang from 2010 until 2020. Forecasts of CO2 emissions in this study show that the average residual error of the GM(1, 1) is below 1.5%. The average increasing rate of CO2 emissions will be about 6.71%; and the carbon intensity will be 2.10 tons/104GDP until year 2020. If the GDP of Shijiazhuang city can be quadruple, the carbon intensity will be half to the 2005 levels until 2020. The findings of this study provide a valuable reference with which the Shijiazhuang government can formulate measures to reduce CO2 emissions by curbing the unnecessary the consumption of energy.

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Forecast of the informatization index of China from 2011 to 2015 using the Grey forecasting model

2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS) ◽

10.1109/gsis.2015.7301861 ◽

2015 ◽

Author(s):

Yancai Zhang

Keyword(s):

Forecasting Model ◽

Grey Forecasting ◽

Grey Forecasting Model

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Study of a discrete grey forecasting model based on the quality cost characteristic curve

Grey Systems Theory and Application ◽

10.1108/gs-06-2017-0016 ◽

2017 ◽

Vol 7 (3) ◽

pp. 376-384 ◽

Cited By ~ 5

Author(s):

Wenjie Dong ◽

Sifeng Liu ◽

Zhigeng Fang ◽

Xiaoyu Yang ◽

Qian Hu ◽

...

Keyword(s):

Prediction Accuracy ◽

Characteristic Curve ◽

Grey System Theory ◽

Forecasting Model ◽

Quality Level ◽

Cost Models ◽

Quality Cost ◽

Content Type ◽

Grey Forecasting ◽

Grey Forecasting Model

Purpose The purpose of this paper is to clarify several commonly used quality cost models based on Juran’s characteristic curve. Through mathematical deduction, the lowest point of quality cost and the lowest level of quality level (often depicted by qualification rate) can be obtained. This paper also aims to introduce a new prediction model, namely discrete grey model (DGM), to forecast the changing trend of quality cost. Design/methodology/approach This paper comes to the conclusion by means of mathematical deduction. To make it more clear, the authors get the lowest quality level and the lowest quality cost by taking the derivative of the equation of quality cost and quality level. By introducing the weakening buffer operator, the authors can significantly improve the prediction accuracy of DGM. Findings This paper demonstrates that DGM can be used to forecast quality cost based on Juran’s cost characteristic curve, especially when the authors do not have much information or the sample capacity is rather small. When operated by practical weakening buffer operator, the randomness of time series can be obviously weakened and the prediction accuracy can be significantly improved. Practical implications This paper uses a real case from a literature to verify the validity of discrete grey forecasting model, getting the conclusion that there is a certain degree of feasibility and rationality of DGM to forecast the variation tendency of quality cost. Originality/value This paper perfects the theory of quality cost based on Juran’s characteristic curve and expands the scope of application of grey system theory.

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A comparative study of univariate models for container throughput forecasting of major ports in Asia

Proceedings of the Institution of Mechanical Engineers Part M Journal of Engineering for the Maritime Environment ◽

10.1177/14750902211023662 ◽

2021 ◽

pp. 147509022110236

Author(s):

Juan Huang ◽

Ching-Wu Chu ◽

Hsiu-Li Hsu

Keyword(s):

Regression Model ◽

Moving Average ◽

Forecasting Model ◽

Short Term ◽

Autoregressive Integrated Moving Average ◽

Port Authorities ◽

Grey Forecasting ◽

Grey Forecasting Model ◽

Short Term Forecasting ◽

Trigonometric Regression

This study aims to make comparisons on different univariate forecasting methods and provides a more accurate short-term forecasting model on the container throughput for rendering a reference to relevant authorities. We collected monthly data regarding container throughput volumes for three major ports in Asia, Shanghai, Singapore, and Busan Ports. Six different univariate methods, including the grey forecasting model, the hybrid grey forecasting model, the multiplicative decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, and the seasonal autoregressive integrated moving average (SARIMA) model, were used. We found that the hybrid grey forecasting model outperforms the other univariate models. This study’s findings can provide a more accurate short-term forecasting model for container throughput to create a reference for port authorities.

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