Grey Forecasting Model for CO2 Emissions

2012 ◽  
Vol 518-523 ◽  
pp. 1664-1668 ◽  
Author(s):  
Guo Lin Bao ◽  
Hong Qi Hui
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Energy Consumption ◽  
Greenhouse Gases ◽  
Carbon Intensity ◽  
Forecasting Model ◽  
Chinese Government ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Average Residual

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.

Vulnerability of hydropower generation to climate change in China: Results based on Grey forecasting model

Energy Policy ◽  
2014 ◽  
Vol 65 ◽  
pp. 701-707 ◽  
Author(s):  
Bing Wang ◽  
Xiao-Jie Liang ◽  
Hao Zhang ◽  
Lu Wang ◽  
Yi-Ming Wei
Keyword(s):  
Climate Change ◽  
Forecasting Model ◽  
Hydropower Generation ◽  
Grey Forecasting ◽  
Grey Forecasting Model

Global Climate Change Opportunity as Well as Challenge to Architecture

2011 ◽  
Vol 243-249 ◽  
pp. 5289-5292
Author(s):  
Jun Hua Yu
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Energy Consumption ◽  
Greenhouse Gases ◽  
Global Climate Change ◽  
Human Activities ◽  
Global Climate ◽  
Relevant Research ◽  
Save Energy ◽  
Emission Of Greenhouse Gases

As known to all, the emission of greenhouse gases is mainly caused by human activities. If we could cut down the emission, we could gradually prevent the influence of climate change. Relevant research shows that in the field of energy consumption, the control of CO2 emission is the most effective way to save energy. Thus, reducing the architectural energy consumption is one of the most crucial factors to realize global climate goals. Although more and more scholars prefer to use the word ‘dilemma’ to describe the urgent contradiction between architectural construction and environment, and energy as well, I still want to discuss the influence of global warming on the architecture industry, and explain why it is an opportunity as well.

scholarly journals Forecasting Carbon Dioxide Emissions for Singapore using Grey Model with Cramer’s Rule

2021 ◽  
Vol 17 (4) ◽  
pp. 437-445
Author(s):  
Assif Shamim Mustaffa Sulaiman ◽  
Ani Shabri
Keyword(s):  
Carbon Dioxide ◽  
Global Warming ◽  
Carbon Dioxide Emissions ◽  
Grey Model ◽  
Forecasting Model ◽  
Fossil Carbon ◽  
Forecasting Models ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Cramer’S Rule

This article analyses and forecasts carbon dioxide () emissions in Singapore for the 2012 to 2016 period. The study analysed the data using grey forecasting model with Cramer’s rule to calculate the best SOGM(2,1) model with the highest accuracy of precision compared to conventional grey forecasting model. According to the forecasted result, the fitted values using SOGM(2,1) model has a higher accuracy precision with better capability in handling information to fit larger scale of uncertain feature compared to other conventional grey forecasting models. This article offers insightful information to policymakers in Singapore to develop better renewable energy instruments to combat the greater issues of global warming and reducing the fossil carbon dioxide emissions into the environment.

scholarly journals Global Warming is an Energy Conundrum

2018 ◽  
Vol 7 (2) ◽  
pp. 1
Author(s):  
Jan-Erik Lane
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Energy Consumption ◽  
Greenhouse Gases ◽  
Emission Reduction ◽  
Energy Demand ◽  
Anthropogenic Source

Climate change is at its core an energy problematic. The main anthropogenic source of greenhouse gases is energy consumption. It is vital, because it makes affluence and wealth feasible. Energy demand is projected to double by 2050 at the same time as governments have obliged themselves to drastic decarbonisation. The risk is obvious that energy demand trumps emission reduction.

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

2017 ◽  
Vol 7 (3) ◽  
pp. 376-384 ◽  
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.

scholarly journals Climate Change and Aerosol Sciences

2021 ◽  
pp. 1-13
Author(s):  
Kehan Li
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Particulate Matter ◽  
Environmental Policy ◽  
Solar Radiation ◽  
Greenhouse Gases ◽  
Atmospheric Aerosols ◽  
Environmental Policies ◽  
Modern Times

Climate change is of great importance in modern times and global warming is considered as a significant part of climate change. It is proved that human’s emissions such as greenhouse gases are one of the main sources of global warming (IPCC, 2018). Apart from greenhouse gases, there is another kind of matter being released in quantity via emissions from industries and transportations and playing an important role in global warming, which is aerosol. However, atmospheric aerosols have the net effect of cooling towards global warming. In this paper, climate change with respect to global warming is briefly introduced and the role of aerosols in the atmosphere is emphasized. Besides, properties of aerosols including dynamics and thermodynamics of aerosols as well as interactions with solar radiation are concluded. In the end, environmental policies and solutions are discussed. Keywords: Climate change, Global warming, Atmospheric aerosols, Particulate matter, Radiation, Environmental policy.

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

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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