Considering Multiple Factors to Forecast CO2 Emissions: A Hybrid Multivariable Grey Forecasting and Genetic Programming Approach

Development of technology and economy is often accompanied by surging usage of fossil fuels. Global warming could speed up air pollution and cause floods and droughts, not only affecting the safety of human beings, but also causing drastic economic changes. Therefore, the trend of carbon dioxide emissions and the factors affecting growth of emissions have drawn a lot of attention in all countries in the world. Related studies have investigated many factors that affect carbon emissions such as fuel consumption, transport emissions, and national population. However, most of previous studies on forecasting carbon emissions hardly considered more than two factors. In addition, conventional statistical methods of forecasting carbon emissions usually require some assumptions and limitations such as normal distribution and large dataset. Consequently, this study proposes a two-stage forecasting approach consisting of multivariable grey forecasting model and genetic programming. The multivariable grey forecasting model at the first stage enjoys the advantage of introducing multiple factors into the forecasting model, and can accurately make prediction with only four or more samples. However, grey forecasting may perform worse when the data is nonlinear. To overcome this problem, the second stage is to adopt genetic programming to establish the error correction model to reduce the prediction error. To evaluating performance of the proposed approach, the carbon dioxide emissions in Taiwan from 2000 to 2015 are forecasted and analyzed. Experimental comparison on various combinations of multiple factors shows that the proposed forecasting approach has higher accuracy than previous approaches.

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

Malaysian Journal of Fundamental and Applied Sciences ◽

10.11113/mjfas.v17n4.2091 ◽

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.

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An Enhanced Multivariable Dynamic Time-Delay Discrete Grey Forecasting Model for Predicting China's Carbon Emissions

SSRN Electronic Journal ◽

10.2139/ssrn.3920295 ◽

2021 ◽

Author(s):

Li Ye ◽

Deling Yang ◽

Yaoguo Dang ◽

Junjie Wang

Keyword(s):

Time Delay ◽

Carbon Emissions ◽

Forecasting Model ◽

Grey Forecasting ◽

Grey Forecasting Model ◽

Dynamic Time

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An Examination of Green Credit Promoting Carbon Dioxide Emissions Reduction: A Provincial Panel Analysis of China

Sustainability ◽

10.3390/su13137148 ◽

2021 ◽

Vol 13 (13) ◽

pp. 7148

Author(s):

Wenjie Zhang ◽

Mingyong Hong ◽

Juan Li ◽

Fuhong Li

Keyword(s):

Carbon Dioxide ◽

Carbon Emissions ◽

Carbon Dioxide Emissions ◽

Industrial Structure ◽

Emissions Reduction ◽

Green Finance ◽

Carbon Emissions Reduction ◽

Negative Effect ◽

Emissions Intensity ◽

The Impact

The implementation of green finance is a powerful measure to promote global carbon emissions reduction that has been highly valued by academic circles in recent years. However, the role of green credit in carbon emissions reduction in China is still lacking testing. Using a set of panel data including 30 provinces and cities, this study focused on the impact of green credit on carbon dioxide emissions in China from 2006 to 2016. The empirical results indicated that green credit has a significantly negative effect on carbon dioxide emissions intensity. Furthermore, after the mechanism examination, we found that the promotion impacts of green credit on industrial structure upgrading and technological innovation are two effective channels to help reduce carbon dioxide emissions. Heterogeneity analysis found that there are regional differences in the effect of green credit. In the western and northeastern regions, the effect of green credit is invalid. Quantile regression results implied that the greater the carbon emissions intensity, the better the effect of green credit. Finally, a further discussion revealed there exists a nonlinear correlation between green credit and carbon dioxide emissions intensity. These findings suggest that the core measures to promote carbon emission reduction in China are to continue to expand the scale of green credit, increase the technology R&D investment of enterprises, and to vigorously develop the tertiary industry.

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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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Social Impacts Due to Carbon Dioxide (CO2) Emissions. Evidence From Sub-Sahara African Countries (SSA) Compared to Southeast Asian Countries (ASEAN).

10.21203/rs.3.rs-177911/v1 ◽

2021 ◽

Author(s):

Jean Baptiste Aboyitungiye ◽

Suryanto Suryanto ◽

Evi Gravitiani

Keyword(s):

Carbon Dioxide ◽

Co2 Emissions ◽

Carbon Emissions ◽

Carbon Dioxide Emissions ◽

Explanatory Power ◽

Value Added ◽

Southeast Asian ◽

Asian Countries ◽

Asean Countries ◽

Southeast Asian Countries

Abstract The recent climatic phenomena observed in developing countries since the 2000s have raised concerns, fears, and debates within the international community and economists. Human activities are largely responsible for atmospheric warming through their emissions of CO2 and polluting substances with dramatic consequences and numerous losses of human life in some countries. Using panel data covering the 2000-2016 period, this study investigated the social vulnerability due to the CO2 emissions through an empirical study of CO2’s determinants in selected countries of sub-Sahara African and Southeast Asian countries. The STIRPAT model gave out the result that; explanatories causes of carbon dioxide emissions are different in the two regions: the agriculture-forestry and fishing value-added, and human development index have a strong explanatory power on CO2 emissions in the ASEAN countries, the per-capita domestic product has a positive and significant influence on carbon emissions in the SSA countries, ceteris paribus, but was statistically insignificant in the ASEAN countries. The growing population decreases carbon emissions in the SSA selected countries while is not statically significant in the ASEAN countries. There is therefore a kind of double penalty: those who suffer, and will suffer the most from the impacts of climate change due to CO2 emissions, are those who contribute the least to the problem. These results provide insight into future strategies for the mitigation of climatic hazards already present in some places and potential for others which will be felt on different scales across the regions. Some of the inevitable redistributive effects of those risks can be corrected by providing financial support to the poorest populations hardest hit by natural disasters.

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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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