An Improved Rolling NGBM(1,1) Forecasting Model with GRG Nonlinear Method of Optimization for Fossil Carbon Dioxide Emissions in Malaysia and Singapore

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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Oxygenated Transport Fuels from Carbon Dioxide

Johnson Matthey Technology Review ◽

10.1595/205651321x16063027322661 ◽

2020 ◽

Author(s):

Peter Styring ◽

George RM Dowson

Keyword(s):

Carbon Dioxide ◽

Carbon Dioxide Emissions ◽

Alternative Fuels ◽

Low Cost ◽

Rapid Development ◽

Transport Sector ◽

Synthetic Fuels ◽

Fossil Carbon ◽

Net Zero ◽

Organic Fuels

The restructuring of the economy post-COVID 19 coupled to the drive towards Net Zero carbon dioxide emissions means we must rethink the way we use transport fuels. Fossil-carbon based fuels are ubiquitous in the transport sector, however there are alternative synthetic fuels that could be used as drop-in or replacement fuels. The main hurdles to achieving a transition to synthetic fuels are the limited availability of low-cost carbon dioxide at an appropriate purity, the availability of renewable hydrogen and, in the case of hydrocarbons, catalysts that are selective for small and particular chain lengths. In this paper we will consider some of the alternative fuels and methods that could reduce cost, both economically and environmentally. We recommend that increased effort in the rapid development of these fuels should be a priority in order to accelerate the possibility of achieving Net Zero without costly infrastructure changes. As ground transportation offers a more straightforward approach legislatively, we will look at oxygenated organic fuels as an alternative drop-in replacement for hydrocarbons.

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Introduction Overview: World Energy Resources and the Need for Biomass for Energy and Lower Fossil Carbon Dioxide Emissions

Plant Biotechnology for Sustainable Production of Energy and Co-products - Biotechnology in Agriculture and Forestry ◽

10.1007/978-3-642-13440-1_1 ◽

2010 ◽

pp. 3-23 ◽

Cited By ~ 2

Author(s):

Charles E. Wyman

Keyword(s):

Carbon Dioxide ◽

Carbon Dioxide Emissions ◽

Energy Resources ◽

Fossil Carbon ◽

World Energy ◽

Biomass For Energy

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Forecasting Energy-Related Carbon Dioxide Emissions in Thailand’s Construction Sector by Enriching the LS-ARIMAXi-ECM Model

Sustainability ◽

10.3390/su10103593 ◽

2018 ◽

Vol 10 (10) ◽

pp. 3593 ◽

Cited By ~ 1

Author(s):

Jindamas Sutthichaimethee ◽

Kuskana Kubaha

Keyword(s):

Carbon Dioxide ◽

Sustainable Development ◽

Error Correction ◽

Co2 Emissions ◽

Development Policy ◽

Carbon Dioxide Emissions ◽

Forecasting Model ◽

Construction Sector ◽

Error Correction Mechanism

The Thailand Development Policy focuses on the simultaneous growth of the economy, society, and environment. Long-term goals have been set to improve economic and social well-being. At the same time, these aim to reduce the emission of CO2 in the future, especially in the construction sector, which is deemed important in terms of national development and is a high generator of greenhouse gas. In order to achieve national sustainable development, policy formulation and planning is becoming necessary and requires a tool to undertake such a formulation. The tool is none other than the forecasting of CO2 emissions in long-term energy consumption to produce a complete and accurate formulation. This research aims to study and forecast energy-related carbon dioxide emissions in Thailand’s construction sector by applying a model incorporating the long- and short-term auto-regressive (AR), integrated (I), moving average (MA) with exogenous variables (Xi) and the error correction mechanism (LS-ARIMAXi-ECM) model. This model is established and attempts to fill the gaps left by the old models. In fact, the model is constructed based on factors that are causal and influential for changes in CO2 emissions. Both independent variables and dependent variables must be stationary at the same level. In addition, the LS-ARIMAXi-ECM model deploys a co-integration analysis and error correction mechanism (ECM) in its modeling. The study’s findings reveal that the LS-ARIMAXi -ECM model is a forecasting model with an appropriate time period (t − i), as justified by the Q-test statistic and is not a spurious model. Therefore, it is used to forecast CO2 emissions for the next 20 years (2019 to 2038). From the study, the results show that CO2 emissions in the construction sector will increase by 37.88% or 61.09 Mt CO2 Eq. in 2038. Also, the LS-ARIMAXi -ECM model has been evaluated regarding its performance, and it produces a mean absolute percentage error (MAPE) of 1.01% and root mean square error (RMSE) of 0.93% as compared to the old models. Overall, the results indicate that determining future national sustainable development policies requires an appropriate forecasting model, which is built upon causal and contextual factors according to relevant sectors, to serve as an important tool for future sustainable planning.

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

Energies ◽

10.3390/en11123432 ◽

2018 ◽

Vol 11 (12) ◽

pp. 3432 ◽

Cited By ~ 4

Author(s):

Chun-Cheng Lin ◽

Rou-Xuan He ◽

Wan-Yu Liu

Keyword(s):

Carbon Dioxide ◽

Genetic Programming ◽

Carbon Emissions ◽

Carbon Dioxide Emissions ◽

Experimental Comparison ◽

Programming Approach ◽

Forecasting Model ◽

Multiple Factors ◽

Grey Forecasting ◽

Grey Forecasting Model

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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