Abstract
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.
Highlights
According to the Intergovernmental Panel on Climate Change (IPCC), the source of global warming is the result of human behavior, and its accumulated effects can severely affect life
In light of the above, this study considered all the factors that affect carbon emissions mentioned in previous works, and proposes an integrated forecasting model called hybrid multivariable grey forecasting and genetic programming model, which is divided into two stages
Among the factors that affect the amount of CO2 emissions mentioned in the literature review in Section 2 [1,2,3,4], the factors that had a higher correlation with the amount of emissions include population, GDP per capita, total energy consumption, number of registered motor vehicles, and foreign investment
Summary
According to the Intergovernmental Panel on Climate Change (IPCC), the source of global warming is the result of human behavior, and its accumulated effects can severely affect life. Because human activities continue emitting greenhouse gases, the IPCC estimated that the average global temperature in 2100 will increase by 1 to 6.3 degrees Celsius. The sea level in 2100 is expected to increase 9 to 88 cm compared to current levels. This will have a huge impact on human habitats, tourism, fisheries, buildings near coastal areas, agricultural land, and wetlands. It is estimated that tens of millions of people will be forced to move, which will result in severe economic losses.
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