Abstract
The forecast of carbon dioxide (CO2) emissions has played a significant role in drawing up energy development policies for individual countries. Since data about CO2 emissions are often limited and do not conform to the usual statistical assumptions, this study attempts to develop a novel multivariate grey prediction model (MGPM) for CO2 emissions. Compared with other MGPMs, the proposed model has several distinctive features. First, both feature selection and residual modification are considered to improve prediction accuracy. For the former, grey relational analysis is used to filter out the irrelevant features that have weaker relevance with CO2 emissions. For the latter, predicted values obtained from the proposed MGPM are further adjusted by establishing a neural-network-based residual model. Prediction accuracies of the proposed MGPM were verified using real CO2 emission cases. Experimental results demonstrated that the proposed MGPM performed well compared with other MGPMs considered.
Highlights
Carbon dioxide (CO2) is mainly produced from fossil fuel combustion [1], and reducing the impact that energy consumption and economic growth have on CO2 emissions has become a global challenge [2]
Urban population (UP), gross domestic product (GDP), and energy consumption have a dominant influence on CO2 emissions
The reduction of greenhouse gas emissions is critical to environmental protection
Summary
Carbon dioxide (CO2) is mainly produced from fossil fuel combustion [1], and reducing the impact that energy consumption and economic growth have on CO2 emissions has become a global challenge [2]. According to the International Energy Agency (IEA) [3], total emissions of greenhouse gas in 2018 were a record 33.1 billion tons, along with a global economic growth rate that increased by 3.2%. Erefore, to keep a green economic growth, the national authorities make an effort to devise energy development policies that reduce the impact of CO2 emissions. Despite huge amount of data we can collect, only a few sample data points are required to achieve reliable and acceptable prediction accuracy [21, 22]. erefore, it is Mathematical Problems in Engineering interesting to apply the multivariate grey prediction models (MGPMs) to CO2 emissions
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